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PATH HOLE DETECTION- A Modern Pothole Detection Technique Using Deep Learning

Road Accident Detection And Avoidance Are A More Difficult And Challenging Problem In India As Poor Quality Of Construction Materials Get Used In Road Drainage System Construction. Due To The Above Problems, Roads Get Damaged Early And Potholes Appear On The Roads Which Cause Accidents. According To A Report Submitted By The Ministry Of Road Transport And Highways Transport Research Wing New Delhi In 2017, Approximately 4,64,910 Accidents Happen Per Year In India. This Paper Proposed A Deep Learning-based Model That Can Detect Potholes Early Using Images And Videos Which Can Reduce The Chances Of An Accident. This Model Is Basically Based On Transfer Learning, Faster Region-based Convolutional Neural Network(F-RCNN) And Inception-V2. There Are Many Models For Pothole Detection That Uses The Accelerometer (without Using Images And Videos) With Machine Learning Techniques, But A Less Number Of Pothole Detection Models Can Be Found Which Uses Only Machine Learning Techniques To Detect Potholes. The Results Of This Work Have Shown That Our Proposed Model Outperforms Other Existing Techniques Of Potholes Detection.

IMAGE ENHANCEMENT- An Effectual Underwater Image Enhancement Using Deep Learning Algorithm

Digital Image Processing Domain Is Growing Day-by-day By Introducing Novel Technologies To Provide Assistance For Several Applications Such As Robotic Activities, Underwater Network Formation, And So On. In Particular, Underwater Image Processing Is Considered As The Crucial Task In Image Processing Industry Due To The Flow Of Light Waves That Are Not In The Specific And Expected Range Under The Water Level. While Image Restoration Technology Can Adequately Consider Removing This Same Haze From Source Images, They Need To Obtain Several Images From A Certain Place That Prevent It From Being Used In A Real-time System. To Overcome This Issue, A Deep Study Approach Is Developed By Providing Excellent Outcomes Of Deep Learning Approaches In Several Other Image Analysis Concerns Such As Colorizing Images Or Object Identification. A Convolution Neural Network (CNN) Model Is Trained To De-haze The Individual Images With Image Restoration In Order To Perform Further With An Image Improvement. The Proposed Approach Can Produce Images With Image Restoration Quality By Including A Standard Image Input And Here, The Neural Network Is Evaluated By Using Images And Features, Which Are Obtained From Separate Areas To Prove Its Capacity To Generalize. The Efficiency Of The Proposed Approach Is High When Compared To Other Existing Methods.

PLANT DISEASE DETECTION USING CNN- Disease Detection Of Plant Leaf Using Image Processing And CNN With Preventive Measures Flask Web Framework

One Of The Important And Tedious Task In Agricultural Practices Is The Detection Of The Disease On Crops. It Requires Huge Time As Well As Skilled Labor. This Paper Proposes A Smart And Efficient Technique For Detection Of Crop Disease Which Uses Computer Vision And Machine Learning Techniques. The Proposed System Is Able To Detect 20 Different Diseases Of 5 Common Plants With 93% Accuracy.

DOG BREED CLASSIFICATION- Breakthrough Conventional Based Approach For Dog Breed Classification Using CNN With Transfer Learning

Dogs Are One Of The Most Common Domestic Animals. Due To A Large Number Of Dogs, There Are Several Issues Such As Population Control, Decrease Outbreak Such As Rabies, Vaccination Control, And Legal Ownership. At Present, There Are Over 180 Dog Breeds. Each Dog Breed Has Specific Characteristics And Health Conditions. In Order To Provide Appropriate Treatments And Training, It Is Essential To Identify Individuals And Their Breeds. The Paper Presents The Classification Methods For Dog Breed Classification Using Two Image Processing Approaches 1) Conventional Based Approaches By Local Binary Pattern (LBP) And Histogram Of Oriented Gradient (HOG) 2) The Deep Learning Based Approach By Using Convolutional Neural Networks (CNN) With Transfer Learning. The Result Shows That Our Retrained CNN Model Performs Better In Classifying A Dog Breeds. It Achieves 96.75% Accuracy Compared With 79.25% Using The HOG Descriptor.

PLANT LEAF DISEASE RECOGNISATION- Leaf Disease Detection And Classification Based On Machine Learning

Detection Of Diseases In Plants Is A Significant Task That Has To Be Done In Agriculture. This Is Something On Which The Economy Profoundly Depends. Infection Discovery In Plants Is A Significant Job In The Agribusiness Field, As Having Diseases In Plants Is Very Common. To Recognize The Diseases In Leaves, A Continuous Observation Of The Plants Is Required. This Observation Or Continuous Monitoring Of The Plants Takes A Lot Of Human Effort And It Is Time-consuming Too. To Make It Simply Some Sort Of Programmed Strategy Is Required To Observe The Plants. Program Based Identification Of Diseases In Plants Makes Easier To Detect The Damaged Leaves And Reduces Human Efforts And Time-saving. The Proposed Algorithm Distinguishing Sickness In Plants And Classify Them More Accurately As Compared To Existing Techniques.

SCRATCH REMOVAL OF IMAGE FROM OLD IMAGE- Research On Repairing Historical Photos Of Damaged Scratches Based On Computer Technology

Historical Photographs Record The True Face Of A Moment In The Development Of Human History, They Have Authenticity, Vividness, And Unique Values. However, Due To Various Factors, Aging And Damage Will Occur. With The Development Of Computer Technology, The Restoration Technology Is More Used In Photo Restoration And Virtual Restoration Of Cultural Relics. This Paper First Analyzes The Principle Of Repairing Photo Archives Based On Computer Technology, And Then Uses The Combination Of Statistics And Computer Image Processing Technology To Detect And Repair The Scratches In Historical Photographs. And The Paper Establishes A Model Repair Framework, Which Provides A New Idea For The Repair Of Such Historical Photos. The Experimental Results Show That The Method Has A Significant Repair Effect.

SKIN CANCER CLASSIFICATION- Skin Cancer Classification Using Image Processing And Machine Learning

One Of The Most Rapidly Spreading Cancers Among Various Other Types Of Cancers Known To Humans Is Skin Cancer. Melanoma Is The Worst And The Most Dangerous Type Of Skin Cancer That Appears Usually On The Skin Surface And Then Extends Deeper Into The Layers Of Skin. However, If Diagnosed At An Early Stage; The Survival Rate Of Melanoma Patients Is 96% With Simple And Economical Treatments. The Conventional Method Of Diagnosing Melanoma Involves Expert Dermatologists, Equipment, And Biopsies. To Avoid The Expensive Diagnosis, And To Assist Dermatologists, The Field Of Machine Learning Has Proven To Provide State Of The Art Solutions For Skin Cancer Detection At An Earlier Stage With High Accuracy. In This Paper, A Method For Skin Lesion Classification And Segmentation As Benign Or Malignant Is Proposed Using Image Processing And Machine Learning. A Novel Method Of Contrast Stretching Of Dermoscopic Images Based On The Methods Of Mean Values And Standard Deviation Of Pixels Is Proposed. Then The OTSU Thresholding Algorithm Is Applied For Image Segmentation. After The Segmentation, Features Including Gray Level Co-occurrence Matrix (GLCM) Features For Texture Identification, The Histogram Of Oriented Gradients (HOG) Object, And Color Identification Features Are Extracted From The Segmented Images. Principal Component Analysis (PCA) Reduction Of HOG Features Is Performed For Dimensionality Reduction. Synthetic Minority Oversampling Technique (SMOTE) Sampling Is Performed To Deal With The Class Imbalance Problem. The Feature Vector Is Then Standardized And Scaled. A Novel Approach Of Feature Selection Based On The Wrapper Method Is Proposed Before Classification. Classifiers Including Quadratic Discriminant, SVM (Medium Gaussian), And Random Forest Are Used For Classification. The Proposed Approach Is Verified On The Publicly Accessible Dataset Of ISIC-ISBI 2016. Maximum Accuracy Is Achieved Using The Random Forest Classifier. The Classification Accuracy Of The Proposed System With The Random Forest Classifier On ISIC-ISBI 2016 Is 93.89%. The Proposed Approach Of Contrast Stretching Before The Segmentation Gives Satisfactory Results Of Segmentation. Further, The Proposed Wrapper-based Approach Of Feature Selection In Combination With The Random Forest Classifier Gives Promising Results As Compared To Other Commonly Used Classifiers.

SKIN CANCER DETECTION USING FLASK API- A Smartphone Based Application For Skin Cancer Classification Using Deep Learning With Clinical Images And Lesion Information

Over The Last Decades, The Incidence Of Skin Cancer, Melanoma And Non-melanoma, Has Increased At A Continuous Rate. In Particular For Melanoma, The Deadliest Type Of Skin Cancer, Early Detection Is Important To Increase Patient Prognosis. Recently, Deep Neural Networks (DNNs) Have Become Viable To Deal With Skin Cancer Detection. In This Work, We Present A Smartphone-based Application To Assist On Skin Cancer Detection. This Application Is Based On A Convolutional Neural Network(CNN) Trained On Clinical Images And Patients Demographics, Both Collected From Smartphones. Also, As Skin Cancer Datasets Are Imbalanced, We Present An Approach, Based On The Mutation Operator Of Differential Evolution (DE) Algorithm, To Balance Data. In This Sense, Beyond Provides A Flexible Tool To Assist Doctors On Skin Cancer Screening Phase, The Method Obtains Promising Results With A Balanced Accuracy Of 85% And A Recall Of 96%.

ACTIVITY RECOGNITION OF THE SPORT PERSON- Computer Vision-based Survey On Human Activity Recognition System Challenges And Applications

For Surveillance Purpose, Lots Of Method Were Used By The Researchers But Computer Vision Based Human Activity Recognition (HAR) Technologies/systems Received The Most Interest Because They Automatically Distinguish Human Behaviour And Movements From Video Data Utilizing Recorded Details From Cameras. But The Extraction Of Accurate And Opportune Information From Video Of Human's Activities And Behaviours Is Most Important And Difficult Task In Pervasive Computing Environment. Due To Lots Of Applications Of HAR Systems Like In Medical Field, Security, Visual Monitoring, Video Recovery, Entertainment And Irregular Behaviour Detection, The Accuracy Of System Is Most Important Factors For Researchers. This Review Article Presents A Brief Survey Of The Existing Video Or Vision-based HAR System To Find Out Their Challenges And Applications In Three Aspects Such As Recognition Of Activities, Activity Analysis, And Decision From Visual Content Representation. In Many Applications, System Recognition Time And Accuracy Is Most Important Factor And It Is Affected Due To An Increase In The Usage Of Simple Or Low Quality Type Cameras For Automated Systems. So, To Obtain A Better Accuracy And Fast Responses, The Usage Of Demanding And Computationally Intelligent Classification Techniques Such As Deep Learning And Machine Learning Is A Better Option For Researchers. In This Survey, We Addressed Numerous Computationally Intelligent Classification Techniques-based Research For HAR From 2010 To 2020 For A Better Analysis Of The Benefits And Drawbacks Of Systems, The Challenges Faced And Applications With Future Directions For HAR. We Also Present Some Accessible Problems And Ideas That Should Be Discussed In Future Research For The HAR System Utilizing Machine Learning And Deep Learning Principles Due To ThPeir Strong Relevance.

AIR CANVAS APPLICATION USING OPENCV AND NUMPY IN PYTHON

Writing In Air Has Been One Of The Most Fascinating And Challenging Research Areas In Field Of Image Processing And Pattern Recognition In The Recent Years. It Contributes Immensely To The Advancement Of An Automation Process And Can Improve The Interface Between Man And Machine In Numerous Applications. Several Research Works Have Been Focusing On New Techniques And Methods That Would Reduce The Processing Time While Providing Higher Recognition Accuracy. Object Tracking Is Considered As An Important Task Within The Field Of Computer Vision. The Invention Of Faster Computers, Availability Of Inexpensive And Good Quality Video Cameras And Demands Of Automated Video Analysis Has Given Popularity To Object Tracking Techniques. Generally, Video Analysis Procedure Has Three Major Steps: Firstly, Detecting Of The Object, Secondly Tracking Its Movement From Frame To Frame And Lastly Analysing The Behaviour Of That Object. For Object Tracking, Four Different Issues Are Taken Into Account; Selection Of Suitable Object Representation, Feature Selection For Tracking, Object Detection And Object Tracking. In Real World, Object Tracking Algorithms Are The Primarily Part Of Different Applications Such As: Automatic Surveillance, Video Indexing And Vehicle Navigation Etc.

BLIND PEOPLE GUIDENCE- Object Detection For Visually Impaired People Using SSD Algorithm

Visually Impaired People Are Unaware Of The Danger That They Are Facing In Their Life. They May Face Many Challenges While Performing Their Daily Activity Even In Their Familiar Environments. Vision Is The Necessary Human Senses And It Plays The Important Role In Human Perception About Surrounding Environment. Hence, There Are Variety Of Computer Vision Products And Services Which Are Used In The Development Of New Electronic Aids For Those Blind People. In This Paper We Designed To Provide Navigation To Those People. It Guides The People About The Object As Well As Provides The Distance Of The Object. The Algorithm Itself Calculates The Distance Of The Object. Here It Also Provides The Audio Jack To Insist Them About The Object. Here We Are Using SSD Algorithm For Object Detection And Calculating The Distance Of The Object By Using Monodepth Algorithm.

CROWD SOCIAL DISTANCE MEASUREMENT AND MASK DETECTION- Monitoring Pandemic Precautionary Protocols Using Real-time Surveillance And Artificial Intelligence

The Worst Possible Situation Faced By Humanity, COVID-19, Is Proliferating Across More Than 180 Countries And About 37,000,000 Confirmed Cases, Along With 1,000,000 Deaths Worldwide As Of October 2020. The Absence Of Any Medical And Strategic Expertise Is A Colossal Problem, And Lack Of Immunity Against It Increases The Risk Of Being Affected By The Virus. Since The Absence Of A Vaccine Is An Issue, Social Spacing And Face Covering Are Primary Precautionary Methods Apt In This Situation. This Study Proposes Automation With A Deep Learning Framework For Monitoring Social Distancing Using Surveillance Video Footage And Face Mask Detection In Public And Crowded Places As A Mandatory Rule Set For Pandemic Terms Using Computer Vision. The Paper Proposes A Framework Is Based On YOLO Object Detection Model To Define The Background And Human Beings With Bounding Boxes And Assigned Identifications. In The Same Framework, A Trained Module Checks For Any Unmasked Individual. The Automation Will Give Useful Data And Understanding For The Pandemic's Current Evaluation; This Data Will Help Analyse The Individuals Who Do Not Follow Health Protocol Norms.

DRIVER DROWSINESS DETECTION USING EYE GAZE DETECTION- Synchronous System For Driver Drowsiness Detection Using Convolutional Neural Network And Computer Vision

One Of The Major Reasons Behind Car Accidents Is The Drowsy Nature Acquired By A Driver While Driving Any Vehicle. Owing To The Ongoing Scenario, In This Project, We Aim To Develop A Real Time Driver Drowsiness Detection System In Order To Detect The Drivers' Fatigue Status, Such As Dozing, Flickering Of Eye Lids And Time Span Of Eye Closure Without Having To Equip Their Bodies With Devices. The Objective Of This Project Is To Build A Drowsiness Detection System That Will Detect That A Person's Eyes Are Closed For A Few Seconds. This System Will Alert The Driver When Drowsiness Is Detected. This Approach Is Based On The Convolutional Neural Network That Can Be Implemented On Android Applications With High Accuracy. Apart From CNN, Computer Vision Also Plays A Major Role To Detect The Drowsiness Pattern Of The Driver. Cloud Architecture Has Also Proved To Be Beneficial In Case Of Capturing And Analyzing Real Time Video Streams.

DRONE MOVEMENT ANALYSIS WITH REINFORCEMENT LEARNING Q- LEARNING Simulation Of Drone Controller Using Reinforcement Learning AI With Hyper Parameter Optimization

Drone Is One Of The Latest Drone Technologies That Grows With Multiple Applications; One Of The Critical Applications Is For Fire-fighting Drones Such As Water Hose Carrying For Firefighting. One Of The Main Challenges Of The Drone Technologies Is The Non-linear Dynamic Movement Caused By A Variety Of Fire Conditions. One Solution Is To Use A Nonlinear Controller Such As Reinforcement Learning. In This Paper, Reinforcement Learning Has Been Applied As Their Key Control System To Improve The Conventional Approach, Which Is The Agent (drone) That Will Interact With The Environment Without Need Of The Controller For The Flying Process. This Paper Is Introduced An Optimization Method For The Hyperparameter In Order To Achieve A Better Reward. In Addition, We Only Concentrate On The Learning Rate (alpha) And Potential Reward Factor Discount (gamma) For Optimization In This Paper. From This Optimization, The Better Performance And Response From Our Result By Using Alpha = 0.1 & Gamma = 0.8 With Reward Produced 6100 And It Takes 49 Seconds In The Learning Process.

OBJECT DETECTION USING CAFFE CNN YOLOV AND SSD- Object Detection And Tracking For Community Surveillance Using Transfer Learning

Automation Of Video Surveillance Is Gaining Extensive Interest Recently, Considering The Public Security Issues. In Recent Times, A Systematic And Exact Detection Of An Object Is A Foremost Point In Computer Vision Technology. With The Unfolding Of Recent Deep Learning Techniques, The Precision Of Detecting An Object Has Increased Greatly Thereby Igniting The Interest In This Area To Large Extent. Also, With The Advent Of Automatic Driving Electric Cars, The Accurate Detection Of Objects Has Gained Phenomenal Importance. The Main Aim Is To Integrate The State-of-the-art Deep Learning Method On Pedestrian Object Detection In Real-time With Improved Accuracy. One Of The Crucial Problems In Deep Learning Is Using Computer Vision Techniques, Which Tend To Slow Down The Process With Trivial Performance. In This Work, An Improved Yolov3 Transfer Learning-based Deep Learning Technique Is Used For Object Detection. It Is Also Shown That This Approach Can Be Used For Solving The Problem Of Object Detection In A Sustained Manner Having The Ability To Further Separate Occluded Objects. Moreover, The Use Of This Approach Has Enhanced The Accuracy Of Object Detection. The Network Used Is Trained On A Challenging Data Set And The Output Obtained Is Fast And Precise Which Is Helpful For The Application That Requires Object Detection.

SELF DRIVING CAR USING COMPUTER VISION- Simulation Of Self-driving Car Using Deep Learning

The Rapid Development Of Artificial Intelligence Has Revolutionized The Area Of Autonomous Vehicles By Incorporating Complex Models And Algorithms. Self-driving Cars Are Always One Of The Biggest Inventions In Computer Science And Robotic Intelligence. Highly Robust Algorithms That Facilitate The Functioning Of These Vehicles Will Reduce Many Problems Associated With Driving Such As The Drunken Driver Problem. In This Paper Our Aim Is To Build A Deep Learning Model That Can Drive The Car Autonomously Which Can Adapt Well To The Real-time Tracks And Does Not Require Any Manual Feature Extraction. This Research Work Proposes A Computer Vision Model That Learns From Video Data. It Involves Image Processing, Image Augmentation, Behavioural Cloning And Convolutional Neural Network Model. The Neural Network Architecture Is Used To Detect Path In A Video Segment, Linings Of Roads, Locations Of Obstacles, And Behavioural Cloning Is Used For The Model To Learn From Human Actions In The Video.

VIOLENCE DETECTION- Violence Detection From Video Under 2D Spatio-Temporal Representations

Action Recognition In Videos, Especially For Violence Detection, Is Now A Hot Topic In Computer Vision. The Interest Of This Task Is Related To The Multiplication Of Videos From Surveillance Cameras Or Live Television Content Producing Complex 2D+t Data. State-of-the-art Methods Rely On End-to-end Learning From 3D Neural Network Approaches That Should Be Trained With A Large Amount Of Data To Obtain Discriminating Features. To Face These Limitations, We Present In This Article A Method To Classify Videos For Violence Recognition Purpose, By Using A Classical 2D Convolutional Neural Network (CNN). The Strategy Of The Method Is Two-fold: (1) We Start By Building Several 2D Spatio-temporal Representations From An Input Video, (2) The New Representations Are Considered To Feed The CNN To The Train/test Process. The Classification Decision Of The Video Is Carried Out By Aggregating The Individual Decisions From Its Different 2D Spatio-temporal Representations. An Experimental Study On Public Datasets Containing Violent Videos Highlights The Interest Of The Presented Method.

RARE ITEM IDENDIFICATION USING APRIORI ALGORITHM- Modern Applications And Challenges For Rare Itemset Mining

Data Mining Is The Process Of Extracting Useful Unknown Knowledge From Large Datasets. Frequent Itemset Mining Is The Fundamental Task Of Data Mining That Aims At Discovering Interesting Itemsets That Frequently Appear Together In A Dataset. However, Mining Infrequent (rare) Itemsets May Be More Interesting In Many Real-life Applications Such As Predicting Telecommunication Equipment Failures, Genetics, Medical Diagnosis, Or Anomaly Detection. In This Paper, We Survey Up-to-date Methods Of Rare Itemset Mining. The Main Goal Of This Survey Is To Provide A Comprehensive Overview Of The State-of-the-art Algorithms Of Rare Itemset Mining And Its Applications. The Main Contributions Of This Survey Can Be Summarized As Follows. In The First Part, We Define The Task Of Rare Itemset Mining By Explaining Key Concepts And Terminology, Motivation Examples, And Comparisons With Underlying Concepts. Then, We Highlight The State-of-art Methods For Rare Itemsets Mining. Furthermore, We Present Variations Of The Task Of Rare Itemset Mining To Discuss Limitations Of Traditional Rare Itemset Mining Algorithms. After That, We Highlight The Fundamental Applications Of Rare Itemset Mining. In The Last, We Point Out Research Opportunities And Challenges For Rare Itemset Mining For Future Research.

STREAMING DATA CLASSIFICATION- Diversity In Ensemble Model For Classification Of Data Streams With Concept Drift

Data Streams Can Be Defined As The Continuous Stream Of Data In Many Forms Coming From Different Sources. Data Streams Are Usually Non-stationary With Continually Changing Their Underlying Structure. Solving Of Predictive Or Classification Tasks On Such Data Must Consider This Aspect. Traditional Machine Learning Models Applied On The Drifting Data May Become Invalid In The Case When A Concept Change Appears. To Tackle This Problem, We Must Utilize Special Adaptive Learning Models, Which Utilize Various Tools Able To Reflect The Drifting Data. One Of The Most Popular Groups Of Such Methods Are Adaptive Ensembles. This Paper Describes The Work Focused On The Design And Implementation Of A Novel Adaptive Ensemble Learning Model, Which Is Based On The Construction Of A Robust Ensemble Consisting Of A Heterogeneous Set Of Its Members. We Used K-NN, Naive Bayes And Hoeffding Trees As Base Learners And Implemented An Update Mechanism, Which Considers Dynamic Class-weighting And Q Statistics Diversity Calculation To Ensure The Diversity Of The Ensemble. The Model Was Experimentally Evaluated On The Streaming Datasets, And The Effects Of The Diversity Calculation Were Analyzed.

AIR AND WEATHER QUALITY- Machine Learning To Improve Numerical Weather Forecasting

This Paper Presents A Brief Overview Of Trends In Numerical Weather Prediction, Difficulties, And The Nature Of Their Occurrence, The Existing And Promising Ways To Overcome Them. The Neural Network Architecture Is Proposed As A Promising Approach To Increase The Accuracy Of The 2m Temperature Forecast Given By The COSMO Regional Model. This Architecture Allows Predicting Errors Of The Atmospheric Model Forecasts With Their Further Corrections. Experiments Are Conducted With Different Histories Of Regional Model Errors. The Number Of Epochs After Which Network Overfitting Happens Is Determined. It Is Shown That The Proposed Architecture Makes It Possible To Achieve An Improvement Of A 2m Temperature Forecast In Approximately 50% Of Cases.

COVID AND GDP GROWTH RATE PREDICTION- Forecasting The Impact Of COVID-19 On GDP Based On Adaboost

Covid-19 Pandemic Has A Unique Impact On The Economy As Other Infectious Diseases. Epidemics Affect People's Daily Consumption Activities, For Example, By Causing Them To Shop Less, Travel Less, Consume Less And Invest Less. The Reduction Of A Large Number Of Economic Activities Leads To The Suppression Of Social Demand And The Reduction Of Consumption Level, Which Further Affects The GDP Of Various Countries Around The World. It Is Necessary To Investigate And Analyze The Impact Of The Epidemic On GDP In Order To Control And Analyze The Economic Situation Under The Impact Of The Epidemic. In This Paper, We Take The Impact Of COVID-19 On The GDP Of Each Country As A Regression Problem, And Propose To Forecast GDP Through Feature Engineering Combined With Aaboost Model. The Model Was Tested On More Than 50,000 Data Records From More Than 200 Countries Provided By The Kaggle Platform To Prove The Validity. The Experiment Shows That Adaboost Has Stronger Robustness Compared With Other Methods, Such As Random Forest, SVR. Adaboost Improves The MSE Of Random Forest By 2.39 And SVR By 0.38.

DIABETICS PATIENTS READMISSION PREDICTION- Prediction Of Diabetic Patient Readmission Using Machine Learning

Hospital Readmissions Pose Additional Costs And Discomfort For The Patient And Their Occurrences Are Indicative Of Deficient Health Service Quality, Hence Efforts Are Generally Made By Medical Professionals In Order To Prevent Them. These Endeavors Are Especially Critical In The Case Of Chronic Conditions, Such As Diabetes. Recent Developments In Machine Learning Have Been Successful At Predicting Readmissions From The Medical History Of The Diabetic Patient. However, These Approaches Rely On A Large Number Of Clinical Variables Thereby Requiring Deep Learning Techniques. This Article Presents The Application Of Simpler Machine Learning Models Achieving Superior Prediction Performance While Making Computations More Tractable.

HOTEL BOOKING WITH DATA ANALYSIS- Performance Analysis Of Machine Learning Techniques To Predict Hotel Booking Cancellations In Hospitality Industry

Hotel Booking Cancellation Is Provided A Substantial Effects On Demand Management Decisions In The Hospitality Industry. The Goal Of This Work Is To Investigate The Effects Of Different Machine Learning Methods In Hotel Booking Cancellation Process. In This Work, We Gathered A Hotel Booking Cancellation Dataset From Kaggle Data Repository. Then, Different Feature Transformation Techniques Were Implemented Into Primary Dataset And Generate Transformed Datasets. Further, We Reduced Insignificant Variables Using Feature Selection Methods. Therefore, Various Classifiers Were Employed Into Primary And Generated Subsets. The Effects Of The Machine Learning Methods Were Evaluated And Explored The Best Approaches In This Step. Among All Of These Methods, We Found That XGBoost Is The Most Frequent Method To Analyze These Datasets. Besides, Individual Classifiers Are Generated The Highest Result For Information Gain Feature Selection Method. This Analysis Can Be Used As The Complementary Tool To Investigate Hotel Booking Cancellation Dataset More Effectively.

SMART IRRIGATION USING IDENTIFYING PLANT DISEASE AND WEATHER CONDITION- Smart Automated Irrigation System With Disease Prediction

Precision Agriculture Have Gained Wide Popularity In Recent Years For Its High-ranking Applications Such As Remote Environment Monitoring, Disease Detection, Insects And Pests Management Etc. In Addition, The Advancement In Internet Of Things (IOT) Through Which We Can Connect Real World Objects To Obtain The Information Such As Physical Phenomenon Through Sensors In The Field Of Agriculture. This Paper Reports On The Smart Automated Irrigation System With Disease Detection. The System Design Includes Soil Moisture Sensors, Temperature Sensors, Leaf Wetness Sensors Deployed In Agriculture Field, The Sensed Data From Sensors Will Be Compared With Pre-determined Threshold Values Of Various Soil And Specific Crops. The Deployed Sensors Data Are Fed To The Arduino Uno Processor Which Is Linked To The Data Centre Wirelessly Via GSM Module. The Data Received By The Data Centre Is Stored To Perform Data Analysis Using Data Mining Technique Such As Markov Model To Detect The Possible Disease For That Condition. Finally, The Analysis Results And Observed Physical Parameters Are Transmitted To Android Smart Phone And Displayed On User Interface. The User Interface In Smart Phone Allows Remote User To Control Irrigation System By Switching, On And Off, The Motor Pump By The Arduino Based On The Commands From The Android Smart Phone.

RAIN FALL PREDICTION USING ANN- Rainfall Prediction Using Machine Learning And Deep Learning Techniques

In India, Agriculture Is The Key Point For Survival. For Agriculture, Rainfall Is Most Important. These Days Rainfall Prediction Has Become A Major Problem. Prediction Of Rainfall Gives Awareness To People And Know In Advance About Rainfall To Take Certain Precautions To Protect Their Crop From Rainfall. Many Techniques Came Into Existence To Predict Rainfall. Machine Learning Algorithms Are Mostly Useful In Predicting Rainfall. Some Of The Major Machine Learning Algorithms Are ARIMA Model(Auto-Regressive Integrated Moving Average), Artificial Neural Network, Logistic Regression, Support Vector Machine And Self Organizing Map. Two Commonly Used Models Predict Seasonal Rainfall Such As Linear And Non-Linear Models. The First Models Are ARIMA Model. While Using Artificial Neural Network(ANN) Predicting Rainfall Can Be Done Using Back Propagation NN, Cascade NN Or Layer Recurrent Network. Artificial NN Is Same As Biological Neural Networks.

RESTAURANT MANAGEMENT SYSTEM- An Android Based Restaurant Automation System With Touch Screen

Online-to-offline (O2O) Commerce Connecting Service Providers And Individuals To Address Daily Human Needs Is Quickly Expanding. In Particular, On-demand Food, Whereby Food Orders Are Placed Online By Customers And Delivered By Couriers, Is Becoming Popular. This Novel Urban Food Application Requires Highly Efficient And Scalable Real-time Delivery Services. However, It Is Difficult To Recruit Enough Couriers And Route Them To Facilitate Such Food Ordering Systems. This Paper Presents An Online Crowdsourced Delivery (OCD) Approach For On-demand Food. Facilitated By Internet-of-Things And 3G/4G/5G Technologies, Public Riders Can Be Attracted To Act As Crowdsourced Workers Delivering Food By Means Of Shared Bicycles Or Electric Motorbikes. An Online Dynamic Optimization Framework Comprising Order Collection, Solution Generation, And Sequential Delivery Processes Is Presented. A Hybrid Metaheuristic Solution Process Integrating The Adaptive Large Neighborhood Search And Tabu Search Approaches Is Developed To Assign Food Delivery Tasks And Generate High-quality Delivery Routes In A Real-time Manner. The Crowdsourced Riders Are Dynamically Shared Among Different Food Providers. Simulated Small-scale And Real-world Large-scale On-demand Food Delivery Instances Are Used To Evaluate The Performance Of The Proposed Approach. The Results Indicate That The Presented Crowdsourced Food Delivery Approach Outperforms Traditional Urban Logistics. The Developed Hybrid Optimization Mechanism Is Able To Produce High-quality Crowdsourced Delivery Routes In Less Than 120 S. The Results Demonstrate That The Presented OCD Approach Can Facilitate City-scale On-demand Food Delivery.

PIZZA ORDERING SYSTEM USING TKINTER GUI- Online Crowdsourced Delivery For On-Demand Food

Online-to-offline (O2O) Commerce Connecting Service Providers And Individuals To Address Daily Human Needs Is Quickly Expanding. In Particular, On-demand Food, Whereby Food Orders Are Placed Online By Customers And Delivered By Couriers, Is Becoming Popular. This Novel Urban Food Application Requires Highly Efficient And Scalable Real-time Delivery Services. However, It Is Difficult To Recruit Enough Couriers And Route Them To Facilitate Such Food Ordering Systems. This Paper Presents An Online Crowdsourced Delivery (OCD) Approach For On-demand Food. Facilitated By Internet-of-Things And 3G/4G/5G Technologies, Public Riders Can Be Attracted To Act As Crowdsourced Workers Delivering Food By Means Of Shared Bicycles Or Electric Motorbikes. An Online Dynamic Optimization Framework Comprising Order Collection, Solution Generation, And Sequential Delivery Processes Is Presented. A Hybrid Metaheuristic Solution Process Integrating The Adaptive Large Neighborhood Search And Tabu Search Approaches Is Developed To Assign Food Delivery Tasks And Generate High-quality Delivery Routes In A Real-time Manner. The Crowdsourced Riders Are Dynamically Shared Among Different Food Providers. Simulated Small-scale And Real-world Large-scale On-demand Food Delivery Instances Are Used To Evaluate The Performance Of The Proposed Approach. The Results Indicate That The Presented Crowdsourced Food Delivery Approach Outperforms Traditional Urban Logistics. The Developed Hybrid Optimization Mechanism Is Able To Produce High-quality Crowdsourced Delivery Routes In Less Than 120 S. The Results Demonstrate That The Presented OCD Approach Can Facilitate City-scale On-demand Food Delivery.

E-HEALTH DATA- Interoperable And Discrete EHealth Data Exchange Between Hospital And Patient

In Order To Prevent Health Risks And Provide A Better Service To The Patients That Have Visited The Hospital, There Is A Need For Monitoring The Patients After Being Released And Providing The Data Submitted By The Patient EHealth Enablers To The Medical Personnel. This Article Proposes An Architecture For Providing The Secure Exchange Of Data Between The Patient Mobile Application And The Hospital Infrastructure. The Implemented Solution Is Validated On A Laboratory Testbed.

VOICE CHATTING AND VIDEO CONFERENCING WebRTC Role In Real-time Communication And Video Conferencing

Real-time Communication (RTC) Is A New Standard And Industry-wide Effort That Expand The Web Browsing Model, Allowing Access To Information In Areas Like Social Media, Chat, Video Conferencing, And Television Over The Internet, And Unified Communication. These Systems Users Can View, Record, Remark, Or Edit Video And Audio Content Flows Using Time-critical Cloud Infrastructures That Enforce The Quality Of Services. However, There Are Many Proprietary Protocols And Codecs Available That Are Not Easily Interoperable And Scalable To Implement Multipoint Videoconference Systems. WebRTC (Web Real-Time Communication) Is A State-of-the-Art Open Technology That Makes Real-time Communication Capabilities In Audio, Video, And Data Transmission Possible In Real-time Communication Through Web Browsers Using JavaScript APIs (Application Programming Interfaces) Without Plug-ins. This Paper Aims To Introduce The P2P Video Conferencing System Based On Web Real-Time Communication (WebRTC). In This Paper, We Have Proposed A Web-based Peer-to-peer Real-time Communication System Using The Mozilla Firefox Together With The ScaleDrone Service That Enables Users To Communicate With Highspeed Data Transmission Over The Communication Channel Using WebRTC Technology, HTML5 And Use Node.js Server Address. Our Experiments Show That WebRTC Is A Capable Building Block For Scalable Live Video Conferencing Within A Web Browser.

VIDEO STERNOGRAPHY- A New Video Steganography Scheme Based On Shi-Tomasi Corner Detector

Recent Developments In The Speed Of The Internet And Information Technology Have Made The Rapid Exchange Of Multimedia Information Possible. However, These Developments In Technology Lead To Violations Of Information Security And Private Information. Digital Steganography Provides The Ability To Protect Private Information That Has Become Essential In The Current Internet Age. Among All Digital Media, Digital Video Has Become Of Interest To Many Researchers Due To Its High Capacity For Hiding Sensitive Data. Numerous Video Steganography Methods Have Recently Been Proposed To Prevent Secret Data From Being Stolen. Nevertheless, These Methods Have Multiple Issues Related To Visual Imperceptibly, Robustness, And Embedding Capacity. To Tackle These Issues, This Paper Proposes A New Approach To Video Steganography Based On The Corner Point Principle And LSBs Algorithm. The Proposed Method First Uses Shi-Tomasi Algorithm To Detect Regions Of Corner Points Within The Cover Video Frames. Then, It Uses 4-LSBs Algorithm To Hide Confidential Data Inside The Identified Corner Points. Besides, Before The Embedding Process, The Proposed Method Encrypts Confidential Data Using Arnold’s Cat Map Method To Boost The Security Level.

SECRET KEY GENERATION- Securing Private Key Using New Transposition Cipher Technique

The Security Of Any Public Key Cryptosystem Depends On The Private Key Thus, It Is Important That Only An Authorized Person Can Have Access To The Private Key. The Paper Presents A New Algorithm That Protects The Private Key Using The Transposition Cipher Technique. The Performance Of The Proposed Technique Is Evaluated By Applying It In The RSA Algorithm's Generated Private Keys Using 512-bit, 1024-bit, And 2048-bit, Respectively. The Result Shows That The Technique Is Practical And Efficient In Securing Private Keys While In Storage As It Produced High Avalanche Effect.

QR CODE GENERATION AND RECOGNITION FOR DATA SECURITY- A Desktop Application Of QR Code For Data Security And Authentication

Initially The Barcodes Have Been Widely Used For The Unique Identification Of The Products. Quick Response I.e. QR Codes Are 2D Representation Of Barcodes That Can Embed Text, Audio, Video, Web URL, Phone Contacts, Credentials And Much More. This Paper Primarily Deals With The Generation Of QR Codes For Question Paper. We Have Proposed Encryption Of Question Paper Data Using AES Encryption Algorithm. The Working Of The QR Codes Is Based On Encrypting It To QR Code And Scanning To Decrypt It. Furthermore, We Have Reduced The Memory Storage By Redirecting To A Webpage Through The Transmission And Online Acceptance Of Data.

E-HEALTH DATA- Detecting The Malicious Application Using FRAppE

Communication Technology Has Completely Occupied All The Areas Of Applications. Last Decade Has However Witnessed A Drastic Evolution In Information And Communication Technology Due To The Introduction Of Social Media Network. Business Growth Is Further Achieved Via These Social Media. Nevertheless, Increase In The Usage Of Online Social Networks (OSN) Such As Face Book, Twitter, Instagram Etc Has However Led To The Increase In Privacy And Security Concerns. Third Party Applications Are One Of The Many Reasons For Facebook Attractiveness. Regrettably, The Users Are Unaware Of Detail That A Lot Of Malicious Facebook Applications Provide On Their Profile. The Popularity Of These Third Party Applications Is Such That There Are Almost 20 Million Installations Per Day. But Cyber Criminals Have Appreciated The Popularity Of Third Party Applications And The Possibility Of Using These Apps For Distributing The Malware And Spam. This Paper Proposes A Method To Categorize A Given Application As Malicious Or Safe By Using FRAppE (Facebook's Rigorous Application Evaluator), Possibly One Of The First Tool For Detecting Malicious Apps On The Facebook. To Develop The FRAppE, The Data Is Gathered From MyPagekeeper Application, A Website That Provides Significant Information About Various Third Party Applications And Their Insight Into Their Behavior.

NEGATIVE REVIEW AND POSITIVE REVIEW CLASSIFICATION- A Review On Machine Learning Based Students Academic Performance Prediction Systems

Prediction Of Academic Performance Of Students Beforehand Provides Scope To Universities To Lower Their Dropout Rate And Help The Students In Improving Their Performance. In This Field, Research Is Being Done To Find Out Which Algorithm Is Best To Use And Which Features Should Be Considered While Predicting The Academic Performance Of Students. This Kind Of Research Work Has Been Increasing Over The Years. This Paper Performs A Survey On The Techniques Used In Various Research Papers For Academic Performance Prediction And Also Point Out The Limitations If Any, In The Methodology Used.

REVIEW PREDICTION FOR AMAZON DATASET- Opinion Mining Based Fake Product Review Monitoring And Removal System

Fake Review Detection And Its Elimination From The Given Dataset Using Different Natural Language Processing (NLP) Techniques Is Important In Several Aspects. In This Article, The Fake Review Dataset Is Trained By Applying Two Different Machine Learning (ML) Models To Predict The Accuracy Of How Genuine Are The Reviews In A Given Dataset. The Rate Of Fake Reviews In E-commerce Industry And Even Other Platforms Is Increasing When Depend On Product Reviews For The Item Found Online On Different Websites And Applications. The Products Of The Company Were Trusted Before Making A Purchase. So This Fake Review Problem Must Be Addressed So That These Large E-commerce Industries Such As Flipkart, Amazon, Etc. Can Rectify This Issue So That The Fake Reviewers And Spammers Are Eliminated To Prevent Users From Losing Trust On Online Shopping Platforms. This Model Can Be Used By Websites And Applications With Few Thousands Of Users Where It Can Predict The Authenticity Of The Review Based On Which The Website Owners Can Take Necessary Action Towards Them. This Model Is Developed Using Naïve Bayes And Random Forest Methods. By Applying These Models One Can Know The Number Of Spam Reviews On A Website Or Application Instantly. To Counter Such Spammers, A Sophisticated Model Is Required In Which A Need To Be Trained On Millions Of Reviews. In This Work “amazon Yelp Dataset” Is Used To Train The Models And Its Very Small Dataset Is Used For Training On A Very Small Scale And Can Be Scaled To Get High Accuracy And Flexibility.

CHATBOT FOR EMOTIOON RECOGNITION- Model Of Multi-turn Dialogue In Emotional Chatbot

The Intent Recognition And Natural Language Understanding Of Multi-turn Dialogue Is Key For The Commercialization Of Chatbots. Chatbots Are Mainly Used For The Processing Of Specific Tasks, And Can Introduce Products To Customers Or Solve Related Problems, Thus Saving Human Resources. Text Sentiment Recognition Enables A Chatbot To Know The User's Emotional State And Select The Best Response, Which Is Important In Medical Care. In This Study, We Combined The Multi-turn Dialogue Model And Sentiment Recognition Model To Develop A Chatbot, That Is Designed For Used In Daily Conversations Rather Than For Specific Tasks. Thus, The Chatbot Has The Ability To Provide The Robot's Emotions As Feedback While Talking With A User. Moreover, It Can Exhibit Different Emotional Reactions Based On The Content Of The User's Conversation.

VIRTUAL ASSISTANT USING NATURAL LANGUAGE PROCESSING- Intelligent Personal Assistant - Implementing Voice Commands Enabling Speech Recognition

Intelligent Personal Assistant (IPA) Is A Software Agent Performing Tasks On Behalf Of An Human Or Individual I Based On Commands Or Questions Which Are Similar To Chat Bots. They Are Also Referred As Intelligent Virtual Assistant Which Interprets Human Speech And Respond Via Synthesized Voices. IPAs And IVAs Finds Their Usage In Various Applications Such As Home Automation, Manage To-do Tasks And Media Playback Through Voice. This Paper Aims To Propose Speech Recognition Systems And Dealing With Creating A Virtual Personal Assistant. The Existing System Serves On The Internet And Is Maintained By The Third Party. This Application Shall Protect Personal Data From Others And Use The Local Database, Speech Recognition And Synthesiser. A Parser Named SURR(Semantic Unification And Reference Resolution) Is Employed To Recognise The Speech. Synthesizer Uses Text To Phoneme.

CHATBOT FOR HEALTH CARE AND DIET PLAN- Artificial Intelligence AI Dietician For Personal Use

Poor Nutrition Can Lead To Reduced Immunity, Increased Susceptibility To Disease, Impaired Physical And Mental Development, And Reduced Productivity. A Conversational Agent Can Support People As A Virtual Coach, However Building Such Systems Still Have Its Associated Challenges And Limitations. This Paper Describes The Background And Motivation For Chatbot Systems In The Context Of Healthy Nutrition Recommendation. We Discuss Current Challenges Associated With Chatbotapplication, We Tackled Technical, Theoretical, Behavioural, And Social Aspects Of The Challenges. We Then Propose A Pipeline To Be Used As Guidelines By Developers To Implement Theoretically And Technically Robust Chatbot Systems.

VOICE BASED ASCENDING AND DECENDING ORDER EXECUTION- Voice Based Sorting Using Pyaudio And Also Searching Operations

Voice Control Is A Major Growing Feature That Change The Way People Can Live. The Voice Assistant Is Commonly Being Used In Smartphones And Laptops. AI-based Voice Assistants Are The Operating Systems That Can Recognize Human Voice And Respond Via Integrated Voices. This Voice Assistant Will Gather The Audio From The Microphone And Then Convert That Into Text, Later It Is Sent Through GTTS (Google Text To Speech). GTTS Engine Will Convert Text Into Audio File In English Language, Then That Audio Is Played Using Play Sound Package Of Python Programming Language.

BLOOD CANCER CELL CLASSIFICATION USING DENSENET- Medical Image Classification Using A Light-Weighted Hybrid Neural Network Based On PCANet And DenseNet

Medical Image Classification Plays An Important Role In Disease Diagnosis Since It Can Provide Important Reference Information For Doctors. The Supervised Convolutional Neural Networks (CNNs) Such As DenseNet Provide The Versatile And Effective Method For Medical Image Classification Tasks, But They Require Large Amounts Of Data With Labels And Involve Complex And Time-consuming Training Process. The Unsupervised CNNs Such As Principal Component Analysis Network (PCANet) Need No Labels For Training But Cannot Provide Desirable Classification Accuracy. To Realize The Accurate Medical Image Classification In The Case Of A Small Training Dataset, We Have Proposed A Light-weighted Hybrid Neural Network Which Consists Of A Modified PCANet Cascaded With A Simplified DenseNet. The Modified PCANet Has Two Stages, In Which The Network Produces The Effective Feature Maps At Each Stage By Convoluting Inputs With Various Learned Kernels. The Following Simplified DenseNet With A Small Number Of Weights Will Take All Feature Maps Produced By The PCANet As Inputs And Employ The Dense Shortcut Connections To Realize Accurate Medical Image Classification. To Appreciate The Performance Of The Proposed Method, Some Experiments Have Been Done On Mammography And Osteosarcoma Histology Images. Experimental Results Show That The Proposed Hybrid Neural Network Is Easy To Train And It Outperforms Such Popular CNN Models As PCANet, ResNet And DenseNet In Terms Of Classification Accuracy, Sensitivity And Specificity.

BREAST CANCER CLASSIFICATION WITH FEATURE DATA- Breast Cancer Detection-Based Feature Optimization Using Firefly Algorithm And Ensemble Classifier

The Accurate Detection Of The Malignant Cell Of Breast Cancer, Decrease The Rate Of Mortality Of Women Around The World. The Process Of Feature Optimization, Increase The Probability Of Feature Selection And Mapping Of Data. This Paper Proposed Ensemble-based Classifier For Detection Of Breast Cancer On An Early Stage. The Proposed Ensemble Classifier Support Vector Machine Is A Base Classifier, And The Other Is Boost Classifier. The Firefly Algorithm Reduces The Variance Of Breast Cancer Features For The Selection Of Feature Components For The Classification Algorithm. The Most Dominated Work Is The Extraction Of Features Of Breast Cancer Image. For The Extraction Of Features Applied Wavelet Packet Transform, Wavelet Packet Transform Overcome The Limitation Of Wavelet Transform And Increase The Diversity Of Feature Extraction Process. The Proposed Algorithm Implemented In MATLAB Software And Tested With The Very Reputed Breast Cancer Image Dataset, MIAS And DDSM. The Proposed Algorithm’s Performance Measured With Standard Parameters Such As Accuracy, Specificity, Sensitivity, And MCC. The Evaluated Results Indicate That The Proposed Algorithm Is Better Than DWPT, SVM And BMC. The Increasing Ratio Of The Classification Algorithm Is 8% Instead Of Existing Algorithms.

HUMAN DISEASE PREDICTION USING SYMPTOMS BY MACHINE LEARNING- Symptoms Based Disease Prediction Using Machine Learning Techniques

Computer Aided Diagnosis (CAD) Is Quickly Evolving, Diverse Field Of Study In Medical Analysis. Significant Efforts Have Been Made In Recent Years To Develop Computer-aided Diagnostic Applications, As Failures In Medical Diagnosing Processes Can Result In Medical Therapies That Are Severely Deceptive. Machine Learning (ML) Is Important In Computer Aided Diagnostic Test. Object Such As Body-organs Cannot Be Identified Correctly After Using An Easy Equation. Therefore, Pattern Recognition Essentially Requires Training From Instances. In The Bio Medical Area, Pattern Detection And ML Promises To Improve The Reliability Of Disease Approach And Detection. They Also Respect The Dispassion Of The Method Of Decisions Making. ML Provides A Respectable Approach To Make Superior And Automated Algorithm For The Study Of High Dimension And Multi - Modal Bio Medicals Data. The Relative Study Of Various ML Algorithm For The Detection Of Various Disease Such As Heart Disease, Diabetes Disease Is Given In This Survey Paper. It Calls Focus On The Collection Of Algorithms And Techniques For ML Used For Disease Detection And Decision Making Processes.

COVID FOR PNEUMONIA CLASSIFICATION USING DENSENET- Deep Learning Based Detection And Segmentation Of COVID-19 - Pneumonia On Chest X-ray Image

The Outbreaks Of COVID-19 Virus Have Crossed The Limit To Our Expectation And It Breaks All Previous Records Of Virus Outbreaks. The Effect Of Corona Virus Causes A Serious Illness May Result In Death As A Consequence Of Substantial Alveolar Damage And Progressive Respiratory Failure. Automatic Detection And Classification Of This Virus From Chest X-ray Image Using Computer Vision Technology Can Be Very Useful Complement With Respect To The Less Sensitive Traditional Process Of Detecting COVID-19 I.e. Reverse Transcription Polymerase Chain Reaction (RT-PCR). This Automated Process Offers A Great Potential To Enhance The Conventional Healthcare Tactic For Tackling COVID-19 And Can Mitigate The Shortage Of Trained Physicians In Remote Communities. Again, The Segmentation Of The Infected Regions From Chest X-ray Image Can Help The Medical Specialists To View Insights Of The Affected Region. So, In This Paper We Have Used Deep Learning Based Ensemble Model For The Classification Of COVID-19, Pneumonia And Normal X-ray Image And For Segmentation We Have Used DenseNet Based U-Net Architecture To Segment The Affected Region. For Making The Ground Truth Mask Image Which Is Needed For Segmenting Purpose, We Have Used Amazon SageMaker Ground Truth Tool To Manually Crop The Activation Region (discriminative Image Regions By Which CNN Identify A Specific Class Using Grad-CAM Algorithm) Of The X-ray Image. We Have Found The Classification Accuracy 99.2% On The Available X-ray Dataset And 92% Average Accuracy From The Segmentation Process.

FALL DETECTION USING MOTION SENSOR DATA- Human Falling Detection Algorithm Based On Multisensor Data Fusion With SVM

Falling Is A Common Phenomenon In The Life Of The Elderly, And It Is Also One Of The 10 Main Causes Of Serious Health Injuries And Death Of The Elderly. In Order To Prevent Falling Of The Elderly, A Real-time Fall Prediction System Is Installed On The Wearable Intelligent Device, Which Can Timely Trigger The Alarm And Reduce The Accidental Injury Caused By Falls. At Present, Most Algorithms Based On Single-sensor Data Cannot Accurately Describe The Fall State, While The Fall Detection Algorithm Based On Multisensor Data Integration Can Improve The Sensitivity And Specificity Of Prediction. In This Study, We Design A Fall Detection System Based On Multisensor Data Fusion And Analyze The Four Stages Of Falls Using The Data Of 100 Volunteers Simulating Falls And Daily Activities. In This Paper, Data Fusion Method Is Used To Extract Three Characteristic Parameters Representing Human Body Acceleration And Posture Change, And The Effectiveness Of The Multisensor Data Fusion Algorithm Is Verified. The Sensitivity Is 96.67%, And The Specificity Is 97%. It Is Found That The Recognition Rate Is The Highest When The Training Set Contains The Largest Number Of Samples In The Training Set. Therefore, After Training The Model Based On A Large Amount Of Effective Data, Its Recognition Ability Can Be Improved, And The Prevention Of Fall Possibility Will Gradually Increase. In Order To Compare The Applicability Of Random Forest And Support Vector Machine (SVM) In The Development Of Wearable Intelligent Devices, Two Fall Posture Recognition Models Were Established, Respectively, And The Training Time And Recognition Time Of The Models Are Compared. The Results Show That SVM Is More Suitable For The Development Of Wearable Intelligent Devices.

BREAST CANCER CLASSIFICATION- Breast Cancer Detection And Classification

Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components I.e. Erythrocytes And Platelets By Using Statistical Parameters Such As Mean, Standard Deviation. For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.

LEUKIMEA CLASSIFICATION- Detection Of Blood Cancer-Leukemia Using K-means Algorithm

Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components I.e. Erythrocytes And Platelets By Using Statistical Parameters Such As Mean, Standard Deviation. For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.

ORAL CANCER CLASSIFIIACTION USING VGG16- Automated Detection And Classification Of Oral Lesions Using Deep Learning For Early Detection Of Oral Cancer

Oral Cancer Is A Major Global Health Issue Accounting For 177,384 Deaths In 2018 And It Is Most Prevalent In Low- And Middle-income Countries. Enabling Automation In The Identification Of Potentially Malignant And Malignant Lesions In The Oral Cavity Would Potentially Lead To Low-cost And Early Diagnosis Of The Disease. Building A Large Library Of Well-annotated Oral Lesions Is Key. As Part Of The MeMoSA ® (Mobile Mouth Screening Anywhere) Project, Images Are Currently In The Process Of Being Gathered From Clinical Experts From Across The World, Who Have Been Provided With An Annotation Tool To Produce Rich Labels. A Novel Strategy To Combine Bounding Box Annotations From Multiple Clinicians Is Provided In This Paper. Further To This, Deep Neural Networks Were Used To Build Automated Systems, In Which Complex Patterns Were Derived For Tackling This Difficult Task. Using The Initial Data Gathered In This Study, Two Deep Learning Based Computer Vision Approaches Were Assessed For The Automated Detection And Classification Of Oral Lesions For The Early Detection Of Oral Cancer, These Were Image Classification With ResNet-101 And Object Detection With The Faster R-CNN. Image Classification Achieved An F1 Score Of 87.07% For Identification Of Images That Contained Lesions And 78.30% For The Identification Of Images That Required Referral. Object Detection Achieved An F1 Score Of 41.18% For The Detection Of Lesions That Required Referral. Further Performances Are Reported With Respect To Classifying According To The Type Of Referral Decision. Our Initial Results Demonstrate Deep Learning Has The Potential To Tackle This Challenging Task.

EFFECTIVE HEART DISEASE PREDICTION USING HYBRID MACHINE LEARNING TECHNIQUES

Heart Disease Is One Of The Most Significant Causes Of Mortality In The World Today. Prediction Of Cardio Vascular Disease Is A Critical Challenge In The Area Of Clinical Data Analysis. Machine Learning (ML) Has Been Shown To Be Effective In Assisting In Making Decisions And Predictions From The Large Quantity Of Data Produced By The Health Care Industry. We Have Also Seen ML Techniques Being Used In Recent Developments In Different Areas Of The Internet Of Things (IoT). Various Studies Give Only A Glimpse Into Predicting Heart Disease With ML Techniques. In This Paper, We Propose A Novel Method That Aims At finding Significant Features By Applying Machine Learning Techniques Resulting In Improving The Accuracy In The Prediction Of Cardiovascular Disease. The Prediction Model Is Introduced With Different Combinations Of Features And Several Known Classification Techniques.

HUMAN SIGNATURE CLASSIFICATION- Handwritten Signature Recognition- A Convolutional Neural Network Approach

Handwritten Signature Recognition Is An Important Behavioral Biometric Which Is Used For Numerous Identification And Authentication Applications. There Are Two Fundamental Methods Of Signature Recognition, On-line Or Off-line. On-line Recognition Is A Dynamic Form, Which Uses Parameters Like Writing Pace, Change In Stylus Direction And Number Of Pen Ups And Pen Downs During The Writing Of The Signature. Off-line Signature Recognition Is A Static Form Where A Signature Is Handled As An Image And The Author Of The Signature Is Predicted Based On The Features Of The Signature. The Current Method Of Off-line Signature Recognition Predominantly Employs Template Matching, Where A Test Image Is Compared With Multiple Specimen Images To Speculate The Author Of The Signature. This Takes Up A Lot Of Memory And Has A Higher Time Complexity. This Paper Proposes A Method Of Off-line Signature Recognition Using Convolution Neural Network. The Purpose Of This Paper Is To Obtain High Accuracy Multi-class Classification With A Few Training Signature Samples. Images Are Preprocessed To Isolate The Signature Pixels From The Background/noise Pixels Using A Series Of Image Processing Techniques. Initially, The System Is Trained With 27 Genuine Signatures Of 10 Different Authors Each. A Convolution Neural Network Is Used To Predict A Test Signature Belongs To Which Of The 10 Given Authors. Different Public Datasets Are Used To Demonstrate Effectiveness Of The Proposed Solution.

ONLINE VOTING USING OTPBY DJANGO- Smart Online Voting System

Our Country, India Is The Largest Democratic Country In The World. So It Is Essential To Make Sure That The Governing Body Is Elected Through A Fair Election. India Has Only Offline Voting System Which Is Not Effective And Upto The Mark As It Requires Large Man Force And It Also Requires More Time To Process And Publish The Results. Therefore, To Be Made Effective, The System Needs A Change, Which Overcomes These Disadvantages. The New Method Does Not Force The Person's Physical Appearance To Vote, Which Makes The Things Easier. This Paper Focusses On A System Where The User Can Vote Remotely From Anywhere Using His/her Computer Or Mobile Phone And Doesn't Require The Voter To Got To The Polling Station Through Two Step Authentication Of Face Recognition And OTP System. This Project Also Allows The User To Vote Offline As Well If He/she Feels That Is Comfortable. The Face Scanning System Is Used To Record The Voters Face Prior To The Election And Is Useful At The Time Of Voting. The Offline Voting System Is Improvised With The Help Of RFID Tags Instead Of Voter Id. This System Also Enables The User The Citizens To See The Results Anytime Which Can Avoid Situations That Pave Way To Vote Tampering.

FACE RECOGNITION BASED ATTENDENCE SYSTEM- Automated Smart Attendance System Using Face Recognition

In The Human Body, The Face Is The Most Crucial Factor In Identifying Each Person As It Contains Many Vital Details. There Are Different Prevailing Methods To Capture Person's Presence Like Biometrics To Take Attendance Which Is A Time-consuming Process. This Paper Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., LBP Algorithm To Record The Student Attendance. LBP (Local Binary Pattern) Is One Among The Methods And Is Popular As Well As Effective Technique Used For The Image Representation And Classification And It Was Chosen For Its Robustness To Pose And Illumination Shifts. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. The Database Is Updated Upon The Enrolment Of The Student Using An Automation Process That Also Includes Name And Rolls Number. ASAS Marks Individual Attendance, If The Captured Image Matches The Image In The Database I.e., If Both Images Are Identical. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence.

MENTAL HEALTH IDENTIFICATION USING FACE EMOTION RECOGNITION- Facial Expression Recognition And Recommendations Using Deep Neural Network With Transfer Learning

Human Facial Emotion Recognition (FER) Has Attracted The Attention Of The Research Community For Its Promising Applications. Mapping Different Facial Expressions To The Respective Emotional States Are The Main Task In FER. The Classical FER Consists Of Two Major Steps: Feature Extraction And Emotion Recognition. Currently, The Deep Neural Networks, Especially The Convolutional Neural Network (CNN), Is Widely Used In FER By Virtue Of Its Inherent Feature Extraction Mechanism From Images. Several Works Have Been Reported On CNN With Only A Few Layers To Resolve FER Problems. However, Standard Shallow CNNs With Straightforward Learning Schemes Have Limited Feature Extraction Capability To Capture Emotion Information From High-resolution Images. A Notable Drawback Of The Most Existing Methods Is That They Consider Only The Frontal Images (i.e., Ignore Profile Views For Convenience), Although The Profile Views Taken From Different Angles Are Important For A Practical FER System. For Developing A Highly Accurate FER System, This Study Proposes A Very Deep CNN (DCNN) Modeling Through Transfer Learning (TL) Technique Where A Pre-trained DCNN Model Is Adopted By Replacing Its Dense Upper Layer(s) Compatible With FER, And The Model Is Fine-tuned With Facial Emotion Data. A Novel Pipeline Strategy Is Introduced, Where The Training Of The Dense Layer(s) Is Followed By Tuning Each Of The Pre-trained DCNN Blocks Successively That Has Led To Gradual Improvement Of The Accuracy Of FER To A Higher Level. The Proposed FER System Is Verified On Eight Different Pre-trained DCNN Models (VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3 And DenseNet-161) And Well-known KDEF And JAFFE Facial Image Datasets. FER Is Very Challenging Even For Frontal Views Alone. FER On The KDEF Dataset Poses Further Challenges Due To The Diversity Of Images With Different Profile Views Together With Frontal Views. The Proposed Method Achieved Remarkable Accuracy On Both Datasets With Pre-trained Models. On A 10-fold Cross-validation Way, The Best Achieved FER Accuracies With DenseNet-161 On Test Sets Of KDEF And JAFFE Are 96.51% And 99.52%, Respectively. The Evaluation Results Reveal The Superiority Of The Proposed FER System Over The Existing Ones Regarding Emotion Detection Accuracy. Moreover, The Achieved Performance On The KDEF Dataset With Profile Views Is Promising As It Clearly Demonstrates The Required Proficiency For Real-life Applications.

NON-CONTACT HEART RATE MONITORING USING MACHINE LEARNING

Health Monitoring Is An Important Parameter To Determine The Health Status Of A Person. Measuring The Heart Rate Is An Easy Way To Gauge Our Health. Normal Heart Rate May Vary From Person To Person And A Usually High Or Low Resting Heart Rate Can Be A Sign Of Trouble. There Are Several Methods For The Measurement Of Heart Rate Monitoring Such As Ecg, Ppg Etc. Such Methods Having A Disadvantage That These Are Invasive And Have A Continuous Contact With The Human Body. In Order To Overcome This Problem A New System Is Proposed Using Camera. In This Method A Blind Source Separation Algorithm Is Used For Extracting The Heart Rate Signal From The Face Image. Viola Jones Based Face Detection Algorithm Is Used To Track The Face. FastICA Algorithm Is Exploited To Separate Heart Rate Signal From Noise And Artefacts. Machine Learning Algorithm Is Implemented To Standardize The Signal. The Data Is Successfully Tested With Real Time Video.

SMART DOOR USING WEBCAM AND FINGERPRINT- Image Processing Technique For Smart Home Security Based On The Principal Component Analysis PCA Methods

Smart Home Is One Application Of The Pervasive Computing Branch Of Science. Three Categories Of Smart Homes, Namely Comfort, Healthcare, And Security. The Security System Is A Part Of Smart Home Technology That Is Very Important Because The Intensity Of Crime Is Increasing, Especially In Residential Areas. The System Will Detect The Face By The Webcam Camera If The User Enters The Correct Password. Face Recognition Will Be Processed By The Raspberry Pi 3 Microcontroller With The Principal Component Analysis Method Using OpenCV And Python Software Which Has Outputs, Namely Actuators In The Form Of A Solenoid Lock Door And Buzzer. The Test Results Show That The Webcam Can Perform Face Detection When The Password Input Is Successful, Then The Buzzer Actuator Can Turn On When The Database Does Not Match The Data Taken By The Webcam Or The Test Data And The Solenoid Door Lock Actuator Can Run If The Database Matches The Test Data Taken By The Sensor. Webcam. The Mean Response Time Of Face Detection Is 1.35 Seconds.

SIGN LANGUAGE RECOGNITION- Real-Time Recognition Of Indian Sign Language

The Real-time Sign Language Recognition System Is Developed For Recognising The Gestures Of Indian Sign Language (ISL). Generally, Sign Languages Consist Of Hand Gestures And Facial Expressions. For Recognising The Signs, The Regions Of Interest (ROI) Are Identified And Tracked Using The Skin Segmentation Feature Of OpenCV. The Training And Prediction Of Hand Gestures Are Performed By Applying Fuzzy C-means Clustering Machine Learning Algorithm. The Gesture Recognition Has Many Applications Such As Gesture Controlled Robots And Automated Homes, Game Control, Human-Computer Interaction (HCI) And Sign Language Interpretation. The Proposed System Is Used To Recognize The Real-time Signs. Hence It Is Very Much Useful For Hearing And Speech Impaired People To Communicate With Normal People.

FACE RECOGNITION- Face Detection And Recognition System Using Digital Image Processing

While Recognizing Any Individual, The Most Important Attribute Is Face. It Serves As An Individual Identity Of Everyone And Therefore Face Recognition Helps In Authenticating Any Person's Identity Using His Personal Characteristics. The Whole Procedure For Authenticating Any Face Data Is Sub-divided Into Two Phases, In The First Phase, The Face Detection Is Done Quickly Except For Those Cases In Which The Object Is Placed Quite Far, Followed By This The Second Phase Is Initiated In Which The Face Is Recognized As An Individual. Then The Whole Process Is Repeated Thereby Helping In Developing A Face Recognition Model Which Is Considered To Be One Of The Most Extremely Deliberated Biometric Technology. Basically, There Are Two Type Of Techniques That Are Currently Being Followed In Face Recognition Pattern That Is, The Eigenface Method And The Fisherface Method. The Eigenface Method Basically Make Use Of The PCA (Principal Component Analysis) To Minimize The Face Dimensional Space Of The Facial Features. The Area Of Concern Of This Paper Is Using The Digital Image Processing To Develop A Face Recognition System.

FACE EXPRESSION RECOGNITION SYSTEM- Facial Expression Recognition With Convolutional Neural Networks

Emotions Are A Powerful Tool In Communication And One Way That Humans Show Their Emotions Is Through Their Facial Expressions. One Of The Challenging And Powerful Tasks In Social Communications Is Facial Expression Recognition, As In Non-verbal Communication, Facial Expressions Are Key. In The Field Of Artificial Intelligence, Facial Expression Recognition (FER) Is An Active Research Area, With Several Recent Studies Using Convolutional Neural Networks (CNNs). In This Paper, We Demonstrate The Classification Of FER Based On Static Images, Using CNNs, Without Requiring Any Pre-processing Or Feature Extraction Tasks. The Paper Also Illustrates Techniques To Improve Future Accuracy In This Area By Using Pre-processing, Which Includes Face Detection And Illumination Correction. Feature Extraction Is Used To Extract The Most Prominent Parts Of The Face, Including The Jaw, Mouth, Eyes, Nose, And Eyebrows. Furthermore, We Also Discuss The Literature Review And Present Our CNN Architecture, And The Challenges Of Using Max-pooling And Dropout, Which Eventually Aided In Better Performance. We Obtained A Test Accuracy Of 61.7% On FER2013 In A Seven-classes Classification Task Compared To 75.2% In State-of-the-art Classification.

Deep Learning And Audio Based Emotion Recognition

While Tightening And Expansion Of Our Facial Muscles Cause Some Changes Called Facial Expressions As A Reaction To The Different Kinds Of Emotional Situations Of Our Brain, Similarly There Are Some Physiological Changes Like Tone, Loudness, Rhythm And Intonation In Our Voice, Too. These Visual And Auditory Changes Have A Great Importance For Human-human Interaction Human-machine Interaction And Human-computer Interaction As They Include Critical Information About Humans' Emotional Situations. Automatic Emotion Recognition Systems Are Defined As Systems That Can Analyze Individual's Emotional Situation By Using This Distinctive Information. In This Study, An Automatic Emotion Recognition System In Which Auditory Information Is Analyzed And Classified In Order To Recognize Human Emotions Is Proposed. In The Study Spectral Features And MFCC Coefficients Which Are Commonly Used For Feature Extraction From Voice Signals Are Firstly Used, And Then Deep Learning-based LSTM Algorithm Is Used For Classification. Suggested Algorithm Is Evaluated By Using Three Different Audio Data Sets (SAVEE, RAVADES And RML).

AN END-TO-END OPTICAL CHARACTER RECOGNITION PIPELINE FOR INDONESIAN IDENTITY CARD

Exponential Growth Of Fake ID Cards Generation Leads To Increased Tendency Of Forgery With Severe Security And Privacy Threats. University ID Cards Are Used To Authenticate Actual Employees And Students Of The University. Manual Examination Of ID Cards Is A Laborious Activity, Therefore, In This Paper, We Propose An Effective Automated Method For Employee/student Authentication Based On Analyzing The Cards. Additionally, Our Method Also Identifies The Department Of Concerned Employee/student. For This Purpose, We Employ Different Image Enhancement And Morphological Operators To Improve The Appearance Of Input Image Better Suitable For Recognition. More Specifically, We Employ Median Filtering To Remove Noise From The Given Input Image.

STONE INSCRIPTION IDENTIFICATION USING OPTICAL CHARACTER RECOGNITION- A Survey On Ancient Marathi Script Recognition From Stone Inscriptions

Rigorous Research Has Been Done On Ancient Indian Script Character Recognition. Many Research Articles Are Published In Last Few Decades. Number Of OCR Techniques Is Available In Market, But OCR Techniques Are Not Useful For Ancient Script Recognition. But More Research Work Is Required To Recognize Ancient Marathi Scripts. This Paper Presents Different Techniques Which Are Published By Different Researchers To Recognize Ancient Scripts. Also Challenges In Recognition Of Ancient Marathi Scripts Are Discussed In This Paper.

LIE OR TRUTH DETECTION USING VGG-16

Deep Neural Networks Achieve Best Classification Accuracy On Videos. However, Traditional Methods Or Shallow Architectures Remain Competitive And Combinations Of Different Network Types Are The Usual Chosen Approach. A Reason For This Less Important Impact Of Deep Methods For Video Recognition Is The Motion Representation. The Time Has A Stronger Redundancy, And An Important Elasticity Compared To The Spatial Dimensions. The Temporal Redundancy Is Evident, But The Elasticity Within An Action Class Is Well Less Considered .Several Instances Of The Action Still Widely Differ By Their Style And Speed Of Execution.

ROAD ACCIDENT DETECTION USING VIDEO CLASSIFICATION- A Novel Approach For Road Accident Detection Using DETR Algorithm

Road Accidents Are Man-made Cataclysmic Phenomena And Are Not Generally Predictable. With Increasing Numbers Of Deaths Due To Accidents In The Roadways, A Smart And Fast Detection System For Road Accidents Is The Need Of The Hour. Often, Precious Few Seconds After The Accidents Make The Difference Between Life And Death. To Address This Problem More Efficiently, “A Novel Approach For Road Accident Detection In CCTV Videos Using DETR Algorithm” Has Been Developed To Aid In Notifying Hospitals And The Local Police At Places Where Instant Notification Is Seldom Feasible. This Paper Presents A Novel And Efficient Method For Detecting Road Accidents With DETR (Detection Transformers) And Random Forest Classifier. Objects Such As Cars, Bikes, People, Etc. In The CCTV Footage Are Detected Using The DETR And The Features Are Fed To A Random Forest Classifier For Frame Wise Classification. Each Frame Of The Video Is Classified As An Accident Frame Or A Non-accident Frame. A Total Count Of Predicted Accident Frames From Any 60 Continuous Frames Of The Video Are Considered Using A Sliding Window Technique Before The Final Decision Is Made. Simulation Results Show That The Proposed System Achieves 78.2% Detection Rate In CCTV Videos.

SPORT ACTION CLASSIFICATION USING GAN- Improving Human Pose Estimation With Self-Attention Generative Adversarial Networks

Human Pose Estimation In Images Is Challenging And Important For Many Computer Vision Applications. Large Improvements In Human Pose Estimation Have Been Achieved With The Development Of Convolutional Neural Networks. Even Though, When Encountered Some Difficult Cases Even The State-of-the-art Models May Fail To Predict All The Body Joints Correctly. Some Recent Works Try To Refine The Pose Estimator. GAN (Generative Adversarial Networks) Has Been Proved To Be Efficient To Improve Human Pose Estimation. However, GAN Can Only Learn Local Body Joints Structural Constrains. In This Paper, We Propose To Apply Self-Attention GAN To Further Improve The Performance Of Human Pose Estimation. With Attention Mechanism In The Framework Of GAN, We Can Learn Long-range Body Joints Dependencies, Therefore Enforce The Entire Body Joints Structural Constrains To Make All The Body Joints To Be Consistent. Our Method Outperforms Other State-of-the-art Methods On Two Standard Benchmark Datasets MPII And LSP For Human Pose Estimation.

SPEECH EMOTION RECOGNITION- Speech Emotion Recognition With Multiscale Area Attention And Data Augmentation

In Speech Emotion Recognition (SER), Emotional Characteristics Often Appear In Diverse Forms Of Energy Patterns In Spectrograms. Typical Attention Neural Network Classifiers Of SER Are Usually Optimized On A Fixed Attention Granularity. In This Paper, We Apply Multiscale Area Attention In A Deep Convolutional Neural Network To Attend Emotional Characteristics With Varied Granularities And Therefore The Classifier Can Benefit From An Ensemble Of Attentions With Different Scales. To Deal With Data Sparsity, We Conduct Data Augmentation With Vocal Tract Length Perturbation (VTLP) To Improve The Generalization Capability Of The Classifier. Experiments Are Carried Out On The Interactive Emotional Dyadic Motion Capture (IEMOCAP) Dataset. We Achieved 79.34% Weighted Accuracy (WA) And 77.54% Unweighted Accuracy (UA), Which, To The Best Of Our Knowledge, Is The State Of The Art On This Dataset.

AUDIO OF SPEAKER RECOGNITION- Automatic Speaker Recognition System Based On Machine Learning Algorithms

Speaker Recognition Is A Technique Used To Automatically Recognize A Speaker From A Recording Of Their Voice Or Speech Utterance. Speaker Recognition Technology Has Improved Over Recent Years And Has Become Inexpensive And And Reliable Method For Person Identification And Verification. Research In The Field Of Speaker Recognition Has Now Spanned Over Five Decades And Has Shown Fruitful Results, However There Is Not Much Work Done With Regards To South African Indigenous Languages. This Paper Presents The Development Of An Automatic Speaker Recognition System That Incorporates Classification And Recognition Of Sepedi Home Language Speakers. Four Classifier Models, Namely, Support Vector Machines, K-Nearest Neighbors, Multilayer Perceptrons (MLP) And Random Forest (RF), Are Trained Using WEKA Data Mining Tool. Auto-WEKA Is Applied To Determine The Best Classifier Model Together With Its Best Hyper-parameters. The Performance Of Each Model Is Evaluated In WEKA Using 10-fold Cross Validation. MLP And RF Yielded Good Accuracy Surpassing The State-of-the-art With An Accuracy Of 97% And 99.9% Respectively, The RF Model Is Then Implemented On A Graphical User Interface For Development Testing.

MUSIC GENRE CLASSIFICATION USING DEEP LEARNING- Music Genre Classification Classification Using Deep Learning And Neural Network Algorithms

Categorizing Music Files According To Their Genre Is A Challenging Task In The Area Of Music Information Retrieval (MIR). In This Study, We Compare The Performance Of Two Classes Of Models. The First Is A Deep Learning Approach Wherein A CNN Model Is Trained End-to-end, To Predict The Genre Label Of An Audio Signal, Solely Using Its Spectrogram. The Second Approach Utilizes Hand-crafted Features, Both From The Time Domain And The Frequency Domain. We Train Four Traditional Machine Learning Classifiers With These Features And Compare Their Performance. The Features That Contribute The Most Towards This Multi-class Classification Task Are Identified. The Experiments Are Conducted On The Audio Set Data Set And We Report An AUC Value Of 0.894 For An Ensemble Classifier Which Combines The Two Proposed Approaches.

SPEECH TO TEXT CONVERTION USING PYTHON- Design Of Voice To Text Conversion And Management Program Based On Google Cloud Speech API

Sexual Crime, Including Sexual Harassment And Sex Assault, Is Prevalent. In Particular, The Number Of Reported Cases Of Sexual Crimes Occurring In The Workplace Is Steadily Increasing. Victims Of Sexual Crime Are Required To Prove The Fact Of The Damage, But It Is Not Easy To Prove The Evidence, So The Sex Offenders Are Often Not Punished Properly Because Of Insufficient Evidence. In This Paper, We Design A Recording Service Called CCVoice. It Uses Mobile Devices To Record Everyday Life. At The Same Time, It Converts The Recorded File To Text Using Google Cloud Speech API And Save The Text File. Therefore, It Is Possible To Easily Obtain Voice Evidence When A User Is Suddenly Sexually Abused Such As Sexual Harassment Or Sex Assault.

BIG DATA ANALYSIS BY US ACCIDENT DATASET- Data Analytics Factors Of Traffic Accidents In The UK

  • Domain: PYSPARK
  • Category: PYTHON
  • Year: 2022
  • Project Code: PYPYS2101

The Traffic And Accident Datasets For This Research Are Sourced By Data.gov.uk. The Data Analytics In This Paper Comprises Three Levels Namely: Descriptive Statistical Analysis; Inferential Statistical Analysis; Machine Learning. The Aim Of The Data Analytics Is To Explore The Factors That Could Have Impact On The Number Of Accidents And Their Associated Fatalities. Some Of The Factors Investigated On Are: Time Of The Day, Day Of The Week, Month Of The Year, Speed Limits, Etc... Machine Learning Approaches Have Also Been Employed To Predict The Types Of Accident Severity.

A PRACTICAL INTERACTIVE CHESS BOARD WITH AUTOMATIC MOVEMENT CONTROL

The Interactive Chess Board Game Is Unlike Games In Its Ordinary Way. This Board Game Together With Tangible Movements Of All Pieces Is Considered To Be Users Attraction. Therefore, The New Chessboard With An Automatic Moving Mechanism For Every Piece Is Chosen. Initially, We Have Designed And Developed An Aluminum Core Structure For Positioning X And Y-axis. Furthermore, A Controllable Magnet Is Deliberated For Holding And Moving An Individual Chess Piece According To Player Manipulations. Purpose Of This Interactive Chess Board Is Applying Technology To Board Game For Excitement, Interest, Amazement, And Attraction. Arduino Microcontroller Is Used For Controlling Every Step Of Piece Movement. The Microcontroller Receives Control Information Through The User Interface And Then Moves The Chess Piece To The Destination On The Board. The Position Calculation Is Brought To Identify The Chess Piece And Drive Accurately The Stepper Motors In X And Y-axis.

SNAKE GAME- Solving The Classic Snake Game Using AI

N This Paper We Did Survey Of Various Papers Based On The Classic Snake Game And Compared Their Various Traits And Features. In This Paper We Introduce An AI Bot To Enhance The Skills Of The Player And The AI Bot Uses The Algorithms Further Discussed In This Paper. Player Can Follow The Simultaneously Running AI Bot To Play The Game Effectively. In This We Use The Classic Snake Game, For That We Present Different Algorithms Or Methods For AI Bot. It Includes Three Searching Algorithms Related To Artificial Intelligence, Best First Search, A* Search And Improved A* Search With Forward Checking, And Two Baseline Methods Random Move And Almighty Move

Property Selling Website Using Django Framework

In A Generation Led By Millennials, Technologies Are Becoming Redundant Each Year. The Organizations Are Competing On A Global Scale And Newer And Innovative Strategies Are Introduced In The Field Of Marketing To Reach Out To The Potential Buyers. Real Estate, Being One Of The Biggest Business Sectors, Needs More Efficiently Targeted Marketing Campaigns As This Is A Very Niche And Unexplored Field In The Indian Scenario. Real Estate Projects Are Highly Priced Products Which Cannot Be Sold Efficiently Without A Well Strategized Marketing Campaign So As To Reach Out To The Exact Targeted Market. The Unsold Inventories In Various Metro Cities Range From 15-60%. The Stipulated Real Estate Sector Growth Trends By Government Do Not Go Hand In Hand With The On-ground Realities Of The Piled-up Inventories. The Marketing Strategies Have Not Evolved With The Digitization Boom, And Still The Real Estate Marketing Techniques Are Conventional And Financially Heavy. Through This Study, Efforts Have Been Made To Pin Point The Existing Supply-demand Problems In The Cities Of Ahmedabad And Mumbai In The Affordable And HIG Housing Sector Specifically. Also, Suitable Solutions For Marketing Campaigns Have Been Proposed Considering The Current Market Realities For Both Cities.

EYE BLINK DETECTION- Eye Blink Detection Using Opencv Computer Vision

The Basic Nonverbal Interaction That Is Now Evolving In The Upcoming Generation Is Eye Gaze. This Eye Blink System Builds A Bridge For Communication Of People Affected With Disabilities. The Operation Is So Simple That With The Eyes Blinking At The Control Keys That Are Built In The Screen . This Type Of System Can Synthesize Speech, Control His Environment, And Give A Major Development Of Confidence In The Individual . Our Paper Mainly Enforces The Virtual Keyboard That Not Only Has The Built In Phrases But Also Can Provide The Voice Notification/ Speech Assistance For The People Who Are Speech Disabled. To Achieve This We Have Used Our Pc/laptop Camera Which Is Built In And It Recognizes The Face And Parts Of The Face. This Makes The Process Of Detecting The Face Much Easier Than Anything. The Eye Blink Serves As The Alternative For A Mouse Click On The Virtual Interface. As Already Mentioned, Our Ultimate Achievement Is To Provide A Nonverbal Communication And Hence The Physically Disabled People Should Get A Mode Of Communication Along With A Voice Assistant. This Type Of Innovation Is A Golden Fortune For The People Who Lost Their Voice And Affected To Paralytic Disorders. We Have Further Explained With The Respective Flowcharts And With Each Juncture

CYBER THREAT DETECTION USING MACHINE LEARNING TECHNIQUES A PERFORMANCE EVALUATION PERSPECTIVE

The Present-day World Has Become All Dependent On Cyberspace For Every Aspect Of Daily Living. The Use Of Cyberspace Is Rising With Each Passing Day. The World Is Spending More Time On The Internet Than Ever Before. As A Result, The Risks Of Cyber Threats And Cybercrimes Are Increasing. The Term 'cyber Threat' Is Referred To As The Illegal Activity Performed Using The Internet. Cybercriminals Are Changing Their Techniques With Time To Pass Through The Wall Of Protection. Conventional Techniques Are Not Capable Of Detecting Zero-day Attacks And Sophisticated Attacks. Thus Far, Heaps Of Machine Learning Techniques Have Been Developed To Detect The Cybercrimes And Battle Against Cyber Threats. The Objective Of This Research Work Is To Present The Evaluation Of Some Of The Widely Used Machine Learning Techniques Used To Detect Some Of The Most Threatening Cyber Threats To The Cyberspace. Three Primary Machine Learning Techniques Are Mainly Investigated, Including Deep Belief Network, Decision Tree And Support Vector Machine. We Have Presented A Brief Exploration To Gauge The Performance Of These Machine Learning Techniques In The Spam Detection, Intrusion Detection And Malware Detection Based On Frequently Used And Benchmark Datasets.

DATA MANAGEMENT SERVICE

The Need For A Method To Create A Collaborative Machine Learning Model Which Can Utilize Data From Different Clients, Each With Privacy Constraints, Has Recently Emerged. This Is Due To Privacy Restrictions, Such As General Data Protection Regulation, Together With The Fact That Machine Learning Models In General Needs Large Size Data To Perform Well. Google Introduced Federated Learning In 2016 With The Aim To Address This Problem. Federated Learning Can Further Be Divided Into Horizontal And Vertical Federated Learning, Depending On How The Data Is Structured At The Different Clients. Vertical Federated Learning Is Applicable When Many Different Features Is Obtained On Distributed Computation Nodes, Where They Can Not Be Shared In Between. The Aim Of This Thesis Is To Identify The Current State Of The Art Methods In Vertical Federated Learning, Implement The Most Interesting Ones And Compare The Results In Order To Draw Conclusions Of The Benefits And Drawbacks Of The Different Methods. From The Results Of The Experiments, A Method Called FedBCD Shows Very Promising Results Where It Achieves Massive Improvements In The Number Of Communication Rounds Needed For Convergence, At The Cost Of More Computations At The Clients. A Comparison Between Synchronous And Asynchronous Approaches Shows Slightly Better Results For The Synchronous Approach In Scenarios With No Delay. Delay Refers To Slower Performance In One Of The Workers, Either Due To Lower Computational Resources Or Due To Communication Issues.

DECENTRALIZED MACHINE LEARNING AND APPLICATIONS

The Need For A Method To Create A Collaborative Machine Learning Model Which Can Utilize Data From Different Clients, Each With Privacy Constraints, Has Recently Emerged. This Is Due To Privacy Restrictions, Such As General Data Protection Regulation, Together With The Fact That Machine Learning Models In General Needs Large Size Data To Perform Well. Google Introduced Federated Learning In 2016 With The Aim To Address This Problem. Federated Learning Can Further Be Divided Into Horizontal And Vertical Federated Learning, Depending On How The Data Is Structured At The Different Clients. Vertical Federated Learning Is Applicable When Many Different Features Is Obtained On Distributed Computation Nodes, Where They Can Not Be Shared In Between. The Aim Of This Thesis Is To Identify The Current State Of The Art Methods In Vertical Federated Learning, Implement The Most Interesting Ones And Compare The Results In Order To Draw Conclusions Of The Benefits And Drawbacks Of The Different Methods. From The Results Of The Experiments, A Method Called FedBCD Shows Very Promising Results Where It Achieves Massive Improvements In The Number Of Communication Rounds Needed For Convergence, At The Cost Of More Computations At The Clients. A Comparison Between Synchronous And Asynchronous Approaches Shows Slightly Better Results For The Synchronous Approach In Scenarios With No Delay. Delay Refers To Slower Performance In One Of The Workers, Either Due To Lower Computational Resources Or Due To Communication Issues.

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