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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.

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