Best Project Center | Best project center in chennai, best project center in t.nagar, best project center in tnagar, Best final year project center, project center in Chennai, project center near me, best project center in mambalam, best project center in vadapalani, best project center in ashok nagar, best project center in Annanagar, best project center

Search Projects Here

44 Results Found

PRIVACY PRESERVING AUTHENTICATION - Shared Authority Based Privacy-preserving Authentication Protocol In Cloud Computing

Cloud Computing Is An Emerging Data Interactive Paradigm To Realize Users' Data Remotely Stored In An Online Cloud Server. Cloud Services Provide Great Conveniences For The Users To Enjoy The On-demand Cloud Applications Without Considering The Local Infrastructure Limitations. During The Data Accessing, Different Users May Be In A Collaborative Relationship, And Thus Data Sharing Becomes Significant To Achieve Productive Benefits. The Existing Security Solutions Mainly Focus On The Authentication To Realize That A User's Privative Data Cannot Be Illegally Accessed, But Neglect A Subtle Privacy Issue During A User Challenging The Cloud Server To Request Other Users For Data Sharing. The Challenged Access Request Itself May Reveal The User's Privacy No Matter Whether Or Not It Can Obtain The Data Access Permissions. In This Paper, We Propose A Shared Authority Based Privacy-preserving Authentication Protocol (SAPA) To Address Above Privacy Issue For Cloud Storage. In The SAPA, 1) Shared Access Authority Is Achieved By Anonymous Access Request Matching Mechanism With Security And Privacy Considerations (e.g., Authentication, Data Anonymity, User Privacy, And Forward Security); 2) Attribute Based Access Control Is Adopted To Realize That The User Can Only Access Its Own Data Fields; 3) Proxy Re-encryption Is Applied To Provide Data Sharing Among The Multiple Users. Meanwhile, Universal Composability (UC) Model Is Established To Prove That The SAPA Theoretically Has The Design Correctness. It Indicates That The Proposed Protocol Is Attractive For Multi-user Collaborative Cloud Applications.

PERFORMACE AND COST EVALUATION - Performance And Cost Evaluation Of An Adaptive Encryption Architecture For Cloud Databases

The Cloud Database As A Service Is A Novel Paradigm That Can Support Several Internet-based Applications, But Its Adoption Requires The Solution Of Information Confidentiality Problems. We Propose A Novel Architecture For Adaptive Encryption Of Public Cloud Databases That Offers An Interesting Alternative To The Tradeoff Between The Required Data Confidentiality Level And The Flexibility Of The Cloud Database Structures At Design Time. We Demonstrate The Feasibility And Performance Of The Proposed Solution Through A Software Prototype. Moreover, We Propose An Original Cost Model That Is Oriented To The Evaluation Of Cloud Database Services In Plain And Encrypted Instances And That Takes Into Account The Variability Of Cloud Prices And Tenant Workloads During A Medium-term Period.

SECURE DATA FORWARDING - A Secure Erasure Code Based Cloud Storage System With Secure Data Forwarding

A Cloud Storage System, Consisting Of A Collection Of Storage Servers, Provides Long-term Storage Services Over The Internet. Storing Data In A Third Party's Cloud System Causes Serious Concern Over Data Confidentiality. General Encryption Schemes Protect Data Confidentiality, But Also Limit The Functionality Of The Storage System Because A Few Operations Are Supported Over Encrypted Data. Constructing A Secure Storage System That Supports Multiple Functions Is Challenging When The Storage System Is Distributed And Has No Central Authority. We Propose A Threshold Proxy Re-encryption Scheme And Integrate It With A Decentralized Erasure Code Such That A Secure Distributed Storage System Is Formulated. The Distributed Storage System Not Only Supports Secure And Robust Data Storage And Retrieval, But Also Lets A User Forward His Data In The Storage Servers To Another User Without Retrieving The Data Back. The Main Technical Contribution Is That The Proxy Re-encryption Scheme Supports Encoding Operations Over Encrypted Messages As Well As Forwarding Operations Over Encoded And Encrypted Messages. Our Method Fully Integrates Encrypting, Encoding, And Forwarding. We Analyze And Suggest Suitable Parameters For The Number Of Copies Of A Message Dispatched To Storage Servers And The Number Of Storage Servers Queried By A Key Server. These Parameters Allow More Flexible Adjustment Between The Number Of Storage Servers And Robustness.

HIERARCHICAL ATTRIBUTE - HASBE-A Hierarchical Attribute Based Solution For Flexible And Scalable Access Control In Cloud Computing

Cloud Computing Has Emerged As One Of The Most Influential Paradigms In The IT Industry In Recent Years. Since This New Computing Technology Requires Users To Entrust Their Valuable Data To Cloud Providers, There Have Been Increasing Security And Privacy Concerns On Outsourced Data. Several Schemes Employing Attribute-based Encryption (ABE) Have Been Proposed For Access Control Of Outsourced Data In Cloud Computing; However, Most Of Them Suffer From Inflexibility In Implementing Complex Access Control Policies. In Order To Realize Scalable, Flexible, And Fine-grained Access Control Of Outsourced Data In Cloud Computing, In This Paper, We Propose Hierarchical Attribute-set-based Encryption (HASBE) By Extending Ciphertext-policy Attribute-set-based Encryption (ASBE) With A Hierarchical Structure Of Users. The Proposed Scheme Not Only Achieves Scalability Due To Its Hierarchical Structure, But Also Inherits Flexibility And Fine-grained Access Control In Supporting Compound Attributes Of ASBE. In Addition, HASBE Employs Multiple Value Assignments For Access Expiration Time To Deal With User Revocation More Efficiently Than Existing Schemes. We Formally Prove The Security Of HASBE Based On Security Of The Ciphertext-policy Attribute-based Encryption Scheme By Bethencourt And Analyze Its Performance And Computational Complexity. We Implement Our Scheme And Show That It Is Both Efficient And Flexible In Dealing With Access Control For Outsourced Data In Cloud Computing With Comprehensive Experiments.

CQA POST VOTING PREDICTION - QAAN Question Answering Attention Networking For Community Question Classification

Community Question Answering (CQA) Provides Platforms For Users With Various Backgrounds To Obtain Information And Share Knowledge. In Recent Years, With The Rapid Development Of Such Online Platforms, An Enormous Amount Of Archive Data Has Accumulated, It Becomes More And More Difficult For Expert Users To Identify Desirable Questions. In Order To Reduce The Proportion Of Unanswered Questions In CQA, Facilitate Expert Users To Find The Questions They Are Interested In, Question Classification Becomes An Important Task Of CQA, Which Aims To Assign A Newly Posted Question To A Specific Preset Category. In This Paper, We Propose A Novel Question Answering Attention Network (QAAN) For Investigating The Role Of The Paired Answer Of Questions For Classification. Specifically, QAAN Studies The Correlation Between Question And Paired Answer, Taking The Questions As The Primary Part Of The Question Representation, And The Answer Information Is Aggregated Based On Similarity And Disparity With The Answer. Our Experiment Is Implemented On Yahoo! Answers Dataset. The Results Show That QAAN Outperforms All The Baseline Models.

REPRESENTATIVE TRAVEL ROUTE RECOMMENDATION- Personalized Tourism Route Recommendation System Based On Dynamic Clustering Of User Groups

Tourism Path Dynamic Planning Is An Asynchronous Group Model Planning Problem. It Is Required To Find Group Patterns With Similar Trajectory Behavior Under The Constraint Of Unequal Time Intervals. Traditional Trajectory Group Pattern Mining Algorithms Often Deal With GPS Data With Fixed Time Interval Sampling Constraints, So They Can Not Be Directly Used In Coterie Pattern Mining. At The Same Time, Traditional Group Pattern Mining Has The Problem Of Lack Of Semantic Information, Which Reduces The Integrity And Accuracy Of Personalized Travel Route Recommendation. Therefore, This Paper Proposes A Semantic Based Distance Sensitive Recommendation Strategy. In Order To Efficiently Process Large-scale Social Network Trajectory Data, This Paper Uses MapReduce Programming Model With Optimized Clustering To Mine Coterie Group Patterns. The Experimental Results Show That: Under MapReduce Programming Model, Coterie Group Pattern Mining With Optimized Clustering And Semantic Information Is Superior To Traditional Group Mode In Personalized Travel Route Recommendation Quality, And Can Effectively Process Large-scale Social Network Trajectory Data.

CREDIT CARD FRAUD DETECTION - Fraud Detection In Credit Card Data Using Unsupervised Machine Learning Based Scheme

Development Of Communication Technologies And E-commerce Has Made The Credit Card As The Most Common Technique Of Payment For Both Online And Regular Purchases. So, Security In This System Is Highly Expected To Prevent Fraud Transactions. Fraud Transactions In Credit Card Data Transaction Are Increasing Each Year. In This Direction, Researchers Are Also Trying The Novel Techniques To Detect And Prevent Such Frauds. However, There Is Always A Need Of Some Techniques That Should Precisely And Efficiently Detect These Frauds. This Paper Proposes A Scheme For Detecting Frauds In Credit Card Data Which Uses A Neural Network (NN) Based Unsupervised Learning Technique. Proposed Method Outperforms The Existing Approaches Of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) And K-Means Clustering. Proposed NN Based Fraud Detection Method Performs With 99.87% Accuracy Whereas Existing Methods AE, IF, LOF And K Means Gives 97%, 98%, 98% And 99.75% Accuracy Respectively.

SECURE MINING OF ASSOCIATION RULES - Scalable Privacy-Preserving Distributed Extremely Randomized Trees For Structured Data With Multiple Colluding Parties

Today, In Many Real-world Applications Of Machine Learning Algorithms, The Data Is Stored On Multiple Sources Instead Of At One Central Repository. In Many Such Scenarios, Due To Privacy Concerns And Legal Obligations, E.g., For Medical Data, And Communication/computation Overhead, For Instance For Large Scale Data, The Raw Data Cannot Be Transferred To A Center For Analysis. Therefore, New Machine Learning Approaches Are Proposed For Learning From The Distributed Data In Such Settings. In This Paper, We Extend The Distributed Extremely Randomized Trees (ERT) Approach W.r.t. Privacy And Scalability. First, We Extend Distributed ERT To Be Resilient W.r.t. The Number Of Colluding Parties In A Scalable Fashion. Then, We Extend The Distributed ERT To Improve Its Scalability Without Any Major Loss In Classification Performance. We Refer To Our Proposed Approach As K-PPD-ERT Or Privacy-Preserving Distributed Extremely Randomized Trees With K Colluding Parties.

TAXI DRIVERS ROUTE CHOICE BEHAVIOR USING THE TRACE RECORDS- A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors In Route Choice

This Paper Introduces A Model To Analyze Route Choice Behavior Of Taxi Drivers For Finding Next Passenger In Urban Road Network. Considering The Situation Of Path Overlapping Between Selected Routes In The Process Of Customer-searching, A Mixed Path Size Logit Model Is Proposed To Analyze Route Choice Behaviors Through Considering Spatio-temporal Features Of Route Including Customer Generation Rate, Path Travel Time, Cumulative Intersection Delay, Path Distance, And Path Size. Specially, Customer Generation Rate Is Defined As Attraction Strength Based On Historical Pick-up Records In The Route, The Intersection Travel Delay And Path Travel Time Are Estimated Based On Large Scaled Taxi Global Positioning System Trajectories. In The Experiment, The GPS Data Were Collected From About 36000 Taxi Vehicles In Beijing At 30-s Interval During Six Months. In The Model Application, An Area Of Approximately 10 Square Kilometers In The Center Of Beijing Is Selected To Demonstrate The Effectiveness Of The Proposed Model. The Results Indicated That The MPSL Model Could Effectively Analyze The Route Choice Behavior In Customer-searching Process And Express Higher Accuracy Than Traditional Multinomial Logit Model And Basic PSL Model.

FILE TRANSFER USING CRYPTOGRAPHIC TECHNIQUE - Enhancing Secure Digital Communication Media Using Cryptographic Steganography Techniques

Data Hiding Technique Is The Process Of Anti-computer Forensic For Making The Data Difficult To Accessible. Steganography Is Merging Texts, Files, Or Other Multimedia Files Within Another Texts, Files, Or Other Multimedia Files To Reduce The Visible Attack And It Is An Approach Of Data Hiding Technique. Cryptography Is Changing The Readable Text To Illegible Information. This Paper Presents About Secure Communication Media Which Is Used In Transferring Text, Multimedia Or Relevant Digital File Between Sender And Receiver Securely. To Have Securing Communication Media, The Media Required To Reduce The Possible Threats And Vulnerabilities. Therefore, Transferred Media Is Main Thing To Consideration For Having Communication System Firmly. Data Hiding Techniques Are Used To Improve The Security Of Communication Media Using Salt Encryption. This Paper Is Proposed The Methodology To Develop The Secure Communication Media Using Combination Of Cryptography And Steganography Techniques By Describing Experimental Results From Difference Technical Analysis.

PREDICT LENGTH OF STAY OF STROKE PATIENTS USING DATA MINING TECHNIQUES - SNOMED CT-Based Standardized E-Clinical Pathways For Enabling Big Data Analytics In Healthcare

Automation Of Healthcare Facilities Represents A Challenging Task Of Streamlining A Highly Information-intensive Sector. Modern Healthcare Processes Produce Large Amounts Of Data That Have Great Potential For Health Policymakers And Data Science Researchers. However, A Considerable Portion Of Such Data Is Not Captured In Electronic Format And Hidden Inside The Paperwork. A Major Source Of Missing Data In Healthcare Is Paper-based Clinical Pathways (CPs). CPs Are Healthcare Plans That Detail The Interventions For The Treatment Of Patients, And Thus Are The Primary Source For Healthcare Data. However, Most CPs Are Used As Paper-based Documents And Not Fully Automated. A Key Contribution Towards The Full Automation Of CPs Is Their Proper Computer Modeling And Encoding Their Data With International Clinical Terminologies. We Present In This Research An Ontology-based CP Automation Model In Which CP Data Are Standardized With SNOMED CT, Thus Enabling Machine Learning Algorithms To Be Applied To CP-based Datasets. CPs Automated Under This Model Contribute Significantly To Reducing Data Missingness Problems, Enabling Detailed Statistical Analyses On CP Data, And Improving The Results Of Data Analytics Algorithms. Our Experimental Results On Predicting The Length Of Stay (LOS) Of Stroke Patients Using A Dataset Resulting From An E-clinical Pathway Demonstrate Improved Prediction Results Compared With LOS Prediction Using Traditional EHR-based Datasets. Fully Automated CPs Enrich Medical Datasets With More CP Data And Open New Opportunities For Machine Learning Algorithms To Show Their Full Potential In Improving Healthcare, Reducing Costs, And Increasing Patient Satisfaction

PREDICT CHANGING STUDENTS ATTITUDE USING DATA MINING - Supporting Teachers To Monitor Students Learning Progress In An Educational Environment With Robotics Activities

Educational Robotics Has Proven Its Positive Impact On The Performances And Attitudes Of Students. However, The Educational Environments That Employ Them Rarely Provide Teachers With Relevant Information That Can Be Used To Make An Effective Monitoring Of The Student Learning Progress. To Overcome These Limitations, In This Paper We Present IDEE (Integrated Didactic Educational Environment), An Educational Environment For Physics, That Uses EV3 LEGO Mindstorms R Educational Kit As Robotic Component. To Provide Support To Teachers, IDEE Includes A Dashboard That Provides Them With Information About The Students’ Learning Process. This Analysis Is Done By Means Of An Additive Factor Model (AFM). That Is A Well-known Technique In The Educational Data Mining Research Area. However, It Has Been Usually Employed To Carry Out Analysis About Students’ Performance Data Outside The System. This Can Be A Burden For The Teacher Who, In Most Cases, Is Not An Expert In Data Analysis. Our Goal In This Paper Is To Show How The Coefficients Of AFM Provide Valuable Information To The Teacher Without Requiring Any Deep Expertise In Data Analysis. In Addition, We Show An Improved Version Of The AFM That Provides A Deeper Understanding About The Students’ Learning Process.

MALWARE DETECTION IN GOOGLE PLAY - Towards De-Anonymization Of Google Play Search Rank Fraud

Search Rank Fraud, The Fraudulent Promotion Of Products Hosted On Peer-review Sites, Is Driven By Expert Workers Recruited Online, Often From Crowdsourcing Sites. In This Paper We Introduce The Fraud De-anonymization Problem, That Goes Beyond Fraud Detection, To Unmask The Human Masterminds Responsible For Posting Search Rank Fraud In Peer-review Sites. We Collect And Study Data From Crowdsourced Search Rank Fraud Jobs, And Survey The Capabilities And Behaviors Of 58 Search Rank Fraud Workers Recruited From 6 Crowdsourcing Sites. We Collect A Gold Standard Dataset Of Google Play User Accounts Attributed To 23 Crowdsourced Workers And Analyze Their Fraudulent Behaviors In The Wild. We Propose Dolos , A Fraud De-anonymization System That Leverages Traits And Behaviors We Extract From Our Studies, To Attribute Detected Fraud To Crowdsourcing Site Workers, Thus To Real Identities And Bank Accounts. We Introduce MCDense, A Min-cut Dense Component Detection Algorithm To Uncover Groups Of User Accounts Controlled By Different Workers, And Use Stylometry And Supervised Learning To Attribute Them To Crowdsourcing Site Profiles. Dolos Correctly Identified The Owners Of 95 Percent Of Fraud Worker-controlled Communities, And Uncovered Fraud Workers Who Promoted As Many As 97.5 Percent Of Fraud Apps We Collected From Google Play. When Evaluated On 13,087 Apps (820,760 Reviews), Which We Monitored Over More Than 6 Months, Dolos Identified 1,056 Apps With Suspicious Reviewer Groups. We Report Orthogonal Evidence Of Their Fraud, Including Fraud Duplicates And Fraud Re-posts. Dolos Significantly Outperformed Adapted Dense Subgraph Detection And Loopy Belief Propagation Competitors, On Two New Coverage Scores That Measure The Quality Of Detected Community Partitions.