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COVID-19 PANDEMIC- A Systematic Review On The Use Of AI And ML For Fighting The COVID-19 Pandemic

Artificial Intelligence (AI) And Machine Learning (ML) Have Caused A Paradigm Shift In Healthcare That Can Be Used For Decision Support And Forecasting By Exploring Medical Data. Recent Studies Have Shown That AI And ML Can Be Used To Fight COVID-19. The Objective Of This Article Is To Summarize The Recent AI- And ML-based Studies That Have Addressed The Pandemic. From An Initial Set Of 634 Articles, A Total Of 49 Articles Were Finally Selected Through An Inclusion-exclusion Process. In This Article, We Have Explored The Objectives Of The Existing Studies (i.e., The Role Of AI/ML In Fighting The COVID-19 Pandemic); The Context Of The Studies (i.e., Whether It Was Focused On A Specific Country-context Or With A Global Perspective; The Type And Volume Of The Dataset; And The Methodology, Algorithms, And Techniques Adopted In The Prediction Or Diagnosis Processes). We Have Mapped The Algorithms And Techniques With The Data Type By Highlighting Their Prediction/classification Accuracy. From Our Analysis, We Categorized The Objectives Of The Studies Into Four Groups: Disease Detection, Epidemic Forecasting, Sustainable Development, And Disease Diagnosis. We Observed That Most Of These Studies Used Deep Learning Algorithms On Image-data, More Specifically On Chest X-rays And CT Scans. We Have Identified Six Future Research Opportunities That We Have Summarized In This Paper.

FACE EXPRESSION RECOGNITION- Automatic Detection Of Pain From Facial Expressions A Survey

Pain Sensation Is Essential For Survival, Since It Draws Attention To Physical Threat To The Body. Pain Assessment Is Usually Done Through Self-reports. However, Self-assessment Of Pain Is Not Available In The Case Of Noncommunicative Patients, And Therefore, Observer Reports Should Be Relied Upon. Observer Reports Of Pain Could Be Prone To Errors Due To Subjective Biases Of Observers. Moreover, Continuous Monitoring By Humans Is Impractical. Therefore, Automatic Pain Detection Technology Could Be Deployed To Assist Human Caregivers And Complement Their Service, Thereby Improving The Quality Of Pain Management, Especially For Noncommunicative Patients. Facial Expressions Are A Reliable Indicator Of Pain, And Are Used In All Observer-based Pain Assessment Tools. Following The Advancements In Automatic Facial Expression Analysis, Computer Vision Researchers Have Tried To Use This Technology For Developing Approaches For Automatically Detecting Pain From Facial Expressions. This Paper Surveys The Literature Published In This Field Over The Past Decade, Categorizes It, And Identifies Future Research Directions. The Survey Covers The Pain Datasets Used In The Reviewed Literature, The Learning Tasks Targeted By The Approaches, The Features Extracted From Images And Image Sequences To Represent Pain-related Information, And Finally, The Machine Learning Methods Used.

CREDIT CARD FRAUDULET TRANSACTION DETECTION- Supervised Machine Learning Algorithm For Credit Card Fraudulent Transaction Detection

The Goal Of Data Analytics Is To Delineate Hidden Patterns And Use Them To Support Informed Decisions In A Variety Of Situations. Credit Card Fraud Is Escalating Significantly With The Advancement Of The Modernized Technology And Become An Easy Target For Fraudulent. Credit Card Fraud Is A Severe Problem In The Financial Service And Costs Billions Of A Dollar Every Year. The Design Of Fraud Detection Algorithm Is A Challenging Task With The Lack Of Real-world Transaction Dataset Because Of Confidentiality And The Highly Imbalanced Publicly Available Datasets. In This Paper, We Apply Different Supervised Machine Learning Algorithms To Detect Credit Card Fraudulent Transaction Using A Real-world Dataset. Furthermore, We Employ These Algorithms To Implement A Super Classifier Using Ensemble Learning Methods. We Identify The Most Important Variables That May Lead To Higher Accuracy In Credit Card Fraudulent Transaction Detection. Additionally, We Compare And Discuss The Performance Of Various Supervised Machine Learning Algorithms Exist In Literature Against The Super Classifier That We Implemented In This Paper.