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Vol 12, Issue 7 (July 2026) Computer Science

An Ensemble Machine Learning Models For Heart And Cardiovascular Disease Prediction

Chronic Health Risks Have Risen Among Young Individuals Due To Several Factors Such As Sedentary Lifestyle, Poor Eating Habits, Sleep Irregularities, Environmental Pollution, Workplace Stress Etc. The Problem Seems To Be More Menacing In The Near Fut... Chronic Health Risks Have Risen Among Young Individuals Due To Several Factors Such As Sedentary Lifestyle, Poor Eating Habits, Sleep Irregularities, Environmental Pollution, Workplace Stress Etc. The Problem Seems To Be More Menacing In The Near Future. One Possible Solution Is Thus To Design Health Risk Prediction Systems Which Can Evaluated Some Critical Features Of Parameters Of The Individual And Then Be Able To Predict Possible Health Risks. As The Data Shows Large Divergences In Nature With Non-correlated Patterns, Hence Choice Of Machine Learning Based Methods Becomes Inevitable To Design Systems Which Can Analyze The Critical Factors Or Features Of The Data And Predict Possible Risks. This Paper Presents An Ensemble Approach For Health Risk Prediction Based On The Steepest Descent Algorithm And Decision Trees. It Is Observed That The Proposed Work Attains A Classification Accuracy Of 93.72% Which Is Comparatively Higher Than Baseline Techniques. Read more Read less

Authors: Saroj Tekam | Prof. Pawan Panchole

Vol 12, Issue 7 (July 2026) Computer Science

An Optimized Deep Learning Model For Churn Rate Prediction

Data Science And Machine Learning Are Being Used Extensively For Business Analytics. One Of The Major Applications Happens To Be Estimating Churn And Attrition Rates. In Today’s Competitive Market Landscape, Retaining Customers Is As Crucial As Acqui... Data Science And Machine Learning Are Being Used Extensively For Business Analytics. One Of The Major Applications Happens To Be Estimating Churn And Attrition Rates. In Today’s Competitive Market Landscape, Retaining Customers Is As Crucial As Acquiring New Ones. Churn Rate, Which Measures The Proportion Of Customers Who Discontinue Their Relationship With A Business Over A Specific Period, Is A Critical Metric For Companies Across Industries. Forecasting Churn Enables Businesses To Proactively Address Customer Dissatisfaction And Refine Their Strategies To Retain Valuable Clients. By Understanding The Likelihood Of Churn, Companies Can Make Informed Decisions To Sustain Growth And Profitability. The Proposed Approach Combines Swarm Intelligence And Neural Networks To Forecast Churn Rates. The Results Clearly Indicate That The Proposed Approach Outperforms Existing Baseline Approaches In Terms Of Forecasting Accuracy. Read more Read less

Authors: Mahak Mansuri | Prof. Pawan Panchole

Vol 12, Issue 7 (July 2026) Computer Science

A Proactive Machine Learning Based Neural-Chaos Architecture For Physical Layer Security Through PAPR Reduction

Proactive Physical Layer Security Has Evolved With The Emergence Of Web 3.0 Which Shifts Control From Centralized Cloud Servers Toward Distributed Edge Nodes, Blockchain Miners/validators, And Peer-to-peer Communication. Many Of These Nodes Rely On L... Proactive Physical Layer Security Has Evolved With The Emergence Of Web 3.0 Which Shifts Control From Centralized Cloud Servers Toward Distributed Edge Nodes, Blockchain Miners/validators, And Peer-to-peer Communication. Many Of These Nodes Rely On Low-power Radios And Operate In Highly Dynamic Environments. One Of The Most Pressing Challenges Is To Implement Proactive Security Through Peak To Average Power Ration (PAPR) Reduction. PAPR Reduction Techniques Such As Clipping, Selective Mapping, And Tone Reservation Ensure That Edge Devices Handle OFDM Waveforms Efficiently, Allowing Decentralized Nodes To Participate Reliably In Consensus Protocols And Data Exchange Without Frequent Downtime Or Excessive Energy Consumption. One Of The Major Challenges That Multiplexed Data Suffers From Is High Value Of PAPR Which Make Data Transmission Perceptible. The Proposed Work Uses A Neural Selective Mapping (N-SLM) Technique And Attains Lower PAPR Compared To Previously Existing Work, Thereby Increasing The Security Of Distributed Wireless Networks. Read more Read less

Authors: Nikita Paliwal | Dr. Neha Jain

Vol 12, Issue 7 (July 2026) Computer Science

A Deep Learning Approach For Predicting Crypto Currency Prices Trends

Machine Learning And Deep Learning Models Are Being Extensively Used At The Backend Of Forecasting Crypto Prices. Many Factors, Including Changes In Regulation, Public Opinion On Social Media, Investor Actions, And Overall Global Economic Trends, Con... Machine Learning And Deep Learning Models Are Being Extensively Used At The Backend Of Forecasting Crypto Prices. Many Factors, Including Changes In Regulation, Public Opinion On Social Media, Investor Actions, And Overall Global Economic Trends, Contribute To The High Degree Of Volatility In The Cryptocurrency Market. Predicting The Future Value Of Cryptocurrencies With Any Degree Of Accuracy Has Emerged As A Top Concern For Academics, Traders, And Investors Due To The Inherent Uncertainty Of The Market. Machine Learning (ML) Provides Excellent Methods For Evaluating Large Volumes Of Real-time And Historical Data To Forecast Price Changes In The Future. ML Models Range From Simple Statistical Methods To Intricate Deep Learning Architectures. The Proposed Work Employs An Optimized Second Order Regularization Based Back Propagation Algorithm Along With The Data Pre-processing Using The Discrete Wavelet Transform (DWT) For Crypto Price Prediction. It Has Been Shown That The Proposed System Attains Lesser Mean Square Percentage Error Compared To Previously Existing Technique. Read more Read less

Authors: Ayush Pal | Dr. Neha Jain

Vol 12, Issue 7 (July 2026) Computer Science

An Optimized Deep Learning Approach For Sentiment Classification Of Social Media Text Data

Of Late, Big Data And Big Data Analytics Has Fund Applications In Diverse Fields. Social Media And Allied Applications Is One Such Domain For Research, Where Artificial Intelligence Has Shown Unprecedented Impact. In This Paper A Mechanism Has Been P... Of Late, Big Data And Big Data Analytics Has Fund Applications In Diverse Fields. Social Media And Allied Applications Is One Such Domain For Research, Where Artificial Intelligence Has Shown Unprecedented Impact. In This Paper A Mechanism Has Been Proposed Which Can Classify Text Data Into Classes Of Different Sentiments. Data In The Form Of Tweets Has Been Used In This Case. Pre-processing Of Raw Data Has Been Done Prior To Using It To Train A Neural Network. A Neural Network Is Then Trained Using The Categories Of The Data Which Are Tweets That Correspond To Happy, Neutral And Sad Moods Of The Twitter Users. The Bayesian Deep Learning Model With Regularization Algorithm Has Been Used For Training The Artificial Neural Network. It Has Been Observed That This Proposed Technique Achieves A Significantly Higher Accuracy Compared To Existing Work In The Domain. Read more Read less

Authors: Vikas Balhon | Dr. Neha Jain