Volume 12 - Issue 7

A Comparative Study Of Machine Learning Algorithms And Response Surface Methodology For Energy Prediction And Optimization In Conveyor Systems

Conveyor Belts Are Fundamental To Any Modern Industrial Or Material-handling Facility, But Energy Efficient Operation Of A Conveyor System Is Essential For Decreasing Operational Cost. This Paper Proposes A Comparative Study Of Energy Optimization Us... Conveyor Belts Are Fundamental To Any Modern Industrial Or Material-handling Facility, But Energy Efficient Operation Of A Conveyor System Is Essential For Decreasing Operational Cost. This Paper Proposes A Comparative Study Of Energy Optimization Using Response Surface Methodology And Machine Learning Models Of A Laboratory Level Conveyor Belt. A Belt Driven Rig Of Size 150cm X 25cm Was Fitted With Current, Voltage Sensors That Were Connected To An ESP32 Microprocessor. A Sensor Was Placed On The Conveyor And Sent Data To A Google Sheet Every 30 Seconds. A Three-level Full-factorial Response Surface Methodology (RSM) Design Was Subsequently Employed To Gain Insight On The Effect Of Using Object Weight (A) And Inter Object Distance (B) On The Performance Of The System. The Results Of The Analysis Of Variance (ANOVA) Showed That There Were Significant Linear Term (A), And The Quadratic Term A2 (P < 0.0001). However, Linear Term (B),a Two-factor Interaction (AB), And B2 Did Not Appear To Be Significant (P = 0.2075). The Regression Model Suggests A Minimum Energy Of 1057.16 J At The Stationary Point With The Adjusted-R2>0.977. Based On The Experimental Data Set, Six Machine Learning Algorithms Were Developed In MATLAB To Predict Conveyor Energy Consumption.The Performance Of The Models Was Evaluated Using R², Root Mean Square Error (RMSE), Mean Absolute Error(MAE), And Mean Absolute Percentage Error (MAPE). Among The Evaluated Models, Extreme Gradient Boosting (XGBoost) Achieved The Highest Prediction Accuracy And Was Subsequently Integrated With A Genetic Algorithm (GA) To Optimize Conveyor Energy Consumption. The Proposed XG Boost–GA Framework Identified The Optimal Operating Conditions Of 500.26 G Weight And 73.60 Cm Spacing, Resulting In A Minimum Predicted Energy Consumption Of 1006.57 J, Which Is Approximately 4.8% Lower Than The 1057.16 J Obtained Using Response Surface Methodology (RSM). These Results Demonstrate That Combining XG Boost With The Genetic Algorithm Is More Effective Than RSM For Minimizing Conveyor Energy Consumption. Read more Read less

EFFECT OF STICKY RICE, JAGGERY, SUGAR, AND ADDITIONAL LIME ON THE COMPRESSIVE AND SPLITTING TENSILE BEHAVIOUR OF LIME MORTAR

Background: Compressive Resistance And Tensile-cracking Capacity Are Central To The Serviceability Of Lime Mortars Used In Masonry And Repair. Objective: The Investigation Quantifies The 7- And 28-day Compressive And Splitting Tensile Behaviour Of A... Background: Compressive Resistance And Tensile-cracking Capacity Are Central To The Serviceability Of Lime Mortars Used In Masonry And Repair. Objective: The Investigation Quantifies The 7- And 28-day Compressive And Splitting Tensile Behaviour Of A Control Mortar And Mortars Containing Jaggery, Sticky Rice, Sugar Or Additional Lime At 3–15% By Lime Mass. Methods: Mixes Used A Lime: Sand: Water Ratio Of 1:2.5:0.45. Cubes Of 150 × 150 × 150 Mm And Cylinders Of 150 Mm Diameter × 300 Mm Were Listed In The Source Report; The Final Chapter 5 Tables Were Used As The Controlling Dataset. Results: The Control Developed Compressive Strengths Of 0.20 And 0.38 MPa And Splitting Tensile Strengths Of 0.052 And 0.096 MPa At 7 And 28 Days. Sticky Rice At 9% Was Strongest, Reaching 0.54 And 0.98 MPa In Compression And 0.108 And 0.198 MPa In Splitting Tension. Additional Lime At 9% Reached 0.51/0.91 MPa And 0.103/0.187 MPa, While Jaggery At 6% Reached 0.49/0.87 MPa And 0.094/0.171 MPa. Sugar At 6% Was The Weakest Optimum, With 0.39/0.72 MPa And 0.079/0.145 MPa. Conclusion: Strength Increased With Age And Peaked At Moderate Dosage; 9% Sticky Rice Provided The Most Consistent Improvement In Matrix Strength And Tensile Crack Resistance. Read more Read less

BACTERIOCINS: BIOPRESERVATION OF FOOD PRODUCTS

Fermentation Of Various Foods By Lactic Acid Bacteria (LAB) Is One Of The Oldest Forms Of Biopreservation Practised By Mankind. Bacterial Antagonism Has Been Recognised For Over A Century But In Recent Years This Phenomenon Has Received More Scientif... Fermentation Of Various Foods By Lactic Acid Bacteria (LAB) Is One Of The Oldest Forms Of Biopreservation Practised By Mankind. Bacterial Antagonism Has Been Recognised For Over A Century But In Recent Years This Phenomenon Has Received More Scientific Attention, Particularly In The Use Of Various Strains Of Lactic Acid Bacteria. One Important Attribute Of LAB Is Their Ability To Produce Antimicrobial Compounds Called Bacteriocins. In Recent Years, Interest In The Compounds Has Grown Substantially Due To Their Potential Usefulness As Natural Substitutes For Chemical Food Preservatives In The Production Of Foods With Enhanced Shelf Life And Safety. This Balance Is Achieved By Its Inhibitory Effect Upon The Harmful Pathogenic Microorganisms. The Potential Application Of Bacteriocins As Consumer-friendly Biopreservatives Either In The Form Of Protective Cultures Or As Additives Is Significant. LAB Are Typically Involved In A Large Number Of Spontaneous Food Fermentations But They Are Also Closely Associated With The Human Environment. Although Most Bacteriocins Have Been Isolated From Food-associated LAB, They Are Not Necessarily Effective In All Food Systems. However, Several Bacteriocins Certainly Do Have Potential In Food Applications When Used Under The Proper Conditions. The Use Of Bacteriocins And Bacteriocin-producing Strains Of LAB Are Of Great Interest As They Are Generally Recognized As Safe Organisms And Their Antimicrobial Products As Biopreservatives. However, It Is Desirable To Continue To Expand Our Understanding Of The Influences That Environmental Factors Have On The Implantation And Survival Of Bacteriocinogenic Strains And The Activity Of Their Bacteriocins In Order To Quantitatively Estimate Their Efficacy For Future Applications In Food Model Systems And Establish Adequate Means Of Application Of These Biopreservatives. Read more Read less

TOPOGRAPHY-INDUCED SPATIAL VARIABILITY IN THE SEISMIC ANALYSIS OF LONG-SPAN ARCH BRIDGES: A SYSTEMATIC REVIEW

Long-span Arch Bridges Are Frequently Selected For Deep Valleys And Canyon Crossings, Yet The Same Topography That Makes The Structural Form Attractive Can Substantially Modify Earthquake Input At The Foundations. This Review Synthesizes Research On... Long-span Arch Bridges Are Frequently Selected For Deep Valleys And Canyon Crossings, Yet The Same Topography That Makes The Structural Form Attractive Can Substantially Modify Earthquake Input At The Foundations. This Review Synthesizes Research On The Influence Of Canyon, Valley, Ridge, Slope, Basin, And Local Geological Conditions On The Seismic Analysis Of Steel, Reinforced-concrete, And Concrete-filled Steel-tube Arch Bridges. A Systematic Structured-review Protocol Was Applied To Literature Indexed In Major Engineering Databases And Publisher Platforms, Prioritizing Peer-reviewed Work From 2020–2026 While Retaining Foundational Studies On Spatially Varying Ground Motion, Wave Passage, Coherency Loss, Local-site Effects, And Multi-support Excitation. Thirty-five Studies Were Included After Duplicate Removal, Relevance Screening, Full-text Assessment, And Quality Appraisal. The Reviewed Evidence Shows That Topography Affects Not Only Peak Acceleration But Also Phase, Duration, Frequency Content, Vertical Motion, Incidence-angle Sensitivity, And Support-to-support Differential Displacement. Consequently, Synchronous Uniform Excitation Can Underestimate Critical Demands, Particularly Arch-foot Axial Force And Bending, Bearing Displacement, Pier Response, Deck Torsion, Pounding Potential, And Soil-foundation Interaction. Recent Bridge-specific Investigations Reported An Average 18.69% Rise In Arch-base Internal Force Under Nonuniform Near-fault Input, While Physics-based Simulations Of A Mountain Earthquake Indicated Period-dependent Peak-ground-velocity Amplification Approaching 50% Near 1–2 S. Advanced Practice Increasingly Combines Three-dimensional Wave Propagation, Finite-element Bridge-soil Models, Nonlinear Response-history Analysis, Fragility Surfaces, Uncertainty Propagation, And Mitigation Devices Such As Friction-pendulum Bearings, Stayed Cables, And Viscous Dampers. However, Research Remains Fragmented Because Recorded Multi-station Canyon Motions Are Scarce, Topographic Geometry And Soil Stratigraphy Are Often Idealized, Vertical And Rotational Inputs Are Inconsistently Treated, And Few Studies Couple Physics-based Hazard Simulation With System-level Bridge Fragility. The Review Proposes A Unified Analysis Taxonomy And Identifies Priorities For Site-specific Input Generation, Validated Soil-structure Interaction, Component Damage Modelling, And Performance-based Design Of Arch Bridges In Mountainous Terrain. Read more Read less

Isolation, Screening, And Morphological Characterization Of Low-Density Polyethylene (LDPE)-Degrading Bacteria From The Waste Yards Of A Polymer Manufacturing Plant

Low-density Polyethylene (LDPE) Is One Of The Most Widely Produced Synthetic Polymers With The Least Biodegradability, And Its Presence In Regions Around Plastic-processing Factories Has Been Noticed As A Unique Area Of Chronically Impacted Ecology T... Low-density Polyethylene (LDPE) Is One Of The Most Widely Produced Synthetic Polymers With The Least Biodegradability, And Its Presence In Regions Around Plastic-processing Factories Has Been Noticed As A Unique Area Of Chronically Impacted Ecology That Has Received Significantly Less Attention Than Municipal Landfills. This Study Isolated Different Indigenous Soil Bacteria From The Waste Area Of The LDPE Manufacturing Plant And Other Area Residential And Institutional Places In The Modinagar–Ghaziabad–Meerut Belt Of Uttar Pradesh, India, That Can Use LDPE As A Source Of Carbon. A Total Of Fourteen Morphologically Different Bacterial Isolates Were Obtained From Seven Sampling Places Using Different Methods Like Serial Dilution And Enrichment Culture Using LDPE Mineral Salt Media; Four Of These Formed A Distinct Clear Zone In The LDPE Agar Measured Using Coomassie Brilliant Blue Technique And Such Clear Zones Were Between 3.2 Mm And 11.2 Mm In Diameter, And Hence Selected For Further Screening. For The Weight Loss Experiments From The Films, The Weight Loss After Incubation For 30 And 60 Days Were In The Range Of 4.83%-12.17% And 14.98%-23.62% Respectively, For The Four Isolates With Isolate With The Largest Clear Zone (B1, 11.2 Mm) Showed About 12.17% Weight Loss At 30 Days And 23.62% At 60 Days As Expected. Microscopic Examination Of Gram-stained Cells From Just One Of Such Isolates That Showed Superior Activity Revealed That Majority Of The Cells Are Small And Gram-positive And Mostly Appear As Short Rods And Coccobacilli. This Indicates That The Polymer Manufacturing Plant Waste Area May Contain Bacteria That Can Degrade LDPE Similar To Isolates From Municipal Dumps And Marine Sediments And Supports The Use Of Areas Of Industry That Have Undergone LDPE Treatment As A Good Target For Isolation Of Candidate Strains Useful Read more Read less

Federated Learning And Explainable Artificial Intelligence For Intrusion Detection In IoT Networks: A Review

The Rapid Enlargement Of The Internet Of Things Has Turned Billions Of Weakly Protected Devices Into An Attractive Attack Surface, And Machine-learning Intrusion Detection Has Become The Default Defensive Answer. Three Difficulties, However, Continue... The Rapid Enlargement Of The Internet Of Things Has Turned Billions Of Weakly Protected Devices Into An Attractive Attack Surface, And Machine-learning Intrusion Detection Has Become The Default Defensive Answer. Three Difficulties, However, Continue To Limit Deployment: High-accuracy Deep Detectors Behave As Black Boxes, Conventional Training Pipelines Demand That Raw Traffic Be Pooled At A Central Site, And Constrained Edge Hardware Restricts Model Size. Two Research Directions Have Emerged In Response—federated Learning, Which Trains A Shared Detector While Traffic Remains On-device, And Explainable Artificial Intelligence, Which Converts Opaque Predictions Into Auditable Evidence. This Paper Reviews Thirty-two Studies Published Between 2009 And 2025 That Span Deep-learning Intrusion Detection, Federated Training Of Security Models, Post-hoc Explanation Techniques, And The Small But Growing Body Of Work That Unites All Of These Strands. We Organise The Literature Into A Common Taxonomy, Catalogue Seven Public Benchmark Datasets With An Emphasis On CIC-IoT2023, Tabulate Reported Detection Results Across Centralised, Federated And Explainable Configurations, And Trace How Hybrid Convolutional–recurrent Architectures Came To Dominate Recent Benchmarks. The Synthesis Shows That Federated Detectors Now Operate Within One Percentage Point Of Their Centralised Equivalents, That SHAP And LIME Are The De-facto Explanation Pair In Security Settings, And That Frameworks Combining Federated Optimisation With Dual Explainability Over Hybrid Deep Models Remain Rare. We Close By Identifying Open Problems—non-IID Client Data, Poisoning Of The Aggregation Step, Explanation Fidelity, And The Absence Of Standard Evaluation Protocol—that Define The Near-term Research Agenda. Read more Read less

Embedded-Based Pest Detection And Prevention System For Smart Agriculture

Agricultural Productivity Is Significantly Affected By Pest Infestations, Leading To Substantial Crop Losses And Increased Dependence On Chemical Pesticides. Early Detection And Timely Prevention Of Pests Are Essential For Improving Crop Yield And En... Agricultural Productivity Is Significantly Affected By Pest Infestations, Leading To Substantial Crop Losses And Increased Dependence On Chemical Pesticides. Early Detection And Timely Prevention Of Pests Are Essential For Improving Crop Yield And Ensuring Sustainable Farming Practices. This Paper Presents An Embedded-based Pest Detection And Prevention System That Integrates Image Sensing, Environmental Monitoring, And Automated Control Mechanisms For Real-time Crop Protection. The Proposed System Employs A Camera Module And Embedded Processor To Detect Pest Presence Using Image Processing And Machine Learning Techniques, While Sensors Continuously Monitor Environmental Parameters Such As Temperature And Humidity. Upon Detecting Pests, The System Automatically Activates Appropriate Preventive Measures, Including Ultrasonic Repellents Or Pesticide Spraying, And Simultaneously Notifies Farmers Through A Wireless Communication Module. Experimental Evaluation Demonstrates That The Proposed System Provides Accurate Pest Detection, Minimizes Pesticide Usage, Reduces Manual Intervention, And Enhances Crop Productivity. The Developed Framework Offers A Cost-effective, Intelligent, And Scalable Solution For Precision Agriculture And Smart Farming Applications. Read more Read less

A Review On Machine Learning Enabled MIMO Systems For Capacity Enhancement In Wireless Networks

Due To Increasing Number Of Users And Multimedia Applications, Multiple Input, Multiple Output (MIMO) Systems Have Become Indispensable From Wireless Technology. The Benefit Of MIMO Systems Is Increase In The Channel Capacity At Low Bandwidths, Howev... Due To Increasing Number Of Users And Multimedia Applications, Multiple Input, Multiple Output (MIMO) Systems Have Become Indispensable From Wireless Technology. The Benefit Of MIMO Systems Is Increase In The Channel Capacity At Low Bandwidths, However, The Accurate Reception Of The MIMO Signal At The Receiver Is Extremely Complex. This Is Due To The Addition Of Noise And Interference, As Well As The Different Behavior Of The Wireless Channel For The Different Channel Paths. Due To The Largeness And The Complexity Of The Data, The Technique Used Off Late Is The Deep Learning Based Approach For Signal Detection. This Paper Presents The Basics Of MIMO Systems And Insight Into Deep Learning Based MIMO Channel Estimation And Signal Detection Techniques. Read more Read less

A Review On Machine Learning And Deep Learning Models For Identifying Potentially Extremist And Radicalized Content

Machine Learning Has Revolutionized Churn Prediction, Enabling Organizations To Anticipate And Mitigate Risks Effectively. By Leveraging Historical Data And Advanced Algorithms, Businesses Can Identify At-risk Customers Or Employees And Take Proactiv... Machine Learning Has Revolutionized Churn Prediction, Enabling Organizations To Anticipate And Mitigate Risks Effectively. By Leveraging Historical Data And Advanced Algorithms, Businesses Can Identify At-risk Customers Or Employees And Take Proactive Measures To Retain Them As Markets Become Increasingly Competitive, Retaining Existing Customers Has Proven To Be More Cost-effective Than Acquiring New Ones. In This Context, Machine Learning (ML) Has Emerged As A Powerful Tool For Analyzing Customer Behavior And Predicting Churn With High Accuracy. By Leveraging Vast Datasets And Sophisticated Algorithms, Businesses Can Proactively Identify At-risk Customers And Take Targeted Actions To Retain Them. The Success Of Churn Prediction Largely Depends On The Quality And Relevance Of Input Features. Important Features Include Customer Demographics, Transaction Frequency, Service Usage Patterns, Complaint Records, And Engagement Metrics. Feature Engineering, Which Involves Creating New Features Or Transforming Existing Ones, Is A Critical Step In Improving Model Performance This Paper Presents A Comprehensive Survey Of Statistical Models For Forecasting Churn Rates Along With Associated Challenges That The Sector Faces. Read more Read less

Brain Tumor Detection From MRI Scans Using Convolutional Neural Networks

Brain Tumors Are Among The Most Life-threatening Neurological Disorders That Require Early And Accurate Diagnosis To Improve Patient Survival And Treatment Outcomes. Magnetic Resonance Imaging (MRI) Is Widely Used For Detecting Brain Abnormalities Be... Brain Tumors Are Among The Most Life-threatening Neurological Disorders That Require Early And Accurate Diagnosis To Improve Patient Survival And Treatment Outcomes. Magnetic Resonance Imaging (MRI) Is Widely Used For Detecting Brain Abnormalities Because It Provides High-resolution Images Of Brain Tissues. However, Manual Examination Of MRI Scans Is Time-consuming And Depends Heavily On The Expertise Of Radiologists, Which May Result In Diagnostic Errors Or Delays. In Recent Years, Artificial Intelligence (AI) And Deep Learning Techniques Have Significantly Improved The Field Of Medical Image Analysis By Providing Automated And Reliable Disease Detection Systems. This Research Presents A Convolutional Neural Network (CNN)-based Approach For Automated Brain Tumor Detection Using MRI Images. The Proposed Methodology Includes Dataset Collection, Image Preprocessing, CNN Model Development, Model Training, Testing, And Performance Evaluation. Image Preprocessing Techniques Such As Resizing, Normalization, And Data Augmentation Are Employed To Improve Image Quality And Enhance Model Learning. The CNN Architecture Automatically Extracts Important Features From MRI Images Without Requiring Manual Feature Engineering, Thereby Improving Classification Accuracy. The Proposed Model Classifies MRI Images Into Two Categories: Tumor And Non-Tumor. Performance Is Evaluated Using Standard Metrics Including Accuracy, Precision, Recall, F1-Score, Sensitivity, Specificity, And Confusion Matrix Analysis. Experimental Results Demonstrate That The CNN-based Approach Achieves High Classification Accuracy While Reducing Diagnostic Time And Improving Consistency. The Proposed System Can Serve As An Effective Computer-aided Diagnosis (CAD) Tool To Assist Radiologists In The Early Detection Of Brain Tumors And Support Clinical Decision-making. Read more Read less

Brain Tumor Detection From MRI Scans Using Convolutional Neural Networks: A Review

Brain Tumor Detection Is One Of The Most Critical Applications Of Artificial Intelligence (AI) In Medical Image Analysis. Early And Accurate Identification Of Brain Tumors Plays A Vital Role In Improving Patient Survival Rates And Assisting Clinician... Brain Tumor Detection Is One Of The Most Critical Applications Of Artificial Intelligence (AI) In Medical Image Analysis. Early And Accurate Identification Of Brain Tumors Plays A Vital Role In Improving Patient Survival Rates And Assisting Clinicians In Treatment Planning. Magnetic Resonance Imaging (MRI) Is Widely Used For Brain Tumor Diagnosis Because It Provides High-resolution Images Of Soft Tissues Without Exposing Patients To Ionizing Radiation. However, Manual Analysis Of MRI Scans Is Time-consuming, Labor-intensive, And Highly Dependent On The Expertise Of Radiologists. To Address These Challenges, Researchers Have Increasingly Adopted Deep Learning Techniques, Particularly Convolutional Neural Networks (CNNs), For Automated Brain Tumor Detection And Classification. This Review Paper Presents A Comprehensive Analysis Of Recent CNN-based Approaches For Brain Tumor Detection Using MRI Images. Various Stages Involved In Automated Tumor Detection, Including Image Acquisition, Preprocessing, Feature Extraction, Classification, And Performance Evaluation, Are Discussed In Detail. The Review Examines Different CNN Architectures Such As AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, U-Net, And Vision Transformers That Have Been Successfully Applied To Brain Tumor Analysis. Furthermore, The Paper Compares Existing Methodologies Based On Datasets, Accuracy, Advantages, And Limitations. The Study Also Highlights Key Challenges Including Limited Dataset Availability, Class Imbalance, Overfitting, Computational Complexity, And Lack Of Model Interpretability. Emerging Research Trends Such As Transfer Learning, Explainable Artificial Intelligence (XAI), Federated Learning, And Multimodal Medical Imaging Are Discussed As Potential Solutions To These Challenges. The Findings Indicate That CNN-based Approaches Have Significantly Improved The Accuracy And Reliability Of Brain Tumor Detection Systems And Have Become Essential Components Of Modern Computer-aided Diagnosis (CAD) Frameworks. This Review Provides Valuable Insights For Researchers And Healthcare Professionals Interested In Developing Advanced AI-driven Brain Tumor Detection Systems. Read more Read less

BRIDGING LEARNING GAPS THROUGH STEM EDUCATION

STEM Education Is Applied To Raise Individuals Having 21st-century Skills Based On The Integration Of Science, Technology, Engineering, And Mathematics. This Paper Aims To Present The Overall Effect Of STEM Education On Students’ Academic Achievement... STEM Education Is Applied To Raise Individuals Having 21st-century Skills Based On The Integration Of Science, Technology, Engineering, And Mathematics. This Paper Aims To Present The Overall Effect Of STEM Education On Students’ Academic Achievement By Analyzing Research Findings Obtained From Qualitative And Quantitative Studies Conducted In School On 300+ Students Between May To July’2025. The Responses Showed That The Effect Of STEM Education On Students’ Academic Success Was Statistically Higher In The Digital Surveys. Further Subgroup Analysis Using F-Test And T- Test, Revealed That Sample 2 Is Having Mean Greater Than Sample 1. For F-test, F= 1.24, Tabulated Value Of F= 1.8, Hence Null Hypothesis Is Accepted, So There Is Equality Of Population Variance. For T-Test, T=2.38, Tabulated T-value= 1.6, Thus Hypothesis Is Rejected. Hence It Is Proved That Sample 2 Showed The Largest Effect Of STEM Education. Read more Read less

AR-IOT FRAMEWORK FOR PREVENTING VEHICLE DEPREDATION

With The Rapid Rise In Automobile Thefts Driven By Inadequate Residential Parking And A Lack Of Sophisticated Security Systems, Enhancing Vehicle Safety Has Become Paramount. This Paper Presents An Advanced, Multi-tiered Vehicle Security System That... With The Rapid Rise In Automobile Thefts Driven By Inadequate Residential Parking And A Lack Of Sophisticated Security Systems, Enhancing Vehicle Safety Has Become Paramount. This Paper Presents An Advanced, Multi-tiered Vehicle Security System That Integrates Biometric Authentication Or Real-Time One-Time Passwords (OTP). Utilizing An Arduino Uno Microcontroller Embedded Platform, The Prototype Interfaces An R307 Fingerprint Sensor Module With A Matrix Keypad. To Prevent Unauthorized Use, The System Allows A User To Turn On The Vehicle But Restricts Motion Until Both Fingerprint And OTP Verifications Are Successful. In The Event Of An Unauthorized Access Attempt, The System Instantly Triggers An Alert To The Vehicle Owner, Pairing Real-time Live Video Streaming Via An Onboard Camera Module With Exact Geographic Tracking Via GPS Technology. The Proposed System Offers A Low-cost, Fast-access, And User-friendly Solution That Significantly Improves Vehicle Security At Every Level. Read more Read less

Andrographis Paniculata: Phytochemistry, Pharmacological Activities, Cardioprotective Potential, And Recent Advances

Traditional Herbal Medicines Remain An Important Source Of Healthcare, Particularly In Developing Countries. This Study Evaluated The Cardioprotective Effect Of An Aqueous Extract Of Andrographis Paniculata Against Dichlorvos-induced Cardiac Toxicity... Traditional Herbal Medicines Remain An Important Source Of Healthcare, Particularly In Developing Countries. This Study Evaluated The Cardioprotective Effect Of An Aqueous Extract Of Andrographis Paniculata Against Dichlorvos-induced Cardiac Toxicity In Male Wistar Rats. Dichlorvos Exposure Caused Cardiovascular Dysfunction, Increased Cardiac Injury Markers, Oxidative Stress, Inflammation, And Apoptosis. Treatment With A. Paniculata Significantly Improved Antioxidant Status, Restored Cardiac Function, Reduced Inflammatory And Apoptotic Responses, And Protected Cardiac Tissue From Damage. These Findings Suggest That A. Paniculata Possesses Promising Cardioprotective Properties Against Dichlorvos-induced Cardiotoxicity And Also Several Studies Noted That Plant Has Limited Genetic Diversity Due To Its Self-pollinating Nature, Restricting The Development Of Improved Cultivators With Enhanced Agronomic Traits And Higher Andrographolide Content. Ethyl Methanesulfonate (EMS)-induced Mutagenesis Has Been Used To Create Genetic Variation, Providing Valuable Resources For Crop Improvement, Functional Gene Studies, And The Development Of High-yielding Medicinal Varieties. Read more Read less

A Review On Machine Learning And Deep Learning Models For Estimating Customer Churn Rates

Machine Learning Has Revolutionized Churn Prediction, Enabling Organizations To Anticipate And Mitigate Risks Effectively. By Leveraging Historical Data And Advanced Algorithms, Businesses Can Identify At-risk Customers Or Employees And Take Proactiv... Machine Learning Has Revolutionized Churn Prediction, Enabling Organizations To Anticipate And Mitigate Risks Effectively. By Leveraging Historical Data And Advanced Algorithms, Businesses Can Identify At-risk Customers Or Employees And Take Proactive Measures To Retain Them As Markets Become Increasingly Competitive, Retaining Existing Customers Has Proven To Be More Cost-effective Than Acquiring New Ones. In This Context, Machine Learning (ML) Has Emerged As A Powerful Tool For Analyzing Customer Behavior And Predicting Churn With High Accuracy. By Leveraging Vast Datasets And Sophisticated Algorithms, Businesses Can Proactively Identify At-risk Customers And Take Targeted Actions To Retain Them. The Success Of Churn Prediction Largely Depends On The Quality And Relevance Of Input Features. Important Features Include Customer Demographics, Transaction Frequency, Service Usage Patterns, Complaint Records, And Engagement Metrics. Feature Engineering, Which Involves Creating New Features Or Transforming Existing Ones, Is A Critical Step In Improving Model Performance This Paper Presents A Comprehensive Survey Of Statistical Models For Forecasting Churn Rates Along With Associated Challenges That The Sector Faces. Read more Read less

A Review On Techniques For Web 3.0 Security Through Reduction Of PAPR Of Data Streams

With Increasing Number Of Cellular Network Users, Large Data Being Generated And Limited Bandwidth Available For Systems, Efficient Multiplexing Techniques Are Needed That Use The Available Bandwidth Efficiently For IoT And Web 3.0 Systems. It Is Wid... With Increasing Number Of Cellular Network Users, Large Data Being Generated And Limited Bandwidth Available For Systems, Efficient Multiplexing Techniques Are Needed That Use The Available Bandwidth Efficiently For IoT And Web 3.0 Systems. It Is Widely Used In Cellular And Internet Of Things (IoT) Based Applications. One Of The Major Challenges That IoT Suffers From Is High Value Of Peak To Average Power Ratio (PAPR), Which Reduces Its Security. High PAPR Causes High Level Of Perceptibility And Low Security. Hence It Is Necessary To Reduce The PAPR Of The OFDM Systems. Several Techniques Have Been Employed So Far For The Reduction Of PAPR In IoT Systems. This Paper Presents A Review On The Most Common PAPR Reduction Techniques For OFDM Systems. Read more Read less

A Comprehensive Study Of Machine Learning (ML) Algorithms And Performance Evaluation: Enhancing The Credit Card Fraud Detection

Credit Card Companies Must Be Able To Identify Fraudulent Credit Card Transactions So That Clients Are Not Charged For Items They Did Not Purchase. Previously, Many Machine Learning Approaches And Classifiers Were Used To Detect Fraudulent Transactio... Credit Card Companies Must Be Able To Identify Fraudulent Credit Card Transactions So That Clients Are Not Charged For Items They Did Not Purchase. Previously, Many Machine Learning Approaches And Classifiers Were Used To Detect Fraudulent Transactions. However, Because Fraud Patterns Are Always Changing, It Is Becoming Increasingly Vital To Investigate New Frauds And Develop The Model Based On The New Patterns. The Purpose Of This Research Is To Create A Machine Learning Classifier That Not Only Detects Fraud But Also Detects Legitimate Transactions. As A Result, The Model Should Have Excellent Accuracy, Precision, Recall, And F1-score. As A Result, We Began With A Large Dataset In This Study And Used Four Machine Learning Classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, And Random Forest. The Random Forest Classifier Scored 99.96% Overall Accuracy With The Best Precision, Recall, F1- Score, And Matthew’s Correlation Coefficient In The Experiments. Read more Read less

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

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

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

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

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

A Survey On Machine Learning And Deep Learning Models For Predicting Traffic Transit Time

The Transition Towards Intelligent Traffic Systems (ITS) Is Inevitable In Future. Accurate Travel Time Estimation Is Vital For The Effectiveness Of Modern Public Transportation Systems. It Plays A Central Role In Applications Such As Real-time Passen... The Transition Towards Intelligent Traffic Systems (ITS) Is Inevitable In Future. Accurate Travel Time Estimation Is Vital For The Effectiveness Of Modern Public Transportation Systems. It Plays A Central Role In Applications Such As Real-time Passenger Information, Transit Planning, And Traffic Management. With Increasing Urbanization And Demand For Efficient Mobility, Transit Agencies Are Turning To Data-driven Models To Improve Service Reliability. One Valuable Data Source Is The General Transit Feed Specification (GTFS), Which Standardizes Public Transportation Schedules And Associated Geographic Information. When Integrated With Statistical Modeling Techniques, GTFS Features Can Significantly Enhance The Precision Of Street-level Travel Time Estimations. This Paper Presents A Comprehensive Review Of Statistical Models For Forecasting Street Level Travel Time Employing GTFS Features, Along With Associated Challenges That The Sector Faces. Read more Read less

A Comprehensive Review On Contemporary Machine Learning And Deep Learning Models For Forecasting Stock Market Trends

Machine Learning Is Transforming Stock Market Prediction By Leveraging Vast Datasets, Advanced Algorithms, And Computational Power To Provide More Accurate Forecasts. While Challenges Remain, Continuous Advancements In Artificial Intelligence And Dee... Machine Learning Is Transforming Stock Market Prediction By Leveraging Vast Datasets, Advanced Algorithms, And Computational Power To Provide More Accurate Forecasts. While Challenges Remain, Continuous Advancements In Artificial Intelligence And Deep Learning Are Improving Predictive Models, Making Them An Essential Tool For Traders And Investors. Stock Market Prediction Extremely Challenging Due To The Dependence Of Stock Prices On Several Financial, Socio-economic And Political Parameters Etc. For Real Life Applications Utilizing Stock Market Data, It Is Necessary To Predict Stock Market Data With Low Errors And High Accuracy. This Needs Design Of Appropriate Artificial Intelligence (AI) And Machine Learning (ML) Based Techniques Which Can Analyze Large And Complex Data Sets Pertaining To Stock Markets And Forecast Future Prices And Trends In Stock Prices With Relatively High Accuracy. This Paper Presents A Comprehensive Review On The Various Techniques Used In Recent Contemporary Papers For Stock Market Forecasting. Read more Read less

Forensic Detection Of Toxic Chatbots, Deepfakes, And Automated Harmful Interactions: A State-of-the-Art Review

The Rapid Expansion Of Generative AI (GenAI) Technologies Has Enabled Unprecedented Synthesised Media And Sophisticated Automated Systems. The Existence Of Advanced Deepfake Technology, Generative Large Language Models (LLMs) That Can Produce Toxic C... The Rapid Expansion Of Generative AI (GenAI) Technologies Has Enabled Unprecedented Synthesised Media And Sophisticated Automated Systems. The Existence Of Advanced Deepfake Technology, Generative Large Language Models (LLMs) That Can Produce Toxic Content, And Automated AI Botnets Has Created Substantial New Obstacles In Digital Forensics. The Present Review Consolidates Recent Work (2024–2025) Concerning The Forensics Of Three Colliding AI Threats: Audio-visual Deepfakes, Toxic Or Weaponised Chatbots, And The Automated Facilitation Of Harm. The Review Reconstructs The Shift From Artefact Detection Systems Toward The Use Of Behavioural, Semantic, And Multimodal Forensics. The Emerging Methodologies, Such As Biological Signal Processing, Audiovisual Correlation, LLM Systematisation Fingerprinting, And Traffic Encryption Biometrics, As Well As The Comprehensive Integration Of These Methodologies, Reveal Vital Yet Unrefined Methodologies That Focus On The Challenges Of Adversarial Attacks, Laundering Via Paraphrasing, And Performance Drops On Real-world Data. The Review Articulates The Necessity Of Well-founded, Transparent, And Standardizable Forensic Systems That Are Meant To Work In Adversarial Conditions. Read more Read less

INFLUENCE OF NATURAL AGGREGATES REPLACEMENT WITH RECYCLED AGGREGATE ON STRENGTH AND SULPHATE RESISTANCE OF M50 GRADE CONCRETE

The Rapid Expansion Of Global Infrastructure Has Led To An Unprecedented Demand For Natural Construction Materials, Necessitating The Exploration Of Sustainable Alternatives. This Research Investigates The Feasibility Of Replacing Natural Coarse Aggr... The Rapid Expansion Of Global Infrastructure Has Led To An Unprecedented Demand For Natural Construction Materials, Necessitating The Exploration Of Sustainable Alternatives. This Research Investigates The Feasibility Of Replacing Natural Coarse Aggregates With Manually Crushed Recycled Concrete Aggregates (RCA) In High-strength M50 Grade Concrete. A Primary Challenge Addressed In This Study Is The Presence Of Adhered Mortar On The Surface Of Recycled Aggregates, Which Significantly Influences Concrete Performance. The Experimental Program Involved Characterizing The Physical Properties Of Recycled Aggregates, Specifically Quantifying The Average Adhered Mortar Content. A Comprehensive Mix Design For M50 Grade Concrete Was Developed, And An Optimum Replacement Ratio Was Determined. The Mechanical Performance Was Evaluated Through Compressive Strength Testing Of Both Natural And Recycled Aggregate Concrete Under Normal Water Curing Conditions. Furthermore, The Study Explores The Effect Of Recycled Aggregate Utilization On Durability By Assessing The Concrete's Resistance To Sulphate Attack. The Findings Provide Critical Insights Into The Viability Of Incorporating Recycled Aggregates Into High-performance Concrete, Offering A Pathway Toward More Sustainable Construction Practices By Mitigating The Reliance On Virgin Natural Resources. Read more Read less

STRENGTHENING OF HIGH-PERFORMANCE LIGHTWEIGHT CONCRETE USING CENOSPHERE AND DOLOMITE HYBRID FILLERS

The Construction Industry’s Rapid Expansion Is Driving An Unsustainable Increase In Global Cement Production, Currently Exceeding 1.6 Billion Tons Annually. This Demand Not Only Depletes Natural Resources But Also Results In Significant Environmental... The Construction Industry’s Rapid Expansion Is Driving An Unsustainable Increase In Global Cement Production, Currently Exceeding 1.6 Billion Tons Annually. This Demand Not Only Depletes Natural Resources But Also Results In Significant Environmental Degradation Due To The Emission Of Greenhouse Gases, Including CO2, SO2, And NOX. To Address These Concerns, This Study Explores The Utilization Of Industrial Byproducts Specifically Cenospheres And Dolomite Powder As Sustainable Additives In Concrete Manufacturing. Cenospheres, Lightweight Hollow Spheres Derived From Coal Combustion, Were Utilized To Reduce The Unit Weight Of The Concrete, While Dolomite Powder Was Introduced As A Mineral-rich Cementitious Material To Enhance Mechanical Performance. This Research Investigates The Mechanical And Physical Properties Of M-30 Grade Concrete Through A Series Of Partial Replacements. The Experimental Program Evaluates Varying Proportions Of Dolomite Powder (0, 10, 20 And 30% By Weight Of Cement) In Combination With Cenospheres (0, 2, 4, And 6% By Weight). The Study Provides A Detailed Experimental Analysis Of Compressive, Flexural, And Split Tensile Strength At 7, 14, And 28 Days Of Curing. Preliminary Results Aim To Determine The Optimal Synergy Between These Materials To Produce A Structural-grade Lightweight Concrete That Minimizes Environmental Impact Without Compromising Mechanical Integrity. This Research Contributes To The Development Of Circular-economy-based Construction Materials, Offering A Viable Pathway For Reducing The Carbon Footprint Of Structural Infrastructure. Read more Read less

FACIAL EXPRESSION RECOGNITION IN CLASSROOMS USING DEEP LEARNING

This Project Builds A System That Checks Student Attention In Real Time. It Uses A Camera Feed To Detect Faces, Track Eyes, And Read Basic Emotions. The System Works With A Single Student Or Many Students At The Same Time. It Also Supports Different... This Project Builds A System That Checks Student Attention In Real Time. It Uses A Camera Feed To Detect Faces, Track Eyes, And Read Basic Emotions. The System Works With A Single Student Or Many Students At The Same Time. It Also Supports Different Platforms Like Webcam, Video Files, Zoom, Google Meet, And Microsoft Teams. Each Frame Is Processed To Find Faces, Detect Eyes, And Estimate Emotions. These Results Are Used To Calculate Attention Levels And Engagement Scores. The System Can Show Alerts, Track Trends, And Create Simple Reports. All Processing Happens On The User’s Machine, So No Data Is Sent Outside. The Goal Is To Help Teachers Understand When Students Are Focused And When They Are Not. Read more Read less

A Review On Chaos Based Mechanisms For Securing Body Area And Body Sensor Networks

Experimental Investigation On The Strength And Engineering Characteristics Of Marine Clay Stabilized With Fly Ash And Lime

Marine Soil Refers To The Deposits Formed Beneath The Seabed And Is Also Commonly Found Along Coastal Areas. The Engineering Behavior Of Fully Saturated Marine Soil Is Considerably Different From That Of Partially Moist Or Dry Soil. It Generally Cont... Marine Soil Refers To The Deposits Formed Beneath The Seabed And Is Also Commonly Found Along Coastal Areas. The Engineering Behavior Of Fully Saturated Marine Soil Is Considerably Different From That Of Partially Moist Or Dry Soil. It Generally Contains A High Percentage Of Organic Matter And Exhibits Expansive Characteristics, Undergoing Swelling And Shrinkage As Its Moisture Content Changes. These Volume Variations Can Adversely Affect The Stability Of Foundations And Other Civil Engineering Structures, Resulting In Increased Construction And Maintenance Costs In Coastal Regions. To Overcome These Challenges, Ground Improvement Techniques Are Widely Adopted To Enhance The Engineering Properties Of Marine Soil So That It Satisfies Foundation And Infrastructure Requirements. Maintaining A Consistent Moisture Condition Around And Beneath Foundations Is Considered One Of The Most Effective Methods For Minimizing The Harmful Effects Associated With Expansive Soils. For The Present Investigation, Marine Clay Samples Were Collected From The Coastal Region Of UPPADA Village In Kakinada District, Andhra Pradesh. The Primary Reason For Selecting Marine Clay From This Location Is The Recent Announcement By The Government Of Andhra Pradesh Regarding The Construction Of A Fishing Harbour In The Area. Therefore, A Preliminary Investigation Has Been Carried Out To Assess The Engineering And Strength Characteristics Of The Locally Available Marine Clay. At The Same Time, The Disposal Of Industrial Waste Materials Has Become A Significant Environmental Concern. Fly Ash, A By-product Generated From Coal-fired Thermal Power Plants, Is One Such Material With Considerable Potential For Reuse. Although Fly Ash Possesses Limited Cementitious Properties On Its Own, It Reacts With Moisture To Produce Cementitious Compounds That Improve The Strength And Compressibility Behavior Of Soils. Accordingly, This Experimental Study Aims To Enhance The Properties Of Marine Clay While Promoting The Beneficial Utilization Of Industrial Waste. The Research Focuses On Evaluating The Influence Of Fly Ash And Lime On The Strength Characteristics Of Marine Clay. Read more Read less

Experimental Investigation On The Synergistic Stabilization Of Expansive Soil Using Calcium Chloride And Shredded Rubber Mulch

Expansive Soils, Commonly Referred To As Black Cotton Soils In India, Present Significant Geotechnical Challenges Because They Undergo Swelling When They Absorb Moisture And Shrink Upon Drying. This Repeated Cycle Of Expansion And Contraction Can Lea... Expansive Soils, Commonly Referred To As Black Cotton Soils In India, Present Significant Geotechnical Challenges Because They Undergo Swelling When They Absorb Moisture And Shrink Upon Drying. This Repeated Cycle Of Expansion And Contraction Can Lead To Considerable Damage To The Foundations Of Structures Constructed On Such Soils. Therefore, Understanding The Engineering Behaviour Of Expansive Soils And Implementing Suitable Stabilization Techniques Has Become An Important Area Of Study For Geotechnical Engineers. Considerable Research Has Been Undertaken To Identify Effective Methods For Minimizing The Expansive Characteristics Of These Soils. Among The Various Ground Improvement Techniques, The Use Of Electrolytes Has Emerged As A Promising Approach For Enhancing The Engineering Properties Of Expansive Soils. In The Present Study, A Systematic Laboratory Investigation Was Carried Out To Evaluate The Effects Of An Electrolyte And An Industrial Waste By-product, Namely Shredded Rubber Mulch, Together With Calcium Chloride, On The Engineering Properties Of Expansive Soil. The Experimental Program Was Conducted Under Carefully Controlled Laboratory Conditions Following A Structured Testing Methodology To Assess The Effectiveness Of These Stabilizing Materials. Read more Read less

AI-Based Early Detection Of Crop Diseases Using Image Classification: Challenges & Solutions