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Vol 12, Issue 7 (July 2026) Electronics And Communication Engineering

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

Authors: G. Roja | Aravind S | Divya R | Mukesh Kumar G | Abinaya M

Vol 12, Issue 7 (July 2026) Computer Science

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

Authors: Noshin Shaikh | Prof. Kamlendra Verma

Vol 12, Issue 7 (July 2026) Computer Science

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

Authors: Mohini Namdev | Prof. Pradeep Pal

Vol 12, Issue 7 (July 2026) Computer Science

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

Authors: Sarthak Rathore | Prof. Pankaj Raghuwanshi

Vol 12, Issue 7 (July 2026) Computer Science

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

Authors: Sarthak Rathore | Prof. Pankaj Raghuwanshi