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Vol 12, Issue 9 (September 2026) Computer Science

A Comprehensive Review On 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

Authors: Narendra Rai | Prof. Shailendra Kumar Shriwastava

Vol 12, Issue 9 (September 2026) Electronics And Communication Engineering

Design And Physical Implementation Of An INT8 Output-Stationary Systolic Array CNN Accelerator For TinyML Inference Using Open-Source SKY130 PDK And OpenLane EDA Flow

This Paper Presents The Design And Complete Physical Implementation Of A 4×4 INT8 Output-stationary Systolic Array Accelerator For TinyML Convolutional Neural Network (CNN) Inference, Targeting Edge Vision Applications Such As Image Classification An... This Paper Presents The Design And Complete Physical Implementation Of A 4×4 INT8 Output-stationary Systolic Array Accelerator For TinyML Convolutional Neural Network (CNN) Inference, Targeting Edge Vision Applications Such As Image Classification And Object Detection On Resource-constrained Devices. The Proposed Architecture Addresses The Fundamental Energy Inefficiency Of General-purpose Processors Executing Fixed Computational Workloads. The Accelerator Was Designed In Verilog HDL Comprising Seven Modules Including MAC Units, Processing Elements, 4×4 Systolic Array Fabric, On-chip Input And Output Data Buffers, And A Finite State Machine Controller And Physically Implemented Using The Open-source OpenLane RTL-to-GDSII EDA Flow Targeting The SkyWater SKY130 130nm/180nm Hybrid Process Design Kit. Two Complete Implementation Runs Achieved Zero Design Rule Check (DRC) Violations. The Complete Buffered System Achieves A Post-route Critical Path Delay Of 3.07 Ns, Enabling Operation Up To 325 MHz, With 23.3 µW Total Power At The Typical Process Corner (TT, 1.8V, 25°C). Functional Correctness Was Verified Using A Sobel-X Edge Detection Conv2D Kernel With Outputs Confirmed Identical Between RTL Simulation And ESP32-WROOM-DA Hardware. Power Comparison Against ESP32 At 347.43 MW Demonstrates A 14,911× Total Power Reduction And 477,187× Better Energy Per Conv2D Operation. Read more Read less

Authors: JUSTUS A | Dr. P. Sivasankar | Dr. G. Kulanthaivel

Vol 12, Issue 9 (September 2026) Library And Information Science

User Satisfaction With Library Services In Management Institutions: A Study

User Satisfaction Is An Important Indicator For Evaluating The Effectiveness And Quality Of Library Services In Management Institutions. Academic Libraries Supporting Management Education Are Expected To Provide Relevant, Timely, Accessible, And Tech... User Satisfaction Is An Important Indicator For Evaluating The Effectiveness And Quality Of Library Services In Management Institutions. Academic Libraries Supporting Management Education Are Expected To Provide Relevant, Timely, Accessible, And Technology-enabled Information Resources To Meet The Changing Academic And Research Requirements Of Their Users. The Present Theoretical Study Examines The Concept Of User Satisfaction With Library Services In Management Institutions And Discusses The Major Factors That Influence Users' Perceptions And Experiences. The Study Focuses On The Relationship Between Library Resources, Service Quality, Accessibility, Staff Support, Technology, Infrastructure, And Overall User Satisfaction. The Paper Also Discusses Important Theoretical Perspectives Related To Service Quality And User Satisfaction And Highlights The Need For Management Institution Libraries To Continuously Evaluate And Improve Their Services. The Study Concludes That User Satisfaction Is Multidimensional And Depends Not Only On The Availability Of Information Resources But Also On The Quality, Accessibility, Responsiveness, And Effectiveness Of Library Services. Read more Read less

Authors: Rajshri Bansidhar Gadkari | Dr.Bhagyashri Yeshwantrao Keskar

Vol 12, Issue 9 (September 2026) Mechanical Engineering

Performance Study Of Diesel Engine Using Pongamia Biodiesel

Rising Environmental Pollution, Ever-increasing Energy Demand, And The Depletion Of Fossil Fuel Reserves Have Driven The Search For Alternative And Sustainable Fuels For Internal Combustion Engines. Biodiesel, A Renewable, Biodegradable, And Non-toxi... Rising Environmental Pollution, Ever-increasing Energy Demand, And The Depletion Of Fossil Fuel Reserves Have Driven The Search For Alternative And Sustainable Fuels For Internal Combustion Engines. Biodiesel, A Renewable, Biodegradable, And Non-toxic Fuel Derived From Non-edible Oil Sources, Has Emerged As One Of The Most Promising Substitutes For Conventional Diesel Fuel. In This Study, Pongamia Biodiesel Was Produced From Pongamia Oil Through The Transesterification Process. The Primary Objective Of This Research Is To Investigate The Performance Of A Single-cylinder Compression Ignition Diesel Engine Fuelled With Pongamia Biodiesel Blends, With Particular Emphasis On How The Supplied Heat Energy Is Utilized And The Associated Losses. Biodiesel Blends — B20, B40, B60, B80, And B100 (20%, 40%, 60%, 80%, And 100% Biodiesel Mixed With Diesel) — Were Prepared And Tested Under Varying Load Conditions. Mechanical Efficiency, Brake Thermal Efficiency, Indicated Thermal Efficiency, And Specific Fuel Consumption Were Analysed Together With A Detailed Heat Balance Sheet For Each Fuel. The Experimental Results Indicate That Pongamia Biodiesel Blends Deliver Engine Performance Comparable To Conventional Diesel. Brake Thermal Efficiency Increases With Engine Load For All Fuel Blends, While Specific Fuel Consumption Is Marginally Higher For Biodiesel Blends Owing To Their Lower Calorific Value. Emission-related Combustion Behaviour Also Improves, With Better Utilization Of Oxygen Content Promoting More Complete Combustion. Among The Tested Blends, B60 Offers A Favourable Overall Compromise Between Performance And Fuel Economy, While B60 Shows The Most Promising Brake Thermal Efficiency. The Study Concludes That Pongamia Biodiesel Is A Viable, Renewable, And Eco-friendly Alternative To Diesel Fuel That Can Be Used In Existing Diesel Engines Without Major Modification, Contributing To Reduced Environmental Pollution And Decreased Dependence On Fossil Fuels. Read more Read less

Authors: Vinod Harikanth¹ | Dr. M. Senthilkumar

Vol 12, Issue 9 (September 2026) ELECTRONICS ENGINEERING

Embedded Edge-AI Framework For Autonomous UAV-Based Human Detection And Remote Alerting Using ESP32-S3 Flight Control And ESP32-CAM Vision

Autonomous Unmanned Aerial Vehicles (UAVs) Equipped With On-board Artificial-intelligence Inference Are Increasingly Used For Surveillance And Monitoring Tasks That Require Rapid, Low-latency Decisions Without Continuous Human Supervision. This Paper... Autonomous Unmanned Aerial Vehicles (UAVs) Equipped With On-board Artificial-intelligence Inference Are Increasingly Used For Surveillance And Monitoring Tasks That Require Rapid, Low-latency Decisions Without Continuous Human Supervision. This Paper Presents The Design And Experimental Implementation Of A Compact Embedded Edge-AI UAV Platform For Autonomous Defense-oriented Human Monitoring, Built Around A Custom-developed Quadcopter Whose ESP32-S3 Microcontroller Performs Real-time Flight Control While A Separate ESP32-CAM Module Performs On-board Visual Inference. The Flight-control Subsystem Interfaces With An Inertial Measurement Unit (MPU6050) And Pulse-width-modulated Motor Drivers To Support Stabilized Takeoff, Hover, Landing And Coordinate/waypoint-based Autonomous Navigation Over A Crazyflie-compatible Wi-Fi Command Interface. For Visual Intelligence, Two Edge Impulse Models Were Trained And Evaluated On The ESP32-CAM: An Image-classification Model Distinguishing “Face” And “no_human” Classes, And An Object-detection Model Providing Spatial Localization. The Classification Model, Deployed With A 0.60 Confidence Threshold, Triggers Transmission Of The Captured JPEG Frame To A Telegram Bot For Remote Notification Whenever A Positive Detection Persists Beyond A Five-second Upload Cooldown. The Classification Model Achieved 96.0% Validation Accuracy, An F1 Score Of 0.97 For The Face Class And 0.95 For The No_human Class, And An Area Under The ROC Curve Of 0.95, While The Comparative Object-detection Model Achieved A Validation F1 Score Of 84.0% Over 60 Training Cycles. Experimental Results Confirm Functional Autonomous Navigation, Correctly Triggered Telegram Alerting And A Working End-to-end Pipeline. The Comparison Shows That Image Classification Is Best Suited To The Binary Alerting Requirement Of This Application, While Object Detection Offers Additional Spatial Information That Is Valuable For Future Localization-based Tasks. Read more Read less

Authors: KATHAR MYDEEN S | Dr. P. Sivasankar | Dr. G. Kulanthaivel