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Vol 12, Issue 9 (September 2026) ELECTRONICS ENGINEERING

AI-Driven Edge-Based Smart Parking System With NFC Authentication For Parking Slot Recommendation And Access Time Prediction

Efficient Parking Management Requires Intelligent, Real-time Decision-making To Minimize Search Time And Improve Space Utilization. This Paper Presents An AI-driven, Edge-based Smart Parking System Built Around A Comparative Evaluation Of Random Fore... Efficient Parking Management Requires Intelligent, Real-time Decision-making To Minimize Search Time And Improve Space Utilization. This Paper Presents An AI-driven, Edge-based Smart Parking System Built Around A Comparative Evaluation Of Random Forest And Neural Network Models For Parking Slot Recommendation And Parking Access-time Prediction, In Which The Neural Network Model Was Selected For Its Higher Accuracy, Consistency, And Smaller Model Size. The Selected Neural Network Models Were Independently Trained As Two TinyML Regression Models In Edge Impulse (test_slot And Test_time), Deployed In Float32 Form, And Merged Into A Single Inference Library (test7time_inferencing_v2.h) Running On An ESP32-WROOM-DA. The Implemented System Performs NFC-based User Authentication Using A PN532 Reader/writer, IR-sensor-based Parking-slot Status Detection Across A 3-lane, 9-slot Layout, Lane-wise Congestion Calculation, On-device Slot Recommendation Based On The Lowest Suitability Score, User Confirmation Through A 2.8-inch SPI TFT Touch Display, On-device Access-time Prediction, Servo-actuated Barrier Control, And RTC DS1307 With Logger.py-based Real-hardware Data Logging. Both TinyML Models Achieved Strong Test-set Performance, With 97.26% Accuracy For Slot Recommendation And 98.68% For Access-time Prediction, While Maintaining Approximately 3 Ms Inference Latency And RAM Usage Below 1.5 KB On The ESP32. Real-time Behavioural Testing Confirmed That The Deployed Models Respond Correctly To Changes In Slot Status, Congestion, And Distance, Dynamically Generating Slot Scores And Access-time Predictions, And Real-hardware Testing Confirmed Successful Integration Of NFC Authentication, IR-based Slot Sensing, TinyML Inference, User Interaction, Barrier Control, RTC Timestamping, And CSV-based Data Logging. The Results Demonstrate That The Selected Neural Network Models Can Be Deployed And Operated As A Real-time, Authenticated, Edge-based Smart Parking System Without Dependence On Cloud-based Processing. Read more Read less

Authors: NITHYA M | Dr. P. Sivasankar | Dr. G. Kulanthaivel

Vol 12, Issue 9 (September 2026) Information Technology

A Review On Machine Learning And Deep Learning Models For Forecasting Renewable Energy Production

The Transition Towards Renewable Energy Is No Longer Just An Option But A Necessity In Addressing The Growing Challenges Of Climate Change, Environmental Degradation, And Energy Security. Fossil Fuels, Which Currently Dominate Global Energy Consumpti... The Transition Towards Renewable Energy Is No Longer Just An Option But A Necessity In Addressing The Growing Challenges Of Climate Change, Environmental Degradation, And Energy Security. Fossil Fuels, Which Currently Dominate Global Energy Consumption, Are Finite And Contribute Significantly To Greenhouse Gas Emissions. Shifting To Renewable Energy Sources Such As Solar, Wind, Hydropower, And Biomass Offers A Sustainable Path Forward, Benefiting Both The Environment And Global Economies. Despite Its Benefits, The Migration To Renewable Energy Faces Challenges, Including The Need For Substantial Initial Investments, Technological Limitations, And Resistance From Entrenched Fossil Fuel Industries. Governments, Private Sectors, And International Organizations Must Work Together To Create Policies And Incentives That Encourage Renewable Energy Adoption. Public Awareness Campaigns, Subsidies, And Research Funding Are Critical In Overcoming These Barriers. To Develop A Sustainable Renewable Energy Infrastructure, We Need To Forecast The Amount Of Renewable Energy Production In Future And Meet Energy Demands In Future Through Renewable Sources. This Paper Presents A Comprehensive Review Of Statistical Models For Forecasting Renewable Energy Along With Associated Challenges That The Sector Faces. This Review Would Enable Future Research In Forecasting The Patterns In A Particular Renewable Energy Source. Read more Read less

Authors: Rakesh Madhukar | Prof. Kamlendra Verma

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