International Journal of AI Electronics and Nexus Energy
A Deep Learning-Assisted FPGA Architecture for Real-Time Signal Classification in Next-Generation Wireless Communication Systems
The rapid evolution of fifth-generation (5G), Beyond 5G (B5G), and sixth-generation (6G) wireless communication systems has significantly increased the complexity of real-time signal processing due to massive spectrum utilization, heterogeneous modulation techniques, ultra-low latency requirements, and intelligent network management. Conventional software-based signal classification approaches often fail to satisfy the stringent latency, throughput, and energy efficiency requirements of next-generation wireless infrastructures. Deep Learning (DL) algorithms have demonstrated remarkable capabilities in automatically extracting complex temporal and spectral features from communication signals; however, their computational complexity limits deployment in real-time embedded systems. Field Programmable Gate Arrays (FPGAs) provide highly parallel, low-latency, and energy-efficient hardware acceleration suitable for implementing deep neural networks directly at the network edge. This research proposes a Deep Learning-Assisted FPGA Architecture for real-time wireless signal classification by integrating optimized Convolutional Neural Networks (CNNs), hardware-friendly quantization techniques, pipelined processing architecture, parallel inference engines, and FPGA-based acceleration. The proposed framework enables rapid modulation recognition, interference identification, spectrum sensing, and signal classification while maintaining low latency and reduced power consumption. Experimental evaluation demonstrates significant improvements in classification accuracy, inference speed, hardware utilization efficiency, throughput, and energy efficiency compared with conventional CPU- and GPU-based implementations. The proposed architecture provides an effective solution for intelligent edge computing, software-defined radio, cognitive radio, and future 6G communication systems requiring real-time artificial intelligence capabilities
T Mukthar Ahamed, Bhumireddy Rajani Kumar Reddy
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
An Optimized VLSI Architecture for High-Speed Digital Signal Processing in Real-Time Image Enhancement Applications
Real-time image enhancement has become an indispensable component of modern digital imaging systems used in medical imaging, autonomous vehicles, surveillance, satellite communication, industrial automation, robotics, and consumer electronics. These applications require high-speed processing of large volumes of image data while maintaining low latency, high throughput, and minimal power consumption. Conventional software-based image enhancement algorithms executed on generalpurpose processors often fail to satisfy stringent real-time requirements because of their limited parallel processing capability and computational overhead. Furthermore, increasing image resolutions, higher frame rates, and complex digital signal processing (DSP) operations significantly increase processing latency, memory bandwidth, and energy consumption. Therefore, designing dedicated hardware architectures capable of accelerating image enhancement algorithms has become a major research challenge in modern VLSI system design. This research presents an Optimized VLSI Architecture for High-Speed Digital Signal Processing in Real-Time Image Enhancement Applications, employing a pipelined and parallel processing architecture implemented using hardware-efficient DSP modules. The proposed architecture integrates image acquisition, preprocessing, digital filtering, edge enhancement, contrast improvement, and output generation into a highly optimized hardware pipeline. Dedicated processing elements are designed using parallel multiply-accumulate (MAC) units, optimized arithmetic circuits, efficient memory organization, and pipeline registers to maximize throughput while minimizing hardware resource utilization. The architecture supports real-time execution of spatial filtering operations with reduced critical path delay and improved computational efficiency. Hardware optimization techniques including parallelism, pipelining, resource sharing, and low-power arithmetic design are incorporated to achieve high operating frequency and energy-efficient implementation. The performance of the proposed VLSI architecture is evaluated using hardware-oriented metrics including operating frequency, processing latency, throughput, logic utilization, power consumption, resource utilization, critical path delay, image quality improvement, and hardware efficiency. Experimental evaluation demonstrates that the proposed architecture achieves significantly higher operating speed, reduced processing latency, lower power consumption, and superior hardware utilization compared with conventional image processing architectures. The optimized VLSI framework provides a scalable and reliable hardware solution for FPGA- and ASIC-based real-time image enhancement systems, making it suitable for embedded vision, medical diagnostics, intelligent transportation systems, industrial inspection, aerospace imaging, and nextgeneration edge AI applications.
An Energy-Efficient Adaptive Beamforming Framework for 6G Massive MIMO Wireless Communication Systems
The rapid evolution of wireless communication technologies has driven the development of sixth-generation (6G) networks capable of supporting ultra-high data rates, massive device connectivity, ultra-low latency, and intelligent communication services. Massive Multiple-Input Multiple-Output (Massive MIMO) technology has emerged as a key enabling technology for 6G because it significantly enhances spectral efficiency, network capacity, and communication reliability by employing a large number of antenna elements. However, conventional beamforming techniques used in Massive MIMO systems require complex signal processing, numerous RF chains, and high transmission power, resulting in increased hardware cost and energy consumption. Moreover, dynamic user mobility, rapidly varying wireless channels, interference, and imperfect channel estimation further degrade beamforming performance in practical deployments. This research proposes an Energy-Efficient Adaptive Beamforming Framework that intelligently optimizes beamforming vectors using real-time Channel State Information (CSI), adaptive user scheduling, hybrid analog-digital beamforming, and power-aware transmission strategies. The proposed framework dynamically adjusts antenna weights to maximize beamforming gain while minimizing interference and unnecessary energy consumption. Hybrid beamforming significantly reduces RF hardware complexity without compromising communication performance, making it suitable for large-scale Massive MIMO deployments in future 6G networks. The performance of the proposed framework is evaluated using communication metrics including Signal-toInterference-plus-Noise Ratio (SINR), spectral efficiency, beamforming gain, throughput, energy efficiency, Bit Error Rate (BER), latency, and total power consumption. Simulation results demonstrate that the proposed adaptive beamforming architecture achieves higher spectral efficiency, improved signal quality, lower transmission power, and reduced computational complexity compared with conventional digital beamforming methods. The proposed framework provides an effective and scalable solution for future 6G wireless communication systems supporting autonomous transportation, Internet of Everything (IoE), extended reality (XR), smart healthcare, industrial automation, intelligent edge computing, and nextgeneration smart city applications.
C. Kumaraswami Reddy, Buturi Grace Jemimah
AI-Driven Prediction of Lithium-Ion Battery Remaining Life from Minimal Cycle Information
Lithium-ion (Li-ion) batteries are widely used in electric vehicles, portable electronics, and renewable energy storage systems due to their high energy density and long lifespan. However, accurately predicting battery life and degradation remains a critical challenge for ensuring reliability, safety, and efficient energy management. Traditional methods for battery life prediction rely on empirical models and statistical techniques, which often fail to capture complex nonlinear degradation patterns under varying operating conditions. This paper presents a deep learning-based approach for predicting the remaining useful life (RUL) of Li-ion batteries. The proposed system utilizes advanced deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) to analyze battery performance data, including voltage, current, temperature, and charge-discharge cycles. These models effectively learn temporal and spatial features from historical data, enabling accurate prediction of battery degradation trends. Experimental results demonstrate that the proposed approach outperforms traditional machine learning methods in terms of prediction accuracy and robustness. This study contributes to improving battery management systems, enhancing operational safety, and optimizing maintenance strategies in energy storage applications.
1Mrs Nirmala Teegala,2Boddupally Jagadeesh chary
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
A Machine Learning Based Hybrid Recommendation System for Smart Learning Environments
The rapid growth of digital education platforms has increased the need for intelligent systems that can provide personalized learning experiences. Traditional recommendation systems often rely on a single recommendation technique, which may not effectively address the diverse learning preferences and requirements of students. This project presents a Machine Learning Based Hybrid Recommendation System for Smart Learning Environments that combines collaborative filtering, content-based filtering, and machine learning algorithms to deliver accurate and personalized learning recommendations. The system analyzes learner profiles, academic performance, learning history, interests, and behavioral patterns to recommend relevant courses, study materials, video lectures, quizzes, and learning activities. The hybrid approach helps overcome challenges such as data sparsity, cold-start problems, and limited recommendation accuracy. By continuously learning from user interactions and feedback, the system adapts to changing learner preferences and improves recommendation quality over time. The proposed system enhances learner engagement, supports adaptive learning, and contributes to better academic outcomes by providing learners with suitable educational resources tailored to their individual needs.
A Hybrid Machine Learning-Based Multi-Stage Framework for Credit Card Fraud Detection
The rapid growth of digital transactions has significantly increased the risk of credit card fraud, leading to substantial financial losses for both consumers and financial institutions. Traditional fraud detection systems often struggle with high false-positive rates and inability to adapt to evolving fraud patterns. To address these challenges, this paper proposes a hybrid machine learning-based multi-stage framework for efficient detection of credit card anomalies and fraudulent activities. The proposed framework integrates multiple machine learning techniques across different stages, including data preprocessing, anomaly detection, and classification. In the initial stage, data cleaning and feature engineering are performed to enhance data quality and extract meaningful patterns. The second stage employs anomaly detection techniques such as Isolation Forest and clustering methods to identify suspicious transactions. In the final stage, supervised learning models such as Random Forest, Support Vector Machines, and Logistic Regression are used to classify transactions as legitimate or fraudulent. This hybrid approach leverages the strengths of both unsupervised and supervised learning, improving detection accuracy while reducing false alarms. Experimental results demonstrate that the proposed system achieves higher precision, recall, and F1-score compared to traditional single-model approaches. The framework is scalable, adaptive, and suitable for real-time fraud detection in modern financial systems.
A NOVEL HYBRID QUANTUM DEEP LEARNING APPROACH FOR CLASSIFICATION OF MULTI-CLASS BRAIN TUMOUR
Accurate and timely brain tumor classification from magnetic resonance imaging (MRI) scans is essential for effective diagnosis and treatment planning. Although conventional deep learning models have achieved promising results, they often face challenges in capturing complex spatial patterns and high-dimensional features present in medical images. To address these limitations, this study proposes an efficient hybrid quantum deep learning model for multi-class brain tumor classification. By integrating quantum computing principles with conventional deep learning architectures, the proposed framework enhances feature representation and classification performance while reducing computational complexity. The model employs convolutional neural networks (CNNs) for robust feature extraction, followed by quantum neural network (QNN)-based processing to improve classification of multiple brain tumor categories. A labelled MRI dataset is used to train and evaluate the proposed model using standard performance metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the hybrid quantum deep learning approach outperforms conventional deep learning models in terms of classification accuracy, scalability, and computational efficiency. The findings highlight the potential of quantumenhanced deep learning as a promising solution for next-generation medical image analysis and intelligent clinical decision support systems. Unlike conventional approaches that rely solely on classical deep learning architectures, the proposed framework integrates quantum computing with deep learning to achieve higher classification accuracy while improving computational efficiency. The model was trained and evaluated on a labelled MRI dataset using standard performance metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed hybrid model consistently outperforms existing deep learning methods in accurately classifying multiple brain tumor types. These findings highlight the transformative potential of quantum-enhanced deep learning in advancing medical image analysis, enabling faster, more reliable, and intelligent diagnostic systems for future healthcare applications.
An Intelligent Generative AI Framework for Adaptive Threat Monitoring and Secure Cloud Computing
The rapid evolution of cloud computing has enabled organizations to deploy scalable applications and distributed services with greater efficiency; however, it has also increased the complexity of protecting cloud infrastructures against sophisticated and continuously evolving cyber threats. Conventional cloud security monitoring solutions primarily rely on static rules, predefined signatures, and manually crafted detection policies, making them less effective against zero-day attacks, advanced persistent threats, and dynamic attack behaviors. To address these limitations, this research proposes an intelligent Generative AI-driven framework for adaptive cloud security monitoring that delivers proactive threat analysis and continuous security assessment across modern cloud environments.The proposed framework integrates security telemetry from multiple cloud sources, including system logs, network traffic, authentication records, application events, and resource utilization metrics, to establish a comprehensive view of cloud security operations. Advanced generative artificial intelligence techniques are employed to model complex behavioral patterns, correlate security events, generate contextual threat intelligence, and identify previously unseen attack scenarios that cannot be effectively recognized using conventional signaturebased approaches. By combining intelligent anomaly detection with adaptive learning, the framework continuously refines its detection capability based on newly observed attack patterns, thereby improving its resilience against evolving cybersecurity threats.Furthermore, the proposed system incorporates automated risk assessment and intelligent alert prioritization to reduce false-positive notifications and assist security analysts in making timely and informed decisions. Experimental evaluation is performed using standard performance metrics, including accuracy, precision, recall, F1- score, detection rate, and false-positive rate, to assess the effectiveness of the proposed framework. The results demonstrate that the integration of Generative AI with adaptive security analytics significantly enhances threat detection performance, strengthens situational awareness, and improves the overall security posture of cloud computing environments. The proposed framework offers a scalable, intelligent, and future-ready solution for safeguarding cloud infrastructures while supporting automated cyber defense against emerging and sophisticated security threats.
Rapid urbanization, increasing vehicle ownership, limited parking infrastructure, and inefficient utilization of available parking spaces have created significant mobility challenges in cities, universities, shopping centers, hospitals, airports, residential complexes, and commercial facilities. Conventional parking management frequently depends on manual inspection, static signboards, entry tickets, security personnel, or driver-based visual searching, resulting in unnecessary circulation, traffic congestion, fuel consumption, time loss, carbon emissions, and poor user experience. This research proposes an intelligent Smart Parking Occupancy System that integrates parking-space sensors, cameraassisted monitoring, embedded processing, wireless communication, Internet of Things connectivity, occupancy analytics, real-time availability management, and user notification within a unified architecture. The proposed system continuously acquires parking information through ultrasonic sensors, infrared sensors, magnetic sensors, or camera-based detection mechanisms and determines whether individual parking spaces are Vacant, Occupied, Reserved, or Unavailable. Sensor readings are validated and processed through an edge controller to reduce noise, resolve temporary fluctuations, and generate reliable occupancy states. The parking information is transmitted through Wi-Fi, LoRaWAN, Zigbee, cellular communication, or other suitable network technologies to a centralized parking management platform. A real-time occupancy engine maintains the status of individual parking slots, calculates zone-level availability, detects abnormal occupancy behavior, and updates mobile applications, web dashboards, entrance displays, and navigation interfaces. The proposed architecture consists of five interconnected layers: Parking Environment and Data Acquisition, Edge Processing and Occupancy Intelligence, Communication and IoT Integration, Smart Parking Management and Decision, and User Application and Monitoring layers. The framework supports real-time space discovery, parking guidance, reservation management, occupancy visualization, historical analytics, administrative monitoring, and optional automated alerts. Illustrative conceptual evaluation indicates improved occupancy detection accuracy, precision, recall, F1-score, system reliability, and lower occupancy-update latency compared with manual parking management, conventional single-sensor systems, and basic IoT parking mechanisms. The proposed system provides a scalable foundation for intelligent parking management across smart cities, universities, shopping malls, hospitals, airports, offices, residential communities, and public transportation facilities.
GAS LEAKAGE AND FIRE DETECTION AND FIRE DETECTION +SMR NOTIFICATION
Gas leakage and fire accidents represent major safety threats in residential buildings, commercial establishments, industrial facilities, laboratories, kitchens, warehouses, and other enclosed environments where combustible gases and ignition sources are present. Conventional safety mechanisms frequently depend on isolated smoke alarms, manual inspection, fixed threshold detectors, or locally activated buzzers that may fail to provide timely remote information when occupants or responsible personnel are away from the affected location. Delayed identification of liquefied petroleum gas leakage, combustible gas accumulation, abnormal temperature rise, smoke formation, or open flame can lead to explosions, structural damage, environmental contamination, financial loss, severe injury, and loss of life. This research proposes an intelligent Gas Leakage and Fire Detection with SMS Notification System that integrates gas sensing, flame detection, temperature monitoring, embedded processing, local alarm generation, actuator control, and GSM-based remote notification within a unified real-time safety architecture. The proposed framework continuously acquires environmental information through an MQ-series combustible gas sensor, flame sensor, and temperature sensor. Sensor readings are processed by a microcontrollerbased decision unit that performs signal conditioning, threshold evaluation, multi-sensor event correlation, and hazard classification. The system categorizes environmental conditions into Normal, Gas Leakage Alert, Fire Warning, and Critical Emergency states according to the detected combination of gas concentration, flame presence, and abnormal temperature behavior. When hazardous gas accumulation is identified, the system activates a local buzzer, warning indicator, ventilation or exhaust mechanism, and GSM communication module to transmit an SMS notification to registered users or emergency personnel. When fire-related indicators are detected, the framework generates an immediate high-priority alarm and sends location-aware emergency information through SMS. The proposed architecture consists of five interconnected layers: Environmental Sensing and Data Acquisition, Signal Processing and Hazard Intelligence, Embedded Decision and Event Classification, Alert and Emergency Response, and User and Monitoring layers. The framework is designed to provide continuous monitoring, rapid hazard recognition, local and remote notification, improved response coordination, and practical deployment across homes, industries, laboratories, warehouses, kitchens, and commercial facilities. Illustrative conceptual evaluation demonstrates that the proposed system can achieve improved detection accuracy, alert reliability, multi-sensor hazard recognition, and lower response latency compared with conventional standalone gas alarms, smoke-only detectors, and isolated threshold-based systems. The proposed framework provides a scalable foundation for intelligent environmental safety monitoring and timely emergency communication.
STRENGTHENING CLOUD SECURITY WITH MACHINE LEARNINGDRIVEN MULTI-FACTOR AUTHENTICATION AND ADAPTIVE CRYPTOGRAPHY
The rapid expansion of cloud computing has transformed modern information technology by enabling scalable infrastructure, elastic resource allocation, remote collaboration, distributed application deployment, and on-demand access to computing services. However, the increasing migration of sensitive information and critical workloads to public, private, hybrid, and multicloud environments has created significant security challenges involving credential theft, account takeover, phishing, session hijacking, insider threats, unauthorized access, behavioral anomalies, and cryptographic compromise. Conventional password-based authentication provides insufficient protection against stolen credentials, while static multi-factor authentication often applies identical verification requirements regardless of user behavior, device trust, contextual risk, or resource sensitivity. Similarly, fixed cryptographic configurations may provide limited adaptability when threat conditions, access patterns, and workload criticality change dynamically. This research proposes an intelligent cloud security framework that integrates Machine Learning-Driven Multi-Factor Authentication (ML-MFA) with Adaptive Cryptography to provide contextaware identity verification and dynamically coordinated data protection. The proposed framework continuously analyzes user behavior, device fingerprints, login history, geolocation consistency, access time, IP reputation, network characteristics, session activity, privilege level, resource sensitivity, and threat intelligence. Machine learning algorithms including Random Forest, Support Vector Machine, XGBoost, Isolation Forest, and Multilayer Perceptron are employed to identify anomalous access behavior and estimate dynamic authentication risk. Based on the calculated risk level, the system adaptively selects appropriate authentication factors such as passwords, passkeys, one-time passwords, authenticator applications, biometric verification, and hardware security keys. The adaptive cryptography module simultaneously evaluates authentication confidence, data sensitivity, device trust, session anomalies, and workload criticality to determine suitable approved encryption policies, key-protection requirements, sessionkey lifetimes, and rekeying frequencies without reducing protection below mandatory security baselines. A hybrid decision engine combines machine learning predictions, behavioral anomalies, authentication confidence, resource sensitivity, and cryptographic posture to classify access requests as Low Risk, Medium Risk, High Risk, or Critical Risk. The proposed architecture consists of five interconnected layers: Cloud Access and Context Acquisition, Security Data Preprocessing and Behavioral Intelligence, Machine Learning-Driven MFA and Adaptive Cryptography, Risk Assessment and Security Enforcement, and Cloud Application and User layers. Illustrative conceptual evaluation demonstrates that the proposed framework can achieve higher security detection accuracy, precision, recall, F1-score, adaptive security efficiency, and faster analytical decision response than password-based authentication, conventional static MFA, and isolated risk-based authentication. The framework provides a scalable foundation for intelligent cloud protection across enterprise systems, financial platforms, healthcare clouds, educational services, government infrastructures, and multicloud environments.
MAL-SCAN :ANDROID MALWARE DETECTION BASED ON SOCIAL NETWORK CENTRALITY ANALYSIS
The rapid expansion of Android-based smartphones, tablets, wearable devices, mobile banking applications, digital payment platforms, social networking services, and enterprise mobility has significantly increased the attack surface available to malicious software. Android malware increasingly employs code obfuscation, repackaging, dynamic payload loading, permission abuse, inter-component communication, hidden API invocation, and coordinated behavioral dependencies to evade conventional signature-based and isolated feature-based detection mechanisms. Traditional Android malware detection methods generally analyze applications as independent collections of permissions, API calls, opcodes, system calls, or behavioral events; however, such approaches may fail to capture the structural relationships among application components and securityrelevant entities. This research proposes MALSCAN, an intelligent Android malware detection framework based on social network centrality analysis, in which an Android application is transformed into a security interaction graph and analyzed using graph-theoretic centrality measures inspired by social network analysis. Nodes represent entities such as application components, permissions, sensitive API calls, intents, services, receivers, URLs, and behavioral events, while edges represent invocation, communication, dependency, permission usage, or information-flow relationships. The framework computes Degree Centrality, Betweenness Centrality, Closeness Centrality, Eigenvector Centrality, and PageRank-based importance scores to identify structurally influential entities and suspicious interaction hubs within an application graph. The resulting centrality profiles are combined with selected static and behavioral indicators and supplied to machine learning classifiers such as Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. A hybrid decision engine produces a malware probability and classifies applications as Benign, Suspicious, or Malicious. The proposed architecture consists of five interconnected layers: Android Application Acquisition, Static Analysis and Security Graph Construction, Social Network Centrality Analysis and Intelligent Detection, Risk Assessment and Response, and Application/User layers. Prototype-oriented evaluation is defined using accuracy, precision, recall, F1-score, graphfeature efficiency, false-positive rate, and detection response time. Illustrative conceptual results indicate that MAL-SCAN can outperform signature-based scanning, conventional permission-based machine learning, and standalone graph analysis by exploiting the structural importance of security-relevant entities. The proposed framework provides a scalable and explainable foundation for Android malware detection in mobile devices, enterprise app stores, application marketplaces, mobile security gateways, and cybersecurity laboratories
Dynamic Priority and Multi-Key -TCAM Architecture for Scalable UltraLow-Latency Network Packet Transmission
The rapid growth of cloud computing, artificial intelligence, and high-speed communication networks has significantly increased the demand for efficient packet lookup mechanisms in modern networking infrastructures. Recent industry reports indicate that the TCAM memory market is projected to grow from approximately USD 2.8 billion in 2025 to over USD 6.4 billion by 2034, while global data center traffic continues to increase annually due to the expansion of cloud services and hyperscale computing environments. However, existing Content Addressable Memory (CAM) and Ternary CAM (TCAM) based lookup architectures suffer from several limitations, including fixed-width memory structures, inefficient memory utilization, increased lookup latency, higher LUT utilization, elevated power consumption, limited scalability, and inadequate priority management under heavy traffic conditions. These challenges negatively impact network throughput and overall hardware efficiency, particularly in large-scale communication environments. To address these issues, this work proposes a Dynamic Priority with MultiKey TCAM (DPMK-TCAM) Architecture for Scalable Ultra-Low-Latency Network Packet Transmission. The proposed architecture integrates a Multikey Search Engine for simultaneous packet attribute matching, a Programmable Dynamic Priority based Multiplexer (PDP Multiplexer) for adaptive priority assignment and conflict resolution, and a Variable Width TCAM (VW-TCAM) for efficient memory utilization and flexible storage management. The architecture further incorporates matching percentage analysis, dynamic comparison, maximum matching identification, adaptive memory allocation, and feedback-driven optimization mechanisms to enhance lookup accuracy and processing efficiency. By combining intelligent priority control with adaptive TCAM organization, the proposed system aims to reduce lookup delay, minimize hardware resource consumption, improve throughput, lower power consumption, and provide scalable performance for next-generation networking and VLSI-based communication systems
Content Addressable Memory (CAM), Ternary Content Addressable Memory (TCAM), Dynamic Priority, Multi-Key Search, Variable Width TCAM (VW-TCAM), Packet Lookup.
DESIGN AND ANALYSIS OF A DIGITALLY CALIBRATED MULTILANE SERDES ARCHITECTURE FOR ULTRA-HIGH-SPEED VLSI INTERCONNECTS
Ultra-high-speed VLSI interconnects have become a critical enabler for modern computing and communication platforms, where serial data rates have rapidly scaled from a few gigabits per second to beyond 56–112 Gb/s per lane, and aggregated multi-lane links routinely exceed 400 Gb/s in contemporary systems. At the same time, interconnect power can account for 20–40% of total chip I/O power, making energy-efficient and scalable Serializer/Deserializer (SerDes) architectures a key research challenge. Traditional single-lane digitally controlled SerDes architectures, while effective at moderate speeds, face inherent problems at ultra-high data rates, including high power consumption and jitter sensitivity due to Phased Lock Loops (PLL)/ Clock and Data Recovery (CDR) blocks, as well as increased area, calibration complexity, and latency caused by independent clock recovery and equalizer adaptation. To address these limitations, this work proposes a digitally calibrated multi-lane SerDes extension architecture that scales throughput by parallelizing multiple lanes while maintaining signal integrity and timing robustness. A shared hierarchical digital PLL and low-skew clock distribution network synchronizes all transmit lanes, while per-lane programmable Finite Impulse Response (FIR) based transmitter equalizers and hybrid Continuous-Time Linear Equalizer with Decision Feedback Equalizer (CTLE–DFE) receiver equalizers with digital adaptation compensate for lane-specific channel impairments. At the receiver, per-lane digital CDRs with phase interpolators are combined with an elastic buffer–based lane bonding and deskew unit to correct inter-lane skew and ensure aligned data boundaries. A centralized digital calibration and control engine continuously monitors phase offsets, delay mismatches, and error metrics, dynamically tuning clock phases and equalizer coefficients. It enables linear throughput scaling with controlled power and area overhead, making the architecture well-suited for next-generation ultra-high-speed VLSI interconnect standards
Serializer/Deserializer (SerDes), Multi-Lane SerDes, Digital Phase-Locked Loop (DPLL), Clock and Data Recovery (CDR), Finite Impulse Response (FIR) Equalizer, Continuous-Time Linear Equalizer (CTLE), Decision Feedback Equalizer (DFE), Ultra-High-Speed VLSI Interconnects
AI BASED EXPIRY AWARE DYNAMIC DISCOUNT SYSTEM
The rapid growth of the retail and supermarket industry has increased the need for intelligent inventory management systems that minimize product wastage while maximizing business profitability. One of the major challenges faced by retailers is the accumulation of products approaching their expiry dates, leading to financial losses, food waste, and inefficient stock management. Traditional methods of manually identifying and discounting nearexpiry products are time-consuming, inconsistent, and often fail to optimize pricing strategies. This project, AI-Based Expiry Aware Dynamic Discount System, proposes an intelligent solution that utilizes Artificial Intelligence (AI) and Machine Learning (ML) techniques to automatically recommend dynamic discounts for products based on their remaining shelf life, demand, inventory levels, and historical sales patterns. The system continuously analyzes product expiry information and predicts the optimal discount percentage that encourages faster sales while maintaining profitability. The proposed system integrates inventory management with predictive analytics to classify products according to their urgency for sale. Products nearing their expiry dates receive dynamically adjusted discounts instead of fixed markdowns, ensuring that pricing decisions are data-driven and responsive to real-time inventory conditions. Additionally, the system generates alerts for store managers regarding products requiring immediate attention and provides analytical dashboards for monitoring stock movement, sales performance, and wastage trends. Machine learning algorithms are employed to identify purchasing behavior and estimate the probability of selling products before expiry. By incorporating AI-driven decision-making, the system improves operational efficiency, reduces manual intervention, minimizes food and product waste, enhances customer satisfaction through attractive pricing, and increases overall revenue for retailers.
Reconfigurable Computing Architecture Using FPGA
Field-Programmable Gate Arrays (FPGAs) have become a powerful platform for implementing highperformance and energy-efficient computing systems due to their ability to be reconfigured according to application requirements. Unlike conventional processors with fixed hardware structures, FPGA-based architectures enable customized hardware acceleration, allowing computational tasks to execute with reduced latency and enhanced parallelism. This project presents a reconfigurable computing architecture using FPGA that supports flexible hardware implementation for a variety of digital applications, including signal processing, embedded systems, image processing, and artificial intelligence. The proposed architecture is designed using Hardware Description Language (HDL) and synthesized on an FPGA development board to evaluate its performance in terms of speed, resource utilization, and power consumption. Dynamic reconfiguration techniques are incorporated to allow hardware modules to be modified without replacing the entire design, thereby improving system adaptability and reducing execution overhead. Experimental results demonstrate that the proposed FPGA-based architecture achieves higher processing efficiency, lower power usage, and greater scalability compared to traditional processor-based solutions. The reconfigurable nature of the architecture also simplifies future upgrades and supports rapid prototyping for evolving application requirements. This work highlights the significance of FPGA technology in modern computing environments where flexibility, performance, and energy efficiency are essential. The proposed architecture provides a reliable and cost-effective solution for developing next-generation embedded and high-performance computing systems.
Deep Learning-Based Real-Time Plant Disease Detection And Crop Health Monitoring Using Convolutional Neural Networks
Agriculture plays a vital role in ensuring food security and economic development, but plant diseases remain one of the major challenges affecting crop productivity and quality. Traditional methods of disease identification rely heavily on manual inspection by agricultural experts, which can be time-consuming, laborintensive, and prone to human error. To overcome these limitations, this project proposes a Deep LearningBased Real-Time Plant Disease Detection and Crop Health Monitoring System Using Convolutional Neural Networks (CNNs). The proposed system leverages advanced image processing techniques and deep learning algorithms to automatically identify plant diseases from leaf images with high accuracy. The system employs a Convolutional Neural Network trained on a large dataset of healthy and diseased plant leaf images representing multiple crop species and disease categories. During operation, images captured through smartphones, drones, or field cameras are preprocessed and passed to the trained CNN model for disease classification. The model extracts meaningful visual features such as color variations, texture patterns, and lesion characteristics to accurately distinguish between healthy and infected plants. In addition to disease identification, the system continuously monitors crop health and provides timely alerts along with suitable disease management recommendations to farmers. Experimental evaluation demonstrates that the proposed CNN-based approach achieves superior classification accuracy, faster inference time, and greater robustness under varying environmental conditions compared to conventional machine learning techniques. The integration of real-time monitoring with intelligent disease diagnosis enables early detection, reducing crop losses and minimizing unnecessary pesticide usage. This solution offers an efficient, scalable, and costeffective approach for precision agriculture, supporting sustainable farming practices while improving crop yield, productivity, and overall agricultural decision-making.
IOT-BASED REAL-TIME SMART CITY MONITORING DASHBOARD
Rapid urbanization has intensified challenges related to waste management, environmental safety, and efficient resource utilization in smart cities. This paper presents an IoT-based real-time smart city monitoring dashboard designed to enhance urban waste management and public safety through continuous sensing and data visualization. The proposed system integrates multiple sensors, including an infrared (IR) sensor for waste bin level detection, a light-dependent resistor (LDR) for smart streetlight status monitoring (LDR– SSL), and a gas sensor for early detection of hazardous gas leaks. Sensor data are collected via a microcontroller-based embedded system and transmitted wirelessly to a cloud platform using Internet of Things (IoT) communication protocols. A centralized web-based dashboard provides real-time monitoring, alert generation, and historical data analysis to assist municipal authorities in proactive decision-making. The system enables timely waste collection, reduces unnecessary fuel consumption, enhances public safety by detecting gas leaks, and improves energy efficiency through smart lighting insights. Experimental results demonstrate reliable realtime data acquisition, low latency communication, and scalable system performance. The proposed solution offers a cost-effective, scalable, and efficient approach for smart city infrastructure management and can be extended to support additional urban monitoring applications.
REMOTE-CONTROLLED HOME APPLIANCE SYSTEM VIA MQTT AND ESP8266
The Remote-Controlled Home Appliance System via MQTT and ESP8266 is an IoTbased smart automation solution designed to enable seamless, real-time, and remote operation of household electrical appliances from anywhere in the world using MQTT communication protocol. As the demand for smart homes grows, users expect efficient, reliable, and secure systems capable of reducing manual effort and optimizing energy consumption. This project focuses on achieving intelligent home automation using the ESP8266 Wi-Fi microcontroller, which connects appliances to the cloud and allows users to switch devices ON or OFF instantly through a mobile app, MQTT dashboard, or automated triggers. The MQTT protocol plays a crucial role by offering lightweight message communication, making it ideal for low-power IoT applications. The system subscribes to specific MQTT topics and listens for control commands such as fan ON, light OFF, or appliance toggle, ensuring fast response and minimal network overhead. MQTT brokers like Mosquitto or CloudMQTT handle realtime message publishing and act as communication hubs between the user interface and the ESP8266 hardware. The ESP8266 receives the commands, processes them, and drives relays connected to appliances, allowing the physical switching of loads. This setup ensures that even basic nonsmart devices can be integrated into an IoT ecosystem. Additional functionalities include real-time status updates sent back to the user interface, two-way MQTT communication, device state memory, and support for automation rules like scheduling or sensorbased activation. The system enhances convenience by enabling remote operation when users are at work, traveling, or managing elderly care. It also promotes energy efficiency since users can turn off forgotten appliances from anywhere. Security concerns are addressed through authentication mechanisms, encrypted MQTT communication, and unique device credentials to prevent unauthorized access. The solution is designed to be scalable, enabling control of multiple appliances simultaneously without interference. Overall, this project delivers an efficient, low-cost, and user-friendly smart home automation system that blends embedded electronics, wireless networking, and cloud-based MQTT communication to create a modern IoTenabled living environment.
REAL-TIME STRESS DETECTION BAND USING GALVANIC SKIN RESPONSE (GSR)
The Real-Time Stress Detection Band using Galvanic Skin Response (GSR) is a wearable physiological monitoring system designed to continuously analyze a user’s emotional and mental state by examining variations in skin conductivity, which is strongly influenced by sweat gland activity controlled by the autonomic nervous system. Stress has increasingly become a major global health concern, affecting productivity, emotional stability, and long-term well-being, and conventional stress assessment methods such as questionnaires or periodic medical checkups fail to provide continuous and accurate realtime insights. This project aims to bridge this gap by integrating sensor technology, embedded computing, and intelligent data analysis into a compact wrist-worn device that measures real-time electrodermal activity. When a person experiences stress, the sympathetic nervous system activates, causing an increase in sweat secretion even in small amounts, and this physiological change results in measurable shifts in skin resistance; the GSR sensor accurately detects these subtle variations and sends them to a microcontroller, which processes the signals and applies calibrated thresholding or machine learning techniques to determine the stress level. The band continuously monitors user data, classifies stress states, and displays or transmits the results through wireless communication to smartphones or cloud dashboards for visualization and long-term trend analysis. The system also provides notifications or alerts whenever stress crosses a predefined threshold, allowing early intervention through relaxation routines, mindfulness techniques, or behavior adjustments. The primary objective of this system is to offer an affordable, non-invasive, and real-time approach to mental wellness monitoring, suitable for students, professionals, patients, or individuals dealing with high-pressure lifestyles. Unlike medicalgrade equipment, this wearable solution is portable and cost-effective, making stress monitoring accessible to the general public without the need for hospital visits. The embedded algorithms ensure fast and accurate readings by filtering noise, stabilizing baseline fluctuations, and adjusting sensitivity according to environmental conditions. The device can also integrate additional sensors such as heart rate, skin temperature, or accelerometers to enable multi-parametric stress analysis for even higher accuracy. Practical applications include workplace stress evaluation, student examination anxiety tracking, early burnout detection, fitness and workout stress monitoring, and therapeutic psychological assessments. The overall system is designed for long battery life, comfortable wearing experience, and consistent performance. The project thus contributes to the growing field of wearable health technology by offering a reliable solution that identifies stress in real time through biometric changes, enabling users to take better control of their emotional health and lifestyle choices while providing researchers and clinicians with valuable physiological data for detailed behavioral modeling and medical insights.
SECURE PASS: STANDALONE BIOMETRIC SECURITY ACCESS CONTROLLER WITH MULTI-MODAL AUTHENTICATION
Secure Pass: Standalone Biometric Security Access Controller with Multi-Modal Authentication is a next-generation embedded security system designed to provide high-level access control using multiple biometric and digital verification methods combined into a single powerful standalone architecture. The system operates without the need for external servers or constant network connectivity, making it ideal for environments requiring continuous, reliable, and tamper-proof security. Traditional security mechanisms such as keys, swipe cards, and PIN codes face significant risks including duplication, theft, guessing, and unauthorized sharing, which compromise security integrity. This project overcomes these limitations through the integration of fingerprint authentication, RFID smart card scanning, PIN-based keypad entry, and optionally facial recognition, ensuring that only verified individuals gain access. The embedded controller handles sensor inputs, processes biometric templates, compares user data, generates secure match results, and triggers door lock mechanisms within milliseconds. All biometric templates are stored locally in encrypted form to prevent misuse, identity theft, or unauthorized replication. The system supports two-factor and three-factor authentication modes, enhancing the security layers further. An intuitive admin interface allows secure user enrollment, deletion, updates, and access log viewing. The system includes intrusion alerts, tamper detection, unauthorized access logging, and real-time decision-making algorithms that make sure each authentication request is validated accurately. The design also emphasizes low power consumption, fast response time, and long-term reliability, making it suitable for research labs, industries, academic institutions, hostels, server rooms, government buildings, and residential smart security installations. Multi-modal authentication significantly reduces false acceptance rates (FAR), false rejection rates (FRR), and spoofing risks. The standalone embedded architecture ensures uninterrupted security even during network failures or cyberattacks, offering complete local control and data privacy. Additional features include event logging, timestamp recording, emergency override options, and support for audit trail creation. The system can be expanded with GSM alerts, IoT dashboards, and wireless monitoring depending on security needs. Overall, Secure Pass represents a modern, intelligent, and highly secure access control solution that delivers robust identity verification, strong data protection, and dependable performance using multi-layer biometric authentication technology integrated within a single independent embedded framework.
GRADE BUZZ: AUTOMATED EMBEDDED RESULT BROADCASTING SYSTEM VIA GSM FOR INSTANT STUDENT MARKS DELIVERY
The rapid expansion of educational institutions and the increasing volume of examination data have created a need for faster, more accurate, and more reliable result delivery systems, motivating the development of automated technologies that reduce manual workload and enhance communication efficiency between institutions and students. Grade Buzz: Automated Embedded Result Broadcasting System via GSM for Instant Student Marks Delivery is a powerful embedded solution designed to transmit student marks directly to their registered mobile numbers using GSM technology without requiring internet connectivity, making it suitable for rural and remote learning environments. The main purpose of this system is to eliminate the traditional delays associated with manual result publication, long queues, notice board display, and slow online result portals, which often become overloaded during peak result periods. The system collects examination data from an internal academic database, processes and converts the information into SMS-ready text format, and automatically dispatches it through a GSM modem connected to a microcontroller, ensuring speed, accuracy, and security. This automated broadcasting mechanism ensures that every student receives their results simultaneously, preventing bias, manipulation, or delays caused by human intervention. The embedded design deploys a microcontroller to interpret marks, handle communication logic, validate phone numbers, prevent unauthorized result access, and generate delivery confirmations to ensure secure transmission. The system provides a simple administrative interface that allows exam controllers to upload marks, initiate broadcasting, monitor delivery status, and maintain logs for institutional records, helping academic staff avoid repetitive processes and reduce paper usage
VLSI-ENABLED AI-BASED DIGITAL IMAGE PROCESSING FRAMEWORK FOR REAL-TIME RIVER WATER QUALITY ASSESSMENT AND TURBIDITY MONITORING
Water quality monitoring plays a crucial role in environmental protection, public health, and sustainable water resource management. Traditional water quality assessment methods often involve labor-intensive sampling procedures, costly laboratory analyses, and delayed result generation. To overcome these limitations, this study proposes a VLSI-enabled artificial intelligence and digital image processing framework for efficient river water quality evaluation, with a primary focus on turbidity estimation and monitoring. Standard turbidity solutions were prepared at 5 NTU intervals and validated using a calibrated nephelometer to establish reference datasets. River water samples were collected from different locations, and high-resolution images were captured under varying illumination and background conditions to analyze environmental influences on image acquisition. Using MATLAB, advanced image processing techniques such as image enhancement, color feature extraction, texture analysis, segmentation, edge detection, and pattern recognition were employed to characterize water quality parameters. Furthermore, the study investigates the integration of Very Large-Scale Integration (VLSI) architectures for accelerating image processing operations and enabling low-power, high-speed implementation suitable for embedded environmental monitoring systems. The extracted image features were compared with standard turbidity references to classify water quality levels accurately. Recent developments in machine learning, computer vision, and VLSI-based hardware acceleration for environmental sensing applications were also explored to enhance monitoring performance. Experimental results indicate that the proposed VLSI-assisted image processing approach provides a rapid, cost-effective, and reliable solution for real-time water quality assessment, demonstrating significant potential for deployment in smart environmental surveillance networks, Internet of Things (IoT)-enabled water management platforms, and next-generation sustainable monitoring systems.
K.S.SUBHASHINI, S. KALYANI, M.K. VARADARAJAN,N. KUMARATHARAN
Water Quality Monitoring & Forecasting System
Water quality monitoring is essential for ensuring safe drinking water, environmental sustainability, and effective resource management. Traditional water quality assessment methods rely on manual sampling and laboratory testing, which are time-consuming and lack real-time insights. This project proposes an intelligent water quality monitoring and forecasting system that integrates Internet of Things (IoT) sensors with machine learning algorithms. The system continuously collects real-time data on parameters such as pH, turbidity, temperature, dissolved oxygen, and conductivity. These data are analyzed using machine learning models to detect anomalies and predict future water quality trends. The proposed approach improves accuracy, enables early detection of contamination, and supports proactive decision-making. Studies show that IoT-based monitoring combined with machine learning can achieve significantly higher accuracy compared to traditional methods and provide predictive insights for sustainable water management .
A MACHINE LEARNING APPROACH FOR EARLY DETECTION OF FISH DISEASES BY ANALYSING WATER QUALITY
Aquaculture plays a vital role in global food production, but fish diseases pose a significant threat to productivity and sustainability. Early detection of fish diseases is crucial to prevent large-scale losses and ensure healthy aquatic ecosystems. This project proposes a machine learning-based system that analyzes water quality parameters to predict and detect fish diseases at an early stage. The system collects real-time data such as pH, temperature, dissolved oxygen, ammonia, and turbidity using sensors. Machine learning models including Random Forest, Support Vector Machine, and Neural Networks are employed to analyze patterns and identify anomalies associated with disease outbreaks. By correlating water quality variations with fish health conditions, the system provides timely alerts and actionable insights to fish farmers. The proposed approach enhances disease prediction accuracy, reduces manual monitoring efforts, and promotes sustainable aquaculture practices.
DEVELOPMENT AND IMPLEMENTATION OF A REAL-TIME FRAUD DETECTION SYSTEM USING KAFKA STREAMING AND XGBOOST ALGORITHM
In the digital banking era, the frequency and sophistication of fraudulent transactions have increased significantly, posing substantial risks to financial institutions and customers. Traditional fraud detection systems often struggle to provide timely and accurate identification of fraudulent activities, resulting in financial losses and damaged trust. This project proposes a realtime bank transaction fraud detection system that integrates Apache Kafka with advanced machine learning techniques to address these challenges effectively. Machine learning models trained on historical transaction data are deployed to analyze incoming transaction streams in real time. These models utilize features such as transaction amount, location, time, and user behavior patterns to distinguish between legitimate and fraudulent transactions. By continuously updating and fine-tuning the models with new data, the system improves its accuracy and adapts to evolving fraud tactics. In conclusion, this project demonstrates the feasibility and effectiveness of combining Kafka’s real-time data streaming with machine learning-based classification for robust fraud detection in banking transactions. The proposed solution not only enhances fraud detection accuracy but also ensures minimal latency, scalability, and adaptability, making it a vital tool in securing financial operations.
CUSTOMER BEHAVIOUR ANALYSIS USING DATA MINING TECHNIQUES WITH AI-DRIVEN RECOMMENDATIONS
In the rapidly evolving business environment, understanding customer behavior has become essential for companies striving to improve customer satisfaction, optimize marketing strategies, and increase profitability. This project employs advanced data mining techniques to analyze vast and complex customer datasets, uncovering hidden patterns and trends related to purchasing habits, preferences, and engagement levels. By utilizing clustering, classification, and association rule mining, the system effectively segments customers into meaningful groups, enabling targeted marketing efforts. Additionally, the project integrates artificial intelligence, particularly machine learning algorithms, to develop an AI-driven recommendation engine that provides personalized product or service suggestions tailored to individual customer profiles. This personalized approach enhances the relevance of recommendations, thereby increasing customer engagement and loyalty. The combined application of data mining and AI not only facilitates deeper insights into customer behavior but also automates the recommendation process, enabling businesses to respond swiftly to changing customer needs. The implementation demonstrates a comprehensive framework that supports data-driven decision-making, helps businesses identify new opportunities, and ultimately drives revenue growth by delivering a superior customer experience. This integrated system highlights the transformative potential of leveraging data analytics and AI technologies in modern customer relationship management.
OPEN-SET RECOGNITION IN UNKNOWN & KNOWN DDOS ATTACKS DETECTION WITH RECIPROCAL POINTS LEARNING
Distributed Denial of Service (DDoS) attacks continue to evolve, with novel and previously unseen variants posing significant challenges to traditional detection systems. Most existing DDoS detection approaches operate under a closed-set assumption, limiting their ability to identify unknown or zero-day attacks. In this paper, we propose a novel open-set recognition framework for DDoS detection based on Reciprocal Points Learning (RPL). Our method enhances the model's ability to distinguish between known attack classes and previously unseen attack patterns by leveraging the concept of reciprocal points in the feature space to improve class separability and detect anomalies. Specifically, RPL dynamically learns feature representations that pull intra-class samples closer while pushing inter-class and unknown samples apart, improving the robustness of the classifier in open-set scenarios. We evaluate our approach using benchmark DDoS datasets, including both known and synthetic unknown attack types. Experimental results demonstrate that our method significantly outperforms state-ofthe-art baselines in detecting unknown attacks while maintaining high accuracy on known classes. This work provides a promising direction for adaptive and resilient DDoS defense systems in the face of increasingly sophisticated threats.
IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN RESOURCE FUNCTIONS AND WORKPLACE EFFECTIVENESS
Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping business operations and organizational practices across various industries. In the field of Human Resource Management (HRM), AI has significantly influenced key functions such as recruitment and selection, employee onboarding, performance management, training and development, workforce planning, employee engagement, and talent retention. Organizations increasingly utilize AI-powered tools and applications to automate routine HR activities, enhance decision-making processes, improve operational efficiency, and create a more productive work environment. The integration of AI into HR functions enables organizations to process large volumes of employee data, identify talent more effectively, predict workforce trends, and deliver personalized employee experiences. The present study examines the impact of Artificial Intelligence on Human Resource functions and workplace effectiveness. The study focuses on understanding how AI technologies contribute to improving HR processes, enhancing employee productivity, reducing administrative workload, and supporting strategic workforce management. Both primary and secondary data sources were considered to analyze employee perceptions and organizational experiences regarding AI adoption in HR practices. The research evaluates the effectiveness of AIdriven recruitment systems, performance evaluation tools, learning and development platforms, employee engagement applications, and predictive analytics in workforce management. The findings indicate that AI has positively influenced HR functions by increasing efficiency, reducing recruitment time, improving talent acquisition accuracy, and facilitating data-driven decision-making. AIpowered solutions have also enhanced workplace effectiveness by supporting employee performance, improving communication, and enabling personalized learning opportunities. However, challenges such as data privacy concerns, implementation costs, employee resistance to technological change, and ethical considerations remain significant issues that organizations must address. The study concludes that Artificial Intelligence serves as a valuable strategic tool for modern Human Resource Management and has the potential to significantly improve workplace effectiveness when implemented responsibly and effectively
Cloud Based Encrypted Online Voting System
In today’s digital era, secure and accessible online platforms have become essential for conducting efficient and transparent election processes. The Cloud-Based Encrypted Online Voting System is a secure and efficient web-based platform designed to enable users to cast their votes remotely through cloud infrastructure. The system ensures confidentiality and integrity by encrypting votes before storing them in the cloud database, thereby preventing unauthorized access and tampering. It includes modules such as user registration, secure login authentication, candidate management, vote casting, and result generation. By leveraging cloud technology, the platform offers scalability, accessibility, and real-time data management while reducing the limitations of traditional voting methods. This project aims to provide a reliable, transparent, and userfriendly solution for modern digital elections.