International Journal of Information Technology and Computer Engineering
JOURNAL SUMMARY: Editor- In-Chief: Dr. Abdul Wasay Title proper: International Journal of Information Technology and Computer Engineering ISSN : 2347-3657 Subject: Computer Sciences & Information Technology, AI & ML, Data Science Publisher: International Journal of Information Technology and Computer Engineering Dates of publication: 2013 Frequency: Quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Indexed by: ROAD Indexed by: THE KEEPERS Title DOI of the Journal: http://doi.org/10.62647/issn.2347-3657 APC: $100/-
The advancement of multirotor unmanned aerial vehicles (UAVs) has enabled their deployment in logistics, emergency response, surveillance, and industrial automation applications. Heavy-lift UAV platforms, particularly hexacopters, provide enhanced thrust redundancy and improved payload stability compared to quadrotor systems [1], [5]. However, increasing payload mass significantly affects thrust-to-weight ratio, energy consumption, and flight endurance due to nonlinear propulsion dynamics and Li-Po battery discharge characteristics [6], [17], [20]. This research presents the design, modeling, implementation, and experimental validation of an AI-enabled heavy-lift hexacopter capable of transporting payloads up to 2.5 kg. The proposed system integrates nonlinear multirotor dynamic modeling [9], [10], adaptive altitude stabilization [2], regression-based energy prediction [18], and PX4-based flight control architecture [14]. Experimental results demonstrate endurance reduction from 19 minutes (no payload) to 9 minutes (2.5 kg payload), while current consumption increases from 18 A to 47 A. The AI prediction model achieved 95% endurance estimation accuracy. The proposed architecture demonstrates scalability for logistics and medium-payload aerial transport systems [4], [19].
Hexacopter UAV, Heavy Payload Drone, AI Energy Modeling, PX4 Autopilot, Hybrid Energy Management, Thrust-to-Weight Optimization, Multirotor Dynamics.
Harsha Siddartha, Jala Srivani, Katta Sanjay Reddy, Gajjelli Rikhil Sai Manikanta
Copyright (c) 2026 Mrs. Boddupally Padmini, Dr. Erigela Radhamma, Harsha Siddartha, Jala Srivani, Katta Sanjay Reddy, Gajjelli Rikhil Sai Manikanta (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Survey of Image Processing, Machine Learning, and Deep Learning-Based PCB Defect Detection Methods
In order to improve quality control in printed circuit board (PCB) production, this study provides a thorough overview and comparative analysis of many deep learning-based defect detection algorithms. We assess cutting-edge object detection models, such as Faster R-CNN, RetinaNet, SSD, and YOLO variations, such as YOLOv3-tiny, YOLOv5s, YOLOv5x6, and YOLOv8, by utilizing developments in deep learning and image processing. Our goal is to give manufacturers information about how well these models identify a variety of PCB faults by examining more than a hundred relevant studies published between 1990 and 2022. Model performance is evaluated using metrics including precision, recall, mean average precision (mAP), and computing economy. According to the study, YOLOv5x6 has a superior mAP of 99.9%, suggesting that it has the ability to greatly improve the accuracy of defect identification. As an extension, the article suggests leveraging the Flask framework to provide an intuitive front end that makes user testing with authentication easier. By providing manufacturers with guidance to enhance quality control procedures, this research advances automated defect detection systems in PCB manufacturing.
Defect detection, PCB, image processing, machine learning, deep learning.
Copyright (c) 2026 konduru Raga Ranjitha, Ch. Praveen Kumar, Dr. P.Siva Krishna (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Heart Pulse Monitoring Sensor With Notification Using Arduino
The rapid advancement of healthcare technology has significantly increased the demand for continuous and real-time health monitoring systems. Cardiovascular diseases remain one of the leading causes of mortality worldwide, making heart rate monitoring a critical component of preventive healthcare. Conventional heart rate monitoring systems such as electrocardiograms (ECG) and hospital-based diagnostic devices are often expensive, bulky, and unsuitable for continuous personal monitoring. To address these limitations, this research presents the design and implementation of a low-cost, portable, and IoT-enabled Heart Pulse Monitoring and Notification System using the Arduino IDE development platform and the ESP8266 NodeMCU microcontroller. The proposed system utilizes a photoplethysmography-based pulse sensor to detect blood volume changes and calculate Beats Per Minute (BPM). The measured data is displayed locally on an OLED screen and transmitted wirelessly to the Blynk cloud platform for remote monitoring. In abnormal conditions, such as tachycardia or bradycardia, automated email alerts are generated to notify caregivers or medical personnel. The system is optimized for low power consumption and compact design, making it suitable for wearable applications and remote healthcare monitoring. Experimental results demonstrate reliable real-time monitoring and effective alert generation, highlighting the feasibility of integrating embedded systems and IoT technologies in affordable healthcare solutions.
Heart Rate Monitoring, Pulse Sensor (HW-827), ESP8266 NodeMCU, IoT-Based Healthcare, Blynk Cloud, OLED Display, Real-Time Monitoring, BPM (Beats Per Minute), Email Notification System, Remote Patient Monitoring.
Bandi. Bhavani, Kandi Harika, Kuppa Jahnavi
Copyright (c) 2026 R Vyshnavi, Bandi. Bhavani, Kandi Harika, Kuppa Jahnavi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Deep Learning Approach for Cardiovascular Risk Prediction
Cardio Vascular Diseases (CVDs) are known to cause high percentage of mortalities in the global population; hence, the necessity of reliable and timely systems of risk prediction has been highlighted in the literature. This paper introduces a new deep learning model, which is called Cardio-RiskNet, to predict cardiovascular risk and offers a rich set of clinical and lifestyle variables as inputs. The model developed in Python was tested on the popular dataset on Hearts Disease Health Indicators that could be obtained on Kaggle. The data includes various information including age, body mass index, blood pressure, cholesterol, glucose level, physical activity, and smoking behaviors. The class imbalance issue is addressed with the SMOTE-ENN hybrid resampling strategy, on the one hand, it gives an opportunity to better identify the minority classes, and on the other, it enhances the model generalization. Cardio-RiskNet is based on the integration of a Conv1D framework with Residual blocks and Squeeze-and-Excitation (SE) modules to perform effective hierarchical feature extraction and powerful representation learning. This was trained over 50 epochs and the model achieved a 84.21 percent accuracy and an AUC of 0.7074 which is very good predictive performance in the field of cardiovascular risk assessment. In addition, the Explainable AI (XAI) methods have been applied to present a more vivid understanding of the decisions made by the model, therefore, enabling the use of the model in a safe and reliable setting in terms of clinical decision support. Altogether, Cardio-RiskNet is an effective, easy-to-understand, and scalable application that will aid in the early prevention of cardiovascular risk, and it is highly likely to be adopted in preventive care and analytics in healthcare.
Copyright (c) 2026 Suneetha Rikhari , E. Aravind , K Mohana Lakshmi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
ESP32 Based Solar-to-Household Power Shifter using Weather Prediction
The increasing demand for reliable and sustainable residential power systems necessitates intelligent energy management solutions. This paper presents the design and implementation of an ESP32-based solar-to-household power shifter integrated with weather-aware decision logic. The proposed system utilizes ambient temperature sensing as a short-term environmental indicator to estimate solar availability and dynamically switch between photovoltaic (PV) supply and battery backup. A relay-based switching module ensures seamless transition between power sources, while a 16×2 LCD and Bluetooth communication provide real-time monitoring and remote supervision. Experimental validation demonstrates improved solar utilization, reduced unnecessary battery discharge, stable switching performance, and uninterrupted power supply under varying environmental conditions. The system offers a low-cost, scalable, and IoT-enabled solution suitable for residential and small-scale renewable installations.
Solar energy management, ESP32, IoT, renewable energy systems, weather-aware control, power shifting, embedded systems.
Jhade Durga, Akula Kalyani, Nimmala Lithiksha
Copyright (c) 2026 Dr J Madhavan, Jhade Durga, Akula Kalyani, Nimmala Lithiksha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Powered Predictive Maintenance: A Deep Learning Approach
The growing level of automation and digitalization in manufacturing systems has increased the demand for intelligent maintenance strategies that can prevent unexpected equipment failures and minimize production interruptions. Conventional maintenance practices, such as corrective and time-based preventive maintenance, are often unable to anticipate failures accurately, resulting in avoidable downtime and increased operational cost. This paper presents an artificial intelligence–based predictive maintenance framework that employs a deep learning model to estimate the probability of machine failure from multivariate time-series sensor data. Synthetic industrial sensor signals reflecting temperature and vibration degradation patterns were generated to emulate realistic machine operating conditions. A Long Short-Term Memory (LSTM) neural network was trained to capture temporal degradation behaviour and to forecast failure risk prior to breakdown. Experimental results indicate that the proposed model achieves high predictive accuracy, precision, and recall, and is able to identify failure conditions significantly in advance of the actual event. The study demonstrates that deep learning models outperform conventional threshold-based and statistical approaches when modelling nonlinear and time-dependent degradation characteristics. The proposed framework supports condition-based maintenance planning, reduces unplanned downtime, and strengthens data-driven decision-making in smart manufacturing environments.
predictive maintenance, deep learning, LSTM, time-series modelling, machine failure prediction, smart manufacturing, Industry 4.0, sensor analytics
Copyright (c) 2026 S. Sasirekha, N. Jagadeesh, S. Thangamani, Paladugu Harshitha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
IoT Based Greenhouse Monitoring and Control System
The wireless sensor network (WSN) is one of the most significant technologies in the 21st century and they are very suitable for distributed data collecting and monitoring in tough environments such as greenhouses. The other most significant technologies in the 21st century is the Internet of Things (IoT) which has rapidly developed covering hundreds of applications in the civil, health, military and agriculture areas. In modern greenhouses, several measurement points are required to trace down the local climate parameters in different parts of a largescale greenhouse in order to ensure proper operation of the greenhouse automation system.
Salivoju Akshaya, Bangaru Akshitha, Patlolla Mounika
Copyright (c) 2026 Salivoju Akshaya, Bangaru Akshitha, Patlolla Mounika (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI Model to Enhance Organizational Decision-Making for Accurate Predictions Using a Machine Learning Algorithm
Organizations increasingly rely on data-driven intelligence to navigate complex, time-sensitive decisions. This study develops and evaluates an AI decision-support model based on a Random Forest ensemble tailored to heterogeneous organizational data and operational constraints. The pipeline combines rigorous preprocessing with feature selection and cross-validated training, and is deployed through lightweight, cloud-based APIs and real-time dashboards for seamless integration into existing Decision Support Systems. On a held-out test set, the model achieved 91.2% accuracy, precision = 0.89, recall = 0.91, F1-score = 0.90, and ROC-AUC = 0.94, with an average prediction latency of < 0.5 s per query—suitable for interactive use. Comparative baselines demonstrated materially lower performance: Logistic Regression (accuracy = 85.3%, F1 = 0.83, ROC-AUC = 0.88) and a Single Decision Tree (accuracy = 83.4%, F1 = 0.81, ROC-AUC = 0.86). External validity was examined via three domain-representative simulations. In finance (loan approvals), the model reduced false approvals by 28% versus a rule-based system while maintaining > 90% overall prediction accuracy. In healthcare (30-day readmission risk), it achieved 92% recall, enabling targeted post-discharge interventions and a 17% reduction in avoidable readmissions. In manufacturing (inventory and supply-chain planning), it improved the inventory turnover ratio by 15% and reduced stockouts by 10%, stabilizing production schedules. Across scenarios, automated analytics cut manual assessment time by > 60%, accelerating decision cycles without sacrificing quality. Collectively, results indicate that the proposed ensemble delivers superior predictive power and operational responsiveness relative to conventional models, while remaining adaptable to sector-specific data and workflows. The model’s modular design, fast inference, and integration-ready architecture position it as a practical augmentation to human expertise—enhancing accuracy, timeliness, and consistency of organizational decisions across finance, healthcare, and manufacturing contexts.
AI Model, Decision-Making, Predictions, Machine Learning, Algorithms
Copyright (c) 2026 Akwi Helene Fomude, Chaoyu Yang, Ofori Makafui, George K. Agordzo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning, Fuzzy Logic, Disease Prediction, Epidemiological Modeling, Clinical Decision Support.
Copyright (c) 2026 Ketankumar Chaturbhai Patel, Prof. (Dr.) Satish Narayanrao Gujar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Secure Bank Transaction System Using Blockchain Technology
Banking functions require stable and safe systems of online transactions. The goal of the present study is to create a banking system based on block-chain and guaranteeing transparency, immutability, and absence of fraudulent activities. The uniqueness of the presented research thesis is the SBTE design that uses automation of smart contracts with the Ethereum blockchain validation to manage essentially banking operations such as registration, transfer of funds, accounts maintenance, etc. The recommended structure performs authentic transactions and documenting them on the blockchain which are verified by miners to ensure trust and traceability. Findings indicate that there are high proficiency, immediate updated records and proper upkeep of ledgers and in general. Ultimately, SBTE framework will be a very positive and stable banking system that allows running various transactions without interruption and will guarantee transparency and reliability to its users and administrators.
Blockchain, Ethereum, Smart Contracts, Secure Banking, Decentralized Transactions, SBTE, Transaction Accuracy.
Copyright (c) 2026 Sharmila Ramasundaram Sudharsanam, Mrs. D.K. Kalai Vani (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The rapid growth of large-scale surveillance infrastructures has created an urgent requirement for automated techniques capable of analysing continuous video streams in real time. Conventional surveillance systems depend almost entirely on prolonged human observation, which is inherently inefficient and vulnerable to fatigue, delayed response, and missed critical incidents. This paper presents an intelligent video surveillance framework based on an unsupervised deep learning approach for detecting abnormal activities in video streams. The proposed system employs a Convolutional Long Short-Term Memory (Conv-LSTM) autoencoder to model normal spatio-temporal patterns in surveillance videos and to identify deviations through reconstruction error analysis. The network is trained exclusively on normal activity sequences, enabling the detection of unforeseen abnormal events without requiring explicit anomaly labels. The complete framework includes a real-time preprocessing pipeline, sequence buffering mechanism, threshold-based decision logic, and visual alert generation. Experimental evaluation conducted on a standard benchmark dataset demonstrates that the system can effectively identify abnormal behaviours such as running and object throwing while maintaining a low false-alarm rate. The results confirm the suitability of the proposed framework for practical real-time intelligent surveillance applications.
Intelligent video surveillance, anomaly detection, Conv-LSTM, autoencoder, unsupervised learning, spatio-temporal modelling.
Copyright (c) 2026 Y. Srinija, Dr.Md Asif (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Efficient Auditing Scheme For Secure Data Storage In Fog-To- Cloud Computing
Cloud computing has become the dominant platform for large-scale data storage and service delivery due to its scalability, flexibility, and cost efficiency. However, the growing reliance on third-party cloud service providers raises serious concerns regarding data integrity, privacy, and transparency. To address these issues, third-party auditors (TPAs) are commonly employed to verify the correctness of outsourced data without retrieving the entire content. At the same time, emerging fog-to-cloud computing architectures, driven by the rapid expansion of the Internet of Things (IoT), introduce new challenges because data is no longer managed solely by centralized cloud servers but also by intermediate fog nodes and mobile sinks. In addition, public digital services such as government welfare schemes require secure and transparent mechanisms that allow citizens to submit applications and track their status without manipulation by intermediaries. This paper presents an integrated study of privacy-preserving cloud data auditing techniques and proposes a secure fog-to-cloud based service framework for public scheme management. The proposed approach enhances data integrity verification, supports user privacy, and improves transparency while reducing computational overhead compared with traditional public auditing methods that rely heavily on expensive cryptographic operations.
Cloud storage, third-party auditing, privacy preservation, fog-to-cloud computing, data integrity, public service systems, IoT.
Copyright (c) 2026 Samala Nilohitha, Mrs M Anusha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Powered Skill Gap Identifier And Career Guidance
Rapid technological change is continuously reshaping job roles, skill requirements, and professional pathways. Traditional career guidance systems, which rely on static assessments and manual counseling, are no longer sufficient to support individuals in navigating complex and evolving labor markets. This paper presents the design, implementation, and evaluation of an Artificial Intelligence (AI)–driven Skill Gap Identifier and Career Guidance Platform (SGICGP). The proposed system integrates machine learning, natural language processing, and labor-market intelligence to extract user skills from unstructured data, identify missing competencies, compute a Skill Gap Index (SGI), and generate personalized learning and career recommendations. A modular architecture is adopted to enable scalability, explainability, and continuous model updates. Experimental results obtained using a prototype implementation demonstrate reliable job-role prediction, accurate skill extraction, and actionable recommendations suitable for educational institutions and training providers.
Career guidance, skill gap analysis, machine learning, NLP, explainable AI, employability analytics
Copyright (c) 2026 Alguvelli Jyothika, Mr. Bheemana Bhuvan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Access to digital information and real-world services remains a major challenge for people with visual impairments, particularly in environments that depend heavily on graphical user interfaces. This paper presents the design and development of a low-cost voice-based assistance system intended to support visually impaired users in performing routine daily activities without visual interaction. The proposed system enables users to interact with the device using spoken commands and receive auditory responses through an integrated speech interface. Core functionalities include printed text reading using optical character recognition, voice-driven information retrieval such as time and weather updates, and basic environmental interaction support. The system integrates speech recognition, text-to-speech synthesis, and optical character recognition modules to provide real-time, hands-free operation. Emphasis is placed on affordability, simplicity of deployment, and usability for non-technical users. Experimental evaluation demonstrates that the proposed solution delivers reliable response times and accurate recognition performance in typical indoor environments. The results indicate that voice-driven assistive technologies can significantly enhance personal independence and digital inclusion for visually impaired individuals.
Assistive technology, voice interface, speech recognition, text-to-speech, optical character recognition, accessibility.
Copyright (c) 2026 B Jyothsna, P Shruthi, R Sirisha, V Sravya Sree (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning–Driven Healthcare: Diagnostic Intelligence, Therapy Optimization, and Patient-Centered Care
This study critically examines the integration of machine learning (ML) techniques in medical diagnosis and treatment, highlighting their transformative potential and the challenges that accompany their adoption. Through a comprehensive literature review of recent advancements—from deep neural networks in medical imaging to reinforcement learning in treatment optimization—we demonstrate how ML enhances diagnostic accuracy, accelerates drug discovery, and enables personalized therapeutic regimens. Case studies, such as three-dimensional neural networks for early lung cancer detection and AI-driven platforms for COVID-19 management, illustrate tangible improvements in clinical outcomes and operational efficiencies. We further explore the technical underpinnings of supervised, unsupervised, reinforcement, and transfer learning methods, alongside feature selection and evaluation metrics critical to model performance. Key barriers, including data quality, privacy concerns, algorithmic bias, and model interpretability, are addressed, and strategies for ethical and effective integration within existing healthcare workflows are proposed. By synthesizing multidisciplinary insights from clinicians, data scientists, and ethicists, this research offers a roadmap for responsible deployment of ML in healthcare, aiming to improve patient outcomes, reduce costs, and foster equitable access to advanced medical technologies.
Machine Learning; Medical Diagnosis; Personalized Treatment; Deep Learning; Clinical Decision Support; Data Privacy.
Copyright (c) 2024 Ketankumar Chaturbhai Patel, Prof. (Dr.) Satish Narayanrao Gujar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Advancing Database Mining: Integrative Approaches of Machine & Deep Learning for Sequential Pattern Analysis
In the vast expanse of data science, database mining has secured itself as an essential thread, allowing organizations to weave patterns amidst billows of bytes. This study examines the combination of machine learning and deep learning methods for heavy-duty sequential pattern discovery in database mining applications. The first targets are to investigate whether it is more efficient to extract sequential patterns using hybrid frameworks combining traditional machine learning algorithms with other deep learning architectures, detect suitable methods for developing efficient hybrid frameworks for SP extraction, and evaluate effectiveness against efficiency in different database environments. I applied a mixed-methods research design using secondary data analysis from benchmark databases including UCI Machine Learning Repository datasets. Finally, we propose that such integrative approaches (using ML preprocessing pipeline for data preparation and DL for pattern recognition) can provide better accuracy comparing to DL alone approaches. Using the hybrid Long Short-Term Memory networks combined with Random Forest preprocessing, results show sequential pattern detection accuracy of 94.7%, a 12.3% improvement compared to using Long Short-Term Memory networks and Random Forest alone. Explains how attention mechanisms improve recognition of patterns in sequences over time. It is concluded that combinational machine and deep learning frameworks are the best way forward for enhancing the database mining of sequential pattern analysis applied settings.
Database Mining, Sequential Pattern Analysis, Machine Learning, Deep Learning, Hybrid Algorithms
Copyright (c) 2024 Bhosarekar Dhanashri Dhananjayrao, Dr. Bechoo Lal (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Blockchain-Based Property Transfer System Using Smart Contracts and IPFS
Property registration and ownership transfer systems in many countries still rely on centralized databases and manual verification processes, which are vulnerable to fraud, data manipulation, and operational inefficiencies. This paper presents a blockchain-based property transfer system that provides a decentralized, transparent and tamper-proof platform for property registration, document verification and ownership transfer. The proposed system utilizes Ethereum smart contracts for secure transaction execution and InterPlanetary File System (IPFS) for decentralized document storage. A web-based application built using React and Spring Boot enables interaction between property owners, buyers and government authorities. Smart contracts enforce access control, verification rules and ownership transfer automatically. Experimental evaluation conducted on a prototype implementation demonstrates that the system ensures strong data integrity, reliable audit trails and efficient transaction processing. The proposed architecture improves transparency, reduces dependency on intermediaries and provides a trustworthy digital ecosystem for property management.
Blockchain, Property transfer, Smart contracts, IPFS, Decentralized applications, Land registry.
Copyright (c) 2026 M. Akanksha, Dr. Narsappa Reddy (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Detecting Botnet Attacks in IoT Environments Using an Optimized Lightweight Hybrid Deep Learning Model
has resulted in billions of interconnected devices operating in smart homes, healthcare systems, industrial automation and intelligent transportation. Although IoT technology provides significant advantages, its limited computational capability and weak security mechanisms expose networks to large-scale cyber threats, particularly botnet attacks. Botnets exploit vulnerable IoT devices and launch coordinated attacks such as distributed denial of service (DDoS), data theft and reconnaissance. Conventional intrusion detection systems based on static rules and signatures fail to effectively detect evolving and previously unseen attacks. This paper presents an optimized deep learning-based intrusion detection framework for detecting botnet attacks in IoT environments. The framework evaluates existing deep learning models including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM) and Recurrent Neural Networks (RNN). In addition, a novel lightweight hybrid deep learning model called Advanced Custom Lightweight Recurrent (ACLR) is proposed. The ACLR model integrates Conv1D, LSTM and Simple RNN layers to efficiently capture both spatial and temporal characteristics of network traffic while maintaining low computational complexity. The Bot-IoT 2018 dataset is used for experimental evaluation. The proposed model achieves an accuracy of 96.42%, outperforming CNN, LSTM and RNN models. The results demonstrate that the proposed framework is effective, scalable and suitable for deployment in resource-constrained IoT environments.
Internet of Things, Botnet attacks, Intrusion Detection System, Deep Learning, CNN, LSTM, RNN, Hybrid model.
Copyright (c) 2026 Aileni Abhinaya, Dr. Md. Asif (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Verilog-Based Simulation of Phase Estimation Block for Range-Doppler Processing in SAR/Radar Imaging Systems
This work is basically about trying to simulate the range-Doppler part of a SAR processor on an FPGA. The idea is to see how the core blocks of a SAR imaging chain behave when pushed toward real-time speeds. I split the whole thing into two parts so it’s easier to build and test step by step. In Phase 1, a Verilog-based simulation of the phase estimation and FFT blocks is developed, which are essential components of the SAR range-Doppler algorithm. The objective is to model the frequency-domain transformation and accurately estimate the phase information of received echoes, enabling the analysis of phase accuracy and resolution performance critical to high-precision SAR imaging. In Phase 2, the full range-Doppler processing pipeline is constructed in Register Transfer Level (RTL), including modules for range compression (convolution), Doppler FFT, and matched filtering. The RTL designs are validated using standard SAR raw datasets in MATLAB, with performance metrics such as image resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) being evaluated. The project builds upon recent advancements in FPGA-accelerated SAR imaging architectures that aim to meet the low-latency, high-throughput demands of airborne and spaceborne radar platforms. The integration of Verilog simulation with MATLAB-based image reconstruction ensures functional correctness while enabling resolution benchmarking. This work demonstrates the feasibility and effectiveness of real-time SAR range-Doppler implementation on FPGA platforms for defense, remote sensing, and surveillance applications.
Synthetic Aperture Radar (SAR), FPGA Implementation, Doppler FFT, PSLR and ISLR Evaluation, MATLAB Integration, Low-Latency Signal Processing
Copyright (c) 2026 Uday Tallapragada, Dr. Krishnanaik (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Modeling and Beam-Steering Control of Hybrid/ACAP-Based FPGA Architectures for Radar and Electronic Warfare (EW) Beamforming Applications
This mini-project looks at how beam steering works when you try to model it on an FPGA-style setup, without using any physical antenna hardware. Everything is done through simulation in Verilog. The idea is to reproduce what a phased array normally does: change the phase across several antenna elements and, as a result, watch the main beam swing toward whatever direction you choose. That’s the fundamental trick behind modern radar and electronic-warfare systems, where the beam has to move quickly and respond to whatever is happening in the environment. In the simulation, the phase for each element is controlled digitally, and that shift is what steers the beam. Because it doesn’t rely on any mechanical movement, the model can show how the beam reacts in real time. You can also see how basic factors—like how far apart the elements are, whether the array is arranged in a line or another shape, and how the phase changes from one element to the next—affect the width of the beam and how strong it is in different directions. To keep things manageable, the project uses the standard formulas from uniform linear array theory along with the basic phase-shift equations that engineers typically use for beamforming. Those models are tied into signal-processing steps so the system can follow a changing angle, similar to angle-of-arrival tracking. When you adjust the target angle or the control values, the beam pattern shifts right away in the plots, which makes it easy to see what’s going on. The whole design was written in Verilog HDL and run in ModelSim, keeping it close to what an eventual FPGA implementation would look like. The end goal here is not just to simulate the beam, but to build a starting point for radar systems that need to steer the beam instantly—something that matters a lot in defense applications where the system may have to track a moving object, push through interference or pick out a signal buried in clutter.
Hybrid FPGA Arrays, Electronic Warfare, Military Radar, Phase-Shift Beamforming, Uniform Linear Array, Angle-of-Arrival Tracking
Copyright (c) 2026 Kalyan Renikunta, Dr. V. Krishnanaik (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Verilog-Based Simulation of UWB Radar Data Acquisition for Pulsed-Radar Signal Processing on FPGA (Military Radar)
This work reports an RTL-level study of a pulsed-radar data-acquisition chain designed entirely in Verilog. The project was approached in two parts. First, a dual-channel front end was created to reproduce 12-bit ADC output and to apply a reduced-rate sampling method before sending the data into independent FIFO buffers. This allowed us to examine how the system reacts when the PRF is pushed high, particularly in terms of timing, throughput, and the way the buffers fill and empty. In the second part, the model was expanded into a real-time processing path similar to what would run on an FPGA, and it was tested at roughly 9,000 pulses per second on each channel. The processing stage uses Verilog modules for pulse averaging, window shaping, and simple interference handling, and these modules were validated using ModelSim. MATLAB was used only to review the captured signals and verify SNR and latency results. The study focuses on decisions that influence real hardware—buffer sizes, clock-domain handling, and timing margins—rather than idealized assumptions. The design can function with low latency and without data loss, according to the simulation results, which makes it appropriate for UWB radar systems that require consistent performance in noisy military scenarios.
Verilog HDL, Subsampling ADC, FIFO Buffer, Radar Signal Processing, Ultra-Wideband (UWB) Radar, High-Speed Data Acquisition
Copyright (c) 2026 M. SVND Praneetha, Dr. V Krishnanaik (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Vision Guard-Smart Eyewear for Driver Alertness
Driver fatigue and loss of alertness remain major contributors to traffic accidents and fatalities across the world. Although several advanced driver monitoring systems have been proposed, many of them rely on complex camera-based approaches or expensive vehicle-integrated solutions that are difficult to deploy in all types of vehicles. This paper presents Vision Guard, a low-cost, wearable and real-time driver alertness monitoring system designed as a smart eyewear platform. The proposed system continuously observes eye blink activity using a dedicated eye blink sensor and determines the alertness level of the driver through an Arduino UNO microcontroller. When prolonged eye closure is detected for more than three seconds, an audible warning is issued using a buzzer. If the eye closure exceeds five seconds, indicating severe drowsiness or unresponsiveness, a servo motor is activated to simulate a vehicle stopping mechanism. An LCD module displays real-time system status to improve user interaction. The prototype also integrates auxiliary sensors, including a Hall effect sensor and an ultrasonic sensor, as well as a relay module to demonstrate scalability and functional expansion. Experimental evaluation under simulated driving conditions confirms that the system can effectively detect drowsiness and trigger appropriate responses. The proposed platform provides an affordable and adaptable solution for enhancing road safety and offers a foundation for future developments such as Internet of Things (IoT) connectivity and intelligent fatigue prediction.
Copyright (c) 2026 R Vyshnavi, M Rithika,T Sriharsha, T Srimahi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Detection of Anaemia using Image Processing
Anaemia is a widespread hematological condition characterized by a reduction in the number of red blood cells (RBCs) or a decrease in haemoglobin concentration, which leads to insufficient oxygen transport to body tissues. Early and accessible screening is particularly important in rural and resource-limited regions where conventional laboratory infrastructure such as automated haematology analysers is unavailable. This paper presents an automated image-processing-based system for detecting anaemia from peripheral blood smear images and a simple graphical user interface (GUI) to facilitate practical clinical use. High-resolution microscopic images acquired at 400× magnification are processed using a two-stage preprocessing approach consisting of gamma correction for contrast enhancement and Wiener filtering for noise suppression. Red blood cells are segmented and detected using the Circular Hough Transform (CHT). The total number of detected RBCs within a field of view is calculated and compared with a clinically motivated threshold of 500 cells per image. Images with RBC counts below this threshold are classified as anaemic, while higher counts are labelled as normal. A Python-based Tkinter GUI enables users to load images, visualize detected cells, and obtain diagnostic results in a single step. The proposed system offers a low-cost, portable, and user-friendly solution suitable for rural health centres, mobile diagnostic units, and telemedicine services. The framework can be further extended with machine learning methods and advanced feature analysis for improved diagnostic accuracy and classification of anaemia subtypes.
Red blood cell counting, Image processing, Anaemia detection, Peripheral blood smear, Automated diagnosis, Tkinter GUI, Python.
Copyright (c) 2026 Ms Radhika Ravikrindi, N Sharanya, K Srividya, T Srujana (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Smart Voter Verification System Using Face Recognition
In an era of digital transformation, ensuring secure and reliable voting mechanisms is vital to protect democratic values. The "Smart Voting and Verification System Using Face Recognition" leverages computer vision and machine learning to provide a contactless, accurate, and tamper-proof method of voter registration and authentication. This system replaces traditional identity verification methods with facial recognition, thus preventing fraudulent voting practices and multiple voting attempts by a single individual. The system is developed using Python and integrates GUI components built with Tkinter for an intuitive and user-friendly interface. Voter facial recognition is implemented using a combination of Haar Cascade Classifiers for face detection and Dlib’s facial encoding for recognition. Voters can register themselves through a dedicated “New Register Voter” window, where they are required to input their Name and Aadhar Number, and capture 10 facial images to ensure accurate recognition. Each image capture is acknowledged on-screen with a live counter to guide users through the process. After successful registration, the system encodes and stores the facial data along with the voter’s information. The voting interface contains options to vote for predefined parties including BJP, CONGRESS, and BRS. A "Start Voting" button initiates the verification process by recognizing the voter’s face in real-time. Upon successful verification, the voter is allowed to cast their vote for the preferred party. To ensure election integrity, each voter can vote only once—validated by facial data previously registered. Additional functionalities include a "Voting Result" section, which aggregates and displays the total number of votes each party has received. An "Exit" button gracefully closes the application. The system ensures transparency and security while simplifying the voting experience and minimizing human errors. This smart voting system demonstrates the potential of integrating biometric authentication into electoral processes, offering a scalable and efficient alternative for modernizing elections in a digital society.
Smart Voting System, Face Recognition, Biometric Authentication, Secure E-Voting, Computer Vision, Machine Learning, Haar Cascade Classifier, Dlib Facial Encoding, Voter Verification, Fraud Prevention, Tkinter GUI, Digital Elections
Vallampally Sanjana, Komuravelly Santhoshi, K Saraswathi
Copyright (c) 2026 B Jyothsna, Vallampally Sanjana, Komuravelly Santhoshi, K Saraswathi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An AI Power Defect Detect In Rail Surface
Ensuring the safety and reliability of railway transportation requires accurate and real-time detection of rail surface defects such as cracks, scars, and fractures. In this study, we present an improved YOLOv8-based defect detection model enhanced with SPD-Conv building blocks, an Efficient Multi-scale Attention (EMA) module, and a Focal-SIoU loss function, enabling robust recognition of small and densely occluded defects without increasing network complexity. Experimental results demonstrate significant improvements in precision, recall, and average accuracy compared to the baseline YOLOv8 model. To further strengthen the system, advanced YOLO variants including YOLOv5x6 and YOLOv9 were integrated, achieving higher reliability in defect identification. Additionally, Flask-based front-end interface with secure user authentication was developed, providing an accessible platform for real-time defect monitoring and analysis. The proposed framework not only improves detection accuracy but also ensures scalability, usability, and security, making it suitable for deployment in practical railway inspection scenarios.
Rail surface defect detection, YOLOv8, YOLOv9, YOLOv5x6, deep learning, SPD-Conv, EMA attention, Focal-SIoU loss, computer vision, real-time detection, railway safety, Flask framework, user authentication
J.Diwakar, G.Sai Krishna, Chekuri Dange Manikanta
Copyright (c) 2026 P.Rama Krishna, J.Diwakar, G.Sai Krishna, Chekuri Dange Manikanta (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Secure Biometric E-Voting System Using ML
In today's digital era, ensuring secure and tamper-proof electoral processes has become a growing necessity. This project, titled Secure Biometric E-Voting System Using Machine Learning, aims to build a reliable and user-friendly voting mechanism that verifies a voter's identity using two critical biometric iris and fingerprint alongside their Aadhar number, name, and age. By integrating machine learning into this biometric verification process, the system not only enhances authentication accuracy but also detects fraudulent or mismatched votes To facilitate this, a custom dataset generation module was developed. It simulates a real- world biometric database by assigning each synthetic voter a unique Aadhar number, a randomly generated name, and an age within valid voting range. For each voter, fingerprint and iris images are carefully paired and organized into dedicated folders. A master summary CSV is also generated to maintain a centralized reference of all user data, allowing the system to index and cross-check voter identities during the machine learning validation phase. This dataset serves as the foundation for training a machine learning model—specifically a Random Forest Classifier to classify votes as either valid or faulty based on biometric match accuracy. The project strengthens the voting process by ensuring that only authenticated individuals can cast a vote, effectively minimizing the chances of identity theft, duplicate voting, and vote rigging. By combining artificial intelligence with biometric data, this system presents a forward- thinking approach to electoral security, ensuring that future elections are not only smart but also safe, inclusive, and tamper- resistant. The rapid advancements in digital technology have necessitated the development of secure and reliable electronic voting systems. Traditional voting mechanisms are often susceptible to fraud, identity theft, and manual errors. This project proposes a secure voting machine that integrates machine learning algorithms to authenticate voters using biometric data—specifically, iris and fingerprint recognition and their Aadhar number.A Random Forest algorithm is employed to accurately classify and validate legitimate voters while detecting anomalies or fraudulent entries.This approach provides a robust solution to safeguard the integrity of the voting process and eliminate the risk of impersonation or duplicate voting.
Secure Biometric E-Voting, Machine Learning, Biometric Authentication, Random Forest Classifier, Electoral Security
Copyright (c) 2026 Ms.S Manjula, A.SaiRuthvika, V.Sangeetha, V.Vaibhavi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine learning for time series forecasting: A statistical perspective
Time series forecasting is a fundamental problem in many real-world applications, including healthcare, finance, and energy systems, where accurate predictions are essential for effective decision-making. Classical statistical models such as autoregressive integrated moving average (ARIMA) have been widely used due to their theoretical rigor and interpretability; however, their performance is often limited by assumptions of linearity, stationarity, and predefined error distributions. Recent advances in machine learning have introduced flexible, data-driven alternatives that demonstrate superior forecasting accuracy in complex environments. This study investigates the advantages of machine learning for time series forecasting from a statistical perspective using a controlled synthetic case study. A synthetic dataset representing daily hospital admissions is generated with trend, multiple seasonalities, and nonlinear dynamics to emulate real-world behavior. Forecasting performance of a classical ARIMA model is compared with a machine learning-based Random Forest regressor. Model evaluation is conducted using standard error metrics and residual diagnostics. The results show that the machine learning model consistently outperforms the statistical model, achieving lower prediction error and improved residual behavior. From a statistical viewpoint, this improvement is attributed to the ability of machine learning models to act as nonparametric estimators of conditional expectations while relaxing restrictive modeling assumptions. The findings highlight that machine learning complements rather than replaces traditional statistical approaches and provides a robust framework for forecasting complex time series data.
Time series forecasting; Machine learning; Statistical modeling; ARIMA; Random Forest; Synthetic data; Healthcare analytics
Copyright (c) 2026 Charanjit Singh, Naimoonisa begum, Paladugu Harshitha, Mr. M.Saravanan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Currency Recognition Using Image Processing
The circulation of counterfeit currency has become a growing concern in modern financial systems, especially in developing countries where cash transactions remain dominant. Traditional methods for detecting fake banknotes mainly rely on manual inspection or dedicated hardware devices such as ultraviolet detectors. These methods are often costly and not easily accessible to common users. In this project, an automated system for Indian currency recognition and counterfeit detection based on digital image processing techniques is presented. The proposed approach uses image acquisition, preprocessing, segmentation, feature extraction and classification to identify and verify currency notes. Statistical and edge-based features are extracted from the currency images and compared with reference feature sets stored in a database. The system is implemented using MATLAB and is evaluated on different denominations of Indian currency. Experimental results indicate that the proposed method is capable of recognizing genuine notes and detecting suspicious notes with satisfactory accuracy, making it suitable for low-cost and small-scale deployment.
Copyright (c) 2026 B Eleena, A.Jahnavi, Archana (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Industrial Investment Payment Strategies a Comprehensive Analysis Using Market Data and Decision Criteria
Industrial Investment are paramount to the enterprise to optimise the distribution of resources, improve profitability, and appropriate payment facilities in the changing market environment. Current approaches, including the conventional AHP, WS models, and classical TOPSIS, usually do not deal with the aspect of uncertainty, indecision, and interrelations between various criteria, and thus result in a non-optimal investment decisions. In order to overcome these shortcomings, the research will introduce a hybrid MCDM model, which will combine Intuitionistic Fuzzy AHP to allocate weight to the payment options and TOPSIS to ensure effective ranking of payment options. The methodology uses the same market indicators of the Market Champions dataset and it is completely carried on the Python platform with libraries including Pandas, NumPy and FuzzyPy. The proposed model shows better performance, and it has high ranking accuracy of 95.2%, low mean absolute error, small ratio of consistency, and good robustness to both scenario and sensitivity analysis. This framework can be used by industrial managers, financial analysts and decision-makers in enterprises to make reliable and accurate and data-driven choices on investment payments.
Integrated Decision-Making Framework, Intuitionistic Fuzzy AHP, Industrial Investment, Market Indicators, TOPSIS.
Copyright (c) 2026 Dr.V S Mege, Dr.Listin P T, Dr.V.K.Ajay (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Analysis of Cloud Computing–Based Computer Networking Architectures for Efficient Internet of Things Ecosystems
The fast growth of Internet of Things (IoT) devices has made it much harder to scale, deal with latency, manage data, and make networks more efficient. Traditional networking architectures have a hard time dealing with the huge amounts of different types of data that IoT environments produce. To overcome these constraints, this study offers a detailed design and examination of cloud computing-based networking architectures specifically designed for optimal IoT ecosystems. The proposed framework combines cloud-based networking ideas with IoT communication models to make data processing more scalable, resources more flexible, and the Quality of Service (QoS) better. To look at many architectural parts, such as the device layer, network layer, cloud service layer, and application layer, in a methodical way, taking into account latency, bandwidth use, fault tolerance, and energy efficiency. Performance tests show that cloud-assisted networking greatly increases data throughput, cuts down on processing delays, and makes it easy for different IoT devices to operate together. The results show that cloud-enabled networking designs are good for developing strong, flexible, and scalable IoT systems that can be used in smart cities, healthcare, industrial automation, and intelligent transportation.
Cloud Computing; Internet of Things; Computer improved Quality of Service , Networking Architecture;
Copyright (c) 2026 Dr. S. Ashok Kumar, Ms. R. Latha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0