Volume 14 | Issue 6

 Abstract

This paper proposes an improved Dadda multiplier architecture aimed at achieving high speed and area efficiency in digital processing systems. Since multipliers consume a significant portion of hardware resources, optimizing their performance is critical in VLSI design. The proposed approach enhances the conventional Dadda multiplier by incorporating 4:2 compressors in the partial product reduction stage, which helps reduce critical path delay. Additionally, parallel prefix adders are employed in the final summation stage to further improve computational speed.

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 Keywords

Dadda Multiplier, 4:2 Compressor, Parallel Prefix Adders, VLSI Design, High-Speed Arithmetic, Area Efficiency, Propagation Delay, LUT Utilization, DSP Applications

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 Abstract

Smart parking management systems are becoming an important part of smart cities due to the increasing number of vehicles and parking problems in urban areas. Traditional parking systems waste time, fuel, and increase traffic congestion. This paper presents a comprehensive literature review of smart parking systems based on recent research work. Various technologies such as Internet of Things (IoT), wireless sensor networks, image processing, and cloud computing are discussed. These technologies help in detecting available parking spaces and guiding drivers efficiently. The review also focuses on different methods like sensor-based, camera-based, and mobile application-based parking systems. Advantages such as reduced traffic congestion, lower fuel consumption, and improved user convenience are highlighted. However, challenges like high installation cost, data privacy, and system maintenance are also identified. From the literature, it is observed that modern systems are moving towards Artificial Intelligence and machine learning for better prediction and automation. This paper also suggests innovative improvements such as integrating face recognition, dynamic pricing, and smart reservation systems. Overall, smart parking systems can significantly improve urban mobility and make parking more efficient, reliable, and user-friendly.

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 Keywords

Smart Parking, IoT, Sensors, Machine Learning, Smart Cities, Automation

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 Abstract

This project presents a portable, energy-efficient, and self-sustaining IoT-based health monitoring system designed to address the limitations of conventional battery-powered devices and the growing need for continuous health tracking. The system utilizes hybrid energy harvesting by combining solar power and ambient radio frequency (RF) energy. These sources are captured through a solar panel and RF harvesting coil, then processed via rectification and voltage regulation circuits. The harvested energy is stored in a lithium-ion battery, serving as the primary power supply and eliminating dependence on external power sources.

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 Keywords

IoT-based Health Monitoring, Hybrid Energy Harvesting ,Solar and RF Energy, NodeMCU (ESP8266) , MAX30102 Sensor , Remote Patient Monitoring , Self-Powered System ,Real-Time Data Transmission

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 Abstract

Facial emotion recognition is an important research area in artificial intelligence and computer vision. Human emotions are commonly expressed through facial expressions, voice, and body language. Among these communication channels, facial expressions are considered one of the most reliable indicators of emotional states. Automatic emotion recognition systems analyse facial images and classify emotions using computational techniques.

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 Keywords

Facial Emotion Recognition, Deep Learning, Convolutional Neural Network (CNN), Computer Vision, Real-time Systems

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 Abstract

The rapid spread of infectious diseases such as COVID-19 has highlighted the importance of preventive measures like wearing face masks in public spaces. This project presents an automated face mask detection system using Machine Learning and Deep Learning techniques to monitor and ensure compliance with mask-wearing guidelines. The system is designed to detect human faces in real-time and classify whether a person is wearing a mask or not. The proposed model utilizes Convolutional Neural Networks (CNN), a class of deep learning algorithms particularly effective in image processing and computer vision tasks. The system is trained on a dataset containing images of people with and without masks. Image preprocessing techniques such as resizing, normalization, and augmentation are applied to improve the model's accuracy and robustness. For face detection, algorithms such as Haar Cascade or deep learning-based detectors are used to identify facial regions in images or video streams. The detected face is then passed through the trained classification model to determine mask presence. The system can be integrated with CCTV cameras to enable real-time monitoring in public areas such as airports, hospitals, schools, and offices. Experimental results demonstrate that the model achieves high accuracy in detecting face masks under varying lighting conditions and orientations. The system is cost-effective, scalable, and can be deployed on embedded devices for widespread use. This solution contributes to public health safety by automating mask detection and reducing the need for manual supervision.

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 Keywords

Face Mask Detection, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Computer Vision Image Processing, Real-Time Detection etc.

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 Abstract

Agricultural productivity is a key component of the economy, but every year crops succumb to several diseases. Artificial Intelligence (AI) models for plant disease detection frequently struggle in real-world farm environments, primarily due to environmental noise and complex backgrounds that fail to capture the pristine variability of lab conditions. This study proposes an innovative, robust framework that leverages the compound scaling of EfficientNetB0 trained on raw, real-environment images to address this domain gap. The performance of EfficientNetB0 is systematically compared against DenseNet121 and MobileNetV2. To further bridge the gap between automated detection and practical agronomy, this system integrates the Gemini API to provide Explainable AI (XAI) and dynamic treatment remedies. The core objective is to develop a reliable, generalizable system that overcomes the "black-box" limitations of traditional CNNs, enhancing the accuracy (achieving 94% in field conditions) and efficiency of plant disease diagnosis in the unpredictable conditions of a real farm.

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 Keywords

EfficientNetB0, Large Language Models (LLMs), Plant Disease Detection, Explainable AI (XAI), Convolutional Neural Networks (CNN), Precision Agriculture.

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 Abstract

This paper focuses on reviewing sorting algorithm visualizers and their role in improving the understanding of algorithms. Visualization helps in learning complex concepts in a simple and interactive way. The proposed system demonstrates sorting algorithms step-by-step using graphical representation, making learning easier and more effective..

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 Keywords

Sorting Algorithms, Visualization, Data Structures, Bubble Sort, Quick Sort, Learning Tool

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 Abstract

Smart Parking System

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Smart Parking System

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 Abstract

Process scheduling plays an essential role in multitasking operating systems. Some of its benefits include high CPU utilization, short waiting times, short turnaround time, and better responsiveness. Round Robin is one of the most widely used techniques today due to its simplicity and fairness. However, it suffers from certain limitations based on a fixed time quantum which is dependent on different process burst times. This leads to increased context switching, wasted resources, and poor performance for processes that have huge differences in execution time. In the view of these issues, there is a need for the ADBRR scheduling algorithm. This self- tuning approach, called ADRR, adjusts time quantum based on changes in the process's burst characteristics. It means that ADRR treats short and long processes equitably. ADRR reduces unnecessary preemptions to ensure that no process starves in CPU time. We compare the performance of the new ADRR algorithm with traditional Round Robin scheduling after running the simulation and making observations. The experimental results show that ADRR maintains fairness and boosts CPU efficiency while significantly lowering average waiting time, turnaround time, and context switching overhead. These results support using an artificial intelligence technique with adaptive scheduling and prove how modern operating systems can be improved through machine learning

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 Keywords

Sensitive Operating Systems, Round Robin, Dynamic Time Quantum, Machine Learning, Decision Trees, CPU scheduling, Adaptive Scheduling.

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 Abstract

RetinoScopeAI is an OCT retinal prediction platform that was created using AI to deliver precise and early identification of reti-nal conditions like Choroidal Ne- ovascularization (CNV), Dia-betic Macular Edema (DME), and Drusen. It is a system that involves deep learning algo- rithms to decode high-resolution Optical Coherence Tomog- raphy (OCT) images. Such images are initially optimized by performing preprocessing on the image to remove noise and enhance the clarity of the image. Once preprocessed, the im- ages go through a trained neural network and disease-spe- cific patterns in the layers on the eye are detected. The site offers secure and easy web interface to physicians and healthcare providers. Users are able to send OCT images and get real-time results of diagnosis. The system shows the pre- dicted disease as well as the visual finding of the OCT scan. It can also give confidence scores and clinical insights to make medical decisions. RetinoScopeAI will help eliminate the reliance on manual interpretation by professionals. This reduces the human error and enhances consistency in diag- nosis. The platform aids in early detection and this aids in preventing loss of vision. It also can be used in remote and tele-ophthalmology. The automatic analysis saves time on the part of the doctors and more efficient on the screen-ing. Ret- inoScopeAI is an AI-based medical imaging company that creates better patient care. In general, the platform provides an efficient, rapid, and smart way of diagnosing and man- management of retinal diseases.

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 Keywords

AI-driven diagnosis, retinal OCT imaging, deep learning models, automated retinal analysis, early eye disease detec- tion, clinical decision support, telemedicine ophthalmology

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 Abstract

Integrating multiple APIs, third-party services, and AI models into single comprehensive workflow has become more difficult in this increasing growth of modern applications. Traditional workflow automation platforms often struggle to manage such complexity, especially when working with long and inter-related tasks, real-time data processing and AI-driven operations. This platform frequently faces issues such as API timeouts, inefficient resource utilization and loss of data consistency between different stages of workflow. Challenges such as maintaining context across multiple steps and ensuring reliability between interconnected components in the system often occurs while integrating different AI models and Services. These drawbacks make current systems difficult to scale and not suited for real-world production environment.

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 Keywords

AI Workflow Orchestration, Type-Safe Full-Stack Systems, Multi-Model LLM Integration, Asynchronous Task Execution, Distributed Workflow Systems, Context-Aware Data Flow

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 Abstract

Cultural heritage sites often face challenges such as structural deterioration, environmental damage, or limited accessibility, making preservation and public engagement dif-ficult. Emerging digital technologies, particularly Augmented Reality (AR), provide innovative ways to experience and interact with such monuments. This paper presents an AR-based system designed to facilitate interactive heritage exploration through digital reconstruction, intelligent narration, and community en-gagement.

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 Keywords

Augmented Reality, Cultural Heritage, Mon-ument Reconstruction, AI Assistant, ChatGPT API, Tourism, Education

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 Abstract

The freshness of fruits is critical for maintaining quality in fruit. This paper represents an automated fruit freshness detection system using YOLOv8 object detection with squeeze-and-Excitation. the freshness of fruits our method balances accuracy and real time performance while detecting the freshness level of fruits the average accuracy is 85.5% with a maximum accuracy of 94.7% for detecting accuracy of 94.7% for detecting the freshness level of single fruits category .the results confirm that the enhanced YOLOv8 - SE model effectively detects and localizes fruit freshness conditions , indicating its suitability for smart agriculture and food quality monitoring applications.

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 Keywords

Fruit recognition, freshness detection, image classification, MobileNet2, computer vision, K means clustering, deep learning, nutrition analysis, React.js, flask.

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 Abstract

The fingertip acts as a virtual colored marker. Using Open CV, the captured frames are converted into HSV colour space, where colour detection and segmentation techniques are applied to isolate the fingertip region. The detected fingertip coordinates are continuously tracked and mapped onto a digital canvas, allowing the system to draw strokes corresponding to the user's hand movement. With designers and performers using digital media, touch and gesture-based interfaces are increasingly widely used in the creative industry. There are issues or flaws with these touchscreens, such as ergonomic strain and accuracy. The goal of this research is to thoroughly analyse how well these interfaces facilitate accuracy, expressiveness, and intuitive interaction while taking ergonomics into account.

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computer vision, virtual drawing, human-computer interaction, gesture recognition, hand tracking, and real-time processing

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 Abstract

This paper presents a critical review and comparative evaluation of machine learning methods to predict the compressive strength of concrete with particular emphasis on mixes that incorporate supplementary cementitious materials. The analysis includes ensemble algorithms (e.g., Random Forest, CatBoost, XGBoost, AdaBoost, Gradient Boosting), regression algorithms (Support Vector Regression, Linear Regression), and neural networks (Artificial Neural Networks, Extreme Learning Machines) by synthesizing the results of the latest studies. CatBoost was the most effective one, with the highest R 2 values of up to 0.94 in several studies. The most significant parameters found are concrete age, water-to-binder ratio, and cement content. Other important gaps in current literature that are identified during the review are inconsistency in datasets, lack of external model validation, lack of interest in hybrid models, and real-world implementation barriers. These results provide a basis on which universal and predictable forecasting models can be developed to more sustainably build concrete constructions.

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 Keywords

RandomForest,,CatBoost,,LinearRegression,,NeuralNetwork

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 Abstract

Word Sense Disambiguation (WSD) is a critical task in Natural Language Processing (NLP) that seeks to resolve lexical ambiguity by determining the appropriate meaning of a word within a specific context. While considerable progress has been made in WSD for high-resource languages, low-resource languages have received limited focus due to a lack of annotated datasets, lexical resources, and computational tools. This paper provides a thorough survey of current WSD techniques, including knowledge-based, supervised, unsupervised, and deep learning methods, and assesses their applicability in low-resource language environments. The study also addresses the challenges faced by low-resource languages, such as data scarcity, morphological complexity, the absence of standardized tools, and multilingual influences. Furthermore, it reviews WSD research in Indian languages, including Manipuri, Malayalam, Punjabi, Bengali, and Sindhi, to highlight both current advancements and limitations. Notably, Sindhi is recognized as one of the least investigated languages concerning WSD research. The findings of this survey underscore the necessity for developing resource-efficient and adaptable approaches, particularly leveraging machine learning and deep learning techniques, to enhance WSD performance in low-resource languages. The paper also outlines future research directions aimed at overcoming existing challenges and closing the research gap in this area.

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 Keywords

Word Sense Disambiguation, Low-Resource Languages, NLP, Machine Learning, Deep Learning, Sindhi

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Driver fatigue and drowsiness are significant contributors to road traffic accidents worldwide, posing serious threats to public safety and increasing the need for reliable driver monitoring technologies. Vision-based driver drowsiness detection systems have emerged as an effective and non-intrusive solution for monitoring driver behavior using computer vision and artificial intelligence techniques. These systems analyze visual indicators such as eye closure, blink rate, yawning behavior, head pose, and gaze direction to assess the driver's level of alertness. This paper presents a PRISMA-guided systematic review of vision-based driver drowsiness detection approaches, focusing on detection algorithms, fatigue indicators, machine learning techniques and real-time monitoring architectures. The review examines widely used techniques such as facial landmark detection, Eye Aspect Ratio (EAR), PERCLOS-based fatigue estimation, yawning detection, gaze tracking, and deep learning models. Comparative analysis of existing methods indicates that deep learning and hybrid vision-based approaches significantly enhance detection accuracy, although challenges related to illumination variation, occlusion, head pose changes, and real-time deployment remain. The study highlights current research trends and outlines future directions for developing robust and efficient driver monitoring systems for intelligent transportation environments.

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 Keywords

Driver Drowsiness Detection, Computer Vision, Eye Aspect Ratio (EAR), PERCLOS, Deep Learning, Driver Monitoring Systems, Intelligent Transportation Systems, PRISMA Systematic Review.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Deepfake technologies driven by generative adversarial networks (GANs), neural vocoders, and end-to-end speech synthesis architectures have significantly advanced the generation of highly realistic synthetic audio. Modern voice cloning systems are capable of replicating speaker identity, linguistic style, and vocal timbre with minimal training data, making synthetic speech increasingly indistinguishable from authentic human recordings. While these developments offer substantial benefits in assistive technologies, entertainment, virtual agents, and content creation, they simultaneously introduce serious security, ethical, and societal risks. Malicious applications include financial fraud, identity impersonation, political misinformation, social engineering attacks, and erosion of public trust in digital media.

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CE-14 Deepfake Detection: Assessment of Speech and Emotion-Based Forensic Analysis

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 Abstract

The proposed system is based on e-certificate system in India's educational framework that leverages blockchain technology to address the widespread issue of certificate forgery. The inherent qualities of blockchain, including its immutability and transparency, create a solid foundation for enhancing the security and reliability of educational certifications. The operational framework is built around creating and storing an electronic file containing essential academic information in a dedicated database. Concurrently, the system generates a unique hash value for this electronic file, which serves as an exclusive identifier. This hash is securely embedded within a blockchain block, benefiting from the technology's resistance to tampering. To enable verification processes, both an inquiry string code and a QR code are linked to each certificate, encapsulating necessary information for authenticity verification. Users can initiate validation by scanning the QR code with a mobile device or entering the inquiry string on a specific website. The hash stored in the blockchain is then checked to confirm that the certificate has not been altered. This proposed solution significantly enhances the credibility of traditional paper certificates by implementing a dependable, transparent, and tamper-resistant verification process.

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 Keywords

Blockchain, Digital Certificate, Hashing, E-Certificate, Certificate Verification, Transparency, QR Code, Secure Storage, Credential Authentication.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Evaluating subjective answers remains a major challenge in educational assessment due to the wide variation in students' writing styles, vocabulary, and expression of concepts. Traditional grading methods, such as manual evaluation or rule-based systems, are time-consuming, inconsistent, and unable to capture the true semantic meaning of answers. This paper presents a machine learning-based framework for automated subjective answer evaluation that leverages Natural Language Processing (NLP) techniques to assess student responses more effectively. The proposed system utilizes semantic embeddings and transformer-based architectures to analyze the contextual meaning of answers, enabling it to recognize paraphrased expressions and evaluate the relevance, completeness, and coherence of responses. The model is trained and validated using the ASAP-SAS dataset and further adapted to evaluate teacher-uploaded question papers. Standard evaluation metrics such as Quadratic Weighted Kappa (QWK), F1-score, and Pearson correlation are used to measure performance. The system aims to reduce manual effort, improve grading fairness, and provide timely feedback to students. Experimental outcomes demonstrate that machine learning can offer a scalable, consistent, and intelligent alternative to traditional grading, enhancing both the efficiency and reliability of academic assessments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Subjective Answer Evaluation, Machine Learning, Natural Language Processing (NLP), Semantic Similarity, Transformer Models, BERT, Automated Grading, Educational Assessment, Deep Learning, Text Evaluation.

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Creative Commons Attribution 4.0 and The Open Definition