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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition