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1045 articles for “U-net”
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An Overview on Energy Harvesting Using Piezoelectric Material for Wi-Fi Systems
Abstract: The rapid proliferation of wireless-networked devices has intensified the demand for sustainable, maintenance-free power sources that can keep small-scale Wi-Fi modules operational in hard-to-reach or infrastructure-limited environments. This study investigates the feasibility of harvesting ambient mechanical energy using piezoelectric transduction technology and directly feeding the harvested power to a low-power Wi-Fi communication subsystem. A compact energy-harvesting module was engineered from lead-zirconate-titanate (PZT) cantilevers with resonant frequencies tuned to the dominant …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 56–63 Read article
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The Evolution and Impact of Numbers: From Ancient Tallies to Quantum Computing: Review Article on Numbers
Abstract: Numbers are among the most fundamental constructs in human civilization, serving as the backbone of mathematics, science, technology, and virtually every aspect of daily life. They represent not only quantities and measures but also relationships, structures, and patterns that underpin the fabric of human understanding. From the earliest tallies etched on bones by prehistoric humans to the sophisticated numerical systems embedded in today’s artificial intelligence and quantum computing, the evolution …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 3, 2025 · pp. 15–19 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report
Abstract: In recent years, diabetes has become the biggest disease in different countries around the world. This disease is caused by adulteration in food ingredients, unhealthy food habits, a lack of physical exercise, and changing the lifestyle every time without a routine chart. The main objective of this review paper is to provide a proper understanding of the machine learning algorithm used in the healthcare system to handle diabetic patients' data. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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Energy performance analysis: An exploratory study of HVAC systems in efficient management of data centres
Abstract: Data center growth is being driven by the rapid proliferation of cloud services. Data centers are using an increasing amount of energy. The network is severely impacted by server workloads, cooling, and supporting equipment. The study aims to identify the energy consumption involved in building services and their operations and analyze the ways in efficient management of performance in data center. The objective of the study is to identify the …
Published in International Journal of Environmental Planning and Development Architecture · Vol. 1, Issue 2, 2023 · pp. 51–71 Read article
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Cyclist Safety Enhancement: A Multi-Modal Hazard Detection System
Abstract: This study presents a multi-modal hazard detection system to enhance cyclist safety in urban environments. Lever- aging a combination of computer vision, object tracking, and predictive modeling, the system offers a comprehensive approach to identifying and mitigating potential risks. Key contributions include improved depth estimation through object size priors, multi-class tracking utilizing KCF and Brisk, and a novel recurrent neural network architecture for predicting bicycle movement. The system’s collision detection …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 1, Issue 2, 2023 · pp. 35–83 Read article
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Health Risk and Evaluation of Atmospheric Pollutants in Owerri Metropolis and Sub-Urban Areas of Imo State, Nigeria Using Chemometric Models
Abstract: Concern about health risk from atmospheric pollutants; Particulate Matter (PM10), Sulphur dioxide (SO2), Nitrogen dioxide (NO2) and Carbon Monoxide (CO) prompted atmospheric monitoring and inhalation health risk assessment for residents of Owerri Metropolis and its Sub-urban areas. Field measurements were carried out in 35 select locations within Imo State. Monitoring was carried out using Chemometric methods as Matrix Laboratory (MATLAB) and Artificial Neural Network (ANN). According to the experiment results, …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 1, 2024 · pp. 47–79 Read article
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Machine Learning Innovations for Effective Spam Comment Filtering in Social Networks
Abstract: The increasing prevalence of social media platforms has revolutionized communication, fostering unparalleled levels of connectivity and data exchange. However, the widespread increase in spam comments presents a serious threat to the integrity of online discussions, potentially undermining the quality of interactions. To confront this issue, our proposed model utilizes machine learning techniques to bolster spam comment detection across various social media platforms. This endeavor involves a thorough investigation encompassing data …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 19–24 Read article
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A Comprehensive Analysis of Machine Learning Models for Credit Card Fraud Detection
Abstract: This paper presents an indepth comparison of various machine learning models—Logistic Regression, Support Vector Classification (SVC), and Neural Networks (NN)—in the context of credit card fraud detection. The analysis spans multiple performance metrics, including accuracy, F1 score, precision, recall, and computational efficiency. Logistic Regression demonstrates competitive performance in terms of accuracy, but its poor precision renders it unsuitable for fraud detection tasks. Conversely, the Neural Network exhibits balanced precision and …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Internet of Things Connectivity Using Millimetre Wave: A Study
Abstract: Internet of Things(IoT) is undergoing rapid development, which is connecting millions of devices and causing sectors to undergo transformation. On the other hand, given this increase, the constraints of conventional wireless communication technologies are being stretched to their limits. Millimetre wave, often known as mmWave, is a high-frequency band that has the potential to revolutionise Internet of Things connectivity by providing much higher capacity levels and lower latency levels. Microwave …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 2, 2025 · pp. 18–30 Read article
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Visual Recognition with Convolutional Neural Networks for Object Detection
Abstract: Various research and development have taken place over the years on computer vision which is a branch of AI. AI disciplines like a vision system is applied in various fields like self-driving cars, face detection by social media apps and law enforcement software’s google lens and so on. The proposed system deals with design and implementation of an efficient way of training a GPU using python libraries to process and …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 2, 2024 · pp. 07–13 Read article
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RTL-to-GDSII Flow Optimization for Low-Power 32-bit RISC-V Processor
Abstract: This paper presents the implementation and optimization of a 32-bit RISC-V processor, transitioning from Register Transfer Level (RTL) design to final GDSII using Synopsys Fusion Compiler over 32nm technology node. The processor architecture is based on the RV32I base instruction set and incorporates a 5-stage pipeline to achieve a balanced trade-off between performance and design complexity. The design methodology involved RTL synthesis, gate-level netlist generation, and successive physical design stages …
Published in Journal of VLSI Design Tools and Technology · Vol. 15, Issue 3, 2025 · pp. 1–10 Read article
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Contextualising health-disaster risk reduction pillars for under-resourced rural secondary schools in Limpopo Province, South Africa
Abstract: School communities in under-resourced rural settings face a disproportionate burden of health-related disasters, including outbreaks, water and sanitation failures, food insecurity and compound events that disrupt learning and wellbeing. Yet school based disaster risk reduction (DRR) evidence in Southern Africa is uneven with limited empirically guidance tailored to the organisational and infrastructural realities of disadvantaged schools. Drawing on the Comprehensive School Safety Framework and the World Health Organization's Health Emergency …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 124–135 Read article
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A Study to Assess the Effectiveness of Self-Instructional Module Regarding Common Psychological Problems of Postpartum Psychosis and Its Management Among Trained Staff Nurses of Netaji Subhash Chandra Bose Medical College, Jabalpur
Abstract: A quasi-experimental study employing a one-group pretest and posttest design was undertaken to evaluate the impact of a self-instructional module on staff nurses’ knowledge of postpartum psychosis and its management. The research was conducted at Netaji Subhash Chandra Bose Medical College Hospital and included 60 trained staff nurses selected through a non-probability convenience sampling technique. Data were collected through a structured questionnaire designed to assess knowledge levels. The reliability of …
Published in International Journal of Women's Health Nursing And Practices · Vol. 4, Issue 1, 2026 · pp. 24–28 Read article
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The Role of Automation in Modernizing Irrigation Practices and Smart Farming: A Comprehensive Review
Abstract: The development of a "Smart Farming Using Automation" system that functions in three modes—timer, soil moisture, and PH sensor—for the farmer's advantage is the focus of this study. An automated drip irrigation system with a PLC is described in this paper. Using sensors for soil moisture and pH. The soil moisture sensor's function is to calculate how much water is required for irrigation. A PLC uses the solenoid valve to …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 1, Issue 1, 2023 · pp. 36–42 Read article
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Smart Waste Management System Using IoT and KNN
Abstract: Smart Cities are being developed with the goal of providing a comfortable living environment for humans. One of the services these cities will offer is eco-friendly waste collection and processing. This study proposes an Internet of Things (IoT)-based system architecture designed to enable dynamic waste collection and delivery to processing plants or designated waste disposal sites. Traditionally, waste collection was managed in a relatively static manner using conventional operations research …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 3, 2025 · pp. 42–46 Read article
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State of the Art: A Pandemic Big HealthCare Analytics Solution: Image Data Classification Using Quantum MAML
Abstract: The modern age is facing many pandemic healthcare problems, e.g., covid 19, infections, inflammations, and many more, leading to critical, deadly situations. Survival rate can be increased with proper diagnosis of such data. We have proposed one of the implementations based on a medical image dataset for classification using deep reinforcement learning (RL) with quantum computing. Deep RL is the combination of DL (deep learning), generative adversarial network (GAN), and …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 · pp. 1–9 Read article
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Effects of Cluster Computing on Big Data Analysis and Network Topology
Abstract: The rapid expansion of big data has posed substantial difficulties for conventional computing systems. As a result, cluster computing has grown to be a potent method for effective large data processing. Cluster computing involves multiple interconnected nodes functioning as a unified system, pooling together their processing, storage, and memory resources. These nodes are typically connected through high-speed networks such as ethernet or InfiniBand, facilitating efficient data sharing and communication among …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 1, 2023 · pp. 31–39 Read article
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Real-Time Object Detection and Tracking in Traffic Surveillance: Implementing Algorithms That Can Process Video Streams for Immediate Traffic Monitoring
Abstract: The rapid growth in urban development and traffic congestion calls for adopting high standards of traffic surveillance systems for monitoring. This paper reviews the current advancement and future trends of real-time object detection and tracking technology and its implications for traffic surveillance. Conventional approaches to traffic monitoring can provide more or less accurate data, but they are not easily scalable and cannot cope with rapidly changing conditions typical within urban …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 18–39 Read article
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Technical Advances in Drone Applications for Environmental Surveillance
Abstract: Unmanned Aerial Vehicles (UAVs), or drones, have rapidly evolved into essential tools for environmental monitoring and conservation due to their advanced sensor integration, real-time data acquisition, and autonomous operational capabilities. This review explores the multidisciplinary convergence of drone technologies with environmental science, emphasizing the technical and engineering aspects that drive these applications. The study outlines key UAV system components—including multispectral and hyperspectral imaging, LiDAR, thermal sensing, and real-time GPS-AI integration—highlighting …
Published in International Journal on Drones · Vol. 1, Issue 2, 2025 · pp. 14–20 Read article