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960 articles for “Machine Learning”
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Unveiling The Seamless Integration: The impact of IOT Technologies on everyday life and organizational Dynamics
Abstract: Internet of Things (IoT) is about connecting devices worldwide to simplify life and save energy. This study talks about these issues and looks ahead to what is next for IoT. Even though IoT mixes the digital and physical worlds, there is still a lot to learn. This study discusses the problems and suggests solution for future research. Currently, IoT is changing everything from homes to industries. This study explains IoT …
Published in Journal Of Network security · Vol. 13, Issue 1, 2025 · pp. 1–9 Read article
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Advanced AI based Energy Monitoring and Demand Prediction with Theft Detection
Abstract: This paper presents a study on an AI-based energy management system, which is designed for real-time monitoring of energy consumption for theft detection and energy demand prediction. Our energy management system has voltage and current sensors for energy consumption measurement and provides real- time data on voltage (V), current (mA), and energy units. We have implemented Machine Learning algorithm SVM to improve the process of theft detection by identifying anomalies …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 Read article
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Multimodal Disease Detection Using Deep Learning
Abstract: Artificial Intelligence (AI) is playing an increasingly pivotal role in modern healthcare, particularly in improving the speed and accuracy of disease detection. With the evolution of Machine Learning (ML), Deep Learning (DL), and high-performance computing, AI-based solutions are now capable of processing extensive medical datasets, ranging from patient records to diagnostic images, with remarkable efficiency. These systems offer immense potential for early intervention, improved clinical decision-making, and alleviating pressure on …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 129–139 Read article
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Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics
Abstract: Bone fracture detection is a critical aspect of medical diagnostics, traditionally relying on manual interpretation of radiographic images by experienced radiologists. This discipline has undergone a revolution with the introduction of machine learning (ML), which can improve accuracy, shorten diagnosis times, and lessen human error. This study investigates the use of different machine learning methods to enhance and automate the identification of bone fractures in radiography pictures. We utilized a …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 2, 2024 · pp. 17–22 Read article
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Detecting Phishing Websites Using Hybrid Methodologies
Abstract: In the digital era, personal information theft has become a widespread and increasingly severe crime. Cybercriminals, often known as hackers, use deceptive strategies, with phishing websites being a major method for stealing confidential data. These fake websites imitate legitimate ones, tricking users into revealing sensitive personal and financial information, which has led to a rise in fraud cases. To address this escalating threat, a comprehensive research paper is proposed. This …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 59–65 Read article
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Innovative Approaches to Fake Product Detection: Blockchain and QR Code Synergy
Abstract: For consumers, corporations, and regulatory agencies, the worldwide market's rise of counterfeit goods presents serious issues. Maintaining consumer confidence, guaranteeing product safety, and preserving brand reputation all depend on the ability to identify counterfeit goods. An overview of the different approaches and difficulties associated with spotting counterfeit goods is given in this abstract. First off, the ability to identify sophisticated counterfeit goods is limited by the use of conventional techniques …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 1, 2024 · pp. 21–27 Read article
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TensorFlow: Architecture, Applications, and Future Challenges
Abstract: TensorFlow, an open-source machine learning platform created by Google, has revolutionized how artificial intelligence (AI) systems are built and implemented. Designed to support scalable and flexible model training across CPUs, GPUs, and TPUs, TensorFlow enables researchers and developers to construct advanced deep learning models with efficiency and precision. This study provides an in-depth examination of TensorFlow's architecture, including its use of dataflow graphs and tensor-based computation. We explore its adaptability …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 41–50 Read article
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Next-Generation Semiconductors: Making AI, 5G, and More Possible
Abstract: The current digital environment is being reshaped by next-generation semiconductors, which are powering revolutionary technologies including enhanced computing systems, 5G connectivity, and artificial intelligence. Next-generation semiconductors are at the heart of new technologies that are changing the world we live in today, such as artificial intelligence (AI), 5G communication, and advanced computer systems. As traditional silicon-based designs reach their limits in terms of size and performance, new materials, architectures, and …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 Read article
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Card Fraud Detection Using Artificial Neural Network and Multilayer Perception Algorithm
Abstract: Fraud has posed a significant challenge for merchants, especially in the online business sector, over the course of many years. This is primarily due to the advancements in technology that have made credit card transactions a common method of payment. Credit card fraud refers to the unauthorized use of a credit card by an individual for personal purposes, without the owner's consent and with no intention of paying for the …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 1, 2023 · pp. 21–30 Read article
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Investigative Study of Adaptive Fault Tolerance in Optical Networks
Abstract: Optical networks have become the backbone of modern telecommunications infrastructure, enabling high-speed data transmission across global networks. However, these networks face significant reliability challenges due to component failures, signal degradation, and environmental factors. This investigative study examines adaptive fault tolerance mechanisms in optical networks, focusing on emerging technologies and methodologies that enhance network resilience. The research analyzes various fault detection techniques, including machine learning-based approaches, self-healing protocols, and dynamic reconfiguration …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 2, 2025 · pp. 24–30 Read article
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Next-Gen Techniques for Bottleneck Detection in High-Performance Computing
Abstract: Modern computing systems face new challenges in bottleneck detection and mitigation due to their increasing complexity which stems from multi-core architectures alongside distributed platforms and real-time processing needs. Traditional methods like hardware profiling and static analysis which used to work well now struggle to keep up with the changing conditions of dynamic system behaviors and diverse computing environments along with variable workload patterns. The current limitations restrict their capability to …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 09–14 Read article
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Assessing the Performance of DL Methods in Handwritten Digit Recognition
Abstract: Handwritten digit recognition is a computer vision task that involves the automatic identification and classification of hand-written digits. The objective is to develop models capable of accurately recognizing and distinguishing digits handwritten by humans. With the development of machine learning and deep learning techniques, this field has advanced remarkably. The convolutional neural network (CNN) is the most often used technique for this purpose. By utilizing CNN, the model can learn …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 1, 2023 · pp. 25–32 Read article
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Supply Chain Optimization Using Big Data
Abstract: In the references to add productivity and short the appendences, and increase customer value, supply chain optimization is a very crucial part of contemporary business operations. Supply chain management has been transformed by the emergence of big data, which can provide analysis and insight from many different sources of information. This content explores the importance of using big data analytics to improve the delivery process. Thanks to the evolution of …
Published in Journal of Production Research & Management · Vol. 14, Issue 2, 2024 · pp. 19–25 Read article
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ConnectED-AI: Connecting Education with Artificial Intelligence
Abstract: In a rapidly evolving educational landscape, the role of technology and artificial intelligence (AI) in enhancing learning experiences has gained immense importance. The “ConnectED-AI” project represents a pioneering effort to leverage AI to empower students with personalized and effective educational resources. This report provides an overview of the “ConnectED-AI” project, which is designed to address the diverse and dynamic learning needs of students. Our project aims to connect students with …
Published in Journal of Open Source Developments · Vol. 11, Issue 1, 2024 · pp. 36–50 Read article
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Time Series Methods in Meteorology: A Review of Predictive Models and Applications
Abstract: The accurate prediction of time series data holds substantial significance in various fields, enabling informed decision-making and resource optimization. In this study, temperature variations over time are predicted using the Autoregressive Integrated Moving Average (ARIMA) model. Reliable temperature projections are more important now than ever because of climate change and its effects. For time series prediction problems, the ARIMA model—which is well-known for its ability to capture temporal dependencies in …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 2, 2024 · pp. 35–46 Read article
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IoT Based Electricity Theft Detection System
Abstract: The proliferation of smart grids and advanced metering infrastructure has paved the way for innovative solutions to tackle the longstanding issue of electricity theft. This study presents an IoT-based electricity theft detection system that leverages real-time data analytics and machine learning algorithms to identify potential theft cases. The proposed system utilizes smart meters to collect electricity consumption data, which is then transmitted to a central server for analysis. The system …
Published in Journal of Semiconductor Devices and Circuits · Vol. 12, Issue 2, 2025 · pp. 18–22 Read article
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Centre Stage of Wireless Sensors in Healthcare: Application and Remote Patient Management
Abstract: The efficiency of wireless sensors in healthcare is greatly influenced by technological progress. The capabilities of these devices have been improved by advancements in wireless communication, data analytics, and sensor technologies. Patients may wear more comfortable, smaller, and more precise modern sensors. They have the ability to track many different physiological characteristics, ranging from simple vital signs to more intricate biomarkers. Understanding the massive volumes of data produced by wireless …
Published in Journal of Microcontroller Engineering and Applications · Vol. 11, Issue 2, 2024 · pp. 10–16 Read article
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Python's Applications in the Profession of Data Science
Abstract: Because of its ease of use, adaptability, and huge ecosystem of libraries, Python has become one of the most influential programming languages in the field of data science. Python is highly valued for its straightforward and versatile nature. This study delves into its various uses in data science, including tasks like data preprocessing, exploratory data analysis (EDA), statistical modeling, machine learning, and creating visualizations. Libraries like Pandas and NumPy make …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 23–30 Read article
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A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction
Abstract: Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Enhancing Power Conversion Efficiency in Tandem Solar Cells with Temporal Dynamic Graph Neural Network
Abstract: In modern homes, people want good comfort and also less electricity bill, so managing heating load and cooling load become very important. Heating Load (HL) and Cooling Load (CL) depend on many things like wall material, window size, sunlight, ventilation, and weather. Because of this many factors, calculation and optimization of HL and CL is little difficult and many time normal formulas give wrong or not perfect results. So in …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 2, 2026 Read article