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543 articles for “predictive algorithm”
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AI-Driven IoT Ecosystems for Real-Time Thermo-Chemical Decision Making
Abstract: The efficient management of thermo-chemical processes is a critical challenge in modern industry, where maintaining precision, operational safety, and energy efficiency directly influences productivity and sustainability. The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) has transformed conventional industrial systems into intelligent, data-driven environments capable of continuous monitoring, predictive analysis, and autonomous decision-making. By integrating interconnected sensors, edge computing, cloud platforms, and advanced machine learning algorithms, industries …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 4, Issue 2, 2026 · pp. 10–15 Read article
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Research on Adversarial Disturbance Based on Meteorological Time Series Data
Abstract: When the deep learning model is used to predict time series data, it is easy to be adversarially attacked. The time series data is sensitive to the abnormal disturbance and has strict requirements on the disturbance amount. To solve these problems, we propose to generate adversarial time series by adding disturbance terms to the original time series, and design an adversarial attack algorithm based on the importance measure (AAIM in …
Published in Journal of Industrial Safety Engineering · Vol. 9, Issue 3, 2022 · pp. 1–19 Read article
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Fractional Homotopy Analysis Transform Method for a Fin Having Temperature Dependent Internal Heat Generation
Abstract: Fractional Homotopy Analysis Transform Method (FHATM) is a new analytical tool for solving homogeneous and non-homogeneous fin equations. FHATM is a novel and innovative modification in Laplace Transform Algorithm (LTA) thus making analysis easier. Non-linear problems are solved by proposed technique without using Adomian and He’s polynomial which is a clear benefit of this new algorithm over decomposition and homotopy perturbation transform methods. This is an elementary technique which gives …
Published in Journal of Experimental & Applied Mechanics · Vol. 10, Issue 1, 2019 · pp. 23–34 Read article
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Modeling of Sintering Process for the Preparation of Magnetic Abrasives by RSM and ANN Models
Abstract: In this study, a modeling has been done for the prediction of the sintering process, as sintering process is one of the best processes to prepare magnetic abrasives. The sintering process is modelled by using RSM and ANN techniques. The ANN model has been developed using a multilayer feed-forward neural network and trained with the help of an error backpropagation learning algorithm based on the generalized delta rule. The indication …
Published in Journal of Experimental & Applied Mechanics · Vol. 10, Issue 3, 2019 · pp. 5–12 Read article
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Optimization of Automatic Energy Management System Using Renewable Resources
Abstract: With the increasing demand for energy efficiency and sustainability, smart home energy management systems (SHEMS) have emerged as promising solutions to optimize residential energy consumption. This paper presents a comprehensive review of SHEMS technologies, focusing on their design, implementation, and impact. SHEMS integrates advanced sensors, real-time data analytics, and intelligent algorithms to monitor, control, and optimize energy usage within the home environment. By leveraging machine learning and predictive modelling techniques, …
Published in Journal of Thermal Engineering and Applications · Vol. 11, Issue 2, 2024 · pp. 15–22 Read article
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Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
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Design of Beam using Artificial Neural Network based Approach
Abstract: Recent developments in artificial neural network (ANN) have opened up new possibilities in the field of structural engineering. This paper demonstrates the applicability of ANN for the design of beams subjected to moment and shear. An attempt has been made to capture the mapping between the design variables using ANN. There is no direct method for design of beams. A feed forward network and back propagation training algorithm has been …
Published in Recent Trends in Civil Engineering & Technology · Vol. 4, Issue 3, 2014 · pp. 1–6 Read article
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Calcareous Soil as a Promising Adsorbent to Remove Fluoride from Aqueous Solution: Equilibrium, Kinetic and Thermodynamic Study
Abstract: Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 In this work, the feasibility of employing calcareous soil to remove fluoride ions from its aqueous solutions was investigated under batch mode. The influence of solution pH, sorbent dose, initial fluoride concentration, contact time, stirring rate and temperature on the removal process were investigated. The equilibrium adsorption data were analyzed using Langmuir, Freundlich and Temkin isotherm models. The kinetics of fluoride …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 3, Issue 3, 2012 · pp. 1–21 Read article
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Improving Software Cost Estimation Process through Classifcation Data Mining Algorithms using WEKA Tool
Abstract: Today, the Software Cost Estimation (SCE) is one of the hot research areas among the researchers. Cost estimation is a method of the achievable cost of a product, thing, program, or an undertaking, enrolled in light of available information. It attempts to data predict good results to combined both of the field software engineering and data mining in this work. It generates the accurate cost of the projects with the …
Published in Journal of Communication Engineering & Systems · Vol. 8, Issue 2, 2018 · pp. 24–33 Read article
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QSAR Modeling Techniques A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
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The Future of Healthcare: Role of Artificial Intelligence in Revolutionizing Nursing
Abstract: Artificial intelligence (AI) is rapidly transforming the healthcare industry, and nursing professionals stand to benefit significantly from its integration. This article explores the role of AI in revolutionizing nursing, highlighting its applications, benefits, challenges, and ethical considerations. Drawing on examples from countries actively integrating AI into nursing services such as the United States, the United Kingdom, China, and Japan, the article discusses how AI supports nursing tasks, enhances patient care, …
Published in Journal of Nursing Science & Practice · Vol. 14, Issue 1, 2024 · pp. 1–5 Read article
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An Innovative Fuzzy-Enhanced Black Widow Spider Optimization for Energy-Efficient Cluster Communication Through the Selection of the Ideal Cluster Head
Abstract: This paper presents a novel approach to enhance energy efficiency in cluster-based wireless sensor networks (WSNs) by integrating fuzzy logic with the black widow spider optimization (BWSO) algorithm. The focus of the suggested fuzzy-enhanced BWSO approach is on cluster head selection, which is essential for lowering energy consumption and enhancing network performance. The program uses fuzzy logic to assess a number of factors, including network density, node proximity, and energy …
Published in International Journal of Wireless Security and Networks · Vol. 2, Issue 2, 2024 · pp. 28–34 Read article
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Exploring the Development of AI Models Using Open-Source Tools to Predict Patient Outcomes and Optimize Treatment Plans
Abstract: Integrating artificial intelligence (AI) into healthcare offers a transformative opportunity to enhance patient care and clinical decision-making. Through the use of predictive analytics, AI can significantly enhance the accuracy of outcome predictions and assist in developing personalized treatment plans that cater to each patient’s specific needs. This paper delves into the development of AI models using open-source tools, which are increasingly favored for their accessibility, collaborative nature, and capacity for …
Published in Journal of Open Source Developments · Vol. 11, Issue 3, 2024 · pp. 37–49 Read article
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Parameter Estimation of Software Reliability Growth Model
Abstract: Reliability normally covers each and every part of the device thinking about hardware, software program and processes but software program reliability paperwork an important factor as we are continually worried with efficient and quality software program. Software reliability is a crucial characteristic of software, nice for predicting the diploma of creditability of the precise software for the specified time frame and in particular surroundings. Software reliability models are categorized into …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 7, Issue 1, 2020 · pp. 15–22 Read article
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Antimicrobial Resistance and AI-Based Strategies for Rapid Pathogen Detection
Abstract: Antimicrobial resistance (AMR) has become a major global health threat, significantly reducing the effectiveness of antimicrobial therapies and increasing the burden of infectious diseases worldwide. The rapid emergence of multidrug-resistant pathogens has created an urgent need for faster, more accurate, and scalable diagnostic approaches to support timely treatment and effective infection control. Artificial intelligence (AI) has emerged as a promising technology capable of transforming pathogen detection and AMR surveillance through …
Published in Recent Trends in Infectious Diseases · Vol. 3, Issue 2, 2026 · pp. 26–36 Read article
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A Study of Parameter Estimation Techniques for Reliability of Software System
Abstract: Abstract—Reliability normally covers every and every a part of the device thinking about hardware, software program and processes but software program reliability paperwork an important factor as we're continually worried with efficient and quality software program. Software reliability is a crucial characteristic of software nice for predicting the diploma of creditability of the precise software for the specified time frame and in a particular surroundings. Software reliability models are categorized …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 6, Issue 1, 2019 · pp. 1–6 Read article
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Enhancing Road Safety with the Latest Breakthrough: Real-time Vehicle Classification, Counting, and Speed Estimation Using YOLOv8n and Deep SORT Algorithm
Abstract: The real-time vehicle classification, counting, and speed estimation system based on YOLOv8n is an important tool for monitoring traffic flow on highways. However, because they are distinct objects from their surroundings, it is still difficult to detect them, which has an impact on how accurate vehicle counts are. To tackle this concern, this paper suggests the implementation of a vision-centric system for real-time vehicle monitoring and identification. The approach involves …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 1, 2024 · pp. 10–18 Read article
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AI-Powered Defense: Advancing Cybersecurity Through Artificial Intelligence Innovations
Abstract: In the current landscape of escalating cyber threats and increasingly sophisticated attack vectors, integrating artificial intelligence (AI) within cybersecurity strategies has become a critical step forward. This study explores the role of AI in strengthening cybersecurity by utilizing its strengths in data analysis, pattern recognition, and predictive modeling. Through AI, organizations can greatly enhance their ability to detect and respond to threats. Machine learning algorithms enable ongoing adaptation to new …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 35–41 Read article
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AI-Powered Solutions for Sustainable Waste Management in Construction Projects
Abstract: The construction industry is a significant contributor to global waste, posing challenges to sustainability and environmental health. This research explores AI-powered solutions for sustainable waste management in construction projects, focusing on optimizing waste reduction, recycling, and resource efficiency. By integrating machine learning algorithms and IoT-enabled sensors, real-time monitoring of waste generation and segregation can be achieved. Predictive analytics and AI-driven decision-making tools are employed to enhance material reuse and minimize …
Published in Recent Trends in Civil Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 1–5 Read article
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 · pp. 10–22 Read article