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117 articles for “forecasting algorithms”
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Smart HVAC Systems: The Role of IoT and AI in Energy Optimization
Abstract: Heating, ventilation, and air conditioning (HVAC) systems are crucial for maintaining interior comfort and air quality in all types of buildings. However, they also rank among the highest energy users in the built environment. Traditional HVAC systems are evolving into intelligent, networked systems through the integration of Internet of Things (IoT) and artificial intelligence (AI) technologies, as energy efficiency and sustainability become increasingly important across the globe. This review article …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 12, Issue 2, 2025 · pp. 38–43 Read article
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Land Use Land Cover Change Detection and Forecasting for Tiruchirappalli City Using Remote Sensing and GIS
Abstract: Normal 0 false false false EN-IN X-NONE X-NONE Land use Land cover (LU/LC) change has taken place in Tiruchirappalli city in Tamil Nadu, India over the past two decades due to induced industrialization and urbanization. In this paper, LU/LC change dynamics were studied by the combined use of remote sensing, geographical information system (GIS) and stochastic modelling technologies. The different land use categories and their spatial and temporal variability in …
Published in Journal of Remote Sensing & GIS · Vol. 3, Issue 1, 2012 · pp. 21–32 Read article
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Application of WSN to Study the Effect of Climate Change on Human Health
Abstract: Wireless Sensor Network (WSN) technology is becoming increasingly popular, particularly as applied to a variety of monitoring and tracking applications. Climate change impacts both the environment and human activity, and imposes a range of new challenges for research, technology development, and knowledge and information exchange. WSN enables monitoring and management of a large set of environmental data including climatic, atmospheric, plant and soil parameters that influence cropland growing environments. Real-time …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 0, Issue 0, 2012 · pp. 91–96 Read article
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Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration
Abstract: Demand forecasting has under gone major changes because to the incorporation of automated analytics into supply chain management (SCM), which has improved company productivity, accuracy, and responsiveness. Central to this transformation is the application of machine learning (ML), which enables the analysis of large and complex datasets to identify patterns, detect trends, and generate precise forecasts. Conventional methods for predicting frequently rely on linear models and historical sales data, which …
Published in International Journal of Industrial and Product Design Engineering · Vol. 3, Issue 2, 2025 · pp. 8–17 Read article
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Artificial Intelligence and Machine Learning Approaches for Corrosion Prediction and Management of Steel Reinforcement in Concrete: A Systematic Review
Abstract: Load bearing concrete structures need steel reinforcement bar (rebar), which are prone to attack by the corrosive environment inside the concrete due to constant ingress of moisture, pollutant gases and anions (mainly Cl−, SO42−). In recent models for the potential life span, the causes of failure of concrete structures have been established principally due to the chloride (Cl−) ion, because of the ease in transportation of Cl− in concrete and …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning
Abstract: It is challenging to estimate the likelihood of complex chronic disease while treating conditions like heart failure. The application of machine learning, an area of artificial intelligence, in cardiovascular care is growing quickly. In essence, it defines how computers classify and understand data, or choose a task with or without human intervention. The theoretical underpinnings of machine learning are models that accept input data (such as images or text) and …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 1, 2023 · pp. 15–20 Read article
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Smart Education through Machine Learning: A Review of Trends, Benefits, and Risks
Abstract: Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern. The article emphasises …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 24–28 Read article
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Crime Prediction and Criminal Identification System Using Machine Learning
Abstract: Advanced machine learning and data analytics-driven crime prediction and criminal identification systems have become game-changing instruments for contemporary law enforcement. Utilizing past crime statistics, surveillance footage, and additional resources, these systems forecast criminal activity, manage resources efficiently, and improve investigation capacities. With an emphasis on their importance in enhancing public safety and lowering crime rates, this paper presents an overview of criminal identification and prediction systems. Examining the technologies and …
Published in International Journal of Electronics Automation · Vol. 2, Issue 1, 2024 · pp. 28–34 Read article
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Artificial Intelligence in Healthcare for Implants and Tissue Regeneration: Advances, Challenges, and Future Directions
Abstract: Artificial intelligence (AI) has been a revolutionary influence in contemporary healthcare, especially in implant design, biomaterials research, and tissue regeneration. In regenerative medicine, AI facilitates predictive modeling, optimization, and decision-making via the analysis of intricate biological, material, and clinical information. This study analyzes current research on AI applications in implant technologies and tissue regeneration, specifically addressing scaffold engineering, biomaterial characterisation, stem cell and gene treatments, smart biomaterials, and implant planning. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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BATTERY MANAGEMENT SYSTEM (BMS)
Abstract: This review of the literature delves into the changes, structure, and the latest BMS (Battery Management Systems) technologies implemented in electric vehicles, smart grids, and energy storage, respectively. The studies emphasize the need for accurate battery modeling, advanced estimation algorithms, robust hardware design, and safety regulations. The research describes how contemporary BMSs perform integration of modeling, monitoring, control, cell balancing, and diagnostics in order to facilitate safe and energy- efficient …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 · pp. 32–43 Read article
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Modelling of Flood in Lagos using Artificial Neural Network
Abstract: Flooding events have caused havoc to live and properties in recent times. Flooding could be caused by several factors some of which are rainfall, temperature, and relative humidity, which lead to rise in water level. The method of artificial neural networks (ANN) was used in this work to model flood occurrence in Lagos, Nigeria. Meteorological data were collected from the Nigeria Meteorological Agency (NIMET) and Nigeria Hydrological survey Agency (NHSA) …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 7, Issue 2, 2018 · pp. 50–57 Read article
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Artificial Intelligence in Early Diagnosis and Personalized Treatment of Alzheimer’s Disease
Abstract: Artificial intelligence (AI) has become a disruptive technology in the medical care industry, with potential solutions to early diagnosis and customized treatment of Alzheimer’s disease (AD), a progressive neurodegenerative disease and the most prevalent cause of dementia globally. Conventional diagnostic techniques, such as cognitive, neuroimaging and biomarker techniques, are usually limited in the ability to detect disease at its most susceptible stage when treatment interventions are most effective. The recent …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 · pp. 15–27 Read article
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A Comparative Study on Various Machine Learning Techniques for the Prediction of Cardiac Ailment
Abstract: Over the last couple of decades, cardiovascular complexities have become the leading source of death in impoverished regions. With heart attack rates on the acceleration at a youthful age, it is necessary to put in place a process to recognize the symptoms of a heart attack early and thus limit it. It is impossible for a common man to often undergo expensive tests such as an ECG and thus there …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 25–30 Read article
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Design and Development of Agrobot Using Feed Forward Network for Smart Farming Agriculture
Abstract: The Agrobot framework is a form of natural language processing that requires training to understand human language and meet user needs accordingly. The development of a chatbot tailored for the agricultural industry represents a significant advancement in agricultural technology. This chatbot serves as an interactive tool to assist farmers in various aspects of agricultural practices, including crop management, pest control, weather forecasting, and market information. The main objective of this …
Published in Journal of Advancements in Robotics · Vol. 11, Issue 1, 2024 · pp. 6–14 Read article
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Enhancing Production Line Efficiency: Simulating and Optimizing Single and Parallel Line Processes
Abstract: During a time of fast-paced industrial growth, increasing production line effectiveness is a core issue for manufacturers looking to maximize output, reduce waste, and stay competitive. This study explores the use of simulation-based optimization methods to enhance single and parallel production line designs. Stepping beyond traditional trial-and-error methods, the research utilizes Siemens Tecnomatix Plant Simulation to simulate actual manufacturing scenarios, considering intricacies like buffer capacities, machine sequencing, and event-driven scheduling. …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 22–32 Read article
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Forecasting of Crushing Strength of Sustainable Concrete by Employing Deep and Random Forest Machine Learning
Abstract: Sustainable concrete is one of the milestone of the concrete industry. This concrete fulfills the requirements of concrete manufacturing industry such as strengthen, Durability, environment friendly and many of other. With this properties of concrete, sustainable concrete is an ideal substitute for ordinary concrete in the concrete industry. In the 21th century Machine learning is a tool which is use to employ the characteristics of sustainable concrete by using deep …
Published in Journal of Polymer & Composites · Vol. 12, Issue 7, 2024 · pp. 41–46 Read article
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Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 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 · pp. 1–8 Read article
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Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges
Abstract: The demand for agricultural is increasing day by day as the population of the world is increasing. So, it becomes necessary for us to increase the production of agricultural products. Traditional ways of agriculture cannot meet such requirements. Nowadays, machine learning based technologies are being used to develop models for agriculture. Machine learning-based applications are very fast and produce high-quality results. It includes recurrent neural networks (RNN), convolution neural networks …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 88–96 Read article
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Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article