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193 articles for “forecasting model”
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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Internet of Things (IoT) towards Green Revolution by monitoring and controlling the microclimate conditions
Abstract: Agriculture is the back bone of many developing as well as developed country. Back in history the agriculture was the key factor in the development of human society but today in this modern world where population is the major issue across the world especially for countries like (India, China) to feed this population, for that they need more food, more food means more production but with limited area of farming …
Published in Journal of Computer Technology & Applications · Vol. 9, Issue 1, 2018 · pp. 24–29 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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A review of the intelligent techniques for load forecasting of UHBVNL
Abstract: The primary aim of load forecasting is to know the change in power demand with the variable factors on a short-term, medium-term, and long-term basis and to evolve our power system network according to the changing variables. It ensures correct values to the operations, stability, demand management, scheduling generating capacity, efficiency, reliability, accuracy, economy, controlling, scheduling, security analysis, environmental sustainability, etc. Various forecasting techniques are there which are making it …
Published in Journal of Power Electronics and Power Systems · Vol. 13, Issue 3, 2023 · pp. 30–38 Read article
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A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket
Abstract: This study explores the application of machine learning techniques to forecasting outcomes in limited overs cricket matches, with a particular focus on One Day Internationals (ODIs). The research investigates how classification algorithms can be effectively utilized to analyze both contextual and dynamic factors that influence match results, including venue details, toss decisions, team strength, and historical performance records. By employing a structured methodology encompassing feature selection, data preprocessing, model training, …
Published in Recent Trends in Sports · Vol. 2, Issue 2, 2025 · pp. 09–19 Read article
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Modeling the Socioeconomic Factors Impact on Household Trip Production for Baghdad
Abstract: In Baghdad, the number of vehicles has increased more than two folds after 2003. The land use in many sectors has changed and expansions of residential zone are noticeable at other sectors while the restrictions on car ownership and commerce are nil. The restrictions on using many streets due to security issues cause a huge traffic jam among the whole network. It was felt that revision to the transportation policies …
Published in Trends in Transport Engineering and Applications · Vol. 2, Issue 3, 2015 · pp. 61–73 Read article
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Study of Social Trends Prediction Using AI
Abstract: AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlations in a variety of available datasets, from social media to economic indicators to public records, and fundamentally changes decision-making based on …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 19–29 Read article
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A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article
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Improving the Forecasting Accuracy of a Project Status Using the Inputs of Earned Value Management – A Review Article
Abstract: The earned value method (EVM) is recognized as a viable method for evaluating and forecasting project cost performance. Its application to schedule performance forecasting has been limited due to poor accuracy. The index based EVM forecasting methods are deterministic approaches having large forecasting errors in the initial stages of a project. Hence this paper gives a brief overview of the advanced accurate approaches like Kalman filter, growth model and linear …
Published in Journal of Construction Engineering, Technology & Management · Vol. 5, Issue 3, 2015 · pp. 13–17 Read article
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A Review on Loan Approval Prediction Based on Machine Learning Techniques
Abstract: The banking industry has also benefited greatly from technological advancements. An increasing number of individuals are submitting loan applications on a daily basis. When deciding which loan applicants to approve, the bank must take certain rules into account. The bank needs to choose the best one for approval based on certain characteristics. The process of carefully verifying every person and recommending them for loan approval is laborious and fraught with …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 1–11 Read article
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Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction
Abstract: Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 32–38 Read article
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Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 55–66 Read article
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Green Q-Commerce: Balancing Speed and Sustainability in Hyper-Fast Delivery Systems
Abstract: The rapid expansion of quick commerce (q-commerce) has transformed consumer expectations with ultra-fast deliveries, yet its environmental impact—marked by carbon-intensive logistics, excessive packaging waste, and energy-heavy operations—poses significant sustainability challenges. This review examines how the q-commerce sector can reconcile speed with ecological responsibility through innovative solutions, including electrified last-mile delivery (e-bikes, electric vehicles, drones), circular packaging models (reusable containers, biodegradable materials), and artificial intelligence (AI)-driven logistics optimization (route efficiency, demand …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 2, 2025 · pp. 22–27 Read article
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Role of Satellite Data Assimilation on ERA-Interim and ERA5 Wave Parameter Ratios – A Case Study based on Year-long In-Situ Observations in the Bay of Bengal
Abstract: The rapid decline in the energy resources forced mankind to tap other forms of natural energy resources in the light of exponential increase in the demand due to over-population. The energy from ocean waves is one of the cleanest sources of energy available perennially that changes seasonally and is site-specific. To assess the wave power potential at any site, knowledge of the wave parameters such as wave height, period and …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 1, 2025 · pp. 1–20 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Customer Churn Prediction Using ML Algorithms
Abstract: Comprehending customer churn is essential for businesses aiming to enhance and sustain customer relationships. This study introduces a machine learning approach aimed at forecasting customer churn by leveraging demographic and behavioral data. Our research involved developing predictive models using support vector machines (SVM), random forests, and decision trees, evaluating their efficacy using real-world data from the telecom industry. Our findings underscore that random forests consistently outperform SVM and decision trees …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 70–75 Read article
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The Strength of Dry, Fresh and Decomposed Raffia Palm Trunk in the Bioremediation of Oil-based Drill Cutting
Abstract: In this research, the impact of dry drill cuttings and compost tea on the environment's ability to degrade oil-based drill cutting contamination is investigated. The experiment was conducted in the research center located at the workshop of Agricultural and Environmental Engineering Department, Rivers State University, Port Harcourt. Oil-based drill cutting samples were placed in bulk in eleven reactors (T1, T2, T3-T11) with four replications. The original drill cuttings' physiochemical characteristics …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 15, Issue 3, 2024 · pp. 26–33 Read article
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Understanding Land Use-Transport Integration: A Literature Review on Theories and Practices in Urban-Transport Planning
Abstract: For many years, it has been understood and can be generalized from various researches and quantitative modelling that there is a causal relationship between land-use typology and travel demand/behaviour as the former influences the later in urban areas. The idea of land-use transport integration models evolved due to the need felt by the urban planners and managers for quantitatively forecasting future pattern of city and regional development as well as …
Published in Trends in Transport Engineering and Applications · Vol. 3, Issue 3, 2016 · pp. 54–67 Read article
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Integrating Sensor Technologies and Machine Learning for Detection and Mitigation of Structural Deformity and Slope Failure in Opencast Mines
Abstract: With furtherance in the mining industry, accidents due to slope failure are frequent in mining sites. Slope instability, a complex process, seriously threatens the miner’s life and properties. The damage inflicted by slope failures in the recent past has pulled the attention of authorities toward implementing disaster risk reduction measures. This research aims to develop an innovative approach that combines sensor technologies and machine learning techniques to detect and mitigate …
Published in Journal of Communication Engineering & Systems · Vol. 13, Issue 3, 2023 · pp. 38–45 Read article
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Computer Data Processing and Empirical Economics
Abstract: This is an article on empirical economics and computer data processing that discusses how computational technology have changed empirical research methods. It analyses how computer systems have changed data management, statistical modelling, and econometric analysis, focussing on large-scale data processing, algorithmic efficiency, and repeatability. Forecasting, policy simulation, and panel data analysis are key uses. The paper also addresses data quality, computational, and ethical issues in economic data use. The paper …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 09–20 Read article