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297 articles for “Forecast”
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Analysis of Existing Scenario of Sewerage Management in Gurugram and Forecasting its Future Demand
Abstract: AbstractWater, food and shelter are the basic needs of human beings but now become inaccessible resources. Due to the increasing demand for these resources, it cannot be met successfully by the conventional sources of energy. Indiscriminate rises in population imply industrialization, migration and the need for energy which leads to the environment degraded. In India, there are 113 river and vast alluvial basins to hold the groundwater. But due to …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 9, Issue 2, 2019 · pp. 32–47 Read article
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Applicability of SWMM Model for Urban Flood Forecasting: A Review
Abstract: Urbanization, the major goal for the emerging economies, with which come the major concerns, flood conditions being one of them. The huge losses of infrastructure and lives have alarmed us with the need to tackle this situation in an effective and sustainable manner. Flood forecasting and its management is the only sail through the narrow gap between the disasters and prosperity. Precipitation is the major component of the hydrologic cycle …
Published in Journal of Water Resource Engineering and Management · Vol. 4, Issue 2, 2017 · pp. 1–4 Read article
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Stock Market Forecasting Using Artificial Neural Networks (ANNs): A Review
Abstract: AbstractThis paper reviews all recent work done for stock market prediction using machine learning and artificial intelligence (AI). Artificial neural networks (ANNs), a field of artificial intelligence (AI), is relatively latest, dynamic and promising technique in stock market forecasting, an area that has been of much research. From this literature review, it is concluded that ANNs is very valuable for predicting world stock markets.Keywords: artificial neural network (ANNs), stock market, …
Published in Journal of Computer Technology & Applications · Vol. 4, Issue 2, 2013 · pp. 18–29 Read article
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Artificial Neural Network Model for Stock Market Forecasting
Abstract: AbstractIn recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks or stock markets. Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. The aim of this paper is to predict stock market using artificial neural networks (ANNs). The authors used feed forward neural network trained by back-propagation algorithm to make …
Published in Journal of Computer Technology & Applications · Vol. 5, Issue 1, 2014 · pp. 7–12 Read article
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Modern Technology Demonstrates the Understanding of Forecasting in the Age of A rtificial I ntelligence and Big Data, Minds Versus Machines
Abstract: AbstractArtificial Intelligence (AI) is a machine learning process and it is the easiest and fastest way to implement business plan. Now a day, automation brings the most value for applying to narrow, repetitive and quick business decisions. It has made thousands of times a day, saving labor hours, replacing the more boring aspects of knowledge work. That’s why AI has proved successful at automating repetitive finance tasks, machine processes such …
Published in Research and Reviews : Journal of Computational Biology · Vol. 9, Issue 1, 2020 · pp. 4–9 Read article
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Student Academic Achievement Forecast Based on Emotional Intelligence, Personality, Demographic Characteristics and Attitude Towards Education and Future Career
Abstract: This study was aimed at predicting students' academic achievement based on emotional intelligence of personality traits, attitudes to education and future career. The present study was a correlational-analytical study. The statistical population of Zanjan University students in the academic year of 1395-1395 was the sample of 489 people selected by cluster random sampling method. Academic Resilience Scale (ARI) and Schotte Emotional Intelligence Questionnaire and Researcher Attitude Questionnaire were used to …
Published in International Journal of Education Sciences · Vol. 1, Issue 2, 2024 · pp. 25–36 Read article
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Development of a Machine Learning and Artificial Intelligence Based Model Aimed at Forecasting the Prognostic Impact of C-Reactive Protein in Myocarditis
Abstract: The specific role of inflammation markers in myocarditis remains uncertain. We investigated the diagnostic and prognostic significance of C-reactive protein (CRP) levels at the initial diagnosis among myocarditis patients. Our retrospective study enrolled patients clinically suspected (CS) or biopsy-proven (BP) with myocarditis, with available CRP data at diagnosis. We collected patient information, including clinical, laboratory, and imaging findings at diagnosis and follow-up visits. We utilized machine learning methods, specifically random …
Published in Research and Reviews: A Journal of Health Professions · Vol. 14, Issue 2, 2024 · pp. 12–24 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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ANN Approach to Forecasting the Strength of Nano Silica Incorporated Geopolymer Composite
Abstract: Coal and steel industry by-products, such as fly ash (FA) and blast furnace slag (GGBS), have gained significant attention as precursors for geopolymer concrete (GPC) due to their high aluminosilicate content, offering a sustainable alternative to conventional cement. Nano silica (NS), recognized for its exceptional pozzolanic activity and ability to refine microstructure, has shown potential to enhance the mechanical and durability properties of GPC. This study investigates the influence of …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 267–278 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
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Forecasting Climate-Driven Healthcare Demand in Agricultural Regions: A Multi-Modal AI Approach
Abstract: The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 28–38 Read article
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A Hybrid Mathematical Model for Epidemic Outbreak Forecasting Using Machine Learning and Cloud Computing
Abstract: The increasing frequency of infectious disease outbreaks has emphasized the necessity for intelligent epidemic surveillance systems capable of predicting disease spread at an early stage. Conventional outbreak detection approaches rely heavily on delayed statistical reporting and manual monitoring techniques, resulting in reduced responsiveness during critical periods. This paper presents a mathematical predictive framework for epidemic outbreak detection using machine learning and cloud computing technologies. The proposed framework integrates the Susceptible–Infected–Recovered …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 01–06 Read article
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Forecasting of Factors Affecting Thermiston Work Productivity Estimation by Using Artificial Neural Network
Abstract: The research aims to find factors affecting of Thermiston work productivity and the derivation of an equation to predict the rates of Thermiston work productivity by using artificial neural network technology and compared with traditional methods. The Artificial Neural Network with multilayer by back-propagation error technique for modeling the productivity estimation is used, it is founded that the ANN are able to manage to, can predict the productivity for Thermiston …
Published in Journal of Construction Engineering, Technology & Management · Vol. 7, Issue 1, 2017 · pp. 10–21 Read article
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To Study About Forecasts of Low-Cost Housing in India
Abstract: The paper presents chip away requiring little to no effort and economical elective structure materials having preferences on regions, for example, India where cement or steel lodging is expensive. The undertaking tends to the difficulties and pigeonholes of utilizing these materials as a basic segment for minimal effort lodging and their equivalent volume for adjustment to the expansive range of elements—physical, biological, social, monetary and specialized—through various foodstuffs created which …
Published in Journal of Industrial Safety Engineering · Vol. 6, Issue 2, 2019 · pp. 14–20 Read article
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PV-Syst based Performance Forecasting of Grid Connected Solar PV System in Indian Scenario
Abstract: The presentation and economy of a solar photovoltaic framework relies upon area and geographic parameters. Anticipating energy productivity is significant for effective arranging and assessment rates. The proposed examination investigates the exhibition assessment of three interconnected geologically associated photovoltaic solar frameworks. In Jaipur, Kolkata and Chennai have been trying a 1 MW solar PV framework for a year. Recreations were performed utilizing PV Syst (a product device created by the …
Published in Trends in Electrical Engineering · Vol. 10, Issue 2, 2020 · pp. 33–39 Read article
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Report on Traffic Flow Forecasting, as One of the important component of the intelligent transportation system.
Abstract: The interest in smart transportation vehicles stems from the integration of new information systems on the problems caused by traffic accidents and the simulation of realtime real and communication. Traffic is increasing worldwide due to driving, urbanization, population growth and rapid population change. Congestion reduces the efficiency of transportation and increases travel time, air pollution and fuel consumption. Now the development of the road has caused new disasters, caused more …
Published in Trends in Machine design · Vol. 10, Issue 1, 2023 · pp. 25–31 Read article
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Election Forecasting with Social Media Data
Abstract: AbstractThe proliferation of social media has provided people with the platform where they can voice their opinions, which in turn has increased the amount of data available to discover user orientation and thereby come up with smarter plans or decisions. One such application is in the field of politics where the sentiments from data are analyzed to understand the opinion of general public that can be used for election prediction. …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 8, Issue 3, 2020 · pp. 26–32 Read article
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Reducing the Bullwhip Effect in a Supply Chain using Artificial Intelligence Technique
Abstract: The bullwhip effect in a supply chain leads to various inefficiencies like excessive inventory, quality problems, higher raw material costs, and poor customer service, which can be reduced by coordination and collaboration among partners of a supply chain with time bound information sharing; it requires full integration. However, full integration of the organizations of a supply chain is not possible in real case scenario due to existing differences of functions, …
Published in Journal of Production Research & Management · Vol. 4, Issue 2, 2014 · pp. 31–42 Read article
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Renewable Solar Energy Prediction in India : 2025-2030
Abstract: As India endeavors to realize its objective of achieving 500 GW of renewable energy capacity by the year 2030, the precise forecasting of solar power generation is rendered increasingly essential. This scholarly article conducts a comprehensive review of the utilization of machine learning (ML) methodologies in the prediction of solar energy across diverse Indian states, underscoring their potential to address the complexities associated with the variability of solar irradiance. Conventional …
Published in Journal of Power Electronics and Power Systems · Vol. 14, Issue 3, 2024 · pp. 41–46 Read article
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Machine Learning-Driven Force Analysis for Tool Wear Prediction Systems
Abstract: A system designed to forecast tool wear by utilizing a force sensor to monitor the wear of the tool's flank and applying a Convolutional Neural Network (CNN) for forecasting purposes. The methodology is demonstrated through experiments in milling, utilizing dry machining with a ball endmill on a stainless-steel component. The flank wear of the tool is directly assessed using a digital microscope throughout the operation. The forecasts produced by the …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 3, 2024 · pp. 16–25 Read article