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117 articles for “forecasting algorithms”
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Disease Prediction Using Machine Learning (ML)
Abstract: A technique called Machine Learning Disease Prediction uses symptoms reported by users or patients to forecast disease. The user-provided symptoms are entered into the system, and it outputs the disease probability. In disease forecasting, various popular supervised machine learning methods are known to be utilized. These algorithm estimates the likelihood of a disease occurrence. Precise analysis of medical information will support timely disease detection and management of patients based on …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 12, Issue 2, 2024 · pp. 40–48 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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Adaptive Task Scheduling and Resource Optimization Using AI Middleware
Abstract: Modern distributed and heterogeneous computing systems face significant challenges in dealing with dynamically changing workloads, resource fragmentation, and changing latencies; existing traditional, or rule-based, specialized schedulers are no longer useful in achieving the best system performance. Such limitations highlight the importance of the adaptive scheduling paradigms that can learn, forecast, and react to the actual real-world conditions in the system. Artificial intelligence middleware is also an attractive solution to this …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 23–31 Read article
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Exploring the Efficiency of Leading and Lagging Indicators in Algorithmic Trading
Abstract: This paper details a comparison of the overall performance of leading and lagging technical indicators used in algorithmic trading over an extended period. While much of the prior research focuses on index price forecasting and some on statistical arbitrage derived from these predictive techniques, there is a scarcity of studies that assess and evaluate trading strategies. The strategies considered for the study were tested on historical data of the 50 …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 8–18 Read article
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An Optimal Bidding Strategy for Social Welfare Maximization of Microgrid
Abstract: Competition in power markets implies benefit to the generators as well as customer. The uncertain nature of bids and behavior of renewables makes the bidding strategy more complex. The power market comprises of Generation companies (GENCOS), Transmission companies (TRANSCOs) and Distribution companies (DISCOs) which actively participate in the auction process. In this paper, a new bidding strategy for a microgrid in day-ahead market is proposed for social welfare maximization considering …
Published in Journal Of Network security · Vol. 8, Issue 1, 2020 · pp. 11–21 Read article
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Prediction of Air Pollution LSTM Model Use in Machine Learning
Abstract: A major environmental problem that has an impact on the health and happiness of millions of people is air pollution. Accurate air quality forecasting is crucial for maintaining human health, the environment, and urban planning. In recent years, machine learning algorithms, such as Long Short-Term Memory (LSTM) models, have been increasingly used to predict air pollution levels. This study presents an abstract for a system that uses an LSTM model …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 1, 2023 · pp. 90–98 Read article
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Using AIML to Enhance Demand Forecasting in Business
Abstract: Artificial intelligence machine learning (AIML) can play a significant role in enhancing demand forecasting in business. AIML is a programming language designed for creating chatbots and conversational agents, but its application extends beyond simple interactions. In the context of demand forecasting, AIML can be utilized to analyze historical data, customer interactions, and market trends. By implementing AIML algorithms, businesses can create intelligent models that learn from past demand patterns, customer …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 35–40 Read article
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Comparative Analysis of Supervised Learning Algorithms
Abstract: Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 25–30 Read article
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Gold Price Prediction Using Naïve Bayes Regression Algorithm and ANN
Abstract: AbstractIn this day and age, the amount of stored data has been immensely expanding step by step which is by and large in the organized or unstructured shape and cannot be utilized for any processing to remove valuable data, so a few systems, for example, synopsis, arrangement, grouping, data extraction, expectation and perception are accessible for the same, which goes under the classification of content examination. Forecast is the branch …
Published in Journal of Open Source Developments · Vol. 4, Issue 2, 2017 · pp. 6–9 Read article
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Machine Learning Based House Price Forecasting
Abstract: This research endeavours to craft a predictive model leveraging machine learning to estimate the market value of houses in Delhi. By integrating Python and its powerful libraries, pandas for data processing, Plot for interactive visualizations, scikit-learn for implementing machine learning algorithms, XGBoost for boosting the model's prediction accuracy, and to evaluate the model's performance cross-validation techniques are used. An interactive user interface is created using a Flask web application to …
Published in Current Trends in Information Technology · Vol. 14, Issue 1, 2024 · pp. 5–11 Read article
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Artificial Intelligence Enhanced Waste Sorting and Classification System for Urban Recycling
Abstract: This study explores the potential of Artificial Intelligence (AI) and Machine Learning (ML) to enhance waste management efficiency within urban environments. Rapid urbanization has resulted in a surge of municipal waste, which current systems often struggle to manage effectively. The proposed AI-enhanced waste sorting and classification system aims to optimize waste collection routes and accurately forecast waste generation trends, thereby reducing operational costs, fuel consumption, and traffic congestion. Additionally, AI-driven …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 23–32 Read article
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Entomo-Analytics: Insect Behavioral Intelligence for Climate-Smart Environmental Monitoring Systems
Abstract: Rapid environmental change driven by climate variability, urbanization, and ecological degradation has intensified the need for innovative monitoring systems capable of providing real-time ecological intelligence. Traditional environmental monitoring methods often rely on satellite imaging and stationary sensors, which may lack fine-scale biological sensitivity. In contrast, insects—due to their abundance, ecological diversity, and rapid responsiveness to environmental shifts—offer a powerful yet underutilized source of bio-sensing data. This paper introduces the concept …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 17–26 Read article
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Detection of Breast Cancer with Python
Abstract: Global cancer data confirms more than 2 million women diagnosed with breast cancer each year reflecting majority of new cancer cases and related deaths, making it significant public health concern. But fortunately, it is also the curable cancer in its early stage. Early diagnosis of breast cancer with timely and effective treatment services improves the prognosis and survival of patients. During classifying tumors, there are significant chances of error and …
Published in Research & Reviews : Journal of Statistics · Vol. 9, Issue 3, 2020 · pp. 5–22 Read article
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A Review on Predicting Wear and Friction of PTFE Composites - Fillers to Machine Learning Models
Abstract: Polytetrafluoroethylene (PTFE) composites, a self-lubricating material with low friction, became an indispensable material in engineering applications where load carrying capacity and wear are crucial. The pure PTFE has poor mechanical strength and wear resistance which can be enhanced by the addition of fillers in appropriate volume fraction. The wear performance is dependent on various factors such as fillers, operating parameters, environmental conditions as well as manufacturing attributes. This makes the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 114–128 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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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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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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Role of Generative AI in Redefining Data Analytics
Abstract: The rapid evolution of data-driven technologies has introduced both significant challenges and promising opportunities within the field of data analytics. Among the most impactful advancements is Generative Artificial Intelligence (Generative AI), a groundbreaking subset of AI that is reshaping how data is interpreted, generated, and utilized. Unlike traditional analytical tools that rely solely on existing data patterns, generative AI possesses the capability to create synthetic data, simulate complex scenarios, and …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 01–07 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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The Rise of AI in E-Commerce: Transforming Shopping Experiences
Abstract: The field of e-commerce is transforming due to the integration of artificial intelligence, offering businesses fresh possibilities to enhance customer experiences, streamline operations, and foster expansion. This article explores the transformative impact of artificial intelligence (AI) on various aspects of e-commerce, including personalized shopping experiences, intelligent chatbots and virtual assistants, predictive analytics for inventory management, dynamic pricing strategies, visual search and image recognition, fraud detection, and security measures, augmented reality …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 22–26 Read article