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332 articles for “algorithm analysis”
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IoT Sensors to Monitor Pipeline Pressure and Flow Rate Combined with ML-Algorithms to Detect Leakages
Abstract: In the field of fluid mechanics, pipelines are the lifeblood of industries, transporting everything from natural gas and oil to water and chemicals. Maintaining their integrity is paramount for safety, economic efficiency, and environmental protection. Traditional leak detection methods explained in fluid mechanics can be slow, expensive, and sometimes fail to identify small leaks early enough to prevent significant damage. However, the convergence of Internet of Things (IoT) and Machine …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 2, 2025 · pp. 40–48 Read article
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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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Intuitionistic Fuzzy Hypergraph Laplacians and Dominating Transversals for Resilient Discrete Network Design
Abstract: This manuscript develops a discrete mathematical framework for resilience analysis on networks whose interactions are polyadic, uncertain, and partially conflicting. Classical graphs compress multi-way coordination into pairwise edges, while ordinary fuzzy graphs often ignore the non-membership information that becomes critical in emergency logistics, infrastructure interdependence, and cyberphysical coordination. We therefore formulate an intuitionistic fuzzy hypergraph in which each vertex hyperedge incidence carries membership, non-membership, and hesitation, and we construct a …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 15–21 Read article
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Recent Advancements and Comprehensive Review on Hyphenated Techniques
Abstract: Hyphenated techniques represent a powerful class of analytical methods that combine two or more established techniques – typically a separation method with a spectroscopic detection technique – to achieve enhanced analytical performance. First introduced by Hirschfeld in 1980, the term “hyphenation” refers to the online coupling of such methods, enabling more precise, sensitive, and comprehensive analysis of complex samples. These techniques exploit the strengths of individual methods while overcoming their …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 16, Issue 3, 2025 · pp. 37–50 Read article
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A Review of Solar-Powered Electric Vehicle Models Handling Unpredictable Changes in Modern Power Grids
Abstract: The transition to Electric Vehicles (EVs) is a critical strategy for mitigating global warming and reducing dependence on diminishing fossil fuel reserves. However, the environmental benefits of EVs are significantly diminished if the charging power is sourced from carbon-intensive electrical grids. To achieve true sustainability, it is vital to integrate Renewable Energy Sources (RES), particularly solar energy, into the charging infrastructure. Beyond transportation, EVs offer a unique opportunity to act …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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AI-Driven Prediction of Mechanical and Thermal Properties in Polymer-Based Functionally Graded Composites
Abstract: The proposed architecture of the current paper is an artificial intelligence (AI)-driven model of forecasting mechanical and thermal aspects of polymer-based functionally-graded composites (FGCs). Traditional micromechanical and finite element models, which are practical in homogeneous composites, might not be able to account in nonlinear interaction that is caused by compositional gradient. To overcome the challenge, machine learning (ML) models like artificial neural network (ANN), support vectors regression (SVR), and gradient-boosted …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 70–89 Read article
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A Comprehensive Analysis of Classification Methods for Churn Prediction in Financial Services
Abstract: Persistent issues that affect long-term revenue in the banking sector include excessive client attrition. Customary churn models depend on measures related to customer satisfaction, which often result in low predictive accuracy due to their subjective nature. This study proposes an effective early warning model to address customer churn in financial services. Data is preprocessed through cleaning, one-hot encoding, Z-score normalization, and Min-max scaling. To handle class imbalance, the SMOTE algorithm …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 2, 2025 · pp. 47–61 Read article
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Experimental Investigation of Boron Carbide Reinforced Al-Mg-Si Alloy Nano Composites by the Stir Casting Method Using Machine Learning Algorithm
Abstract: For the purpose of this experiment, Al-Mg-Si alloys were manufactured with the use of the stir casting process with a metal mold. The alloys had different weight percentages of nano boron carbide, which ranged from 0 to 4% by weight. The intervals between the percentages were 0.5. Following that, the microstructural and mechanical parameters of the as-cast alloy and the nano composites were compared and contrasted according to ASTM Standards …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1766–1779 Read article
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Electromagnetic Transients in Compensated Power Lines- Revisited: New Approach and Case Studies
Abstract: This paper addresses the simulation of the electromagnetic transients developed in high voltage power lines, which are initiated by lightning discharges. The analysis can handle both individual and simultaneous series inductive and shunt capacitive compensation types. The sizes and locations of the compensating elements are included in the analysis. The derived Laplace s-domain mathematical model applies the concept of the ABCD transmission constants representing equivalent two-port networks. The resulting currents …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 1, 2024 · pp. 19–26 Read article
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Live Integrated Facial Observation (L.I.F.O.)
Abstract: A human face is the most influential part of humans that can uniquely identify a person. Using all the facial characteristics as biometric, the LIFO system can be applicable in many different ways. Like in everyday life, the most mandatory task in any organization is attendance marking. Earlier, people used to mark their presence using paperwork but now along with the advancement of technology, this system has also changed and …
Published in Journal of Advancements in Robotics 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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Real-Time Cab Fare and ETA Prediction Using API Integration
Abstract: The exponential proliferation of ride-hailing platforms has necessitated the formulation of sophisticated and highly responsive predictive models for cab fare estimation and estimated time of arrival (ETA) computation. This work elucidates a robust framework leveraging real-time application programming interface (API) integration from Uber and Ola within a Flutter-based ecosystem to enhance predictive analytics. By assimilating real-time geospatial data, dynamic pricing algorithms, and latency-optimized API responses, this study investigates the empirical …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 08–15 Read article
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Fusion of deep learning autoencoders with random forest for wetland classification using Sentinel-2A data: A case study on Sirpur wetland
Abstract: Present study analyses the performance of deep leaning algorithm-autoencoder to reduce data dimension as compared to conventional models. Classification accuracies of Sirpur wetland using Sentinel 2A dataset with different inputs have also been studied. These inputs sets comprise the reconstructed data through compression of original 13 bands into 4 bands using decoder algorithm, first four Principal Components, all spectral bands, and spectral indices. Random Forest classifier (RF) is used to …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 1, 2026 · pp. 25–35 Read article
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Design and Development of an Automated Robotic Algorithm for Blasting, Painting, and Barnacle Cleaning of Offshore Structures
Abstract: Marine surface maintenance tasks such as barnacle removal, abrasive blasting, and protective painting are traditionally carried out using manual methods that are labor-intensive, hazardous, and prone to variability in quality. These challenges are particularly significant for offshore structures, where harsh environmental conditions and restricted accessibility increase operational risks and maintenance costs. This paper presents the design and evaluation of an autonomous robotic system for automated surface preparation and coating of …
Published in Journal of Offshore Structure and Technology · Vol. 13, Issue 1, 2026 · pp. 1–12 Read article
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A Review of Machine and Deep Learning Techniques for Cyber Security
Abstract: Nowadays in the digital landscape, cyber threats and attacks are increasing in an exponential manner, posing server risks to organizations and critical infrastructures. Data breaches often result from sophisticated threat models that exploit vulnerabilities in networks, systems and user behaviors. Cyber solutions are increasingly incorporating machine learning and deep learning to prevent and mitigate these security issues. These technologies have the potential to detect anomalies, classify threats and predict potential …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 01–07 Read article
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AI, Robotics, and the Future of Waste Management: A Systematic Review of Advanced Collection and Sorting Systems
Abstract: The rapid growth of cities and rise in population have made waste management a major concern that calls for innovative and efficient solutions. Conventional waste collecting techniques are dangerous, time-consuming, and frequently ineffective. The development of automated waste management systems powered by cutting-edge technology like robotics, deep learning, artificial intelligence (AI), and the Internet of Things (IoT) is examined in this study. Vision-based systems, convolutional neural networks (CNN) for garbage …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 1, 2025 · pp. 1–6 Read article
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Human-in-the-Loop AI in HR Decision-Making: Insights from Big 4 AI Governance Reports
Abstract: The integration of artificial intelligence (AI) in human resource (HR) decision-making has transformed recruitment, performance evaluations, and talent management. However, biases embedded in AI-driven HR systems present significant ethical and operational challenges. Human-in-the-Loop (HITL) AI offers a hybrid approach that combines AI efficiency with human oversight to enhance fairness and accountability. This paper systematically analyses HITL AI in HR decision-making using qualitative analysis of AI governance reports from Big 4 …
Published in Current Trends in Information Technology · Vol. 16, Issue 1, 2025 · pp. 43–50 Read article
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Analysis and Design of Modern Parking Structure by Considering Optimised Bracing Systems Under the Dynamic Load
Abstract: The design and implementation of effective bracing systems are crucial for ensuring the structural stability and safety of G+10 parking buildings, particularly in regions prone to seismic and wind loads. This study evaluates and optimizes various bracing systems, including X-type, V-type, Inverted V-type, and Eccentric bracing, to determine their suitability for use in G+10 parking structures in the Chhatrapati Sambhajinagar City area. The research focuses on assessing each bracing system's …
Published in Recent Trends in Civil Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 61–76 Read article
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Demand Forecasting for Perishable Food Commodities Using Data Analytics
Abstract: This paper introduces a comprehensive study aimed at enhancing the forecasting of perishable food item demand. Focusing on solving the critical issue of waste management within the supply chain of food products, the research undertakes a comparative analysis of various machine learning models. The development of an optimized model that is capable of accurately forecasting the demand for perishable food items is the focus of this research. The research includes …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 3, 2024 · pp. 27–37 Read article