Search
154 articles for “Statistical models”
-
Tribological Performance and Wear Coefficient Prediction of AA2024–TiC Composites via Python-Based Machine Learning
Abstract: Determining wear coefficient accurately serves as a critical factor to maximize engineering materials' tribological characteristics. The experiment examines the wear characteristics of TiC-reinforced AA2024 aluminum alloy subjected to different tribological operating conditions. A pin-on-disc tribometer performed wear tests under different conditions of load and TiC weight fraction and sliding speed and duration. ANOVA statistical results show that load intensity and TiC reinforcement density stand out as principal variables that affect …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 1099–1112 Read article
-
Tailoring the Compressive Behavior of Tetra-Chiral Auxetic Structures through FDM Process Parameters
Abstract: This research evaluates how fused deposition modeling (FDM) fabrication process parameters affect the compressive behavior of tetra-chiral auxetic structures created from Polylactic Acid (PLA). Auxetic materials have a number of useful properties, including reversible deformation and high-energy absorbing capabilities, which are beneficial to creating ultra-lightweight structural, protective, and shock-resistance designs. Among the available auxetic topologies, the tetra-chiral configuration is particularly attractive for engineering use, because its rotation-dominated node–ligament deformation gives …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
-
Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
-
A Theoretical Model for a Fermi–Boson Hybrid Particle in Nuclear and Particle Physics
Abstract: We present a theoretical model for a Fermi–Boson Hybrid Particle (FBHP) that unifies fermionic half-integer spin matter fields with bosonic integer-spin force fields within a single quantum framework. By extending conventional quantum field theory, a hybrid creation operator is formulated that combines fermionic and bosonic operators through a continuous mixing parameter, allowing smooth interpolation between Fermi–Dirac and Bose–Einstein statistical behaviour. A generalized statistical mechanics formalism is developed, leading to quantitative …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 10–18 Read article
-
Association between Diabetes Type and Family History of Diabetes: A Cross-Sectional Analysis of Gender Differences and Genetic Influences
Abstract: Introduction: Diabetes mellitus DM is one of the fast-growing chronic metabolic disorder in the world, with high impact and burden in low- and middle-income country. Although genetic predisposition is a central determinant of diabetes risk, particularly for Type 2 diabetes (T2D), the contribution of familial aggregation varies across populations. In many parts of the world where diabetes is rising very fast understanding the relationship between diabetes type and family history …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 15, Issue 3, 2025 · pp. 43–50 Read article
-
Internet of Things in Smart Grid: Applications, challenges, Conditions, Architecture- A Review
Abstract: Future-generation intelligent optimisation in electrical system design is essential for managing electrical networks and distribution systems. It also requires interoperability variations in the implementation of physical or graphical models. The Internet of Things (IoT) plays a significant role in smart grids and distributed electricity systems. IoT enables the monitoring of the smart grid's electrical energy and facilitates the integration of real-time data into electrical grid architecture at various levels. Industrial …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 2, Issue 2, 2024 · pp. 1–9 Read article
-
Advanced Anomaly Detection in Cloud Infrastructures Using Deep Learning Algorithms
Abstract: It is critical to guarantee the stability and security of cloud environments as cloud computing is becoming the backbone of contemporary IT infrastructures. Neglecting to quickly identify and resolve anomalies, which might point to security breaches, performance problems, or system breakdowns, can lead to disastrous outcomes. The increasing size and complexity of cloud infrastructures are challenging the effectiveness of traditional anomaly detection methods. These approaches often depend on rule-based systems …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 1–11 Read article
-
AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering
Abstract: The remarkable mechanical strength increased functional qualities, lightweight structure, and thermal stability of polymer nanocomposites have prompted modern materials research to prioritize their rapid commercialization. Advanced materials can be created by adding nanoscale fillers such as carbon nanotubes, graphene, silica, and metal oxides to polymer matrices. These materials have applications in biomedical engineering, aerospace, electronics, packaging, and automobile manufacture. Research and development of polymer nanocomposites has traditionally relied on costly …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1–19 Read article
-
AI-Enabled Feedback Management for Enhancing Education
Abstract: Institutions are becoming more aware of the importance of student input in improving learning experiences in the current educational environment. However, the intricate and complex patterns found in this feedback are frequently missed by conventional techniques like manual reviews and simple statistics. Our proposal suggests a novel method for analyzing student input and more accurately predicting sentiment by utilizing Long Short-Term Memory (LSTM) algorithms. We can learn more about student …
Published in International Journal of Electronics Automation · Vol. 3, Issue 2, 2025 · pp. 21–27 Read article
-
Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 Read article
-
A research study into the interactions between the monoherbal formulations of arjuna and Aloe vera in a rat model of isoproterenol-induced cardiotoxicity
Abstract: Aloe vera is a herbal dietary supplement, and arjuna is used for its cardioprotective properties. Rats were given isoproterenol hydrochloride subcutaneously to cause myocardial infarction. The purpose of the study was to identify any potential pharmacodynamic interactions between the commercially available formulations of Aloe vera and arjuna. Materials and Procedures: The electrocardiogram (heart rate, ST segment elevation time, QRS complex amplitude), serum cardiac markers (creatine kinase, isoform of creatine kinase, …
Published in International Journal of Toxins and Toxics · Vol. 2, Issue 2, 2025 · pp. 1–10 Read article
-
Psychological Profile of Patients Undergoing Hemodialysis and Its Association with Physiological Parameters
Abstract: Background: End-stage renal disease (ESRD) is a chronic, progressive condition requiring maintenance hemodialysis, which imposes substantial physiological, psychological, and social burdens on patients. While physiological indicators are routinely monitored during treatment, the influence of psychological factors on these indicators and patients' quality of life remains inadequately explored, particularly in the Indian context. Objective: To investigate the relationship between psychological variables (depression, anxiety, illness intrusiveness, and quality of life) and physiological …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 16, Issue 2, 2026 Read article
-
Engine Performance Analysis of Cottonseed Based Biodiesel Using Design Expert Statistical Based Tool
Abstract: Biodiesel is a biofuel acquired by substance forms from vegetable oils or creature fats and liquor that can be utilized in diesel engines alone or mixed with diesel oil. It is characterized as the mono-alkyl esters of unsaturated fats got from vegetable oils or creature fats. In basic words biodiesel is the item that gotten when vegetable oil or creature fat is artificially responded with a liquor to deliver unsaturated …
Published in International Journal of Industrial and Product Design Engineering · Vol. 1, Issue 1, 2023 · pp. 1–5 Read article
-
The Impact of Climate Change on Jute Production in Gaibandha District, Bangladesh
Abstract: Jute plays a crucial role in Bangladesh’s agricultural economy, especially in Gaibandha District, where it is a key crop for rural livelihoods. However, climate change, characterized by rising temperatures and unpredictable rainfall, poses significant threats to jute production. While numerous studies have explored the broader impact of climate change on agriculture, there is a gap in understanding how localized climate conditions specifically affect jute farming in Gaibandha, with many existing …
Published in International Journal of Climate Conditions · Vol. 2, Issue 1, 2025 · pp. 1–17 Read article
-
Assessing the Knowledge and Attitudes of Eligible Couples Regarding Small Family Norm in a Selected Rural Area of Gwalior
Abstract: This study aimed to determine the level of knowledge and attitude of eligible couples toward permanent family planning methods in a selected rural area of the district. The research method was an evaluation. Sixty couples were chosen at random who met the inclusion and exclusion criteria. The primary purpose of this research is to evaluate the level of understanding and satisfaction with permanent family planning methods among eligible couples in …
Published in International Journal of Community Health Nursing And Practices · Vol. 1, Issue 2, 2023 Read article
-
Determinants of Smallholder Farmers Quantity of Coffea arabica L., Supply to Market: A Case of Gimbo District, Kaffa Zone, Ethiopia
Abstract: This study investigates the factors influencing the market supply of coffee in various districts of the Gimbo District, Kaffa Zone, in Southwest Ethiopia. Coffee is Ethiopia's most significant export crop, recognized for its extensive genetic diversity and its substantial contribution to the country's GDP. Despite the district's strong production capacity, the marketing structure remains predominantly traditional, compelling producers to sell through conventional channels that do not offer premium prices, thereby …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 107–120 Read article
-
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
-
Patient Profile and Antimicrobial Susceptibility Testing of Clinically Isolates In E. coli
Abstract: Escherichia coli, commonly known as E. coli, is a prevalent bacterium capable of causing infections in humans. The development of antimicrobial resistance in E. coli poses a significant public health issue. This study aimed to determine the patient profile and antimicrobial susceptibility patterns of clinically isolated E. coli. A retrospective study was carried out on E. coli samples collected from clinical specimens within a hospital environment. Patient profiles, including age, …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 14, Issue 1, 2024 · pp. 32–47 Read article
-
Study of Diurnal Anisotropy Variation in Cosmic Ray Intensity During Minimum Solar Activity Period
Abstract: we present a comprehensive study of cosmic ray variations over the period from 1964 to 2018, encompassing solar cycles 20, 21, 22, 23, and 24. Both annual average and day-to-day variations have been analyzed to capture the temporal dynamics of cosmic ray intensity across multiple solar cycles. The study focuses particularly on periods of minimum solar activity, namely the years 1965, 1976, 1986, 1996, and 2008, when cosmic ray modulation …
Published in Research & Reviews : Journal of Physics · Vol. 14, Issue 2, 2025 · pp. 30–37 Read article
-
Rapid Forecasting of Short-run Electric Power Demand Profiles of India Using the Statistical Method of Z Scores
Abstract: Power demand profile prediction for a region or nation is a critical part of the energy system design and operational planning process. A simplified method based on non-dimensionalizing power demand data from previous years using Z scores calculated from the mean and standard deviation of the profiles is developed in this study to forecast monthly demand profiles at time resolution of 1 hour for future years. The Z score range …
Published in Research & Reviews : Journal of Statistics · Vol. 14, Issue 1, 2025 · pp. 38–48 Read article