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332 articles for “randomization”
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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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Methods Based on Machine Learning for Large-scale Classification of Crop Leaf Diseases
Abstract: Worldwide productivity of crops is seriously threatened by crop leaf diseases, which can result in large crop losses and negative economic effects. Effective disease management and crop protection depend on the early and precise detection and classification of these illnesses. Machine learning approaches have gained popularity recently due to their ability to automate procedures related to illness diagnosis and classification. An overview of the several machine learning–based methods used for …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 11–23 Read article
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Crop Yield Prediction Using Machine Learning Algorithm Based on Climate Variables
Abstract: India's economy is based primarily on agriculture, as over 50% of the country's population depends on it for their livelihood. The long-term viability of agriculture is seriously threatened by variations in the weather, climate, and other environmental factors. Because machine learning provides tools for decision assistance in agricultural yield prediction, including guidance on which crops to plant and when to plant them during the growing season, it is essential to …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 49–52 Read article
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Identification of Brain Stroke Using Artificial Intelligence
Abstract: Globally, strokes are the primary cause of disability and mortality. Recently, machine learning (ML) and deep learning (DL) have been employed by artificial intelligence algorithms as effective stroke diagnosing techniques. These days, machine learning and data mining technologies are used in the construction of the main models. We have used five machine learning algorithms to determine if a stroke has occurred or is likely to occur based on a patient’s …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 15–22 Read article
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Machine Learning Approaches for Phishing Detection: A Comparative Study
Abstract: These days, everyone has an internet addiction. All of us have used the internet for banking, booking, recharging, and buying. Phishing is a type of website threat that exists online. On the original website, phishing is an attempt to illegally obtain information such as login ID, password and credit card information. In this research, we proposed an efficient phishing detection system based on machine learning. Overall, the experimental findings demonstrated …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 35–45 Read article
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Deep Learning -Based Dental Issue Detection
Abstract: Dentistry is vital for preserving oral health, a key component of overall wellness. Early identification of dental issues is crucial for effective treatment and avoiding further complications. Conventional approaches to diagnosing dental problems typically depend on physical examinations and visual assessments by skilled professionals, which can be both time-intensive and influenced by individual judgment.In recent years, the application of deep learning algorithms has demonstrated significant potential in automating and enhancing …
Published in Research and Reviews: A Journal of Dentistry · Vol. 16, Issue 1, 2025 · pp. 18–23 Read article
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VLSI – Power, Expansion and Versatility
Abstract: The evolution of Very Large-Scale Integration (VLSI) technology has significantly transformed the landscape of modern electronics, driving innovations across diverse industries. This article explores the key attributes of VLSI, focusing on its power, expansion, and versatility. VLSI's power lies in its ability to integrate thousands to millions of transistors on a single chip, enabling the development of high-performance, energy-efficient devices. The expansion of VLSI technology is exemplified by its application …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 3, Issue 1, 2025 · pp. 1–8 Read article
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Designing and Modeling of Giant Magnetoresistance (GMR) Materials based Devices in Electrical Power and Biomedical Systems
Abstract: This paper presents Designing and Modeling of Giant Magnetoresistance (GMR) Devices in Electrical Power and Biomedical Systems and the related materials. The GMR, inverse GMR, and Spin valve using exchange bias have been analytically derived and discussed from the designing point of view for optimizing the performance of the GMR based Devices in Electrical systems. More recently, GMR has been used in sensors, magnetic memory chips, and hard-disk read-heads. The …
Published in Research & Reviews : Journal of Physics · Vol. 14, Issue 1, 2025 · pp. 22–32 Read article
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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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A Survey on Ensemble Technique for Enhanced Cyberattack Detection
Abstract: It is now more difficult than ever to safeguard enterprises against cyberattacks due to their fast growth and growing sophistication. Stronger cyberattack detection systems are becoming more and more necessary as hostile strategies continue to evolve in order to safeguard information, preserve corporate trust, and protect sensitive data. An overview of contemporary detection techniques is given in this study, with a focus on integrating machine learning (ML) to increase efficacy. …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 50–54 Read article
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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 Read article
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Lightweight Models for Per-PC Energy Consumption Forecasting: Comparative Study with ML and DL Approaches
Abstract: We have collected primary data from automated logging of parameters like CPU utilization, estimated power, active or idle state, user logging activity, and the type of day. Additionally, survey data showed user awareness, energy-saving behaviour, and PC usage patterns. The data is pre-processed and merged by applying processes such as data cleaning, normalization, and feature extraction, i.e., determining the peak active timings and downtime. Developed lightweight prediction models based on …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 Read article
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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
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Development of Polymer Based SRAM Cell with Enhance Low Power Performance
Abstract: Low-power memory technologies are in high demand with the rapid growth of portable electronics and energy-efficient computing systems. Static random-access memory (SRAM) plays a crucial role in processors, cache memories, and system-on-chip applications due to their speed and reliability. Static Random Access Memory (SRAM) is typically implemented using complementary MOS (CMOS) technology, which integrates both PMOS (P-channel Metal–Oxide–Semiconductor) and NMOS (N-channel Metal–Oxide–Semiconductor) transistors. However, as technology scales to deep submicron …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1439–1448 Read article
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Comparison of Models of Machine Learning and Hyperparameter optimization methods on various datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameters tuning step. The traditional exhaustive methods of search (Grid Search and others) ensure that the search space is covered, but are computationally very inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian Optimization, Tree-structured Parzen Estimator (TPE), Genetic Algorithms) are thought to …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 Read article
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Assessment of Effective use of Online Webinars, Workshop, Conference and Quizzes on Library Science Professionals during Covid Scenario: A Survey
Abstract: Due Covid-19 virus around the globe all sectors have been jeopardized and came to halt. Libraries were not exceptions too, where library professionals started learning and sharing professional knowledge through different e-platforms on different Library Science themes, issues and challenges after a covid scenario in the libraries. The study explored the library science professionals on effective use of online webinars and other series during covid times. The study employed quantitative …
Published in Journal of Advancements in Library Sciences Read article
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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
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Perceptions and Attitude of Registered Nurses Towards Professionalism in Nursing from Selected Hospitals of Guntur District, Andhra Pradesh: A Comparative Study
Abstract: Background: Nursing professionalism refers to a collection of principles that are essential to raising the standard of patient care while enhancing the methodology, norms, and judgement that direct nursing activities on a daily basis. The main objective of the study is to assess the perceptions and attitude of registered nurses towards professionalism in nursing. Materials and Methods: A comparative descriptive research design adopted for this study. The study conducted at …
Published in International Journal of Evidence Based Nursing And Practices · Vol. 1, Issue 1, 2023 · pp. 1–7 Read article
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Effect of Energy/Protein Ratio and Strain on Performance, Nutrient Digestibility and Serum Biochemical Indices of Exotic and Locally Adapted Turkey
Abstract: This study investigated the response of locally adapted and exotic turkeys raised under the same environmental conditions to diets with varying energy/protein ratio. One hundred and fourty-four (144) four-week old poults of each strain of turkey were randomly selected and assigned to six different diets in a 2 x 2 x 3 factorial arrangement. Each treatment diet were replicated twice with twelve (12) birds per replicate. Diets formulated with 28 …
Published in International Journal of Biochemistry and Biomolecule Research · Vol. 1, Issue 2, 2023 · pp. 37–45 Read article