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906 articles for “Learning Effect”
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Role of Reinforcement Learning in Improvement of Semiconductor Doping
Abstract: The semiconductor industry faces increasing challenges in achieving optimal doping profiles as device dimensions shrink and performance requirements intensify. Traditional doping optimization methods, while effective, often struggle with the complex, multi-dimensional parameter spaces characteristic of modern semiconductor manufacturing. This study explores the transformative role of reinforcement learning (RL) in improving semiconductor doping processes, examining how RL algorithms can autonomously optimize doping parameters to enhance device performance, reduce manufacturing costs, and …
Published in Journal of Semiconductor Devices and Circuits · Vol. 12, Issue 2, 2025 · pp. 23–34 Read article
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GenChrome-ML: A Machine Learning Framework for Early Detection of Chromosomal Disorders Using Genomic Data
Abstract: The increasing burden of chronic disease and cancer demands innovative, more rapid and effective diagnostic tools in the field of healthcare. The majority of current diagnostic tools are dependent upon clinical symptomology and manual evaluation, leading to delays in early detection and treatment. The development of artificial intelligence (AI) and machine learning (ML), in recent years, has offered opportunities for the enhancement of disease prediction, diagnosis and personalization of treatment …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 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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Intelligent Aquaculture System for Fish Disease Detection Using Machine Learning
Abstract: Aquaculture is one of the key factors for global food security, but fish diseases bring about heavy economic losses and jeopardize sustainability. One of the most important aspects of global food security is aquaculture, but fish infections endanger sustainability and cause significant financial losses. Early diagnosis is not possible since traditional disease detection techniques are laborious and necessitate expert intervention. To effectively detect fish infections, this study suggests an Intelligent …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 2, 2025 · pp. 30–37 Read article
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Stock Price Analysis and Forecasting Using Linear Regression and SVM Classifiers
Abstract: In this study, we attempt to implement a Machine Learning approach to predict stock market prices. Linear Regression is very effectively implemented in forecasting stock prices, returns, and stock modelling. This project is for common users as the prediction is done on all of the companies. We outline the design of the Linear Regression model with its salient features and customizable parameters. We select a certain group of parameters with …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 62–66 Read article
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Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing
Abstract: The exponential growth of internet of things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 46–62 Read article
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Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation
Abstract: Deepfake technology, powered by highly advanced deep learning models, has raised significant concerns regarding media manipulation, identity theft, and the spread of online disinformation. Due to the increasing sophistication of deepfake content, traditional forensic methods often fail to detect such artificially generated images with high accuracy. Consequently, deep learning-based approaches have become essential in combating this challenge. This study compares six prominent deep learning architectures: VGG16, ResNet50, MobileNetV2, InceptionV3, EfficientNetB0, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 27–36 Read article
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The Roles of Language and Communication in Transforming and Sustaining National Development in Nigeria
Abstract: The objective of this paper is to assess the roles of language and communication in transforming and sustaining national development in Nigeria. There is the need to educate the people on how to use language and communication skills to achieve peace. An attempt is also made to highlight the objectives of language and communication skills, the processes involved and the channels to use in communicating. The paper finally suggests ways …
Published in Emerging Trends in Languages · Vol. 1, Issue 2, 2024 · pp. 31–36 Read article
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Comparative Analysis of AI-Based Approach vs. Traditional Methods in Climate Modeling
Abstract: Climate modeling helps to predict the future of climate variations and human interference with environment. The traditional General Circulation Models (GCMs) are based on physics-derived mathematical equations but are very expensive in terms of computation. There are alternative ways to perform climate modeling in recent years with the rise and improvement of Artificial Intelligence (AI) based approaches in term of predictability, efficiency, and classification of extreme events compared to conventional. …
Published in Current Trends in Information Technology · Vol. 15, Issue 3, 2025 · pp. 26–32 Read article
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Catalyzing Scientific Exploration: Essential Algorithms in Python for Computational Excellence
Abstract: This research work delves into the foundational algorithms that underlie scientific computing, leveraging the Python programming language. It navigates through essential numerical techniques, optimization approaches, and linear algebra operations, highlighting their critical importance in diverse scientific fields. By offering illustrative examples and concise Python code snippets; the study emphasizes Python's role as a versatile and accessible platform for researchers and practitioners engaged in scientific computation. Python's widespread use in scientific …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 10, Issue 1, 2023 · pp. 33–42 Read article
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Facial Expression Recognition using Convolutional Neural Network
Abstract: Machine learning is a subset of Artificial intelligence. Machine learning enables self-learning from past data and predicts the future. Machine learning is used in many applications such as self-driving cars, Google translate etc. Deep learning is a subset of machine learning. CNN is a convolutional neural network. The CNN approach is very effective in image classification. The target of this exploration is to assist a look acknowledgment framework dependent on …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 1, 2021 · pp. 30–35 Read article
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Advanced Polymer Nanocomposite EEG Electrodes for Enhanced Epileptic Seizure Detection: A Comparative Analysis
Abstract: Electroencephalography (EEG) has been very important in the detection of epileptic seizures so as to enable successful diagnosis, surveillance and therapy of epilepsy. Nevertheless, EEG electrodes based on traditional metals may be limited due to high or high contact impedance, lack of biocompatibility, discomfort to patients and prone to motion artifacts, which interfere with signal quality and diagnostic adequacy. The recent progress in material science has resulted in coming up …
Published in Journal of Polymer & Composites · Vol. 14, 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 · Vol. 9, Issue 4, 2022 · pp. 1–11 Read article
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Fake Product Detection Using Convolutional Neural Networks
Abstract: The widespread circulation of counterfeit products in global markets presents a significant threat to both consumer trust and the integrity of established brands. With the advancement of artificial intelligence, particularly deep learning, there is growing potential to develop more sophisticated systems to combat this issue. This study introduces a novel counterfeit detection framework using the VGG16 Convolutional Neural Network (CNN) to distinguish between authentic and counterfeit products through image analysis. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 08–15 Read article
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System Learning Method Using Spam Filter
Abstract: System learning method can figure out how to perform essential tasks by generalizing from examples. This is often feasible and more effective where human programming is not. As large data becomes available, more ambitious problems can be tackled. As a result, System learning method is broadly used in computer science and other fields. However, developing successful System learning method using spam filter applications requires a substantial amount of “black art” …
Published in Journal of Advances in Shell Programming · Vol. 3, Issue 1, 2016 · pp. 19–27 Read article
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AGRISMART: Crop and Soil Management System
Abstract: Agriculture has played a crucial role in developing countries where the majority of the rural population relies on it for their livelihoods. A finer-grade crop classification has become crucial in the context of precision agriculture. In recent years, the volume of open image data has grown significantly. This can be used in combination with machine learning techniques to classify crop types in the agricultural industry. The proposed crop species recognition …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 3, 2025 · pp. 50–55 Read article
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Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines
Abstract: Mango leaf diseases pose a significant threat to mango production, impacting both yield and fruit quality. Early and accurate detection of these diseases is crucial for effective management. This paper evaluates the use of hybrid techniques, specifically the integration of neural networks (NNs) and support vector machines (SVM), in the identification and classification of mango leaf diseases. NN excel in extracting complex features from images, while SVMs are robust classifiers, …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 19–27 Read article
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Accurate & Efficient Plant Disease Detection using Transfer Learning with Edge Impulse
Abstract: Transfer learning is a powerful machine learning technique that allows optimization of pre-trained models for related tasks on small datasets. In this research paper, we explore the application of Edge Impulse & transfer learning for plant diseases and aim to detect on edge devices more effectively at low cost. We collected and preprocessed many plant images and used this data to fine-tune a neural network model pre-trained by Edge Impulse. …
Published in Recent Trends in Sensor Research & Technology · Vol. 10, Issue 1, 2023 · pp. 30–41 Read article
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Automated Car License Plate Detection and Recognition Using Deep Learning
Abstract: The use of automated license plate detection and recognition (ALPR) systems to automate processes such as number plate detection is gaining popularity in traffic control, security, and law enforcement. This research focuses on achieving more accurate and efficient detection and recognition of number plates by leveraging deep learning techniques. The systems outlined in this study aim to improve the effectiveness of ALPR systems using advanced convolutional neural networks (CNNs) and …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 23–29 Read article
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article