Search
435 articles for “machine learning techniques”
-
Automating Compiler Optimization: A Machine Learning Approach
Abstract: This study reports on an ML-based approach to compiler optimization, complementing traditional optimization methods that rely strongly on hand-tuned settings. Compiler optimization plays a key role in performance-speedup and energy optimization of complex contemporary software systems. However, the traditional approach to optimizer settings involves laborious, error-prone, and scale-insensitive human-in-the-loop intervention, especially in the complex and high-demand environments in which today's computing application thrives. By integrating RL and GA, we can …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 12–16 Read article
-
Text to Image Using Machine Learning
Abstract: In the era of digital transformation, our project addresses the convergence of computer vision and natural language processing to enhance user interaction and visual content creation. This project comprises three distinct modules: user authentication and session management, image colorization from grayscale inputs, and text-to-image generation. The login registration module provides secure access to the system, ensuring user privacy and data integrity. Once authenticated, users can utilize advanced computer vision techniques …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 2, 2024 · pp. 35–41 Read article
-
AI Hindi Poem Generator
Abstract: The Hindi Poetry Generator project represents a pioneering initiative in the domain of computational creativity, blending machine learning algorithms and natural language processing methodologies to craft poetic expressions in the Hindi language. Rooted in the vast landscape of Hindi literature, this project harnesses the power of deep learning models to generate evocative and culturally significant poetry. At its core, the system relies on neural networks and sophisticated language modeling techniques …
Published in Recent Trends in Programming languages · Vol. 11, Issue 2, 2024 · pp. 10–16 Read article
-
Innovations in Mineral Science and Engineering for Sustainable Resource Development
Abstract: The growing global demand for mineral resources, coupled with increasing environmental and social concerns, has intensified the need for sustainable approaches in mineral science and engineering. Traditional mining and mineral processing practices, while essential for industrial development, are often associated with high energy consumption, resource depletion, and environmental degradation. In response, recent innovations in mineral science and engineering have focused on improving resource efficiency, minimizing environmental impact, and ensuring long-term …
Published in International Journal of Minerals · Vol. 3, Issue 1, 2026 · pp. 31–36 Read article
-
Algorithm for the prediction of cardiovascular disease (CVD)
Abstract: cardiovascular diseases (CVD) still claim a significant number of deaths globally and remain the number one killer with an annual death toll of nearly 17.9 million. While several medical advancements have been made, an early diagnosis is still hard to obtain, which often leads to worsening conditions and intricate treatment options. With the advancement of modern technology, Machine learning has demonstrated to be a miraculous tool which can greatly impact …
Published in Research and Reviews : A Journal of Immunology · Vol. 15, Issue 2, 2025 Read article
-
Role of Machine Learning Principles for Efficient Nuclear Fuel Management and Design
Abstract: The introduction of machine learning (ML) and evolutionary computation methods in addressing complex nuclear fuel management challenges has brought a significant positive change in the domain of nuclear fuel management. Key applications include fuel assembly design optimization, core loading pattern determination, burnup calculation acceleration, fuel performance prediction, and spent fuel characterization. The analysis reveals significant improvements in computational efficiency, prediction accuracy, and optimization capabilities when ML techniques are properly integrated …
Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 22–33 Read article
-
The Analysis of Deep Learning-Based Methods for Identifying Diabetic Retinopathy
Abstract: Diabetic retinopathy (DR) is a degenerative eye condition resulting from diabetes mellitus, where high blood glucose levels lead to lesions on the retina. This condition is considered the leading cause of blindness among working-age diabetic patients, particularly in developing countries. As the disease is irreversible, the treatment aims to preserve the patient’s current vision. Early detection is crucial for effective management of DR to maintain vision. One of the main …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 3, 2024 · pp. 15–31 Read article
-
Time Series Sales Forecasting Using ARIMA Model
Abstract: Sales forecasting is a critical application in various industries and presents one of the most challenging problems worldwide. One method of prediction involves identifying patterns in historical data, where the outcome is known in advance and can be validated using more recent data. If a pattern consistently leads to the same outcome, it can be considered a genuine relationship. This method is highly flexible and can be utilized with diverse …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 1, 2024 · pp. 17–27 Read article
-
Remote Sensing and GIS-Based Approaches for Soil Salinization Assessment: A Comprehensive Review
Abstract: Soil salinization, a critical environmental challenge, significantly impacts land productivity, agricultural yields, and contributes to desertification, particularly in arid and semi-arid regions. Early detection and effective management of soil salinity are essential for sustainable agriculture and land management. Remote sensing (RS) and geographic information systems (GIS) have emerged as indispensable tools for mapping, monitoring, and analyzing soil salinity over vast areas. RS provides multi-temporal and multi-spectral data that helps identify …
Published in Journal of Remote Sensing & GIS · Vol. 15, Issue 3, 2024 · pp. 29–38 Read article
-
Recent Advances in Quality Control and Quality Assurance: Enhancing Pharmaceutical Product Integrity and Compliance
Abstract: The pharmaceutical industry is undergoing a paradigm shift driven by stringent regulatory expectations and the demand for high-quality, safe, and efficacious drug products. Quality Control (QC) and Quality Assurance (QA) serve as the two foundational pillars that ensure pharmaceutical integrity from raw material acquisition through to product release. Traditional QC and QA practices, while effective, have been challenged by complex formulations, biologics, and personalized medicine, requiring innovative methodologies and technologies. …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 16, Issue 2, 2025 · pp. 54–62 Read article
-
Detection and Classification of Alzheimer’s Disease Using Deep Learning Technique
Abstract: It is crucial that people with Alzheimer's disease (AD) receive a proper diagnosis to begin preventative action before irreparable brain damage develops. Most people who suffer from Alzheimer's disease (AD), a neurological condition that progresses, are older than 65. The area of interest (ROI) in the hippocampus has been extensively studied for several purposes, including neurological illness research, stress development monitoring, and memory function analysis. Moreover, a connection between Alzheimer's …
Published in Recent Trends in Sensor Research & Technology · Vol. 12, Issue 1, 2025 · pp. 15–20 Read article
-
Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis
Abstract: Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 67–85 Read article
-
Leveraging Digital Marketing Techniques for Newly Developed Software Using AI
Abstract: In today’s digital era, most software developers prefer using paid advertising campaigns for their newly developed software to strengthen their online visibility and attract new users; this research explores some strategies to increase and maximize the reach of newly developed software through digital marketing campaigns, particularly focusing on the use of artificial intelligence (AI) tools to optimize campaign performance and increase the reach of newly developed software. To fully assess …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 12–22 Read article
-
AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review
Abstract: The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1083–1097 Read article
-
Fuzzy Mathematics in Decision-Making: A Quantitative Perspective
Abstract: Fuzzy mathematics plays an increasingly generalized role in decision-making, and thus, this paper details different types of fuzzy mathematics and highlights other possible alternatives alongside fuzzy methodologies. Fuzzy models offer a versatile and precise approach to assessing complex and uncertain situations using fuzzy sets, membership functions, linguistic variables, and aggregation methods. Through the lenses of time, cost, and quality, the project management case study illustrates how fuzzy logic effectively evaluates …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 1, 2025 · pp. 6–12 Read article
-
Damage Evolution and Delamination Resistance in Polymer Matrix Functionally Graded Laminates
Abstract: Functionally graded laminates (FGLs) in polymer-matrix systems represent a promising pathway to enhance damage tolerance and delay delamination in advanced structural composites. In this study, we explore the mechanisms of damage initiation, propagation, and delamination resistance in polymer matrix functionally graded laminates (PM-FGLs) through a combined experimental–computational approach. Laminates with linear, exponential, and bio-inspired gradation profiles were fabricated using vacuum-assisted resin transfer molding (VARTM) and additive manufacturing techniques. Comprehensive mechanical …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 321–337 Read article
-
An Investigative Study on Cache-Oblivious Data Structures
Abstract: Cache-oblivious data structures and data management systems have emerged as critical components in modern computing environments, aiming to optimize memory access patterns across different levels of the memory hierarchy without explicit knowledge of cache sizes or configurations. This study presents an overview of cache-oblivious techniques, including adaptive data structures, compression, parallel processing, and security considerations. The workexplores future directions in cache-oblivious systems, such as non-volatile memory support, graph processing, edge …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 2, 2023 · pp. 33–37 Read article
-
Accelerating Unsupervised Feature Learning: Parallelized Training of Denoising Autoencoders
Abstract: Unsupervised representation learning has become a cornerstone of contemporary machine learning, enabling algorithms to extract informative features from un-labelled, high-dimensional data. This work investigates the efficacy of stacked denoising autoencoders (SDAEs) trained via parallelized stochastic gradient descent (SGD) as a scalable approach to feature extraction. By strategically leveraging multi-threaded computation, our study systematically examines the trade-offs between increased parallelism, training efficiency, and the preservation of model accuracy. Experiments on the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 56–64 Read article
-
Biomarkers in Cancer Research: Discovery and Future Directions
Abstract: Biomarkers have transformed the study of cancer, providing critical information regarding prognosis and therapy response and promoting earlier diagnosis. This paper discusses the different parts of cancer biomarkers, starting with their description, classification, and major types, which are the groundwork for understanding their clinical role. The discussion on the development and validation process of biomarkers is then undertaken, focusing on state-of-the-art techniques and the importance of ensuring accuracy and reproducibility. …
Published in Trends in Drug Delivery · Vol. 12, Issue 1, 2025 · pp. 22–26 Read article
-
A Random Forest Approach to Navigating Cryptocurrency Market Fluctuations
Abstract: This study looks at the main elements influencing daily price variations to improve our analysis and prediction of Bitcoin values. Our forecasting algorithm is based on comprehensive data that we have collected and analyzed over the last few years. Because the Random Forest algorithm provides more accurate forecasts than previous techniques, that is why we chose it. Predicting the price swings of cryptocurrencies, like Bitcoin, can be challenging due to …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 2, 2024 · pp. 7–11 Read article