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114 articles for “Task complexity”
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Optimizing Data Processing Efficiency in Big Data: Advanced MapReduce Algorithm Innovations
Abstract: The exponential growth of big data in recent years has created an urgent need for innovative and efficient processing frameworks capable of managing and analyzing massive and complex datasets. Among these, MapReduce has gained prominence as a powerful tool for distributed data processing due to its simplicity and scalability. However, traditional MapReduce frameworks often encounter significant limitations in terms of efficiency, scalability, and resource optimization, particularly when handling large-scale and …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 1–7 Read article
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Optimization of Robotic Path Planning Algorithms for Autonomous Material Handling Systems
Abstract: For autonomous systems for handling materials (AMHS) to operate as efficiently as possible in industrial and logistical settings, robotic route planning is essential. This study examines many robotic route planning algorithms, emphasizing their use, ways of optimization, and difficulties in material handling systems. To improve the effectiveness, precision, and computational viability of these algorithms, the study also examines a number of optimization strategies, including machine learning, parallelization, heuristic search, and …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 2, Issue 2, 2024 · pp. 15–20 Read article
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Identification and Categorization of Brain Tumors
Abstract: Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Strategic and well-thought-out treatment planning significantly contributes to improving a patient's overall quality of life. Many different imaging techniques, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and also ultrasound are used to evaluate tumors in different parts of the body, with a focus on using MRI images for brain tumors. It is …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 2, 2023 · pp. 32–37 Read article
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Computational Simulations in Drug Discovery: Modeling Protein Folding and Drug Binding
Abstract: Computational simulations have become essential tools in drug discovery, offering unprecedented insights into molecular behavior at the atomic level. These simulations, particularly in the domains of protein folding and drug binding, allow for the exploration of complex biological systems that are often difficult to study experimentally. Protein folding, a critical aspect of drug discovery, involves the transition of a polypeptide chain from an unfolded to a biologically active structure. Understanding …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 23–29 Read article
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Epidemiology and transmission of infectious diseases study using Machine learning
Abstract: Infectious diseases remain a formidable global health challenge, characterized by rapid evolution and complex transmission dynamics that often outpace traditional epidemiological surveillance and response mechanisms. This study investigates the transformative potential of machine learning (ML) methodologies to enhance our understanding and prediction of infectious disease epidemiology and transmission. Leveraging diverse datasets—including clinical records, genomic sequences, environmental factors, social mobility data, and real-time digital footprints—we studies and presented various ML models …
Published in International Journal of Pathogens · Vol. 2, Issue 2, 2025 Read article
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Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection
Abstract: Sickle Cell Anemia (SCA) is a prevalent genetic blood disorder characterized by the presence of abnormal hemoglobin, resulting in the distinctive sickle shape of red blood cells. Timely and accurate identification of Sickle Cell Anemia (SCA) is essential for effective management and treatment. This study presents a new method that utilizes Convolutional Neural Networks (CNNs), a deep learning model particularly effective for image analysis. The process involves using microscopic images …
Published in Research and Reviews: A Journal of Medicine · Vol. 14, Issue 3, 2024 · pp. 9–15 Read article
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A Review on Loan Approval Prediction Based on Machine Learning Techniques
Abstract: The banking industry has also benefited greatly from technological advancements. An increasing number of individuals are submitting loan applications on a daily basis. When deciding which loan applicants to approve, the bank must take certain rules into account. The bank needs to choose the best one for approval based on certain characteristics. The process of carefully verifying every person and recommending them for loan approval is laborious and fraught with …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 1–11 Read article
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Design And Optimization Of High-Speed Array Multiplier
Abstract: An array multiplier is a digital circuit used for the rapid multiplication of binary numbers. It employs an array of logic gates to generate partial products concurrently, significantly enhancing speed. The array structure divides the multiplication task into smaller, parallel processes, allowing for efficient parallel processing of multiple bits. Each cell within the array manages specific bit-level multiplications, and their results are summed to produce the final product. This parallel …
Published in Journal of Microelectronics and Solid State Devices · Vol. 11, Issue 3, 2024 Read article
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Data Integration and Visualization in Bioinformatics: Techniques and Challenges
Abstract: Data integration and visualization play essential roles in bioinformatics, facilitating the thorough analysis, and interpretation of intricate biological datasets. In the field of bioinformatics, vast amounts of data are generated from various experimental platforms, such as genomic sequencing, proteomics, transcriptomics, and metabolomics. However, the heterogeneity of these datasets, coupled with their large scale and complexity, presents significant challenges in terms of integration, analysis, and visualization. Data integration techniques aim to …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 1–8 Read article
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Applying Text Analysis Methods for Emotion Recognition
Abstract: This article presents a comprehensive study of sentiment analysis, a vital task in the realms of natural language processing (NLP) and artificial intelligence (AI). Sentiment analysis involves the extraction and classification of subjective information from textual data, determining whether the sentiment expressed is positive or negative. This paper investigates different approaches and methodologies used in sentiment analysis, encompassing machine learning models as well. Additionally, it discusses the challenges faced in …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 2, 2024 · pp. 12–22 Read article
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AI and Machine Learning Approaches for Estimating Depression Severity: Techniques, Trends, and Applications
Abstract: Depression is a very common mental health disorder that results in a disorder of a person’s behavior, emotions, and cognitive abilities. Depression can be caused by environmental factors or hereditary factors. The person suffering from depression might have symptoms of suicidal thoughts, altering food patterns as well as sleeping issues. Depression is a global issue that has impacted millions of people globally having more effect on women worldwide. The complexity …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 3, 2024 · pp. 29–38 Read article
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Machine Learning Approaches Towards Resume Classification
Abstract: Finding the right person for an open position can be an unnerving task, especially when there are many applicants, and if the recruiter or the Human Resources department must sort and further categorize all those resumes then it will be a labor-intensive, time-consuming, and tiresome task. Additionally, human assessment of resumes may be biased and prone to mistakes. Manually screening the proper candidate's resume from the pool is not practicable; …
Published in International Journal of Electronics Automation · Vol. 1, Issue 2, 2023 · pp. 1–7 Read article
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A Comparison of Different Generative AI Models
Abstract: Generative models have significantly advanced the field of artificial intelligence by allowing machines to produce complex and realistic outputs such as images, text, and other forms of data. Among the leading frameworks in this domain are generative adversarial networks (GANs), variational autoencoders (VAEs), and architectures based on Transformers. Each model offers specific benefits and drawbacks concerning design structure, training demands, and range of applications. This paper provides a detailed comparison …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 16–22 Read article
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Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection
Abstract: The expanding realm of data analysis holds considerable importance in healthcare, particularly in the medical sector where forecasting heart disease is considered a complex endeavor. Early prediction of serious health conditions can be the determining factor between survival and fatality, with heart disease being one such critical health issue. Over the past decade, the main reason for death has been heart disease. Heart disorders come in many different forms, and …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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The Role of Automation in Modern Healthcare: Innovations and Impact.
Abstract: Automation technology encompasses a broad array of systems and tools designed to perform tasks with minimal human intervention, thereby increasing efficiency, reducing errors, and enhancing productivity. This study explores the core components of automation technology, including sensors, actuators, controllers, and software, and examines their applications across various industries. In manufacturing, automation leads to significant improvements in production speed and precision. In healthcare, it enhances patient care and operational efficiency. In …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 2, 2024 · pp. 1–9 Read article
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Artificial Intelligence in Robotics: Current Trends, Applications, and Future Challenges
Abstract: The incorporation of artificial intelligence (AI) into robotics has transformed the industry by greatly improving robots' capacity to carry out complex and autonomous functions in a wide range of sectors. This paper explores the evolution, applications, and challenges associated with AI-driven robotics. It examines key AI methodologies employed in robotics, including machine learning, natural language processing (NLP), computer vision, and planning/control algorithms, which enable robots to perceive, learn, and interact …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 2, 2025 · pp. 31–43 Read article
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Smart Cities: How AI and IoT Shape Our Cities
Abstract: In this modern world where urbanization and digital technologies intersect, smart cities have emerged as a symbol of progress and innovation. Smart cities are urban areas built to operate in a more sustainable, connected, and efficient way. As our cities grow and become more complex, it is important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This study examines how AI and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 3, 2025 · pp. 19–28 Read article
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The Future of Robotics: A Review of AI-Enabled Robotics Research, Development, and Applications
Abstract: Robotics powered by artificial intelligence (AI) is transforming contemporary industries by empowering machines to learn, adapt, and operate on their own in intricate, changing contexts. The breadth and capabilities of automation have been greatly expanded by the convergence of AI technologies with robots, including machine learning, deep learning, computer vision, and natural language processing (NLP). With an emphasis on technological advancements, application areas, and research advances, this study examines current …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 2, 2025 · pp. 33–38 Read article
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AI-Powered Architectural Ideation: From Sketch to Script
Abstract: Artificial Intelligence (AI) is redefining the landscape of architectural education by altering how students conceptualize, design, and execute their projects. As emerging technologies continue to permeate architecture, AI holds a central role in shaping future architectural pedagogy. The integration of AI in architectural studies has introduced new methodologies, facilitating complex design processes, sustainability assessments, and improved efficiency. This abstract explores how AI tools like generative design software, digital twins, and …
Published in International Journal of Architectural Design and Planning · Vol. 3, Issue 1, 2025 · pp. 1–6 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article