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261 articles for “Deep complex”
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Advanced Perspectives on Fluid Dynamics in Environmental Systems: Interactions, Impacts, and Innovations
Abstract: Environmental fluid mechanics is a multidisciplinary field that investigates the movement and behavior of natural fluids—such as air and water—in the environment, with a particular focus on understanding and mitigating the effects of human activities and natural processes. This study presents an analysis of fluid flow phenomena relevant to environmental systems, examining the transport of momentum, heat, and contaminants in both natural and engineered environments. Emphasis is placed on the …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 2, 2025 · pp. 1–16 Read article
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Ecological, Economic, and Medical Roles of Fungi: Insights from Mycology in Addressing Global Challenges"
Abstract: The large and diverse kingdom of fungi is vital to ecosystems, industry, agriculture, and medicine. Due to its ecological and biotechnological value, mycology has grown in importance. Fungi are crucial to natural systems and human life, decomposing organic materials, developing mutualistic interactions with plants, and producing antibiotics and fermented products. Recent advancements in molecular biology and genomics have significantly enhanced our understanding of fungal diversity, physiology, and evolutionary processes. These …
Published in International Journal of Fungi · Vol. 2, Issue 2, 2025 Read article
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Development of a Novel Analytical Framework for Investigating Non-Symmetric Deformation Behavior in Strip Rolling
Abstract: In recent years, the asymmetrical rolling process has attracted considerable research attention due to its ability to induce non-uniform deformation characteristics within metallic workpieces. In this context, the present study introduces a novel analytical framework for asymmetrical cold rolling based on an enhanced slab method, specifically designed to overcome the inherent limitations of existing analytical models when applied to a wide range of asymmetric rolling conditions. A newly developed mathematical …
Published in Journal of Experimental & Applied Mechanics · Vol. 17, Issue 1, 2026 · pp. 1–21 Read article
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Autonomous 6G Physical Layer Architectures for Space-Air-Ground Integrated Networks
Abstract: The emergence of sixth generation (6G) wireless systems calls for a significant shift away from conventional deterministic communication models. As communication infrastructures evolve into Space- Air-Ground Integrated Networks (SAGIN), traditional physical layer (PHY) techniques struggle to operate effectively under the severe Doppler effects and long propagation delays associated with space environments. This paper examines the role of artificial intelligence embedded directly within the 6G transceiver architecture to enable ultra-reliable and …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 · pp. 21–31 Read article
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Research Paper A Review of Symmetry in Mechanical Systems: Theoretical Systems Foundations and Engineering Applications
Abstract: In mechanical system analysis and design, symmetry is of mechanical systems. This article examines the idea of symmetry in mechanical systems, exploring its mathematical foundations (such as Lie algebras and group theory) and how these ideas help explain the behavior, stability, and control of the system. We explore the applications of symmetry in a range of mechanical systems, from basic mechanical connections to intricate multi-body dynamics, emphasizing the benefits of …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 1, 2025 · pp. 15–19 Read article
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Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article
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Measuring Microstructure, Wear Resistance, and Mechanical Reliability Enhancement in Polymer Nanocomposites via Data-Driven Analysis with Deep Learning
Abstract: Polymer nanocomposites have gained great attention owing to their superior mechanical performance, better wear resistance and customizable microstructural properties for aerospace, automotive, medicinal and industrial engineering applications. However, the correct evaluation of the link between the microstructure evolution and the material reliability is a huge issue due to the intricacy of nanoscale interactions and diverse material characteristics. In this study, we propose a data-driven approach that integrates deep learning and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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From Differential Equations to Data Science: A Survey on Analytical Methods in Contemporary Problems
Abstract: The integration of differential equations and data science methods represents a dynamic and evolving approach to solving contemporary challenges across a wide range of disciplines, including engineering, physics, biology, economics, and finance. Differential equations have long served as fundamental tools for modeling continuous systems and processes, offering powerful insights into the behavior of natural and man-made phenomena. For example, they describe how heat diffuses through materials, how populations grow in …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Advances in Deep Learning for Medical Image Analysis in the Era of Precision Medicine
Abstract: Medical imaging is fundamental to modern healthcare but analyzing the high-dimensional data requires advanced techniques. Manual image interpretation is time-consuming, subjective and limited in detecting complex patterns and minute details. Recent breakthroughs in Deep Learning offer transformative advances for unlocking clinically relevant information from medical images. This paper provides a comprehensive 6000+ word review of the current state-of-the-art Deep Learning techniques for medical image analysis including detailed coverage of key …
Published in Research and Reviews : Journal of Computational Biology · Vol. 12, Issue 2, 2023 · pp. 10–23 Read article
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Handwritten Sanskrit Word Recognition: A Deep Learning Approach Using AlexNet
Abstract: Handwritten Sanskrit word recognition poses significant challenges due to the intricate structure of the script and the considerable variations in handwriting across individuals. To address these challenges, this research introduces a novel methodology employing transfer learning with the AlexNet convolutional neural network. The study utilized two distinct datasets: a specifically curated Sanskrit word image dataset containing 2616 samples, alongside a broader Devanagari character dataset used for validation purposes. The established …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 33–43 Read article
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Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction
Abstract: Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 32–38 Read article
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The Renaissance and Resilience of CB1 Reverse Agonists: From Central Liabilities to Peripheral Promise
Abstract: Building upon these foundational insights, current research has increasingly focused on refining the pharmacological profile of CB1 reverse agonists to maximize therapeutic benefit while minimizing central adverse effects. The adverse neuropsychiatric outcomes associated with first-generation agents such as Rimonabant including anxiety, depression, and suicidal ideation highlighted the critical role of central CB1 receptors in mood regulation. Consequently, drug development strategies have shifted toward peripherally restricted CB1 reverse agonists that exhibit …
Published in International Journal of Cheminformatics · Vol. 4, Issue 1, 2026 · pp. 30–35 Read article
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Optimization of Hybrid Precoding in Massive MIMO using Kalman’s Algorithm
Abstract: The ever-growing demand in the market for the high data rate and high spectral efficiency increases the need to use the available band ofspectrum more efficiently. The future is dependent on using the frequency bands more efficiently. The most recent and trending 5G technologyuses the mm Wave band for its transmission. This band in the 60 GHz frequency uses MIMO systems along with many other leadingtechnologies or methodologies like hybrid …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 2, 2023 · pp. 7–19 Read article
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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
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Ramifications of Artificial Intelligence and Cyber Security
Abstract: Artificial intelligence (AI) has pros and cons for cyber security: AI can improve network security, anti-malware, and fraud detection. AI can simulate cyberattacks, automate responses, and analyse enormous databases. AI-powered phishing and deepfakes are cyber risks. AI can potentially be attacked and become a liability for corporations. AI has transformed cyber security, bringing both new opportunities and challenges. AI-powered tools discover abnormalities faster, automate threat responses, and improve threat detection …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 1, 2025 · pp. 40–45 Read article
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A Study on AI-Enhanced Environmental Toxicology: Sensor-Driven Predictive Framework
Abstract: Traditional environmental toxicology relies heavily on labor-intensive, often retrospective, sampling and analysis, limiting our understanding of dynamic pollutant behaviors and their real-time impact on ecosystems and human health. This study presents a novel, integrated framework leveraging advanced sensor networks and artificial intelligence (AI) to revolutionize the monitoring, assessment, and predictive modeling of environmental contaminants. We deployed a sophisticated array of multi-parameter sensors (e.g., electrochemical, optical, biosensors for heavy metals, organic …
Published in Research and Reviews: A Journal of Toxicology · Vol. 15, Issue 3, 2025 Read article
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Designing Self-Optimizing Operating Systems: Information-Theoretic Approaches to Thread Scheduler Implementation
Abstract: Thread Level Scheduling (TLS) in multi-core and many-core processor environments represents a critical frontier in next-generation operating system design. As computing systems grow increasingly heterogeneous and concurrent, traditional scheduling strategies often rely on heuristics or localized resource metrics, frequently overlooking the deeper, quantifiable relationships and uncertainties inherent in complex concurrent workloads. This study explores the application of information-theoretic approaches, specifically entropy-based task allocation, mutual information-driven dependency analysis, and channel capacity-inspired …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 31–39 Read article
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Understanding Trichotillomania: Exploring and Managing Hair-Pulling Disorder
Abstract: One form of traumatic alopecia is called trichotillomania, which is characterized by an overwhelming impulse to take out hair and a feeling of comfort once the hair has been removed. Alopecia in trichotillomania results from the patient’s deliberate act of pulling out their own hair, often triggered by feelings of stress, tension, or psychological distress. This condition, classified as a mental health disorder, leads to noticeable hair loss that can …
Published in Research and Reviews: A Journal of Health Professions · Vol. 14, Issue 3, 2024 Read article
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Charting the Path Forward: An In-Depth Analysis of Breakthroughs and Hurdles in Artificial Intelligence
Abstract: Recent years have witnessed tremendous progress in artificial intelligence (AI), fueled by exponential increases in processing power and data accessibility. These developments have made it possible for AI to be widely used in a variety of industries, such as healthcare, finance, autonomous driving, and more. Significant difficulties are presented by the "black-box" nature of many AI systems, which lack transparency and the capacity to explain. By encouraging algorithms that can …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 1, 2025 · pp. 13–23 Read article
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A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems
Abstract: Large amounts of diverse medical data have been produced because of the quick development of digital healthcare systems, offering substantial chances to use machine learning methods for clinical decision support and illness prediction. By identifying intricate patterns in clinical data, machine learning-based models have shown great promise in early disease detection, risk assessment, and personalised healthcare. However, issues with transparency, interpretability, and reliability have been brought up by the growing …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 20–28 Read article