Search
47 articles for “hidden”
-
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
-
Contemporary Life of Lady Umm Musa in Islamic History
Abstract: The revision reports the issue of seeing angels by non-prophets, a controversial topic that has sparked debate among scholars. It reviews the views of Islamic scholars on the possibility of non-prophets witnessing angels and the circumstances that might lead to such visions, pointing out the diversity of opinions between those who permit such visions and those who believe they are exclusive to prophets In the linguistic sense, the concept of …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 1, 2025 · pp. 28–33 Read article
-
GHUMO.in: A Comprehensive Software to Boosting Tourism and Promoting Cultural Exchange
Abstract: India, with its vast and diverse landscapes, rich cultural heritage, and hidden gems, holds immense potential as a top tourist destination. However, unlocking this potential requires a comprehensive approach that addresses various aspects of travel, accommodation, cultural immersion, and safety. This research paper introduces “GHUMO.in”, an innovative platform designed to enhance the Indian tourism sector by showcasing remote areas, simplifying trip bookings, facilitating stays in local accommodations, providing life insurance …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 1, 2025 · pp. 34–41 Read article
-
CMOS-Based Process-Scalable Analog Circuits for Machine Learning: A Comprehensive Review and Future Directions.
Abstract: Analog computing techniques are gaining attention for machine learning (ML) applications due to their ability to reduce computational complexity. Continuous operations such as addition and subtraction offer a simpler and more efficient approach compared to probabilistic product decoding, which can be sensitive to noise and inconsistent measurements. This paper presents a simulated VLSI implementation of a broadcast edge connection, independent of the MOS component model, along with experimental results. The …
Published in Journal of VLSI Design Tools and Technology · Vol. 15, Issue 1, 2025 · pp. 8–17 Read article
-
Great Women of India Like Ahilyabai Holkar, Rani Durgavati, Rani Bhabani, Rani Bhavashankari, Rani Rasmoni: Lessons for Making New India 2047
Abstract: Introduction: Since ancient times, women have struggled to navigate a society dominated by men. Despite years of planned development in India, women still have a lower status in society, and their socio-economic conditions remain significantly worse than those of men. Time has come to take Lessons from our Great Women, like Ahilyabai Holkar, Rani Durgavati, Rani Bhabani, Rani Bhavashankari, Rani Rasmoni and others to make New India 2047. Objective: The …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 2, 2025 · pp. 39–44 Read article
-
TensorFlow-Based Big Data Analytics for IoT Networks: A Study
Abstract: Internet of Things (IoT) has exploded in recent years, connecting billions of devices generating massive amounts of data. This deluge presents both a significant opportunity and a considerable challenge. While the potential insights hidden within this data are transformative, customary data processing techniques frequently fall short when handled with the velocity, volume, and variety of IoT-generated data. This is where Big Data technologies step in, offering the tools and infrastructure …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 31–38 Read article
-
Virulence Factors in pathogens :mechanisms of infection & disease progression
Abstract: Pathogens, like bacteria, viruses, fungi, and parasites, have evolved specialized tools – known as virulence factors – that help them infect hosts, escape the immune system, and cause disease. These factors play a key role in determining how severe an infection becomes, how quickly it spreads, and how the body responds. Some pathogens use adhesins to stick to host cells, while others produce toxins that damage tissues or disrupt normal …
Published in International Journal of Pathogens · Vol. 2, Issue 1, 2025 · pp. 6–11 Read article
-
Bridging the Gap and Unlocking Health Literacy: A Guide to Medical Report Clarity
Abstract: This research highlights the significant challenges faced by the general population in interpreting laboratory reports, medical charts, and other health-related documents. Studies show that approximately 9 out of 10 individuals struggle to understand such medical information, primarily due to low health literacy levels. This lack of understanding has become a hidden epidemic, affecting the way people engage with and respond to their own healthcare. While medical tests are essential for …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 31–36 Read article
-
Study of Social Trends Prediction Using AI
Abstract: AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlations in a variety of available datasets, from social media to economic indicators to public records, and fundamentally changes decision-making based on …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 19–29 Read article
-
Efficient Clustering Techniques for Data Stream Mining
Abstract: Data mining mainly works on a massive database for storing heavy amount of data. It is generally essential for extracting the meaning insights from the massive, continuously growing database. The traditional method often struggles with sheer volume and the dynamic nature of the modern data. Data stream mining allows for the real-time analysis, means insights are generated as the data arrives, and not after the long batch process. This continuous …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 26–32 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
-
Optimizing Marketing Campaigns Using Random Forest and A/B Testing
Abstract: Marketing initiatives play a vital role in driving business growth by reaching targeted consumer segments through tailored strategies across multiple channels. The success of these initiatives is influenced by various factors, including the type and duration of the campaign, the characteristics of the target audience, the communication channels employed, and the overall efficiency of each strategy. These factors collectively impact key performance metrics such as conversion rates, customer acquisition costs, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 01–09 Read article
-
Artificial Intelligence and the Future of Education
Abstract: Artificial Intelligence (AI) refers to the capability of a computer system to mimic human intelligence by performing tasks such as learning, reasoning, problem-solving, and decision-making. As a rapidly evolving and emerging technology, AI holds the potential to transform a wide range of sectors, including healthcare, transportation, business, and notably, education. Its application in the educational domain is gaining momentum, aiming to create more efficient, engaging, and personalized learning environments. Incorporating …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 39–44 Read article
-
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
-
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
-
Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems
Abstract: In the era of intelligent technologies and data-driven systems, multi-objective decision-making (MODM) has become an essential aspect of managing complex environments such as smart cities, autonomous systems, and cyber-physical networks. As decision-making scenarios become increasingly dynamic and uncertain, there is a growing need for advanced methodologies that can handle diverse objectives, conflicting constraints, and incomplete information. This review highlights the emerging role of entropy, symmetry, and data fusion as foundational …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 44–49 Read article
-
Symmetry Breaking in Mathematical Models: Bifurcation, Chaos, and Pattern Formation
Abstract: Symmetry breaking serves as a central organizing principle in the understanding of nonlinear systems across physics, biology, chemistry, and engineering. When a system transitions from a symmetric state to an asymmetric configuration, it often signals the onset of new structures, dynamic behaviors, or even chaotic regimes. This review explores symmetry breaking from the theoretical and mathematical perspectives of bifurcation theory, chaos theory, and pattern formation. We discuss how small parameter …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 25–30 Read article
-
Emotion Recognition from Electroencephalogram Signal and Eye Movement Based on Deep Learning
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
-
Experimental Analysis of Glass Fiber Composite on Low Velocity Impacts
Abstract: This research specifically deals with determining the retained tensile strength after loading Glass Fiber Reinforced Polymer (GFRP) composites following low-velocity impact. The purpose is to determine the degree to which these impacts affect the structural performance and mechanical integrity of GFRP materials. Experimental tests were conducted on glass fiber composite specimens in order to observe variation in tensile strength upon impact. The results indicated significant tensile strength reduction in impact …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 789–796 Read article
-
Animal Species Prediction Using Deep Learning
Abstract: In the face of escalating biodiversity loss, effective monitoring of animal species is critical for conservation efforts. This study presents a deep learning approach for species detection and a multimodal feature identification technique for animals vulnerable to poaching. The suggested prediction system recognizes objects automatically by the application of deep learning techniques to detect objects and then recognize them by using computer vision techniques, and it is triggered when an …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 14–22 Read article