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501 articles for “Data Insights”
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Anisotropic Debye-Waller Factors and Debye Temperatures in Hexagonal Close-Packed Elements: A Comprehensive Compilation and Analysis
Abstract: In this study, we have investigated the anisotropic behavior of Debye-Waller factors (DWFs) and Debye temperatures (DTs) in three distinct materials: hexagonal rhenium (Re), osmium (Os), and thallium (Tl). We conducted a comparative analysis, aligning our experimental data on directional Debye temperatures with theoretical calculations. This exercise provided valuable insights into the concurrence between practical and theoretical approaches, thereby offering a critical evaluation of the accuracy and reliability of theoretical …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 32–39 Read article
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Phubbing, Social Anxiety and Perceived Control in Young Adults
Abstract: This study examines the complex interactions among ten empirical studies on social anxiety, phubbing (phone snubbing), and perceived control. Psychological research has focused a great deal of attention on social anxiety, which is characterized by the fear of being negatively evaluated in social situations. Phubbing, the practice of people prioritizing their phones over interpersonal interactions, has become more common in recent years due to the widespread use of smartphones. This …
Published in International Journal of Behavioral Sciences · Vol. 2, Issue 2, 2025 · pp. 18–25 Read article
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Identification of Hub Genes and Enriched Gene Ontology & Pathways in Idiopathic Pulmonary Fibrosis Through Bioinformatics Approaches
Abstract: Idiopathic Pulmonary Fibrosis (IPF) is a progressive interstitial lung disease marked by aberrant remodeling of lung tissue and excessive extracellular matrix deposition, ultimately leading to respiratory failure. Despite ongoing research, the molecular mechanisms underlying IPF remain incompletely understood. This research aims to uncover differentially expressed genes (DEGs) and related biological pathways through an integrated analysis of microarray data. Two publicly available datasets, GSE110147 and GSE53845, were obtained from the Gene …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 1–13 Read article
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Attitude of Undergraduate Agriculture Students Towards Farming: Empirical Evidence From a Premier State Agricultural University in India
Abstract: Agricultural education plays a critical role in developing skilled human resources for sustaining and revitalizing the farming sector in India. Despite agriculture being the backbone of the national economy, there is increasing concern about the declining inclination of educated youth towards farming as a profession. The present study was undertaken to assess the attitude of undergraduate agriculture students towards farming and to examine selected socio-demographic, educational, economic, and psychological factors …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 2, 2026 · pp. 41–56 Read article
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Artificial Intelligence in Cybersecurity: Emerging Trends, Technological Advancements, and Future Directions for Cyber Defense
Abstract: Artificial Intelligence (AI) is revolutionizing the field of cybersecurity by automating complex security tasks, improving threat detection capabilities, and enhancing the precision of threat response mechanisms. With the rapid evolution of cyber threats such as malware, ransomware, phishing, and data breaches, conventional security systems are often insufficient to provide timely and accurate protection. AI, powered by machine learning algorithms and neural networks, enables the analysis of vast datasets to detect …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 103–112 Read article
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A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction
Abstract: The adoption of ML technologies in agriculture is reshaping farming practices, empowering producers to make informed, data-oriented decisions that improve yields, sustainability, and long-term resilience. In mango cultivation, ML analyzes data from weather, soil, and pests to optimize irrigation, fertilization, and pest control. Predictive analytics help forecast ideal farming practices, minimizing resource wastage and improving yield. Real-time monitoring and image-based disease detection allow timely interventions to maintain plant health and …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 1, 2026 · pp. 12–17 Read article
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Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India
Abstract: Commodity price forecasting is instrumental in financial markets, providing framework for investment choices and risk management practices. Traditional models, including statistical and machine learning approaches, have limitations in capturing the nonlinear and volatile nature of commodity prices. Deep learning (DL) techniques have emerged as promising alternatives, leveraging advanced neural networks to enhance predictive accuracy. This study presents a thorough and comprehensive examination of deep learning applications in commodity price prediction, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 08–12 Read article
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Multiple Disease Prediction Using Machine Learning Algorithms
Abstract: The incorporation of machine learning algorithms into healthcare has transformed disease prediction and diagnosis. This research introduces a method for predicting various diseases using machine learning techniques. A comprehensive dataset, consisting of patient records, medical histories, and key disease-related features, was utilized to build predictive models. Data preprocessing methods, including feature selection and normalization, were implemented to clean and prepare the dataset. Several machine learning algorithms, such as Decision Trees, …
Published in Research and Reviews : A Journal of Immunology · Vol. 14, Issue 3, 2024 · pp. 34–38 Read article
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A Descriptive Study to Assess the Effectiveness of Instructional Modules on Knowledge Regarding Challenges Faced by Rural Community People on Cancer Preventive Measures Among People Living in the Selected Community Area, Palakkad District
Abstract: This study employed a quantitative approach with a descriptive study design to assess the knowledge and challenges faced by rural communities regarding cancer preventive measures. A total of sixty participants were selected using purposive sampling, which is non-probability based. Data were gathered through a semi-structured questionnaire to capture demographic information, alongside a structured questionnaire specifically designed to evaluate knowledge on cancer prevention challenges within the rural population. The collected data …
Published in International Journal of Oncological Nursing and Practices · Vol. 2, Issue 2, 2024 · pp. 26–33 Read article
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AI and Big Data for Optimized Water Resource Management in Arid Regions
Abstract: Water scarcity in arid regions is an escalating global challenge, driven by climate change, population growth, and increasing demands from urban, industrial, and agricultural sectors. Effective water resource management (WRM) is crucial for sustaining livelihoods, economic stability, and infrastructure resilience. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and big data offer innovative solutions for optimizing water use, enhancing efficiency, and improving sustainability in water-scarce environments. This paper …
Published in Trends in Transport Engineering and Applications · Vol. 12, Issue 1, 2025 · pp. 1–5 Read article
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Gatividhi Guard: The Activity Guardian—Revolutionizing Security Information and Event Management (SIEM) Technology
Abstract: In the dynamic landscape of cybersecurity, organizations confront increasingly intricate cyber threats that necessitate sophisticated security measures. Conventional systems such as Security Information and Event Management (SIEM) systems face ongoing challenges, they often struggle to effectively detect and mitigate sophisticated attacks within extensive data sets. To address these limitations, the introduction of Gatividhi Guard signifies a paradigm shift in SIEM technology. Gatividhi Guard is an innovative SIEM platform leveraging advanced …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 1, 2024 · pp. 29–44 Read article
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Investigation of Mechanical Properties of Banana, Linen and Their Hybrid Reinforced Composite Laminates in Adverse Condition and Analyze Using ML
Abstract: This research investigates the mechanical performance of composite laminates reinforced with banana and linen fibers, focusing on both individual and hybrid fiber combinations. The primary objective is to assess how these natural fiber composites behave under extreme environmental conditions, particularly high humidity and fluctuating temperatures, which are common in aerospace and automotive applications.Key mechanical properties—tensile strength, flexural strength, and impact resistance—are experimentally evaluated to assess the performance and long-term reliability …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 25–31 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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Leveraging Large Language Models for Personalized Document Summarization and Question Answering: An Architecture for Stoner-Friendly Chatbots
Abstract: This study presents a detailed framework for developing personalized chatbots that utilize large language models (LLMs) to process and extract information from extensive documents while effectively responding to user inquiries. The proposed system is designed to mitigate information overload by employing advanced natural language processing techniques, leveraging technologies such as OpenAI, LangChain, and Streamlit. By integrating these tools, the framework enhances knowledge retrieval, simplifies document comprehension, and improves overall productivity. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 88–93 Read article
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Effective Use of Library Resources and Services by the End Users at PPSavani University, Surat
Abstract: This qualitative research study examines the perceptions of students and faculty regarding the utilization of library resources and services at P. P. Savani University (PPSU), Surat. While the library has modern infrastructure, an extensive print collection (25,000+ books) and digital collection (64,200+ e-journals, 18,500+ e-books), and full automation via KOHA (a widely used open-source Integrated Library System (ILS)), a nuanced understanding of user experiences is paramount to truly optimize its …
Published in Journal of Advancements in Library Sciences · Vol. 12, Issue 3, 2025 · pp. 33–38 Read article
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The Role of BIM and Parametric Intelligence in Architectural Practice: A Study of Architects in Uttarakhand
Abstract: Dehradun, the capital city of Uttarakhand, represents one of India’s youngest and most dynamic urban centers in Uttarakhand. Since its designation as the state’s capital, the city has experienced a rapid evolution in architectural development and construction technology. As urbanization and design demands increase, architectural practices in Dehradun and across Uttarakhand are progressively shifting from conventional methods toward advanced digital tools that promote precision, efficiency, and sustainable outcomes. Among these, …
Published in International Journal of Architectural Design and Planning · Vol. 4, Issue 1, 2026 · pp. 19–38 Read article
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Consumer Awareness and Adoption Barriers of E-Pharmacy Services in Delhi/NCR: A Mixed-Methods Investigation
Abstract: Background: The rapid digitization of India's pharmaceutical market has positioned e-pharmacies as a transformative force; yet, consumer adoption remains significantly low despite growing awareness. Objectives: This study investigates consumer awareness levels, purchasing behaviors, trust perceptions, and adoption barriers regarding e-pharmacy services among urban Indian consumers. Methods: A sequential explanatory mixed-methods design was employed. Quantitative data were gathered via a structured questionnaire administered to 65 respondents in Delhi. Qualitative insights were …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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High-Energy Astrophysics: Exploring the Extreme Universe Through Radiation, Relativistic Phenomena, and Cosmic Cataclysms
Abstract: High-energy astrophysics is a fast-developing area of astrophysical research dedicated to exploring the universe’s most powerful and extreme events. It involves investigating dense and energetic cosmic objects and phenomena, including black holes, neutron stars, supernova remnants, gamma-ray bursts, and active galactic nuclei. These sources emit radiation predominantly in the X-ray and gamma-ray regions of the electromagnetic spectrum and are often associated with high-energy particles, including cosmic rays and neutrinos. Investigating …
Published in International Journal of Universe · Vol. 2, Issue 1, 2026 · pp. 10–15 Read article
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IoT-Enabled Remote Patient Monitoring System Using Wearable Sensors
Abstract: In recent years, the Internet of Things (IoT) has revolutionized healthcare by enabling seamless connectivity between patients, medical devices, and healthcare professionals. The increasing demand for continuous health monitoring and early disease detection has driven the development of IoT-based remote patient monitoring systems. This paper presents an IoT-enabled framework that integrates wearable physiological sensors, wireless communication modules, and cloud- based analytics to facilitate real-time health tracking. The proposed system continuously …
Published in Recent Trends in Electronics Communication Systems · Vol. 13, Issue 1, 2026 Read article