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1193 articles for “agriculture chat bot.csv dataset”
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Analysis and Identification of Malicious Mobile Applications Using Machines Learning
Abstract: Over the past few years, malware attacks on the Android platform have surged, posing significant risks to users' financial security, personal information, and device integrity. In the first half of 2019 alone, approximately 25 million smartphones were infected, highlighting the severity of these threats. The model ranks manifest features based on their frequency in normal and malicious apps, identifying key components that distinguish benign from malicious applications. To improve detection …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 17–24 Read article
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Identification of Groundwater Potential Zone of Chhatrapati Sambhajinagar Tehsil Using Remote Sensing and GIS Technique
Abstract: Groundwater is one of the most vital and renewable natural resources, playing a key role in sustaining domestic, agricultural, and industrial activities, particularly in semi-arid regions like Chhatrapati Sambhajinagar tehsil in Maharashtra. The growing population, rising water demand, and irregular rainfall patterns have resulted in a pressing need to locate potential groundwater zones for sustainable water management. This research employs Remote Sensing (RS) and Geographic Information System (GIS) techniques to …
Published in Journal of Water Resource Engineering and Management · Vol. 12, Issue 2, 2025 · pp. 61–73 Read article
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Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques
Abstract: The use of polymers in biomaterials was now key to designing the next generation of medical implants, which need to be strong and also compatible with living tissue. Tests for biocompatibility, such as those done in the laboratory and by doing experiments on animals, require much time and many resources, so the need for computer-based approaches becomes clear. An artificial intelligence approach was provided in this study to determine how …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 665–692 Read article
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A Review of Machine and Deep Learning Techniques for Cyber Security
Abstract: Nowadays in the digital landscape, cyber threats and attacks are increasing in an exponential manner, posing server risks to organizations and critical infrastructures. Data breaches often result from sophisticated threat models that exploit vulnerabilities in networks, systems and user behaviors. Cyber solutions are increasingly incorporating machine learning and deep learning to prevent and mitigate these security issues. These technologies have the potential to detect anomalies, classify threats and predict potential …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 01–07 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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AI and ML in the Chemical Industry: A Review of Transformative Applications and Future Prospects
Abstract: The chemical industry, a key growth indicator of the global manufacturing ecosystem, is experiencing a digital transformation driven mainly by advancements in Artificial Intelligence (AI) and Machine Learning (ML) in this sector. These technologies are totally revolutionizing current and traditional methodologies by significantly improving process efficiency, reducing costs of manufacturing, accelerating R&D, and improving safety and sustainability standards. Proper utilization of Artificial intelligence (AI) and machine learning (ML) in chemical …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 1–6 Read article
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Dosing Control of Urea in Selective Catalytic Reduction (SCR) to enhance the reduction of Nitrogen oxides
Abstract: Selective Catalytic Reduction (SCR) is an effective aftertreatment technique designed to comply with rigorous emission criteria established by global regulatory authorities for the elimination of nitrogen oxides from exhaust streams. Since NOx and ammonia reagents are poisonous and an excess of either is therefore very undesired, it poses an intriguing control problem, particularly at high conversion. SCR systems must reduce NOx emissions as much as possible and reduce the possibility …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 110–120 Read article
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Review of Thermal Performance of Solar Air Heaters: Influence of Composite Materials and Advanced Thermal Storage Techniques
Abstract: Solar air heaters (SAHs) are essential devices for capturing solar energy and converting it into heat, finding applications in steam generation, refrigeration, agricultural drying, and space and water heating in residential and commercial environments. However, the thermal performance of SAHs is often limited by the low heat transfer coefficient of air and the absence of solar energy during nighttime, impacting efficiency and reliability. To address these challenges, this study explores …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 488–506 Read article
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Machine Learning-Assisted Design and Optimization of Lightweight Polymer Composites for IoT-Enabled Automotive Applications
Abstract: This study aims to develop an integrated machine learning and optimization framework for the intelligent design of lightweight polymer composites suited for IoT-enabled automotive applications. The goal is to enhance material performance while satisfying multiple design constraints such as mechanical strength, thermal stability, and process compatibility. A curated dataset of polymer composite formulations was used to train a Random Forest Regression (RFR) model capable of predicting tensile strength, thermal conductivity, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 12–27 Read article
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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
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Advanced Micromachining with Abrasive Jet Machining: Experimental Observations and Model Comparisons
Abstract: Abrasive Jet Machining (AJM), also known as Micro Blast Machining, is a non-traditional machining process that removes material through the erosive action of a high-velocity gas jet carrying fine abrasive particles. This process is particularly effective for machining intricate shapes in hard and brittle materials that are heat-sensitive and prone to chipping. Similar to sandblasting, AJM is widely utilized for tasks such as deburring, rough finishing, and micromachining, especially in …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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Feature Extraction and Analysis of Bearing Faults: A Review
Abstract: One of the most important steps in identifying bearing problems is feature extraction. In order to provide a more meaningful dataset, it entails locating and extracting pertinent features from raw bearing vibration signals. Tasks involving categorization and prediction can then make use of these attributes. In many practical applications, such as monitoring rotating machinery or electronic components, the raw signals collected (e.g., vibration, current, temperature) are often complex, high-dimensional, and …
Published in Trends in Mechanical Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 20–28 Read article
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Study of a Western Disturbance of 2023 Using Satellite-Based Observation, Reanalysis Data, and Numerical Simulation
Abstract: Western Disturbances (WDs) are synoptic-scale, extratropical storm systems that influence winter precipitation across northwest India. This study focuses on a specific WD event that occurred from 24– 25 March 2023, affecting Jammu & Kashmir, Himachal Pradesh, Uttarakhand, and Punjab. The analysis integrates satellite observations, ERA-5 reanalysis data, and simulations from the Weather Research and Forecasting (WRF) model to evaluate the model's performance. The novelty of this study lies in its …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 3, 2025 · pp. 16–38 Read article
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Topology and Geometry in Data Science: Persistent Homology and Beyond
Abstract: In recent years, the interplay between topology, geometry, and data science has gained substantial momentum, offering powerful frameworks to analyze and interpret complex datasets. Traditional statistical and machine learning methods often rely on linear or metric- based assumptions, which may fail to capture the intrinsic structure of high-dimensional or nonlinear data. In contrast, topological and geometric methods provide shape-oriented, scale- invariant tools that focus on the continuity, connectivity, and global …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 21–27 Read article
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Optimizing Glass to Metal Composite Seal Performance: An integrated Approach with Artificial Neural Network, Multiple Regression, and Taguchi
Abstract: Composite materials, particularly glass to metal composites, are critical components in solar receiver tubes, where vacuum leakage can significantly compromise the efficiency of solar plants. This research addresses the technical barriers associated with the development of durable and high-quality glass to metal composite seals. We investigate the principles that can enhance the physical and chemical properties of these composite seals, focusing on the incorporation of TiO2 and MgO nanoparticles into …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 418–435 Read article
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Fuzzy Probability Distributions and Their Applications in Uncertain Data Analysis
Abstract: This study explores the use of fuzzy probability distributions in data analysis under uncertain conditions, with a specific focus on their implementation in evaluating call center customer satisfaction. Traditional probability models rely on precise parameters, often failing to account for the inherent variability and subjectivity present in real-world data. In contrast, fuzzy probability distributions, which integrate fuzzy logic principles, offer a more adaptable and realistic framework for addressing such complexities. …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 3, 2024 · pp. 1–8 Read article
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Aquaculture Advancements Through Bioactive Molecules
Abstract: A wide range of seafood species – including fish, shellfish, squid, and bivalves – generate by-products that are often discarded as waste. However, these by-products hold great potential for developing innovative functional food products. Because there is limited opportunity to recycle natural resources, the loss of profit may be accompanied by the loss of ecological sustainability, given that the “wastes” of industrial processing can account for up to 75% of …
Published in International Journal of Marine Life · Vol. 2, Issue 2, 2025 Read article
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Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article
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Assessment of Water Quality and Quantity Trends at Reuse Locations within the Bahr El- Baqar Drainage Catchment under Water Stress Reduction Scenarios
Abstract: Many countries face the pressing challenge of water scarcity, affecting around 20% of the global population in areas with water shortages. Egypt, one of the most populous countries in Africa and the Middle East, is severely impacted by this issue. To address this, Egypt is focusing on developing alternative water resources, including the reuse of agricultural drainage water for irrigation. In this study, the SIWARE model was employed to evaluate …
Published in Journal of Water Resource Engineering and Management · Vol. 12, Issue 3, 2025 · pp. 1–11 Read article