Search
140 articles for “AI-driven techniques”
-
Cloud-driven Fraud Detection: Evaluating Decision Tree and Random Forest Classifiers for Credit Card Transaction Security
Abstract: With the alarming rise in global financial fraud, necessitating substantial annual losses, modern techniques for fraud detection are continuously evolving across various business domains. Fraud detection involves constant monitoring of user activities to estimate, perceive, or prevent undesirable behaviour. Cloud Computing emerges as a promising solution, accelerating application deployment, fostering creativity and innovation, reducing costs, and enhancing overall business acumen. This study introduces a cloud-driven approach to fraud detection, specifically …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 13–27 Read article
-
Sustainable Feeding Practices for Dairy Development in the tropics: Exploring Cost Reduction through Resource Optimization
Abstract: Sustainable feeding practices are crucial for enhancing the economic viability and environmental responsibility of dairy farming. This review explores innovative strategies for cost reduction through resource optimization, aiming to minimize feed expenses while maintaining animal health and productivity. A comprehensive examination of over 100 cost-saving measures reveals diverse approaches, including nutritional planning, pasture management, and the incorporation of alternative feed sources. The integration of precision feeding techniques, waste utilization, and …
Published in Research and Reviews : Journal of Dairy Science and Technology · Vol. 14, Issue 2, 2025 · pp. 11–19 Read article
-
Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models
Abstract: The AI-Based Decision Support System for Water Well Construction utilizes data from the National Aquifer Mapping and Management System (NAQUIM) and employs advanced AI techniques like regression analysis, decision trees, and neural networks. This system predicts crucial parameters for water well construction, including location suitability, water-bearing zone depths, and groundwater quality. By integrating large datasets such as lithology, geophysical logs, and aquifer maps provided by the Central Ground Water Board …
Published in Journal of Water Resource Engineering and Management · Vol. 12, Issue 1, 2025 · pp. 16–28 Read article
-
Statistical Modeling for Weld Quality Assessment using AI SAW Welding of Mild Steel
Abstract: The main issue to the industries that apply Submerged Arc Welding (SAW) is quality assurance since the structural integrity dictates safety and the performance of the industry. The existing system of checking manuals is not only time consuming but also has human errors that make it mandatory to deploy automated intelligent systems. This study carries out an extensive comparison of the leading approaches based on the use of Artificial Intelligence …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 892–907 Read article
-
Text to Image Using Machine Learning
Abstract: In the era of digital transformation, our project addresses the convergence of computer vision and natural language processing to enhance user interaction and visual content creation. This project comprises three distinct modules: user authentication and session management, image colorization from grayscale inputs, and text-to-image generation. The login registration module provides secure access to the system, ensuring user privacy and data integrity. Once authenticated, users can utilize advanced computer vision techniques …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 2, 2024 · pp. 35–41 Read article
-
Neuromorphic Spiking Neural Networks and Event-Driven Hardware: Architectures, Learning Paradigms, and Experimental Realizations
Abstract: With the current computational boom the research community is seeking for more sustainable energy efficient i.e. biologically inspired models of conventional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs) known as the third generation of neural network models, provide a revolutionary approach by mimicking the asynchronized event-driven and temporally accurate signaling of the mammalian brain. Whereas conventional deep learning models operate with real-valued activations and dense matrix multiplications, SNNs use …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 Read article
-
Polymer Chemistry-Driven Approaches for Improved Therapeutic Delivery
Abstract: Polymer chemistry has revolutionized pharmaceutical technology by enabling the development of advanced drug delivery systems with improved precision, stability, and therapeutic outcomes. This review explores the role of polymer design, synthesis, and functionalization in the development of responsive and targeted drug carriers. Emphasis is placed on various types of polymers—biodegradable, synthetic, natural, and stimuli-responsive—and their suitability for drug delivery platforms such as nanoparticles, micelles, hydrogels, dendrimers, and implants. The physicochemical …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 Read article
-
Role of Generative AI in Redefining Data Analytics
Abstract: The rapid evolution of data-driven technologies has introduced both significant challenges and promising opportunities within the field of data analytics. Among the most impactful advancements is Generative Artificial Intelligence (Generative AI), a groundbreaking subset of AI that is reshaping how data is interpreted, generated, and utilized. Unlike traditional analytical tools that rely solely on existing data patterns, generative AI possesses the capability to create synthetic data, simulate complex scenarios, and …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 01–07 Read article
-
3D-Printed Polymeric Drug Delivery Systems for Personalized Medicine
Abstract: Personalized medicine has revolutionized healthcare by tailoring treatments to individual patient needs. Among the emerging technologies, 3D printing has demonstrated immense potential in fabricating polymeric drug delivery systems with precise control over drug release, dosage, and bioavailability. These systems offer customized therapeutic solutions, improving efficacy while reducing adverse effects. This paper provides an overview of 3D-printed polymeric drug delivery systems, discussing the types of polymers used, fabrication techniques, applications in …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 313–325 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
-
Photochemical Materials for Light-responsive Optical Switching: AI-optimized Design of Dynamic Visual Effects
Abstract: This paper presents an in-depth investigation into the design and behavior of photochemical materials that generate optical illusions and dynamic visual effects through light-induced molecular transformations. The study focuses on advanced photoresponsive compounds such as azobenzene and spiropyran derivatives, emphasizing their reversible optical transitions governed by photoisomerization, phase transitions, and photochromism in solid-state and polymeric matrices. Spectroscopic and kinetic analyses are employed to evaluate the influence of light wavelength, material …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 13–27 Read article
-
Depression Detection Using Machine Learning: A Comprehensive Review
Abstract: Depression remains one of the most prevalent mental health conditions globally, yet it frequently goes undiagnosed due to the reliance on subjective evaluation methods. With the growing availability of digital behavioral data and significant progress in machine learning (ML), new possibilities have emerged for the automated detection of depression. This review offers a detailed examination of recent advancements in ML-driven approaches to identifying depressive symptoms. It covers a range of …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 27–32 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
-
AI-Driven Carbon Capture and Utilization: Advancing Sustainable Solutions for Climate Change Mitigation
Abstract: Climate change stands out as one of the biggest challenges for our planet today, as it poses a threat unprecedented to global ecosystems and human societies. This phenomenon is primarily driven by the increase in atmospheric greenhouse gases, particularly carbon dioxide (CO2), which trap heat in the atmosphere and cause rising global temperatures. These sources are largely industrial processes, transportation, and energy production, all of which rely very much on …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 1, 2025 · pp. 40–48 Read article
-
Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 Read article
-
A Comprehensive survey of robust image quality metrics for satellite imagery
Abstract: Satellite imagery is essential for applications like environmental monitoring, urban development, precision agriculture, defence surveillance, and disaster response. The reliability of these applications is closely tied to the quality of the captured images, which may be compromised by atmospheric effects, sensor imperfections, compression artifacts, and transmission noise. As a result, accurate image quality assessment (IQA) is essential to ensure trustworthy analysis and informed decision-making in satellite-based systems. The distinctive properties …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 1, 2026 · pp. 7–20 Read article
-
Aura Pulse: An AI-Powered System for Real-Time Emotional Support and Personalized Recommendations
Abstract: Aura Pulse is a cutting-edge AI-powered platform developed to provide real-time emotional support, tackling the growing challenges of stress, anxiety, and burnout in today’s fast-moving digital era. Utilizing advanced facial expression analysis, Aura Pulse interprets visual cues to create a comprehensive emotional profile of the user. This instant emotional evaluation enables the platform to deliver personalized suggestions aligned with the user’s mood and mental state, promoting overall well- being and …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 1, 2026 · pp. 18–25 Read article
-
Experimental Investigation of Date palm Leaf Fiber reinforced with polymer matrix composite.
Abstract: The demand for composites incorporating robust fibers is significant across diverse industries, driven by the favorable attributes of strength, lightness, and cost-effectiveness. Within GCC countries, date palm (DP) serves as a valuable source of cellulosic fibers, highly esteemed for its widespread availability. These fibers can be sourced from midribs, spadix stems, leaflets, and mesh. Date palm leaf fibers are becoming more and more popular as a way to strengthen composite …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 40–48 Read article
-
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
-
ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article