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198 articles for “Automating Data Analysis”
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Recent Advancements and Comprehensive Review on Hyphenated Techniques
Abstract: Hyphenated techniques represent a powerful class of analytical methods that combine two or more established techniques – typically a separation method with a spectroscopic detection technique – to achieve enhanced analytical performance. First introduced by Hirschfeld in 1980, the term “hyphenation” refers to the online coupling of such methods, enabling more precise, sensitive, and comprehensive analysis of complex samples. These techniques exploit the strengths of individual methods while overcoming their …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 16, Issue 3, 2025 · pp. 37–50 Read article
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Comparison and Analysis of Facial Emotion Detection Using Various Deep Learning Neural Networks
Abstract: Facial emotion recognition employs Convolutional Neural Networks (CNNs), Residual Networks (ResNet), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) to automatically identify various emotions, including disgust, anger, fear, happiness, sadness, surprise, and neutrality. This study utilizes transfer learning along with data preprocessing techniques such as rotation, flipping, brightness adjustment, and enhancement methods. Traditional machine learning models achieve an accuracy range of 45 to 50%. In contrast, our proposed …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 37–42 Read article
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Applying Text Analysis Methods for Emotion Recognition
Abstract: This article presents a comprehensive study of sentiment analysis, a vital task in the realms of natural language processing (NLP) and artificial intelligence (AI). Sentiment analysis involves the extraction and classification of subjective information from textual data, determining whether the sentiment expressed is positive or negative. This paper investigates different approaches and methodologies used in sentiment analysis, encompassing machine learning models as well. Additionally, it discusses the challenges faced in …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 2, 2024 · pp. 12–22 Read article
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Leveraging Deep Learning for Accurate Weed Identification
Abstract: Weed control is very important for all types of agricultural businesses. The project here revolves around the application of computer vision techniques and, more concretely, deep learning techniques, for the effective recognition and classification of weeds. The EfficientNetB4 architecture is an appropriate backbone as its scalability and performance optimization is adequate. The modifier used is Adam optimization algorithm which will serve as a pre- processor for the model. Weeds at …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 90–99 Read article
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Xypher Bot: Autonomous Surveying Robot
Abstract: Xypher Bot is a fully autonomous robot built to carry out tasks like height measurement, estimating distance, and detecting objects. It uses affordable and easily available components such as the MPU6050 sensor, ultrasonic sensors, and a laser pointer. By using simple trigonometry, the bot can measure object height with good accuracy. The top part of the bot, which is responsible for height measurement, has already been built and tested. The …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 2, 2025 · pp. 49–62 Read article
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A Comparative Analysis of Machine Learning Techniques for Fruit Defect Detection Systems
Abstract: With evolving technologies in machine learning, significant advancements have been made in the livestock industry, helping to reduce waste, increase yield, achieve cost savings, and improve competitiveness in the marketplace. Fruit defect detection models support precision agriculture by providing valuable data for decision-making and enhancing overall efficiency through automated inspection processes. This study implements and comparatively evaluates machine learning models including MobileNetV2, a custom-designed convolutional neural network (CNN) model, ResNet50, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 37–47 Read article
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Efficient Machine Defect Detection with Sugeno Fuzzy Membership and GRU Networks for Robust Industrial Automation
Abstract: Machine fault detection is of immense significance in industrial automation to achieve efficient operations, reduced downtime, and reduced economic losses. Sugeno fuzzy logic and Gated Recurrent Unit (GRU) networks are used in this research to provide a new hybrid solution that addresses problems such as noisy data, evolving defect patterns, and real-time detection. To improve readability and reliability, the Sugeno fuzzy logic unit preprocesses fuzzy and uncertain input data into …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 2, 2025 · pp. 17–26 Read article
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Using MCDM Methods in automotive industry- A Review
Abstract: In the automobile sector, choosing the best car necessitates weighing a number of factors, including cost, fuel economy, performance, safety, and environmental impact. In order to solve complicated situations that need the simultaneous evaluation of multiple conflicting aspects, Multi-Criteria Decision Making (MCDM) procedures are essential. Among the various MCDM approaches, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and MOORA are widely recognized for their straightforward structure …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 20–26 Read article
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Design and Development of a Smart Automated Packaging System for Poultry Drumsticks
Abstract: The present work aims to design and implement an automated packaging system for poultry drumsticks that enhances operational efficiency, product quality, and adaptability. The integrated system offers a solution that complies with industrial quality and safety standards by sorting products, getting accurate weighing, and efficiently portioning food into 1-kilogram standardized units. By effectively reducing human involvement, operational inefficiencies, workforce requirements, and contamination hazards, the automation system becomes sufficiently adaptable to …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Life Cycle Assessment (LCA): A Tool for Sustainable Development and Environmental Management of Products
Abstract: Life cycle assessment (LCA) is a crucial technique for clearly understanding how much room there is for product improvement in a given good or service. Because of its high cost and application in eco-design, supply chain management, green buying, sustainable investing, and other relevant activities, manual data engineering and analysis processes. Traditional LCA methodologies and technology may need more scalability regarding substantial product portfolios and expanding reporting requirements. The Sustainability …
Published in International Journal of Industrial and Product Design Engineering · Vol. 1, Issue 2, 2023 · pp. 16–25 Read article
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ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction
Abstract: Fiber-reinforced polymer (FRP) composites are widely used in aerospace and structural systems; nevertheless, the potential for microcracking and fatigue-induced performance degradation remains an obstacle with respect to improved service life. Traditional self-healing methods, while performing well on a chemical level, often lack real-time diagnostic awareness and adaptive control. To circumvent this, we developed a machine-learning augmented self-healing FRP composite, in which a DCPD–Grubbs catalytic matrix was combined with IoT sensor …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 188–208 Read article
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Enhancing Road Safety: A Robotic System for Automatic Pothole Identification and Filling
Abstract: Innovative technologies called automatic pothole detection systems are made to automatically locate and identify potholes on road surfaces. These systems incorporate many components, including sensors such as cameras, LiDAR, and accelerometers, to acquire data on the road conditions. Potholes on roads provide serious risks to passing cars and pedestrians, which can result in collisions, damage to cars, and deterioration of the infrastructure. Automatic pothole detection systems, which make use of …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 3, 2024 · pp. 1–6 Read article
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Smart Agriculture in India: Advancements in Image Processing for Automated Plant Disease Detection and Crop Analysis
Abstract: The adoption of image processing technologies in agriculture is emerging as a revolutionary method for tackling persistent challenges in the farming industry. These techniques are increasingly used for different tasks such as detecting plant diseases, assessing crop health, and predicting yields, especially in the framework of smart agriculture systems. This study paints a detailed picture of the latest progress in image processing techniques applied to automated disease detection and detailed …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 13–19 Read article
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Human-robot Collaboration in Manufacturing: Safety, Efficiency, and Technological Developments
Abstract: Human-robot collaboration (HRC) has emerged as a transformative force in modern manufacturing, significantly enhancing productivity, operational flexibility, and overall efficiency. This review article explores the fundamental aspects of HRC, with a particular focus on safety protocols, efficiency optimization, and technological advancements that are shaping the future of collaborative robotics. Ensuring safety in HRC environments is a primary concern, necessitating the implementation of advanced safety measures, risk assessment methodologies, and compliance …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 1, 2025 · pp. 18–23 Read article
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IoT-based Black Box Monitoring for Vehicle Crash Data Analysis and Improving Safety
Abstract: Road accidents are a significant global concern. This project proposes an Internet of Things (IoT)-based black box monitoring system for vehicles to enhance road safety through comprehensive crash data analysis. The system expands on traditional black boxes by incorporating various sensors (accelerometers, gyroscopes, GPS) and potentially in-cabin cameras (with privacy safeguards). This data offers a deeper understanding of crash dynamics, including impact severity, vehicle motion, and safety system deployment timing. …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 3, 2024 · pp. 7–13 Read article
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IoT Integration in Sustainable Agriculture
Abstract: The increasing demand for food production, environmental concerns, and resource limitations have necessitated the adoption of Internet of Things (IoT)-based innovative farming solutions. The current paper introduces an IoT-based system that integrates hydroponics, aquaponics, and poultry to promote sustainability, resource utilization, and agricultural productivity. Conventional farming practices are riddled with ineffective use of resources, uncertain environmental effects, and expensive operations. The new system facilitates real-time monitoring, automated decision support, and …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 69–84 Read article
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Design and Development of Screw Detection System : A case study
Abstract: This study explores the design of a vision-based screw detection and orientation system for industrial automation, inspection, and robot disassembly. By integrating machine learning algorithms like region-based convolutional neural networks (R-CNN) with traditional image processing and impedance sensing, the system performs real-time screw presence detection, head type identification, and alignment. Three key technologies—deep learning classification, edge-based geometric analysis, and impedance verification—are integrated into a single modular system. The findings indicate …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 30–36 Read article
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An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 Read article
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A Study on Smart Healthcare Innovations
Abstract: The desire for effective, patient-centred solutions and the rapid growth of technology are driving forces in the healthcare industry. The term "smart healthcare innovation" refers to a broad category of approaches, tools, and procedures that are intended to improve patient outcomes, optimize resource use, and enhance overall healthcare delivery. These innovations integrate cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), wearable devices, big data analytics, and …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 · pp. 63–68 Read article