Automation Techniques Research Articles

Machine Learning In Production Engineering: A Comprehensive Review

The study examines how machine learning (ML) methods can be incorporated into production engineering practices. The paper highlights data preprocessing and cleaning as essential steps to maintain data quality and reliability for ML applications. The review shows the production environment challenges that include missing data values and the presence of outliers along with data inconsistencies. The text explains how advanced automation techniques decrease human involvement while improving feature extraction methods, which produce uniform features across different manufacturing systems. The paper emphasizes that effective model deployment relies on rigorous data engineering pipelines that perform comprehensive data ingestion, transformation, and feature engineering. The review intends to explore the existing ML applications within production engineering while identifying key practices that enable model readiness and reliability.

Special Issue on Advanced Image Processing Techniques for Robotics and Automation (Part 1)

The demand for sensing in robotics and automation has increased because of a decrease in the labor force. Recent advances in computational performance have bolstered the widespread use of image processing technology across various applications. This special issue aims to provide researchers with the opportunity to access the latest research and practical case studies on advanced image processing, computer vision, and sensing techniques for robotics and automation. The topics of interest in this special issue are as follows: 1) Theory and algorithms: Image Processing, Computer Vision, Pattern Recognition, Object Detection, Image Understanding, Media Understanding, Machine Learning, Deep Learning, 3D Measurement, Simultaneous Localization and Mapping (SLAM), Multispectral Image Processing, Visualization, Virtual Reality (VR) / Augmented Reality (AR) / Mixed Reality (MR) , Datasets for Image Processing; 2) Industrial applications: Factory automation, machine vision, visual inspection, monitoring, surveying, logistics; 3) Sensing techniques for robotics and automation: Robot vision, advanced driver-assistance systems (ADAS), autonomous driving, robotic picking, assembly, and palletizing; 4) Image processing hardware and software: Image acquisition devices, image sensors, image processing systems, sensor information processing; 5) Man machine interface: Visualization, human interface devices. We extend our heartfelt gratitude to all the contributors, reviewers, and editorial staff for their dedication and support in realizing this special issue.

AI-Augmented DevOps: Autonomous Software Delivery with Large Language Models

The evolution of DevOps has accelerated software delivery through continuous integration, deployment, and infrastructure automation. However, modern systems' scale, dynamism, and complexity have outpaced traditional automation techniques. This paper introduces AI-augmented DevOps: an architectural and operational model that embeds intelligent agents powered by Large Language Models (LLMs) and machine learning into the DevOps lifecycle. We propose a modular five-layer framework consisting of observation, inference, action, feedback, and interaction layers, each designed to support autonomous, traceable, and policy-compliant decision-making. Our implementation leverages GPT-4 and reinforcement learning to enhance tasks such as log summarization, Infrastructure-As-Code (IaC) generation, and real-time incident remediation. A simulated CI/CD environment and real-world case studies demonstrate significant improvements in deployment frequency, MTTR, and change failure rates. The paper provides a blueprint for integrating AI into software delivery pipelines, enabling systems that continuously learn, adapt, and improve while maintaining human oversight and operational governance.

Improving brain tumor diagnosis: A self-calibrated 1D residual network with random forest integration.

Improving brain tumor diagnosis: A self-calibrated 1D residual network with random forest integration.

Cognitive Automation in T2 RTGS Testing: Reducing Integration Risks Across 53+ Interfaces

Testing large-scale systems like T2 RTGS, which integrates numerous interfaces for end-to-end payment flows, requires robust automation to reduce complexity and risks. This article explores the application of cognitive automation techniques, combining genetic algorithms and computer vision, to transform traditional quality assurance workflows in financial infrastructure testing. Genetic algorithms are utilized to optimize test case prioritization, focusing resources on high-risk integration points and enabling faster validation cycles. For monitoring SWIFT message queues in Opics and FX systems, computer vision techniques automate real-time anomaly detection, flagging discrepancies without manual oversight. Additionally, the article highlights the implementation of machine learning-enhanced reconciliation models that significantly reduce false positives in payment discrepancies by learning from historical resolution records. By presenting measurable results and demonstrating AI-centric testing strategies, this article offers a technical roadmap for QA professionals facing complex integration challenges in financial systems, showing how cognitive automation not only detects errors faster but also fosters greater collaboration through end-to-end integration testing.

The Design of a Stem-Themed Model for a Smart Home Integrated with AI & IoT Technologies for High School Students

Abstract: The application of AI (Artificial Intelligence) and IoT (Internet of Things) technologies in teaching and learning has become a powerful trend, positively impacting and transforming both teaching methods and students' learning approaches. In the field of education, the use of AI and IoT technologies is still relatively new and is being gradually implemented. Therefore, this paper focuses on the design of STEM themes, specifically regarding AI & IoT technologies. Particularly, designing STEM topics around smart homes controlled by AI & IoT technology will help students understand real-world applications, as well as the operational principles of control circuits. It will also enable them to grasp automation techniques that enhance device monitoring, ultimately guiding career paths and fostering creative thinking skills while developing students' competencies in high school.

Data Reconciliation (Recon) Transformation Strategies for Finance Compliance Reports

Financial institutions are required to ensure data accuracy, integrity, and compliance when reporting to regulatory authorities. Reports such as FR 2052a (Liquidity Monitoring), Y-14Q (Stress Testing) necessitate robust data reconciliation (Recon) strategies to maintain regulatory compliance and mitigate risks. This paper explores technical and functional aspects of data reconciliation, highlighting key automation techniques, AI-driven solutions, and statistical methodologies for optimizing financial compliance processes. We analyze data integration challenges, anomaly detection models, and best practices in recon automation to enhance efficiency. Furthermore, case studies demonstrate how leveraging machine learning, cloud computing, and robotic process automation (RPA) can significantly improve financial data integrity. The future of reconciliation strategies is discussed, focusing on real-time monitoring, AI-driven decision intelligence, and blockchain-based audit trails. By implementing these strategies, financial institutions can reduce compliance costs, improve operational efficiency, and enhance regulatory transparency.

Model-driven engineering for digital twins: a systematic mapping study

Abstract Digital twins (DTs) are proliferating in a multitude of domains, including agriculture, automotive, avionics, logistics, manufacturing, medicine, smart homes, etc. As domain experts and software experts both have to contribute to the engineering of effective DTs, several model-driven engineering (MDE) approaches have been recently proposed to ease the design, development, and operation of DTs. However, the diversity of domains in which MDE is currently applied to DTs, as well as the diverse landscape of DTs and MDE applications to DTs, makes it challenging for researchers and practitioners to get an overview of what techniques and artifacts are already applied in this context. In this paper, we shed light on the aforementioned aspects by performing a systematic mapping study on the application of MDE automation techniques, i.e., model-to-model transformation, code generation, and model interpretation, in the context of DTs as well as on the characteristics of DTs including the twinned systems to which these techniques are applied in different domains. We systematically retrieved a set of 189 unique publications, of which 66 were selected for further investigation in this paper. Our results indicate that the distribution of employed MDE techniques (136 applications of automation techniques) is balanced between the different techniques, but there are significant variations for different DT types. With respect to the different domains, we found that even though applications are available in many domains, a small number of domains currently dominate applications of MDE to DTs, i.e., more than half of included papers are in the manufacturing and transportation domains.

Spectrofluorophotometric Analysis of Phytoconstituents, Biomarkers, Enzyme Kinetics and Trace Metals: A Comprehensive Review.

Spectrofluorophotometry is a highly sensitive and selective analytical technique widely employed in pharmaceutical, biomedical, and environmental sciences. This review provides a comprehensive application for detecting and quantifying phytoconstituents, biomarkers, enzyme kinetics, and trace metals. This technique offers detection of analyte in the femtomolar (fM) range. Phytochemicals such as flavonoids, alkaloids, and polyphenols were analyzed with high precision and accuracy, and the reported analytical method can be adopted for quality control analysis. Spectrofluorophotometry has been utilized to estimate biomarkers, which plays a critical role in disease diagnostics and therapeutic monitoring. It also facilitates the monitoring of enzyme kinetics, offering insights into metabolic processes and drug development. Additionally, its ability to detect trace metals through chelation and fluorescence quenching mechanisms proves vital for environmental and toxicological analysis. Despite challenges such as fluorescence quenching and environmental susceptibility, advancements in miniaturization, automation, and hybrid analytical techniques continue to enhance their capabilities. This review underscores the transformative impact of spectrofluorophotometry and its expanding role in modern analytical sciences.

Industrial Automation and Data Processing Techniques in IoT-Based Digital Twin Design for Thermal Equipment: A case study

Industrial Automation and Data Processing Techniques in IoT-Based Digital Twin Design for Thermal Equipment: A case study

The Transformative Impact of ASICs across Industries: A Technical Analysis

Application-Specific Integrated Circuits (ASICs) have emerged as a transformative technology in the semiconductor industry, revolutionizing various sectors, including healthcare, retail, agriculture, and computing. This comprehensive article explores the technical architecture, industry-specific implementations, challenges, and future directions of ASIC technology. The article examines the evolution of ASIC design methodologies and their impact on performance optimization, power efficiency, and manufacturing processes. It further investigates the integration of artificial intelligence in design automation, verification processes, and optimization techniques while addressing the complexity challenges in modern ASIC development. The article also highlights the significant advancements in industry-specific applications, from medical imaging systems to edge computing implementations, demonstrating the versatile impact of ASICs across different domains.

Simulation models verification and validation: Recent development and challenges: A review

Nowadays, verification and validation activities have become the prominent parameters to check the acceptability of a simulation model by the users for the intended purpose. This review paper aims to identify and analyze the recent development and challenges of simulation model verification and validation. To achieve this objective, a systematic review of the literature was carried out which mainly consisted of the methodological development of verification and validation process paralleled with simulation model development. Initially, a cumulative total of 980 records was found sourced from Google Scholar via an advanced search method, Science Direct, Web of science and from Scopus. Through a rigorous screening process about 72 sources or publications were included for analysis. The verification and validation techniques that have been developed so far were classified into five categories methodologically. From intensive analysis, it is found that researchers extensively scrutinize the traditional methods and graphical/ statistical tools, escalating interest in data-driven and automation techniques, and limited focus on agent-based and hybrid models. Though agent-based and hybrid models are increasingly vital in the realm of complex system simulations, their verification and validation processes remain relatively under-explored. Though reasonable efforts have been exerted on the verification and validation methods development, still academicians and researchers agreed on the lack of verification and validation methodology for the recently developed simulation model paradigms such as agent-based and hybrid models, autonomous robotics models, high fidelity and data-driven models, and real-time prediction models such as digital twins. As a challenge of verification and validation processes, lack of universal methodologies, lack of reliable real-world data for validation, inaccuracy of real-world data for the intended purpose, different world views by different individuals, and the rapid growth and complexity of simulation modeling are identified as the hindering factors of verification and validation process.

Design and optimization of renewable energy-powered automation transformer coil winding machines

The automation of transformer coil winding machines powered by renewable energy sources offers a sustainable solution to address the growing demand for energy-efficient manufacturing in the electrical industry. This study explores the design and optimization of renewable energy-powered automation systems for transformer coil winding machines, integrating cutting-edge renewable energy technologies such as solar and wind power with advanced automation techniques. The research focuses on achieving optimal machine performance, energy efficiency, and environmental sustainability while reducing operational costs and carbon footprints. The study employs a multidisciplinary approach, incorporating renewable energy system modeling, mechanical design engineering, and automation control strategies. The design framework integrates renewable energy sources with energy storage systems to ensure uninterrupted operation, even in fluctuating energy conditions. Optimization algorithms, including machine learning techniques and computational simulations, are utilized to refine machine performance and enhance the precision of coil winding operations. Key parameters such as torque, speed, and winding accuracy are analyzed to achieve superior results. Experimental validations demonstrate the feasibility and efficiency of the proposed system, showing significant improvements in energy consumption, reduced downtime, and higher operational reliability compared to conventional coil winding machines. Additionally, the economic and environmental impact assessment highlights the potential for widespread adoption of such renewable energy-powered systems in the transformer manufacturing industry. The findings underscore the importance of integrating renewable energy with industrial automation to promote sustainable manufacturing practices. Future work will explore advanced control systems and hybrid renewable energy setups to further enhance the performance and scalability of these machines.

Automating Dataset Generation for Object Detection in the Construction Industry with AI and Robotic Process Automation (RPA)

The construction industry is increasingly adopting artificial intelligence (AI) to enhance productivity and safety, with object detection in visual data serving as a vital tool. However, developing robust object detection models demands extensive, high-quality datasets, which are often difficult to generate and maintain in construction due to the dynamic and complex nature of job sites. This paper presents an innovative approach to automating dataset generation using robotic process automation (RPA) and generative AI techniques, specifically, DALL-E 2. This approach not only accelerates dataset creation but also improves model performance by delivering balanced, high-quality inputs. To validate the proposed methodology, a case study of a building construction site is conducted. In this study, three commonly used convolutional neural network architectures—RetinaNet, Faster R-CNN, and YOLOv5—are trained with the artificially generated dataset to automate the identification of formworks and rebars during construction.

Empowering Database Administrators: The Essential Role of Automation in Modern Practices

Managing large servers and databases while maintaining the uninterrupted functioning of critical applications presents significant challenges for database administrators. Automating database administration has become a crucial component in enhancing high availability, reliability, efficiency, and scalability. To reduce mistakes, minimize manual interventions, and improve database process performance, automation is essential. Important components include disaster recovery plans, security monitoring, performance review, and automatic backups, all of which are intended to prevent data loss. This study explores how businesses use automated database administration and contemporary automation techniques to maintain a competitive edge in an increasingly data-centric environment. Keywords: Jenkins, data, ansible, automation, mistakes, CICD, access, backups, and consistency.

A Complete Pipeline to Extract Temperature from Thermal Images of Pigs.

Using deep learning or artificial intelligence (AI) in research with animals is a new interdisciplinary area of research. In this study, we have explored the potential of thermal imaging and AI in pig research. Thermal cameras play a vital role in obtaining and collecting a large amount of data, and AI has the capabilities of processing and extracting valuable information from these data. The amount of data collected using thermal imaging is huge, and automation techniques are therefore crucial to find a meaningful interpretation of the changes in temperature. In this paper, we present a complete pipeline to extract temperature automatically from a selected Region of Interest (ROI). This system consists of three stages: the first one checks whether the ROI is completely visible to observe the thermal temperature, and then the second stage uses an encoder-decoder structure of a convolution neural network to segment the ROI, if the condition was met at stage one. In the last stage, the maximum temperature is extracted and saved in an external file. The segmentation model showed good performance, with a mean Pixel Class accuracy of 92.3%, and a mean Intersection over Union of 87.1%. The extracted temperature observed by the model entirely matched the manually observed temperature. The system showed reliable results to be used independently without human intervention to determine the temperature in the selected ROI in pigs.

Health & Gait: a dataset for gait-based analysis

Acquiring gait metrics and anthropometric data is crucial for evaluating an individual’s physical status. Automating this assessment process alleviates the burden on healthcare professionals and accelerates patient monitoring. Current automation techniques depend on specific, expensive systems such as OptoGait or MuscleLAB, which necessitate training and physical space. A more accessible alternative could be artificial vision systems that are operable via mobile devices. This article introduces Health&Gait, the first dataset for video-based gait analysis, comprising 398 participants and 1, 564 videos. The dataset provides information such as the participant’s silhouette, semantic segmentation, optical flow, and human pose. Furthermore, each participant’s data includes their sex, anthropometric measurements like height and weight, and gait parameters such as step or stride length and gait speed. The technical evaluation demonstrates the utility of the information extracted from the videos and the gait parameters in tackling tasks like sex classification and regression of weight and age. Health&Gait facilitates the progression of artificial vision algorithms for automated gait analysis.

Adoption of Data-Driven Automation Techniques to Create Smart Key Performance Indicators for Business Optimization

Key performance indicators (KPIs) are crucial for managing business performance and optimization strategies. However, traditional KPIs are inflexible and cannot adapt to changes in staff, business units, functions, and processes. To address this issue, this paper proposes a method that combines statistics, machine learning (ML), and artificial intelligence (AI) to augment traditional KPIs with the flexibility of data-driven automation (DDA) techniques. This study builds a model that takes traditional KPIs generated by an integrated ecosystem as input data and assesses the suitability and correlation of the data using statistical techniques, such as Bartlett’s test of sphericity and the Kaiser–Meyer–Olkin (KMO) test of sampling adequacy. The model then employs exploratory Factor Analysis (FA) techniques to identify correlations and patterns, prioritize KPIs, and automatically generate smart KPIs for business optimization. The model is designed to adapt automatically by creating new KPIs as the business evolves and data change. A case study evaluation validates this approach, showing that DDA techniques can effectively create smart KPIs for business optimization. This approach provides a flexible and adaptable way to manage business performance and optimization strategies, enabling organizations to stay ahead of the competition and achieve their goals.

Toward spatial glycomics and glycoproteomics: Innovations and applications.

Toward spatial glycomics and glycoproteomics: Innovations and applications.

Extent of Implementation of Accounting Automation Techniques on Job Performance of Accounting Officers in Tertiary Institutions in Anambra State

This study x-rayed the extent of implementation of accounting automation techniques on job performance of account officers in tertiary institutions in Anambra State. Two research questions guided the study and four null hypotheses were tested at .05 level of significance. Related literature pertinent to the study were reviewed which exposed the need for the study. Survey research design was adopted using a population of 593 account officers in all the six public tertiary institutions (two universities, two polytechnics and two colleges of education) in Anambra State. A sample size of 239 account officers was used for the study. The sample size was derived using the Taro Yamani formula. Thereafter, stratified random sampling technique was used to determine the sample from each institution. A structured questionnaire developed by the researcher was used for data collection. Cronbach Alpha method was used to establish the reliability of the instrument. Reliability coefficient values of 0.84 and 0.80 were obtained for the two clusters with an overall reliability coefficient value of 0.82. Data collected were analyzed using mean and standard deviation to answer the research questions and ANOVA to test the null hypotheses at 0.05 level of significant. Statistical Package for Social Sciences (SPSS) was used to analyze data collected. The results showed that data entry and transaction processing automation are implemented at a high extent by accounting officers on their job performance in tertiary institutions in Anambra State. Types of institution and years of experience do not significantly influence the mean ratings of the respondents on the extent of their implementation of data entry automation and transaction processing automation on job performance in tertiary institutions in Anambra State. Based on the findings, it was recommended among others that tertiary educational institutions and professional bodies should prioritize the implementation of data entry automation and transaction automation techniques to improve the efficiency, accuracy and transparency of accounting processes. Institutions should also invest in training and capacity-building programmes to ensure that accounting staff have the necessary skills to effectively use automated accounting systems.