AI Learning Research Articles

Integrating AI and Machine Learning in Quality Assurance for Automation Engineering

The integration of AI and Machine Learning (ML) into Quality Assurance (QA) for Automation Engineering represents a transformative shift, leveraging data-driven decision-making and automation across industries. Despite their promising benefits, the reliability, fairness, and generalizability of ML models remain significant concerns. This paper addresses these challenges by exploring the complexities inherent in assessing and validating ML programs. Firstly, it identifies obstacles such as bias, model robustness, and adaptability to new data, emphasizing the necessity for rigorous testing frameworks. Secondly, the paper reviews existing methodologies and solutions proposed in scholarly literature to enhance the assessment of ML programs, ensuring they perform as intended and meet ethical standards.This comprehensive manual serves as a guiding resource for professionals and scholars navigating the dynamic convergence of QA and ML. It underscores the need for continual learning and adaptation in an era where AI's potential is matched by the responsibilities of ethical and resilient model development. By offering profound insights and methodologies, the paper equips QA practitioners and AI enthusiasts alike to navigate the intricate terrain of quality assurance in the era of machine learning effectively.

Decisional value scores: A new family of metrics for ethical AI-ML

AbstractResearch in ethical AI has made strides in quantitative expression of ethical values such as fairness, transparency, and privacy. Here we contribute to this effort by proposing a new family of metrics called “decisional value scores” (DVS). DVSs are scores assigned to a system based on whether the decisions it makes meet or fail to meet a particular standard (either individually, in total, or as a ratio or average over decisions made). Advantages of DVS include greater discrimination capacity between types of ethically relevant decisions and facilitation of ethical comparisons between decisions and decision-making systems, including across different modalities (for instance: human, machine, or coupled human–machine systems). After clarifying ambiguities in the concept of “decision” itself, including the question of how to individuate the decisions made by a system, we discuss the role and meaning of “decision” in common AI and machine learning approaches such as decision trees, neural networks, SVMs, and unsupervised classifiers. We then show how DVSs may be defined for several ethical values of interest, with an extended discussion of transparency. Finally, we explore how such metrics can be applied to real decision-making systems through two case studies: evaluations of LLMs for transparency; and evaluations of criminal risk assessment tools for utility, rights violations, fairness, and transparency.

Effects of artificial intelligence learning environments on engineers’ intercultural communication competence: mediating role of learning adaptability

ABSTRACT The purpose of this study is to investigate the impact of artificial intelligence learning environments on the intercultural communication competence of engineers. Engineering students at Chinese higher education institutions were invited to participate, and 990 students (510 males and 480 females) from different school types, majors, and grades completed an online survey. After analyses with multivariate linear regression and structural equation modelling, the results show that different dimensions of AI learning environments – including personalized learning, use of technology, and cooperation and interaction – have significant positive impacts on engineers’ intercultural communication competence and learning adaptability. At the same time, learning adaptability plays a mediating role between AI learning environments and intercultural communication competence. This study suggests that AI learning environments may have the potential to (i) enhance engineers’ intercultural communication competence and learning adaptability, an (ii) indirectly improve engineers’ intercultural communication competence through enhancements in their learning adaptability.

Synergistic insights: Exploring continuous learning and explainable AI in handwritten digit recognition

Deep Neural Networks achieve outstanding results; however, their reliance on a static environment with fixed data poses challenges in dynamic scenarios where data continuously evolves. Being capable of learning, adapting, and generalizing continually in a scalable, successful, and efficient manner is crucial for the sustainable development of AI systems. The classical solution of retraining the model using both old and new data is time-consuming and expensive. Continual Learning tackles the problem of learning new data distributions without the need for retraining from scratch. Furthermore, the task of recognizing unlabeled images using previously acquired knowledge becomes challenging, particularly when the new data needs to be incrementally annotated without starting the training process from scratch.To gain a deeper understanding of how “Black Box” neural networks make decisions, it is important to visualize components inside the model that affect the error rate throughout the decision-making process. The Continual Self-Learning model on label-less historical digits yields increasingly perceptive interpretations. This paper aims to establish a literature review of the latest advances in continual learning for computer vision tasks, to articulate catastrophic forgetting using Explainable Artificial Intelligence on both split MNIST and the historical digit dataset DIDA, and to shed light on important but still understudied topics.

Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0

Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models.

The Effect of AI Learning System on Student Learning Outcomes and Motivation

The Effect of AI Learning System on Student Learning Outcomes and Motivation

Evaluating the impact of knowledge management and database management on decision-making process: A case study of subsea project services

The oil and gas industry is known for its rapid technological advancements and the complexity of its operations, increasingly relying on data-intensive management. As subsea projects—from exploration to decommissioning—become more complex and data-driven, integrating knowledge management (KM) and database management systems (DBMS) has become essential. The research specifically explores how KM and DBMS contribute to decision-making, utilizing a quantitative methodology through a questionnaire survey. Findings reveal that KM processes significantly improve the effectiveness of non-spatial data management, highlighting KM's crucial role in facilitating technology adoption and operational efficiency. However, the influence of KM on spatial data management and overall decision-making is found to be limited, indicating the necessity for more adaptive and integrated KM strategies that serve the unique requirements of database systems. This study underscores the critical but nuanced role of KM and DBMS in the oil and gas industry, advocating for tailored KM strategies to optimize database management. It suggests future research into the interplay of KM with emerging technologies like AI and machine learning to enhance database management. This investigation stresses the importance of holistic KM practices in effectively managing the intricate data landscapes of subsea project services, thereby contributing to the broader discourse on KM, DBMS, and decision-making in complex settings.

A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine Learning

A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine Learning

Unlocking Security: The Significant Role of Machine Learning and Deep Learning Techniques in Fingerprint Recognition Systems

One of the more widely utilized biometrics for in-person identification is a fingerprint. It has been discovered that no two people have the same fingerprints and that each one is distinct. A person's fingerprint traits remain constant as they age. Compared to DNA, fingerprints are more distinctive. Identical twins cannot have the same fingerprints even though they have the same DNA. Artificial Intelligence encompasses the broader goal of creating intelligent systems, while Machine Learning focuses on developing algorithms that enable machines to learn from data. Deep Learning, as a subset of Machine Learning, leverages neural networks with multiple layers to learn complex representations of data. While Deep Learning has become increasingly prominent and successful in recent years, it is just one of the many approaches within the broader field of AI and Machine Learning. Fingerprint recognition stands as one of the oldest and most widely used biometric authentication methods, finding applications across diverse domains such as law enforcement, access control, and mobile device security. With the advent of machine learning and deep learning techniques, significant strides have been made in enhancing the accuracy, efficiency, and scalability of fingerprint recognition systems. This comprehensive review aims to provide a thorough examination of the pivotal role played by machine learning and deep learning methodologies in advancing fingerprint recognition technology. The paper commences with an introduction to the importance and ubiquity of fingerprint recognition, elucidating its significance in various real-world applications. Subsequently, it delves into the foundational concepts of machine learning and deep learning, elucidating their relevance to fingerprint recognition tasks. Key machine learning algorithms such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Random Forests, and neural network-based approaches are discussed, highlighting their strengths and limitations in the context of fingerprint recognition.

Current understanding and future directions in diagnosis and treatment in breast asymmetry: a literature review

Breast asymmetry can be caused by hormonal fluctuations, surgery, or developmental abnormalities that result in disparities in the size, shape, or location of the breasts. When it comes to identifying and assessing asymmetry, diagnostic techniques such as mammography, ultrasonography, MRI, and 3D tomosynthesis provide clear advantages. To address both functional needs and aesthetic concerns, there are various treatment options available, including non-surgical techniques such as hormone therapy and fat grafting, as well as surgical techniques such as augmentation, reduction, and mastopexy. With the help of AI and machine learning, future advancements in imaging improve the accuracy of diagnoses, enabling customized treatment plans based on molecular and genetic profiles. The field is evolving due to advancements in minimally invasive, 3D-printed implants enabling customization, and regenerative therapies such as stem cell treatments, with the goal of optimizing outcomes while minimizing recovery time and complications. Non-surgical alternatives such as focused ultrasound and injectable fillers provide additional choices for patients preferring less invasive treatments. Holistic patient care integrates virtual reality for surgical planning and psychosocial support, ensuring comprehensive management of both physical and emotional aspects. Overall, ongoing innovations are poised to revolutionize breast asymmetry management, offering tailored solutions that improve patient outcomes and quality of life.

Adaptive Security in Hybrid Cloud Environments: Leveraging AI and Machine Learning

Adaptive Security in Hybrid Cloud Environments: Leveraging AI and Machine Learning

Decentralized Governance to Optimize Human Output Datasets for AI Learning

Decentralized Governance to Optimize Human Output Datasets for AI Learning

681 Application of Low-Cost Automated Progress Tracking System (APS) on Laparoscopic Appendicectomy Simulation Models (LaApSi) for the Laparoscopic Simulation Training in the Lower-Middle-Income-Countries (LMICs)

Abstract Aim To assess the effectiveness of the Low-Cost Automated Progress Tracking System on Laparoscopic Appendicectomy (LA) Simulation Models (LaApSi) for Laparoscopic Training in Lower-Middle-Income-Countries (LMICs). To assess and record the progression of trainees’ performance during simulation training. Method After the successful application and validation of the LaApSi model during laparoscopic courses at LMICs, the APS system has been prototyped to assess the trainee’s progression of LA. This system helps to assess surgical steps during simulated LA with the help of the LaApSi model integrated with wires at various anatomical positions giving real-time computer feedback and allowing users to assess the timing of operation, blood vessel excision and injury to the base of the appendix/caecum. Results The prototype version tested in LMIC showed excellent results in the detection of the above-mentioned parameters (shown in photos). This cost-effective model (LaApSi< £5 and docking system <£15) showed huge potential as a laparoscopic training model in LMICs. There will be additional validation of 16 APS models during the Laparoscopic Surgery Course in Nepal in Jan 2024 in collaboration with Nepalese Doctors Association-UK which we believe will further solidify its application in the LMICs Conclusions There is a need for the transition to high-fidelity low-cost laparoscopic simulation models from the traditional training methods in the LMICs which in the long-term address global surgery values. These systems have the potential for remote proctorship in the rural parts adapting the Hub & Spoke model and integrating AI and Machine Learning which eliminates the use of wires and sensors in the models.

P-047 Sperm moving pattern analysis with artificial intelligence solution predicts aneuploidy ratios of zygotes

Abstract Study question Can an analysis of sperm movements with artificial intelligence predict aneuploidy ratios of zygotes? Summary answer And sperm movement patterns analyzed by AI diagnosis predicted the aneuploidy ratios of zygotes. What is known already Sperm motilities present a specific pattern like strength, strength with curving, curving only and none progressive et al. Sperm movement patterns are determined by the sperm neck protein which is the centromere protein. Also, this centromere protein of the sperms was involved in the chromosome segregation in the zygote. Study design, size, duration A total of 8,000 clinical participants were recruited by CHA Infertility Center Seoul Station and 5 videos were taken from the semen sample of each participant. Machine learning with sperm moving clip for the diagnosis algorithm predicted aneuploidy ratios of fertilized embryos. Participants/materials, setting, methods We used YOLO_v8 and EfficientNet algorithm on the extracted sperm movement trajectory image to classify sperm into classes according to their motion pattern. This information was combined with the age of the patient to predict the possibility of infertility by analyzing the association with aneuploidy ratios of zygotes using deep learning model. Main results and the role of chance Deep learning AI algorithm utilized diagnosis for unexplained male infertility. No strength moving sperm pattern has shown association with male origin aneuploidy after fertilization. Especially high ratios of cycle moving sperm significantly increased the aneuploidy ratios in the zygote. But paternal aging has no correlation with aneuploidy ratios in the embryo. AI diagnosis have a 0.9 accuracy score. It is expected that the AI solution generated through this system can help overcome the problem of finding the cause of male infertility by predicting aneuploidy ratios of zygotes. Limitations, reasons for caution This study is designed for and performed with a minimum number of sperm move data. For clinical application, further mass data was set-up to increase accuracy of diagnosis. Wider implications of the findings Our AI solution was constructed to predict the possibility of aneuploidy ratios in the zygote by analyzing and classifying the movement patterns of sperm. Therefore, sperm movement-based AI diagnosis solution can be applied to define unexplained male infertility. Trial registration number non-clinical trials

제품디자인에서 생성형 인공지능 활용을 통한 협업프로세스 활용 가능성 탐색

With the rapid advancement of generative AI, most industries are entering a new phase. Generative AI is being utilized across a wide range of fields, including design. Consequently, this study explored the application of AI in the product design field and integrated it into the design process. The goal was to identify the differences between traditional product design processes and those enhanced by AI. Through this research, the potential of AI in product design was confirmed, and future methods of its application were investigated. First, the study considered the domains of human and AI design separately. AI's role was seen as generating and displaying various images, while humans decided on keywords and concrete designs. Next, experimental results indicated that at crucial moments in the design process, the designer’s ability and judgment in selecting designs remained important. Additionally, the effective use of prompts through questions became crucial in applying generative AI. Language, as the culmination of human knowledge and the most fundamental means of expression, plays a critical role. Ultimately, the effectiveness and efficiency of AI usage will depend on the user's level of knowledge in the respective field or their ability to extract knowledge from AI learning. As AI technology advances, paradigms across all industries are shifting. This phenomenon will merge with traditional design methods, resulting in an evolved form of design approach.

Enhancing Road Safety Using AI and Machine Learning

Enhancing Road Safety Using AI and Machine Learning

Innovative solutions for critical infrastructure resilience against cyber-physical attacks

Critical infrastructure must withstand cyber and physical assaults in today's interconnected world. This study explores innovative strategies to shield these systems from cyber-physical threats. As networks become more connected, cyberattacks grow more sophisticated, making it imperative to safeguard sectors like transportation, energy, water, and information. The paper analyzes the present and future threat landscape, identifying vulnerabilities within critical infrastructures and proposing targeted mitigation strategies to enhance security. The research leverages AI and machine learning to develop detection tools that identify and predict cyber-physical attacks, enhancing response times and preventive measures. Additionally, resilience engineering and architecture are crucial in fortifying infrastructure, enabling it to withstand and recover from attacks while maintaining essential functions. The study also reviews legislation and policies surrounding infrastructure protection, pinpointing shortcomings and recommending improvements to bolster national security. Effective countermeasures against cyber-physical threats require collaboration and information sharing among government bodies, industry stakeholders, and educational institutions. These partnerships facilitate the exchange of knowledge, best practices, and tools to address threats efficiently. Furthermore, the paper highlights the importance of training programs that equip professionals with skills to integrate cyber and physical security defenses. Exploring the integration of blockchain, IoT, and autonomous technologies could further enhance the resilience of critical systems. These technologies foster secure communications and automated responses to incidents. Lastly, community awareness initiatives play a vital role in preparing for and mitigating cyber-physical attacks, ensuring that public readiness and resilience are maintained. This comprehensive approach aims to fortify critical infrastructure against evolving cyber threats, ensuring continuous operation and security.

Simulation and Modeling of the Adhesion of Staphylococcus aureus onto Inert Surfaces under Fluid Shear Stress.

Bacterial adhesion to biotic and abiotic surfaces under fluid shear stress plays a major role in the pathogenesis of infections linked to medical implants and tissues. This study employed an automated BioFlux 200 microfluidic system and video microscopy to conduct real-time adhesion assays, examining the influence of shear stress on adhesion kinetics and spatial distribution of Staphylococcus aureus on glass surfaces. The adhesion rate exhibited a non-linear relationship with shear stress, with notable variations at intermediate levels. Empirical adhesion events were simulated with COMSOL Multiphysics® and Python. Overall, COMSOL accurately predicted the experimental trend of higher rates of bacterial adhesion with decreasing shear stress but poorly characterized the plateauing phenomena observed over time. Python provided a robust mathematical representation of the non-linear relationship between cell concentration, shear stress, and time but its polynomial regression approach was not grounded on theoretical physical concepts. These insights, combined with advancements in AI and machine learning, underscore the potential for synergistic computational techniques to enhance our understanding of bacterial adhesion to surfaces, offering a promising avenue for developing novel therapeutic strategies.

Precision in practice: Exploring the impact of AI and machine learning on ultrasound guided regional anaesthesia.

Ultrasound Guided Regional Anaesthesia (UGRA) is a commonly utilized practice in both elective and emergency situations during surgical procedures and for pain management. Its benefits include, being non-invasive, cost-effective, readily accessible, and providing the anaesthetist with clear visualization of essential anatomical landmarks, needle progression, and the spread of local anaesthetic. Ultrasonography has been shown to increase success rates for regional anaesthesia and decrease complications. One of the critical steps during UGRA is identifying relevant anatomical structures like nerves or vertebras. However, this aspect can be hindered by external influences such as variations in nerve structure and position, interference from noise, and positional instability. Machine learning is a promising branch of artificial intelligence. It is used to conduct predictive tasks without programming instructions by creating algorithms. Extensive research has been conducted to evaluate the influence of machine learning on innovative anaesthesia methods. In 2023, Lopez et al. published a systematic review on how Artificial Intelligence could positively impact traditional anaesthesia practices.1 Various studies included in the review employed different models to achieve variable targets during the induction of anaesthesia. In one experiment, Alkhatib et al. used Convolutional neural network (CNN) based deep trackers to track the median and sciatic nerve with a surprising accuracy of 0.87.2 Another study employed the same CNN model to locate and discriminate accurate images of sacrum, vertebral levels and intervertebral gaps during percutaneous spinal needle insertion.3 Another study used a different AI model called SVM (support vector machine) classification, image processing, and template matching to locate lumbar level L3-L4 and the ideal puncture site for epidural anaesthesia in real-time. In this experiment, anaesthetists with minimal experience in ultrasonography were able to successfully determine needle puncture sites accurately.4 All these studies utilizing AI models not only yielded beneficial results but also led to significant time savings. Considering this, anaesthesiologists nationwide must integrate effective AI models to improve their clinical practice. This would minimize adverse outcomes in regional anaesthesia and ultimately enhance patient care and satisfaction.

Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies.

The integration of augmented reality (AR) in spine surgery marks a significant advancement, enhancing surgical precision and patient outcomes. AR provides immersive, three-dimensional visualizations of anatomical structures, facilitating meticulous planning and execution of spine surgeries. This technology not only improves spatial understanding and real-time navigation during procedures but also aims to reduce surgical invasiveness and operative times. Despite its potential, challenges such as model accuracy, user interface design, and the learning curve for new technology must be addressed. AR's application extends beyond the operating room, offering valuable tools for medical education and improving patient communication and satisfaction. A literature review was conducted by searching PubMed and Scopus databases using keywords related to augmented reality in spine surgery, covering publications from January 2020 to January 2024. In total, 319 articles were identified through the initial search of the databases. After screening titles and abstracts, 11 articles in total were included in the qualitative synthesis. Augmented reality (AR) is becoming a transformative force in spine surgery, enhancing precision, education, and outcomes despite hurdles like technical limitations and integration challenges. AR's immersive visualizations and educational innovations, coupled with its potential synergy with AI and machine learning, indicate a bright future for surgical care. Despite the existing obstacles, AR's impact on improving surgical accuracy and safety marks a significant leap forward in patient treatment and care.