The proposed evaluated Capability Maturity Model (CMM) for long-term management of Zero Defect Manufacturing (ZDM) implementations, ZDM-CMM, is intended as a tool for organizations initiating or advancing their ZDM initiatives. This ZDM-CMM offers guidance on key perspectives crucial for successful ZDM development, deployment, and potential objectives for measuring maturity and progress over time. The evaluation of the ZDM-CMM highlights use in balancing conflicting objectives such as speed, cost, and quality, with sensitivity analysis recommended to determine their relative importance. While additional objectives were suggested, the current model provides a foundational framework to be refined with further experience. The CMM, designed primarily for tracking maturity rather than comparing implementations, emphasizes the significance of considering various perspectives early in ZDM project planning to ensure comprehensive management. Although not yet widely adopted, the companies that participated in the evaluation expressed interest in using the CMM for visualization of progress and holistic assessment of ZDM efforts. Further, the ZDM-CMM is expected to provide significant insights for project planning, potentially saving time, resources, and ensuring thorough consideration of critical ZDM aspects. 

Explainable artificial intelligence is beneficial in converting opaque machine learning models into transparent ones and outlining how each one makes decisions in the healthcare industry. To comprehend the variables that affect decision-making regarding diabetes prediction that can be accounted for by model-agnostic techniques. In this project, we investigate how to generate local and global explanations for a machine-learning model built on a logistic regression architecture. We trained on 253,680 survey responses from diabetes patients using the explainable AI techniques LIME and SHAP. LIME and SHAP were then used to explain the predictions produced by the logistic regression and Random forest-based model on the validation and test sets.With a discussion of future work, the comparative analysis and discussion of various experimental findings between LIME and SHAP are provided, along with their strengths and weaknesses in terms of interpretation. With a high accuracy of 86% on the test set, we used LR architecture with a spatial attention mechanism, demonstrating the possibility of merging machine learning and explainable AI to improve diabetes prediction, diagnosis, and treatment.We also focus on various applications, difficulties, and probable future directions of machine learning models for LIME and SHAP interpreters.

Alzheimer’s Disease (AD) poses a major challenge for healthcare systems worldwide, as timely and accurate diagnosis is crucial for patient management and outcome improvement. While experienced medical professionals can often identify AD through conventional assessment methods, limited resources and growing patient populations make large-scale and rapid screening increasingly necessary. In this work, we explore whether the FixMatch algorithm—a semi-supervised learning approach—can aid in classifying Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) by using the ADNI fMRI dataset of 5,182 images. This approach supplements rather than replaces clinical expertise, offering a faster, more standardized classification process where expert labeling is limited. We first assessed various supervised models and determined that VGG19-FFT provided the strongest balance of classification accuracy and computational efficiency. Integrating VGG19-FFT into FixMatch as the teacher model, initial tests using 10% labeled and 90% unlabeled data yielded modest results. A more systematic examination of different labeled-to-unlabeled data splits revealed that a 60:40 ratio enabled FixMatch to achieve classification accuracies of 100% for AD, 99% for CN, and 99% for MCI—on par with fully supervised training. This outcome highlights the potential of FixMatch to significantly reduce labeling requirements, a particular advantage in resource-constrained settings where expert annotations are costly. By striking an effective balance between labeling effort and model performance, the identified 60:40 ratio helps make advanced diagnostic methods both feasible and practical in real-world clinical applications.

Malicious URLs represent a significant cybersecurity threat, facilitating malware distribution and data theft. This paper explores a ML-based framework for malicious URL detection, providing a comparative analysis of DL and traditional ML approaches. Eight ML models─LR, SVM, DT, KNN, GNB, RF, XGBoost, and LightGBM─are benchmarked against three DL models: LSTM, BiLSTM, and GRU. The results reveal that traditional ML models, particularly RF, XGBoost, and LightGBM, achieve superior performance with accuracy scores of up to 92%, outperforming DL models, which achieve accuracy rates of 90%, 91%, and 88%, respectively. To further enhance detection performance, a stacking model combining these techniques is proposed, achieving a remarkable accuracy of 99.99%. This research underscores the potential of stacked models to significantly improve malicious URL detection, offering advanced solutions to strengthen cybersecurity frameworks for both individuals and organizations.

The widespread adoption of Artificial Intelligence (AI) in critical domains, such as healthcare, finance, law, and autonomous systems, has brought unprecedented societal benefits. Its black-box (sub-symbolic) nature allows AI to compute prediction without explaining the rationale to the end user, resulting in lack of transparency between human and machine. Concerns are growing over the opacity of such complex AI models, particularly deep learning architectures. To address this concern, explainability is of paramount importance, which has triggered the emergence of Explainable Artificial Intelligence (XAI) as a vital research area. XAI is aimed at enhancing transparency, trust, and accountability of AI models. This survey presents a comprehensive overview of XAI from the dual perspectives of challenges and opportunities. We analyze the foundational concepts, definitions, terminologies, and taxonomy of XAI methods. We then review several application domains of XAI. Special attention is given to various challenges of XAI, such as no universal definition, trade-off between accuracy and interpretability, and lack of standardized evaluation metrics. We conclude by outlining the future research directions of human-centric design, interactive explanation, and standardized evaluation frameworks. This survey serves as a resource for researchers, practitioners, and policymakers to navigate the evolving landscape of interpretable and responsible AI.

Predicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the limited availability of supervised labeled data to train Artificial Intelligence (AI) models. This data availability becomes either expensive or difficult due to privacy protection. To overcome the scarcity of balanced labeled data, semi-supervised learning utilizes extensive unlabeled data. Motivated by this, we propose semi-supervised learning to train AI model. For the AI model, we employ the Belief Rule-Based Expert System (BRBES) because of its domain knowledge-based prediction and uncertainty handling mechanism. For improved accuracy of the BRBES, we utilize initial labeled data to optimize BRBES’ parameters and structure through evolutionary learning until its accuracy reaches the confidence threshold. As semi-supervised learning, we employ a self-training model to assign pseudo-labels, predicted by the BRBES, to unlabeled data generated through weak and strong augmentation. We reoptimize the BRBES with labeled and pseudo-labeled data, resulting in a semi-supervised BRBES. Finally, this semi-supervised BRBES explains its prediction to the end-user in nontechnical human language, resulting in a trust relationship. To validate our proposed semi-supervised explainable BRBES framework, a case study based on Skellefteå, Sweden, is used to predict and explain energy consumption of buildings. Experimental results show 20 ± 0.71% higher accuracy of the semi-supervised BRBES than state-of-the-art semi-supervised machine learning models. Moreover, the semi-supervised BRBES framework turns out to be 29 ± 0.67% more explainable than these semi-supervised machine learning models.

Background: Alzheimer’s disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. Methods: To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. Results: The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. Conclusions: The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions.

Employee attrition is one of the most important factors for any company to get benefits because it has a muscular impact and hampers the organization’s long-term growth strategies. For employee attrition, an organization loses its valuable skills and experience, which is why organizations try to keep their employees in order to minimize the cost of training and recruitment. An organization can avoid the stressful and unfair manual prediction process by utilizing machine learning approaches that predict the likelihood of attrition based on employee attributes. This paper proposed an approach which enables an organization to find out the risk factor for employee attrition. In the preprocessing part, the dataset will be balanced using Borderline-SMOTE Technique, and after that, the linear discriminant analysis technique used to reduce the dimension. Then AdaBoost algorithm is performed on this dataset to get a perfect score. The method claims 93.75% accuracy. In this paper, explainable artificial intelligence (XAI) is used to improve model prediction transparency, interpretation, and trustworthiness. This paper explained the AdaBoost algorithm using XAI techniques including SHapley Additive Values(SHAP). The effort intends to precisely determine the employee by means of an automated screening system including ML methods with capacity for explanations.