Supply chain agility has become a key success factor for businesses trying to handle upheavals and uncertainty in today’s quickly changing business environment. Proactive risk reduction is essential for achieving this agility. To facilitate real-time risk prevention and improve agility, this research study proposes an innovative strategy that makes use of machine learning as well as predictive analytics approaches. Traditional supply chain risk management frequently uses post-event analysis as well as historical data, which restricts its ability to address real-time interruptions. This research, on the other hand, promotes a futuristic methodology that uses predictive analytics to foresee possible disruptions. Based on contextual and historical data, machine learning models can be trained to find patterns and correlations as well as anomalies that point to imminent dangers. Organizations can identify risks as they arise and take preventative measures by incorporating these models into a real-time monitoring system. This study examines numerous predictive analytics methods, showing how they can be used to spot supply chain risks. These methods include time series analysis and anomaly detection as well as natural language processing. Additionally, risk assessment models are continuously improved and optimized using machine learning algorithms, assuring their accuracy and adaptability in changing contexts. This research clarifies the symbiotic relationship among predictive analytics and machine learning as well as supply chain agility using a synthesis of theoretical discourse and practical evidence. Case studies from various sectors highlight the usefulness and advantages of the suggested strategy. The advantages of this novel technique include improved risk visibility and quicker response times as well as the capacity to quickly modify operations. The development of a holistic framework that incorporates predictive analytics and machine learning into risk management procedures, setting the path for real-time risk identification as well as mitigation, is one of the theoretical contributions. On the practical side, the case studies offered in this paper show the actual benefits as well as the adaptability of the proposed approach across a wide range of businesses.

Background: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts. Method: Developing a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment. Result: The hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis. Conclusion: In summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology’s potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare.

Due to recent developments in deep learning and artificial intelligence, the healthcare industry is currently going through a significant upheaval. Despite a considerable advance in medical imaging and diagnostics, the healthcare industry still has a lot of unresolved problems and unexplored applications. The transmission of a huge number of medical images in particular is a difficult and time-consuming problem. In addition, obtaining new medical images is too expensive. To tackle these issues, we propose deep pix2pix generative adversarial networks (GAN) for generating synthetic medical images. For the comparison, we implemented CycleGAN, Pix2Pix GAN and Deep Pix2Pix GAN. The result has shown that our proposed approach can generate a new synthetic medical image from a different image with more accuracy than that of the other models. To provide a robust model, we trained and evaluated our models on a widely used brain image dataset, the IXI Dataset.

Advanced data analytics are increasingly being employed in healthcare research to improve patient classification and personalize medicinal therapies. In this paper, we focus on the critical problem of clustering electronic health record (EHR) data to enable appropriate patient categorization. In the era of personalized medicine, optimizing patient classification is critical to healthcare analytics. This research presents a comparative assessment of different clustering algorithms for Electronic Health Record (EHR) data, with the goal of improving the efficacy and productivity of patient clustering methods. Our study focuses on Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) as a Multi-Criteria Decision-Making (MCDM) strategy, includes an in-depth assessment of eight clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering, Mean Shift, Affinity Propagation, Spectral Clustering, Gaussian Mixture Models (GMM), as well as Self-Organizing Maps. The evaluation factors used for evaluation in this research are Cluster Quality Metrics, Scalability, Robustness to Noise, Cluster Shape and Density, Interpretability, Cluster Number, Dimensionality, and Consistency and Stability. These criteria and alternatives were chosen after conducting a thorough assessment of the literature and consulting with domain experts. All participated specialists actively engaged in the decision-making process, bringing unique insights into the best clustering algorithms for healthcare data. The results of this study illustrate each algorithm’s strengths and weaknesses in the setting of patient stratification, providing insight into their performance across multiple dimensions. The fuzzy TOPSIS MCDM strategy is a reliable instrument for synthesizing expert opinions and methodically evaluating the found clustering alternatives. This study advances healthcare analytics by giving practitioners and researchers with informative perspectives on the selection of clustering algorithms designed to address the unique problems of patient stratification utilizing EHR data.

With the increasing global environmental and social challenges, it is more urgent than ever to implement effective strategies for sustainable development. Environmental, Social and Governance (ESG) criteria are also necessary requirements guiding organizations toward responsible and sustainable practices. However, the multi-dimensionality of the criteria and the uncertainty associated with judgment by an expert makes evaluation and choice of the best ESG-driven strategy a very complex task. The current paper introduces an innovative approach to the assessment of ESG strategies via Fuzzy based multi-criteria decision-making tools. Those selected shall be addressed particularly through the Fuzzy TOPSIS method, considering and ranking seven key strategies driven by ESG focus areas- AI-Powered Predictive Analytics, Renewable Energy Integration, Smart Waste Management Systems, Blockchain for Transparent Governance, AI-Enhanced Workforce and Community Development, Sustainable Supply Chain Optimization and Generative AI for Eco-Friendly Innovation. The results of this assessment indicate that the most popular ESG approach is renewable energy integration, which is in line with the industry’s pivotal role in advancing energy transition and climate action. AI-Powered Predictive Analytics and Sustainable Supply Chain Optimization are closely related, emphasizing the strategic value of data intelligence as well as operational efficiency in improving sustainability practices. These results offer important novel insights about how AI-powered methods might guide environmentally friendly choices in intricate industrial settings. Our method provides a strong and transparent framework for assessing ESG strategies under sustainability by incorporating fuzzy logic into decision-making. The study adds to the rapidly expanding body of research on AI-driven sustainability evaluations, from which companies, policymakers and other stakeholders could gain insight how to enhance their ESG performance as well as advance sustainable development.

Mobile computing and technology are becoming more common in many parts of private life and public services, and they are playing an increasingly important role in healthcare, not just for sensory devices but also for communication, recording, and display. They are used for more than only sensory devices but also for communications, recording, and display. Numerous medical indications and postoperative days must be monitored carefully. As a result, the most recent development in Internet of Things‐ (IoT‐) based healthcare communication has been embraced. The Internet of Things (IoT), which is employed in a wide range of applications, is a catalyst for the healthcare industry. Healthcare data is complicated, making it difficult to handle and evaluate in order to derive useful information for decision‐making. On the other hand, data security is a vital requirement in a healthcare data systems. Determining the need for a smart and secure IoT platform for healthcare applications, we create one in this study. Here, a cutting‐edge encryption algorithm is used to protect the health data. Normalization is first used to preprocess the data and remove any irrelevant information. Using principal component analysis and logistic regression, the data’s features are extracted (LR‐PCA). To choose the pertinent features, a feature selection process based on genetic algorithms is used. We have put out a brand‐new kernel homomorphism. To increase the security of the IoT network, use the two‐fish Encryption algorithm (KHTEA). EBSMO (exponential Boolean spider monkey optimization) is used to further boost the encryption process’ effectiveness. Utilizing the MATLAB simulation tool, the proposed system is assessed, and the metrics are contrasted with the accepted practices. Our suggested solution has been shown to be effective in protecting medical healthcare data. The effectiveness of the proposed and existing approaches is assessed using metrics for encryption time, execution time, and security level. The security precautions we suggested for healthcare data worked well.