Chronic Metabolic Syndrome Diabetes is often called a “silent killer” due to how little symptoms appear early on. High blood sugar occurs in people with diabetes because their bodies have a hard time maintaining normal glucose levels. Care for a recurrent sickness would be permanent. The two most common forms of diabetes are type 1 and type 2. A better prognosis can help reduce the high risk of developing diabetes. In order to better predict the likelihood that a PIMA Indian may develop diabetes, this study will use a machine learning-based algorithm. The demographic and health records of 768 PIMA Indians were used in the analysis. Standardisation, feature selection, missing value filling, and outlier rejection were all parts of the data preparation process. Machine learning techniques such as logistic regression, decision trees, random forests, the KNN model, the AdaBoost classifier, the Naive Bayes model, and the XGBoost model were used in the study. Accuracy, precision, recall, and F1 score were the only metrics utilised to assess the models' efficacy. The results demonstrate that. The results of this study reveal that diabetes risk may be reliably predicted using machine learning-based models, which has important implications for the early detection and prevention of this illness among PIMA Indians.

Lack of network security is a major roadblock for Internet of Things (IoT) implementations. New attacks have emerged in recent times, taking advantage of vulnerabilities in IoT gadgets. The sheer scale of the IoT will also make standard network attacks more potent. Machine learning has found a lot of use in traffic classification and intrusion detection. We present a methodology in this piece that can be used to spot fraudulent communications and determine the identity of IoT devices. To determine the origin of the generated traffic, the nature of the traffic, and the presence of network hazards, this framework collects features per network flow. To achieve this, it relocates the network’s brains to its periphery. In order to discover which of several Machine Learning algorithms is superior to random forest, a number of them are pitted against one another. Using these Machine Learning methods, attacks can be ranked in terms of their potential damage. After running the tests, it was determined that TABNET has the highest accuracy (94.62%) for categorizing the network severity (93.51%) and that CNN has the lowest accuracy (93.51%) of the two.

Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution.

Mobile wallets have gained widespread popularity as a convenient, secure, and user-friendly payment method embraced by consumers. However, the pace of mobile wallet adoption has exhibited variations across different markets, primarily due to a range of factors. In this study, we present an all-encompassing examination of existing literature, aiming to pinpoint the fundamental elements influencing the sustainable acceptance of mobile wallets by consumers. Through an exhaustive analysis of 80 research papers published between 2010 and 2022, we discern the prevailing factors that hold sway over the adoption of mobile wallets. Our scrutinization highlights that factor such as perceived sustainability, usefulness, ease of use, security, social influence, trustworthiness, and compatibility stand out as the most formidable propellants of mobile wallet adoption. Furthermore, our investigation uncovers hurdles that hinder the wider acceptance of mobile wallets, encompassing insufficient awareness, perceived intricacies, and lingering uncertainties regarding the technology’s sustainability. Our in-depth evaluation underscores the necessity of comprehending consumer perspectives and dispositions towards mobile wallets to galvanize their adoption sustainably. The culmination of our inquiry involves a discourse on the implications drawn from our discoveries, catering to researchers and practitioners vested in fostering the sustainable adoption of mobile wallets.