This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.

Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

The occurrence of scorch during the production of flexible polyurethane is a significant issue that negatively impacts foam products' resilience and generally jeopardizes their integrity. The likelihood of foam product failure can be decreased by optimizing production variables based on machine learning algorithms used to predict the occurrence of scorch. Investigating technology is required because prevention is the best approach to dealing with this problem. Hence, machine learning algorithms were trained to predict the occurrence of scorch using the thermodynamic profile of polyurethane foam, which is made up of recorded production variables. A variety of heuristics algorithms were trained and assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, and Logistic Regression. The XGboost ensemble was found to perform best. It outperformed others with an accuracy of 98.3% (i.e., 0.983), followed by logistic regression, decision tree, random forest, K-nearest neighbors, and naïve Bayes, yielding a training accuracy of 88.1%, 66.7%, 84.2%, 87.5%, and 67.5% respectively. The XGBoost was finally used, yielding 2-distinct cases of non(occurrence) of scorch. Ensemble demonstrates that it is quite capable and is an effective way to predict the occurrence of scorch.

Burglary involves forced or unauthorized entry, which leads to damage or loss of property having monetary or emotional value and, more severely, puts lives at risk. The dire need for the safety of lives and properties has attracted so much research on burglary alert system using Internet of Things (IoT) technology. Most of the research focused on alerting the users of burglary attempts using any or a combination of two notification methods: SMS, call, and email. This study emphasizes three-mode notification that combines SMS, call, and email using the application of IoT technology in a burglary alert system, which uses a Passive Infrared (PIR) sensor for burglar detection to ensure that Homeowners or authorized personnel get alerts in events of imminent attempt to break-ins. The study also details the sensor integration with its supporting components, such as the central hub or microcontroller, buzzer, LED, and network interface in the development of the system. The software was developed to facilitate seamless integration with the hardware, ensuring timely and accurate event detection and subsequent alert generation using Arduino IDE programming language, a framework based on the C++ language. The system effected the 3-mode notification to ensure that users get notification in case of an imminent break-in since the failure of the three modes simultaneously is extremely rare. The system’s performance based on its responsiveness on the 3-mode notifications was evaluated, and an average of 83.56% responsiveness was obtained, indicating an acceptable response time.

Blockchain platforms propagate into every facet, including managing medical services with professional and patient-centered applications. With its sensitive nature, record privacy has become imminent with medical services for patient diagnosis and treatments. The nature of medical records has continued to necessitate their availability, reachability, accessibility, security, mobility, and confidentiality. Challenges to these include authorized transfer of patient records on referral, security across platforms, content diversity, platform interoperability, etc. These, are today – demystified with blockchain-based apps, which proffers platform/application services to achieve data features associated with the nature of the records. We use a permissioned-blockchain for healthcare record management. Our choice of permission mode with a hyper-fabric ledger that uses a world-state on a peer-to-peer chain – is that its smart contracts do not require a complex algorithm to yield controlled transparency for users. Its actors include patients, practitioners, and health-related officers as users to create, retrieve, and store patient medical records and aid interoperability. With a population of 500, the system yields a transaction (query and https) response time of 0.56 seconds and 0.42 seconds, respectively. To cater to platform scalability and accessibility, the system yielded 0.78 seconds and 063 seconds, respectively, for 2500 users.

Besides the inherent benefits of exchanging information and interactions between nodes on a social graph, they can also become a means for the propagation of knowledge. Social graphs have also become a veritable structure for the spread of disease outbreaks. These and its set of protocols are deployed as measures to curb its widespread effects as it has also left network experts puzzled. The recent lessons from the COVID-19 pandemic continue to reiterate that diseases will always be around. Nodal exposure, adoption/diffusion of disease(s) among interacting nodes vis-a-vis migration of nodes that cause further spread of contagion (concerning COVID-19 and other epidemics) has continued to leave experts bewildered towards rejigging set protocols. We model COVID-19 as a Markovian process with node targeting, propagation and recovery using migration-interaction as a threshold feat on a social graph. The migration-interaction design seeks to provision the graph with minimization and block of targeted diffusion of the contagion using seedset(s) nodes with a susceptible-infect policy. The study results showed that migration and interaction of nodes via the mobility approach have become an imperative factor that must be added when modeling the propagation of contagion or epidemics.

The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.