Protecting payment gateways from emerging cyber-attacks requires strong and resilient machine learning models. This research examines the efficacy of ensemble techniques like Random Forest, Gradient Boosting, and reinforcement learning to identify suspicious transactions and prevent advanced cyber-attacks. Through transactional data, past cyber threat patterns, and simulated attack patterns, the models proved adaptable in real-time and highly accurate in identifying novel and unknown patterns of attacks. Issues like excessive false positive rates and overfitting were resolved using fine-tuning, feature selection, and cost-sensitive learning. Reinforcement learning also contributed to the system's robustness by allowing repeated learning from feedback and enhancing the ability to detect threats over time. Precision, recall, F1-score, and receiver operating characteristic-area under the curve (ROC-AUC) were used to measure the models' performance, which showed considerable real-time threat detection improvements. This adaptive strategy identifies the promise of self-improving systems to protect digital financial transactions securely. The results emphasize the need to incorporate adaptive machine learning methods into cybersecurity measures to counter the increasing complexity of cyber-attacks.
Integrating artificial intelligence (AI) and machine learning (ML) into smart home systems has significantly advanced and improved residential energy efficiency, addressing growing concerns around energy conservation and sustainability. Choosing appropriate AI/ML techniques to optimize energy consumption in the dynamic and contemporary smart home environment remains a complex challenge. This study investigates a range of AI/ML algorithms such as regression models, deep learning, clustering, and decision trees to enhance energy management in smart homes. The study highlights the core processes of smart home energy optimization, including data acquisition, feature extraction, and model evaluation, as well as the specific roles of each AI/ML technique in optimizing energy usage. The study also discusses the strengths and weaknesses of the AI/ML techniques used for smart homes. It further explores the application areas and emerging challenges such as data security risks, high implementation costs, and gaps in existing technology that impact the scalability of AI/ML solutions in smart home contexts. The findings reveal that AI/ML techniques can effectively transform energy management in smart homes, enabling real-time optimization and adaptive decision-making to minimize energy consumption and reduce costs. Additionally, the study highlights future research directions.
Infections, inflammation, and progression of multifactorial diseases are found to be integratively linked, including most Cancers. Dysfunctional microbiomes are also associated with several cancers in their tumor microenvironments. Antimicrobial peptides (AMPs) are short, positively charged peptides found in a diverse range of species, including bacteria and humans. As host defense peptides, they can destroy pathogenic infections, particularly those that are multidrug resistant. AMPs have raised hopes in the biomedical and pharmaceutical industries as fresh non-antibiotic strategies for combating infectious diseases. However, in vitro and in vivo verification of AMPs is problematic and may miss new antimicrobial drugs. Creating computational methods for quick and precise identification of AMPs and their functional forms is critical for developing new and more effective antimicrobial drugs. Machine learning techniques were recently discovered effective at mining, predicting, and producing efficient antimicrobial peptides from a large AMP database. We reviewed 76 articles, after following literature search rubrics to come to the following conclusions. Distance metric-constant K-based nearest neighbor algorithms (KNN), hidden Markov models (HMMs), support vector machine models (SVMs), random forest models (RFs), decision tree models, and deep neural network (DNN)-based models are some of the most popular AI tools for detecting antimicrobial activity in peptide sequence-derived structure and function. Knowledge graphs can further assist in identifying hub genes and antimicrobial peptides that target and block quorum sensing (QS) signals within the microbial networks. In conclusion, we state that currently no single AI method has been found appropriate for AMP discovery and accurately capable of predicting high-efficacy AMPs. Our current literature review and analysis identify cutting-edge algorithms or innovations that might be included in hybrid machine-learning approaches for the most effective AMP identification, creation, and prediction. Non-peptide, natural molecule-based approaches to AMR reduction are also being studied for development, with natural peptide scaffolds serving as the foundation.
Construction risk management has evolved significantly by integrating artificial intelligence (AI) technologies, particularly machine learning (ML), to enhance predictive capabilities. Transfer learning (TL), a promising subfield of ML, has the potential to further revolutionize construction safety by enabling models trained in one domain to be adapted for related tasks in construction risk scenarios. This systematic review explores the current trends in applying TL to construction risk management, identifies key challenges, and highlights future opportunities for advancement. The review first assesses TL's ability to mitigate common issues such as data scarcity, overfitting, and lengthy model training times, which often hinder traditional ML approaches. Key challenges include the complexity of domain adaptation, the lack of standardized datasets, and the need for robust validation methods. Despite these barriers, the potential for TL to improve predictive accuracy, efficiency, and cross-project learning makes it a transformative tool. Finally, future research directions are proposed to optimize TL techniques for real-time, intelligent construction risk management systems.
Energy and power resources must be managed well to guarantee their best use. To ensure that the required demand for energy generation is met, it is necessary to estimate Energy Demand (ED) accurately. Regression analysis and time series analysis were the mainstays of ED prediction in the past. But thanks to recent developments, accuracy has been increased by utilizing machine learning (ML) techniques to identify trends in data on electricity consumption. The purpose of this study is to offer insightful information on the relationships between various variables and how those relationships affect trends in energy usage. The goal is to create a prediction model that can reliably predict ED in a certain study area. In Lagos State, Nigeria, the Elman neural network (ELNN) and support vector machine (SVM) model are used for ED forecasting. The performance of SVM is optimized by selecting suitable kernel functions. The research region's 24-hour dataset was used to train the SVM model. Metrics including the correlation coefficient (R), Pearson correlation coefficient (PCC), mean square error (MSE), mean absolute error (MAE), and Root mean square error (RMSE) were used to evaluate the ML model. The findings show that the ELNN-M3 model fits the data well (R > 0.9). It is noteworthy that the study considered temperature, wind speed, and sun radiation as predicting variables. This study adds to the ongoing attempts to improve the accuracy of energy demand prediction, especially in dynamic contexts such as Lagos State.
