The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detection. The methodology involves experimenting with three feature scaling techniques (no scaling, normalization, and min-max scaling), three feature selection methods (no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)), and twelve ML models, including traditional algorithms and ensemble methods. A publicly available dataset with 11,598 samples and 139 features is utilized, and model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Results reveal that the Light Gradient Boosting Machine (LGBM) achieves the highest accuracy of 97.16% when PCA and either min-max scaling or normalization are applied. Additionally, ensemble models consistently outperform traditional ML models, demonstrating their effectiveness in enhancing malware detection. These findings offer valuable insights into optimizing preprocessing and model selection strategies for developing reliable and efficient malware detection systems.

The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. We explore three feature transformation techniques, no transformation, normalization, and min-max scaling, to assess their influence on the performance of various ML models. To comprehensively evaluate the effectiveness of these preprocessing techniques, we experimented with twelve different ML models, including both traditional algorithms and ensemble methods. A publicly available dataset has been used for this research, containing 768 samples and 8 features. To ensure their effectiveness, the models are assessed using several evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the ML models, Light Gradient Boosting Machine (LGBM) achieved the highest accuracy of 82.91% when min-max scaling was applied to the data. Our results demonstrate the varying effectiveness of different combinations of feature transformation techniques and ML models in enhancing diabetes detection performance. Furthermore, it has been observed that the ensemble models generally achieved better performance than traditional ML models. These findings provide valuable insights for optimizing preprocessing and model selection strategies in the development of robust early diabetes detection systems.

Integrating artificial intelligence (AI) into agriculture is a pivotal solution to address the pressing challenges posed by rapid population growth and escalating food demand. Traditional farming methods, unable to cope with this surge, often resort to harmful pesticides, deteriorating soil health. However, the advent of AI promises a transformative shift toward sustainable agricultural practices. In the context of the United States, AI's historical trajectory within the agricultural sector showcases a remarkable evolution from rudimentary applications to sophisticated systems focused on optimizing production and quality. The future of American agriculture lies in AI-driven innovations, spanning various facets such as image sensing for yield mapping, labor management, yield optimization, and decision support for farmers. Despite its numerous advantages, the deployment of AI in agriculture does not come without challenges. This paper delved into both the benefits and drawbacks of AI adoption in the agricultural domain, examining its impact on the agro-industry and the environment. It scrutinized the emergence of robot farmers and AI's role in reshaping farming practices while acknowledging the inherent problems associated with AI implementation, including accessibility, data privacy, and potential job displacement. Moreover, the study explored how AI tools can catalyze the development of agribusiness, offering insights into overcoming existing challenges through innovative solutions. By comprehensively understanding the opportunities and obstacles entailed in AI integration, stakeholders can navigate the agricultural landscape adeptly, fostering a more sustainable and resilient food system for future generations.

The growing prevalence of advanced persistent threats (APTs), zero-day exploits, and the rapid proliferation of IoT devices have exposed limitations in traditional cybersecurity approaches. In response, this study presents a comparative analysis of deep learning models—specifically Long Short-Term Memory (LSTM) and Transformer-based architectures—for cybersecurity threat classification from textual data. Leveraging a standardized dataset and consistent preprocessing pipeline, both models are evaluated across key performance metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that Transformer models significantly outperform LSTM-based approaches, exhibiting superior capacity to capture long-range dependencies, handle complex threat narratives, and generalize to previously unseen data. These findings offer valuable insights into the practical application of modern deep learning techniques in cybersecurity and provide a foundation for designing more robust and adaptive threat detection systems.

Big data analytics’ incorporation into customized cancer care is a revolutionary development in medical science. Using knowledge from genomic sequencing, electronic health records (EHRs), clinical trial databases, and social determinants of health, this systematic review investigates the relationship between artificial intelligence (AI), machine learning, and big data analysis in customizing cancer treatments for each patient. The study highlights how AI‐driven prediction models might improve treatment outcomes, reduce side effects, and increase patient safety by synthesizing insights from the last 10 years of academic research. A systematic literature review that follows PRISMA guidelines and includes both quantitative and qualitative evaluations of academic publications is among the key approaches. The findings show that multiomics approaches, which combine transcriptomics, proteomics, metabolomics, and genomics, are becoming increasingly important for customized medicine. Real‐time data analytics and wearable technology are also noted as promising resources for prompt responses. Notwithstanding obstacles, including data heterogeneity, moral dilemmas, and validation problems, the results highlight how crucial AI is to treating tumor complexity and developing precision medicine. In order to improve regulatory frameworks, promote interdisciplinary collaboration, and optimize AI applications in cancer treatment planning, the study concludes by suggesting future research areas. For scientists, physicians, and legislators hoping to transform cancer treatment using big data analytics–driven tailored medicine, this review provides insightful information.

This research paper introduced a groundbreaking approach to greenhouse gas (GHG) monitoring by leveraging artificial intelligence (AI) technology to enhance both accuracy and efficiency. Traditional GHG monitoring methods are often hampered by high costs, labor-intensive processes, and significant delays in data analysis. This study harnessed advanced AI techniques, such as machine learning algorithms and neural networks, to facilitate real-time data collection and analysis from diverse sources including satellite imagery, Internet of Things (IoT) sensors, and atmospheric models. By implementing AI models like random forest, support vector machines, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks, the research achieved substantial improvements such as reducing data reporting latency from 24 hours to just 1 hour, increasing spatial resolution from 30 meters to 10 meters, and enhancing detection accuracy from 80% to 95%. Additionally, the AI systems identified previously unknown emission sources and accurately forecasted future emission trends with a high correlation (R² = 0.89). These advancements not only allow for more precise identification of emission hotspots and tracking of changes over time but also facilitate more effective regulatory responses and policy-making. The findings underscore the transformative potential of AI-driven monitoring systems in bolstering global sustainability efforts and combating climate change.