Our study focused on plant breeding, from traditional methods to the present most advanced genetic and data-driven concepts. Conventional breeding techniques, such as mass selection and cross-breeding, have been instrumental in crop improvement, although they possess inherent limitations in precision and efficiency. Advanced molecular methods allow breeders to improve crops quicker by more accurately targeting specific traits. Data analytics and information technology (IT) are crucial in modern plant breeding, providing tools for data management, analysis, and interpretation of large volumes of data from genomic, phenotypic, and environmental sources. Meanwhile, emerging technologies in machine learning, high-throughput phenotyping, and the Internet of Things (IoT) provide real-time insights into the performance and responses of plants to environmental variables, enabling precision breeding. These tools will allow breeders to select complex traits related to yield, disease resistance, and abiotic stress tolerance more precisely and effectively. Moreover, this data-driven approach will enable breeders to use resources judiciously and make crops resilient, thus contributing to sustainable agriculture. Data analytics integrated into IT will enhance traditional breeding and other key applications in sustainable agriculture, such as crop yield improvement, biofortification, and climate change adaptation. This review aims to highlight the role of interdisciplinary collaboration among breeders, data scientists, and agronomists in absorbing these technologies. Further, this review discusses the future trends that will make plant breeding even more effective with this new wave of artificial intelligence (AI), blockchain, and collaborative platforms, bringing new data transparency, collaboration, and predictability levels. Data and IT-based breeding will greatly contribute to future global food security and sustainable food production. Thus, creating high-performing, resource-efficient crops will be the foundation of a future agricultural vision that balances environmental care. More technological integration in plant breeding is needed for resilient and sustainable food systems to handle the growing population and changing climate challenges.

Data science and predictive analytics are revolutionizing plant biotechnology by revealing crop performance and tolerances. Data science is important in a global context where agricultural demand is rising and crops' yields, resilience, and sustainable resource usage are maximized daily. We explore predictive models in plant biotechnology and how they may be developed utilizing agronomic, environmental, phenotypic, and genomic data to improve agricultural solutions. Predictive analytics extrapolates genome, transcriptomics, and proteomics data to promote precision farming and climate-resilient crop adaptive breeding. Agricultural data science uses IoT sensors, drones, and image technologies, but integration and data quality are still difficulties. The review also explores machine learning approaches including decision trees, neural networks, regression, and others to help predictive analytics overcome restrictions. These models can quantify resilience and response to biotic and abiotic stresses, predict yields, and choose breeding genes. Examples demonstrate how predictive models can boost crop resilience, yields, and water and pest management early intervention. Predictive analytics in plant biotechnology faces data shortages, processing needs, and model interpretability challenges. These barriers may prohibit many agricultural stakeholders from adopting advanced models like deep neural networks. The study concludes that plant scientists, data scientists, and agronomists must work together, integrate AI with multi-omics for advanced predictive modeling, and use blockchain for data security. These advances can help predictive analytics improve sustainable agriculture by fostering resilient crop growth and resource efficiency for a more predictable food supply.

The integration of machine learning (ML) into health information technology (HIT) is revolutionizing data-driven healthcare systems, yet several key challenges and areas of focus remain. Electronic health records (EHRs) constitute most of the data source (60%), with wearable devices and interviews/focus groups comprising smaller portions. This indicates a continued reliance on traditional health records, although emerging technologies are beginning to play a role. Another key preprocessing challenge, with data cleaning consuming the most effort (40%), followed by data anonymization and feature selection, each requiring substantial effort in ensuring the accuracy and privacy of patient data. Supervised learning dominates in healthcare applications, followed by deep learning and unsupervised learning. In terms of accuracy, EHR data consistently yields the highest performance, around 85%, closely followed by wearable devices, genetic data, and lifestyle data. However, challenges remain in addressing data privacy and algorithm transparency, as highlighted by the distribution of effort in ensuring compliance and maintaining data privacy. The findings suggest a need for further exploration into wearable devices and the real-time monitoring capabilities they bring to healthcare, alongside tackling data preprocessing and ethical challenges in HIT.

In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.

This study investigates the barriers, adoption rates, performance impacts, and cost-benefit dynamics associated with sustainable IT implementation across various industries. The analysis highlights key barriers to sustainable IT adoption, including cultural resistance, regulatory requirements, lack of expertise, and cost of implementation. Cost, identified as the most significant barrier, affects approximately 40% of organizations, emphasizing the need for affordable and scalable sustainable IT solutions. The relationship between the sustainable IT index and business performance was also examined, revealing a positive correlation and organizations with higher sustainable IT investments demonstrated greater revenue growth, supporting the economic viability of green initiatives. Further, industry-specific adoption rates of sustainable IT practices were evaluated, showing that the technology sector leads in adopting energy-efficient hardware, green data centers, and cloud computing, with adoption rates exceeding 80%. Conversely, retail exhibits comparatively lower adoption, likely due to budget constraints and differing operational priorities. Findings indicate that sustainable IT practices significantly enhance customer satisfaction and operational efficiency, though their direct impact on revenue growth is moderate. Finally, a cost-benefit analysis over a ten-year period reveals that while initial implementation costs are high, cumulative benefits from sustainable IT practices increase substantially over time, surpassing costs by year six. These findings underscore the long-term financial and operational advantages of sustainable IT investments, providing valuable insights for businesses considering such transitions.

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies.