The financial sector is grappling with significant challenges in detecting cyber attacks, leading to potential short- and long-term financial losses for banks and other institutions. The statistical and machine learning methods have been effective in identifying suspicious activities, they have struggled to achieve a balance between recall and precision. To improve accuracy, this paper introduces a novel approach that employs deep learning and bio-inspired algorithms to detect suspicious activities. The proposed model analyzes transactional patterns, quantities, and temporal aspects using a carefully curated dataset of labeled transactions. The model shows promising results in distinguishing between legitimate and fraudulent operations, achieving a balance between recall and precision. Further, many in the industry are transitioning to cloud computing infrastructures to enhance application performance. However, these infrastructures are not ideal for delay-sensitive applications, such as those in the medical and finance sectors. To address communication delays, fog computing has emerged as a new paradigm. The  proposed model was simulated using Python and the Google Colab framework, and  experimental results shows that  improved accuracy and a balanced recall and precision.

In conventional monolithic operating system architecture, the kernel delivers all the necessary services to the application programs. The microkernel operating system is important in several areas such as industrial control systems, embedded systems, and real-time systems. As the requirements of the operating system increase, the kernel expands in size and increases complexity. The introduction of Operating Systems focusing on Microkernels was due to the difficulties mentioned above caused by Operating Systems with Monolithic kernels. Operating systems based on microkernels offer enhanced security and flexibility to the system. A comprehensive review of eleven distinct operating systems based on the microkernel architecture is presented in this study. Some microkernels provide great advantages like strong security, better performance, and reliability also some microkernels have disadvantages like increased complexity, high cost in making, and low performance. The three main components of the study are process scheduling, memory management, and inter-process communication. This overview provides a comprehension of the various advances in creating operating systems based on microkernel technology. The data sources included books, research papers, and official documentation of individual microkernels.

This research paper explores Blockchain (BC) technology-based identity verification's role in streamlining and securing the employee onboarding process within Human Resource (HR) management. It addresses this technology's potential benefits, challenges, and limitations in enhancing HR practices. This study is grounded in the theoretical foundation of BC technology and its applications. It examines existing identity verification systems in HR management and delves into the potential implications of adopting BC-based solutions. This research employs a comprehensive design encompassing a discussion of the background, research problem, objectives, and significance. A detailed overview of BC technology and its applications and an analysis of existing identity verification systems are presented. The study employs a well-defined research design, including a sampling strategy, sample size determination, data collection methods, and data analysis techniques. The study's findings reveal that BC-based identity verification has the potential to streamline and secure the employee onboarding process in HR management. However, the investigation also identified scalability, interoperability, and data security challenges. These findings contribute to understanding the feasibility of adopting BC technology in HR practices. The study informs HR managers and BC developers on the potential benefits and hurdles of implementing BC-based identity verification, enabling them to make informed decisions.

Forecasting electrical energy consumption is becoming increasingly important for a country's citizens as it addresses rising energy demand and energy waste issues. A useful electrical energy consumption prediction scheme could help users estimate their monthly electricity bills and the use of new electrical appliances in their homes. Traditional energy consumption prediction methods are time-consuming and necessitate expert assistance to analyze and calculate energy use over time. The limitations of the existing works are that the existing literature does not accurately predict monthly energy consumption and costs using machine learning. They concentrated on electrical energy consumption over a short period of time in a single building, using seasonal data rather than automating the system for repeated use. To address these issues, this paper proposes machine learning-based automation systems that predict monthly energy consumption, estimate costs, and identify relevant features using data from electrical home appliances in Bangladesh. Several regression models, including Random Forest, Decision Tree, XGBoost, Boosting, and LightGBM Regressor, are tested to find the best prediction model. We have performed dataset collection, dataset cleaning, feature extraction, scaling, normalization, hyper parameter tuning, training, testing, and model selection activities. The simulation results clearly indicated that the Random Forest regressor model performed better than the other models, with higher R squared values and lower error values. The comparison results revealed that the proposed random forest regression model outperforms previous works by at least 4% in accuracy and 7% in mean absolute error. The proposed mobile application helps users make informed decisions by calculating energy consumption for new home appliances, making recommendations, delivering updated news from the power board, and providing required guidelines. The mobile application feature evaluation results revealed that our proposed application received an excellent rating from more than 70% of customers.

The research aims to develop an innovative solution to address the marketing challenges faced by Kutai Kartanegara Regency, where a majority of the population is engaged in agriculture. Limited access for farmers to sell their harvests poses a significant obstacle. The proposed solution involves the implementation of a Business-to-Business (B2B) e-commerce system to enhance sales and expand marketing opportunities. In the context of global digital transformation, the integration of e-commerce is not only advantageous but also deemed a necessity. Adopting a research and development approach, the study utilizes the PHP programming language and MySQL database to create a dynamic, web-based B2B sales application. The potential broader impacts of this research extend beyond facilitating agricultural product sales; the envisioned e-commerce system holds the promise of catalyzing economic growth. By providing farmers with a platform not only for selling their products but also for engaging in strategic B2B transactions, the system becomes a driver of economic empowerment. The research incorporates insights from relevant studies emphasizing the role of e-commerce in enhancing agricultural outcomes, addressing both technological and socio-economic factors comprehensively. The research methods, including interviews, direct observations, and literature studies, reflect a holistic understanding of the challenges and needs faced by the agricultural community. The outcome is the proposed B2B e-commerce system, which has the potential to streamline processes, enhance efficiency, and broaden market reach. Use case diagrams and class diagrams serve not only as technical guides but also as visual representations of a user-friendly system, emphasizing a user-centric approach and the potential for transformative change from the main page to sales reports. The significance of this research extends beyond technological advancements; it holds the potential to redefine the socio-economic landscape of Kutai Kartanegara Regency. By empowering farmers, facilitating strategic transactions, and fostering collaboration between traditional agriculture and modern commerce, the proposed B2B e-commerce system becomes a catalyst for sustainable growth and a potential contributor to broader economic prosperity.

Precision agriculture is revolutionizing the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In aquaculture, precision agriculture can significantly improve fish farming practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving fish health. This paper presents the development and implementation of an IoT-based Fish Recommendation System designed to optimize aquaculture practices through a mobile application. This system uses different sensors for extracting data continuously regarding temperature, PH and Turbidity etc. These parameters can be analysed in real-time to help fish farmers make decisions on when or how much the system should feed and aerate, and what approach of water treatment is best for their fishes. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for each fish species. This can enhance their survival rates and promote growth. In this study, we evaluate a series of machine learning algorithms for their ability to predict the optimal fish species based on water quality parameters. Among these algorithms, Random Forest demonstrated superior performance, achieving an accuracy of 92.5%, precision of 93%, recall of 93%, and F1-score of 92%. These findings highlight the effectiveness of our approach in integrating machine learning with IoT for precise aquaculture management. Implemented through a user-friendly mobile application, our system enhances accessibility and usability for fish farmers.