Various critical issues in liver diseases include cirrhosis, hepatitis, and liver cancer, which can be fatal. They indeed require early diagnosis with appropriate diagnosis of the disease. The different conventional diagnostic methods generally can't identify these diseases during their early stages; consequently, prognosis is not always good. Recently, in subsequence to improve this gap, ML/DL has emerged as the tool for transformation. It gives an overview of different ML and DL models used for predicting liver diseases, including supervised, unsupervised, semi-supervised learning, and reinforcement learning, and emphasizes the better performance that deep learning models like Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks are providing in handling complex medical data. These DL models perform significantly better in diagnostic accuracy when compared to the traditional ML methods, hence holding tremendous potential in their medical applications. Besides, hybrid and ensemble methods, which are combined models, are emphasized for their ability to overcome the limitations of individual algorithms and enhance diagnostic precision and robustness. This study further underlines the need to develop more advanced DL methodologies for the early detection and intervention in liver diseases, which is necessary to reduce the global burden and improve patient outcomes.

The article addresses existing methods for enhancing the performance of microservice architectures under high-load conditions, where stability and scalability are required to adapt to changing demands. The objective of the study is to systematize existing optimization methods. The methodological framework includes data analysis, a comparison of various approaches such as containerization, auto-scaling, and the use of frameworks for asynchronous request processing. The research was conducted based on an analysis of publicly available articles, providing a comprehensive examination of the topic. The analyzed studies demonstrate that implementing hybrid solutions incorporating machine learning for load forecasting and dynamic infrastructure configuration significantly improves performance. Additionally, the studies address the management of service states and interactions, which is critical for maintaining data integrity under high loads. The information presented in the article will be valuable for system architects, DevOps engineers, and cloud computing specialists working with resource-intensive services. These solutions enable the creation of scalable, reliable infrastructures capable of efficiently handling large volumes of real-time data. The conclusion confirms the necessity of a comprehensive approach to optimizing microservice systems, focusing on dynamic adaptation and the integration of new technologies.

Diabetic retinopathy, a condition resulting from prolonged high blood sugar levels that damage the retina, can cause vision impairment and, if untreated, lead to blindness. With advances in medical imaging and the availability of fundus image collections such as Madrid Messidor and DRIVE, computer-aided diagnosis (CAD) systems have become instrumental in identifying and categorizing cases. Machine learning, a branch of artificial intelligence, has demonstrated remarkable success in medical image processing, showing great potential for the early detection of diabetic retinopathy—a condition often challenging to diagnose in its early stages due to a lack of symptoms. This review examines prior studies leveraging machine learning algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest neighbors (KNN), for diabetic retinopathy detection using fundus image datasets. It also explores existing challenges, including dataset variability, computational demands, and the generalizability of models across diverse populations. Highlighting methodologies, datasets, and performance metrics like accuracy, sensitivity, and specificity, this article aims to provide a cohesive understanding of the current landscape, delineate strengths and limitations, and suggest directions for future research.

Drunk driving is one of the major cause of road accidents especially in Nigeria where there is no controls or checks to the level of alcohol content of drivers. In this study, a vehicle alcohol and temperature monitoring system was simulated and constructed using an Arduino Uno microcontroller ATMega328. The circuit was simulated in Proteus8.1 design suit comprising an alcohol sensor, contactless temperature sensor, motor/relay driver, ignition, 16×2 liquid crystal display, and 5V DC power. The simulated circuit was constructed on a printed circuit board and tested for continuity and power ‘ON’. An evaluation performance test was also carried out using 40 volunteers that have consumed varying amounts of alcohol content ranging from two to fifteen standard drink and their blood alcohol content as well as body temperature were measured. The true positive, false positive, true negative and false negative were recorded. Results showed that the constructed device has sensitivity of 92% and specificity of 75% depicting that the system can correctly identify 92% presence of alcohol and 75% of alcohol absence with 90% accuracy. It however, failed to identify only 8% alcohol presence and 25% alcohol absence. The device if installed in a vehicle could be significantly useful in averting road accidents caused by drunk driving. Ensuring the public and commercial drivers have a clear understanding of the law and raising awareness of the impact drink drive has on road users is important to reducing its prevalence.

In today's organizational landscape, predicting employee attrition has emerged as a critical concern. The departure of trained, technical, and pivotal staff members poses significant challenges, including financial setbacks incurred in their replacement. To address this, organizations harness current and historical employee data to discern prevalent attrition triggers. Employing established classification methodologies such as Decision Tree, Logistic Regression, Random Forest, Support vector machine, and Gradient boosting Algorithms are constructed using human resource data. Leveraging feature selection techniques, these models facilitate proactive measures to mitigate attrition risks. By accurately forecasting attrition, companies not only fortify their workforce stability but also enhance economic resilience through diminished human resource expenditures. This proactive approach not only aids in retaining valuable talent but also fosters sustainable growth by fostering an environment conducive to employee retention and organizational stability.

This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.

This study aims to analyze the adoption of Artificial Intelligence (AI) in South and Southeast Asia and Africa, focusing on how AI technologies shape the digital economies in these regions. The objective is to assess AI's role in key sectors such as energy, manufacturing, healthcare, and finance, and examine its impact on policy formulation, public service delivery, and governance. The paper employs a comparative literature review methodology. Data is gathered from secondary sources, including academic papers and reports, to analyze AI infrastructure, policy frameworks, sectoral impacts, and barriers to adoption. The study identifies similarities and differences in AI integration across the regions. The study reveals that South and Southeast Asia have made greater advancements in AI adoption, driven by stronger infrastructure and government policies, while Africa faces challenges due to infrastructure deficits and limited AI expertise. However, both regions show potential for leveraging AI to drive economic growth, particularly in underserved sectors like agriculture and financial inclusion. Additionally, the research highlights the critical role of AI governance, with a strong need for both regions to develop robust regulatory frameworks to ensure responsible and ethical AI implementation. The study is limited by its reliance on secondary data, which restricts deeper insight into on-the-ground challenges. Future research should incorporate field studies and interviews with AI practitioners for a more comprehensive understanding of AI’s impact. This paper contributes to the limited comparative research on AI adoption between South and Southeast Asia and Africa. It offers insights for policymakers and industry leaders on how AI can be harnessed for inclusive economic growth and transformation in these regions.

Login data refers to information used by individuals who have purchased or subscribed to a specific membership. Typically, this data includes an OTP code. In this research, the OTP code is utilized to grant users access to view the list of movies associated with the account that added them as a member. OTPs are particularly vulnerable to attacks, as they are often transmitted via phone numbers or email. To mitigate risks such as eavesdropping or interception, implementing robust data security systems is essential. Cryptography, the science of data security and authenticity, plays a crucial role in protecting sensitive information. Rivest-Shamir-Adleman (RSA) is one of the algorithms employed to secure data. In this research, the RSA algorithm is used to encrypt the OTP code provided to members. This encrypted OTP code is then converted into a QR code format and sent to the user via email. An application of the RSA algorithm in this research has proven effective in securing data. This is supported by the results of avalanche effect testing, which showed an average security improvement of 51.9%. Based on this percentage, it can be concluded that the system provides a high level of data security, making it challenging for unauthorized individuals to breach.

Heart rate and body temperature are important health parameter that need to be monitored especially for the old aged that needs constant medical care. In this study, simulation and construction of wireless body temperature and heartbeat monitoring device was carried out using PIC16F886 microcontroller and RF module. The circuit was simulated using Proteus8.4, while a prototype was constructed on a Vero board. The circuit was tested by measuring temperature and heartbeat of 20 volunteers during different levels of activities. Result shows that, the transmitter section, reads body temperature and heartbeat using sensors. The signal is then processed by the microcontroller and wirelessly transmitted to the receiver section through RF module at 433MHz, while detail is displayed on the LCD. At the receiver section, the signals are received through RF antenna operating at the same frequency with the transmitting RF module (433MHz). The data processed by the receiver microcontroller is also displayed on LCD. The readings of body temperature and heartbeat can be taken simultaneously by the same device and transmitted wirelessly to the care giver or patient relation. The device recorded a heartbeat rate of 76bpm at normal body temperature of 36.2°C and for every degree increases in body temperature, the heart beats about 10bpm faster. The device is reliable and cost effective to enable it accessible and affordable to the care giver and patient’s relative who can monitor the patient remotely.

The target of this study is to use GFTSC (Group for time series modules/components) to classify the constituents components of time series existing in the Ethiopia Gross Domestic Product (GDP). This statistics is the GDP yearly data of Ethiopian Gross Domestic Product (GDP). The Gross fixed capital formation (% of GDP) was available. The (Ethiopia GDP) data for the period of twelve years. The GDP of Ethiopia is a secondary data obtained from the DataStream of National University Singapore Library. The softness of BFAST (Break for Additive/multiplicative Seasonal and Trend) were inspected by the extension of BFAST to GFTSC. GFTSC was created to involve the cyclical and irregular constituents that was not involved by BFAST technique. GFTSC is aimed to synchronous the image of all the 4 time series constituents. Experiential statistics of Ethiopia was employed to GFTSC and subsequently the next forecast was made. The simulated and real data findings suggested that BFTSC can provide a better time series components identification better than manual process and hence caution should be taken because Ethiopia GDP had only stationery trend, hence not really improving and not dropping, so caution should be taken less it got to ruin. Improvement in Ethiopia GDP is recommended.