Special Issue on Computing, Engineering and Multidisciplinary Sciences 2024

Bearing in mind that patient data is extremely sensitive, it is crucial to establish strong protection when the security and privacy of healthcare data are concerned. Prioritizing data security and privacy is essential for the overall healthcare industry in order to maintain the reliability of electronic healthcare (eHealth) information systems. This study explores the gathered data and information from the surveys and interviews by looking at the security and privacy concerns in using eHealth information technologies. The surveys and interviews were performed on the medical practitioners in N. Macedonia. The main goal is to find out how well-informed are the medical practitioners on the already in-place privacy measures that have been implemented by the medical authorities and to assess their attitudes regarding the need for additional improvements of the system. From the executed interviews, eight healthcare professionals participated in a thorough email interview in order to discover security and privacy issues associated with eHealth systems usage. This information served as the groundwork for administrating an online survey, to which 370 medical practitioners responded from primary and secondary healthcare. The findings emphasize how essential it is to promptly address the system usability concerns on the security and privacy procedures that are implemented when using eHealth technologies.

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As the global community increasingly shifts its focus towards sustainable development and combating climate change, renewable energy policies have become pivotal in shaping national and regional energy landscapes. Malaysia, as a developing nation with a rapidly growing economy, has recognized the importance of renewable energy sources in achieving its socio-economic goals while addressing environmental concerns. This paper explores Malaysia’s renewable energy policy framework and assesses its impact on neighboring countries in the Southeast Asian region. Through a comprehensive analysis of policy measures, incentives, challenges, and achievements, this paper elucidates the significance of Malaysia’s renewable energy endeavors in fostering regional energy security, sustainability, and cooperation.

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The solar photovoltaic (PV) power generation industry has experienced substantial, ongoing growth over the past decades as a clean, cost-effective energy source. As electric grids use ever-larger proportions of solar PV, the technology’s inherent variability—primarily due to clouds—poses a challenge to maintaining grid stability. This is especially true for geographically dense, electrically isolated grids common in rural locations which must maintain substantial reserve generation capacity to account for sudden swings in PV power production. Short-term solar PV forecasting emerges as a solution, allowing excess generation to be kept offline until needed, reducing fuel costs and emissions. Recent studies have utilized networks of light sensors deployed around PV arrays which can preemptively detect incoming fluctuations in light levels caused by clouds. This research examines the potential of such a sensor network in providing short-term forecasting for a 575-kW solar PV array in the arctic community of Kotzebue, Alaska. Data from sensors deployed around the array were transformed into a forecast at
a 2-minute time horizon using either long short-term memory (LSTM) or gated recurrent unit (GRU) as base models augmented with various combinations of 1-dimensional convolutional (Conv1D) and fully connected (Dense) model layers. These models were evaluated using a novel combination of statistical and event-based error metrics, including Precision, Recall, and Fβ. It was found that GRU-based models generally outperformed their LSTM-based counterparts along statistical error metrics while showing lower relative event-based forecasting ability. This research demonstrates the potential efficacy of a novel combination of LSTM/GRU-based deep learning models and a distributed sensor network when forecasting the power generation of an actual solar PV array. Performance across the eight evaluated model combinations was mostly comparable to similar methods in the literature and is expected to improve with additional training data.

 This research is an extension of the research (ISEEIE 2023), which dealt with Time-Series Clustering (TSC) of Vegetation Index (VI) for paddy rice. The novelty of this research is “Visualization of growth changes before and after additional fertilization,” “Analyzing the appropriate amount of additional fertilizer,” and “Optimization of monitoring period to minimize the number of monitoring days for its workload reduction using Unmanned Aerial Vehicle (UAV).” For visualization of growth changes before and after fertilization, a UAV was used to obtain VIs for each mesh and make its time-series data during the monitoring period. Then, TSC was performed on the data. As a result of clustering, NDVI and NDRE increased with additional fertilizer, making it possible to visualize the fertilizer effects. For analyzing the appropriate amount of fertilizer, the its amount applied was changed for each paddy field (2.8, 3.5, 4.2g/m2). In a field experiment conducted, both the TSC results and the crop estimates by unit acreage sampling for each paddy field revealed no difference in yield among fields, indicating that the paddy field with the least fertilizer amount (2.8g/m2) is optimal. It was estimated that this would reduce nitrate nitrogen, which is harmful to soil and the human body, by 0.070mg/L. In addition, for optimization of the monitoring period, the importance of each independent variable outputted by Random Forest (RF) was used to find a subset of monitoring dates. In any VI, there is a period, determined by the range of effective accumulated temperature, when the clustering result does not change even if the number of monitoring dates is reduced (The period could be reduced from 30 to 40 days, which is particularly important for three vegetation indices). From these results, the technologies can help reduce fertilizer costs, excessive fertilization and environmental impacts, and promote the use of UAV.

 Polyfluoroalkyl substances (PFAS) are persistent chemicals that accumulate in the body and environment. Although recent studies have indicated that PFAS may disrupt kidney function, the underlying mechanisms and overall effects on the organ remain unclear. Therefore, this study aims to elucidate the impact of PFAS on kidney health using machine learning techniques. Utilizing a dataset containing PFAS chemical features and kidney parameters, dimensionality reduction and clustering were performed to identify patterns. Machine learning models, including XGBoost classifier, regressor, and Random Forest regressor, were then developed to predict kidney type from PFAS descriptors, estimate PFAS accumulation in the body, and predict the ratio of glomerular surface area to proximal tubule volume, which indicates kidney filtration efficiency. The kidney type classifier achieved 100% accuracy, confirming that PFAS exposure alters kidney morphology. The PFAS accumulation model attained an R2 of 1.00, providing a tool to identify at-risk individuals. The ratio prediction model reached an R2 of 1.00, offering insights into PFAS effects on kidney function. Furthermore, PFAS descriptors and anatomical variables were identified through analyses using feature importance, demonstrating discernible links between PFAS and kidney health, offering further biological significance. Overall, this study can significantly contribute to the current findings on the effect of PFAS while offering machine learning as a contributive tool for similar studies.

Machine learning-based predictive models often face challenges, particularly biases and a lack of trust in their predictions when deployed by individual agents. Establishing a robust deployment methodology that supports validating the accuracy and fairness of these models is a critical endeavor. In this paper, we introduce a novel approach to deploying predictive models, such as pre-trained neural network models, in a public blockchain network using smart contracts. Smart contracts are encoded in our approach as self-executing protocols for storing various parameters of the predictive models. We develop efficient algorithms for uploading and retrieving model parameters from smart contracts on a public blockchain, thereby ensuring the trustworthiness and immutability of the stored models, making them available for testing and validation by all peers within the network. In addition, users can rate and comment on the models, which are permanently recorded in the blockchain. To demonstrate the effectiveness of our approach, we present a case study focusing on storing vehicle price prediction models and review comments. Our experimental results show that deploying predictive models on a public blockchain network provides a proficient and reliable way to ensure model security, immutability, and transparency.

Recently Network safety has become an important or hot topic in the security society (i.e., Encryption and Decryption) developed as a solution of problem that have an important role in the security of information systems (IS). So protected/secure the shared data and information by many methods that require in all internet faciality, data health and the cloud computing that suggestively increased our data every in milliseconds unit. This performance analysis by two factors namely Encryption, Decryption and throughput time of three Hybrid Encryption schemes namely; Hybrid AES-RSA, Hybrid AES-ECC, and Hybrid AES-ElGamal which are based on Encryption and Decryption times by milliseconds unit in the form of throughput. The results evaluation shows clear distinctions schemes capabilities such as; Encryption and Decryption as well as throughput time consume. Nevertheless, the Hybrid AES-RSA emerges as the fastest types. Both encryption and decryption outcome with superior throughput. Hybrid AES-ECC and Hybrid AES-ElGamal results are slower processing times and making them more suitable for scenarios where performance is not the primary concern. The choice between these schemes should consider not only performance but also security requirements and specific application required for testing and realize to select Hybrid AES-RSA due to better performance in milliseconds. the programing language for proposed system is JAVA, this mean that all testing is by JAVA and discover that the Hybrid AES-RSA is better in performance. The security proposed is Hybrid AES-RSA for automated recruitment system is best.

Aquaculture is one of the most important industries worldwide, and most marine products are produced by aquaculture. On the other hand, the aquaculture farmers are faced on the challenge of damage to marine products due to abnormal seawater temperatures. Research on seawater temperature prediction have been conducted, but many of them require a large amount of training data. Collecting seawater temperature data is not easy, and it takes an enormous time to introduce in new aquaculture farms. Therefore, the purpose of this study is to predict seawater temperature even with a small amount of training data for about one year. In this paper, we propose a seawater temperature prediction model using transition learning. The proposed model also attempts to improve the prediction accuracy by considering the difference in water depth between observation points. The results of the evaluation experiment showed that the prediction accuracy can be improved by transfer learning when learning with a small amount of data. In addition, we also confirmed that adding water depth values to the input layer may not lead to improved prediction accuracy for transfer learning.