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\"With metaverse expected to be the next generation of the Internet and its online activities., this book explores its fundamental study and application from the business information systems perspectives\"-- Provided by publisher.

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

The ubiquitous problem of pesticide in aquatic environment are receiving worldwide concern as pesticide tends to accumulate in the body of the aquatic organism and sediment soil, posing health risks to the human. Many pesticide formulations had introduced due to the rapid growth in the global pesticide market result from the wide use of pesticides in agricultural and non-agricultural sectors. The occurrence of pesticides in the water body is derived by the runoff from the agricultural field and industrial wastewater. Soluble pesticides were carried away by water molecules especially during the precipitation event by percolating downward into the soil layers and eventually reach surface waters and groundwater. Consequently, it degrades water quality and reduces the supply of clean water for potable water. Long-time exposure to the low concentration of pesticides had resulted in non-carcinogenic health risks. The conventional method of pesticide treatment processes encompasses coagulation-flocculation, adsorption, filtration and sedimentation, which rely on the phase transfer of pollutants. Those methods are often incurred with a relatively high operational cost and may cause secondary pollution such as sludge formation. Advanced oxidation processes (AOPs) are recognized as clean technologies for the treatment of water containing recalcitrant and bio-refractory pollutants such as pesticides. It has been adopted as recent water purification technology because of the thermodynamic viability and broad spectrum of applicability. This work provides a comprehensive review for occurrence of pesticide in the drinking water and its possible treatment.

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research is conducted, exploring AI applications, benefits, challenges, regulations, and relevant theories. This study identifies key trends and suggests future research directions. The major findings include an overview of AI applications, benefits, challenges, and ethical issues in the banking and finance industries. Recommendations are provided to address these challenges and ethical issues, along with examples of existing regulations and strategies for implementing AI governance frameworks within organizations. This paper highlights innovation, regulation, and ethical issues in relation to AI within the banking and finance sectors. Analyzes the previous literature, and suggests strategies for AI governance framework implementation and future research directions. Innovation in the applications of AI integrates with fintech, such as preventing financial crimes, credit risk assessment, customer service, and investment management. These applications improve decision making and enhance the customer experience, particularly in banks. Existing AI regulations and guidelines include those from Hong Kong SAR, the United States, China, the United Kingdom, the European Union, and Singapore. Challenges include data privacy and security, bias and fairness, accountability and transparency, and the skill gap. Therefore, implementing an AI governance framework requires rules and guidelines to address these issues. This paper makes recommendations for policymakers and suggests practical implications in reference to the ASEAN guidelines for AI development at the national and regional levels. Future research directions, a combination of extended UTAUT, change theory, and institutional theory, as well as the critical success factor, can fill the theoretical gap through mixed-method research. In terms of the population gap can be addressed by research undertaken in a nation where fintech services are projected to be less accepted, such as a developing or Islamic country. In summary, this study presents a novel approach using descriptive analysis, offering four main contributions that make this research novel: (1) the applications of AI in the banking and finance industries, (2) the benefits and challenges of AI adoption in these industries, (3) the current AI regulations and governance, and (4) the types of theories relevant for further research. The research findings are expected to contribute to policy and offer practical implications for fintech development in a country.

The Fourth Industrial Revolution (4IR) offers optimum productivity and efficiency via automation, expert systems, and artificial intelligence. The Fourth Industrial Revolution deploys smart sensors, Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), big data and analytics, Augmented Reality (AR), autonomous robots, additive manufacturing (3D Printing), and cloud computing for optimization purposes. However, the impact of 4IR has brought various changes to digital humanities, mainly in the occupations of people, but also in ethical compliance. It still requires the redefining of the roles of knowledge management (KM) as one of the tools to assist in organization growth, especially in negotiating tasks between machines and people in an organization. Knowledge management is crucial in the development of new digital skills that are governed by the ethical obligations that are necessary in the Fourth Industrial Revolution. The purpose of the study is to examine the role of KM strategies in responding to the emergence of 4IR, its impact on and challenges to the labor market, and employment. This paper also analyzes and further discusses how 4IR and employment issues are being viewed in the context of ethical dilemmas.

Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention. To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices.

The release of polycyclic aromatic hydrocarbons (PAHs) into the environment has become a serious concern with rapidly increasing human activities. PAHs are one of the hazardous pollutants generated primarily from the incomplete combustion of fossil fuels, industrial emissions, and the expenditure of vehicles. These toxic compounds are very dangerous to ecosystems and human health due to being persistent, bioaccumulative, and carcinogenic. Composting is considered a form of bioremediation for eliminating PAHs in contaminated soils. The method utilizes microbial communities to break down organic pollutants and is low-cost and environmentally friendly. The efficiency factor depends on many aspects, including soil pH, oxygen, temperature provision, and the diversity of microbes, among others. Thermophilic conditions help in the decomposition of both low- and high-molecular-weight PAHs. This paper focuses on the effectiveness of composting as a bioremediation technology for remediating PAH-contaminated soils and its impact on the environment and human health. Due to its safety and high efficiency, composting should be improved and prioritized for its widespread application as a principal remediation technology for PAH pollution at the earliest opportunity.

The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate a high cost of energy production. Therefore, in this study, we proposed an energy usage forecasting model and parameter analysis using long short-term memory (LSTM) and explainable artificial intelligence (XAI), respectively. A public energy usage dataset from a steel company was used in this study to evaluate our models and compare them with previous study results. The results showed that our models achieved the lowest root mean squared error (RMSE) scores by up to 0.08, 0.07, and 0.07 for the single-layer LSTM, double-layer LSTM, and bi-directional LSTM, respectively. In addition, the interpretability analysis using XAI revealed that two parameters, namely the leading current reactive power and the number of seconds from midnight, had a strong influence on the model output. Finally, it is expected that our study could be useful for industry practitioners, providing LSTM models for accurate energy forecasting and offering insight for policymakers and industry leaders so that they can make more informed decisions about resource allocation and investment, develop more effective strategies for reducing energy consumption, and support the transition toward sustainable development.