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It is worth noting that this 21st century has experienced so many economic, social, cultural and political turbulences throughout the world. The 2019 novel coronavirus (COVID-19) outbreak has been regarded by the World Health Organization (WHO) as a public health crisis of global concern. Nowadays, the chest X-ray (CXR) and chest computed tomography (CT) are a more effective imaging technique for diagnosing lung related problems. Deep learning has been more mature in the field ofsupervised learning, but other areas of machine learning have just started, especially for the areas of unsupervised learning and reinforcement learning. Deep learning has very good performance in speech recognition and image recognition. Using deep learning approaches to diagnose COVID-19 can achieve better cures and treatments. This research presents the data augmentation and L2 regularization approach for transfer learning in several state-of-the-art deep learning models such as VGG16, VGG19, ResNet, and AlexNet, with Convolutional Block Attention Module (CBAM) to perform binary classification ( such as normal and COVID-19/pneumonia cases or COVID-19 and pneumonia cases) and also multi-class classification (such as COVID-19, pneumonia, and normal cases) of covid-chestxray-dataset and NIH datasets. In addition, the performance evaluation adopted the confused matrix to evaluate the results of these models. To sum up, the CBAM can improve the accuracy of all deep learning models to achieve better performance in contrast to that without this architecture. The findings can be a reference for the related COVID-19 diagnosis researches especially during the post-pandemic era. [ABSTRACT FROM AUTHOR]

The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally resultsin the propagation of erroneousinformation, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of Coronavirus Disease (COVID-19), a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an “infodemic” by the World Health Organization (WHO), posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation. The research uses multiple deep learning model frameworks to detect misinformation in Chinese and English, and compare them based on different text feature selections. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The long and short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the bidirectional long and short-term memory (BiLSTM) model were selected for fake news detection. BiLSTM produces the best detection result, with detection accuracy reaching 94% for short-sentence English texts, and 99% for long-sentence English texts, while the accuracy for Chinese texts was 82%. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented impact on society, especially in densely populated areas. Schools have implemented distance learning, which has spawned many related problems. This paper focuses on the difficulties arising from the epidemic in indigenous communities and how appropriate information strategies may be used to solve these. Four main suggestions are provided to assist indigenous students and their teachers to protect themselves and learn during the pandemic and to ensure that educational goals are achieved. [ABSTRACT FROM AUTHOR]

The article focuses on the integration of advanced technologies such as RFID, NFC, and Artificial intelligence (AI) within the Internet of Everything (IoE) framework to enhance value chain processes and smart city applications. Topics include the use of deep learning for predicting groundwater levels and thyroid diseases, energy-efficient path planning for autonomous underwater vehicles, and smart appliance adoption.

The popularity of 5G networks has made smart manufacturing not limited to high-tech industries such as semiconductors due to its high speed, ultra-high reliability, and low latency. With the advance of system on chip (SoC) design and manufacturing, 5G is also suitable for data transmission in harsh manufacturing environments such as high temperatures, dust, and extreme vibration. The defect of the screw head is caused by the wear and deformation of the die forming the head after mass production. Therefore, the screw quality inspection system based on the tactile network in this article monitors the production quality of the screw; the system will send awarning signal through the router to remind the technician to solve the production problem when the machine produces a defective product. Sensors are embedded into the traditional screw heading machine, and sensing data are transmitted through a gateway to the voluntary computing node for screw slot quality inspection. The anomaly detection data set collected by the screw heading machine has a ratio of anomaly to normal data of 0.006; thus, we propose a time-series deep AutoEncoder architecture for anomaly detection of screw slots. Our experimental results show that the proposed solution outperforms existing works in terms of efficiency and that the specificity and accuracy can reach 97% through the framework proposed in this article. [ABSTRACT FROM AUTHOR]

The World Health Organization defines Smart Healthcare as "Information and Communication Technology applications in the medical and health fields, including medical care, disease management, public health monitoring, education, and research." In addition, many scholars believe that "Smart Healthcare" refers also to the integration of medical informatics, public health, and business applications mainly through the Internet and related artificial intelligence and data mining technologies in order to provide more accurate personal healthcare services and health information. The concept of deep learning has gained ground rapidly in recent years. While deep learning is usually applied to the studies of image/object recognition such as board game notations, paintings, people/things/objects in pictures, and so on, it is also often applied to the extraction of features. However, researchers have rarely used deep learning methods to predict outcomes in the medical and healthcare fields, preferring instead to make these predictions using algorithms based in traditional statistical methods and regression analysis. This paper introduces and investigates deep learning methods in the context of predicting outcomes in the medical and healthcare fields. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technology has recently been applied to outdoor learning in an attempt to overcome the drawbacks associated with traditional teaching environments. This study conducted an experiment designed to examine how augmented reality (AR) technology in mobile devices can be used to generate virtual objects to create a context-aware, AR-enabled guided tour application for outdoor learning. The participants were 70 elementary school students (average age: 11 years), who were randomly divided into control and experiment groups. The results showed the proposed system provided learners with a friendly, interactive interface and rich, engaging media to improve learning performance and stimulate the students' internal motivation to learn. The system's quantification of the learning motivations noted in Keller's ARCS model and Kolb's learning style theory can be used to improve the design of the learning materials. In conclusion: (1) The proposed system and activity helps stimulate learning intention via the pursuit of outdoor learning objectives, (2) the AR technology provides learners with contextual information related to the outdoor learning environment, and (3) the benefits of the proposed model do not differ for students with different learning styles. [ABSTRACT FROM AUTHOR]

In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings. [ABSTRACT FROM AUTHOR]

Motivation: Ovarian cancer is the fifth leading cause of cancer deaths in women in the western world for 2013. In ovarian cancer, benign tumors turn malignant, but the point of transition is difficult to predict and diagnose. The 5-year survival rate of all types of ovarian cancer is 44%, but this can be improved to 92% if the cancer is found and treated before it spreads beyond the ovary. However, only 15% of all ovarian cancers are found at this early stage. Therefore, the ability to automatically identify and diagnose ovarian cancer precisely and efficiently as the tissue changes from benign to invasive is important for clinical treatment and for increasing the cure rate. This study proposes a new ovarian carcinoma classification model using two algorithms: a novel discretization of food sources for an artificial bee colony (DfABC), and a support vector machine (SVM). For the first time in the literature, oncogene detection using this method is also investigated. Results: A novel bio-inspired computing model and hybrid algorithms combining DfABC and SVM was applied to ovarian carcinoma and oncogene classification. This study used the human ovarian cDNA expression database to collect 41 patient samples and 9600 genes in each pathological stage. Feature selection methods were used to detect and extract 15 notable oncogenes. We then used the DfABC-SVM model to examine these 15 oncogenes, dividing them into eight different classifications according to their gene expressions of various pathological stages. The average accuracy of the eight classification experiments was 94.76%. This research also found some oncogenes that had not been discovered or indicated in previous scientific studies. The main contribution of this research is the proof that these newly discovered oncogenes are highly related to ovarian or other cancers. [ABSTRACT FROM AUTHOR]

Energy is limited in wireless sensor networks (WSNs) so that energy consumption is very important. In this paper, we propose a hybrid architecture based on power-efficient gathering in sensor information system (PEGASIS) and low-energy adaptive clustering hierarchy (LEACH). This architecture can achieve an average distribution of energy loads, and reduced energy consumption in transmission. To further extend the system lifetime, we combine the intersection-based coverage algorithm (IBCA) with LEACH architecture and the hybrid architecture to prolong the system lifetime that introducing sensor nodes to enter sleep mode when inactive. This step can save more energy consumption. Simulation results show that the performance of our proposed LEACH architecture with IBCA and the hybrid architecture with IBCA perform better than LEACH architecture with PBCA in terms of energy efficiency, surviving nodes and sensing areas. [ABSTRACT FROM AUTHOR]

Due to the importance of power dissipation in the wireless sensor networks and embedded systems, real-time scheduling has been studied in terms of various optimization problems. Real-time tasks that synchronize to enforce mutually exclusive access to the shared resources could be blocked by lower priority tasks. While dynamic voltage scaling (DVS) is known to reduce dynamic power consumption, it causes increased blocking time due to lower priority tasks that prolong the interval over which a computation is carried out. Additionally, processor slowdown to increase execution time implies greater leakage energy consumption. In this paper, a leakage-controlled method is proposed, which decreases both priority inversion and power consumption. Based on priority ceiling protocol (PCP) and a graph reduction technique, this method can decrease more energy consumption and avoid priority inversion for real-time tasks. [ABSTRACT FROM AUTHOR]

In recent years, many emerging information technologies have been applied to learning environments in an attempt to overcome drawbacks associated with traditional teaching environments. Some of these technologies have been shown to improve learning outcomes and learning motivation. Advances in wireless communications have raised research interest in the development of ubiquitous learning (u-learning) environments and their impact on learner attitudes and behavior. This study reports the use of augmented reality (AR) technology to create virtual objects for use in mobile devices to create a context-aware, AR-enabled guided tour application for outdoor learning. The goal is to provide learners with a friendly, interactive interface and rich, engaging media to stimulate intrinsic motivation and learning performance. The main advantages of the proposed system include the following: (1) it helps stimulate learning intention through pursuing outdoor learning objectives, (2) AR technology provides learners with contextual information related to the outdoor learning environment, and (3) it enhances learner retention of teaching contents easily with the situated learning strategy. [ABSTRACT FROM AUTHOR]

As the use of Information Technology (IT) applications has become more widespread, focus has turned toward applying IT to the learning domain, with policy initiatives aimed at integrating IT with teaching. E-Portfolios have assumed growing importance in higher education as institutions seek new teaching-learning-assessment tools to increase learner autonomy in virtual learning environments. Based on the Technology Acceptance Model (TAM) and Information System Success Model (ISSM), this research investigates and analyzes the attitudes, degree of satisfaction and acceptance of e-Portfolio system users. The results indicate that, among prospective users, attitude appears to have the strongest and most significant direct effect on usage intentions, while satisfaction serves as a preliminary condition to mediate the effect of the users' attitude on intention to use. In addition, the utility users perceive in the e-portfolio system influences their attitude and intention more than it does their perceived ease of use. Moreover, service quality utility has a greater influence on user satisfaction and intention than does system quality and information quality. This model provides a systematic understanding of prospective users' perceptions of the e-Portfolio system, and such an understanding could help in the development and promotion of e-Portfolio systems in educational contexts. [ABSTRACT FROM AUTHOR]

Recently, several spectacular bankruptcies, including Fannie Mae, Freddie Mac, Washington Mutual, Merrill Lynch, and Lehman Brothers, have caught the world by surprise. To improve the accuracy of financial distress predictions, this research compares traditional statistical methods (i.e., linear discriminant analysis, logistic regression), decision tree classification methods (i.e., C5.0, CART, CHAID, QUEST) and artificial neural network techniques (i.e., multi-layer perceptron, support vector machine) to distinguish their respective capabilities for predicting financial distress. The experimental results showed that the support vector machine (SVM) technique could be a more suitable method for predicting financial distress than traditional statistical or DT techniques. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this research is to illustrate the use of artificial neural network (ANN) and data-mining (DM) technologies as a good approach for satisfying the requirements of library users. Design/methodology/approach - This research presents the Intelligent Library Materials Recommendations System (ILMRS) which uses the adaptive resonance theory (ART) network to distribute readers into different clusters according to their personal background. When clusters of related personal background have been established, the Apriori algorithm is used to discover the suitable materials in which readers are interested and which they may need. Findings - The investigation results indicate that the ART and Apriori mining techniques can be used to improve the accuracy of the recommendations for reading materials in the library. Moreover, readers can be divided by means of demographic variables into three segments. Finally, the questionnaire survey proved that the proposed recommender system will be a suitable approach for stimulating the readers' motivation and interest. Research limitations/implications - This research is limited by its datasets from a digital library of a university in Taiwan, and it is applied by ART and Apriori mining techniques to recommend materials of readers. Originality/value - Today, digital information is becoming ever more popular. The huge quantity and the diversity of digital information are its main features. Therefore, readers are interested in obtaining useful information in an efficient manner. In this research, a digital library can use this approach to anticipate a reader's needs in advance based on the mining results. [ABSTRACT FROM AUTHOR]

Abstract  As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules; however, the only drawback of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance: genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population; (2) the experiments prove that the rule sets derived by the proposed approach are more accurate than GP. [ABSTRACT FROM AUTHOR]

The single most important task of knowledge management (KM) performance measurement is comparing your company with its main rivals. Most of the metrics and methods of knowledge measurement that have been developed are concentrated on measuring the knowledge within the organization, which may be nice to know, but is not critical. In this paper, we propose a methodology for comparing a firm's knowledge management performance with its major rivals using the Analytical Network Process (ANP) to obtain a clear direction of the effort required to gain or maintain a competitive advantage. The ANP approach employed in the present study is a theory of multiple criteria decision making (MCDM), and is good at dealing with tangible and intangible information. Our methodology is designed to make a detailed comparison of a firm's KM performance with that of its main rivals, in order to be able to provide effective information for improving its KM and to increase its decision-making quality. This paper makes three important contributions: (1) it develops a comprehensive model, which incorporates a variety of issues for conducting KM performance measurements in comparison with major rivals; (2) case experience is provided to help us understand the advantages and disadvantages of the methodology for KM performance measurement from a practical point of view, and (3) the results obtained from exploring the case firm present changes that the case firm can make, implying that the case firm must reinforce its knowledge creation and internalization so as to improve [ABSTRACT FROM AUTHOR]

In this paper, the development of knowledge management (KM) was surveyed, using a literature review and classification of articles from 1995 to 2004. With a keyword index and article abstract, we explored how KM performance evaluation has developed during this period. Based on a scope of 108 articles from 80 academic KM journals (retrieved from six online databases), we surveyed and classified methods of KM measurement, using the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-orientated analysis and organization- orientated analysis, together with their measurement matrices for different research and problem domains. Future development directions for KM performance evaluation are presented in our discussion. They include: (1) KM performance measurements have tended towards expertise orientation, while evaluation development is a problem- orientated domain; (2) different information technology methodologies, such as expert systems, knowledge-based systems and case-based reasoning may be able to evaluate KM as simply another methodology; (3) the ability to continually change and obtain new understanding is the driving power behind KM methodologies, and should be the basis of KM performance evaluations in the future. [ABSTRACT FROM AUTHOR]

The knowledge-based economy is coming, and knowledge management (KM) has rapidly disseminated in academic circles as well as in the business world. While an increasing number of companies have launched into knowledge management initiatives, a large proportion of these initiatives are limited to a technical focus. The problem with this type of focus is that it excludes and neglects the true potential benefits that can be derived from knowledge management. This paper develops a new metric, knowledge management performance index (KMPI), for evaluating the performance of a firm in its KM at a point in time. We therefore suggest that a KMPI can be used to determine KM activities from the following perspectives: knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. When KM activities efficiency is increased, KMPI will also be expanded, enabling firms to become knowledge intensive. This paper makes three important contributions: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes model that might be applied to KM; (2) it proposes a measurement framework to enable knowledge assets to be leveraged effectively and efficiently; and (3) it presents the first application of the Black-Scholes model that uses a real-world business situation involving KM as its test bed. The results prove the option pricing model can act as a measurement guideline to the whole range of KM activities. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the journal including an article on the development and application of artificial intelligence and another on the knowledge engineering of metadata in electronic libraries.