• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.63-69
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• 2021

Flipped learning is necessary for modern education but quite difficult to implement. In pedagogical science, the question remains to what extent the practical work of the teacher in combination with the technologies of flipped learning will improve the quality of higher education. The aim of this article is to study the effectiveness and feasibility of using flipped learning technologies, assessing their perception by students (advantages and problems), identified an algorithm for introducing flipped learning technology in higher education institutions. Research methods. The main method is an experiment. An evaluation of the effectiveness of the study was conducted using a questionnaire and observation method. Statistical methods were used to evaluate the results of the experiment. The research hypothesis is that flipped learning allows the teacher to spend more time on an individual approach, to understand the real needs of students, and provide effective feedback, thereby improving the quality of learning and motivation of students, especially while studying complex material. The results of the study are to prove the effectiveness of the technology of flipped education in the study of complex disciplines, courses, topics. The use of flipped learning strategies improves the self-regulation of the educational process, group work skills, improves students' ability to learn, overcome difficulties. The technology of flipped learning in the presence of modern technical means and constant work on improving the level of digital literacy is an effective means for students to master complex topics and problematic issues that require additional consideration and discussion. The perspective of further research is the consideration of integrated approaches to the application of flipped learning technologies to the principles of STEAM-education, multilingual and multicultural programs, etc. It is also worth continuing to develop a set of methods aimed at enhancing the student's learning activities, the formation of group work skills, direct participation in creating the foundations of higher education.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Asia pacific journal of information systems
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• v.9 no.3
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• pp.177-194
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• 1999

Identifying knowledge as the single most important asset ultimately defining organizational competitiveness, enterprises are trying to move towards knowledge-oriented practices. Such practices have given rise to learning and agile organization, This paper presents applied information technologies to realize the learning and agile organization, focusing on systems thinking. Firstly, in order to establish a framework for the systems thinking, an information systems development method based on process chain is proposed. Then, an agent-based microworld simulation approach is presented. The approaches provide visible and analytical information to knowledge workers so that they can have systems thinking capabilities eventually. Various microworlds on the top of the information system can be constructed with agents and simulated for possible business events. All decision makings are dynamic in nature. To let knowledge workers look ahead the possible outcomes of the whole relevant processes is the core capability of the approaches. Through watching, the knowledge workers would be able to acquire new insights or problem solving knowledge for the problem in hand.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.366-370
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• 2022

The article deals with the theoretical and methodological foundations of innovative approaches in the modern education system. The issues of introducing computerized and telecommunication technologies are characterized, which allow switching to distance learning (DL), which is a promising form of the system of open education support in the modern educational process. Special attention is paid to the study of practical technologies of vocational training and the activities of a teacher and innovative areas of vocational training of students.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.248-256
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• 2021

The article discusses the evolution of the development of distance learning in world practice; investigated the essence and modern content of the concepts of "distance learning" and "distance education"; studied the principles of distance learning in the educational process; analyze the use of distance learning in higher educational institutions of Ukraine; substantiated the effectiveness of introducing distance learning into the higher education system; formed new management approaches in the distance learning system; proposals for the organization and improvement of distance learning at the university were developed on the basis of the analysis.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.175-181
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• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.204-214
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• 2022

Speedy development has been witnessed in communication technologies and the adoption of the Internet across the world. Information dissemination is the primary goal of these technologies. One of the rapidly developing nations in the Middle East is Saudi Arabia, where the use of communication technologies, including mobile and Internet, has drastically risen in recent times. These advancements are relatively new to the region when contrasted to developed nations. Thus, offenses arising from the adoption of these technologies may be new to Saudi Arabians. This study examines cyber security awareness among Saudi Arabian citizens in distinct settings. A comparison is made between the cybersecurity policy guidelines adopted in Saudi Arabia and three other nations. This review will explore distinct essential elements and approaches to mitigating cybercrimes in the United States, Singapore, and India. Following an analysis of the current cybersecurity framework in Saudi Arabia, suggestions for improvement are determined from the overall findings. A key objective is enhancing the nationwide focus on efficient safety and security systems. While the participants display a clear knowledge of IT, the surveyed literature shows limited awareness of the risks related to cyber security practices and the role of government in promoting data safety across the Internet. As the findings indicate, proper frameworks regarding cyber security need to be considered to ensure that associated threats are mitigated as Saudi Arabia aspires to become an efficient smart nation.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.667-684
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• 2022

Gene expression data present the level of mRNA abundance of each gene, and analyses of gene expressions have provided key ideas for understanding the mechanism of diseases and developing new drugs and therapies. Nowadays high-throughput technologies such as DNA microarray and RNA-sequencing enabled the simultaneous measurement of thousands of gene expressions, giving rise to a characteristic of gene expression data known as high dimensionality. Due to the high-dimensionality, learning models to analyze gene expression data are prone to overfitting problems, and to solve this issue, dimension reduction or feature selection techniques are commonly used as a preprocessing step. In particular, we can remove irrelevant and redundant genes and identify important genes using gene selection methods in the preprocessing step. Various gene selection methods have been developed in the context of machine learning so far. In this paper, we intensively review recent works on gene selection methods using machine learning approaches. In addition, the underlying difficulties with current gene selection methods as well as future research directions are discussed.