Computer Science Disciplines Research Articles

A Review on Current Applications of Artificial Intelligence in Medical Field

Artificial Intelligence is a powerful and revolutionary discipline of computer science that has the potential to radically transform medical practise and healthcare delivery. Artificial intelligence (AI) is the intelligence displayed by machines that can aid in the performance of various tasks via sentiment analysis and natural language processing (NLP). Using past data and information, this technology allows machines to learn on their own. When it comes to equipping computers, AI is a subset of machine learning and deep learning, each of which has its own set of tasks. In medical research, artificially intelligent computer systems are commonly used. Patient diagnosis, end-to-end drug discovery and development, enhancing physician-patient communication, transcribing medical documents such as prescriptions, and remotely treating patients are all common applications. We outline current developments in the application of AI in healthcare in this review paper, applications of AI in different medical specialization, and assess the probable future direction of AI-augmented healthcare systems

Visual Computing Models in Cancer: PET/CT, CAD, CNN, and ST-Net

Visual computing is a discipline in computer science that allows computers to acquisite, process, and analyze visual data. In the application field of medicine, it enables to development of new methods for the visualization, detection, and analysis of diseases. Therefore, it plays a significant role in developing the understanding of diseases. Taking the fact that cancer is currently one of the deadliest and most frequent diseases around the world into consideration, it is crucial to generate effective methods for the study of cancer in order to provide appropriate management strategies for the disease and treatment methods. Research has shown that methods based on visual computing can detect, classify, and analyze the cancerous tissues in a patient successfully with high accuracy. This paper focuses on the PET/CT, CAD, CNN, and ST-Net visual computing models by evaluating their working mechanisms, their efficacy in the use of cancer with the help of previous research made in the literature, and their limitations in a medical approach.

79 Cross-Training Future Workforce on Data Handling and Interpretation for Precision Agriculture Systems

Abstract The objective of this paper is to report observations from a 10-week summer training program on Data Management in Animal and Plant Sciences. A total of six undergraduate students from three universities in the Texas A&M University System [Texas A&M University (TAMU)-College Station, TAMU-Kingsville, and TAMU-Corpus Christi] representing disciplines of Animal Science, Plant Science, and Computer Science learned handling and interpretation of sensor-based data derived from both animal sensors and UAV images of plant fields. Students attended weekly training sessions on data collection, management, processing, and visualization software including ArcGIS, MySQL and power BI. In addition, students also worked as a team and developed a database project and gave an oral presentation on their team project. On the pretraining survey, two students indicated that they had a background in animal science, two students indicated a background in plant science, one student in computer science, and one student with experience in spatial data. To begin with, the students had a mean score of 4.3 (range 0–8; on a scale of 0-10) for knowledge of data management in agriculture. One third of the students indicated that they did not have data handling experience, and one half of the students had some data analysis experience whereas one student indicated partial experience of working with data. In general, students indicated that they were very satisfied with the internship experience. On a scale of 0-10, the mean satisfaction score was 8.75 (range 7.5-10). Students indicated that they were more confident to work with and talk about data from animal systems (mean 8.5; range 7-10) and plant systems (mean = 8.4; range = 7-10). All students agreed that they learned at least one new concept related to animal and plant data ecosystems. Students indicated that the program was a good start for understanding the overall data architecture, indicated progress on data handling and thought helpful to understand opportunities in agriculture. All students agreed that their understanding of data management in agriculture changed significantly because of the course. Consequently, four students indicated that they were interested in career opportunities related to data in agriculture whereas two students indicated their interest in application of the developed tools for the on-farm use. When evaluating effect of the cross disciplinary training, students agreed that the training was helpful in learning concepts outside of their own discipline with the mean score 9.4(range 8-10) on a scale of 0-10. All students indicated that they learned team management skills and skills working with people that are different from their own. Students suggested improvement on the communications, prerecorded videos, in-person meetings, weekly reporting reviews in the future iterations for students to benefit more from the program.

Increasing the competitiveness of future economists for work in industry 4.0

Introduction. Currently, in the context of the development of digitalization and information technology, the business environment is changing. This must be taken into account when preparing economics students so that students have a high level of professional competence and possess the knowledge and skills necessary to work in Industry 4.0. Given the rapid development of ICT in the field of economics and education, it is essential to prioritize the development of students' ability to navigate information flows and use modern data processing methods and programming skills.. The aim of the study is to develop recommendations aimed at creating organizational and pedagogical conditions for the increasing the competitiveness of future economists for working in industry 4.0. Materials and methods. The 285 students took part in the survey – future economists studying in the specialty "Accounting and taxation" of 5 universities (Chechen State University named after Akhmat Abdulkhamidovich Kadyrov, Peoples' Friendship University of Russia, Moscow Aviation Institute, Financial University under the Government of the Russian Federation, Plekhanov Russian University of Economics). To assess the significance of differences in indicators in samples of 1st and 3rd year students, a nonparametric Pearson's chi-squared test was used. Results. The results obtained showed that when characterizing a competitive specialist, 1st year students in economics give priority to theoretical and methodological knowledge and knowledge of a foreign language, while 3rd year students attribute a competitive specialist and high knowledge of modern ICT to the priority characteristics of a competitive specialist. The results of Pearson's chi-squared test confirmed the statistical significance of differences in such characteristics as foreign language proficiency (χ2 = 58.7), theoretical and methodological knowledge proficiency (χ2 = 46.8), modern ICT proficiency at a high level (χ2 = 52.5) when comparing the answers of 1st and 3rd year students who are in the zone of significance, exceeding the critical value (p < 0.001). A survey of students on the factors contributing to an increase in the level of training of competitive specialists for work in Industry 4.0 showed that, compared with first-year students, third-year students are more interested in the introduction of special additional disciplines, electives in the information and professional areas. The calculation results of Pearson's chi-squared test confirmed the statistical significance of differences in students' assessment of such factors that contribute to an increase in the level of their IC, such as an increase in classroom hours for computer science disciplines (χ2 = 41.9), the introduction of special additional disciplines, electives in the information and professional areas (χ2 = 61 ,3) when comparing the answers of 1st and 3rd year students.. Conclusions. The data identified in the study make it possible to formulate recommendations aimed at creating organizational and pedagogical conditions for increasing the competitiveness of future economists. First of all, it is necessary to organize a sufficient level of technical support for teachers and students, as well as to attract competent teachers who are fluent in basic applications and professional software. Keywords: information culture, software, MySQL, Python, education

Artificial Intelligence: Current Challenges and Future Perspectives

Artificial intelligence is a discipline of computer science that gives machines intelligence that resembles that of humans and empowers them to acquire knowledge, evaluate, and handle issues when exposed to information in different formats. AI is now useful as a powerful tool to help researchers collect, deal with, and analyze large sets of data in a fast and cost-effective way; additionally, a statistical collection and analysis of a vast amount of dataset of enormous numbers of published research articles will be with a high level of accuracy.
 However, Artificial intelligence has many struggles, conflicts, and challenges that need to be discussed and clarified.

Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.

Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human-computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC.

TOWARD PSYCHOINFORMATICS: COMPUTER SCIENCE MEETS PSYCHOLOGY

Psycoinformatics is a present paper that provides insight into emerging research. This also emphasize the cooperation between the disciplines of psychology and computer science by handling large data sets derived from heavy used device, as smartphones or online social networking sites, to shed light on a large number of psychological traits, including personality and mood. New challenges are waiting for the psychologists in light which result in “Big Data” sets, because classic psychological methods will only be able to analyse the data derived from ubiquitous mobile devices, as well as other everyday technologies. As a result, psychologists must enrich their scientific methods through the inclusion of methods from informatics. The paper provides a clear view of one area of this research field, dealing mainly with social networks and smartphones. Additionally, we also highlight how data derived from Psychoinformatics can be in a meaningful way with data from human by neuroscience. We close the paper with some observations of areas for future research and problems that require consideration within this new discipline. KEYWORDS: Psycoinformatics, humans, computer, researchers, neuroscience.

A New Computer Science Academic Word List

This corpus-based vocabulary study aimed to develop a new computer science academic word list across ten sub-disciplines of computer science defined by Association for Computing Machinery (hereafter ACM). A corpus of Computer Science containing 2,500,990 running words was developed from 300 Computer Science Research Articles (hereafter CSRAC) as a database of this study. Drawing on and combining procedures and methods from Coxhead (2000), Gardner and Davies (2014) and other previous studies, this study developed a New Computer Science Academic Word List (hereafter NCSAWL), containing the most frequently-used computer words in computer research articles from the corpus. The NCSAWL contains 444 words, which accounts for approximately 20.33% of the coverage in the CSRAC, the NCSAWL has a much better coverage of computer English. The result of this study has numerous implications for computer science learners, English teachers, researchers, as well as material writers and course syllabus designers. For examples, English computer teachers should focus on teaching learners the most high-frequent words which have a dispersed coverage and have special meaning and use in the discipline of computer science. Teachers could also raise the awareness of learners that some words have different meanings and uses in general English. The material designers for English for academic and special purposes could incorporate the NCSAWL vocabulary into their academic reading and writing materials for computer science students. Researchers and English language teachers who are interested in expanding their computer science academic vocabulary could also use this NCSAWL.

An Evaluation Tool for Extracurricular Activities to Reduce the Gender Gap in Computer Science

In the last few years, workforce with STEM (Science, Technology, Engineering and Math) competencies has proved to be crucial for countries' innovative capacity and global competitiveness. Yet women are vastly underrepresented in STEM and, in particular, in ICT and computer science fields, both among workers and degree holders: this gap hinders the possibility for ICT employment to be strengthened and for women to take advantage of career opportunities, thus perpetuating gender inequalities in these disciplines. To counteract these effects and attract girls towards ICT-related fields of study and careers, several initiatives have been organised all around the world, such as summer camps and dedicated extracurricular activities. However, these initiatives are usually not supported by proper evaluation tools allowing researchers and practitioners to understand the actual benefits of the carried-out activities on girls' competencies and future attitudes. In this paper, we propose an evaluation tool for extracurricular activities aimed at reducing the gender gap in ICT. The proposed tool aims at capturing both a quantitative and a qualitative evaluation, including an Implicit Association Test (IAT) along with a more traditional questionnaire consisting of thematic sections designed to analyse various aspects of the activities' impact on girls. The tool has been applied in the context of two summer camps related to national and international projects aimed at attracting girls towards computer science and STEM disciplines: the ‘Digital Girls’ project, organised since 2014 by the University of Modena and Reggio Emilia in collaboration with other universities and local institutions, and the “STEM for Future” Erasmus+ project. Based on the results obtained by the summer camp case studies, we discuss some critical elements that can hinder the efficacy of the evaluation tool, giving suggestions to overcome these potential issues.

Using Kolb's Experiential Learning Theory to Improve Student Learning in Theory Course

Abstract : Data structures and Algorithms (DSA) is a mandatory course for all discipline students to get placement in IT companies and to participate in competitive examinations including GATE and TANCET for their higher studies in a computer science discipline. DSA course focuses on how to organize, manage and store data in an efficient manner, which facilitates to access data easily and at a faster rate. Different types of data structures, its functionality and its applicability are discussed in this course. At the end of the course, students will have the capability to identify the suitable data structure for a problem. Due to its importance and complexity, pressure will be created on faculty members, who are handling this course. From the perspective of the student, some students understand the concept but lack knowledge of how to apply it. The majority of students struggle to comprehend the data structure and are perplexed by it. Recent work focuses on how faculty members play a major role in active learning like developing models to explain the concept, conducting activities like role play, think-pair share, flipped classrooms and so on. In this work, a study was conducted in the course DSA which focused on reflective practice led by David Kolb's experiential learning theory. An experiment was conducted during Academic Year 2021-22 (Odd) in the course 18CS340 – Data Structures and Algorithms for a set of 54 students. It is inferred that the student gained a higher or deeper knowledge level in this course and is confident to identify appropriate data structures for real world problems. By engaging in reflective practice, faculty members can think around and reflect on their experiences, learn from them, make changes, and enhance their learning and instructional skills. Keywords : Activity-based learning, Data Structures and Algorithms, Kolb's experiential learning, Reflective practice, Self-Learning.

ОРГАНІЗАЦІЯ ДИСТАНЦІЙНОГО ТА ЗМІШАНОГО НАВЧАННЯ В ЗАКЛАДАХ ВИЩОЇ ОСВІТИ ЗАСОБАМИ ХМАРНИХ СЕРВІСІВ

The article examines the theoretical and practical aspects of the application of cloud services in the process of IT training of students of higher education institutions during distance and mixed learning. In recent years, the national education system has faced a number of problems related to the organization of the educational process in the conditions of quarantine restrictions, military operations and the need to ensure the quality and availability of higher education. In particular, among the main activities of the teaching staff is the search for and effective use of modern technologies, methods and means of organizing distance and mixed forms of education. In today's realities, the distance and, later, the mixed form of education have become an integral part of the organization of the educational process of higher education institutions in Ukraine. Regardless of the specifics of the training of higher education applicants, training is, to varying degrees, based on the use of remote technologies and cloud services. There is a sufficient number of educational materials management systems (LCMS Moodle, ATutor, NEO LMS, etc.) and cloud services (Google Classroom, Microsoft Teams, etc.) to support and organize the educational process. Among a large number of various systems and services, it is advisable to pay attention to the Microsoft Teams cloud service, as it is integrated with other Microsoft tools. An analysis of the functionality of the Microsoft Teams cloud service, which is the "core" of the virtual learning environment, was carried out. The advantages of its use are highlighted and the expediency of introducing cloud services into the educational process is substantiated. General approaches to the use of the Microsoft Teams cloud service are proposed, in particular, the organization and division of academic groups, methods of communication between participants in the educational process, methods of presenting methodical and educational materials, methods of evaluating the results of educational activities. According to the results of the research, it can be stated that the use of cloud services during distance and mixed learning can fully ensure the assimilation of the theoretical and practical aspects of computer science disciplines, regardless of certain problems in the self-organization of students. In our opinion, it is caused by the lack of direct student-teacher communication, which is obvious in the case of distance education.

Investigating Functional Variation across Academic Disciplines: A Multidimensional Analysis of Research Articles

The current study aims to investigate the functional variation in academic writing across six major disciplines of sciences, namely biology, physics, and computer science and of social sciences, namely linguistics, sociology, and psychology research articles. Furthermore, the current study also compares the use of language in science and social sciences research articles (RAs). For this purpose, a corpus of 300 research articles from six sub-disciplines of science and social sciences journals was compiled to conduct the analysis. A Multidimensional Analysis Tagger using version 1.3.2 was selected to tag and evaluate the RAs corpus. The triangulation research method was deployed to analyze the data quantitatively and to interpret the results qualitatively. The findings indicate that there are functional differences in the language employed in academic disciplines, particularly in terms of being informational, non-narrative, and persuasive. Likewise, comparable outcomes were noticed in dimension 3. The findings indicate that there are functional differences in the language employed in academic disciplines, particularly in terms of being informational, non-narrative, and persuasive. Likewise, comparable outcomes were noticed in dimension 3. The results may be helpful for the researchers, students of different disciplines, and teachers to spot trends across various sub-disciplines.

Gamers’ Total Experience and Game Motivation for Further Education Digital Manpower

Digital Manpower is very necessary in Thailand today as the growth of technology continues to increase. For example, tech giant Gartner has announced new technology trends every year including 2022, announcing the following technology trends: Total Experience, Composable Application, Decision Intelligence, Hyperautomation, Distributed Enterprises, Autonomic Systems, Generative AI, AI Engineering etc. The application of technology in daily life is increasing. The development of Digital Manpower is therefore very important in order to increase the knowledge and expertise of the country's people in technology. From the statistics, the country has increased internet usage in many ways. For example, from the statistics in 2021, Thai children playing games were number 1 in the world, and in 2022, Thai children playing games are in 2nd place in the world. But the problem faced with Thai education today is that children like to play games and think that they can study in the field of computers. But, in fact, students are not directed into the technology field taking into account their own aptitudes. causing the study to be not fully effective and some leave their courses midway. Therefore, this research is interested in the confirmation factors of children playing games by collecting data on Gamers' Total Experience and Game Motivation in order to analyze the data and discover the confirmation factors that direct the admissions to various computer disciplines. What direction and aptitude do you need to be able to enter a group of computer science disciplines in order to become a Digital Manpower in the country's future development. The researcher has collected data from a sample of 630 people from all six groups in computer science. The confirmation factor was analyzed by the LISREL program. It was found that each group of computer disciplines had different confirmation factors.

Investigating Persuasive Strategies in Scientific and Literature Books Blurbs

This study is conducted to identify the ways in which Scientific book blurbs differ from and/or similar to literature book blurbs and explores the linguistic features that are used in Scientific and literature book blurbs. To meet these aims, this study focuses on sixteen Scientific blurbs in different disciplines involving (Physics, Chemistry, Geology, Medicine, Biology, Computer Science, Linguistics, Agriculture) and sixteen literature book blurbs in four different disciplines involving (Novel, Drama, Poetry, Literature). These data are analyzed by adopting Gea valor (2005:52) classifications of linguistic features. The results have revealed that different linguistic strategies are employed in Scientific and literature books blurbs such as the ellipses, personal pronouns and complement languages. Imperative constructions are rarely used in Scientific blurbs, it has been found only in computer science discipline while they are absent in literature blurbs.

Studying and enhancing the methods for distance teaching of computer science in Kazakh secondary school students during the pandemic

Introduction. In 2020, the first two weeks of the enforced transformation of all the levels of school education, which was initiated as one of the measures against the Coronavirus disease (COVID-19), revealed a range of issues hampering the appropriate distance education. The absence of the methodological basis for conducting online classes in the Kazakh pedagogical community defined the need to study and enhance forms and technologies that would be efficient to use to interact with students during the transition of the national education system to distance education.Aim. The aim of this research lies in defining efficient methods for distance teaching of computer science in the Kazakh secondary school students in terms of ensuring the maintenance of the quality of knowledge and the academic progress of students at the sufficient level corresponding to that of the traditional in-person education.Methodology and research methods. A total of five educators and 320 students of three Kazakh schools took part in the study. At the moment of the experiment, the students were aged 12 to 18 years old. The participants were divided into seven groups according to the educational level (5th–11th forms) in order to make it more convenient to trace qualitative changes in the academic progress depending on the selected method for distance teaching of computer science. The authors conducted three control evaluations of the quality of knowledge in each of the 320 participants. The t-test for unpaired samples for every group was conducted to prove the statistical certainty of the calculated average reference values, which were required to confirm the viability of the conducted research. The analysis of the data obtained at the concluding stage of the experiment allowed to compare them with the reference values calculated at the preliminary stage of the research in question. For the comparison, The authors applied the Mann–Whitney U test for independent samples.Results. The preliminary analysis of the quality of knowledge related to the discipline of computer science in the participants revealed generally high and average level of both acquisition of theoretical information and development of the subject-related skills, which was registered based of the results of in-person education. The leading experience of the specialists composing the authors’ initiative research group allowed developing a structural scheme for an online lesson. The lessons applying this scheme were conducted up to the end of the academic quarter. By conducting the Mann–Whitney U test, we discovered that the obtained average values of the quality of teaching computer science to the participants statistically increased (I group – Uemp = 6.49 (p ≤ 0.05), II group – Uemp = 7.46 (p ≤ 0.05), III group – Uemp = 6.05 (p ≤ 0.01), IV group – Uemp = 6.71 (p ≤ 0.05), V group – Uemp = 6.91 (p ≤ 0.01), VI group – Uemp = 6.65 (p ≤ 0.05), VII group – Uemp = 6.21 (p ≤ 0.05)). Despite temporary fall in the efficiency of teaching computer science registered during the transition to the distance model, it was managed to achieve the level of academic progress and acquisition of knowledge corresponding to that of in-person education.Scientific novelty. TThe significance of the collected and analysed data was statistically proved. The data confirmed the efficiency of the use of defined and adapted approaches and teaching techniques, which were able to compensate the absence of traditional in-person lessons, while preventing the fall in the academic progress and the quality of knowledge in students.Practical significance. The obtained results evidence the success of the arrangements aimed at the enhancement of the methods for distance teaching of computer science in the Kazakh secondary school students during the transition to distance education enforced due to the pandemic.

Research Landscape of Adaptive Learning in Education: A Bibliometric Study on Research Publications from 2000 to 2022

Adaptive learning is an approach toward personalized learning and places the concept of “learner-centered education” into practice. With the rapid development of artificial intelligence and other technologies in recent years, there have been many breakthroughs in adaptive learning. Thus, it is important to gain insight into the evolution of related research and to track the research frontiers to further promote its development. This study used CiteSpace and VOSviewer to conduct a bibliometric analysis of 644 adaptive learning journal papers indexed in the WoS database from 2000 to 2022. This study presented a general view of the field of adaptive learning research over the last two decades using quantitative analysis. Currently, adaptive learning research is rapidly developing. In terms of the major research forces, a core group of authors including Qiao J. F., Han H. G. and Song Q has been formed; the major publishing country in this field is China; the core publishing journals include IEEE Transactions on Neural Networks and Learning Systems. Four major research topics in this field were identified using cluster analysis, namely the application of deep learning in educational data analysis, the development and application of adaptive learning model in AI education, the development and application of intelligent tutoring system in tutoring and teaching, cutting-edge modeling technology for feature modeling and knowledge tracing. Through evolution analyses, the logic of adaptive learning research’s development was determined; that is, technological changes have played a key role in the development of this field. Following the logic, we presented three frontiers of adaptive learning with burst terms: feature extraction, adaptation model and computational modeling. Adaptive learning is a core research topic for both computer science and educational technology disciplines, and it is also an important field where emerging technologies empowering education and teaching can play a part. The findings of the study clearly presented the current research status, evolutionary logic and research frontiers of this topic, which can provide references for the further development of this research field.

A Comparative Study on Promotion of Modal Adjuncts in Research Article Introductions

Promotion is a major strategy in Research article introductions (RAIs) to attract reader's attention and gain recognition. Building on previous researches that suggest promotion in RAIs could be realized by different kinds of linguistic elements and rhetoric structures, this study collected 100 SCI RAIs in the discipline of computer science as data and explored the promotion realized by interpersonal metafunction and rhetoric structure of modal adjuncts. Meanwhile, in order to identify if there are differences and similarities of promotion between Chinese EFL RAIs (English RAIs written by Chinese scholars) and English L1 RAIs (English RAIs written by English and American scholars), this study quantified lexical features by examining lexical density, lexical variety and position of modal adjuncts. Results showed that the modal adjuncts could realize promotion through varying interpersonal metafunctions (temporality, modality, intensity and comment) and the model of Create a Research Space (CARS). However, more comprehensive promotion was realized in English L1 RAIs through interpersonal metafunction conveyed by more frequent, more kinds of and more positions of modal adjuncts. Besides, this study also verified that modal adjuncts were encoded of CARS to realize promotion in both Chinese EFL RAIs and English L1 RAIs in the same way because of the same strategies (familiarity and logic) and similar linguistic features. These results are discussed in terms of "constraint communication", and discipline background drawn on for knowledge claims in same and different types of academic writing of promotion between Chinese EFL RAIs and English L1 authors.

Magic in Computer Networks Class

Many institutions have been pushed by the recent economic crisis to reduce expenses by cramming students into huge lecture groups. Large auditoriums have the drawback of discouraging student-faculty interaction and limiting participation. Additionally, students typically discover that the discipline of computer science is replete with theoretical and technological notions. They get disinterested and/or unmotivated when there is a lack of understanding. The lecturer should use alternate teaching techniques, particularly for first-year undergraduate students, to pique their attention and introduce fundamental ideas, according to classroom experience. This paper has outlined a few strategies for engaging students and fostering a sense of ease while they learn fundamental ideas about computer networks. Keywords— Dynamic teaching; Engineering Education; Flipped classroom; JIGSAW; Lecture improvement.

A Computer Program For The System Of Weak Fuzzy Complex Numbers And Their Arithmetic Operations Using Python

Weak fuzzy complex number system is considered as a generalization of complex numbers by using an algebraic element with fuzzy property. It is believed that this new numerical system will play many important roles in various computer science disciplines, proceeding from this importance this paper aims to introduce for the first time the algebraic operations defined on weak fuzzy complex numbers using Python and Jupyter Notebook. Hence, by building a new class and its functions in Python for this new set, we can now use weak fuzzy complex numbers and the main arithmetic operations on them.

Pronunciation Tutor for Deaf Children based on ASR

ASR, whose full name is Automated Speech Recognition, is a technology that converts human speech into text. Speech recognition, a multidisciplinary field, is closely related to acoustics, phonetics, linguistics, digital signal processing theory, information theory, computer science and other disciplines. ASR has been applied in educational technology such as deaf children's education in this day and age. This paper makes a preview of a project in which a computer-aided tutor for deaf children instruction based on the speech recognition technology. This tutor utilizes three effective models and is combined with data mining technology. Two evaluation approaches and overview of embedded experiment are also detailed in this paper.