Integration Of Computer Technology Research Articles

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Integrating sports education with data analysis and computer technology: A new paradigm for enhanced athletic performance

This comprehensive study delves into the transformative integration of sports science, data analytics, and computer technology in athletic training and education. It highlights the multidisciplinary approach to enhancing athletic performance through evidence-based methodologies, including biomechanical analysis, physiological assessment, and psychological strategies. With a focus on data-driven training methodologies, real-time performance monitoring, educational implications, and the incorporation of ethical considerations, the article showcases how modern technology and data analysis can personalize training programs, optimize performance, and minimize injury risks. Through quantitative analysis and case studies, it demonstrates the significant impact of these integrations on athletic performance metrics, while also addressing the challenges and limitations faced in adopting these approaches. The findings suggest a promising future for sports education and training, advocating for interdisciplinary collaboration and the adoption of data analytics in sports.

Computer Aided Process Planning for Sheet Metal Cutting Operations in the Manufacturing Industry

This paper describes computer-aided process planning for sheet metal cutting operations. The two designs to production stages that is highlighted are sheet metal processing and machining. There are four modules in this object's system. Virtual factory environments, feature-based designs, process planning, and process-based feature mapping. Feature-based design is utilized for the conception, modeling, and representation of the components for manufacturing applications. Whenever it involves sheet metal cutting operations in the manufacturing sector, computer-aided process planning, is extremely important for streamlining manufacturing procedures. The provides a general introduction to computer-aided process planning as it relates to sheet metal cutting, emphasizing its importance in boosting productivity, saving costs, and raising product quality. The main goal of computer-aided process planning for sheet metal cutting is to integrate computer technology, CAD/CAM systems, and sophisticated algorithms to automate and streamline the planning process. Based on design requirements, material characteristics, and production limitations, this method enables manufacturers to produce exact and ideal cutting plans. To create blanking and piercing holes, stamped or punched die are utilized in generative shape design; the generative computer-aided process planning system is created in C++ and used in various case studies presented in the present work. The application of computer-aided process planning for sheet metal cutting has several advantages, including greater output, less material waste, shortened lead times, and improved competitiveness in the industrial sector. The potential of computer-aided process planning to revolutionize sheet metal cutting operations, making them more efficient and cost-effective while ensuring high-quality final products is highlighted in the present research.

Empowering Sustainable Development and Security through Computer Education and Technology Integration

This study explores the pivotal role of computer education and technology integration in empowering sustainable development and security. The study employs a comprehensive literature review to establish the theoretical framework, identifying gaps in current knowledge. Utilizing a mixed-methods approach, we investigate the impact of technology on sustainable development and security outcomes. Empirical findings demonstrate the positive correlation between computer education, technological integration, and the enhancement of both sustainable development initiatives and security measures. The discussion delves into the implications of these results, emphasizing the potential for technology to catalyze positive change. Acknowledging challenges and limitations, the paper concludes with actionable recommendations for policymakers, educators, and practitioners seeking to leverage technology for societal advancement.

Implementation of the Simple Additive Weighting (SAW) Method in Selection of Students Recipients of Single Tuition Fee Assistance

Higher education in Indonesia, including at Budi Darma University, continues to be committed to creating an inclusive and equitable learning environment for all students. In line with this determination, Budi Darma University is implementing the Single Tuition Assistance (BUKT) program to support educational accessibility. Obstacles in the selection process, which is subjective and lacks a structured framework, can lead to inequality in the distribution of aid. This can result in students who should receive greater support being overlooked, while those who may need less may receive greater aid. In the BUKT selection process, there are a number of requirements that must be met, such as parents' income, PKH card ownership, completeness of documents, parents' dependents and home ownership. It is hoped that the use of a decision support system can be a solution to overcome this challenge. Decision Support Systems (DSS) integrate computer technology, mathematical models, and data to provide structured and organized support within a decision-making framework. The Simple Additive Weighting (SAW) method is a multi-criteria decision making method that allows relative weighing between criteria to determine the final score for each alternative. By applying SAW in the selection of BUKT recipient students, it is hoped that more objective and data-based decisions can be obtained. The research results produced the best alternative with a value of 100.00 in the alternative with code A5 in Fitri's name, so that Fitri was declared entitled to receive single tuition assistance.

Emergency Online Teaching and COVID-19: Teachers’ Use and Integration of Computer Technologies

The aim of the research was to determine the use of computer technologies and the status of integration into lessons during online teaching by class teachers during the COVID-19 pandemic. The research used a descriptive survey model. The study group for the research comprised 464 class teachers employed in official primary schools linked to the Ministry of National Education in Rize province. Two scales were used in the research; the Technology Integration Self-Efficacy Scale and the Teachers’ Information and Communication Technologies Integration Approach Scale. Data were analyzed with the t test, ANOVA and correlation test. Male teachers were determined to have higher use of computer technologies and higher levels of ability to integrate it into lessons compared to female teachers. Though there was no difference in use of technology levels of teachers, their levels of ability to integrate technology into lessons differed according to seniority. The use of computer technology by teachers and levels of ability to integrate into lessons did not differ according to receiving in-service training related to technology and educational status. Teachers’ use of technology and levels of ability to integrate into lessons differed according to the hours of lessons given using technology. As the levels of use of education technology by class teachers increased, their levels of ability to integrate computer technology into lessons also increased. 
 
 Keywords: Online teaching, technology integration, class teacher, Covid 19.

Empowering Sustainable Development and Security through Computer Education and Technology Integration

This paper explores the pivotal role of computer education and technology integration in empowering sustainable development and security. The study employs a comprehensive literature review to establish the theoretical framework, identifying gaps in current knowledge. Utilizing a mixed-methods approach, we investigate the impact of technology on sustainable development and security outcomes. Empirical findings demonstrate the positive correlation between computer education, technological integration, and the enhancement of both sustainable development initiatives and security measures. The discussion delves into the implications of these results, emphasizing the potential for technology to catalyze positive change. Acknowledging challenges and limitations, the paper concludes with actionable recommendations for policymakers, educators, and practitioners seeking to leverage technology for societal advancement.

The Role of Computer Technologies in Music Teacher Training: Implementing the Humane Education Strategy

The ever-evolving nature of society dictates educational policies, which is mirrored in the challenges of training music teachers in higher education. Pedagogical universities are constantly investigating ways to revolutionize education and introduce new pedagogical tools. This article analyzes the experiences, difficulties and directions of music teacher training in the scope of the humane education strategy. Surveying the condition of music education, its current status and potential for progress has demonstrated that the career of a musician-educator is and will remain in high demand as long as it is augmented with new professional content. This can be used to broaden the role of a music teacher in both regular and extra schooling, such as designing and implementing numerous musical and creative progression programmes. Importantly, the article delves into the issue of developing a model for teaching information technologies to future music teachers, which follows an interdisciplinary and context-oriented content selection principle. It advocates for a comprehensive approach to shaping a continuous sequence of educational subjects throughout the entire period of university education. It also highlights the integration of music and computer technologies as a means of teaching information technology and as a subject for study. Finally, the article highlights the significance of computer technologies in training future music teachers as there is a growing demand for music education specialists who are proficient in the technological processes associated with music software and can effectively employ computer technologies in their professional duties.

Insights into a Community of Inquiry that emerged during academics’ emergency remote university teaching of chemistry in response to concern for students

ABSTRACT Background Effective teaching of university chemistry in an online environment not only requires teachers to have specific discipline content knowledge; it also requires them to have knowledge and skills in technology-enhanced teaching practices. When transitioning from traditional face-to-face teaching into online contexts, teachers must learn new strategies and methods, along with a shift in their beliefs. Whilst online learning of chemistry is commonplace in many higher education institutions globally, the integration of computer and digital technologies in teaching chemistry in developing countries had been limited prior to the 2020 pandemic. Purpose In this study, the experiences, perceptions, and teaching strategies of university chemistry teachers in the Philippines have been explored during the transition of their instruction to emergency remote teaching at two timepoints during the COVID-19 pandemic. Sample Data were collected in the form of survey responses from 139 participants representing 35 universities and colleges across the nation. Results No strong relationships were found between participants’ experiences and their academic rank, length of service, or institution type, suggesting university chemistry teachers took similar approaches to quickly adopt and adapt available technologies to deliver the required curriculum. Since the Philippines continues to struggle to establish a robust infrastructure to promote online and technology-enhanced learning, participants had to devise pedagogies and practices that would allow accessible learning experiences for their students by curating online tools that would require minimal bandwidth and low internet data consumption. Using abductive methodology, the Community of Inquiry (CoI) framework was employed as a guide in interpreting university chemistry teachers’ experiences and perceptions about their implementation of emergency remote teaching using online technologies. Whilst participants had limited prior experiences in employing online learning pedagogies, key elements of the CoI framework became apparent through the new technologies and practices implemented by university chemistry teachers which fostered teaching presence, cognitive presence, and social presence.

Water contamination analysis in IoT enabled aquaculture using deep learning based AODEGRU

Water contamination presents a significant challenge in aquaculture, impacting the sustainability of ecosystems and the health of aquatic organisms. Precisely assessing water contamination levels is crucial for effective monitoring and safeguarding aquatic life within the aquaculture industry. Traditional methods for evaluating water contamination are characterized by their costliness, time-consuming nature, and susceptibility to errors. Integrating computer technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics offers promising potential in addressing this issue. Nevertheless, current deep learning solutions have limitations related to data variability, interpretability, and performance. To address these limitations, this study proposes a comprehensive framework that incorporates IoT-based data collection and data segregation techniques to enhance the accuracy of water contamination classification in aquaculture. Real-time data collected through IoT devices, encompassing parameters like temperature, pH levels, dissolved oxygen, nitrate concentration, and other water quality indicators, enables a holistic evaluation of water quality. By considering predefined acceptable ranges for aquatic life, this framework calculates a water contamination index, facilitating the classification of data into categories such as contaminated and non-contaminated. To ensure robust classification, the study introduces an innovative attention-based model known as the Ordinary Differential Equation Gated Recurrent Unit (AODEGRU). This attention mechanism directs the model's focus towards salient features associated with water contamination, while the AODEGRU architecture captures temporal patterns within the data. Experimental results underscore the effectiveness of the proposed model. It demonstrates its superiority with high performance, achieving an accuracy rate of approximately 98.69% on a publicly available dataset and an impressive 99.89% accuracy on a real-time dataset, clearly outperforming existing methodologies.

An Empirical Study on Chinese Futures Market Based on Bollinger Bands Strategy and R

Abstract Quantitative investment trading is becoming more and more popular due to the gradual integration of computer technology, mathematics, and statistics. It is of great practical significance to develop a multi-species portfolio investment model that takes into account various transaction costs and conforms to live trading. In this paper, we use the free software R to program the Bollinger Bands trading strategy and test it on the historical data of the Chinese futures market. Through in-sample optimization, out-of-sample testing and correlation test, the varieties with good back testing effect are selected for risky investment portfolio to provide investors involved in the Chinese futures market with specific trading strategies that can be used for reference, and at the same time to provide investors with a way of thinking to develop quantitative investment portfolio models. JEL classification numbers: C60. Keywords: Quantitative investment, R language, Chinese futures market, Bollinger Bands.

Progressively Multi-Scale Feature Fusion for Image Inpainting

The rapid advancement of Wise Information Technology of med (WITMED) has made the integration of traditional Chinese medicine tongue diagnosis and computer technology an increasingly significant area of research. The doctor obtains patient’s tongue images to make a further diagnosis. However, the tongue image may be broken during the process of collecting the tongue image. Due to the extremely complex texture of the tongue and significant individual differences, existing methods fail to fully obtain sufficient feature information, which result in inaccurate inpainted tongue images. To address this problem, we propose a recurrent tongue image inpainting algorithm based on multi-scale feature fusion called Multi-Scale Fusion Module and Recurrent Attention Mechanism Network (MSFM-RAM-Net). We first propose Multi-Scale Fusion Module (MSFM), which preserves the feature information of tongue images at different scales and enhances the consistency between structures. To simultaneously accelerate the inpainting process and enhance the quality of the inpainted results, Recurrent Attention Mechanism (RAM) is proposed. RAM focuses the network’s attention on important areas and uses known information to gradually inpaint image, which can avoid redundant feature information and the problem of texture confusion caused by large missing areas. Finally, we establish a tongue image dataset and use this dataset to qualitatively and quantitatively evaluate the MSFM-RAM-Net. The results shows that the MSFM-RAM-Net has a better effect on tongue image inpainting, with PSNR and SSIM increasing by 2.1% and 3.3%, respectively.

Internet of Things and Cloud Computing Integration of Opera Elements into Vocal Singing Teaching Practice Exploration

INTRODUCTION: With the rapid development of computer technology, the application of the Internet of Things and cloud computing technology is becoming more and more common. However, there is no more computer technology integration in integrating opera elements into vocal singing teaching. Therefore, compared with other industries, the development of verbal singing teaching could be faster and adapt to the current era of the Internet of Things and cloud computing. Thus studies the exploration of the practice of integrating opera elements into vocal singing teaching based on the Internet of Things and cloud computing.
 OBJECTIVES: To improve the level of China's Internet of Things and cloud computing technology-based opera elements into vocal singing teaching practice; to solve the current problem of China's traditional culture not being integrated into verbal singing teaching; to promote the development of China's opera industry, the modernization of oral singing teaching, and to enhance the relevance of the Internet and economic development.
 METHODS: In the study, firstly, the Internet of Things (IoT) and cloud computing technology are used for the establishment of the evaluation model; secondly, the feasibility of integrating opera elements into vocal singing teaching is analyzed through theoretical discussions; lastly, through the analysis of the model of the Internet of Things (IoT) and cloud computing, the feasibility of integrating opera elements into vocal singing teaching is illustrated by the analysis of the model of the Internet of Things (IoT) and cloud computing, and how the Internet of Things and cloud computing can assist opera elements to be integrated into vocal singing teaching.
 RESULTS: Using the Internet of Things and cloud computing technology to enrich the vocal singing teaching model from multiple perspectives can better enhance the integration of opera elements into verbal singing teaching. Internet of Things and cloud computing technology play an auxiliary role in the integration of opera elements into vocal singing teaching; the study of the additional mechanism reveals that the effect of the integration of opera elements into verbal singing teaching can be improved through the improvement of the means of the auxiliary function.
 CONCLUSION: As a representative of Chinese culture, the integration of opera elements into vocal singing teaching practice is of great significance, and the Internet of Things and cloud computing technology play an essential role in helping the integration of opera elements into vocal singing teaching practice.

The use of ICT for mathematical calculations in the water quality assessment in the teaching of ecologists

Research goal of the paper is the demonstration of computer technology integration in the modern educational process of teaching ecologists in practical classes on assessing the quality of water in water bodies, containing mathematical calculations. The use of modern information computer technologies in education will effectively contribute to the acquisition of a range of knowledge and skills on methods for water quality assessing in the field of water use and protection of water resources. The ability to use ICT will provide the ecologists with the ability to respond quickly to changes in the environment and make effective decisions in environmental management in future production activities.

FACTORS INFLUENCING THE INTENTION TO USE COMPUTER TECHNOLOGY FOR E-LEARNING AMONG UNIVERSITY STUDENTS

The utilisation of gadgets and computer technology has become a significant aspect of e-learning. This condition requires students to use various online platforms for learning purposes. The current study aims to explore the potential factor in explaining the intention to use computer technology during e-learning among private university students. Online questionnaires were distributed to 174 university students from various programmes in a university in Selangor. The items in the questionnaire covered three independent variables: perceived ease of use of technology, perceived usefulness of technology, and computer self-efficacy. The dependent variable is the intention to use computer technology in e-learning. Pearson Correlation was used to assess the relationship between the independent and dependent variables. Furthermore, Hierarchal Multiple Regression analysis was used to determine the predictor of the intention to use computer technology. The result showed that the intention to use computer technology for e-learning was significantly related to technology’s perceived ease of use, usefulness, and computer self-efficacy. However, when computer self-efficacy is controlled, students’ intention to use computer technology is driven by their perceived notion that technology is easy to use and usefulness of the technology. The study concludes that when students perceive technology as easy to use and sense its practicality, they will have a stronger intention of using technology for learning. This research implies that a systematic strategy should be practised by integrating computer technology into teaching and learning in the current situation. Future studies could further explore the potential of differences across students’ backgrounds towards e-learning.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

Soybean Leaf Disease Detection

Abstract: With the continuous integration of computer technology into agricultural production, it also reduces personnel costs while improving agricultural production efficiency and quality. Crop disease control is an important part of agricultural production, and the use of computer vision technology to quickly and accurately identify crop diseases is an important means of ensuring a good harvest of agricultural products and promoting agricultural modernization. In this paper, a recognition method based on deep learning is proposed based on soybean brown spot. The method is divided into image pre- treatment and disease identification. Based on traditional threshold segmentation, the pre-processing process first uses the HSI colour space to filter the information of the normal area of the leaf, adopts OTSU to set the threshold to segment the original image under the Lab colour space, and then merges the segmented images. The final spot segmentation image is obtained. Compared with the renderings of several other commonly used methods of segmentation, this method can better separate the lesions from the leaves. In terms of disease identification, in order to adapt to the working conditions of large samples of farmland operations, a convolutional neural network (CNN) of continuous convolutional layers was constructed with the help of Caffe to extract more advanced features of the image. In the selection of activation functions, this paper selects the Max out unit with stronger fitting ability, and in order to reduce the parameters in the network and prevent the network from overfitting, the sparse Max out unit is used, which effectively improves the performance of the Max out convolutional neural network. The experimental results show that the algorithm is superior to the algorithm based on ordinary convolutional neural network in identifying large sample crop diseases.

3D Dynamic Image Modeling Based on Machine Learning in Film and Television Animation

With the deep integration and rapid development of computer technology and film and television animation in recent years, computer animation technology has gradually created objective practical value. 3D animation technology also plays a key role in film and television special effects and advertising special effects. As a new type of multimedia data, human motion capture data can be used for 3D human model modeling and human motion simulation because of its high fidelity. A human motion pattern recognition method based on long short-term memory network (LSTM) is proposed. The model uses a deep learning neural network composed of a 2-layer LSTM network to automatically extract features from the collected human body feature information. Then, the multi-class motion patterns are modeled in time series to quickly identify different motion patterns in real time. To evaluate the performance of this model, the effectiveness of this method in identifying six different motion modes is validated using an open dataset. At the same time, this method is compared with four methods based on in-depth learning model. Experimental results verify the effectiveness of the method. It provides a feasible method for human motion recognition and modeling based on capture data in video animation.

A study of an online cyber identity course designed on the basis of the community of inquiry model

The current study examines whether an online cyber identity course, which was based on the community of inquiry model, significantly affected the self-efficacy perception of school counselor candidates (SCC) in terms of technology integration. Forty-four SCC, selected using the criterion sampling method, participated in a mixed-method design study by taking a one-week online cyber identity course. The online version of the Computer Technology Integration Survey (CTIS) was used as a pre and post-test measure to collect quantitative data. Two subscales are used in the survey to measure the self-efficacy of participants. Qualitative data was collected through the use of a structured interview form which included open-ended questions regarding the social, cognitive, and teaching presence of the community of inquiry model. T-tests were conducted to detect any significant differences between the pre and post-test scores of the CTIS subscales. The results of these tests revealed that the online cyber identity course created significant differences in both SCC’s self-efficacy perception of the computer technology capabilities and strategies, and the external influences of computer technology uses. Moreover, qualitative analysis results showed that students reflected more on cognitive and teaching presence than on social presence.

Another Normal: A Techno‐Social Alternative to Techno‐Feudal Cities

AbstractInnovator, writer, educator and practising designer Kas Oosterhuis, who leads the multidisciplinary innovation studio ONL [Oosterhuis_ Lénárd], gives us a potted history of his urban inspirations – from architectural school with its preoccupation with linear urbanism and its speculative architecture precedents, to the great linear city being proposed in Saudi Arabia. The integration of computer technology has been crucial to ONL's substantial output and their quest for ‘real‐time’ architecture and city planning that interacts with its inhabitants and adapts its fabric and form accordingly.