Tumor Segmentation and Classification Using Machine Learning Approaches

Medical image processing has recently developed progressively in terms of methodologies and applications to increase serviceability in health care management. Modern medical image processing employs various methods to diagnose tumors due to the burgeoning demand in the related industry. This study uses the PG-DBCWMF, the HV area method, and CTSIFT extraction to identify brain tumors that have been combined with pancreatic tumors. In terms of efficiency, precision, creativity, and other factors, these strategies offer improved performance in therapeutic settings. The three techniques, PG-DBCWMF, HV region algorithm, and CTSIFT extraction, are combined in the suggested method. The PG-DBCWMF (Patch Group Decision Couple Window Median Filter) works well in the preprocessing stage and eliminates noise. The HV region technique precisely calculates the vertical and horizontal angles of the known images. CTSIFT is a feature extraction method that recognizes the area of tumor images that is impacted. The brain tumor and pancreatic tumor databases, which produce the best PNSR, MSE, and other results, were used for the experimental evaluation

Assessment of Information Technology Use Competence for Teachers: Identifying and Applying the Information Technology Competence Framework in Online Teaching

This paper proposes a theoretical framework as a foundation for building information technology competence framework and the requirements for using information technology competence of teachers in online teaching at training institutions. The parameters in this paper survey was conducted on sample space (n = 342) and 42 expert opinions to identify information technology competence framework with criteria and skill sets necessaries to successfully organize online teaching. This paper discusses on teaching developing information technology competence to change minds and develop teachers' competency to meet the online teaching trend of the digitalization today. So, building information technology competence framework in online teaching has many meanings in training process contribute to improving the learning capacity of students

DEVELOPING THE INFORMATION TECHNOLOGY APPLICATION COMPETENCE OF TEACHERS IN ONLINE TEACHING

Developing the competence to use information technology in teaching is one of the important occupational competencies for teachers in the digital age. Information technology application development has many implications in promoting the training process to train and develop students, in accordance with the actual conditions of education in Vietnam and the general trend of the world is essential.Research paper on needs assessment using information technology of teachers in online teaching, proposing the process of identifying the structure of information technology competencies and requirements for capacity development to use information technology in online teaching of training institutions. The parameters in this paper present an empirical research result to address the need to develop the information technology application competence in online teaching, necessary to successful organize online teaching with a variety of theoretical and practical pedagogies in technology in education

Consideration of Data Security and Privacy Using Machine Learning Techniques

As artificial intelligence becomes more and more prevalent, machine learning algorithms are being used in a wider range of domains. Big data and processing power, which are typically gathered via crowdsourcing and acquired online, are essential for the effectiveness of machine learning. Sensitive and private data, such as ID numbers, personal mobile phone numbers, and medical records, are frequently included in the data acquired for machine learning training. A significant issue is how to effectively and cheaply protect sensitive private data. With this type of issue in mind, this article first discusses the privacy dilemma in machine learning and how it might be exploited before summarizing the features and techniques for protecting privacy in machine learning algorithms. Next, the combination of a network of convolutional neural networks and a different secure privacy approach is suggested to improve the accuracy of classification of the various algorithms that employ noise to safeguard privacy. This approach can acquire each layer's privacy budget of a neural network and completely incorporates the properties of Gaussian distribution and difference. Lastly, the Gaussian noise scale is set, and the sensitive information in the data is preserved by using the gradient value of a stochastic gradient descent technique. The experimental results showed that a balance of better accuracy of 99.05% between the accessibility and privacy protection of the training data set could be achieved by modifying the depth differential privacy model's parameters depending on variations in private information in the data

A Recent Connected Vehicle - IoT Automotive Application Based on Communication Technology

Realizing the full potential of vehicle communications depends in large part on the infrastructure of vehicular networks. As more cars are connected to the Internet and one another, new technological advancements are being driven by a multidisciplinary approach. As transportation networks become more complicated, academic, and automotive researchers collaborate to offer their thoughts and answers. They also imagine various applications to enhance mobility and the driving experience. Due to the requirement for low latency, faster throughput, and increased reliability, wireless access technologies and an appropriate (potentially dedicated) infrastructure present substantial hurdles to communication systems. This article provides a comprehensive overview of the wireless access technologies, deployment, and connected car infrastructures that enable vehicular connectivity. The challenges, issues, services, and maintenance of connected vehicles that rely on infrastructure-based vehicular communications are also identified in this paper

STATE MANAGEMENT INSTITUTIONS ON MARINE RESOURCES AND ENVIRONMENT OF SOME COUNTRIES: EXPERIENCE FOR VIETNAM

The state management institution for marine and island resources and environment plays a very important role. It guides, regulates, and protects marine and island resources. This state management institution aims to protect national interests, ensure sustainable economic development activities, and protect the environment and national security. The objective of the study is to find experiences in building state management institutions for marine and island resources and the environment of some countries in the world, including Japan, Korea, China, Philippines, and Australia. The study analyzed the management models, organizational structures, policies, and legal tools that these countries use to sustainably manage marine resources. The study proposed a model for Vietnam. It emphasized the importance of building an effective marine management institution, the need for coordination between state agencies, and the participation of the community and businesses. In addition, the article also recommends that Vietnam needs to improve the legal and policy framework, strengthen the management capacity of state agencies, and promote international cooperation in this field.  Article visualizations

CAPACITY BUILDING FOR CIVIL SERVANTS AND STATE MANAGEMENT ORGANIZATIONS OF VIETNAM'S SEAS AND ISLANDS

Vietnam's seas and islands greatly influence political and legal life for national, regional, and international development in history, present, and future. The article analyzes the need and importance of capacity building for civil servants and state management organizations on seas and islands in the context of developing the marine economy and protecting Vietnam's sea and island sovereignty. With a long coastline and a rich island system, Vietnam is facing many challenges from resource exploitation, and climate change to national security and defense issues. The article discusses the current status of the sea and island management capacity of civil servants, officials, and related organizations, including limitations in expertise, management skills, and the ability to respond to complex situations. The article also proposes strategic solutions to improve management capacity through training, professional development, enhanced international cooperation, and application of modern technology in sea and island management. The article emphasizes that improving the capacity of civil servants and state management organizations is the key to effectively implementing policies for sustainable marine economic development and protecting national maritime interests, making an important contribution to the comprehensive development of Vietnam.  Article visualizations

EVALUATION OF PRESCRIBING INDICATORS FOR PEADIATRIC OUTPATIENTS UNDER SIX YEARS OLD IN DISTRICT HOSPITALS OF CAN THO CITY IN THE PERIOD OF 2015-2016

Objective: Examining and comparing the primary and supplementary prescribing indicators in pediatric outpatients under six years old. Methods: We performed a comparative cross-sectional study, over nine months, from September 2015. 800 prescriptions for peadiatric patients under 6 y old were collected at 8 district hospitals in Can Tho city to evaluate the primary and supplementary prescribing indicators. The sample was collected prospectively by the systematic selection, with the interval between the patients is 5. The data was analysed and compared to the standard drug use indicators in developing countries recommended by WHO. Results: Average number of drugs per encounter: 4.1, percentage of drugs prescribed by generic name: 94.2%, percentage of encounters with an antibiotic prescribed: 85.8%, percentage of drugs prescribed from essential drugs list by Ministry of Health: 78.7%, percentage of encounters with a corticoid prescribed: 41.7%, percentage of encounters with a vitamin prescribed: 13.1%, average drug cost per encounter: 37.5 thousands VND, percentage of drug costs spent on antibiotics: 55.2%, percentage of drug costs spent on essential drugs: 75.7%, percentage of drug costs spent on corticoid: 1.9%, percentage of drug costs spent on vitamin: 1.4%. Conclusion: The results of this research have identified some issues in outpatient prescribing, which may lead to intervention studies for evaluating changes in these issues in the outpatient clinic

Xác định yêu cầu cho sản xuất lúa ứng dụng công nghệ cao tại tỉnh An Giang trên cơ sở tham vấn các chủ thể khác nhau

Nghiên cứu nhằm xác định các yêu cầu cho sản xuất lúa ứng dụng công nghệ cao dưới hình thức sản xuất đại trà, từ đó làm cơ sở khoa học xây dựng vùng có khả năng phát triển lúa ứng dụng công nghệ cao tại tỉnh An Giang. Nghiên cứu đã sử dụng phương pháp hệ thống hóa cơ sở lý luận về nông nghiệp ứng dụng công nghệ cao, tham vấn ý kiến chuyên gia và phương pháp đánh giá đa tiêu chí. Kết quả nghiên cứu đã xác định được bốn yêu cầu chung và 20 yêu cầu cụ thể cho sản xuất lúa ứng dụng công nghệ cao tại tỉnh An Giang. Trong đó, yêu cầu cụ thể về thị trường tiêu thụ, lợi nhuận, chi phí đầu tư, khả năng quản lý, phương thức tổ chức sản xuất, thổ nhưỡng, nguồn nước và các yêu cầu môi trường được quan tâm nhiều, yêu cầu nguồn lao động và quyền sử dụng đất ít được quan tâm từ các chủ thể. Bên cạnh đó, các chủ thể cũng cho rằng cần quan tâm đến quy mô diện tích canh tác khi thực hiện ứng dụng công nghệ cao. Kết quả này là cơ sở bước đầu xây dựng được các tiêu chí đánh giá khả năng phù hợp để xác định vùng sản xuất lúa ứng dụng công nghệ cao, hướng đến phát triển nông nghiệp bền vững trong thời kỳ hội nhập

ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese

Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several significant drawbacks, including imbalanced data and noisy data on social media platforms. Imbalanced and noisy are two essential issues that need to be addressed in Vietnamese social media texts. Graph Convolutional Networks can address the problems of imbalanced and noisy data in text classification on social media by taking advantage of the graph structure of the data. This study presents a novel approach based on contextualized language model (PhoBERT) and graph-based method (Graph Convolutional Networks). In particular, the proposed approach, ViCGCN, jointly trained the power of Contextualized embeddings with the ability of Graph Convolutional Networks, GCN, to capture more syntactic and semantic dependencies to address those drawbacks. Extensive experiments on various Vietnamese benchmark datasets were conducted to verify our approach. The observation shows that applying GCN to BERTology models as the final layer significantly improves performance. Moreover, the experiments demonstrate that ViCGCN outperforms 13 powerful baseline models, including BERTology models, fusion BERTology and GCN models, other baselines, and SOTA on three benchmark social media datasets. Our proposed ViCGCN approach demonstrates a significant improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized Language Models, including multilingual and monolingual, on three benchmark datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our integrated model ViCGCN achieves the best performance compared to other BERTology integrated with GCN models