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Implementation of Virtual Laboratory of Electronic Commerce Into University Course 将电子商务虚拟实验室纳入大学课程

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-71

M. Vejačka

引用次数: 0

A Comparative Study on Software-Defined Network with Traditional Networks 软件定义网络与传统网络的比较研究

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-17

Berty Smitha Evelin Zoraida, Ganesan Indumathi

{"title":"A Comparative Study on Software-Defined Network with Traditional Networks","authors":"Berty Smitha Evelin Zoraida, Ganesan Indumathi","doi":"10.18421/tem131-17","DOIUrl":"https://doi.org/10.18421/tem131-17","url":null,"abstract":"The emergence of a technological age, in which nearly everything is linked and accessible from anywhere, is largely due to the Internet. Hence, traditional Networks continue to be difficult and complex to operate despite their widespread deployment. As a consequence, it is challenging to set up the networks in accordance with the established protocols and respond to variations in load and defects by reconfiguring the structure. The data and control layers are packaged together in the contemporary systems, further complicating problems by vertically integrating them. Because of their limited resources and unsecured transmission channel, wireless sensor networks (WSNs) are particularly vulnerable to persistent security attacks. Many hundreds of self-organized and resource-constrained sensor nodes make up the WSN. It is possible to build an ad-hoc network of sensors without a specified architecture or centralised control because these sensor nodes are often organised in a scattered form. The underlying problem is to strengthen the privacy enforcement in these systems because WSNs will have power over real-time applications, where unauthorized conduct might lead to significant harm. In order to address this issue, the software-defined network (SDN) technology was developed. With the creation of SD, network operators now have more legitimacy and influence over the networks. Based on a global perspective and centralised management of the network topology, SDN has increased the rigour of its enhancing security. This study describes the software-defined network, outlining its fundamental ideas, how it differs from conventional networking, and its architectural tenets. Also, it highlighted the key benefits of SDN while emphasizing availability, maliciousness, packet delivery and duplication ratio, and efficiency.","PeriodicalId":515899,"journal":{"name":"TEM Journal","volume":"58 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Book Preferences Among Visitors in Library System 图书馆系统读者图书偏好分析

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-44

Zhi-Yao Foo, Kok-Why Ng, S. Haw, E. Anaam

引用次数: 0

Exploratory Data Analysis and the Rise of Large Language Models - Gaming Industry Insights 探索性数据分析和大型语言模型的崛起 - 游戏产业观察

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-59

Denitsa Zhecheva

{"title":"Exploratory Data Analysis and the Rise of Large Language Models - Gaming Industry Insights","authors":"Denitsa Zhecheva","doi":"10.18421/tem131-59","DOIUrl":"https://doi.org/10.18421/tem131-59","url":null,"abstract":"The applications of modern large language models are diverse and new at the same time. It is forecasted that the scientific society and businesses may experience a long period of time exploring all opportunities and challenges for using them which will allow analysis of the impact on how advanced generative artificial intelligence is changing work occupation activities and performance efficiency. Undoubtedly, today’s question that every business must answer is not if but how to implement large language models, due to their ability to transform numerous business processes. This study aims to give a better understanding on how large language models are contributing to the process of exploratory data analysis as they are not here to replace the traditional methods but to add generative artificial intelligence capabilities to the well-established ones. The results of this paper reveal high level of accuracy of the paired output between operation prompts in OpenAI’s large language model and human-mediated entry. However, such output comparison highlighs the need for more informative and specific input prompts to ascertain this accuracy. Further caveats that need to be placed in consideration refer to possible system downtimes, as well as the expenses incurred with every prompt execution. Nevertheless, the comparative speed of operation of large language models remains their most substantial competitive advantage. Overall, the findings in this paper contribute to understanding that large language models streamline with ease the desired extraction of insightful information which may further be used for better decision-making, good data management, and design of winning growth strategy as is the case of the gaming industry.","PeriodicalId":515899,"journal":{"name":"TEM Journal","volume":"24 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework 用于车辆检测的高效机器学习预测方法：数据分析框架

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-02

Herison Surbakti, Prashaya Fusiripong

{"title":"An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework","authors":"Herison Surbakti, Prashaya Fusiripong","doi":"10.18421/tem131-02","DOIUrl":"https://doi.org/10.18421/tem131-02","url":null,"abstract":"The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.","PeriodicalId":515899,"journal":{"name":"TEM Journal","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting the Financial Soundness of Indonesia’s National Board of Zakat (BAZNAS) Using Artificial Neural Network Models 利用人工神经网络模型预测印度尼西亚国家天课委员会（BAZNAS）的财务稳健性

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-63

S. Syamsuri, F. Johari, Nadhilah Nadhilah, Yaumi Sa’adah

引用次数: 0

Algorithmic Prediction of Students On-Time Graduation from the University 学生按时从大学毕业的算法预测

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-72

Ayman Alfahid

{"title":"Algorithmic Prediction of Students On-Time Graduation from the University","authors":"Ayman Alfahid","doi":"10.18421/tem131-72","DOIUrl":"https://doi.org/10.18421/tem131-72","url":null,"abstract":"This study develops statistical learning models to assess the probability of undergraduate students graduating within a predetermined period, utilizing admission, performance, and demographic data. The urgency of addressing student attrition is highlighted by recent data from the National Center for Education Statistics (NCES), indicating a 59% completion rate by full-time undergraduates within six years. This research leverages institutional data from a Saudi University, focusing on freshmen enrolled in the 2012-2013 and 2013-2014 academic years, to identify students at risk of dropping out, thereby enabling timely interventions. Ten algorithms, including decision trees, ensemble models, SVM, and ANN, were built and evaluated on a test set representing 33.3% of the entire dataset using precision, recall, accuracy, and Matthews correlation coefficient (MCC). The findings show that SVM and Random Forest models were the most reliable, achieving accuracies of 0.830 and 0.831 respectively, and maintaining balance in precision, recall, and MCC. Conversely, the naïve Bayes model recorded the worst performance. The comparative analysis revealed the superior performance of ensemble models over decision tree models in predicting student attrition, emphasizing the importance of model selection in developing effective early intervention strategies. In addition, our analysis revealed that academic data is a better predictor of on-time graduation than admission data, emphasizing the need for institutions to focus on continuous academic assessment data.","PeriodicalId":515899,"journal":{"name":"TEM Journal","volume":"30 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140426288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D Visualizations in Learning: An Evaluation of an AR+Core Application for Computer Hardware Education using the Hedonic Motivation System Adoption Model 学习中的三维可视化：使用 "享乐动机系统采用模型 "对计算机硬件教育 AR+Core 应用程序进行评估

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-48

Agariadne Dwinggo Samala, Terence Govender, Dana Tsoy, Ljubiša Bojić, Abelriadne Gentarefori Samala, Melurqeybya Putisaylane Samala, Febri Prasetya, Doni Tri Putra Yanto, R. Zainul, Aprilla Fortuna

引用次数: 0

Enhancing Motivation and Attitudes of Teacher Candidates Through Digital Citizenship Training 通过数字公民培训提高师范生的动机和态度

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-19

Hasan Tangül, Emrah Soykan, Nalan Geli̇rli̇

引用次数: 0

Information and Feedback in Professional Communication in a Medical Setting 医疗环境中专业交流的信息与反馈

TEM Journal Pub Date : 2024-02-27 DOI: 10.18421/tem131-10

Gilbert Hofmann, S. Seljan, Ivan Dunđer

引用次数: 0