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53rd IEEE ASEE Frontiers in education International Conference, FIE 2023

作者： Walker, Justice T. Barany, Amanda Acquah, Alex Reza, Sayed Mohsin Barrera, Alan Del Rio Guzman, Karen Johnson, Michael A. University of Texas at El Paso Department of Education El PasoTX United States Graduate School of Education University of Pennsylvania PhiladelphiaPA United States Pennsylvania State University Harrisburg Department of Computer Science MiddletownPA United States University of North Texas Department of Learning Technologies DentonTX United States

ISBN: (纸本)9798350336429

Personal health tracking devices and internet-based digital platforms with the capacity to collect, aggregate, and store data at massive scales are examples of tools that have broadened priorities in computing to include data science. In response, there has been growing attention in research and practice emphasizing pre-college groups. This is partly because of the growing recognition-reflected in initiatives like CS4ALL, ***, Bootstrap: Data science, Exploring computer science-that learning experiences before college are consequential in sustaining a robust pipeline of computer scientists and engineers. Despite these inroads, there is justifiable concern that existing efforts might not fully support learner development in the necessary conceptual, epistemological, and heuristic styles needed to productively parse and understand 'big data.' This is because computing-based curricula that include data science often involve data curated by others (rather than learners directly), which results in simulated versions of practice instead of engagement that is realistically discursive and messy. This is further complicated by the persistent shortage of K-12 computer science teachers in general and even fewer who can design and implement curricula that support authentic engagement with data science. To address these issues, we leverage culturally relevant and constructionist perspectives in a sandbox (i.e., open-ended) science where tools like Scratch and electronic textiles (E-textiles) have had success expanding possibilities in computing to also include activities where learners can engage broadly along varied pursuits-and encounter challenges that spur computational thinking and problem-solving. The literature suggests that learning activities framed in this way encourage knowledge construction, practice literacies, and seriously impact learner attitudes, interest, and perceptions of growth in the field. This latter set of self-concept measures represents a few of m

关键词： computer science education computer science Learning Constructionism Curriculum Design Data science education

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An Evaluation and Implementation of Pretty Good Privacy (PGP) in Wireless Network Security 3

An Evaluation and Implementation of Pretty Good Privacy (PGP...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Hans, Arvind Vigenesh, M. Yuvaraj, S. Bhagwat, Suvarna R. Zaki, Hamza Yamang, Chiging Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India

ISBN: (纸本)9798350319125

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks. © 2023 IEEE.

关键词： destiny infrastructures networks packages wireless networks

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Machine Learning as a Predictive Technology And Its Impact on Digital Pricing and Cryptocurrency Markets 2

Machine Learning as a Predictive Technology And Its Impact o...

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2nd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2022

作者： Sattaru, Naveen Chakravarthy Umrao, Dhananjay Ramachandran, K.K. Karthick, K.K. Tiwari, Mohit Kumar, M V Suresh Telangana Hyderabad India Computer Science and Engineering Naraina Vidya Peeth Engineering & Management Institute Kanpur India Commerce/ Management Science Dr G R D College of Science India Management Science Grd College India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (纸本)9781665437899

In this current era, Machine Learning (ML) Approach is widely used as a predictive technology in transportation, finance, advertising, travel, healthcare, and various manufacturing industries across the globe. The modern-day financial market around the globe has experienced its deep impact on various aspects of digital pricing. Enormous organizations use numerous digital pricing techniques in order to generate maximum profit percentages for a sustainable future while conducting global business. On the other hand, the effective applications of the machine learning approach also offer relevant advantages in the Cryptocurrency Markets worldwide. It has been identified that with the use of ML approaches, organizations can conduct faster as well as cheaper transfers and exchange of money through virtual mediums easier than before. Moreover, Machine learning can easily improve all the trading strategies in the cryptocurrency and digital pricing markets for gaining more profit as well as adaptable business experience for future experiments. Researchers are going to investigate the particular research topic with an effective quantitative method by conducting secondary method as well as asking topic-related questions. In the modern share markets, undertaking numerous innovative ML technologies for enhancing efficiency in digital pricing and cryptocurrency has become easier. Therefore, the research paper sheds some important light on the impacts of ML approaches and their contributions in cryptocurrency trading and digital pricing for obtaining further research scopes. © 2022 IEEE.

关键词： Costs

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Fraud Detection and Prevention in E-Commerce: Machine Learning Approaches to Secure Transactions 1

Fraud Detection and Prevention in E-Commerce: Machine Learni...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Srinivas, Madhira Kilaru, Mrudula Jain, Divya Sidhu, Kawerinder Singh Department of Computer Science & Engineering - Data Scienc Cheeryala Hyderabad501301 India Siddhartha Academy of Higher Education Velagapudi Ramakrishna Siddhartha School of Engineering India Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India

ISBN: (纸本)9798331518578

E-commerce has grown as a leading marketplace for world trade, but this market has been contaminated by new risks like fraud. Some of the past techniques of detecting fraud do not do well in handling new methods of fraud in the market. The purpose of this research is to understand the offers of the ML approaches for increasing the efficiency of the fraud detection and prevention in e-Commerce processer. This study shows the utility of using different types of the ML solution alone and in comparison, to one another for real-time cyber fraud identification by demonstrating how and why their false positives and overall effectiveness are lower than other methods, as well as how the models adapt to new tactics. Issues like quality of data used, bias in either selection or process of the algorithm are also covered. © 2025 IEEE.

关键词： Marketplaces

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The Most Important Factors Chosen in Medical Clinical Data by Using Multi-Facet Clustering Variational Autoencoders (MFCVAE)

The Most Important Factors Chosen in Medical Clinical Data b...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zhihong Zeng Dong-Meau Chang Tse-Chuan Hsu Zhou Liang College of Mathematics and Information Engineering Longyan University China School of Computer Science and Intelligence Education Lingnan Normal University China Department of Computer Science & Information Management Soochow University Taipei Taiwan

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

In medical practice, doctors often need the support of various medical measuring data to diagnose diseases. In clinical observation, doctors will sort out various factors related to the cause of the disease based on the patient's condition. MFCVAE is a principled, probabilistic clustering model which finds multiple partitions of data simultaneously through its multiple Mixtures-of-Gaussians (MoG) prior. Our model lies in the framework of variational autoencoders, and is trained fully unsupervised. MFCVAE discovers a disentangled multi-facet structure in an exploratory setting, allows to compose representations of inputs in the latent space, perform generative classification, and generate diverse, synthetic samples. This article used the patient data of hospital patients and the MFCVAE method be used to find the most relevant factors and provide them as a reference for doctors in disease diagnosis. The research results was shown that the top 10 important factors could be found from 186 clinical factors using the MFCVAE method.

关键词： Heart Training Target tracking Software quality Probabilistic logic Software reliability Security Medical diagnosis Medical diagnostic imaging Diseases

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Cloud-Based AI Solutions for Personal Finance management

Cloud-Based AI Solutions for Personal Finance Management

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Pervasive Computational technologies (ICPCT), International Conference on

作者： Gurpreet Kaur Chhabra Ashutosh Kumar Mishra Varsha Tyagi Chandra Mani Vani Tyagi Pramod Kumar Sagar Department of Management Management Education and Research Institute Delhi India Department of Management SRM Business School Lucknow UP India Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Computer Science & Engineering Mangalmay Institute of Engineering & Technology Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Cloud-based AI answers for private finance control are out-of-the-field tools that leverage synthetic intelligence and cloud computing technologies to manage people's money higher. In this article, I have described various solutions with features ranging from budgeting to personalized financial planning and investment suggestions. Utilizing superior system learning algorithms and cloud-based AI solutions can help understand an individual's fiscal information, including expenses, profits, or savings transactions, for generating personalized insights and advice. This lets people learn about their spending habits and make more informed financial choices. Through the power of the cloud, these solutions can securely store and access financial data from multiple devices, which helps individuals, manage their finances on the go easily. It also enables the client to get real-time updates and notifications, which offers him more comprehensive knowledge regarding his existing financial scenario. These offerings also come with more automation features like reminders to pay payments and computerized enough transfers for a price range, keeping customers from spending further effort and time on their funds. They also offer financial advisors, tax planning tools, and wealth management.

关键词： Cloud computing Fintech Finance Market research Real-time systems Planning Pattern recognition Artificial intelligence Hypertext systems Investment

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A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing

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Mathematical Problems in Engineering 2023年第1期2023卷

作者： Praveen, S. Phani Ghasempoor, Hesam Shahabi, Negar Izanloo, Fatemeh Department of CSE PVPSIT Andhra Pradesh Vijayawada India Department of IT Management Hadaf Institute of Higher Education Sari Iran Department of Computer Science Islamic Azad University Babol Branch Babol Iran Department of Computer Science Rouzbahan Institute of Higher Education Sari Iran

The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing's performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments. © 2023 S. Phani Praveen et al.

关键词： Cloud computing

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Analysis of the Energy Efficiency of Smart City Buildings Based on Deep Learning Algorithms Using AI

Analysis of the Energy Efficiency of Smart City Buildings Ba...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Abid, Yaser M. Jalil, Mustafa A. Abd, Sura Khalil Sabah, Hawraa Ali Bakri, Bilal Ibrahim Jaleel Maktoof, Mohammed Abdul Mohammed, Aymen Adile, Mohaned Mustafa Ahmed, Mohammed Sulaiman Mohammed, Mohammed Abdulamer Alzubaidi, Laith H. University of Information Technology and Communication Baghdad Iraq Mustansiriyah University Faculty of Engineering Electrical Engineering Department Baghdad Iraq Ministry of Higher Education and Scientific Research Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Hilla University College Babil Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Energy represents a promising innovation notion concerning enhancing smart city structures. A 'smart city' is an urban area wired to receive data from various electrical technology and gadgets. The typical problems with energy efficiency include less rain due to pollution, higher energy use, and water shortages if people aren't prepared to reduce their energy use. With the proper use of AI, improving technological operations' efficiency and reducing energy consumption may be possible. In particular, artificial intelligence may shorten the time it takes businesses to realize their full potential for energy savings by improving the effectiveness of their technological infrastructure. The suggested method of deep learning-based energy-efficient management on a convolutional network (EEMCN-DL) is made to withstand energy efficiency problems in smart cities. The system employs sensor modules to engage with environmental issues. For detecting the pollution factor and forecasting rainfall, EEMCN-DL employs the convolutional networking technique (CNS) on the hardware units of sensors. By grouping the parts to integrate into cloud unit storage, the useful statistic methodology may identify energy usage and water shortage and then present the data as a percentage. Hence, EEMCN-methodologies DLs are incorporated to improve energy efficiency for future generations. Energy efficiency is improved by 98.9 percent thanks to the emergent system's study in simulation. © 2024 IEEE.

关键词： Sustainable city

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence education Large language models Learning systems Natural language processing systems Neural networks

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Effects of an automated programming assessment system on the learning performances of experienced and novice learners

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Interactive Learning Environments 2023年第8期31卷 5347-5363页

作者： Cheng, Li-Chen Li, Wei Tseng, Judy C. R. Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan STEM Education Research Center Wenzhou University Zhejiang Province Wenzhou China Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

Programming ability is the core ability of this era and can be obtained and improved through practice. In this paper, an Automated Programming Assessment system based on Mastery learning and Peer competition (APAMP) was proposed and developed. APAMP allows students to practice repeatedly by providing immediate feedback after their programs are submitted. It also presents an analytical dashboard as a competition mechanism for students to visualize their learning performance and compare their performance with peers. By incorporating APAMP into programming courses, students can master programming skills through repeated practice, and their enthusiasm for learning programming can also be encouraged by peer competition. To evaluate the effects of APAMP on the learning performance of students with programming language learning and novice students, a quasi-experiment was conducted in a high school. The experimental results showed that the learning achievement of the two groups of students improved significantly. Moreover, the learning attitude of students in the experienced group improved significantly, with the experienced students benefiting more from the system than the novice students. In fact, the novice students showed a significant decline in learning attitude and learning motivation, which was contrary to our intuition. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： automated programming assessment systems learning performance mastery learning peer competition Programming instruction

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