2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)最新文献_第3页

A Proposed Hybrid Deep Learning Model for Wind Power Forecasting 用于风能预测的混合深度学习模型建议

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459633

Hamed H. Aly

引用次数: 0

How Does Chat GPT Influence Human Capital Development Amongst Malaysian Undergraduate Students? 聊天 GPT 如何影响马来西亚大学生的人力资本发展？

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459491

Ishaq Ibrahim, Ali Nasser Altahitah, Kalsom Ali, A. Ateeq, M. Alaghbari

{"title":"How Does Chat GPT Influence Human Capital Development Amongst Malaysian Undergraduate Students?","authors":"Ishaq Ibrahim, Ali Nasser Altahitah, Kalsom Ali, A. Ateeq, M. Alaghbari","doi":"10.1109/ICETSIS61505.2024.10459491","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459491","url":null,"abstract":"This study aims to explore the outcomes of Chat GPT on the education and learning process in the Malaysian undergraduate students. The influence of Chat GPT on the Malaysian undergraduate students predicted to impact the human capital development. This is a qualitative study conducting ethnography design approach, established an interview's questions and examined the reliability of the instruments, containing four interviewees from different four universities enrolled in undergraduate programs. The study resulted as there is a huge impact of Chat GPT on Malaysian undergraduate students, found that Chat GPT known by all the students and frequently used in their assignments and learning process. In addition, this research finds that Chat GPT impacts the undergraduate students all over the world positively by enforcing further monitoring and prepare a space for the students and lecturers to communicate and discuss the ideas to avoid the absence of innovation and creativity amongst the Malaysian undergraduate students.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530246","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

Enhancing Shared Image Security in Networked Environments: A Digital Watermarking Approach 增强网络环境中共享图像的安全性：数字水印方法

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459380

Rania Hamami, Narima Zermi, L. Boubchir, Amine Khaldi

引用次数: 0

Optimizing Requirements Prioritization: Majority Voting Goal-Based Approach with Vertical Binary Search 优化需求优先级：基于多数票的目标法与垂直二元搜索

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459533

Madala Guru Brahmam, Vijay Anand R, Veena Grover, Saikat Gochhait

引用次数: 0

Do Ownership, Financing and Dividend Decisions, and COVID-19 Matter for Firm Value? Evidence from Indonesia 所有权、融资和分红决策以及 COVID-19 对公司价值有影响吗？印度尼西亚的证据

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459531

Faisal Faisal, M. Shabri, Abd. Majid, A. Sakir

引用次数: 0

The Impact of Business Intelligence on Decision-Making Process and Customer Service 商业智能对决策过程和客户服务的影响

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459599

Abdallah Shatat, Mariam Altahoo, Munira Almannaei

{"title":"The Impact of Business Intelligence on Decision-Making Process and Customer Service","authors":"Abdallah Shatat, Mariam Altahoo, Munira Almannaei","doi":"10.1109/ICETSIS61505.2024.10459599","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459599","url":null,"abstract":"Business Intelligence (BI) is critical in enhancing decision-making processes, operational efficiency, and positive outcomes such as improved customer service, stronger customer relationships, increased profitability, and lower failure rates. This study investigates and analyses the impact of Business intelligence on decision-making and customer service. The secondary data collection methodology employed in this paper involves a systematic review of existing knowledge by researchers about Business Intelligence. Several keywords were used, such as “Business Intelligence,” “BI in customer service and decision-making process”, and “BI Tools”. The collected research was published between 2018 and 2023 to ensure up-to-date information. This method facilitated the detection of the effect of business intelligence on decision-making and customer service by presenting its tools and challenges of implementation and examining its impact on Uber as a case study. Finally, the results have shown a positive effect on the decision-making and customer service level at Uber after using business intelligence and its tools efficiently.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530396","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

Determining the Impact Artificial Intelligence on Development of Higher Education 确定人工智能对高等教育发展的影响

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459626

Mudasir Ali Rind, Mohammad Ali Al Qudah, Pirali Aliyev

{"title":"Determining the Impact Artificial Intelligence on Development of Higher Education","authors":"Mudasir Ali Rind, Mohammad Ali Al Qudah, Pirali Aliyev","doi":"10.1109/ICETSIS61505.2024.10459626","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459626","url":null,"abstract":"In recent years, there has been significant progress in the field of Artificial Intelligence, both in terms of technological advancements and knowledge acquisition. These advancements have led to the development of unorthodox learning methodologies in Artificial Intelligence applications. Artificial Intelligence is the field of study focused on developing advanced systems that can effectively learn and teach, providing learners with the most relevant information based on their own learning requirements and preferences. AI applications have made significant contributions to the education industry, particularly in higher education, where they play a crucial role in facilitating learning. This research examined the motivation and efficacy of learners in relation to the artificial intelligence learning strategy for learning applications. Data were gathered from a total of 121 respondents who were selected from five higher education colleges in the Sindh region of Pakistan. This study revealed that most learners expressed satisfaction with the utilization of artificial intelligence (AI) applications in various aspects. Specifically, they acknowledged that AI applications enhance learning capabilities and productivity. Moreover, they recognized the usefulness of AI applications in augmenting knowledge and facilitating the learning process by providing easily understandable content. What is your opinion on the potential of AI applications in these areas? Most learners expressed good motivation for the questions and expressed optimism about the usefulness of artificial intelligence applications. In conclusion, more engagement between learners and AI learning applications will provide positive outcomes in comprehending the material of the relevant topic. This paper proposes the implementation of training programs for learners specifically focused on AI learning applications.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530412","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 Neural Network Algorithm in Comparison to Multiple Linear Regression and Random Forest Algorithm 神经网络算法与多重线性回归和随机森林算法的比较分析

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459496

Pagolu Meghana, Visalakshi Annepu, M. Jweeg, Kalapraveen Bagadi, H. Aljibori, M. N. Mohammed, O. Abdullah, S. Aldulaimi, M. Alfiras

{"title":"Analysis of Neural Network Algorithm in Comparison to Multiple Linear Regression and Random Forest Algorithm","authors":"Pagolu Meghana, Visalakshi Annepu, M. Jweeg, Kalapraveen Bagadi, H. Aljibori, M. N. Mohammed, O. Abdullah, S. Aldulaimi, M. Alfiras","doi":"10.1109/ICETSIS61505.2024.10459496","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459496","url":null,"abstract":"Regression analysis, a stalwart in statistical methodology, offers a robust framework for predicting outcomes based on historical data. It hinges on the premise that by scrutinizing past input data, one can discern the relationships between independent and dependent variables, enabling the forecasting of final results. In the dynamic landscape of Machine Learning, a multitude of regression techniques exists. Nevertheless, many real-world companies grapple with optimizing their return on investment due to the perplexing task of selecting the most apt model for their specific datasets. This research endeavor seeks to bridge this knowledge gap by conducting a comprehensive comparative analysis of three widely used and highly proficient regression algorithms: Multiple Linear Regression (MLR), Random Forest (RF), and Neural Networks (NNs). MLR offers a simple and interpretable linear model, while RF harnesses ensemble learning to handle complex relationships, and NN s employ intricate, nonlinear modeling capabilities. The study subjects two distinct datasets, Crop Yield, and Cardiovascular Disease, to scrutiny. The former addresses agricultural productivity forecasting, while the latter explores healthcare applications. By evaluating these datasets using the three regression models, the research aims to determine the most suitable model for each dataset's unique characteristics, enabling data-driven decision-making and enhancing the efficacy of regression analysis in practical, real-world scenarios.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530240","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

Outdoor Obstacle Detection for Visually Impaired using AI Technique 利用人工智能技术为视障人士提供户外障碍物检测服务

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459374

Loubna Bougheloum, M. B. Salah, M. Bettayeb

{"title":"Outdoor Obstacle Detection for Visually Impaired using AI Technique","authors":"Loubna Bougheloum, M. B. Salah, M. Bettayeb","doi":"10.1109/ICETSIS61505.2024.10459374","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459374","url":null,"abstract":"Obstacle detection is a crucial factor in ensuring the safety and mobility of visually impaired individuals. This paper introduces a comprehensive system designed to support individuals with visual impairments in outdoor environments, employing recent advancements in artificial intelligence (AI). The core of the system involves the use of YOLOv5 for efficient object recognition and Google Text-to-Speech (GTTS) for the conversion of detection results into clear and informative audio feedback. The model is trained on a customized dataset encompassing 10 specific outdoor object categories, in addition with the widely used MS COCO dataset. This strategic combination allows the system to attain heigh accuracy in obstacle detection, surpassing the performance of previous techniques. The model's ability to accurately identify and classify outdoor objects contributes to its efficacy in real-world scenarios. To ensure user accessibility, the system transforms output labels into text, which is then converted into an audio format. This audio feedback is seamlessly delivered to visually impaired users via earphones, providing real-time information about their surroundings. This approach represents a significant advancement in AI-driven outdoor obstacle detection, promising not only improved accuracy but also enhanced usability for individuals with visual impairments. By addressing the challenges of outdoor navigation, this new approach has the capacity to significantly enhance the autonomy and well-being of people with visual impairments in their everyday activities.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530483","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

Internship Management System (Planning Phase) 实习管理系统（规划阶段）

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459487

Ahmad Shatat, Abdallah Saleh, Islam Nassar, Rifat Hussain

引用次数: 0