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

{"title":"Use of Three-Dimensional Maps in the Study of the Political Geography Course as One of the Applications of Artificial Intelligence","authors":"Mahmoud Khalifa, W. Nageab","doi":"10.1109/ICETSIS61505.2024.10459504","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459504","url":null,"abstract":"In recent years, Geospatial Information Systems (GIS) have witnessed a significant shift towards smart spatial analytics, which involves the processing of large volumes of geographic data, known as big geographic data (Geodata Big). This processing and analysis enable the extraction of valuable information that can be utilized in decision-making processes. Smart maps, as geographic information platforms, play a crucial role in decision-making by providing data, statistics, and information about geographic features and phenomena. Three-dimensional maps offer the capability to visualize and analyze geographic and temporal data on Earth's surface or custom maps. They enable users to observe changes over time and create interactive visual tours that can be shared with others. The applications of smart maps are diverse and include surveillance, monitoring, navigation, and determining optimal routes to specific locations. Artificial-Geo intelligence (AI-Geo) applications have further enhanced GIS capabilities by utilizing intelligent models trained on various datasets. These models enable the identification and extraction of geographic phenomena from satellite imagery, aerial photographs, and other sources. AIGeo applications have found utility in numerous domains beyond mapping, providing valuable insights and facilitating decision-making processes. This abstract highlights the growing importance of smart spatial analytics in GIS, particularly through the use of smart maps and AI-Geo applications. By leveraging these technologies, organizations can harness the power of geospatial data to make informed decisions and gain deeper insights into geographic phenomena.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"237 4","pages":"230-234"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530241","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}

{"title":"Preservation Conservation Engineering: A Case Study","authors":"Nada Tarek","doi":"10.1109/ICETSIS61505.2024.10459594","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459594","url":null,"abstract":"Through a case study of a 19th century residential structure in Glina, Croatia, this research paper investigates preservation engineering application of an earthquake-damaged historic buildings. Following the 2020 Petrina quakes, the cultural site of the case study received studies and minimally invasive retrofits to strike a ratio between preservation and seismic resistance. fabric-reinforced cementitious matrix FCRM and adding concrete to the floors was employed to improve structural integrity and increase its ability to resist seismic waves. The research paper concludes to effective approaches for preserving authentic heritage elements while improving life safety by combining preservation standards and structural engineering.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"185 1","pages":"1996-2000"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530250","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}

{"title":"Unveiling the Retention Puzzle for Optimizing Employee Engagement and Loyalty Through Analytics-Driven Performance Management: A Systematic Literature Review","authors":"A. Al-Alawi, Fatema Ahmed AlBinAli","doi":"10.1109/ICETSIS61505.2024.10459383","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459383","url":null,"abstract":"Disengagement and turnover of employees are significant costs to organizations worldwide. In many organizations, it isn't easy to foster continuous engagement among employees. Analytically-driven performance management aims to capture and analyze workplace data with advanced analytical techniques to develop a sustainable solution. This systematic literature review (SLR) examines and analyzes frameworks proposed for optimizing engagement and retention through performance analytics. Among the forty initial papers screened, twenty-four highly relevant sources were selected and analyzed. Human resources (HR) related key themes included bias issues, text analysis of reviews, personalized HR management, talent assessments, augmenting HR work with Artificial Intelligence (AI), and integration challenges. According to the findings, a reliable emphasis was placed on the balance of human and machine perspectives. While analytics and algorithms offer insightful information, human judgment is needed to contextualize this data. If datadriven methods are the only ones used, complicated personal aspects that influence experience may be overlooked. Consequently, a human-machine strategy working together is crucial. Furthermore, effective integration requires both strategy alignment and cultural preparedness. Longitudinal evaluations and more real-world case studies help close gaps in the literature. Analytics with human-centric frameworks can maximize engagement and performance management.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"210 2","pages":"292-296"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530072","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}

{"title":"Signature Forgery and Veracity Detection using Machine Learning","authors":"Navneet Tiwari, Jinesh Thakkar, Om Bansode, Hanmant Magar","doi":"10.1109/ICETSIS61505.2024.10459390","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459390","url":null,"abstract":"This research paper addresses the escalating risk of fraud signatures in banking transactions. It introduces a Signature Forgery Detection System that utilizes offline verification and diverse geometric measures to discern genuine from forged signatures. With the prevalence of signature-based identity verification in financial transactions and the absence of foolproof systems, the proposed system aims to enhance the security of banking by efficiently detecting and preventing signature forgery.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"193 3","pages":"1756-1759"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530083","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}

{"title":"WhatsApp Chat Analysis: Unveiling Insights through Data Processing and Visualization Techniques","authors":"Saira Yaqub, Saikat Gochhait, Hafiz Abdul Haseeb Khalid, Syeda Noreen Bukhari, Ayesha Yaqub, Muhammad Abubakr","doi":"10.1109/ICETSIS61505.2024.10459604","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459604","url":null,"abstract":"WhatsApp has become a widely used medium to communicate in the modern era of technology, fostering diverse conversations and expressions among millions of users worldwide. This research introduces a robust analytical tool, the “WhatsApp Chat Ana-lyzer,” crafted to dissect and visualize the multifaceted landscape of group chats on WhatsApp. Imbued with Python's prowess and fortified by Streamlit, matplotlib, and Seaborn, the tool transcends conventional analyses by providing nuanced insights into user behavior, message statistics, and emerging content trends. In this research, we embark on an exploratory journey to decipher the complex dynamics embedded within WhatsApp group chats. By amalgamating sophisticated data preprocessing techniques, advanced statistical analyses, and captivating visualizations, the “WhatsApp Chat Analyzer” stands as a testament to our commitment to unraveling the facts of modern digital communication.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"334 2","pages":"862-865"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530497","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}

{"title":"Other reviewers","authors":"","doi":"10.1109/icetsis61505.2024.10459577","DOIUrl":"https://doi.org/10.1109/icetsis61505.2024.10459577","url":null,"abstract":"","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"407 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530018","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}

{"title":"A Multi-Line Production System Lot Sizing Problem (Desing and Resolution)","authors":"Meroua Sahraoui, Fouad Maliki, M. Bennekrouf","doi":"10.1109/ICETSIS61505.2024.10459669","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459669","url":null,"abstract":"Production planning and control research has long emphasized scheduling strategies to address fluctuating demand and resource limitations. The Lot Sizing and Scheduling Problem (LSP) remains a significant challenge due to its complexity. In multi-line production systems, finding the right amount of each product to create each time is the difficult task of lot sizing. Getting lot sizing right is crucial because it directly affects both inventory levels and customer satisfaction. This work proposes a novel model for optimizing production planning in multi-line workshops, implemented using the CPLEX solver. The model aims to maximize gains by determining the optimal production batches.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"260 4","pages":"1317-1322"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530056","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}

{"title":"Integrating Technology in Human Resource Management: Innovations and Advancements for the Modern Workplace","authors":"M. Alaghbari, A. Ateeq, Mohammed Alzoraiki, Marwan Milhem, B. Beshr","doi":"10.1109/ICETSIS61505.2024.10459498","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459498","url":null,"abstract":"This research investigates the significant impact of technology on the transformation of Human Resource Management (HRM), with a specific emphasis on the modernization of conventional HR procedures. The integration of technology into Human Resource Management (HRM) is essential in the contemporary business landscape to augment organizational efficiency and effectiveness. This research provides a critical evaluation of the influence of digital breakthroughs, such as Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and cloud computing, on human resources (HR) operations. The use of these technologies is significantly influencing diverse human resources strategies, namely in the areas of recruiting, performance management, and employee engagement. This is achieved via the incorporation of predictive analytics and datadriven approaches, which facilitate informed decision-making processes. The study emphasizes the impact of these technology instruments on enhancing operational effectiveness and enabling HR practitioners to transition from administrative responsibilities to strategic positions in workforce planning, talent management, and organizational growth. This research explores the ramifications of technological advancements on workers and employers, specifically focusing on the difficulties and advantages associated with incorporating technology into human resource management. Through a comprehensive examination of contemporary scholarly literature and empirical investigations, this research tries to establish a connection between theoretical constructs and their tangible application. This resource is very beneficial for professionals in the field of human resources, as well as researchers and organizational leaders. It assists them in effectively navigating and managing the intricate challenges of modern human resource management within a technologically sophisticated environment.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"260 2","pages":"307-311"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530057","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}

{"title":"Identification of Strategic Planning Factors to Achieve Smart Mobility for New Cities in Developing Countries Using CIB Method","authors":"Raya Fadel, S. Abu-Eisheh","doi":"10.1109/ICETSIS61505.2024.10459709","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459709","url":null,"abstract":"This research explores the application of the Cross-Impact Balances (CIB) method in identifying the factors that need to be included in the strategic planning process for the adoption of smart mobility solutions in new cities within developing countries. Smart mobility systems use emerging technologies to arrive at solutions to many of the mobility related problems that affect the urban environment by creating connected and sustainable transportation systems that can move people more efficiently and safely. The CIB method, known for its ability to assess interdependencies and uncertainties in complex systems, is employed as a decision support tool. The research investigates the descriptors influencing smart mobility success in developing cities, and found that relevant aspects such as infrastructure readiness, technological disparities, socio-economic dynamics, and regulatory environments. Factors like citizen engagement, strategic region, and sustainable mobility urban plans are high-priority factors, emphasizing community involvement and thoughtful planning. Medium-priority factors highlight the need for comprehensive infrastructure and strategic collaboration. Low-priority factors, that include employed population and political situation, are found to have a comparatively lesser impact. Based on the outcome of the CIB method, the paper recommends using the resulting high- and medium-priority factors for the preparation of the strategic planning framework (the goals, objectives, and broad strategies) to achieve the vision of establishing new cities that could be characterized to have smart mobility systems.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"247 3-4","pages":"1963-1967"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530235","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}

{"title":"Design of Brain Tumor Detection System on MRI Image Using CNN","authors":"Indira Salsabila Ardan, R. Indraswari","doi":"10.1109/ICETSIS61505.2024.10459651","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459651","url":null,"abstract":"Brain tumor is an abnormal proliferation of brain cells, which may be benign or malignant in nature. Brain cancer, which is frequently diagnosed in individuals of all ages, is a malignant form of a brain tumor and one of the most severe forms of cancer. Each year, an estimated 300 cases of brain tumors, including those in children, are diagnosed in Indonesia. To detect brain tumors, imaging methods such as Magnetic Resonance Imaging (MRI) are utilized. However, radiologists' manual examination of MRI scans might lead to conclusions that differ from one doctor to the next (interobserver error). Research on brain tumor type classification on MRI images is also limited. To identify various types of brain tumors in MRI images, we will therefore construct a system utilizing Convolutional Neural Networks (CNN) and transfer-learning methods. In this study, the Flask framework was successfully used to develop a web-based application to identify distinct form of brain tumors in MRI scans. The model makes use of CNN architecture, a ResNet50V2 base model trained on the ImageNet dataset, a head model with 512 nodes and one entirely connected layer, and an output layer that forecasts the input into four classes of brain MRI images, including “Normal”,”Glioma”, “Meningioma”, and”Pituitary”. Appropriate parameter settings were used to achieve the highest accuracy. In this study, Adam optimization algorithm was used with 60 epochs and a batch size of 32. Additionally, a ten-fold cross-validation technique was implemented. 95% accuracy rate was achieved by implementing the proposed architecture.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"246 4","pages":"1388-1393"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530236","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}