As a product of technological advancement, electronic management has become a pivotal tool in modern administrative practices. It is acknowledged that organizational conflict, an almost ubiquitous phenomenon in institutions, has significant implications for productivity and workplace harmony. This study aimed to investigate the potential of electronic management in attenuating organizational conflict within the context of the Northern Technical University (NTU). Electronic management was conceptualized as an independent variable, with organizational conflict treated as the dependent variable. A questionnaire was distributed to a sample of administrative leaders, and the responses were subsequently analyzed. The study adopted a hypothesis positing no significant effect of electronic management on reducing organizational conflict. Key findings of the study include the value of integration and interaction amongst organizational units, the essential balance between quality, cost, speed, and accuracy in administrative activities, and a noted reduction in the cycle time of executing administrative operations. The results affirm a significant relationship between the application of electronic management and the reduction of organizational conflict at both micro and macro levels. Based on these findings, it is recommended that the surveyed organization increases its focus on the tenets of electronic management and invests in enhancing the understanding of these concepts among managers and employees. Additionally, efforts should be made to augment the organization's competency in the dimensions of electronic management and to further develop the skills of its managerial staff and employees.
{"title":"Exploring the Impact of Electronic Management on Mitigating Organizational Conflict: An Examination at the Northern Technical University","authors":"Raghad Amer Al-Soufi, Niebal Younis Mohammed","doi":"10.18280/isi.280523","DOIUrl":"https://doi.org/10.18280/isi.280523","url":null,"abstract":"As a product of technological advancement, electronic management has become a pivotal tool in modern administrative practices. It is acknowledged that organizational conflict, an almost ubiquitous phenomenon in institutions, has significant implications for productivity and workplace harmony. This study aimed to investigate the potential of electronic management in attenuating organizational conflict within the context of the Northern Technical University (NTU). Electronic management was conceptualized as an independent variable, with organizational conflict treated as the dependent variable. A questionnaire was distributed to a sample of administrative leaders, and the responses were subsequently analyzed. The study adopted a hypothesis positing no significant effect of electronic management on reducing organizational conflict. Key findings of the study include the value of integration and interaction amongst organizational units, the essential balance between quality, cost, speed, and accuracy in administrative activities, and a noted reduction in the cycle time of executing administrative operations. The results affirm a significant relationship between the application of electronic management and the reduction of organizational conflict at both micro and macro levels. Based on these findings, it is recommended that the surveyed organization increases its focus on the tenets of electronic management and invests in enhancing the understanding of these concepts among managers and employees. Additionally, efforts should be made to augment the organization's competency in the dimensions of electronic management and to further develop the skills of its managerial staff and employees.","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977601","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":"Refining the ISO 9126 Model for Enhanced Decision Support System Evaluation in the Manufacturing Industry","authors":"Johanes Fernandes Andry, None Hadiyanto, Vincensius Gunawan","doi":"10.18280/isi.280519","DOIUrl":"https://doi.org/10.18280/isi.280519","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977619","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}
Fatima Zohra Trabelsi, Amal Khtira, Bouchra El Asri
ABSTRACT
{"title":"Employing Data and Process Mining Techniques for Redundancy Detection and Analystics in Business Processes","authors":"Fatima Zohra Trabelsi, Amal Khtira, Bouchra El Asri","doi":"10.18280/isi.280529","DOIUrl":"https://doi.org/10.18280/isi.280529","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"107 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977657","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}
Sara Ali Abd Al Hussen, Elham Mohammed Thabit A. Alsaadi
The need for automated diagnostic systems in medical imaging, particularly in the detection and categorization of brain tumors, is paramount. This research proposes a hybrid model to identify and classify MRI-detected brain tumors into four categories: pituitary, meningioma, glioma, or absence of a tumor. This hybrid approach leverages the strengths of both deep learning and traditional machine learning techniques, enabling the extraction of complex features and the recognition of intricate patterns, such as those found in brain tumors. Machine learning further enhances the model's capacity to classify accurately based on these specific features, reducing time and cost. The proposed system consists of several stages: initial pre-processing of brain MRI images, the application of two distinct segmentation techniques (region-based and edge-based), morphological operations, feature extraction, and finally classification. The classification employs a hybrid model (VGG16) in conjunction with four traditional classifiers: Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). The experimental results highlight that the use of Random Forest with region-based segmentation yields the highest accuracy, reaching 99.17%. This combination excels at focusing on minute yet crucial details in MRI images and maintains stability in the presence of distortion and outliers. The dataset employed in this study is an amalgamation of three: Figshare, SARTAJ, and Br35H, each containing MRI images of the aforementioned four types of brain tumors.
{"title":"Automated Identification and Classification of Brain Tumors Using Hybrid Machine Learning Models and MRI Imaging","authors":"Sara Ali Abd Al Hussen, Elham Mohammed Thabit A. Alsaadi","doi":"10.18280/isi.280518","DOIUrl":"https://doi.org/10.18280/isi.280518","url":null,"abstract":"The need for automated diagnostic systems in medical imaging, particularly in the detection and categorization of brain tumors, is paramount. This research proposes a hybrid model to identify and classify MRI-detected brain tumors into four categories: pituitary, meningioma, glioma, or absence of a tumor. This hybrid approach leverages the strengths of both deep learning and traditional machine learning techniques, enabling the extraction of complex features and the recognition of intricate patterns, such as those found in brain tumors. Machine learning further enhances the model's capacity to classify accurately based on these specific features, reducing time and cost. The proposed system consists of several stages: initial pre-processing of brain MRI images, the application of two distinct segmentation techniques (region-based and edge-based), morphological operations, feature extraction, and finally classification. The classification employs a hybrid model (VGG16) in conjunction with four traditional classifiers: Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). The experimental results highlight that the use of Random Forest with region-based segmentation yields the highest accuracy, reaching 99.17%. This combination excels at focusing on minute yet crucial details in MRI images and maintains stability in the presence of distortion and outliers. The dataset employed in this study is an amalgamation of three: Figshare, SARTAJ, and Br35H, each containing MRI images of the aforementioned four types of brain tumors.","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"168 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977610","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}
Herwin Herwin, Lantip Diat Prasojo, Bambang Saptono, Shakila Che Dahalan
ABSTRACT
{"title":"Analyzing the Impact of Augmented Reality on Student Motivation: A Time Series Study in Elementary Education","authors":"Herwin Herwin, Lantip Diat Prasojo, Bambang Saptono, Shakila Che Dahalan","doi":"10.18280/isi.280507","DOIUrl":"https://doi.org/10.18280/isi.280507","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"249 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977241","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":"Predicting Used-Vehicle Resale Value in Developing Markets: Application of Machine Learning Models to the Kazakhstan Car Market","authors":"Alibek Barlybayev, Arman Sankibayev, Yenglik Kadyr, Nurzada Amangeldy, Talgat Sabyrov","doi":"10.18280/isi.280512","DOIUrl":"https://doi.org/10.18280/isi.280512","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976304","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":"Evaluating the Efficacy of Resampling Techniques in Addressing Class Imbalance for Network Intrusion Detection Systems using Support Vector Machines","authors":"Swarnalatha Kudithipudi, Nirmalajyothi Narisetty, Gangadhara Rao Kancherla, Basaveswararao Bobba","doi":"10.18280/isi.280511","DOIUrl":"https://doi.org/10.18280/isi.280511","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976482","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":"Enhancing Data Communication Performance: A Comprehensive Review and Evaluation of LDPC Decoder Architectures","authors":"Maryam Imad Subhi, Qusay Al-Doori, Omar Alani","doi":"10.18280/isi.280501","DOIUrl":"https://doi.org/10.18280/isi.280501","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"26 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976663","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}
Ahmed Amine Fariz, Jaafar Abouchabaka, Najat Rafalia
In response to economic, political, and technological stimuli, governments across the globe are progressively embracing digital transformation to devise innovative digital solutions. Despite these advancements, challenges persist in the integration of information resources, including deficiencies in government information systems and threats to network and information security. This paper investigates a novel algorithm for the filling and classification of big data within E-government systems, which comprises data management and governance, cultural and industrial shifts tied to human resource development, and data exchange protocols. A cloud computing environment serves as the infrastructure for constructing an E-government big data intelligence system. The system enables parallel data processing and classification via decision trees, thereby promoting the efficacious and sustainable employment of big data analytics in policy formulation and digital innovation. Additionally, the paper delineates the hurdles and issues that confront these agencies, and proposes potential solutions to augment citizen satisfaction and to deliver value within and beyond governmental sectors. The findings suggest that the integration of big data technologies in E-government presents an effective strategy for the provision of interactive services, thereby addressing citizens' demands for enhanced services.
{"title":"Harnessing the Power of Cloud-Based Big Data Analytics for E-Government Advancement in Morocco: A Catalyst for Development","authors":"Ahmed Amine Fariz, Jaafar Abouchabaka, Najat Rafalia","doi":"10.18280/isi.280517","DOIUrl":"https://doi.org/10.18280/isi.280517","url":null,"abstract":"In response to economic, political, and technological stimuli, governments across the globe are progressively embracing digital transformation to devise innovative digital solutions. Despite these advancements, challenges persist in the integration of information resources, including deficiencies in government information systems and threats to network and information security. This paper investigates a novel algorithm for the filling and classification of big data within E-government systems, which comprises data management and governance, cultural and industrial shifts tied to human resource development, and data exchange protocols. A cloud computing environment serves as the infrastructure for constructing an E-government big data intelligence system. The system enables parallel data processing and classification via decision trees, thereby promoting the efficacious and sustainable employment of big data analytics in policy formulation and digital innovation. Additionally, the paper delineates the hurdles and issues that confront these agencies, and proposes potential solutions to augment citizen satisfaction and to deliver value within and beyond governmental sectors. The findings suggest that the integration of big data technologies in E-government presents an effective strategy for the provision of interactive services, thereby addressing citizens' demands for enhanced services.","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977620","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}
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