Cosmos Scholars Publishing House: Journals Management System
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Readiness of Galileo PNT Service for NATO Operations Threats and Challenges
No full textIn the domain of the Defense ICT-based technology: (Information and Communication Technology), most of the worldwide defense operations, including the North Atlantic Treaty Organization (NATO) operations, are significantly relying on the space support services. One of the most essential services is the Positioning, Navigation and Timing (PNT) service, which is currently provided by the United States’ Global Positioning System (GPS). The GPS has become a global utility comparable to the internet, and it does not ‘just’ provide positioning data, but also provides the most important civilian/military use of the timing signal, which synchronizes all the communication and the encryption in all operations. While the USA has formulated first requirements to strengthen the resiliency of GPS in terms of the availability of accuracy, the availability of integrity and the availability of continuity, NATO has additional options to improve resiliency by integrating the European Galileo constellation. This paper focus mainly on the military implications of these two Global Navigation Satellite Systems (GNSS), to order to enrich the knowledge of the NATO members and policymakers about the PNT options available to the alliance, as well as addressing the various threats and challenges facing the PNT services
A Role of Artificial Intelligence in the Context of Economy: Bibliometric Analysis and Systematic Literature Review
No full textThis bibliometric analysis aims to explore and assess the multifaceted impact of technology on the economy through a comprehensive review of academic literature. By systematically examining a wide range of research articles, this study intends to provide insights into the various ways in which technology influences economic growth, productivity, innovation, and other key indicators. The analysis will identify trends, influential authors, and prominent journals in the field, shedding light on the evolving relationship between technology and the economy. The paper might highlight a gap in research on how AI is being adopted and leveraged in developing economies. The actual research gaps addressed in the paper will depend on the findings of their systematic literature review and bibliometric analysis. The significance of this paper reflects a recognition that AI has become a driving force behind innovation, productivity improvements, and competitive advantage across industries. The main objective of this research is to use bibliometric analysis to investigate the publication output about artificial intelligence, using data sourced from the Scopus database, to contribute to economic improvement. The data has been gathered using specified terms, including "artificial intelligence, technologies," and "economy," with a focus on the fields of business management, accounting, and computer sciences. The bibliometric analysis included the use of Scopus to gather data, using VOSviewer, Rstudio, and Excel as analytical tools. The data collection spanned from 2016 to 2023. The primary aims of this work are doing a comprehensive bibliometric analysis, examining the co-citation structure, analyzing keyword co-occurrence, and conducting a geographical analysis within the domain of artificial intelligence and economics. The research yields findings about the implications of publishing in the field of artificial intelligence and its impact on business, management, accounting, and computer science from an academic standpoint. This article aims to assist scholars and business experts seeking to construct contemporary company structures, serving as a valuable resource for future endeavors in this field.  
The Influence of Knowledge Hiding on University Innovation and Employee Performance: The Private Universities in Mogadishu
No full textThe primary objective of this study paper is to examine the concealed aspects of knowledge possessed by academic personnel and their correlation with employee performance and dimensions of innovation. The researchers employed a quantitative research methodology, conducting a field study on private universities in Mogadishu. The study included a sample size of 120 academic staff members. The researchers used various statistical tests, including measurement and structured models, in their study. The results of this study indicate that the two categories of knowledge concealment have a negative impact on employee performance and innovation dimensions, whereas evasive knowledge concealment improves employee performance and process innovation. The present research paper provides a significant contribution to the extant literature on knowledge management, specifically in the area of knowledge hiding. It focuses on the behavior of coworkers in academic settings and explores how they respond to explicit demands. The study sheds light on important aspects within academia and offers insights that are relevant to the academic community
Unraveling the Intercultural Sensitivity of Foreign Teachers: A Cross-Sectional Examination in Secondary Education across Public and Private Schools, Thailand
No full textThis research aims to explore cultural distinctions and investigate the adaptability of foreign teachers in both public and private secondary schools. A mixed methods approach was employed, utilizing semi-structured interviews and questionnaires as study instruments. The study instruments included the Self-Perceived Communication Competence Scale (SPCC), Intercultural Sensitivity Scale (ISS), and Tromso Social Intelligence Scale (TSIS). Data analysis relied on descriptive statistics. The findings revealed that foreign teachers in both public and private secondary schools demonstrated the highest levels in three key factors: Friends, Interaction Attention, and Social Information Processing. These factors corresponded to self-perceived communication competence, self-reported intercultural sensitivity, and social intelligence, respectively.  
Anomaly Detection for Network Security
Full text linkToday, network security is crucial due to the rapid development of network and internet technologies, as well as the continuous growth in network threats. Detecting network anomalies is one of the approaches that may be used to safeguard a network's security. Recent research has focused extensively on techniques for identifying abnormalities. Using the Autoencoder model together with data pre-processing techniques such as data resampling and feature selection, this research describes a novel approach for identifying network abnormalities. It has been shown that the suggested strategy is applicable to network intrusion data. A comparison of the reconstruction error to a threshold value determines whether the traffic data is normal or anomalous. CICIDS2017 dataset is selected to evaluate the implementation of the proposed Autoencoder model based on real-world, large-scale, current network traffic data The proposed model with data pre-processed achieved F1-Score of 76% which outperformed the baseline model without feature selection and data resampling in data pre-processing stages. This research project investigated the effect of data pre-processing techniques on the performance of the proposed Autoencoder. At the end of this research project, it is demonstrated that the proposed methodologies are applicable towards imbalanced network intrusion data
Privacy Protection Schemes in Internet of Vehicles (IoV): A Review and Analysis
Full text linkInternet of Vehicles (IoV) refers to a dynamic network of vehicles where data exchange is a way of communication among them. Existing work focus on mitigating the data leakage and strengthening the privacy protection during data exchange among vehicles in IoV, specifically on the Safety Beacon Message (SBM) as it contains location and identity of the vehicles. Limited work reported on the design of privacy protection schemes explicitly for SBM, and the success case studies in implementation of privacy protection schemes in various types of IoV architectures. This paper aims to review past research on privacy protection schemes for SBM in IoV architectures and the challenges in pursuing similar research in current environment. Firstly, this paper discusses the privacy protection issues related to SBM that arise in the centralized IoV architectures. Next, decentralization and tamper- proof features of blockchain in IOV is introduced as the enhancement to the centralized IoV architectures. Then, privacy protection issues related to SBM in a blockchain-based IoV architecture will be discussed. Finally, this paper concludes with future direction of work in privacy protection schemes in blockchain- based IoV architecture.  
Machine Learning Prediction Model for Early Student Academic Performance Evaluation in Video-Based Learning
Full text linkThe transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) has created the emergence of educational technology domain for many students to access e-learning platforms. However, there are some drawbacks especially in asynchronous video-based learning. A sense of isolation could occur between teacher and students if the teachers do not interact much with the students in the asynchronous video-based learning. Consequently, the knowledge that is delivered by the teacher may not reach students effectively and cause a drop in student performance in the coming examination. Moreover, the growth of video-based learning has created a huge amount of data on the student learning process on the educational video which may provide a boost for educational data mining research. Therefore, this research study aims to introduce a predictive model that scrutinize the number of video view data based on each chapter in the video as well as student learning style, Felder-Silverman (FS) learning style model to deliver a prediction on individual student early performance in asynchronous video-based learning. This research has tested the different combination of feature selection methods with several handle of imbalance data methods such as Synthetic Minority Oversampling Technique (SMOTE), SMOTE-TOMEK and Adaptive Synthetic (ADASYN) algorithms to build the machine learning model and compare the model performance. As a result, proposed machine learning classifier algorithms with the combination of Maximum Relevance and Minimum Redundancy (MRMR) as feature selection method and SMOTE has been achieved the highest Area Under Curve (AUC) rate of 0.93
Digitizing ECG Signal using 2D Signal Convolution Approach
Full text linkECG signal printed on a graph paper has been widely used by medical examiners to analyze diseases related to the heart. Medical practitioners rely on historical records to perform diagnosis. Constantly accessing the ECG printed graph paper manually could be time consuming as there are bulk of graph papers for examination. The proposed work aims to convert the printed ECG graph paper into digitized ECG for remote diagnosis. The ECG printed graph paper undergoes conversion into ECG artifact before transforming as digitized ECG. In the initial phase, patient information in the ECG artifact is preserved by encoding into a QR Code. In phase two, preliminary processing is done on ECG artifact for removal of gridline in the background. Image convolution method is proposed as the process for background gridline removal. Then, morphological image processing is implemented to enhance the ECG artifact. In phase three, segmentation process takes place, in which the ECG artifact is divided into segments for separating the waveforms. In the final phase of ECG digitization, the location of the signal is traced for reshaping the ECG artifact as digitized ECG. The accuracy of the ECG digitization is measured through the heart rate that is calculated using our approach and compared with the one on ECG printed graph paper. The average sum of squared error of the heart rate between the ECG printed graph paper and digitized ECG is 0.005618. The digitized ECG can be useful for medical examiners and practitioners in telemedicine where remote diagnosis may be needed
Efficient Fill-Level Monitoring for Smart E-waste Recycling
Full text linkProper electronic waste management is compulsory nowadays because, with improper management, the harmful substances in electronic waste will put humans and the environment at risk. The production of e-waste differs from domestic waste as it is not regular. It is vital to improve the e-waste management process. This paper presents a smart e-waste bin fill level monitoring system. This paper proposes a module for identifying fill level, display, and communication systems integrated with a mobile application. Different sensors are implemented to monitor the bin status in real-time. The system will alert the collection party on various scenarios through a mobile application, including the full bin and a fire in the container.  
Improving Students’ Collaborative Learning Experiences within a Game-Based Augmented Reality Learning Environment
Full text linkIn today’s post-COVID 19 world, the need to re-establish and strengthen collaborative activities is crucial in improving their learning experiences, and technology has become more prevalent as an enabler and a necessity to support this. Therefore there is a need for the development of student learning approaches that can capture their learning experiences and bridge the gap between formal learning and a more authentic, collaborative approach towards learning. Research has suggested that the use of emerging technologies such as Augmented Reality (AR) have been conducive in promoting better understanding of complex and difficult content in classes and offer learners the rich and engaging learning experience of visualizing course content, but have not yet been fully evaluated for their effectiveness in improving the learning experiences of students. As such, this study sought to design a game-based AR environment that investigated students’ attitudes and perceptions of using game-based AR, within a Team-Based Learning (TBL) class structure in their learning process, and its impact on student learning experiences. 56 Undergraduate level students participated in this mixed method research study and in this game-based AR learning environment. Data was collected on their attitudes and triangulated to obtain the study’s results. Findings showed that students were motivated to learn more, that the learning environment improved their collaboration, and were positive to having such learning experiences in their future courses, and a game-based AR learning framework, GALE, was presented. Such findings have important implications for the use of augmented reality as an instructional tool in 21st century learning environments