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The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment. [ABSTRACT FROM AUTHOR]

Introducing computational thinking at elementary school can develop students' capabilities and interest in Computing skills. In this study, we introduced the Computer Science unplugged (CS-unplugged) technique in Pakistan. We use paper-based activities to equip students with basic Computer Science skills without introducing any programming language. This study contributes twofold: First, we report the impact of CS-Unplugged training on more than 350 elementary students. The empirical study reveals that the students perform better in solving problems after unplugged training. Improved results in the post-training activity support this impact. Second, we applied machine learning to predict students' performance. We employed different supervised machine learning algorithms to predict the students' performance. Our results indicate that the Logistic regression-based model can predict the positive response of the student with a 0.91 receiver operating characteristic curve (ROC curve). This pilot study results encourage introducing unplugged techniques at elementary schools in third-world countries. The goal is to have minimal changes in infrastructure and focus on better student learning. In the future, we plan to introduce more unplugged problem-solving techniques to elementary students by providing little training to the science or math teacher. [ABSTRACT FROM AUTHOR]

Analyzing human behaviors using computer-based approaches is a new dimension of digital humanity. It integrates computing with healthcare, psychology, and social media. Like other healthcare and medical-related problems, anxiety and depressive disorders are common in Pakistan. In the existing era, this disease is not only affecting people of all ages but the impact of depression is also witnessed on social media communications. On social media, anyone could not guess about the mental conditions of a user through his/ her tweets or comments. Especially, the prediction of depressed unknown users through his behavior on social media is a hard task. Computer science and the medical domain can eliminate such healthcare-related issues. In this paper, a system has been proposed to detect and analyze the behavior of a user on social media. The proposed system comprises of an algorithm that performs sentimental analysis of users’ tweets and comments and a web-based platform to share best practices and success stories of users who have recovered from the disease. The system has been tested on real-time data obtained from tweeter using Application Programming Interface (API). Top trends have been focused to obtain the data and the proposed system successfully detected the users under depression. After working on the huge dataset, the proposed system will be a good contribution to humanity. [ABSTRACT FROM AUTHOR]

Background: With the increasing advancement in the field of information technology, it's about time we realize that our future will be shaped by this field. With more and more people using smartphones, we need to adapt them to the medical field. Already many advancements in medical field are done thanks to the advancement of computer science. But we need to implement this into our teaching and learning as well. Almost all students and faculty members use smartphones in one way or another if we can utilize the smartphone to enhance the learning opportunities for our medical students, it would greatly benefit them. But before the implementation, we need to find out if our faculty is willing to adopt this technology. The objective of this study is to find out what are the perceptions of dental faculty members about using a smartphone as a teaching tool. Methodology: A validated questionnaire was distributed among the faculty members of all the dental colleges of KPK. The questionnaire had 2 sections. First one contains information regarding the demographics. The second one had questions related to the faculty members' perception regarding using a smartphone as a teaching tool. Results: The results of our study showed that the faculty (Mean 2.08) had positive perceptions regarding using a smartphone as a teaching tool. Conclusion: Most of the Dental Faculty members of KPK agree that smartphone can be used as a teaching tool, and it can have better outcomes if proper applications and teaching strategies are used. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSN), flooding increases the reliability in terms of successful transmission of a packet with higher overhead. The flooding consumes the resources of the network quickly, especially in sensor networks, mobile ad-hoc networks, and vehicular ad-hoc networks in terms of the lifetime of the node, lifetime of the network, and battery lifetime, etc. This paper aims to develop an efficient and reliable protocol by using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Unicasting is used when the desired destination is at a minimum distance to avoid an extra overhead and increases the efficiency of the network in terms of overhead and energy because unicasting is favorable where the distance is minimum. Similarly, multicasting is used when the desired destination is at maximum distance and increases the network's reliability in terms of throughput. The results are implemented in the Department of Computer Science, Bacha Khan University Charsadda (BKUC), Pakistan, as well as in the Network Simulator-2 (NS-2). The results are compared with benchmark schemes such as PUMA and ERASCA, and based on the results, the performance of the proposed approach is improved in terms of overhead, throughput, and packet delivery fraction by avoiding flooding. [ABSTRACT FROM AUTHOR]