Generative AI’s swift progress advances onto profound cybersecurity dilemmas. Its usage by malevolent entities to automate intricate malware creation poses a significant threat, circumventing conventional defensive measures. This paradigm shift enables the generation of polymorphic malware, eluding signature-based detection and facilitating precision-targeted assaults. The democratization of Generative AI exacerbates these threats by extending advanced capabilities to a broader spectrum of malicious actors. A comprehensive examination of AI-generated malware’s prevalence, and its repercussions is imperative to fortify cyber resilience. Such scrutiny informs proactive defense strategies vital for safeguarding digital assets within increasingly interconnected systems. Robust threat intelligence frameworks and AI-centric defensive mechanisms emerge as imperative shields against evolving cyber perils. Addressing this emergent challenge stands as an indispensable endeavor in contemporary cybersecurity discourse.

Ranking of researchers based on their scientific impact in a scientific community is indeed a very crucial task. However, identifying the researchers ranking helps the scientific community in various decisions such as awarding scholarships, selection for tenure, awarding achievements, giving promotions, etc. In literature numerous parameters have been proposed for the ranking of researchers, such as publication count, citation count, coauthor count, h-index, and its extensions. The current state-of-the-art research delineates that no such universally accepted parameter exists which can identify the most influential researchers. Therefore, it is necessary to determine an optimal parameter that can effectively rank authors. Furthermore, to identify the best parameter, few of the researchers conducted evaluative surveys as reflected in the literature. In these evaluative surveys, the researchers utilized a limited number of indices on the small and imbalanced datasets, followed by fictional cases and scenarios, this has made it challenging to ascertain the relative importance and impact of each parameter in comparison to the others. This research evaluates the h-index and its thirty-two variants, which are based on the number of publications and citation count category used for ranking the authors. We have collected data from 1050 researchers working in the mathematical domain for our experimental purposes. For the benchmark dataset, we have collected the awardees’ data of the last two decades of four different societies belonging to mathematical domain. First and foremost, we have computed the correlations among the obtained values of the indices to assess their similarities and differences to evaluate indices. The result revealed that there is a high degree of correlation observed among h-index and it’s twenty-four different variants. However, some of the indices represented weak correlation, signifying that their rankings are highly dissimilar to those of other indices. Secondly, the position of awardees is checked in the top 10, top 50, and top 100 return records based on the ranking list of each index. The outcome of the last step divulges that A index, E index, H core citation, H2 lower index, K index, M index, and woginger index retrieved almost 80% of awardees in top 10% ranked list. Further, the analysis revealed that most of the winners (awards) were in the top tier, belonging to IMS, LMS, and AMS society returned by hg index, g index, k index, etc. indicating a relationship between the stated societies and the indices.

Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.

Underwater wireless sensor networks (UWSNs) are comprised of sensor nodes that are deployed under the water having limited battery power and other limited resources. Applications of UWSNs include monitoring the quality of the water, mine detection, environment monitoring, military surveillance, disaster prediction, and underwater navigation. UWSNs are more vulnerable to security attacks as compared to their counterparts such as wireless sensor networks (WSNs). The possible attacks in UWSNs can abrupt the operation of entire network. This research work presents the analysis of relevant research done on security-based schemes in UWSNs. The security-based schemes are categorized into five sub-categories. Each technique in each category is analyzed in detail. The major contribution in each security-based scheme along with technique used, possible future research issues and implementation tool are discussed in detail. The open research issues and future trends identified and presented in this research can be further explored by the research community.

This study examines the relationship between virtual team performance and its critical explaining factors, i.e., individual, team, organizational, and technological factors, by exploring the mediating effect of knowledge sharing. For this purpose, a conceptual framework is proposed based on an in-depth Systematic Literature Review (SLR) analysis. Next, data is collected through a survey from 252 IT practitioners working in Pakistan’s small and medium-sized enterprises (SMEs). Statistical tests on collected data are applied in statistical package for social sciences (SPSS) and smart partial least squares (SmartPLS) to evaluate the proposed conceptual framework empirically. Finally, 19 industrial and 5 academic experts verified the framework. There is a consensus among researchers and practitioners regarding the factors that contribute to the explanation of knowledge sharing and virtual team performance. Thus, the current study’s outcome may facilitate the management of software organizations to improvise their knowledge sharing infrastructure, make the development process more efficient, avoid project failure and increase the productivity of virtual teams.

The exponentially growing concern of cyber-attacks on extremely dense underwater sensor networks (UWSNs) and the evolution of UWSNs digital threat landscape has brought novel research challenges and issues. Primarily, varied protocol evaluation under advanced persistent threats is now becoming indispensable yet very challenging. This research implements an active attack in the Adaptive Mobility of Courier Nodes in Threshold-optimized Depth-based Routing (AMCTD) protocol. A variety of attacker nodes were employed in diverse scenarios to thoroughly assess the performance of AMCTD protocol. The protocol was exhaustively evaluated both with and without active attacks with benchmark evaluation metrics such as end-to-end delay, throughput, transmission loss, number of active nodes and energy tax. The preliminary research findings show that active attack drastically lowers the AMCTD protocol’s performance (i.e., active attack reduces the number of active nodes by up to 10%, reduces throughput by up to 6%, increases transmission loss by 7%, raises energy tax by 25%, and increases end-to-end delay by 20%).