10 documents found, page 1 of 1
Enhancing security in 5G edge networks: predicting real-time zero trust attacks...
Ashfaq, Fiza; Wasim, Muhammad; Shah, Mumtaz Ali; Ahad, Abdul; Pires, Ivan MiguelThe Internet has been vulnerable to several attacks as it has expanded, including spoofing, viruses, malicious code attacks, and Distributed Denial of Service (DDoS). The three main types of attacks most frequently reported in the current period are viruses, DoS attacks, and DDoS attacks. Advanced DDoS and DoS attacks are too complex for traditional security solutions, such as intrusion detection systems and fi...
PERI_DEP: a dataset of mother's mental health in Pakistan
Zafar, Amna; Akram, Beenish Ayesha; Wasim, Muhammad; Maham; Pires, Ivan Miguel; Coelho, Paulo JorgePerinatal depression (PND) represents a multifaceted mental health issue that impacts women throughout the perinatal period. Existing datasets have a class imbalance issue, resulting in biased outcomes. In Pakistan, we developed a novel dataset called PERI_DEP. This dataset leverages the Patient Health Questionnaire (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS), and socio-demographic questionnaires to ga...
DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relatio...
Gul, Haji; Al-Obeidat, Feras; Amin, Adnan; Wasim, Muhammad; Moreira, FernandoKnowledge graphs (KGs) possess a vital role in enhancing the semantic comprehension of extensive datasets across many fields. It facilitate activities like recommendation systems, semantic searching, and intelligent data mining. However, lacking information can sometimes limit the usefulness of knowledge graphs (KGs), as the lack of relationships between entities could severely limit their practical application...
Effectiveness of internal evaluation metrics for community detection based on c...
Wasim, Muhammad; Ullah, Ubaid; Al-Obeidat, Feras; Amin, Adnan; Moreira, FernandoThe exploration of complex networks and the arrangement of communities is a widely researched topic across various fields, reflecting research interest in a multitude of domains. Clustering algorithms have emerged as a prominent tool for community detection, gaining considerable attention in recent decades. To assess the effectiveness of clustering algorithms, various evaluation metrics are employed, including ...
Advancing fake news detection with Graph Neural Network and Deep Learning
Gul, Haji; Al-Obeidat, Feras; Wasim, Muhammad; Amin, Adnan; Moreira, FernandoIn the modern era of digital technology, the rapid distribution of news via social media platforms substantially contributes to the propagation of false information, presenting challenges in upholding the accuracy and reliability of information. This study presents an updated approach that utilizes Graph Neural Networks (GNNs) alongside with advanced deep learning techniques to improve the identification of fal...
Semantic features analysis for biomedical lexical answer type prediction using ...
Hussain, Fiza Gulzar; Wasim, Muhammad; Cheema, Sehrish Munawar; Pires, Ivan MiguelLexical answer type prediction is integral to biomedical question–answering systems. LAT prediction aims to predict the expected answer’s semantic type of a factoid or list-type biomedical question. It also aids in the answer processing stage of a QA system to assign a high score to the most relevant answers. Although considerable research efforts exist for LAT prediction in diverse domains, it remains a challe...
LocustLens: leveraging environmental data fusion and machine learning for deser...
Khan, Sidra; Akram, Beenish Ayesha; Zafar, Amna; Wasim, Muhammad; Khurshid, Khaldoon S.; Pires, Ivan MiguelThe desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three...
Enhancing link prediction efficiency with shortest path and structural attributes
Wasim, Muhammad; Al-Obeidat, Feras; Amin, Adnan; Gul, Haji; Moreira, FernandoLink prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, so...
Forecasting Networks Links with Laplace Characteristic and Geographical Informa...
Wasim, Muhammad; Al-Obeidat, Feras; Gul, Haji; Amin, Adnan; Moreira, FernandoForecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our meth...
Normalized effect size (NES): a novel feature selection model for Urdu fake new...
Wasim, Muhammad; Cheema, Sehrish Munawar; Pires, Ivan MiguelSocial media has become an essential source of news for everyday users. However, the rise of fake news on social media has made it more difficult for users to trust the information on these platforms. Most research studies focus on fake news detection in the English language, and only a limited number of studies deal with fake news in resource-poor languages such as Urdu. This article proposes a globally weight...