8 documents found, page 1 of 1
Introducing the Hyperdynamic Adaptive Learning Fusion (HALF) model for superior...
Islam, Umar; Alali, Ibrahim Khalil; Alotaibi, Shoayee Dlaim; Alzaid, Zaid; Shah, Babar; Ali, Ijaz; Moreira, FernandoToday’s complex world is defined by digital changes in educational paradigms to which E-learning has contributed significantly, and as such, accurate prediction methods are needed for student performance modeling. In this paper a new and complex model is proposed, namely the Hyperdynamic Adaptive Learning Fusion (HALF) model that leverages adaptive computing paradigms and artificial intelligence to build a fusi...
Empowering global ethereum price prediction with EtherVoyant: A state-of-the-ar...
Islam, Umar; Shah, Babar; Al-Atawi, Abdullah A.; Arnone, Gioia; Abonazel, Mohamed R.; Ali , Ijaz; Moreira, FernandoEthereum has emerged as a major platform for decentralized apps and smart contracts with the heightened interest in cryptocurrencies in recent years. Investors and market participants in the cryptocurrency space will find it increasingly important to use reliable price prediction models as Ethereum's popularity grows. To better estimate Ethereum prices around the world, we propose "EtherVoyant," a novel hybrid ...
Semantic orientation of crosslingual sentiments: Employment of lexicon and dict...
Raza, Arslan Ali; Habib, Asad; Ashraf, Jawad; Shah, Babar; Moreira, FernandoSentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Ana...
A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Ne...
Ali, Gohar; Alfandi, Omar; Shah, Babar; Ilyas, Mohammed; Moreira, FernandoReal-time flows using time division multiple access (TDMA) scheduling in cluster-based wireless sensor networks try to schedule more flows per time frame to minimize the schedule length to meet the deadline. The problem with the previously used cluster-based scheduling algorithm is that intra-cluster scheduling does not consider that the clusters may have internal or outgoing flows. Thus, intra-cluster scheduli...
Features Weight Estimation Using a Genetic Algorithm for Customer Churn Predict...
Amin, Adnan; Shah, Babar; Abbas, Ali; Anwar, Sajid; Alfandi, Omar; Moreira, FernandoThe high dimensional dataset results in more noise, will require more computations, has huge sparsity linked with high dimensional features and has thus introduced great challenges in data analysis. To efficiently manipulate and address the impact of the challenges faced by high dimensional dataset, researchers used several features reduction methods. Feature reduction is a formidable step, when dealing with im...
Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based...
Ahmad, Masood; Shah, Babar; Ullah, Abrar; Alfandi, Omar; Ali, Gohar; Hameed, Abdul; Moreira, FernandoIn wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobject...
Cross-company customer churn prediction in telecommunication: a comparison of d...
Amin, Adnan; Shah, Babar; Khattak, Asad Masood; Ali, Gohar; Rocha, Álvaro; Anwar, Sajid; Moreira, FernandoCross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation me...
Compromised user credentials detection in a digital enterprise using behavioral...
Shah, Saleh; Shah, Babar; Amin, Adnan; Al-Obeidat, Feras; Chow, Francis; Anwar, Sajid; Moreira, FernandoIn today’s digital age, the digital transformation is necessary for almost every competitive enterprise in terms of having access to the best resources and ensuring customer satisfaction. However, due to such rewards, these enterprises are facing key concerns around the risk of next-generation data security or cybercrime which is continually increasing issue due to the digital transformation four essential pill...