Browsing by Author "Ali, Liaqat"
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Item Breast Cancer Prediction Using Machine Learning and Image Processing Optimization(Springer Science and Business Media Deutschland GmbH, 2023) Al-Dmour, Nidal A.; Said, Raed A.; Alzoubi, Haitham M.; Alshurideh, Muhammad; Ali, LiaqatItem DDoS Intrusion Detection with Ensemble Stream Mining for IoT Smart Sensing Devices(Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Al-Dmour, Nidal A.; Said, Raed A.; Omidvar, Alireza; Khan, Urooj Yousuf; Soomro, Tariq Rahim; Soomro, Tariq Rahim; Alshurideh, Muhammad; Abdellatif, Tamer Mohamed; Moubayed, Abdullah; Ali, LiaqatSecurity threats in the Smart City Systems are becoming a challenge. These Smart City Systems, generating Big Data, are a revolutionizing application of the Internet of Things(IoT). Data Stream Mining, which is an efficient way of handling Big Data, is now of great concern. The acquired information is computationally expensive to process in terms of efficiency and runtime. Detection of suspicious activities on decentralized servers, generating and computing massive data streams requires time. Moreover, several stakeholders should be engaged to train the heterogenous malware data streams in the level of service application. Small experiments can be performed on the functionality of Batch ML on IoT datasets with available heap size resources. Among these candidate datasets, a little contribution has been already represented on the Mirai Attack. This research aims at the study of Data Stream Mining algorithms. Owing to the accuracy and interferences of the measurement, these algorithms are able to handle the non-hierarchical and unbalanced datasets similar to the Mirai Attacks. No single method can solely improve these critical standpoints. Thus, an Ensemble technique should be implemented. According to our study, a pool of meta or selective classifiers that interact based on the temporal Data Mining swiftly can outperform others. The maintainability and security concerns of such applications can be best fulfilled in meta-heuristics with the one-time scanning network approach for the recognition of the most frequent attacking pattern with the on-the-fly scheme. These are implemented in Create, Read, Update and Delete (CRUD) operations of the Big Data Systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Impacts of Big-Data Technologies in Enhancing CRM Performance(Institute of Electrical and Electronics Engineers Inc., 2020) Taleb, Nasser; Salahat, Mohammad; Ali, Liaqatbig data is a hot business topic today. In business organizations, customer-relationship management (CRM) is an important pillar to achieve competitive advantages. Big data refers to practices of integrating big data into an organizational CRM process to achieve the objectives of improving and sustaining customer service. The alternative goal of big data is to combine internal CRM data with customer behavior and buying patterns from the environment external to the organization. Several tools exist that can integrate big data with other CRM data to improve customer analysis and understand buying behavior and patterns. This paper reports research evaluating the role of big-data technology in enhancing the effective use of CRM. Research proves that data's predictive model is enhanced by analyzing customer buying patterns. Issues and challenges related to the implementation of big data are discussed and benefits of appropriate implementation of big-data technologies are highlighted. The research further demonstrates some tangible benefits of implementing big-data technologies in CRM. © 2020 IEEE.Item Information Systems Solutions for the Database Problems(Springer Science and Business Media Deutschland GmbH, 2023) Al-Dmour, Nidal A.; Ali, Liaqat; Salahat, Mohammed; Alzoubi, Haitham M.; Alshurideh, Muhammad; Chabani, ZakariyaItem Linear Discrimination Analysis Using Image Processing Optimization(Springer Science and Business Media Deutschland GmbH, 2023) Said, Raed A.; Al-Dmour, Nidal A.; Ali, Liaqat; Alzoubi, Haitham M.; Alshurideh, Muhammad; Salahat, MohammedWhen we talk about Machinery Vision and Deep Learning, we often talk about algorithms. In fact, mathematical models with computer knowledge are the basis of how we deal with graphical data to process the Image and make decision. Machine learning can play an important role in determining agricultural plant type in order to optimize the harvesting steps in an automated way. How to process and introduce the products to the market often requires detailed information about the stages of planting and harvesting. In addition, by using this method, sophisticated research can be designed in plant genetics and effect of environmental variables on the end product. The ultimate goal of this work is to use Linear Discrimination Analysis for the Image Processing and classification of harvested wheat grain which are belonged to different types of grain namely Rosa, Kama and Canadian. The above discovery has proved with the statistics to have with more than 94% of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Playing the video games during COVID-19 pandemic and its effects on player's well-being(Institute of Electrical and Electronics Engineers Inc., 2023) Noaman, Samar Billi; Ibrahim, Amer; Ali, Liaqat; Iqbal M.W.; Ashraf, Asma; Haseeb, Usama; Muneer, Salman; Almajed, Rasah; Hamid K.Item Post-Covid-19 Pandemic IT Project Management Skills and Challenges(Institute of Electrical and Electronics Engineers Inc., 2023) Ali, Liaqat; Taleb, Nasser; Ali, Atif; Abu-Alsondos, Ibrahim A.; Naseem, Hina; Yousaf, Farhan; Abdelhakim, Mohamed