Heejae Park¹, Tri-Hai Nguyen², Laihyuk Park^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 13-41, 2024, DOI:10.32604/cmes.2024.052394 - 20 August 2024

Abstract The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services will burden network operators with rising infrastructure costs. Recently, the Open Radio Access Network (O-RAN) has been introduced as a solution for growing financial and operational burdens in Beyond 5G (B5G) and 6G networks. O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs. By disaggregating conventional Base Band Units (BBUs) into O-RAN Distributed Units (O-DU) and O-RAN Centralized Units (O-CU), O-RAN offers greater flexibility for upgrades and network automation. However, this openness introduces new security More >

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

Jalil Abbas^1,*, Arshad Ahmad², Syed Muqsit Shaheed³, Rubia Fatima⁴, Sajid Shah⁵, Mohammad Elaffendi⁵, Gauhar Ali⁵

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2839-2855, 2024, DOI:10.32604/cmc.2024.052218 - 15 August 2024

Abstract The software development process mostly depends on accurately identifying both essential and optional features. Initially, user needs are typically expressed in free-form language, requiring significant time and human resources to translate these into clear functional and non-functional requirements. To address this challenge, various machine learning (ML) methods have been explored to automate the understanding of these requirements, aiming to reduce time and human effort. However, existing techniques often struggle with complex instructions and large-scale projects. In our study, we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier (FNRC). By combining the… More >

Deema Mohammed Alsekait^1,*, Asif Nawaz², Ayman Nabil³, Mehwish Bukhari², Diaa Salama AbdElminaam^3,4,5,6,*

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1031-1054, 2024, DOI:10.32604/csse.2024.051847 - 17 July 2024

Abstract The article describes a novel method for sentiment analysis and requirement elicitation from social media feedback, leveraging advanced machine learning techniques. This innovative approach automates the extraction and classification of user requirements by analyzing sentiment in data gathered from social media platforms such as Twitter and Facebook. Utilizing APIs (Application Programming Interface) for data collection and Graph-based Neural Networks (GNN) for feature extraction, the proposed model efficiently processes and analyzes large volumes of unstructured user-generated content. The preprocessing pipeline includes data cleaning, normalization, and tokenization, ensuring high-quality input for the sentiment analysis model. By classifying… More >

Qazi Mazhar ul Haq¹, Fahim Arif^2,3, Khursheed Aurangzeb⁴, Noor ul Ain³, Javed Ali Khan⁵, Saddaf Rubab⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172 - 26 March 2024

Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature… More >

Jie Chen¹, Wei Wang^1,*, Wenyuan Sun¹, Yuming He¹, Shunchen Miu¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2022.08801

Abstract Characteristic parameters of oil and gas gathering system (OGGS), such as the liquid holdup, flow rate and pressure of wells, fluctuate dynamically during the production cycle. Furthermore, with the call for energy transition and digitalization, it is critical to grasp the operation status of OGGS in real time. A generalized process simulation model for multi-phase gathering system was established by coupling several models (mass balance, pressure balance, hydraulic and thermal model of a single pipe, power and thermal equipment model, etc.). Because the hydraulic equation of the pipe contains nonlinear terms, the hydraulic model of… More >

Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Alwan Ahmed Abdulrahman Alwan, Shankar Karuppayah^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3375-3398, 2023, DOI:10.32604/cmc.2023.036587 - 31 March 2023

Abstract The cyber-criminal compromises end-hosts (bots) to configure a network of bots (botnet). The cyber-criminals are also looking for an evolved architecture that makes their techniques more resilient and stealthier such as Peer-to-Peer (P2P) networks. The P2P botnets leverage the privileges of the decentralized nature of P2P networks. Consequently, the P2P botnets exploit the resilience of this architecture to be arduous against take-down procedures. Some P2P botnets are smarter to be stealthy in their Command-and-Control mechanisms (C2) and elude the standard discovery mechanisms. Therefore, the other side of this cyberwar is the monitor. The P2P botnet… More >

Muhammad Hasnain¹, Imran Ghani², Seung Ryul Jeong^3,*, Muhammad Fermi Pasha⁴, Sardar Usman⁵, Anjum Abbas⁶

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 783-799, 2023, DOI:10.32604/cmc.2023.030162 - 22 September 2022

Abstract Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry. In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework? What human factors contribute to success or failure of a software? What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work? In order to… More >

Fang Zhao¹, Dongming Xiang^2,*, Guanjun Liu¹, Changjun Jiang¹, Honghao Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1337-1363, 2022, DOI:10.32604/cmes.2022.018872 - 19 April 2022

Abstract Workflow system has become a standard solution for managing a complex business process. How to guarantee its correctness is a key requirement. Many methods only focus on the control-flow verification, while they neglect the modeling and checking of data-flows. Although some studies are presented to repair the data-flow errors, they do not consider the effect of delete operations or weak circulation relations on the repairing results. What's more, repairing some data-flow errors may bring in new errors. In order to solve these problems, we use workflow net with data (WFD-net) systems to model and analyze More >

Hatim Elhassan¹, Mohammed Abaker¹, Abdelzahir Abdelmaboud², Mohammed Burhanur Rehman^1,*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 259-273, 2022, DOI:10.32604/iasc.2022.023750 - 05 January 2022

Abstract The research community has well recognized the importance of requirement elicitation. Recent research has shown the continuous decreasing success rate of IS projects in the last five years due to the complexity of the requirement conflict refinement process. Requirement conflict is at the heart of requirement elicitation. It is also considered the prime reason for deciding the success or failure of the intended Information System (IS) project. This paper introduces the requirements conflict detection automation model based on the Mean shift clustering unsupervised machine learning model. It utilizes the advantages of Artificial Intelligence in detecting… More >