Radwan Abu-Gdairi¹, R. Mareay^2,*, M. Badr³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1025-1038, 2024, DOI:10.32604/cmc.2024.048647

Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life application from medical records is… More >

Sang-min Lee, Namgi Kim^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1897-1914, 2024, DOI:10.32604/cmc.2023.046346

Abstract Recommendation Information Systems (RIS) are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet. Graph Convolution Network (GCN) algorithms have been employed to implement the RIS efficiently. However, the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process. To address this issue, we propose a Weighted Forwarding method using the GCN (WF-GCN) algorithm. The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning. By applying the WF-GCN… More >

Maan Habib^1,*, Maan Okayli²

Revue Internationale de Géomatique, Vol.32, pp. 1-16, 2023, DOI:10.32604/rig.2023.043399

Abstract This study provides a comprehensive overview of modern cartography innovations and emerging trends, highlighting the importance of geospatial representation in various fields. It discusses recent advancements in geospatial data collection techniques, including satellite and aerial imagery, Light Detection and Ranging (LiDAR) technology, and crowdsourcing. The research also investigates the integration of big data, machine learning, and real-time processing in Geographic Information Systems (GIS), as well as advances in geospatial visualization. In addition, it examines the role of cartography in addressing global challenges such as climate change, disaster management, and urban planning in line with the International Cartographic Association’s (ICA) perspectives.… More >

Manjiang Xing^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1081-1090, 2023, DOI:10.32604/iasc.2023.034750

Abstract At present, the database cache model of power information system has problems such as slow running speed and low database hit rate. To this end, this paper proposes a database cache model for power information systems based on deep machine learning. The caching model includes program caching, Structured Query Language (SQL) preprocessing, and core caching modules. Among them, the method to improve the efficiency of the statement is to adjust operations such as multi-table joins and replacement keywords in the SQL optimizer. Build predictive models using boosted regression trees in the core caching module. Generate a series of regression tree… More >

V. Prakash^*, S. Gopalakrishnan

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2575-2590, 2023, DOI:10.32604/iasc.2023.032122

Abstract Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software. The existing research applies various optimization methods such as Genetic Algorithm, Crow Search Algorithm, Ant Colony Optimization, etc., for test case optimization. The existing methods have limitations of lower efficiency in fault diagnosis, higher computational time, and high memory requirement. The existing methods have lower efficiency in software test case optimization when the number of test cases is high. This research proposes the Tournament Winner Genetic Algorithm (TW-GA) method to improve the efficiency of software test case optimization. Hospital Information… More >

X. Zhang¹, H. Liu¹, Z. H. Liu¹, J. R. Ming^1,*, Y. Zhou²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 357-369, 2023, DOI:10.32604/csse.2023.033251

Abstract To meet the needs of today’s library users, institutions are developing library mobile apps (LMAs), as their libraries are increasingly intelligent and rely on deep learning. This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA. A research model was constructed based on the technology acceptance model. A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data. The analysis was based on structural equation modeling. The empirical results show that the perceived ease of use, the… More >

Firas Abedi¹, Subhi R.M. Zeebaree², Zainab Salih Ageed³, Hayder M.A. Ghanimi⁴, Ahmed Alkhayyat^5,*, Mohammed A.M. Sadeeq⁶, Sarmad Nozad Mahmood⁷, Ali S. Abosinnee⁸, Zahraa H. Kareem⁹, Ali Hashim Abbas¹⁰, Waleed Khaild Al-Azzawi¹¹, Mustafa Musa Jaber^12,13, Mohammed Dauwed¹⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5691-5704, 2023, DOI:10.32604/cmc.2023.034435

Abstract As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren’t needed can… More >

Mohsen Eid¹, Tamer Medhat^2,*, Manal E. Ali³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1531-1544, 2023, DOI:10.32604/csse.2023.031745

Abstract An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a useful approach for identifying superfluous… More >

Ahmad Mohammed Alamri¹, Ahmad Ali AlZubi^2,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1595-1609, 2023, DOI:10.32604/csse.2023.027971

Abstract In the era of rapid information development, with the popularity of computers, the advancement of science and technology, and the ongoing expansion of IT technology and business, the enterprise resource planning (ERP) system has evolved into a platform and a guarantee for the fulfilment of company management procedures after long-term operations. Because of developments in information technology, most manual accounting procedures are being replaced by computerized Accounting Information Systems (AIS), which are quicker and more accurate. The primary factors influencing the decisions of logistics firm trading parties are investigated in order to enhance the design of decision-supporting modules and to… More >

V. A. Vijayakumar^*, J. Shanthini, S. Karthik, K. Srihari

Computer Systems Science and Engineering, Vol.45, No.1, pp. 659-669, 2023, DOI:10.32604/csse.2023.028400

Abstract The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The… More >