Khaled M. Alalayah¹, Fatma S. Alrayes², Jaber S. Alzahrani³, Khadija M. Alaidarous¹, Ibrahim M. Alwayle¹, Heba Mohsen⁴, Ibrahim Abdulrab Ahmed⁵, Mesfer Al Duhayyim^6,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3121-3139, 2023, DOI:10.32604/csse.2023.036352

Abstract With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL)… More >

Hanan Abdullah Mengash^*, Manel Farouk Ayadi

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2777-2795, 2022, DOI:10.32604/cmc.2022.015434

Abstract Feelings influence human beings’ decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people’s preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multi-criteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multi-attribute selection procedure that incorporates both regret and rejoice to select… More >

M. Govindarajan^1,*, V. Chandrasekaran², S. Anitha³

Computer Systems Science and Engineering, Vol.40, No.3, pp. 851-863, 2022, DOI:10.32604/csse.2022.019298

Abstract Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time. With the use of mobile devices, communication services generate numerous data for every moment. Given the increasing dense population of data, traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation. A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning (RKLSTM-CTMDSL) model is introduced for traffic prediction with superior accuracy and minimal time consumption. The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction. In… More >

Nazish Naheed¹, Muhammad Shaheen¹, Sajid Ali Khan¹, Mohammed Alawairdhi^2,*, Muhammad Attique Khan^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 315-344, 2020, DOI:10.32604/cmes.2020.011380

Abstract In the area of pattern recognition and machine learning, features play a key role in prediction. The famous applications of features are medical imaging, image classification, and name a few more. With the exponential growth of information investments in medical data repositories and health service provision, medical institutions are collecting large volumes of data. These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other hand, this growth also made it difficult to comprehend and utilize data for various purposes. The results of imaging data can become biased because of… More >