Muaadh A. Alsoufi^1,*, Maheyzah Md Siraj¹, Fuad A. Ghaleb², Muna Al-Razgan³, Mahfoudh Saeed Al-Asaly³, Taha Alfakih³, Faisal Saeed²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 823-845, 2024, DOI:10.32604/cmes.2024.052112 - 20 August 2024

Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >

Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607 - 30 January 2024

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >

Deepthi K. Oommen^*, J. Arunnehru

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640 - 08 June 2023

Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the… More >

Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 593-611, 2023, DOI:10.32604/csse.2023.039479 - 26 May 2023

Abstract Visual motion segmentation (VMS) is an important and key part of many intelligent crowd systems. It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes, which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades. Trajectory clustering has become one of the most popular methods in VMS. However, complex data, such as a large number of samples and parameters, makes it difficult for trajectory clustering to work well with accurate motion segmentation… More >

Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Yu-Chieh Lin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128 - 31 August 2022

Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time More >

Helen Josephine V L¹, Ramchand Vedaiyan², V. M. Arul Xavier³, Joy Winston J⁴, A. Jegatheesan⁵, D. Lakshmi⁶, Joshua Samuel Raj^7,*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 701-714, 2023, DOI:10.32604/csse.2023.030611 - 16 August 2022

Abstract The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or… More >

Ashit Kumar Dutta^1,*, Yasser Albagory², Manal Al Faraj¹, Yasir A. M. Eltahir³, Abdul Rahaman Wahab Sait⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1517-1529, 2023, DOI:10.32604/csse.2023.026482 - 15 June 2022

Abstract The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the More >

Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Ibrahim Abunadi³, Fahd N. Al-Wesabi^2,4, Hadeel Alsolai⁵, Anwer Mustafa Hilal¹, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2581-2596, 2022, DOI:10.32604/cmc.2022.024764 - 29 March 2022

Abstract Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique More >

Yun-Kyu Lee¹, Dong-Sung Pae², Dae-Ki Hong³, Myo-Taeg Lim¹, Tae-Koo Kang^4,*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 657-673, 2022, DOI:10.32604/iasc.2022.020849 - 17 November 2021

Abstract With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record… More >

A. Jayamathi¹, T. Jayasankar^2,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 311-324, 2022, DOI:10.32604/iasc.2022.019473 - 03 September 2021

Abstract Orthogonal frequency division multiplexing is one of the efficient and flexible modulation techniques, and which is considered as the central part of many wired and wireless standards. Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) achieves maximum spectral efficiency and data rates for wireless mobile communication systems. Though it offers better quality of services, high peak-to-average power ratio (PAPR) is the major issue that needs to be resolved in the MIMO-OFDM system. Earlier studies have addressed the high PAPR of OFDM system using clipping, coding, selected mapping, tone injection, peak windowing, etc. Recently, deep… More >