Shayla Islam¹, Anil Kumar Budati^1,*, Mohammad Kamrul Hasan², Saoucene Mahfoudh³, Syed Bilal Hussian Shah³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 813-825, 2023, DOI:10.32604/cmes.2023.027595

Abstract In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promising approach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio (SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambient Radio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum for the transmission of data without loss or without collision at a specific time. In this paper, the authors proposed a novel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing… More >

Moody Alhanaya, Khalil Hamdi Ateyeh Al-Shqeerat^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3709-3724, 2023, DOI:10.32604/iasc.2023.036856

Abstract The increasing number of security holes in the Internet of Things (IoT) networks creates a question about the reliability of existing network intrusion detection systems. This problem has led to the developing of a research area focused on improving network-based intrusion detection system (NIDS) technologies. According to the analysis of different businesses, most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques. However, these techniques are not suitable for every type of network. In light of this, whether the optimal algorithm and feature reduction techniques can be generalized across various datasets… More >

Ierin Babu^1,*, R. MathuSoothana², S. Kumar²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3621-3636, 2023, DOI:10.32604/iasc.2023.033791

Abstract Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately… More >

Xueyu Zhao^*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.7, pp. 1897-1905, 2023, DOI:10.32604/fdmp.2023.025247

Abstract A method for the treatment of hazardous waste drilling fluids, potentially leading to environmental pollution, is considered. The waste drilling fluid is treated with an inorganic flocculant, an organic flocculant, and a pH regulator. The profile control agent consists of partially hydrolyzed polyacrylamide, formaldehyde, hexamethylenetetramine, resorcinol, phenol, and the treated waste drilling fluid itself. For a waste drilling fluid concentration of 2500 mg/L, the gelling time of the profile control agent is 25 h, and the gelling strength is 32,000 mPa.s. Compared with the profile control agent prepared by recirculated water under the same conditions, the present profile control agent displays better… More >

Muhammad Irfan¹, Ch. Anwar Ul Hassan², Faisal Althobiani³, Nasir Ayub^4,*, Raja Jalees Ul Hussen Khan⁵, Emad Ismat Ghandourah⁶, Majid A. Almas⁷, Saleh Mohammed Ghonaim³, V. R. Shamji³, Saifur Rahman¹

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1723-1740, 2023, DOI:10.32604/csse.2023.035141

Abstract The smart grid has enabled users to control their home energy more effectively and efficiently. A home energy management system (HEM) is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy. Here, we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm (GWA) and Harmony Search Algorithms (HSA). Moreover, a fusion initiated on HSA and GWA operators is used to optimize energy intake. Furthermore, many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge. Hybridization has proven beneficial in achieving numerous… More >

M. Amirthavalli^*, S. Chithra, R. Yugha

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3079-3095, 2023, DOI:10.32604/csse.2023.032486

Abstract Internet of Things (IoT) enables devices to get connected to the internet. Once they are connected, they behave as smart devices thereby releasing sensitive data periodically. There is a necessity to preserve the confidentiality and integrity of this data during transmission in public communication channels and also permitting only legitimate users to access their data A key challenge of smart networks is to establish a secure end-to-end data communication architecture by addressing the security vulnerabilities of data users and smart devices. The objective of this research work is to create a framework encompassing Ciphertext policy Attribute-based Encryption scheme using block… More >

L. Amudha^1,*, R. PushpaLakshmi²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2299-2312, 2023, DOI:10.32604/csse.2023.031169

Abstract Automated live video stream analytics has been extensively researched in recent times. Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame. We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score. The second stage uses the least square method for optimal anomaly score generation. The third stage adopts award-based reinforcement learning to update the model. The proposed Hybrid Ensemble… More >

Xianli Gong¹, Chongwen Huang^2,3,4, Xinwei Yue⁵, Zhaohui Yang^2,4,6, Feng Liu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2531-2555, 2023, DOI:10.32604/cmes.2023.024940

Abstract In this paper, we consider a downlink non-orthogonal multiple access (NOMA) network assisted by two reconfigurable intelligent surfaces (RISs) over Rician fading channels, in which each user communicates with the base station by the virtue of a RIS to enhance the reliability of the received signal. To evaluate the system performance of our proposed RIS-NOMA network, we first derive the exact and asymptotic expressions for the outage probability and ergodic rate of two users. Then, we derive the exact and asymptotic upper bound expressions for the ergodic rate of the nearby user. Based on asymptotic analytical results, the diversity orders… More >

Kolthoum Missaoui^1,2,*, Nader Frikha^2,3, Abdelhamid Kheiri¹, Slimane Gabsi^2,3, Mohammed El Ganaoui⁴

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.4, pp. 1067-1081, 2023, DOI:10.32604/fdmp.2022.021969

Abstract A study is conducted on the performances of a solar powered continuous-adsorption refrigerator considering two particular days as references cases, namely, the summer solstice (June 21st) and the autumn equinox (September 21st). The cooling capacity, system performance coefficient and the daily rate of available cooling energy are assessed. The main goal is to compare the performances of a solar adsorption chiller equipped with a hot water tank (HWT) with an equivalent system relying on solar collectors with no heat storage module. The daily cooling rates for the solar refrigerator are found to be 102.4 kWh and 74.3 kWh, respectively, on… More >

Hyun-Sam Shin¹, Jun-Ki Hong^2,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 235-248, 2023, DOI:10.32604/iasc.2023.033082

Abstract Recently, artificial-intelligence-based automatic customer response system has been widely used instead of customer service representatives. Therefore, it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly. Therefore, we analyzed the performance of the emotion recognition (ER) accuracy as a function of the simulation time using the proposed chunk-based speech ER (CSER) model. The proposed CSER model divides voice signals into 3-s long chunks to efficiently recognize characteristically inherent emotions in the customer’s voice. We evaluated the performance of the ER of voice signal chunks by applying four RNN techniques—long… More >