Awais Mahmood^1,*, Muhammad Imran², Aun Irtaza³, Qammar Abbas⁴, Habib Dhahri^1,5, Esam Mohammed Asem Othman¹, Arif Jamal Malik⁶, Aaqif Afzaal Abbasi⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 963-979, 2022, DOI:10.32604/cmc.2022.019291 - 07 September 2021

Abstract Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that… More >

Mavra Mehmood¹, Talha Javed², Jamel Nebhen³, Sidra Abbas^2,*, Rabia Abid¹, Giridhar Reddy Bojja⁴, Muhammad Rizwan¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 91-107, 2022, DOI:10.32604/cmc.2022.019127 - 07 September 2021

Abstract Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the More >

Arshia Arif¹, M. Jawad Khan^1,2,*, Kashif Javed¹, Hasan Sajid^1,2, Saddaf Rubab¹, Noman Naseer³, Talha Irfan Khan⁴

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 535-555, 2022, DOI:10.32604/cmc.2022.018318 - 07 September 2021

Abstract For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been… More >

Maryam Zaffar^1,2,*, Manzoor Ahmed Hashmani¹, Raja Habib², KS Quraishi³, Muhammad Irfan⁴, Samar Alqhtani⁵, Mohammed Hamdi⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1893-1920, 2022, DOI:10.32604/cmc.2022.018295 - 07 September 2021

Abstract Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions, for the improvement of quality of education and to meet the dynamic needs of society. The selection of features for student's performance prediction not only plays significant role in increasing prediction accuracy, but also helps in building the strategic plans for the improvement of students’ academic performance. There are different feature selection algorithms for predicting the performance of students, however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection… More >

Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 661-675, 2022, DOI:10.32604/iasc.2022.020132 - 03 September 2021

Abstract Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, More >

M. Bhoopathi^1,*, P. Palanivel²

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 143-159, 2022, DOI:10.32604/iasc.2022.017705 - 03 September 2021

Abstract At present, the restructured electricity market has been a prominent research area and attracted attention. The motivation of the restructuring in the power system is to introduce the competition at various levels and to generate a correct economic signal to reduce the generation cost. As a result, it is required to have an effective price scheme to deliver useful information about the power. The pricing mechanism is dependent on the demand at the load level, the generator bids, and the limits of the transmission network. To address the congestion charges, Locational Marginal Pricing (LMP) is… More >

R. Muthaiyan^1,*, Dr M. Malleswaran²

Computer Systems Science and Engineering, Vol.40, No.1, pp. 299-312, 2022, DOI:10.32604/csse.2022.018034 - 26 August 2021

Abstract In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of… More >

Qusai Y. Shambour^*, Abdelrahman H. Hussein, Qasem M. Kharma, Mosleh M. Abualhaj

Computer Systems Science and Engineering, Vol.40, No.1, pp. 113-125, 2022, DOI:10.32604/csse.2022.017221 - 26 August 2021

Abstract Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes More >

Alejandro Ibarra-Sánchez, Juan Enrique Rodríguez-Pérez, Aureliano Peña-Lomelí, Clemente Villanueva-Verduzco, Jaime Sahagún-Castellanos^*

Phyton-International Journal of Experimental Botany, Vol.91, No.1, pp. 33-43, 2022, DOI:10.32604/phyton.2022.016136 - 16 August 2021

Abstract Synthetic varieties (SVs) are populations generated by randomly mating their parents. They are a good alternative for low-input farmers who grow onions, maize, and other allogamous crops since the seed produced by a SV does not change from one generation to the next. Although SV progenitors are commonly pure lines, in this case a synthetic (Syn_TC) whose parents are t three-way line crosses, a very common type of maize hybrid grown in Mexico, is studied. The aim was to develop a general and exact equation for the inbreeding coefficient of a Syn_TC Syn_TC because of … More >

Linbei Wang¹ , Zaoyu Tao¹, Lina Wang^2,*, Yongjun Ren³

Journal of Quantum Computing, Vol.3, No.3, pp. 107-118, 2021, DOI:10.32604/jqc.2021.016857 - 21 December 2021

Abstract With the accelerating process of social informatization, our personal information security and Internet sites, etc., have been facing a series of threats and challenges. Recently, well-developed neural network has seen great advancement in natural language processing and computer vision, which is also adopted in intrusion detection. In this research, a hybrid model integrating MultiScale Convolutional Neural Network and Long Short-term Memory Network (MSCNN-LSTM) is designed to conduct the intrusion detection. Multi-Scale Convolutional Neural Network (MSCNN) is used to extract the spatial characteristics of data sets. And Long Short-term Memory Network (LSTM) is responsible for processing More >