Kanika Bhalla¹, Deepika Koundal^2,*, Surbhi Bhatia³, Mohammad Khalid Imam Rahmani⁴, Muhammad Tahir⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5503-5518, 2022, DOI:10.32604/cmc.2022.021125 - 11 October 2021

Abstract Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared (IR)/visible (VS) images. Dissimilarities in various kind of features in these images are vital to preserve in the single fused image. Hence, simultaneous preservation of both the aspects at the same time is a challenging task. However, most of the existing methods utilize the manual extraction of features; and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image. Therefore, this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images.… More >

Sagheer Abbas¹, Yousef Alhwaiti², Areej Fatima³, Muhammad A. Khan⁴, Muhammad Adnan Khan^5,*, Taher M. Ghazal^6,7, Asma Kanwal^1,8, Munir Ahmad¹, Nouh Sabri Elmitwally^2,9

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4563-4581, 2022, DOI:10.32604/cmc.2022.021102 - 11 October 2021

Abstract This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy… More >

Taher M. Ghazal^1,2, Sagheer Abbas³, Sundus Munir^3,4, M. A. Khan⁵, Munir Ahmad³, Ghassan F. Issa², Syeda Binish Zahra⁴, Muhammad Adnan Khan^6,*, Mohammad Kamrul Hasan¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5005-5019, 2022, DOI:10.32604/cmc.2022.020866 - 11 October 2021

Abstract Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems More >

K. S. Bhuvaneshwari¹, K. Venkatachalam², S. Hubálovský^3,*, P. Trojovský⁴, P. Prabu⁵

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5949-5965, 2022, DOI:10.32604/cmc.2022.020769 - 11 October 2021

Abstract With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because… More >

Mohamed Yacin Sikkandar^1,*, S. Sabarunisha Begum², Abdulaziz A. Alkathiry³, Mashhor Shlwan N. Alotaibi¹, Md Dilsad Manzar⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4279-4291, 2022, DOI:10.32604/cmc.2022.020571 - 11 October 2021

Abstract Knee Osteoarthritis (KOA) is a degenerative knee joint disease caused by ‘wear and tear’ of ligaments between the femur and tibial bones. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired movement. Identifying this space between bones through the anterior view of a knee X-ray image is solely subjective and challenging. Automatic classification of this process helps in the selection of suitable treatment processes and customized knee implants. In this research, a new automatic classification More >

Dharmendra Dangi^*, Amit Bhagat, Dheeraj Kumar Dixit

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5399-5419, 2022, DOI:10.32604/cmc.2022.020431 - 11 October 2021

Abstract There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites. Today, customers throughout the world share their points of view on all kinds of topics through these sources. The massive volume of data created by these customers makes it impossible to analyze such data manually. Therefore, an efficient and intelligent method for evaluating social media data and their divergence needs to be developed. Today, various types of equipment and techniques are available for automatically estimating the classification of sentiments. Sentiment analysis involves determining people's emotions using… More >

Saadullah Farooq Abbasi^1,2, Harun Jamil³, Wei Chen^2,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4619-4633, 2022, DOI:10.32604/cmc.2022.020318 - 11 October 2021

Abstract Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs More >

Pijush Kanti Dutta Pramanik¹, Nilanjan Sinhababu², Anand Nayyar^3,4,*, Mehedi Masud⁵, Prasenjit Choudhury¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5199-5212, 2022, DOI:10.32604/cmc.2022.019630 - 11 October 2021

Abstract In mobile crowd computing (MCC), people’s smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today’s SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users’ mobility poses a serious issue in the QoS of MCC. To… More >

Muneeb Ur Rehman¹, Fawad Ahmed¹, Muhammad Attique Khan², Usman Tariq³, Faisal Abdulaziz Alfouzan⁴, Nouf M. Alzahrani⁵, Jawad Ahmad^6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4675-4690, 2022, DOI:10.32604/cmc.2022.019586 - 11 October 2021

Abstract Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features More >

Javaria Amin¹ , Muhammad Almas Anjum² , Abida Sharif³ , Mudassar Raza⁴ , Seifedine Kadry⁵, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6023-6039, 2022, DOI:10.32604/cmc.2022.019115 - 11 October 2021

Abstract

Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected

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