Naglaa. F. Soliman^1,*, Samia M. Abd-Alhalem², Walid El-Shafai², Salah Eldin S. E. Abdulrahman³, N. Ismaiel³, El-Sayed M. El-Rabaie², Abeer D. Algarni¹, Fatimah Algarni⁴, Amel A. Alhussan⁵, Fathi E. Abd El-Samie^1,2

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1881-1891, 2022, DOI:10.32604/iasc.2022.017683 - 09 December 2021

Abstract Recently, deep learning has opened a remarkable research direction in the track of bioinformatics, especially for the applications that need classification and regression. With deep learning techniques, DNA sequences can be classified with high accuracy. Firstly, a DNA sequence should be represented, numerically. After that, DNA features are extracted from the numerical representations based on deep learning techniques to improve the classification process. Recently, several architectures have been developed based on deep learning for DNA sequence classification. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the default deep learning architectures used for this… More >

Vatsala Anand¹, Sheifali Gupta¹, Deepika Koundal^2,*, Shubham Mahajan³, Amit Kant Pandit³, Atef Zaguia⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3145-3160, 2022, DOI:10.32604/cmc.2022.022788 - 07 December 2021

Abstract Biomedical image analysis has been exploited considerably by recent technology involvements, carrying about a pattern shift towards ‘automation’ and ‘error free diagnosis’ classification methods with markedly improved accurate diagnosis productivity and cost effectiveness. This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images. The proposed model has four convolutional layers, two maxpool layers, one fully connected layer and three dense layers. All the convolutional layers are using the kernel size of 3 * 3 whereas the maxpool layer is using the kernel size of 2… More >

Iqra Afzal¹, Fiaz Majeed¹, Muhammad Usman Ali², Shahzada Khurram³, Akber Abid Gardezi⁴, Shafiq Ahmad⁵, Saad Aladyan⁵, Almetwally M. Mostafa⁶, Muhammad Shafiq^7,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2191-2207, 2022, DOI:10.32604/cmc.2022.022711 - 07 December 2021

Abstract In this era of electronic health, healthcare data is very important because it contains information about human survival. In addition, the Internet of Things (IoT) revolution has redefined modern healthcare systems and management by providing continuous monitoring. In this case, the data related to the heart is more important and requires proper analysis. For the analysis of heart data, Electrocardiogram (ECG) is used. In this work, machine learning techniques, such as adaptive boosting (AdaBoost) is used for detecting normal sinus rhythm, atrial fibrillation (AF), and noise in ECG signals to improve the classification accuracy. The… More >

CSS Anupama¹, T. J. Benedict Jose², Heba F. Eid³, Nojood O Aljehane⁴, Fahd N. Al-Wesabi^5,*, Marwa Obayya⁶, Anwer Mustafa Hilal⁷

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3969-3983, 2022, DOI:10.32604/cmc.2022.022701 - 07 December 2021

Abstract Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues. Biomedical image processing concepts are identical to biomedical signal processing, which includes the investigation, improvement, and exhibition of images gathered using x-ray, ultrasound, MRI, etc. At the same time, cervical cancer becomes a major reason for increased women's mortality rate. But cervical cancer is an identified at an earlier stage using regular pap smear images. In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model… More >

Anwer Mustafa Hilal¹, Imène ISSAOUI², Marwa Obayya³, Fahd N. Al-Wesabi⁴, Nadhem NEMRI⁵, Manar Ahmed Hamza^1,*, Mesfer Al Duhayyim⁶, Abu Sarwar Zamani¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3853-3867, 2022, DOI:10.32604/cmc.2022.022663 - 07 December 2021

Abstract The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified and classified at early stage itself in order to reduce health crisis. With this background, the current paper presents XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD precisely. The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, More >

Abdulqader M. Almars^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3091-3106, 2022, DOI:10.32604/cmc.2022.022609 - 07 December 2021

Abstract Depression is a common mental health issue that affects a large percentage of people all around the world. Usually, people who suffer from this mood disorder have issues such as low concentration, dementia, mood swings, and even suicide. A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods. Therefore, the analysis of social media content provides insight into individual moods, including depression. Several studies have been conducted on depression detection in English and less in Arabic. The detection of depression from Arabic social media… More >

Amna Mir¹, Umer Yasin¹, Salman Naeem Khan¹, Atifa Athar^3,*, Riffat Jabeen², Sehrish Aslam¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3733-3746, 2022, DOI:10.32604/cmc.2022.022524 - 07 December 2021

Abstract Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically.… More >

Ahmed Elaraby^1,*, Walid Hamdy², Madallah Alruwaili³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4019-4031, 2022, DOI:10.32604/cmc.2022.022161 - 07 December 2021

Abstract Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and… More >

Ala’ A. Eshmawi¹, Hesham Alhumyani², Sayed Abdel Khalek³, Rashid A. Saeed², Mahmoud Ragab⁴, Romany F. Mansour^5,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2543-2557, 2022, DOI:10.32604/cmc.2022.021833 - 07 December 2021

Abstract Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recent years. Likewise, Machine Learning (ML) approaches is one of the interesting research domains that are highly helpful and are increasingly applied in several business domains. In this background, the current research paper focuses on the design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviated as DHOA-FNN model. The proposed DHOA-FNN technique involves four different stages namely, preprocessing, feature extraction, classification, and parameter tuning. In addition to the above, More >

Ahmed R. Abas¹, Ibrahim Elhenawy¹, Mahinda Zidan^2,*, Mahmoud Othman²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2943-2961, 2022, DOI:10.32604/cmc.2022.021671 - 07 December 2021

Abstract Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users’ feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective… More >