Abdul Haseeb¹, Muhammad Attique Khan^2,*, Majed Alhaisoni³, Ghadah Aldehim⁴, Leila Jamel⁴, Usman Tariq⁵, Taerang Kim⁶, Jae-Hyuk Cha⁶

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3895-3920, 2023, DOI:10.32604/cmc.2023.045244

Abstract Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks.… More >

Wei Zhang¹, Ping He^2,*, Ting Li², Fan Yang¹, Ying Liu³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1893-1910, 2023, DOI:10.32604/cmc.2023.044253

Abstract Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these… More >

Muhammad Attique Khan^1,2, Reham R. Mostafa³, Yu-Dong Zhang², Jamel Baili⁴, Majed Alhaisoni⁵, Usman Tariq⁶, Junaid Ali Khan¹, Ye Jin Kim⁷, Jaehyuk Cha^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838

Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the… More >

Goma Tshivetta Christian Fersein Jorvialom^1,2, Lord Amoah^1,2,*

Journal of Quantum Computing, Vol.4, No.3, pp. 183-197, 2022, DOI:10.32604/jqc.2022.039913

Abstract Prior versions of reversible data hiding with contrast enhancement (RDHCE) algorithms strongly focused on enhancing the contrast of grayscale images. However, RDHCE has recently witnessed a rise in contrast enhancement algorithms concentrating on color images. This paper implies a method for color images that uses the RGB (red, green, and blue) color model and is based on bi-histogram shifting and image adjustment. Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final… More >

Jieyu An^1,*, Wan Mohd Nazmee Wan Zainon¹, Binfen Ding²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1673-1689, 2023, DOI:10.32604/iasc.2023.039763

Abstract Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes, such as text and image, to accurately assess sentiment. However, conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities. This limitation is attributed to their training on unimodal data, and necessitates the use of complex fusion mechanisms for sentiment analysis. In this study, we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method. Our approach harnesses… More >

Deepthi K. Oommen^*, J. Arunnehru

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

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 >

Muhammad Jan^1,*, Muhammad Anwar-Ul-Haq², Talha Javed³, Sadam Hussain^4,*, Ilyas Ahmad⁵, Muhammad Ashraf Sumrah⁶, Javed Iqbal⁷, Babar Hussain Babar⁸, Aqsa Hafeez⁹, Muhammad Aslam⁵, Muhammad Tahir Akbar¹⁰, Marjan Aziz⁶, Khadiga Alharbi¹¹, Izhar Ullah¹²

Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1361-1375, 2023, DOI:10.32604/phyton.2023.026620

Abstract Abiotic stresses are among the major limiting factors for plant growth and crop productivity. Among these, salinity is one of the major risk factors for plant growth and development in arid to semi-arid regions. Cultivation of salt tolerant crop genotypes is one of the imperative approaches to meet the food demand for increasing population. The current experiment was carried out to access the performance of different rice genotypes under salinity stress and Zinc (Zn) sources. Four rice genotypes were grown in a pot experiment and were exposed to salinity stress (7 dS m⁻¹), and Zn (15… More >

Xiangwei Chen¹, Zhijin Zhao^1,2,*, Xueyi Ye¹, Shilian Zheng², Caiyi Lou², Xiaoniu Yang²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1385-1400, 2023, DOI:10.32604/csse.2023.034543

Abstract Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms. However, there is no unsupervised interference signals recognition algorithm at present. In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed. Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning. In the second phase, the original dataset’s k-nearest… More >

Pengpeng Jian¹, Fucheng Guo^1,*, Yanli Wang², Yang Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1707-1728, 2023, DOI:10.32604/cmes.2023.023243

Abstract This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data. A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems. Existing methods either focus on single-modal or multimodal problems, and they cannot fit each other. A general geometry problem solver should obviously be able to process various modal problems at the same time. In this paper, a shared feature-learning model of multimodal data is adopted to learn the unified feature representation More >

Samra Rehman¹, Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Fayadh Alenezi³, Abdullah Alqahtani⁴, Khean Vesal⁵, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1179-1194, 2023, DOI:10.32604/cmc.2023.035324

Abstract Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation. The authors present computer vision techniques for detecting and classifying fruit leaf diseases. Examples of computer vision techniques are preprocessing original images for visualization of infected regions, feature extraction from raw or segmented images, feature fusion, feature selection, and classification. The following are the major challenges identified by researchers in the literature: (i) low-contrast infected regions extract irrelevant and redundant information, which misleads classification accuracy; (ii) irrelevant and redundant information may increase computational time and reduce the designed… More >