Huixiang Xu^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2309-2335, 2024, DOI:10.32604/cmc.2024.046253

Abstract The Internet of Things (IoT) has revolutionized how we interact with and gather data from our surrounding environment. IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights. The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation and connectivity. These IoT devices, equipped with many sensors and actuators, continuously produce vast volumes of data. However, the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges. However, transmitting all this data to a… More >

Ehab Bahaudien Ashary¹, Sahar Jambi², Rehab B. Ashari², Mahmoud Ragab^3,4,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2953-2969, 2023, DOI:10.32604/csse.2023.041523

Abstract Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste, enhancing waste management and reducing… More >

Nida Aslam^1,*, Irfan Ullah Khan², Salma Abdulrahman Bader², Aisha Alansari³, Lama Abdullah Alaqeel², Razan Mohammed Khormy², Zahra Abdultawab AlKubaish², Tariq Hussain^4,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3167-3188, 2023, DOI:10.32604/cmc.2023.039721

Abstract One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to classify Android applications into malware… More >

Huanhua Liu, Wei Wang^*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

T. Satyanarayana Murthy¹, Kollati Vijaya Kumar², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gi-Cheon Park⁵, Hyoung-Kyu Song⁶, Gyanendra Prasad Joshi⁷, Hyeonjoon Moon^7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1633-1650, 2023, DOI:10.32604/csse.2023.034769

Abstract Fruit classification utilizing a deep convolutional neural network (CNN) is the most promising application in personal computer vision (CV). Profound learning-related characterization made it possible to recognize fruits from pictures. But, due to the similarity and complexity, fruit recognition becomes an issue for the stacked fruits on a weighing scale. Recently, Machine Learning (ML) methods have been used in fruit farming and agriculture and brought great convenience to human life. An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts. CNN’s (convolutional neural networks) have attained incredible outcomes in image classifiers… More >

C. S. S. Anupama¹, Rafina Zakieva², Afanasiy Sergin³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Chomyong Kim⁸, Yunyoung Nam^8,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321

Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning… More >

Amjed Al Fahoum^*, Ala’a Zyout, Hiam Alquran, Isam Abu-Qasmieh

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1173-1193, 2023, DOI:10.32604/cmc.2023.038304

Abstract Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.… More >

Hadeel Alsolai¹, Mohammed Rizwanullah^2,*, Mashael Maashi³, Mahmoud Othman⁴, Amani A. Alneil², Amgad Atta Abdelmageed²

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936

Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the presented GBODL-ASC technique, three major… More >

Mohamed Ibrahim Waly^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3159-3174, 2023, DOI:10.32604/csse.2023.035900

Abstract Recently, computer assisted diagnosis (CAD) model creation has become more dependent on medical picture categorization. It is often used to identify several conditions, including brain disorders, diabetic retinopathy, and skin cancer. Most traditional CAD methods relied on textures, colours, and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain notions. Recent deep learning (DL) models have been published, providing a practical way to develop models specifically for classifying input medical pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL) method for classifying medical images facilitated by deep transfer… More >

Jae-Seok Jang¹, Hyuk-Yoon Kwon^2,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3471-3489, 2023, DOI:10.32604/csse.2023.035570

Abstract Question-answering (QA) models find answers to a given question. The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets. In this paper, we deal with the QA pair matching approach in QA models, which finds the most relevant question and its recommended answer for a given question. Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies. In contrast, we aim to automatically find the category to which the question belongs by employing the text classification model and… More >