Ahsan Wajahat¹, Jingsha He¹, Nafei Zhu¹, Tariq Mahmood^2,3, Tanzila Saba², Amjad Rehman Khan², Faten S. Alamri^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 651-673, 2024, DOI:10.32604/cmc.2024.047530

Abstract The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This means that while attempting to… More >

Xiao Lu^1,*, Chengling Jiang¹, Zhoujun Ma¹, Haitao Li², Yuexin Liu²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 373-390, 2024, DOI:10.32604/cmc.2024.047469

Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect tiny objects. Secondly, a simplified… More >

Kazim Jawad¹, Muhammad Ahmad², Majdah Alvi³, Muhammad Bux Alvi^3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1463-1480, 2024, DOI:10.32604/cmc.2024.047466

Abstract Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify the sentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internet using various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer to communicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect. Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limited linguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompasses extracting subjective expressions in Roman Urdu… More >

Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418

Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition… More >

Yuhang Wang^1,*, Zhiqin He¹, Qinmu Wu¹, Tingsheng Lu², Yu Tang¹, Maoyun Zhu¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1377-1398, 2024, DOI:10.32604/cmc.2024.047379

Abstract This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’ diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic system with multi-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanism and the residual connection between the… More >

Zenglun Guan^1,2, Murad S. Alfarzaeai^1,3,*, Eryi Hu^1,3,*, Taqiaden Alshmeri⁴, Wang Peng³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 279-306, 2024, DOI:10.32604/cmc.2024.047159

Abstract In the coal mining industry, the gangue separation phase imposes a key challenge due to the high visual similarity between coal and gangue. Recently, separation methods have become more intelligent and efficient, using new technologies and applying different features for recognition. One such method exploits the difference in substance density, leading to excellent coal/gangue recognition. Therefore, this study uses density differences to distinguish coal from gangue by performing volume prediction on the samples. Our training samples maintain a record of 3-side images as input, volume, and weight as the ground truth for the classification. The prediction process relies on a… More >

Yan Xiang^1,2, Daofeng Li^1,2,*, Xinyi Meng^1,2, Chengfeng Dong^1,2, Guanglin Qin^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1005-1023, 2024, DOI:10.32604/cmc.2024.047143

Abstract The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasing demands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has caught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. This has resulted in a myriad of security challenges, including information leakage, malware propagation, and financial loss, among others. Consequently, developing an intrusion detection system to identify both active and potential intrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practical intrusion detection… More >

Yuejiao Wang, Zhong Ma^*, Chaojie Yang, Yu Yang, Lu Wei

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 819-836, 2024, DOI:10.32604/cmc.2024.047108

Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

Yashan Feng¹, Yafang Tian¹, Yongxin Yang¹, Yufang Zhang¹, Haiwei Guo¹, Jing’an Li^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 263-278, 2024, DOI:10.32604/cmc.2024.047004

Abstract Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibrium calculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment.… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Md. Maniruzzaman¹, Taiki Watanabe¹, Issei Jozume¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1205-1222, 2024, DOI:10.32604/cmc.2024.046954

Abstract Person identification is one of the most vital tasks for network security. People are more concerned about their security due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprints and faces have been widely used for person identification, which has the risk of information leakage as a result of reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiable pattern, which will not be reproducible falsely by capturing psychological and behavioral information of a person using vision and sensor-based techniques. In existing studies, most of the researchers used very… More >