Jahanzaib Latif¹, Shanshan Tu^1,*, Chuangbai Xiao¹, Anas Bilal², Sadaqat Ur Rehman³, Zohaib Ahmad⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152

Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed… More >

Wei Wu¹, Xiulai Li², Zhuhua Hu¹, Xiaozhang Liu^3,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 489-498, 2023, DOI:10.32604/cmc.2023.039929

Abstract In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data… More >

Muhammad Rashid^1,*, Omar S. Sonbul¹, Muhammad Arif², Furqan Aziz Qureshi³, Saud. S. Alotaibi⁴, Mohammad H. Sinky¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1009-1025, 2023, DOI:10.32604/cmc.2023.039901

Abstract This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography (ECC) and PRESENT for cryptographic applications. The features of the proposed work are (i) computation of only the point multiplication operation of ECC over for a 163-bit key generation, (ii) execution of only the variant of an 80-bit PRESENT block cipher for data encryption & decryption and (iii) execution of point multiplication operation (ECC algorithm) along with the data encryption and decryption (PRESENT algorithm). To establish an area overhead for the flexible design, dedicated hardware architectures of ECC and PRESENT are implemented in the first step, and a sum of… More >

Shah Faisal¹, Kashif Javed¹, Sara Ali¹, Areej Alasiry², Mehrez Marzougui², Muhammad Attique Khan^3,*, Jae-Hyuk Cha^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781

Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using… More >

Qurat-ul-Ain Arshad¹, Mudassar Raza¹, Wazir Zada Khan², Ayesha Siddiqa², Abdul Muiz², Muhammad Attique Khan^3,*, Usman Tariq⁴, Taerang Kim⁵, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752

Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous… More >

Weifei Wang¹, Jinguo Li^1,*, Na Zhao², Min Liu¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 471-487, 2023, DOI:10.32604/cmc.2023.039733

Abstract With the advancement of network communication technology, network traffic shows explosive growth. Consequently, network attacks occur frequently. Network intrusion detection systems are still the primary means of detecting attacks. However, two challenges continue to stymie the development of a viable network intrusion detection system: imbalanced training data and new undiscovered attacks. Therefore, this study proposes a unique deep learning-based intrusion detection method. We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data. Then the original data is fed into the triplet network… More >

Sharad Saxena¹, Shailendra Mishra^2,*, Mohammed Baljon^2,*, Shamiksha Mishra³, Sunil Kumar Sharma², Prakhar Goel¹, Shubham Gupta¹, Vinay Kishore¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1027-1045, 2023, DOI:10.32604/cmc.2023.039677

Abstract In recent years, the growth of female employees in the commercial market and industries has increased. As a result, some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety. The exponential increase in assaults and attacks on women, on the other hand, is posing a threat to women’s growth, development, and security. At the time of the attack, it appears the women were immobilized and needed immediate support. Only self-defense isn’t sufficient against abuse; a new technological solution is desired and can be used as quickly as hitting a switch or button.… More >

A. Manju¹, R. kaladevi², Shanmugasundaram Hariharan³, Shih-Yu Chen^4,5,*, Vinay Kukreja⁶, Pradip Kumar Sharma⁷, Fayez Alqahtani⁸, Amr Tolba⁹, Jin Wang¹⁰

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 993-1007, 2023, DOI:10.32604/cmc.2023.039567

Abstract The medical community has more concern on lung cancer analysis. Medical experts’ physical segmentation of lung cancers is time-consuming and needs to be automated. The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques. Computer-Aided Diagnostic (CAD) system aids in the diagnosis and shortens the time necessary to detect the tumor detected. The application of Deep Neural Networks (DNN) has also been exhibited as an excellent and effective method in classification and segmentation tasks. This research aims to separate lung cancers from images of Magnetic Resonance Imaging… More >

Yuna Han¹, Hangbae Chang^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 221-237, 2023, DOI:10.32604/cmc.2023.039463

Abstract Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains… More >

Zunliang Chen^1,2, Chengxu Huang^1,2, Lucheng Duan^1,2, Baohua Tan^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1085-1102, 2023, DOI:10.32604/cmc.2023.039451

Abstract In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce… More >