Kaiyue Wang¹, Jian Gao^1,2,*, Xinyan Lei¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 619-638, 2023, DOI:10.32604/iasc.2023.036701

Abstract Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that combines Transformer and 1D-CNN for… More >

Arfa Siddiqu, Abdul Basit^*, Waheed Noor, Muhammad Asfandyar Khan, M. Saeed H. Kakar, Azam Khan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 875-885, 2023, DOI:10.32604/iasc.2023.036426

Abstract The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages. Moreover, the absence of a standard publicly available dataset for several low-resource languages, including the Pashto language remained a hurdle in the advancement of language processing. Realizing that, a clean dataset is the fundamental and core requirement of character recognition, this research begins with dataset generation and aims at a system capable of complete language understanding. Keeping in view the complete and full autonomous recognition of the cursive… More >

R. D. Dhaniya¹, K. M. Umamaheswari^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1129-1143, 2023, DOI:10.32604/iasc.2023.035905

Abstract Current revelations in medical imaging have seen a slew of computer-aided diagnostic (CAD) tools for radiologists developed. Brain tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging (MRI). In this work, we reported on new observations based on binary brain tumor categorization using HYBRID CNN-LSTM. Initially, the collected image is pre-processed and augmented using the following steps such as rotation, cropping, zooming, CLAHE (Contrast Limited Adaptive Histogram Equalization), and Random Rotation with panoramic stitching (RRPS). Then, a method called particle swarm optimization (PSO) is used to segment tumor regions in an MR image. After… More >

Kha Tu Huynh¹, Thi Phuong Linh Le¹, Muhammad Arif², Thien Khai Tran^3,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 401-418, 2023, DOI:10.32604/iasc.2023.036981

Abstract To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent, the traffic signs detection system has become one of the necessary topics in recent years and in the future. The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image. To accomplish this goal, the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional Neural Networks (R-CNN) implementation for identifying and extracting signs in panoramic images. Data augmentation and… More >

Zainab Ali Abbood^1,2,*, Doğu Çağdaş Atilla^3,4, Çağatay Aydin⁵

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 269-281, 2023, DOI:10.32604/iasc.2023.035276

Abstract Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to MANET’s potential to provide small-cost solutions for real-world contact challenges, decentralized management, and restricted bandwidth, MANETs are more vulnerable to security threats. When protecting MANETs from attack, encryption and authentication schemes have their limits. However, deep learning (DL) approaches in intrusion detection systems (IDS) can adapt to the changing environment of MANETs and allow a system to make intrusion decisions… More >

V. Banupriya^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5597-5613, 2023, DOI:10.32604/cmc.2023.038696

Abstract In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly,… More >

Sobia Nawaz¹, Sidra Rasheed², Wania Sami³, Lal Hussain^4,5,*, Amjad Aldweesh^6,*, Elsayed Tag eldin⁷, Umair Ahmad Salaria^8,9, Mohammad Shahbaz Khan¹⁰

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543

Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The… More >

Ibrahim Aliyu¹, Tai-Won Um², Sang-Joon Lee³, Chang Gyoon Lim^4,*, Jinsul Kim^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5947-5964, 2023, DOI:10.32604/cmc.2023.037202

Abstract In the quest to minimize energy waste, the energy performance of buildings (EPB) has been a focus because building appliances, such as heating, ventilation, and air conditioning, consume the highest energy. Therefore, effective design and planning for estimating heating load (HL) and cooling load (CL) for energy saving have become paramount. In this vein, efforts have been made to predict the HL and CL using a univariate approach. However, this approach necessitates two models for learning HL and CL, requiring more computational time. Moreover, the one-dimensional (1D) convolutional neural network (CNN) has gained popularity due to its nominal computational complexity,… More >

Yan Du^1,2, Shibin Zhang^1,2,*, Guogen Wan^1,2, Daohua Zhou³, Jiazhong Lu^1,2, Yuanyuan Huang^1,2, Xiaoman Cheng⁴, Yi Zhang⁴, Peilin He⁵

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5743-5759, 2023, DOI:10.32604/cmc.2023.035919

Abstract Nowadays, industrial control system (ICS) has begun to integrate with the Internet. While the Internet has brought convenience to ICS, it has also brought severe security concerns. Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts. They are not aimed at the original network data, nor can they capture the potential characteristics of network packets. Therefore, the following improvements were made in this study: (1) A dataset that can be used to evaluate anomaly detection algorithms is produced, which provides raw network data. (2) A request response-based convolutional neural… More >

Adnan Hussain¹, Sareer Ul Amin², Muhammad Fayaz³, Sanghyun Seo^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3509-3525, 2023, DOI:10.32604/csse.2023.037258

Abstract Hand Gesture Recognition (HGR) is a promising research area with an extensive range of applications, such as surgery, video game techniques, and sign language translation, where sign language is a complicated structured form of hand gestures. The fundamental building blocks of structured expressions in sign language are the arrangement of the fingers, the orientation of the hand, and the hand’s position concerning the body. The importance of HGR has increased due to the increasing number of touchless applications and the rapid growth of the hearing-impaired population. Therefore, real-time HGR is one of the most effective interaction methods between computers and… More >