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 >

Saud Yonbawi¹, Sultan Alahmari², T. Satyanarayana murthy³, Ravuri Daniel⁴, E. Laxmi Lydia⁵, Mohamad Khairi Ishak⁶, Hend Khalid Alkahtani^7,*, Ayman Aljarbouh⁸, Samih M. Mostafa⁹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3847-3864, 2023, DOI:10.32604/csse.2023.036552

Abstract Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification… More >

Xiaorui Zhang^1,2,3,*, Chunlin Yuan¹, Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4273-4291, 2023, DOI:10.32604/cmc.2023.036975

Abstract The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this study designs a multilayer attentional… More >

Majdy M. Eltahir¹, Adil Yousif², Fadwa Alrowais³, Mohamed K. Nour⁴, Radwa Marzouk⁵, Hatim Dafaalla⁶, Asma Abbas Hassan Elnour⁶, Amira Sayed A. Aziz⁷, Manar Ahmed Hamza^8,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.034037

Abstract The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high,… More >

Zhiguo Chen^1,2,*, Jiabing Cao^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4445-4465, 2023, DOI:10.32604/cmc.2023.038639

Abstract The Corona Virus Disease 2019 (COVID-19) effect has made telecommuting and remote learning the norm. The growing number of Internet-connected devices provides cyber attackers with more attack vectors. The development of malware by criminals also incorporates a number of sophisticated obfuscation techniques, making it difficult to classify and detect malware using conventional approaches. Therefore, this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning (VMCTE). VMCTE has a strong anti-interference ability. Even if malware uses obfuscation, fuzzing, encryption, and other techniques to evade detection, it can be accurately classified into its corresponding malware family. Unlike traditional… More >

Fadwa Alrowais¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi^4,*, Anwer Mustafa Hilal⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3325-3342, 2023, DOI:10.32604/iasc.2023.036354

Abstract Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to… More >

Samah Alhazmi^1,*, Shahnawaz Khan², Mohammad Haider Syed¹

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3487-3499, 2023, DOI:10.32604/iasc.2023.036297

Abstract Quality education is one of the primary objectives of any nation-building strategy and is one of the seventeen Sustainable Development Goals (SDGs) by the United Nations. To provide quality education, delivering top-quality content is not enough. However, understanding the learners’ emotions during the learning process is equally important. However, most of this research work uses general data accessed from Twitter or other publicly available databases. These databases are generally not an ideal representation of the actual learning process and the learners’ sentiments about the learning process. This research has collected real data from the learners, mainly undergraduate university students of… More >

Mohamed Ezz, Waad Alanazi, Ayman Mohamed Mostafa^*, Eslam Hamouda, Murtada K. Elbashir, Meshrif Alruily

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2299-2317, 2023, DOI:10.32604/csse.2023.036567

Abstract Palmprint identification has been conducted over the last two decades in many biometric systems. High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues. This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication. The proposed model has two stages of learning; the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model. The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity. The proposed model achieves robust and reliable end-to-end Palmprint… More >

Ping-Huan Kuo^1,2, Sing-Yan Chen¹, Her-Terng Yau^1,2,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1577-1596, 2023, DOI:10.32604/csse.2023.036292

Abstract Cars are regarded as an indispensable means of transportation in Taiwan. Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded. In this study, a price prediction system for used BMW cars was developed. Nine parameters of used cars, including their model, registration year, and transmission style, were analyzed. The data obtained were then divided into three subsets. The first subset was used to compare the results of each algorithm. The predicted values produced by the two algorithms with the most satisfactory results were used as the input… More >

José Escorcia-Gutierrez^1,*, Roosvel Soto-Diaz², Natasha Madera³, Carlos Soto³, Francisco Burgos-Florez², Alexander Rodríguez⁴, Romany F. Mansour⁵

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1337-1353, 2023, DOI:10.32604/csse.2023.035253

Abstract Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification… More >