Jungpil Shin^1,*, Md. Al Mehedi Hasan², Abu Saleh Musa Miah¹, Kota Suzuki¹, Koki Hirooka¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2605-2625, 2024, DOI:10.32604/cmes.2023.046334

Abstract Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities. In Japan, approximately 360,000 individuals with hearing and speech disabilities rely on Japanese Sign Language (JSL) for communication. However, existing JSL recognition systems have faced significant performance limitations due to inherent complexities. In response to these challenges, we present a novel JSL recognition system that employs a strategic fusion approach, combining joint skeleton-based handcrafted features and pixel-based deep learning features. Our system incorporates two distinct streams: the first stream extracts crucial handcrafted features, emphasizing the capture of hand and body movements within JSL gestures. Simultaneously,… More >

Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697

Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

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 >

Sherly Alphonse^*, Harshit Verma

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2067-2086, 2024, DOI:10.32604/cmc.2024.046070

Abstract Although lots of research has been done in recognizing facial expressions, there is still a need to increase the accuracy of facial expression recognition, particularly under uncontrolled situations. The use of Local Directional Patterns (LDP), which has good characteristics for emotion detection has yielded encouraging results. An innovative end-to-end learnable High Response-based Local Directional Pattern (HR-LDP) network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work. By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions, this network considerably minimizes the number of network… More >

Muhammad Rizwan^1,2, Muhammad Faheem Mushtaq¹, Maryam Rafiq², Arif Mehmood³, Isabel de la Torre Diez⁴, Monica Gracia Villar^5,6,7, Helena Garay^5,8,9, Imran Ashraf^10,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2047-2066, 2024, DOI:10.32604/cmc.2024.037347

Abstract Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter… More >

Guohui Song^1,2, Yongbin Wang^1,*, Jianfei Li¹, Hongbin Hu¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 275-295, 2023, DOI:10.32604/iasc.2023.041873

Abstract Recommending high-quality news to users is vital in improving user stickiness and news platforms’ reputation. However, existing news quality evaluation methods, such as clickbait detection and popularity prediction, are challenging to reflect news quality comprehensively and concisely. This paper defines news quality as the ability of news articles to elicit clicks and comments from users, which represents whether the news article can attract widespread attention and discussion. Based on the above definition, this paper first presents a straightforward method to measure news quality based on the comments and clicks of news and defines four news quality indicators. Then, the dataset… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 139-151, 2023, DOI:10.32604/iasc.2023.033940

Abstract Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires a wide range of body… More >

Li Li^*, Youran Kong, Qing Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 551-567, 2024, DOI:10.32604/cmc.2023.045512

Abstract With the growth of the Internet, more and more business is being done online, for example, online offices, online education and so on. While this makes people’s lives more convenient, it also increases the risk of the network being attacked by malicious code. Therefore, it is important to identify malicious codes on computer systems efficiently. However, most of the existing malicious code detection methods have two problems: (1) The ability of the model to extract features is weak, resulting in poor model performance. (2) The large scale of model data leads to difficulties deploying on devices with limited resources. Therefore,… More >

Zahid Farooq Khan¹, Muhammad Ramzan^1,*, Mudassar Raza¹, Muhammad Attique Khan^2,3, Khalid Iqbal⁴, Taerang Kim⁵, Jae-Hyuk Cha⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491

Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization utilizes the binary dragonfly algorithm… More >