Rabia Abid¹, Muhammad Rizwan², Abdulatif Alabdulatif^3,*, Abdullah Alnajim⁴, Meznah Alamro⁵, Mourade Azrour⁶

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the security of e-healthcare systems. The… More >

Xiaoying Chen^1,*, Jie Kang¹, Cong Hu²

Journal of New Media, Vol.6, pp. 1-16, 2024, DOI:10.32604/jnm.2024.045833

Abstract With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements.… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

Nasser Alshammari¹, Shumaila Shahzadi², Saad Awadh Alanazi^1,*, Shahid Naseem³, Muhammad Anwar³, Madallah Alruwaili⁴, Muhammad Rizwan Abid⁵, Omar Alruwaili⁴, Ahmed Alsayat¹, Fahad Ahmad^6,7

Computer Systems Science and Engineering, Vol.48, No.2, pp. 363-394, 2024, DOI:10.32604/csse.2023.040721

Abstract Software Defined Network (SDN) and Network Function Virtualization (NFV) technology promote several benefits to network operators, including reduced maintenance costs, increased network operational performance, simplified network lifecycle, and policies management. Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration (NFV MANO), and malicious attacks in different scenarios disrupt the NFV Orchestrator (NFVO) and Virtualized Infrastructure Manager (VIM) lifecycle management related to network services or individual Virtualized Network Function (VNF). This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order… More >

Yizhao Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270

Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related to impulsive ground-shaking. Thirdly, a… More >

Yanqiong Zhang, Xianwei Jiang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2399-2450, 2024, DOI:10.32604/cmes.2023.045731

Abstract Sign language, a visual-gestural language used by the deaf and hard-of-hearing community, plays a crucial role in facilitating communication and promoting inclusivity. Sign language recognition (SLR), the process of automatically recognizing and interpreting sign language gestures, has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world. The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR. This paper presents a comprehensive and up-to-date analysis of the advancements, challenges, and opportunities in deep learning-based sign language recognition, focusing on the… More >

Xinlong Diao, Xiao Wang^*, Junkang Qin, Qinmu Wu, Zhiqin He, Xinghong Fan

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2617-2665, 2024, DOI:10.32604/cmc.2024.047377

Abstract In recent years, Artificial Intelligence (AI) has revolutionized people’s lives. AI has long made breakthrough progress in the field of surgery. However, the research on the application of AI in orthopedics is still in the exploratory stage. The paper first introduces the background of AI and orthopedic diseases, addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases, draws out the advantages of deep learning and machine learning in image detection, and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years, describing the contributions, strengths and weaknesses,… More >

Fatma Harby¹, Mansor Alohali², Adel Thaljaoui^2,3,*, Amira Samy Talaat⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2689-2719, 2024, DOI:10.32604/cmc.2024.046623

Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture the periodic nature of audio… More >

Ali Hamid Farea^1,*, Omar H. Alhazmi¹, Kerem Kucuk²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1525-1545, 2024, DOI:10.32604/cmc.2023.045794

Abstract While emerging technologies such as the Internet of Things (IoT) have many benefits, they also pose considerable security challenges that require innovative solutions, including those based on artificial intelligence (AI), given that these techniques are increasingly being used by malicious actors to compromise IoT systems. Although an ample body of research focusing on conventional AI methods exists, there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures. To contribute to this nascent research stream, a novel AI-driven security system denoted as “AI2AI” is presented in this work. AI2AI employs AI techniques to… More >

Al Hussien Seddik Saad¹, Mohammed S. Reda², Gamal M. Behery², Ahmed A. El-harby², Mohammed Baz³, Mohamed Abouhawwash^4,5,*, Ahmed Ismail Ebada⁶

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 125-138, 2023, DOI:10.32604/iasc.2023.032084

Abstract The trend of digital information transformation has become a topic of interest. Many data are threatening; thus, protecting such data from attackers is considered an essential process. Recently, a new methodology for data concealing has been suggested by researchers called coverless steganography. Coverless steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an image. This paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional systems. The system… More >