Shabana Ziyad Puthu Vedu^1,*, Wafaa A. Ghonaim², Naglaa M. Mostafa³, Pradeep Kumar Singh⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2599-2617, 2025, DOI:10.32604/cmc.2025.062695 - 16 April 2025

Abstract Hearing and Speech impairment can be congenital or acquired. Hearing and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their challenges. However, the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of study. Assistive learning devices enable students to access institutional resources and facilities fully. The proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and classmates. This tool converts the audio signals into sign language videos for the… More >

Zaid Derea^1,2,*, Beiji Zou¹, Xiaoyan Kui^1,*, Monir Abdullah³, Alaa Thobhani¹, Amr Abdussalam⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 115-136, 2025, DOI:10.32604/cmc.2025.062729 - 26 March 2025

Abstract Improving website security to prevent malicious online activities is crucial, and CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has emerged as a key strategy for distinguishing human users from automated bots. Text-based CAPTCHAs, designed to be easily decipherable by humans yet challenging for machines, are a common form of this verification. However, advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising efficiency. In our comprehensive investigation into CAPTCHA recognition, we have tailored the renowned UpDown image captioning model specifically for this… More >

Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070 - 18 July 2024

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

Muhammad Ahmad Nawaz Ul Ghani¹, Kun She^1,*, Muhammad Arslan Rauf¹, Shumaila Khan², Javed Ali Khan³, Eman Abdullah Aldakheel⁴, Doaa Sami Khafaga⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2609-2623, 2024, DOI:10.32604/cmc.2024.049611 - 15 May 2024

Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing… More >

Jianying Shi¹, Shengchao Chen¹, Benguo Yu², Yi Ren^3,*, Guanjun Wang^1,4,*, Chenyang Xue⁵

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3235-3258, 2023, DOI:10.32604/iasc.2023.041392 - 11 September 2023

Abstract Respiratory infections in children increase the risk of fatal lung disease, making effective identification and analysis of breath sounds essential. However, most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system, and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy. In this work, we collected three types of breath sounds from children with normal (120 recordings), bronchitis (120 recordings), and pneumonia (120 recordings) at the posterior chest position using an off-the-shelf 3M electronic stethoscope.… More >

Muhammad Ishaq¹, Mustaqeem Khan^1,2, Soonil Kwon^1,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3355-3369, 2023, DOI:10.32604/csse.2023.037373 - 03 April 2023

Abstract Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines. Speech Emotion Recognition (SER) is one of the critical sources for human evaluation, which is applicable in many real-world applications such as healthcare, call centers, robotics, safety, and virtual reality. This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state. The authors designed a Temporal Convolutional Network (TCN) core block to recognize long-term dependencies in speech signals and then feed these temporal 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 - 03 April 2023

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… More >

Ahmed Ben Atitallah^1,*, Mohamed Amin Ben Atitallah^2,3, Yahia Said^4,5, Mohammed Albekairi¹, Anis Boudabous⁶, Turki M. Alanazi¹, Khaled Kaaniche¹, Mohamed Atri⁷

Computer Systems Science and Engineering, Vol.46, No.1, pp. 701-717, 2023, DOI:10.32604/csse.2023.035871 - 20 January 2023

Abstract The challenge faced by the visually impaired persons in their day-to-day lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction… More >

Saiyed Umer^1,*, Ranjeet Kumar Rout², Shailendra Tiwari³, Ahmad Ali AlZubi⁴, Jazem Mutared Alanazi⁴, Kulakov Yurii⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1165-1185, 2023, DOI:10.32604/cmes.2022.023312 - 27 October 2022

Abstract A deep fusion model is proposed for facial expression-based human-computer Interaction system. Initially, image preprocessing, i.e., the extraction of the facial region from the input image is utilized. Thereafter, the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial regions. To prevent overfitting, in-depth features of facial images are extracted and assigned to the proposed convolutional neural network (CNN) models. Various CNN models are then trained. Finally, the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions, i.e., fear, More >

Lamiaa A. Abou Elazm^1,2, Walid El-Shafai^3,4, Sameh Ibrahim², Mohamed G. Egila¹, H. Shawkey¹, Mohamed K. H. Elsaid², Naglaa F. Soliman⁵, Hussah Nasser AlEisa^6,*, Fathi E. Abd El-Samie³

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 929-955, 2023, DOI:10.32604/iasc.2023.031386 - 29 September 2022

Abstract Biometric security is a growing trend, as it supports the authentication of persons using confidential biometric data. Most of the transmitted data in multimedia systems are susceptible to attacks, which affect the security of these systems. Biometric systems provide sufficient protection and privacy for users. The recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of attacks. In addition, they have not been studied on different and large biometric datasets. Another point that deserves consideration is the hardware implementation of cancellable biometric recognition systems. This paper presents a suggested… More >