New Special Issue: Advances and Opportunities of Image and Speech Recognition through Machine Learning


Effective interactive communications equipment is intended to assist deaf and partially deaf people connect socially. Numerous best adapted for recognizing sign languages now integrate different sensors of various kinds, particularly dig-deep cameras and cutting-edge algorithms for machine learning, because of the recent increase in interest in the study of movement and motion identification. Humans provide a comprehensive view of learning in the classroom approaches for recognizing sign language in this work. They examine many of these strategies for extracting the features, segmentation, and detection from the perspectives of the paradigms readily accessible by sensor technology. Also, designers summarize the existing multiple language databases, encompassing thumb printing motions and language terms, that can serve as an evaluation tool for the deaf or hard of hearing.


The important current study works are then explored, focusing on how they handle various information. They cover their key characteristics and point up prospects and difficulties for scientific enhancement. Speech impairment is a psychosomatic disorder. A handicap characterized as communication difficulties limits a person's oral and auditory communication capacity. People with this disability use sign language as a way of interaction. Although the communication system has become increasingly common in recent years, it can still be challenging for people who still need to sign to interact with petitioners. With the contemporary advances in machine recognition and algorithms for deep learning, there has been significant improvement in movement and gesture detection. The main goal of this effort is to develop a deep studying program offering different languages to textual translating and communicate ideas between petitioners and non-signers.


Communication is used by individuals who have language and deafness to communicate. Disability users with non-verbal multiple languages symbols, individuals can express their own opinions and emotions to additional commoners. But those regular people have trouble understanding their language. Thus professional signs during healthcare and legal appointments, training and educational sessions, and communicative competence is required. The requirement for these activities has increased over the past several generations. Additional service types, including video remote slightly elevated broadband connections, have enabled humans to understand data. Therefore these programs provide quick and simple ways to employ a gesture recognition interpreter, which has certain benefits but some drawbacks. Since there are millions of deaf people everywhere, legislative-level multiple-language identification techniques must be developed.


Based on the preceding, we invite academics to submit original research articles and review papers to the current Special Issue focusing on the developments and opportunities of image and speech recognition through machine learning.

Possible topics include, but are not limited to:

  1. Quantitative evidence for the spectral paradigm of selective processing.

  2. Utilizing optical relevance relying on graphs to recognize monuments from images.

  3. Socializing with hand monitoring and expression recognition.

  4. Computational characteristic hydrocarbon auditory measurements.

  5. An overview of significant innovations in textural analytical techniques for material fault identification.

  6. Implementation of vertical gradient scanned documents would be a prevalent graph rediscovery.

  7. K-means and the support vector machine for diagnosing tumors in chest radiographs.

  8. Quantitative epidermis recognition and classification censoring of mature images.

  9. An efficient kaleidoscopic quantization technique utilizing unpredictability.

  10. A unique background subtraction technique to enhance recognition memory.

  11. Recognition of facial gestures using a fresh extraction of features technique.

  12. Automobile fatigue and attentiveness could be recognized via image processing techniques to recognize physical expressions.


Submission Timeline: 

Submission Deadline: 10/07/2023

Authors Notification: 15/09/2023

Final notification:10/01/2024

Submissions are set to open soon.