Y. Gizatdinova, O. Špakov, O. Tuisku, Matthew Turk, Veikko Surakka
We examined two vision-based interfaces (VBIs) for performance and user experience during character-based text entry using an on-screen virtual keyboard. Head-based VBI uses head motion to steer the computer pointer and mouth-opening gestures to select the keyboard keys. Gaze-based VBI utilizes gaze for pointing at the keys and an adjustable dwell for key selection. The results showed that after three sessions (45 min of typing in total), able-bodied novice participants (N = 34) typed significantly slower yet yielded significantly more accurate text with head-based VBI with gaze-based VBIs. The analysis of errors and corrective actions relative to the spatial layout of the keyboard revealed a difference in the error correction behavior of the participants when typing using both interfaces. We estimated the error correction cost for both interfaces and suggested implications for the future use and improvement of VBIs for hands-free text entry.
{"title":"Vision-Based Interfaces for Character-Based Text Entry: Comparison of Errors and Error Correction Properties of Eye Typing and Head Typing","authors":"Y. Gizatdinova, O. Špakov, O. Tuisku, Matthew Turk, Veikko Surakka","doi":"10.1155/2023/8855764","DOIUrl":"https://doi.org/10.1155/2023/8855764","url":null,"abstract":"We examined two vision-based interfaces (VBIs) for performance and user experience during character-based text entry using an on-screen virtual keyboard. Head-based VBI uses head motion to steer the computer pointer and mouth-opening gestures to select the keyboard keys. Gaze-based VBI utilizes gaze for pointing at the keys and an adjustable dwell for key selection. The results showed that after three sessions (45 min of typing in total), able-bodied novice participants (N = 34) typed significantly slower yet yielded significantly more accurate text with head-based VBI with gaze-based VBIs. The analysis of errors and corrective actions relative to the spatial layout of the keyboard revealed a difference in the error correction behavior of the participants when typing using both interfaces. We estimated the error correction cost for both interfaces and suggested implications for the future use and improvement of VBIs for hands-free text entry.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"47 1","pages":"8855764:1-8855764:23"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139248982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in speech recognition have achieved remarkable performance comparable with human transcribers’ abilities. But this significant performance is not the same for all the spoken languages. The Arabic language is one of them. Arabic speech recognition is bounded to the lack of suitable datasets. Artificial intelligence algorithms have shown promising capabilities for Arabic speech recognition. Arabic is the official language of 22 countries, and it has been estimated that 400 million people speak the Arabic language worldwide. Speech disabilities have been one of the expanding problems in the last decades, even in kids. Some devices can be used to generate speech for those people. One of these devices is the Servox Digital Electro-Larynx (EL). In this research, we developed an autoencoder with a combination of long short-term memory (LSTM) and gated recurrent units (GRU) models to recognize recorded signals from Servox Digital EL Electro-Larynx. The proposed framework consisted of three steps: denoising, feature extraction, and Arabic speech recognition. The experimental results show 95.31% accuracy for Arabic speech recognition with the proposed model. In this research, we evaluated different combinations of LSTM and GRU for constructing the best autoencoder. A rigorous evaluation process indicates better performance with the use of GRU in both encoder and decoder structures. The proposed model achieved a 4.69% word error rate (WER). Experimental results confirm that the proposed model can be used for developing a real-time app to recognize common Arabic spoken words.
语音识别的最新进展已经取得了与人类转录能力相当的显著表现。但这种显著的表现并不适用于所有的口语。阿拉伯语就是其中之一。阿拉伯语语音识别受限于缺乏合适的数据集。人工智能算法在阿拉伯语语音识别方面表现出了很好的能力。阿拉伯语是22个国家的官方语言，据估计全世界有4亿人说阿拉伯语。在过去的几十年里，语言障碍一直是一个不断扩大的问题，甚至在儿童中也是如此。一些设备可以用来为这些人生成语音。其中一种装置是伺服数字电喉(EL)。在这项研究中，我们开发了一种结合长短期记忆(LSTM)和门控循环单元(GRU)模型的自编码器，以识别来自Servox Digital EL电喉的记录信号。该框架包括三个步骤:去噪、特征提取和阿拉伯语语音识别。实验结果表明，该模型对阿拉伯语语音识别的准确率为95.31%。在这项研究中，我们评估了LSTM和GRU的不同组合来构建最佳的自编码器。严格的评估过程表明，在编码器和解码器结构中使用GRU具有更好的性能。该模型的错误率为4.69%。实验结果表明，该模型可用于开发实时识别阿拉伯语常用口语单词的应用程序。
{"title":"Deep Learning Methods for Arabic Autoencoder Speech Recognition System for Electro-Larynx Device","authors":"Z. J. M. Ameen, A. Kadhim","doi":"10.1155/2023/7398538","DOIUrl":"https://doi.org/10.1155/2023/7398538","url":null,"abstract":"Recent advances in speech recognition have achieved remarkable performance comparable with human transcribers’ abilities. But this significant performance is not the same for all the spoken languages. The Arabic language is one of them. Arabic speech recognition is bounded to the lack of suitable datasets. Artificial intelligence algorithms have shown promising capabilities for Arabic speech recognition. Arabic is the official language of 22 countries, and it has been estimated that 400 million people speak the Arabic language worldwide. Speech disabilities have been one of the expanding problems in the last decades, even in kids. Some devices can be used to generate speech for those people. One of these devices is the Servox Digital Electro-Larynx (EL). In this research, we developed an autoencoder with a combination of long short-term memory (LSTM) and gated recurrent units (GRU) models to recognize recorded signals from Servox Digital EL Electro-Larynx. The proposed framework consisted of three steps: denoising, feature extraction, and Arabic speech recognition. The experimental results show 95.31% accuracy for Arabic speech recognition with the proposed model. In this research, we evaluated different combinations of LSTM and GRU for constructing the best autoencoder. A rigorous evaluation process indicates better performance with the use of GRU in both encoder and decoder structures. The proposed model achieved a 4.69% word error rate (WER). Experimental results confirm that the proposed model can be used for developing a real-time app to recognize common Arabic spoken words.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124778583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent years have seen a surge in interest in the multifaceted topic of human-computer interaction (HCI). Since the advent of the Fourth Industrial Revolution, the significance of human-computer interaction in the field of safety risk management has only grown. There has not been a lot of focus on developing human-computer interaction for identifying potential hazards in buildings. After conducting a comprehensive literature review, we developed a study framework for the use of human-computer interaction in the identification of construction-related hazards (CHR-HCI). Future studies will focus on the intersection of computer vision, VR, and ergonomics. In this research, we have built a theoretical foundation for past studies’ findings and connections and offered concrete recommendations for the improvement of HCI in danger identification in the future. Moreover, we analyzed two cases studies related to the domain of CHR-HCI in terms of wearable vibration-based systems and context aware navigation.
{"title":"CHR vs. Human-Computer Interaction Design for Emerging Technologies: Two Case Studies","authors":"Sharefa Murad, A. Qusef, Muhanna A. Muhanna","doi":"10.1155/2023/8710638","DOIUrl":"https://doi.org/10.1155/2023/8710638","url":null,"abstract":"Recent years have seen a surge in interest in the multifaceted topic of human-computer interaction (HCI). Since the advent of the Fourth Industrial Revolution, the significance of human-computer interaction in the field of safety risk management has only grown. There has not been a lot of focus on developing human-computer interaction for identifying potential hazards in buildings. After conducting a comprehensive literature review, we developed a study framework for the use of human-computer interaction in the identification of construction-related hazards (CHR-HCI). Future studies will focus on the intersection of computer vision, VR, and ergonomics. In this research, we have built a theoretical foundation for past studies’ findings and connections and offered concrete recommendations for the improvement of HCI in danger identification in the future. Moreover, we analyzed two cases studies related to the domain of CHR-HCI in terms of wearable vibration-based systems and context aware navigation.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124634751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many unstructured documents contain segments with specific topics. Extracting these segments and identifying their topics helps to access the required information directly. This can improve the quality of many NLP applications such as information extraction, information retrieval, summarization, and question answering. Resumes (CVs) are unstructured documents that have diverse formats. They contain various segments such as personal information, experience, and education. Manually processing resumes to find the most suitable candidates for a particular job is a difficult task. Due to the increased amount of data, it has become very necessary to manipulate resumes by computer to save time and effort. This research presents a new algorithm named TSHD for topic segmentation based on headings detection. We apply the algorithm to extract resume segments and identify their topics. The proposed TSHD algorithm is accurate and addresses many weaknesses in previous studies. Evaluation results show a very high F1 score (about 96%) and a very low segmentation error (about 2%). The algorithm can be easily adapted to deal with other textual domains that contain headings in their segments.
{"title":"TSHD: Topic Segmentation Based on Headings Detection (Case Study: Resumes)","authors":"Majd E. Tannous, Wassim Ramadan, Mohanad A. Rajab","doi":"10.1155/2023/6044007","DOIUrl":"https://doi.org/10.1155/2023/6044007","url":null,"abstract":"Many unstructured documents contain segments with specific topics. Extracting these segments and identifying their topics helps to access the required information directly. This can improve the quality of many NLP applications such as information extraction, information retrieval, summarization, and question answering. Resumes (CVs) are unstructured documents that have diverse formats. They contain various segments such as personal information, experience, and education. Manually processing resumes to find the most suitable candidates for a particular job is a difficult task. Due to the increased amount of data, it has become very necessary to manipulate resumes by computer to save time and effort. This research presents a new algorithm named TSHD for topic segmentation based on headings detection. We apply the algorithm to extract resume segments and identify their topics. The proposed TSHD algorithm is accurate and addresses many weaknesses in previous studies. Evaluation results show a very high F1 score (about 96%) and a very low segmentation error (about 2%). The algorithm can be easily adapted to deal with other textual domains that contain headings in their segments.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129415618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Riera, Sebastià Verger, P. Montoya, Francisco J. Perales López
Chronic pain affects the quality of life of those affected. The need to investigate alternative and complementary methods to the pharmacological one to alleviate chronic pain is evident, so virtual reality and binaural tones have become a topic of interest in this field in recent years. This study aims to analyze the contributions of the combination of these two techniques in pediatric patients with chronic pain. For this, data on psychophysiological responses (heart rate and galvanic skin response) and pain perception are collected during and after interaction with this technology using a mixed pre- and posttest experimental methodology. The physiological data and answers in the Pediatric Pain Questionnaire (PPQ) have been collected in a sample of n = 13 healthy participants and n = 9 pediatric patients with chronic pain. The results show a significant difference between baseline and after applying virtual reality and binaural beats, md = 1.205 (t = 3.32; p < 0.05 ). There are great effects on the perception of chronic pain if virtual reality and binaural beats are combined, even greater than with virtual reality alone, making this combination of technologies a very useful tool to be exploited for the management of chronic pain in pediatric patients with rheumatic diseases.
{"title":"Advances in the Cognitive Management of Chronic Pain in Children through the Use of Virtual Reality Combined with Binaural Beats: A Pilot Study","authors":"L. Riera, Sebastià Verger, P. Montoya, Francisco J. Perales López","doi":"10.1155/2022/2495182","DOIUrl":"https://doi.org/10.1155/2022/2495182","url":null,"abstract":"Chronic pain affects the quality of life of those affected. The need to investigate alternative and complementary methods to the pharmacological one to alleviate chronic pain is evident, so virtual reality and binaural tones have become a topic of interest in this field in recent years. This study aims to analyze the contributions of the combination of these two techniques in pediatric patients with chronic pain. For this, data on psychophysiological responses (heart rate and galvanic skin response) and pain perception are collected during and after interaction with this technology using a mixed pre- and posttest experimental methodology. The physiological data and answers in the Pediatric Pain Questionnaire (PPQ) have been collected in a sample of n = 13 healthy participants and n = 9 pediatric patients with chronic pain. The results show a significant difference between baseline and after applying virtual reality and binaural beats, md = 1.205 (t = 3.32; \u0000 \u0000 p\u0000 <\u0000 0.05\u0000 \u0000 ). There are great effects on the perception of chronic pain if virtual reality and binaural beats are combined, even greater than with virtual reality alone, making this combination of technologies a very useful tool to be exploited for the management of chronic pain in pediatric patients with rheumatic diseases.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123934422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Speech emotion recognition (SER) has grown to be one of the most trending research topics in computational linguistics in the last two decades. Speech being the primary communication medium, understanding the emotional state of humans from speech and responding accordingly have made the speech emotion recognition system an essential part of the human-computer interaction (HCI) field. Although there are a few review works carried out for SER, none of them discusses the development of SER system for the Indo-Aryan or Dravidian language families. This paper focuses on some studies carried out for the development of an automatic SER system for Indo-Aryan and Dravidian languages. Besides, it presents a brief study of the prominent databases available for SER experiments. Some remarkable research works on the identification of emotion from the speech signal in the last two decades have also been discussed in this paper.
{"title":"A Review of the Advancement in Speech Emotion Recognition for Indo-Aryan and Dravidian Languages","authors":"Syeda Tamanna Alam Monisha, S. Sultana","doi":"10.1155/2022/9602429","DOIUrl":"https://doi.org/10.1155/2022/9602429","url":null,"abstract":"Speech emotion recognition (SER) has grown to be one of the most trending research topics in computational linguistics in the last two decades. Speech being the primary communication medium, understanding the emotional state of humans from speech and responding accordingly have made the speech emotion recognition system an essential part of the human-computer interaction (HCI) field. Although there are a few review works carried out for SER, none of them discusses the development of SER system for the Indo-Aryan or Dravidian language families. This paper focuses on some studies carried out for the development of an automatic SER system for Indo-Aryan and Dravidian languages. Besides, it presents a brief study of the prominent databases available for SER experiments. Some remarkable research works on the identification of emotion from the speech signal in the last two decades have also been discussed in this paper.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Poinsot, G. Gorisse, O. Christmann, Sylvain Fleury, S. Richir
Nowadays, both text-based and 3D online applications rely on conversational agents and autonomous characters to interact with users. Previous experiments demonstrated that perceived agency, that is to say, one’s belief in interacting with a computer- or another human-controlled entity could impact social interaction. At present, theories and results still diverge and no consensus has been reached. Therefore, we developed an experiment to investigate the effect of perceived agency and emotional communication on social presence. Participants were told that they would play an online image recognition game against a computer- or a human-controlled opponent. In both cases, participants faced a computer-controlled opponent designed to provide a challenging yet balanced competitive experience. Depending on the experimental conditions, participants were able to communicate with their opponents using emoticons between the game rounds. Results demonstrate a significant main effect of emotional communication on the three dimensions of social presence we considered in this experiment. An interaction effect between perceived agency and emotional communication was observed in copresence, another core dimension of social presence. The impact of emotional communication on participants’ sense of copresence depends on the perceived agency of the opponent. A significant increase was observed for participants facing a computer-controlled opponent when emotional communication was allowed. The sense of copresence was even higher when they were facing a computer-controlled opponent rather than a presumed human-controlled one. These results are discussed with regard to theories of social interaction in computer-mediated communication.
{"title":"Effect of Social Actors Perceived Agency on Social Presence in Computer-Mediated Communication","authors":"K. Poinsot, G. Gorisse, O. Christmann, Sylvain Fleury, S. Richir","doi":"10.1155/2022/9632119","DOIUrl":"https://doi.org/10.1155/2022/9632119","url":null,"abstract":"Nowadays, both text-based and 3D online applications rely on conversational agents and autonomous characters to interact with users. Previous experiments demonstrated that perceived agency, that is to say, one’s belief in interacting with a computer- or another human-controlled entity could impact social interaction. At present, theories and results still diverge and no consensus has been reached. Therefore, we developed an experiment to investigate the effect of perceived agency and emotional communication on social presence. Participants were told that they would play an online image recognition game against a computer- or a human-controlled opponent. In both cases, participants faced a computer-controlled opponent designed to provide a challenging yet balanced competitive experience. Depending on the experimental conditions, participants were able to communicate with their opponents using emoticons between the game rounds. Results demonstrate a significant main effect of emotional communication on the three dimensions of social presence we considered in this experiment. An interaction effect between perceived agency and emotional communication was observed in copresence, another core dimension of social presence. The impact of emotional communication on participants’ sense of copresence depends on the perceived agency of the opponent. A significant increase was observed for participants facing a computer-controlled opponent when emotional communication was allowed. The sense of copresence was even higher when they were facing a computer-controlled opponent rather than a presumed human-controlled one. These results are discussed with regard to theories of social interaction in computer-mediated communication.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132764205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The number of children with mental health problems is ever-growing; as a result, nearly 850,000 children in the UK are believed to have clinically significant problems, and only a quarter show evidence of mental illness. Family members often have a hard time dealing with children with mental health problems. As a result, digital mental health interventions are becoming popular for people seeking professional mental health services. Previous studies in this area have also shown that parents who are divorced or working away from home struggle to maintain contact with their children. This lack of communication between the parents and their children can worsen the children’s mental health conditions and prevent early diagnosis. Human-centred design thinking is applied step by step in this paper to provide an intuitive understanding of the design process. Five stages of the design thinking process were examined to follow a correct path. The results were promising, and the feedback received assured that the product helps parents to better monitor their children’s mental health and provides support when needed. The design thinking process was followed in concordance with the user needs identified from previous studies in this area, which led to a working solution that benefits both parents and children in tackling these problems.
{"title":"A Digital Mental Health Intervention for Children and Parents Using a User-Centred Design","authors":"A. Mahlous, Bersan Okkali","doi":"10.1155/2022/4322177","DOIUrl":"https://doi.org/10.1155/2022/4322177","url":null,"abstract":"The number of children with mental health problems is ever-growing; as a result, nearly 850,000 children in the UK are believed to have clinically significant problems, and only a quarter show evidence of mental illness. Family members often have a hard time dealing with children with mental health problems. As a result, digital mental health interventions are becoming popular for people seeking professional mental health services. Previous studies in this area have also shown that parents who are divorced or working away from home struggle to maintain contact with their children. This lack of communication between the parents and their children can worsen the children’s mental health conditions and prevent early diagnosis. Human-centred design thinking is applied step by step in this paper to provide an intuitive understanding of the design process. Five stages of the design thinking process were examined to follow a correct path. The results were promising, and the feedback received assured that the product helps parents to better monitor their children’s mental health and provides support when needed. The design thinking process was followed in concordance with the user needs identified from previous studies in this area, which led to a working solution that benefits both parents and children in tackling these problems.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the day-to-day life of communities, good communication channels are crucial for mutual understanding. The hearing-impaired community uses sign language, which is a visual and gestural language. In terms of orientation and expression, it is separate from written and spoken languages. Despite the fact that sign language is an excellent platform for communication among hearing-impaired persons, it has created a communication barrier between hearing-impaired and non-disabled people. To address this issue, researchers have proposed sign language to text translation systems for English and other European languages as a solution. The goal of this research is to design and develop an Amharic digital text converter system using Ethiopian sign language. The proposed system was created with the help of two key deep learning algorithms: a pretrained deep learning model and a Long Short-Term Memory (LSTM). The LSTM was used to extract sequence information from a sequence of image frames of a specific sign language, while the pretrained deep learning model was used to extract features from single frame images. The dataset used to train the algorithms was gathered in video format from Addis Ababa University. Prior to feeding the obtained dataset to the deep learning models, data preprocessing activities such as cleaning and video to image frame segmentation were conducted. The system was trained, validated, and tested using 80%, 10%, and 10% of the 2475 images created during the preprocessing step. Two pretrained deep learning models, EfficientNetB0 and ResNet50, were used in this investigation, and they attained an accuracy of 72.79%. In terms of precision and f1-score, ResNet50 outperformed EfficientNetB0. For the proposed system, a graphical user interface prototype was created, and the best performing model was chosen and implemented. The proposed system can be utilized as a starting point for other researchers to improve upon, based on the outcomes of the experiment. More high-quality training datasets and high-performance training machines, such as GPU-enabled computers, can be added to the system to improve it.
{"title":"A Generic Approach towards Amharic Sign Language Recognition","authors":"Netsanet Yigzaw, M. Meshesha, Chala Diriba","doi":"10.1155/2022/1112169","DOIUrl":"https://doi.org/10.1155/2022/1112169","url":null,"abstract":"In the day-to-day life of communities, good communication channels are crucial for mutual understanding. The hearing-impaired community uses sign language, which is a visual and gestural language. In terms of orientation and expression, it is separate from written and spoken languages. Despite the fact that sign language is an excellent platform for communication among hearing-impaired persons, it has created a communication barrier between hearing-impaired and non-disabled people. To address this issue, researchers have proposed sign language to text translation systems for English and other European languages as a solution. The goal of this research is to design and develop an Amharic digital text converter system using Ethiopian sign language. The proposed system was created with the help of two key deep learning algorithms: a pretrained deep learning model and a Long Short-Term Memory (LSTM). The LSTM was used to extract sequence information from a sequence of image frames of a specific sign language, while the pretrained deep learning model was used to extract features from single frame images. The dataset used to train the algorithms was gathered in video format from Addis Ababa University. Prior to feeding the obtained dataset to the deep learning models, data preprocessing activities such as cleaning and video to image frame segmentation were conducted. The system was trained, validated, and tested using 80%, 10%, and 10% of the 2475 images created during the preprocessing step. Two pretrained deep learning models, EfficientNetB0 and ResNet50, were used in this investigation, and they attained an accuracy of 72.79%. In terms of precision and f1-score, ResNet50 outperformed EfficientNetB0. For the proposed system, a graphical user interface prototype was created, and the best performing model was chosen and implemented. The proposed system can be utilized as a starting point for other researchers to improve upon, based on the outcomes of the experiment. More high-quality training datasets and high-performance training machines, such as GPU-enabled computers, can be added to the system to improve it.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131039835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.
{"title":"Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation","authors":"B. Ahamed, Meenakshi S. Arya, V. O. Nancy","doi":"10.2139/ssrn.4060653","DOIUrl":"https://doi.org/10.2139/ssrn.4060653","url":null,"abstract":"The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.","PeriodicalId":192934,"journal":{"name":"Adv. Hum. Comput. Interact.","volume":"2022 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130904153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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