2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)最新文献

{"title":"A Brainwave-based Attention Diagnosis and Music Recommendation System for Reading Improvement","authors":"Tingting Zhang, Tiffany Chang, Ming-Hao Wu","doi":"10.1109/ICAIRC52191.2021.9544927","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9544927","url":null,"abstract":"The sense of ‘Attention’ has a certain degree of influence on the effectiveness of learning. It was found that good music boosts reading interest. Music referral systems have been proactively explored using hybrid methods. In this work, first, a select electroencephalographic(EEG) was used to record and review attention according to the individual's choices for music material due to the introduction of an attention device. Then, the collected brainwave data values are filtered for invalid data and then combined with a Supper Vector Machine (SVM) classifier. SVM classifier for calculation and analysis identifies two types of brainwave data values (attention vs. non-attention). The recognition rate was up to 72%. We suggest a hybrid music recommendation model based upon an exciting device, which incorporates the user's electroencephalographic(EEG) information and records the individual's choices for music material due to the introduction of an attention device. The experimental outcomes reveal that the system, without a doubt, gives customers a personalized list of music, making them more attentive and continue reading.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131595745","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}

{"title":"iPonder: A Novel Approach to Multimodal Mental Health Diagnosis and Therapy for Teens via Deep Transfer Learning and Computer Vision","authors":"Vivek Kogilathota","doi":"10.1109/ICAIRC52191.2021.9545055","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9545055","url":null,"abstract":"Suicide is the second leading cause of death for teenagers, tarnishing the future potential of the world. More teenagers die from suicide than from cancer, heart disease, stroke, and many more diseases, combined. Although the greatest mental health improvement occurs from a conversation, over 50% of suicidal teenagers stay quiet about their depression due to shame. Thus, iPonder, a cross-platform accessible mobile application for teenagers to privately discuss, confront, and overcome their depression, was created. Using a four-level diagnosis approach based on user data, responses to psychological questionnaires, choice specification, and free-response answers, iPonder constructs a hyper-personalized user profile, which can then be used to pair teenagers anonymously online with similar profiles to discuss and vent about their issues. Questionnaires include the Patient Health Questionnaire-9, General Anxiety Disorder- 7, UCLA Loneliness Scale, and Myers-Briggs Personality Type Indicator test. Using Reddit's Self-Reported Mental Health Diagnosis text-corpus with over 200,000 data points, 11 artificial neural networks were constructed to predict user risk of various mental illnesses based on their free-text-responses, using transfer learning from a token-based text embedding neural network with over 48 million parameters trained on the English Google News 7B corpus with over 4 billion data points. Additionally, two other artificial neural networks were created, trained on the Distress Analysis Interview Corpus with tremendous labeled video data regarding facial landmark tracking and audio analysis for depression-diagnosed people, to predict the risk of depression from a user-submitted video. All neural networks were evaluated on their accuracy, precision, recall, and f1-score. Besting state-of-the-art metrics on similar tasks in the relevant literature, iPonder achieves over 95% accuracy, recall, precision, and f1-score per neural network, pointing to a new age of accessible, effective, and anonymous mental health therapy in teens.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129345746","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}

{"title":"The Mobile Robot for Garbage Sorting and Handling Based on Machine Vision","authors":"Qihua Zhou, Zhangwen Fang, Xiaowen Mao, Qiang Mai","doi":"10.1109/ICAIRC52191.2021.9544768","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9544768","url":null,"abstract":"Recent years, garbage sorting has attracted wide attention from all aspects of society. Various methods and policies for garbage sorting have been put forward, which started from cultivating people's active awareness of garbage sorting. With the development of machine vision and deep learning, applying these techniques to intelligent garbage sorting will be a worthy direction. A mobile robot for garbage sorting and handling based on machine vision and GPS auto-drive technology was designed in this paper. The scheme was elaborated from three aspects of mechanical structure, hardware system, and software system. The test results showed that the PID speed regulation response of the mobile robot was fast, and the accuracy rates for recyclable garbage and hazardous garbage were respectively 100% and 80%.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129181129","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}

{"title":"Artificial Intelligence Aided Innovation Education Based on Multiple Intelligence","authors":"Fasain Lo, Fang-Liu Su, Shaoze Chen, Jinfeng Qiu, Jiong Du","doi":"10.1109/ICAIRC52191.2021.9544874","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9544874","url":null,"abstract":"Innovation education is an education with the basic value of cultivating students' innovative spirit and ability, which integrates science, technology, engineering, art, and mathematics. However, how to effectively understand the talents of different students in the curriculum and further cultivate talents with problem-solving and creativity is important. This paper integrates the theory of multiple intelligence and artificial intelligence technology, observes the teaching situation through smart voice and image interaction, and provides smart teaching auxiliary services for teachers and students based on multiple intelligence framework analysis and machine learning. This paper proposes a smart and innovative education model and designs a smart teaching assistant system according to the innovative education model. We focus on artificial intelligence-aided innovation education, interdisciplinary integration as the goal so that teachers and students have a better interactive relationship than before. The research result effectively achieves individualized teaching, learning in fun, cultivate innovative talents with multiple abilities.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128200035","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}

{"title":"Reversible Data Hiding in Color Image with Grayscale Invariance using Sorting","authors":"Junying Yuan, Min-Hao Wu, Ting-Cheng Chang, Zengrong Zhan, Haishan Chen","doi":"10.1109/ICAIRC52191.2021.9544663","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9544663","url":null,"abstract":"Most reversible data hiding (RDH) algorithms for color images directly embed secret information in the luminance channel. As a result, the grayscale version of the color image is usually distorted. Since many color image processing algorithms work on the grayscale version, their performance on marked color images may get seriously impacted. Therefore, RD H with grayscale invariance (RDH-GI) is advanced to eliminate such shortcomings and has received great attention thereafter. However, their performance is still far from satisfactory. This paper presents a novel RDH-GI method by exploiting the pixel clustering technique to segment host pixels into different complexity levels. Data embedding tends to take place in less complex regions, and thus image distortion is reduced while maintaining grayscale invariance. Experiments prove the effectiveness of the proposed work.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115388880","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}

{"title":"A Wearable Foot Pressure Sensor","authors":"Xinyu Zhao, Xiu-rong Ma, Yunlong Shan, Xiaopeng Gong","doi":"10.1109/ICAIRC52191.2021.9544992","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9544992","url":null,"abstract":"A foot pressure sensor with an impedance adaptive RFID chip is proposed in this paper. It is used as a shoe pad and is easy to wear. The newly designed sensor consists of three components: RFID chip and antenna, supporting layer, and reflective surface. The distance between the RFID antenna and the reflective surface corresponds to the impedance sensor code of the RFID chip, and the supporting layer transforms the foot pressure into the distance. The detective device is portable as the designed antenna is only in a baby foot size (90x24x 0.03mm). The simulations and the test validate the effectiveness of such a sensor.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123114676","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}

{"title":"Convolutional Neural Networks for Automated Detection and Classification of Bone Tumors in Magnetic Resonance Imaging","authors":"V. Georgeanu, Mădălin Mămuleanu, D. Selișteanu","doi":"10.1109/ICAIRC52191.2021.9545036","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9545036","url":null,"abstract":"Today, medical imaging techniques are useful diagnostic tools in every specialty. The images are analyzed for diagnosis and treatment planning. Furthermore, medical imaging analysis is performed by specialized medical staff who, depending on work conditions tend to be subjective. Malignant bone tumors, like osteosarcoma, destroy the cortex of the bone and extend into surrounding soft tissues. So, it is important to detect and classify the bone tumor in an early stage with high accuracy. This work introduces a convolutional neural network approach along with image processing techniques to detect and classify bone magnetic resonance imaging scans into a malignant or benign tumor. Using transfer learning techniques, we compared the performance of two pre-trained CNN models VGG-16 and ResNet-50.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131114790","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}

{"title":"IEEE ICAIRC 2021 Conference Agenda","authors":"","doi":"10.1109/icairc52191.2021.9545018","DOIUrl":"https://doi.org/10.1109/icairc52191.2021.9545018","url":null,"abstract":"","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114053418","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}

{"title":"Domain Adaptation Algorithm based on Manifold Regularization","authors":"Xi Liu, Zengrong Zhan, Junying Yuan","doi":"10.1109/ICAIRC52191.2021.9544928","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9544928","url":null,"abstract":"Domain adaptation for classification is often encountered in recent years. A popular approach consists in transforming the source and target data to an identical linear space. Then the Maximum Mean Discrepancy (MMD) is used to evaluate the dissimilarity of distributions. However, the MMD only makes the source and target domain distribution consistent according to the global probability distribution, and cannot effectively protect the local geometric structure of the data. To make better use of the structure of local geometry, this paper proposes a method called domain adaptation based on manifold regularization (DAMR). First, this algorithm embeds the input data into a reproducing kernel Hilbert space (RKHS). Second, subspace-based dimensionality reduction is conducted on the RKHS. Third, a manifold regularization term is added to the learning method. Furthermore, the classification experiments demonstrate that DAMR is an accurate and effective method.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128756273","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}

{"title":"Design of Automatic Test System for Magnetic Head Based on Machine Vision","authors":"Long Gao, Jianyong Bian, Feng-Peng Wang","doi":"10.1109/ICAIRC52191.2021.9544632","DOIUrl":"https://doi.org/10.1109/ICAIRC52191.2021.9544632","url":null,"abstract":"Hard Disk head testing needs to be done in a dust-free environment and an ESD-free environment. Especially with the rapid development of magnetic heads from MR, TMR to PMR, the flying height of the magnetic head is reduced from 18 nm to less than 2 nm, which makes the production and test conditions of magnetic heads increasingly harsh. As a result, the traditional low- tech labor-intensive production does not meet the new demand. In terms of the particularity of the dynamic testing of hard disk heads, an automatic testing system for the dynamic performance of hard disk heads was developed. In the system, an Epson industrial robot and a CCD camera were used as the actuator, and to build the robot hand-eye Vision System. Thus, the difficulty in the development process from all-man to robot automation in the dynamic performance test of the magnetic head is solved.","PeriodicalId":348083,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130128187","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}