智能学习系统与应用(英文)最新文献

{"title":"Application of Artificial Intelligence Algorithm in Image Processing for Cattle Disease Diagnosis","authors":"Bezawit Lake, Fekade Getahun, Fitsum T. Teshome","doi":"10.4236/jilsa.2022.144006","DOIUrl":"https://doi.org/10.4236/jilsa.2022.144006","url":null,"abstract":"","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330690","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":"Gamification in Education: A Study of Design-Based Learning in Operationalizing a Game Studio for Serious Games","authors":"James Hutson, Ben Fulcher, Joseph Weber","doi":"10.4236/jilsa.2022.144010","DOIUrl":"https://doi.org/10.4236/jilsa.2022.144010","url":null,"abstract":"","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330790","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":"Virtual Reality and Learning: A Case Study of Experiential Pedagogy in Art History","authors":"James Hutson, Trent Olsen","doi":"10.4236/jilsa.2022.144005","DOIUrl":"https://doi.org/10.4236/jilsa.2022.144005","url":null,"abstract":"","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330649","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":"Digital Biomarker Identification for Parkinson’s Disease Using a Game-Based Approach","authors":"Ilman Shazhaev, Dimitry Mihaylov, Abdulla Shafeeg","doi":"10.4236/jilsa.2022.144007","DOIUrl":"https://doi.org/10.4236/jilsa.2022.144007","url":null,"abstract":"","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330724","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":"Towards Immunizing Infodemic: Comprehensive Study on Assessing the Role of Artificial Intelligence and COVID-19 Pandemic","authors":"Maryam Roshanaei, Greggory Sywulak","doi":"10.4236/jilsa.2022.143003","DOIUrl":"https://doi.org/10.4236/jilsa.2022.143003","url":null,"abstract":"","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330634","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":"Synchronization of Stochastic Memristive Neural Networks with Retarded and Advanced Argument","authors":"R. Xian","doi":"10.4236/JILSA.2021.131001","DOIUrl":"https://doi.org/10.4236/JILSA.2021.131001","url":null,"abstract":"In this paper, we discuss the driving-response synchronization problem for two memristive neural networks with retarded and advanced arguments under the condition of additional noise. The control law is related to the linear time-delay feedback term, and the discontinuous feedback term. Moreover, the random different equation is used to prove the stability of this theory. At the end, the simulation results verify the correctness of the theoretical results.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47013436","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":"Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFAStTM)","authors":"A. Aravind, A. G. Bahirvani, Ronald Quiambao, T. Gonzalo","doi":"10.4236/jilsa.2020.122003","DOIUrl":"https://doi.org/10.4236/jilsa.2020.122003","url":null,"abstract":"Using the latest available artificial intelligence (AI) technology, an advanced algorithm LIVERFAStTM has been used to evaluate the diagnostic accuracy of machine learning (ML) biomarker algorithms to assess liver damage. Prevalence of NAFLD (Nonalcoholic fatty liver disease) and resulting NASH (nonalcoholic steatohepatitis) are constantly increasing worldwide, creating challenges for screening as the diagnosis for NASH requires invasive liver biopsy. Key issues in NAFLD patients are the differentiation of NASH from simple steatosis and identification of advanced hepatic fibrosis. In this prospective study, the staging of three different lesions of the liver to diagnose fatty liver was analyzed using a proprietary ML algorithm LIVERFAStTM developed with a database of 2862 unique medical assessments of biomarkers, where 1027 assessments were used to train the algorithm and 1835 constituted the validation set. Data of 13,068 patients who underwent the LIVERFAStTM test for evaluation of fatty liver disease were analysed. Data evaluation revealed 11% of the patients exhibited significant fibrosis with fibrosis scores 0.6 - 1.00. Approximately 7% of the population had severe hepatic inflammation. Steatosis was observed in most patients, 63%, whereas severe steatosis S3 was observed in 20%. Using modified SAF (Steatosis, Activity and Fibrosis) scores obtained using the LIVERFAStTM algorithm, NAFLD was detected in 13.41% of the patients (Sx > 0, Ay 0). Approximately 1.91% (Sx > 0, Ay = 2, Fz > 0) of the patients showed NAFLD or NASH scorings while 1.08% had confirmed NASH (Sx > 0, Ay > 2, Fz = 1 - 2) and 1.49% had advanced NASH (Sx > 0, Ay > 2, Fz = 3 - 4). The modified SAF scoring system generated by LIVERFAStTM provides a simple and convenient evaluation of NAFLD and NASH in a cohort of Southeast Asians. This system may lead to the use of noninvasive liver tests in extended populations for more accurate diagnosis of liver pathology, prediction of clinical path of individuals at all stages of liver diseases, and provision of an efficient system for therapeutic interventions.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43140561","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":"Application of Dual Attention Mechanism in Chinese Image Captioning","authors":"Yong Zhang, Jing Zhang","doi":"10.4236/jilsa.2020.121002","DOIUrl":"https://doi.org/10.4236/jilsa.2020.121002","url":null,"abstract":"Objective: The Chinese description of images combines the two directions of computer vision and natural language processing. It is a typical representative of multi-mode and cross-domain problems with artificial intelligence algorithms. The image Chinese description model needs to output a Chinese description for each given test picture, describe the sentence requirements to conform to the natural language habits, and point out the important information in the image, covering the main characters, scenes, actions and other content. Since the current open source datasets are mostly in English, the research on the direction of image description is mainly in English. Chinese descriptions usually have greater flexibility in syntax and lexicalization, and the challenges of algorithm implementation are also large. Therefore, only a few people have studied image descriptions, especially Chinese descriptions. Methods: This study attempts to derive a model of image description generation from the Flickr8k-cn and Flickr30k-cn datasets. At each time period of the description, the model can decide whether to rely more on images or text information. The model captures more important information from the image to improve the richness and accuracy of the Chinese description of the image. The image description data set of this study is mainly composed of Chinese description sentences. The method consists of an encoder and a decoder. The encoder is based on a convolutional neural network. The decoder is based on a long-short memory network and is composed of a multi-modal summary generation network. Results: Experiments on Flickr8k-cn and Flickr30k-cn Chinese datasets show that the proposed method is superior to the existing Chinese abstract generation model. Conclusion: The method proposed in this paper is effective, and the performance has been greatly improved on the basis of the benchmark model. Compared with the existing Chinese abstract generation model, its performance is also superior. In the next step, more visual prior information will be incorporated into the model, such as the action category, the relationship between the object and the object, etc., to further improve the quality of the description sentence, and achieve the effect of “seeing the picture writing”.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41874573","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":"Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model","authors":"Liam Close, R. Kashef","doi":"10.4236/jilsa.2020.124005","DOIUrl":"https://doi.org/10.4236/jilsa.2020.124005","url":null,"abstract":"Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330570","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":"Transcription Factor Bound Regions Prediction: Word2Vec Technique with Convolutional Neural Network","authors":"Rixin Chen, Ruoxi Dai, Mingye Wang","doi":"10.4236/jilsa.2020.121001","DOIUrl":"https://doi.org/10.4236/jilsa.2020.121001","url":null,"abstract":"Genome-wide epigenomic datasets allow us to validate the biological function of motifs and understand the regulatory mechanisms more comprehensively. How different motifs determine whether transcription factors (TFs) can bind to DNA at a specific position is a critical research question. In this project, we apply computational techniques that were used in Natural Language Processing (NLP) to predict the Transcription Factor Bound Regions (TFBRs) given motif instances. Most existing motif prediction methods using deep neural network apply base sequences with one-hot encoding as an input feature to realize TFBRs identification, contributing to low-resolution and indirect binding mechanisms. However, how the collective effect of motifs on binding sites is complicated to figure out. In our pipeline, we apply Word2Vec algorithm, with names of motifs as an input to predict TFBRs utilizing Convolutional Neural Network (CNN) to realize binary classification, based on the ENCODE dataset. In this regard, we consider different types of motifs as separate “words”, and their corresponding TFBR as the meanings of “sentences”. One “sentence” itself is merely the combination of these motifs, and all “sentences” compose of the whole “passage”. For each binding site, we do the binary classification within different cell types to show the performance of our model in different binding sites and cell types. Each “word” has a corresponding vector in high dimensions, and the distances between each vector can be figured out, so we can extract the similarity between each motif, and the explicit binding mechanism from our model. We apply Convolutional Neural Network (CNN) to extract features in the process of mapping and pooling from motif vectors extracted by Word2Vec Algorithm and gain the result of 87% accuracy at the peak.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330924","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}