2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)最新文献

{"title":"Association analysis of Primary liver cancer based on Apriori Algorithm","authors":"Y. Liu, Qi Pan","doi":"10.1109/ICIIBMS46890.2019.8991456","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991456","url":null,"abstract":"This paper briefly describes the Apriori algorithm for primary liver cancer data, and then performs data preprocessing based on the characteristics of primary liver cancer patients, including data import and extraction, and embedding the algorithm into primary liver cancer. The implementation of clinical warning. After that, the Apriori algorithm is used to realize the association of the data association rules of primary liver cancer, and the internal valuable association rules are obtained, which provides suggestions for improving the doctor's remediation and prevention, so as to prevent the occurrence and reduction of primary liver cancer. The incidence of primary liver cancer has important practical significance.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116369227","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":"[ICIIBMS 2019 Title Page]","authors":"","doi":"10.1109/iciibms46890.2019.8991511","DOIUrl":"https://doi.org/10.1109/iciibms46890.2019.8991511","url":null,"abstract":"","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123408120","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 the Development of Artificial Intelligence-based Systems: Human-Centered Functional Requirements and Open Problems","authors":"T. Fagbola, S. Thakur","doi":"10.1109/ICIIBMS46890.2019.8991505","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991505","url":null,"abstract":"The increasing capability of AI-powered systems, including self-aware and unmanned systems, at automating simple to sophisticated tasks and their wide areas of real-world interventions in boosting productivity and enhancing competitiveness has offered transformative potentials leading to better quality of life. These systems have lately become an inseparable part of human lives. However, incorrect use leading to unintended consequences, safety, fairness, trustworthiness are major concerns of these emerging ubiquitous systems. In this paper, an attempt was made to concisely present and discuss key human-centered functional requirement specifications of emerging Artificial intelligence-based systems especially interpretability, explainability, fairness, transparency and security. Some emerging toolkits and open libraries for developing and evaluating AI-based systems are also discussed. A number of open problems with respect to managing the tradeoff among these requirements and systems’ performance are presented to guide future researches in this direction.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124789591","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 fire equipment enterprise performance game management system based on SaaS cloud platform","authors":"Cang Nai-meng, Wu Xiaoyu, Y. Wan-jun, Wang Zi-chen, Zhao Huai-lin, Li Jia-lan","doi":"10.1109/ICIIBMS46890.2019.8991448","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991448","url":null,"abstract":"In order to improve performance assessment participation, we need to add a new module for management performance appraisal system. However, the traditional performance appraisal is too programmed, the lack of interest. In order to solve this problem, this paper combines the advantages of performance cloud and mobile game, and designs and develops a performance game management software. It is based on the SaaS cloud platform. According to business needs assessment indicators to quantify, for the completion of basic performance appraisal module. Then, choose a mature SaaS vendors, designed a fusion of performance and gaming cloud platform. Then, scene settings are made for the newly added game module, and a performance game credit system and a challenge system are added respectively. Finally, with the mobile phone as the carrier, the system is spread through APP. In order to test the practicability of the system, the author put the application into a fire-fighting equipment enterprise for testing, and collected and analyzed the data of the sales department of the fire-fighting enterprise. The results showed that the system improved sales performance in the sector, and improve employee participation.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131635276","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":"Research on the multi-scale network crowd density estimation algorithm based on the attention mechanism","authors":"Li Wang, Huailin Zhao, Yaoyao Li","doi":"10.1109/ICIIBMS46890.2019.8991496","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991496","url":null,"abstract":"Whether it is daily urban traffic or some special gatherings, crowd gathering scenes are common, and it is becoming more and more important to calculate the number of people in terms of safety and planning. Calculating the number of people in high-density crowd is a very difficult challenge due to the diversity of ways people appear in crowded scenes. This paper proposes a multi-branch network structure that combines the dilated convolution and attention mechanism. By combining dilated convolution, the context information of different scales of the crowd image are extracted. The attention mechanism is introduced to make the network pay more attention to the position of the head of the crowd and suppress the background noise, so as to obtain a higher quality density map. Then add all the pixels in the density map to get the total number of people. Through a large number of experiments, this network can better provide effective crowd density estimation features and improve the dissimilarity of density map distribution, which has better robustness.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120994203","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":"Research on Model for Sensory Quality of Yogurt Based on Bagging","authors":"Lizhong Xiao, Yuan Liu, H. Tian","doi":"10.1109/ICIIBMS46890.2019.8991466","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991466","url":null,"abstract":"Yogurt is a common dairy product in daily life. How to quickly and accurately identify the sensory quality of yogurt is of great significance to the control of sensory quality of yogurt. In this paper, sensor data of 120 yogurt samples were obtained by electronic nose, and the measured sensor data were used as inputs to construct 2-layer back propagation neural network(BPNN) models. Then the Bagging method was employed to integrate the BPNN models, which constructed the sensory quality classification model for yogurt. The comparative experiment showed that the sensory quality classification model based on Bagging-BPNN has better accuracy and generalizability than the model based on single BPNN and k-nearest neighbors(kNN) algorithm.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123822522","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":"Classification of Salivary Adulterated NS1 SERS Spectra Using PCA-Cosine-KNN","authors":"N. H. Othman, K. Y. Lee, A. Radzol, W. Mansor, U. M. Rashid","doi":"10.1109/ICIIBMS46890.2019.8991490","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991490","url":null,"abstract":"Review of literature on dengue fever (DF) reveals the most popular biomarkers and diagnostic medium are IgG/IgM and blood plasma respectively. As such, the current diagnostic methods are prone to blood borne infection. Presence of nonstructural protein 1 (NS1), another biomarker of DF, was detected in saliva of DF infected subjects using Enzyme-Linked Immunosorbent Assay (ELISA), but of low sensitivity. Our previous work has found Surface Enhanced Raman Spectroscopy (SERS), a confluent of photonic and nano-technology, is able to detect and produce a molecular fingerprint of NS1 from its salivary spectra. This implies an early, non-invasive, blood borne infection free detection method for DF, with the many associated advantages. Since K-nearest neighbor (KNN) is known for its strength in pattern recognition of signals and images, it is chosen to classify between NS1 positively and negatively adulterated NS1 samples here. Our work here intends to investigate the effect of number of nearest neighbours (k-value), classifier rules on KNN classifier with Cosine distance rule, subjected to three termination criteria of Principal Component Analysis (PCA). Healthy and adulterated NS1 samples from our UiTM-NMRR-12-1278-12868-NS1-DENV database were first analyzed with SERS. After pre-processing to remove undesired features, performance of the different KNN classifiers with Cosine distance rule as k-value and classifier rules were varied, with optimized features sets derived from the termination criteria of PCA, were evaluated and compared, in terms of sensitivity, specificity, precision and accuracy. From the results, it is observed that all the classifier models attained the highest performance of 100% in accuracy, precision and ROC performance, except for the Scree-Cosine-KNN models with Consensus classifier rule. And the CPV- KNN models with k-value of 1, 3 or 5 are the best in view of trade-off between computation load and performance, for all classifier rules, when Cosine distance rule is used.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121862295","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":"Influence of daily life behavior with listening to music on stress structure","authors":"Masahiro Seo, K. Nagumo, K. Oiwa, A. Nozawa","doi":"10.1109/ICIIBMS46890.2019.8991434","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991434","url":null,"abstract":"The objective of this study was to evaluate the influence listening to music during daily life behavior on the stress structure. A dishwashing task simulating an actual daily life behavior was performed. The experiments were conducted during dishwashing (Control) and during dishwashing while listening to music (Favorite). It was observed that the mental fatigue in the stress structure was decreased by listening to music. Therefore, listening to music might affect the psychological state, and might result in improved performance.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123166233","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":"Research of Pedestrian Re-identification Method Based on Video Surveillance","authors":"Li Yao, Zihan Feng, Tiantian Zhu, Yan Wan","doi":"10.1109/ICIIBMS46890.2019.8991439","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991439","url":null,"abstract":"In order to solve the problem of person recognition in cross-view video sequences of non-overlapping camera, most of the current person re-identification models based on deep learning either need to manually label features as their attributes, or learn the overall single semantic level of feature representation. This paper proposes a person re-identification method based on DNN with multi-level feature fusion, it can automatically learn multi-level discriminative visual factors that are insensitive to viewing condition changes, and identify and utilize them when matching images. Firstly, this paper uses the HOG feature to perform person detection on the video of the two cameras respectively. The person images detected of the camera1 are used as the prob, the person images detected in the camera2 are used as the gallery, and then the two parts are put into the person re-ID model and completed by the training. Finally, the cross-view tracking is implemented for the re-identified persons in combination with the KCF algorithm. The experimental results confirm the accuracy and efficiency of the method.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124498202","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":"Brain Tumor Recognition Based on Data Augmentation and Convolutional Neural Network","authors":"Xu Han, Huang Zheng, Zhao Yiwen, Song Guoli","doi":"10.1109/ICIIBMS46890.2019.8991503","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991503","url":null,"abstract":"The brain tumor is one of the most dangerous diseases at present. Accurate diagnosis of brain tumors can contribute to improving the prognosis conditions of patients. Existing methods have some shortcomings, such as manual extraction of features and insufficient amount of data. Since the convolutional neural network (CNN) can extract features automatically, we propose a deep Convolutional Neural Network to diagnose the brain tumors. In this paper, an automatic system based on CNN is proposed to classify three categories of brain Magnetic Resonance Images, including normal images, brain images with meningiomas and brain images with gliomas. Several steps, including image preprocessing, data augmentation and image classification, are applied to the original brain images. And the experiments show that the accuracy of the proposed system on testing set can reach 93.33%, which indicates that our model can achieve a comparable classification result.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132899670","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}