In recent years, with the development of the Internet, information security has become the focus of our attention. With the advent of the era of big data, the detection of large-scale malicious code has attracted a lot of researches' attention. For solving the problem, we propose a malware detection method based on operation and data flow of instructions, which is used by malicious code. It combines the operation and data flow of the instructions being used by malware, then reflects itself in an rgb image. Then, it uses the convolutional neural network that has advantages in image processing for deep-learning to detect the rgb image of malicious code. We have carried out a series of experiments. And through these experiments, it is proved that this kind of rgb image, which is generated by the fusion of the operation and data flow of instructions used by malware, could be well applied to the detection of malicious code. The experiment shows that the highest detection accuracy could be as high as 97.95% and the false positive rate could be as low as 2.618%.
{"title":"A Malware Detection Method Based on Rgb Image","authors":"Jinrong Chen","doi":"10.1145/3404555.3404622","DOIUrl":"https://doi.org/10.1145/3404555.3404622","url":null,"abstract":"In recent years, with the development of the Internet, information security has become the focus of our attention. With the advent of the era of big data, the detection of large-scale malicious code has attracted a lot of researches' attention. For solving the problem, we propose a malware detection method based on operation and data flow of instructions, which is used by malicious code. It combines the operation and data flow of the instructions being used by malware, then reflects itself in an rgb image. Then, it uses the convolutional neural network that has advantages in image processing for deep-learning to detect the rgb image of malicious code. We have carried out a series of experiments. And through these experiments, it is proved that this kind of rgb image, which is generated by the fusion of the operation and data flow of instructions used by malware, could be well applied to the detection of malicious code. The experiment shows that the highest detection accuracy could be as high as 97.95% and the false positive rate could be as low as 2.618%.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968963","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}
Contagion of negative events under virtual environment is lack of studies although previous researches have showed that the effect of contagion stands out when facing negative events in reality. Therefore, to test the effect of contagion on attitude toward online collective action is of significance. The data of experiment was collected via Internet. 90 people participated. The results showed: a) the phenomenon of attitude contagion happened under virtual environment; b) the participants were more sensitive to positive attitude than negative one of online collective events; c) male were more sensitive to the change of attitudes than female; d) participants at the ages between 25 to 30 years old were more sensitive to positive attitude than the rest. Finally implications and suggestion for further researches are discussed.
{"title":"Social Contagion on the Internet: Evidence from Experiment of Attitude towards Online Collective Event","authors":"Chunlei Liu, Mi Shi","doi":"10.1145/3404555.3404619","DOIUrl":"https://doi.org/10.1145/3404555.3404619","url":null,"abstract":"Contagion of negative events under virtual environment is lack of studies although previous researches have showed that the effect of contagion stands out when facing negative events in reality. Therefore, to test the effect of contagion on attitude toward online collective action is of significance. The data of experiment was collected via Internet. 90 people participated. The results showed: a) the phenomenon of attitude contagion happened under virtual environment; b) the participants were more sensitive to positive attitude than negative one of online collective events; c) male were more sensitive to the change of attitudes than female; d) participants at the ages between 25 to 30 years old were more sensitive to positive attitude than the rest. Finally implications and suggestion for further researches are discussed.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123021382","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 development of big data has brought changes to society and brought us challenges. How to extract useful information from complex data has become the focus of research in recent years. Personalized recommendation as an effective solution has received widespread attention in academia and industry. Collaborative filtering has been widely used by finding users with similar user behaviors as the preferences of similar users. However, the existing methods ignore the interaction information between the user and the item during feature extraction, which leads to imperfect feature extraction and affects the algorithm effect. This paper proposes a graph convolution collaborative filtering model based on the attention model, which uses the graph convolution network to embed user and item interaction information into feature vectors, and uses the attention model to highlight the relatively important interaction information among them, so as to obtain more excellent feature vector. The experimental results show that the model has a good effect on the two commonly used evaluation metrics: recall and normalized discounted cumulative gain(NDCG).
{"title":"Attention-Based Graph Convolution Collaborative Filtering","authors":"Xiao-Zhe Han, Xiaobin Xu","doi":"10.1145/3404555.3404641","DOIUrl":"https://doi.org/10.1145/3404555.3404641","url":null,"abstract":"The development of big data has brought changes to society and brought us challenges. How to extract useful information from complex data has become the focus of research in recent years. Personalized recommendation as an effective solution has received widespread attention in academia and industry. Collaborative filtering has been widely used by finding users with similar user behaviors as the preferences of similar users. However, the existing methods ignore the interaction information between the user and the item during feature extraction, which leads to imperfect feature extraction and affects the algorithm effect. This paper proposes a graph convolution collaborative filtering model based on the attention model, which uses the graph convolution network to embed user and item interaction information into feature vectors, and uses the attention model to highlight the relatively important interaction information among them, so as to obtain more excellent feature vector. The experimental results show that the model has a good effect on the two commonly used evaluation metrics: recall and normalized discounted cumulative gain(NDCG).","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124182982","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}
Transaction automation has always received widespread attention in the field of financial research. As one of the most popular technical indicators of traders, the Moving Average Convergence Divergence(MACD) indicator sometimes performs worse than expected in unstable financial markets. In this paper, we use Residual Networks to improve the effectiveness of traditional trading MACD algorithm in technical analysis. The rationale behind our research is that deep learning networks can learn market behavior and be able to estimate whether a given trading point is more likely to succeed. We verify our strategy (MACD-KURT) which is based on the combination of Residual Networks prediction and technical analysis on CSI300 index constituent stocks in the Chinese market, and the results show that the strategy based on the combination of Residual Networks prediction and technical analysis is better than the one based on technical analysis alone, ether in strategy's return or risk control.
{"title":"Deep Learning for Algorithmic Trading: Enhancing MACD Strategy","authors":"Y. Lei, Qinke Peng, Yiqing Shen","doi":"10.1145/3404555.3404604","DOIUrl":"https://doi.org/10.1145/3404555.3404604","url":null,"abstract":"Transaction automation has always received widespread attention in the field of financial research. As one of the most popular technical indicators of traders, the Moving Average Convergence Divergence(MACD) indicator sometimes performs worse than expected in unstable financial markets. In this paper, we use Residual Networks to improve the effectiveness of traditional trading MACD algorithm in technical analysis. The rationale behind our research is that deep learning networks can learn market behavior and be able to estimate whether a given trading point is more likely to succeed. We verify our strategy (MACD-KURT) which is based on the combination of Residual Networks prediction and technical analysis on CSI300 index constituent stocks in the Chinese market, and the results show that the strategy based on the combination of Residual Networks prediction and technical analysis is better than the one based on technical analysis alone, ether in strategy's return or risk control.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132564423","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 this paper, we propose an efficient model compression method for object detection network. The key to this method is that we combine pruning and training into a single process. This design benefits in two aspects. First, we have a full control on pruning of convolution kernel, which ensures the model's accuracy to maximum extent. Second, compared with previous works, we overlap pruning with the training process instead of waiting for the model to be trained before pruning. In such a way, we can directly get a compressed model that is ready to use once training finished. We took experiments based on SSD(Single Shot MultiBox Detector) for verification. Firstly, when compressing the ssd300 model with dataset of Pascal VOC, we got model compression of 7.7X while the model accuracy only drops by 1.8%. Then on the COCO dataset, under the premise that the accuracy of the model remains unchanged, we got the model compressed by 2.8X.
{"title":"An Efficient Model Compression Method of Pruning for Object Detection","authors":"Junjie Yin, Li Wei, Ding Pu, Q. Miao","doi":"10.1145/3404555.3404612","DOIUrl":"https://doi.org/10.1145/3404555.3404612","url":null,"abstract":"In this paper, we propose an efficient model compression method for object detection network. The key to this method is that we combine pruning and training into a single process. This design benefits in two aspects. First, we have a full control on pruning of convolution kernel, which ensures the model's accuracy to maximum extent. Second, compared with previous works, we overlap pruning with the training process instead of waiting for the model to be trained before pruning. In such a way, we can directly get a compressed model that is ready to use once training finished. We took experiments based on SSD(Single Shot MultiBox Detector) for verification. Firstly, when compressing the ssd300 model with dataset of Pascal VOC, we got model compression of 7.7X while the model accuracy only drops by 1.8%. Then on the COCO dataset, under the premise that the accuracy of the model remains unchanged, we got the model compressed by 2.8X.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131780086","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}
It is critical for higher education institutions to work on improvement of their teaching and learning strategy by examining feedback of students. Analyzing these feedbacks typically requires manual interventions which are not only labor intensive but prone to errors as well. Therefore, automatic models and techniques are needed to handle textual feedback efficiently. To this end, we propose a model for aspect-based opinion mining of comments of students that are posted in online learning platforms. The model aims to predict some of the key aspects related to an online course from students' reviews and then assess the attitude of students toward these commented aspects. The proposed model is tested on a large-scale real-world dataset which is collected for this purpose. The dataset consists of more than 21 thousand manually annotated students' reviews that are collected from Coursera. Conventional machine learning algorithms and deep learning techniques are used for prediction of the aspect categories and the aspect sentiment classification as well. The obtained results with respect to precision, recall, and F1 score are very promising.
{"title":"Aspect-Based Opinion Mining of Students' Reviews on Online Courses","authors":"Zenun Kastrati, Blend Arifaj, Arianit Lubishtani, Fitim Gashi, Engjëll Nishliu","doi":"10.1145/3404555.3404633","DOIUrl":"https://doi.org/10.1145/3404555.3404633","url":null,"abstract":"It is critical for higher education institutions to work on improvement of their teaching and learning strategy by examining feedback of students. Analyzing these feedbacks typically requires manual interventions which are not only labor intensive but prone to errors as well. Therefore, automatic models and techniques are needed to handle textual feedback efficiently. To this end, we propose a model for aspect-based opinion mining of comments of students that are posted in online learning platforms. The model aims to predict some of the key aspects related to an online course from students' reviews and then assess the attitude of students toward these commented aspects. The proposed model is tested on a large-scale real-world dataset which is collected for this purpose. The dataset consists of more than 21 thousand manually annotated students' reviews that are collected from Coursera. Conventional machine learning algorithms and deep learning techniques are used for prediction of the aspect categories and the aspect sentiment classification as well. The obtained results with respect to precision, recall, and F1 score are very promising.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133360913","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}
Zhen Zhang, Hongqiang Li, Zheng Gong, Rize Jin, Tae-Sun Chung
The ECG signal analysis and diagnosis algorithms have been studied for decades. There are some state of art algorithms that have been developed. In this paper, we proposed a compatible ECG automatic diagnosis Cloud Computing framework in order to integrate these exist algorithms. On the other hand, there are many studies regarding the IoT based health diagnosis system. But there are few of that aiming at the personal use health monitor and diagnose. Basing on our proposed framework, users can diagnose their heart health status by themselves conveniently anywhere and anytime through the mobile application. The ECG character automatic classification computing algorithm is compatible for Python and MATLAB by introducing the hybrid programming technic on the cloud computing side. So that, it is easy for researchers to integrate their developed algorithm into this framework to build an application quickly. We developed a prototype application as well to verify the availability of this framework.
{"title":"A Compatible ECG Diagnosis Cloud Computing Framework and Prototype Application","authors":"Zhen Zhang, Hongqiang Li, Zheng Gong, Rize Jin, Tae-Sun Chung","doi":"10.1145/3404555.3404640","DOIUrl":"https://doi.org/10.1145/3404555.3404640","url":null,"abstract":"The ECG signal analysis and diagnosis algorithms have been studied for decades. There are some state of art algorithms that have been developed. In this paper, we proposed a compatible ECG automatic diagnosis Cloud Computing framework in order to integrate these exist algorithms. On the other hand, there are many studies regarding the IoT based health diagnosis system. But there are few of that aiming at the personal use health monitor and diagnose. Basing on our proposed framework, users can diagnose their heart health status by themselves conveniently anywhere and anytime through the mobile application. The ECG character automatic classification computing algorithm is compatible for Python and MATLAB by introducing the hybrid programming technic on the cloud computing side. So that, it is easy for researchers to integrate their developed algorithm into this framework to build an application quickly. We developed a prototype application as well to verify the availability of this framework.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123015599","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 basic random walk link prediction method, the probability of a walking particle when selecting a neighbor node for a walk is determined only by the degree of the current node, and it is fixed and uniform, without considering the impact of degree of the neighboring nodes on the transition probability. In view of this, a link prediction algorithm is proposed in which the degrees of the current node and its neighbor nodes jointly determine the transition probability. First, using the transition probability model of Metropolis-Hasting Random Walk (MHRW) algorithm to redefine the transition probability of the walking particles between the neighbor nodes, then combining Random Walk with Restart (RWR) similarity index to propose the Metropolis-Hasting Random Walk with Restart (MHRWR) algorithm in this paper for link prediction. The link prediction comparison experiments been performed on 6 different scale real network datasets. Compared with the benchmark algorithm, the MHRWR algorithm not only improved the AUC index, but also improved the Precision and Ranking score; compared with the RWR algorithm, the AUC value has increased by an average of 2.10%, and the highest is 5.34%. Experimental results show that the MHRWR algorithm of our proposed leads to superior accuracy in link prediction.
在基本随机行走链路预测方法中，行走粒子选择行走邻居节点时的概率仅由当前节点的程度决定，并且是固定的、均匀的，没有考虑相邻节点的程度对转移概率的影响。鉴于此，提出了一种当前节点与其相邻节点度共同决定转移概率的链路预测算法。首先，利用Metropolis-Hasting Random Walk (MHRW)算法的转移概率模型，重新定义行走粒子在相邻节点之间的转移概率，然后结合Random Walk with Restart (RWR)相似度指标，提出本文的Metropolis-Hasting Random Walk with Restart (MHRWR)算法进行链路预测。在6个不同规模的真实网络数据集上进行了链路预测对比实验。与基准算法相比，MHRWR算法不仅提高了AUC指数，而且提高了Precision和Ranking得分;与RWR算法相比，AUC值平均提高了2.10%，最高达到5.34%。实验结果表明，本文提出的MHRWR算法具有较高的链路预测精度。
{"title":"An Improved Link Prediction Algorithm Based on Comprehensive Consideration of Joint Influence of Adjacent Nodes for Random Walk with Restart","authors":"Liang Lv, Can Yi, Banglv Wu, Mingxuan Hu","doi":"10.1145/3404555.3404598","DOIUrl":"https://doi.org/10.1145/3404555.3404598","url":null,"abstract":"In the basic random walk link prediction method, the probability of a walking particle when selecting a neighbor node for a walk is determined only by the degree of the current node, and it is fixed and uniform, without considering the impact of degree of the neighboring nodes on the transition probability. In view of this, a link prediction algorithm is proposed in which the degrees of the current node and its neighbor nodes jointly determine the transition probability. First, using the transition probability model of Metropolis-Hasting Random Walk (MHRW) algorithm to redefine the transition probability of the walking particles between the neighbor nodes, then combining Random Walk with Restart (RWR) similarity index to propose the Metropolis-Hasting Random Walk with Restart (MHRWR) algorithm in this paper for link prediction. The link prediction comparison experiments been performed on 6 different scale real network datasets. Compared with the benchmark algorithm, the MHRWR algorithm not only improved the AUC index, but also improved the Precision and Ranking score; compared with the RWR algorithm, the AUC value has increased by an average of 2.10%, and the highest is 5.34%. Experimental results show that the MHRWR algorithm of our proposed leads to superior accuracy in link prediction.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125006726","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}
Jianzhi Deng, Yuehan Zhou, Xiaohui Cheng, Tianyu Li, C. Qin
In our research, we try to find out the Competing Endogenous RNA Network (ceRNA) and the biomarker of Liver cancer (LC). 490 differentially expressed mRNAs, 248 differentially expressed lncRNAs and 66 differentially expressed miRNAs were screened from the TCGA liver data. Among then, the differentially expressed mRNAs were enriched in 88 biological process, 16 cellular component and 27 molecular function of the gene ontology. And they were mostly enriched in extracellular region, extracellular space, integral component of plasma membrane, regulation of transcription and DNA-templated sequence-specific DNA binding. 14 DElncRNAs, 11 DEmiRNAs and 4 DEmRNAs were built the ceRNA network based on their inter-regulatory. The up-regulated mRNA in liver tumor samples, CCNE1, was regard as the biomarker of liver cancer by the proof of survival analysis and receiver operating characteristic analysis.
{"title":"Biological Big Data Analysis of Competing Endogenous RNA Network and mRNA Biomarker in Liver Cancer","authors":"Jianzhi Deng, Yuehan Zhou, Xiaohui Cheng, Tianyu Li, C. Qin","doi":"10.1145/3404555.3404602","DOIUrl":"https://doi.org/10.1145/3404555.3404602","url":null,"abstract":"In our research, we try to find out the Competing Endogenous RNA Network (ceRNA) and the biomarker of Liver cancer (LC). 490 differentially expressed mRNAs, 248 differentially expressed lncRNAs and 66 differentially expressed miRNAs were screened from the TCGA liver data. Among then, the differentially expressed mRNAs were enriched in 88 biological process, 16 cellular component and 27 molecular function of the gene ontology. And they were mostly enriched in extracellular region, extracellular space, integral component of plasma membrane, regulation of transcription and DNA-templated sequence-specific DNA binding. 14 DElncRNAs, 11 DEmiRNAs and 4 DEmRNAs were built the ceRNA network based on their inter-regulatory. The up-regulated mRNA in liver tumor samples, CCNE1, was regard as the biomarker of liver cancer by the proof of survival analysis and receiver operating characteristic analysis.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121531394","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}
Suleman Khan, F. Azam, Muhammad Waseem Anwar, Yawar Rasheed, Mudassar Saleem, N. Ejaz
Polio is an epidemic disease, which may lead to paralysis and may be fatal enough to cause even death of the infected person. In most of the cases, polio virus has mild symptoms, so, there is a high probability that it can remain unnoticed. This paper aims to understand the eruption, severity and spread of polio virus from a spatio-temporal point of view. This research proposed a novel machine learning model to predict the chances of polio. Particularly, data sets are developed by getting data from several sources such as NIH (National Institute of Health), databases of medical stores and transport logs. Subsequently, K-mean algorithm is applied on the given data to predict the chances of polio's breakout. The preliminary study proved that the proposed model is significant step towards mitigating the challenges of this fatal disease. Furthermore, it also provides a platform/ framework, which can be extended in the development of an automated tool for polio virus detection.
{"title":"A Novel Data Mining Approach for Detection of Polio Disease Using Spatio-Temporal Analysis","authors":"Suleman Khan, F. Azam, Muhammad Waseem Anwar, Yawar Rasheed, Mudassar Saleem, N. Ejaz","doi":"10.1145/3404555.3404591","DOIUrl":"https://doi.org/10.1145/3404555.3404591","url":null,"abstract":"Polio is an epidemic disease, which may lead to paralysis and may be fatal enough to cause even death of the infected person. In most of the cases, polio virus has mild symptoms, so, there is a high probability that it can remain unnoticed. This paper aims to understand the eruption, severity and spread of polio virus from a spatio-temporal point of view. This research proposed a novel machine learning model to predict the chances of polio. Particularly, data sets are developed by getting data from several sources such as NIH (National Institute of Health), databases of medical stores and transport logs. Subsequently, K-mean algorithm is applied on the given data to predict the chances of polio's breakout. The preliminary study proved that the proposed model is significant step towards mitigating the challenges of this fatal disease. Furthermore, it also provides a platform/ framework, which can be extended in the development of an automated tool for polio virus detection.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115851997","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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