2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)最新文献

Anomaly detection in cloud network data 云网络数据中的异常检测

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313014

T. Yasarathna, Lankeshwara Munasinghe

{"title":"Anomaly detection in cloud network data","authors":"T. Yasarathna, Lankeshwara Munasinghe","doi":"10.1109/SCSE49731.2020.9313014","DOIUrl":"https://doi.org/10.1109/SCSE49731.2020.9313014","url":null,"abstract":"Cloud computing is one of the most rapidly expanding computing concepts in the modern IT world. Cloud computing interconnects data and applications served from multiple geographic locations. A large number of transactions and the hidden infrastructure in cloud computing systems have presented a number of challenges to the research community. Among them, maintaining the cloud network security has become a key challenge. For example, detecting anomalous data has been a key research area in cloud computing. Anomaly detection (or outlier detection) is the identification of suspicious or uncommon data that significantly differs from the majority of the data. Recently, machine learning methods have shown their effectiveness in anomaly detection. However, identifying anomalies or outliers using supervised learning methods still a challenging task due to the class imbalance and the unpredictable nature and inconsistent properties or patterns of anomaly data. One-class classifiers are one feasible solution for this issue. In this paper, we mainly focused on analyzing cloud network data for identifying anomalies using one-class classification methods namely One Class Support Vector Machine(OCSVM) and Autoencoder. Here, we used a benchmark data set, YAHOO Synthetic cloud network data set. To the best of our knowledge, this is the first study that used YAHOO data for detecting anomalies. According to our analysis, Autoencoder achieves 96.02 percent accuracy in detecting outliers and OCSVM achieves 79.05 percent accuracy. In addition, we further investigated the effectiveness of a one class classification method using another benchmarked data set, UNSW-NB15. There we obtained 99.10 percent accuracy for Autoencoder and 60.89 percent accuracy for OCSVM. The above results show the neural network-based methods perform better than the kernel-based methods in anomaly detection in cloud network data.","PeriodicalId":163774,"journal":{"name":"2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273058","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}

引用次数: 1

Detecting human emotions on Facebook comments 在Facebook评论中检测人类情绪

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313015

E. J. A. Chathumali, S. Thelijjagoda

{"title":"Detecting human emotions on Facebook comments","authors":"E. J. A. Chathumali, S. Thelijjagoda","doi":"10.1109/SCSE49731.2020.9313015","DOIUrl":"https://doi.org/10.1109/SCSE49731.2020.9313015","url":null,"abstract":"Human emotion detection plays a vital role in interpersonal relationships. From the early eras, automatic recognition of emotions has been an active research topic. Today, sharing emotions on social media is one of the most popular activities among internet users. However, when it comes to a specific domain like emotion detection in social media, it is still on a research-level. There are less number of applications have been developed to detect emotions online, using online comments and user comments. The aim of this research is to develop a system that identifies human emotions on Facebook comments. Among the different social media platforms, this research specifically focuses on Facebook comments written in the English language to narrow down the problem. The research is based on Semantic analysis, which comes under Natural Language Processing (NLP) and the system development consists of four major steps, including the extraction of Facebook comments via Graph API, preprocessing, classification and emotion detection. To classify the emotions, a classification model was created by using Naïve Bayes Algorithm. When it comes to marketing, emotions are what lead your onlookers to purchase. By using the detected emotions, marketers can promote their campaigns by changing online advertisements dynamically. The results obtained through testing the system show that it is capable of accurately identifying human emotions hidden in Facebook comments with an accuracy level of 80%, making it highly useful for marketing purposes.","PeriodicalId":163774,"journal":{"name":"2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115098141","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}

引用次数: 3

SCSE 2020 Cover Page SCSE 2020封面

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/scse49731.2020.9313045

引用次数: 0

Sentiment classification of Sinhala content in social media 社交媒体中僧伽罗语内容的情感分类

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313023

P. Jayasuriya, Sarith Ekanayake, Ranjiva Munasinghe, B. Kumarasinghe, Isuru Weerasinghe, S. Thelijjagoda

引用次数: 2

IoT based animal classification system using convolutional neural network 基于物联网的卷积神经网络动物分类系统

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313018

L. G. C. Vithakshana, W. Samankula

{"title":"IoT based animal classification system using convolutional neural network","authors":"L. G. C. Vithakshana, W. Samankula","doi":"10.1109/SCSE49731.2020.9313018","DOIUrl":"https://doi.org/10.1109/SCSE49731.2020.9313018","url":null,"abstract":"The kingdom “Animalia” is used to represent all living creatures on the planet earth, which is fallen into six categories. The language is the most common factor to divide humans and animals. Numerous classification techniques can be used for classification purposes, and the classification commonly can be done acoustically and visually. The classification systems are playing a considerable role, and bioacoustics monitoring was a significant field of study. Visual classification of animals is done by using either satellite images or established camera images. Nevertheless, due to some circumstances, image processing techniques cannot be applied. Then the acoustical classification techniques are taken place to encounter those problems. Even with acoustical methods, a remote observing method is required due to a few issues. Applying an IoT based acoustic classification system was designed using Convolutional Neural Networks (CNN), which is beneficial for those who are interested in monitoring ecosystems such as animal scientists, zoologists, and environmentalists. The hardware implementation was designed to collect the data from the place it was placed. The audio clips were preprocessed using the Mel-frequency Cepstral Coefficient (MFCC). A CNN architecture based on TensorFlow was used for the training process. To train and test the network, 400 sound clips of two seconds, such that 40 per each ten animal species, which were gathered from online libraries and formatted using Audacity, were used. The network was trained by changing the different gradient descent optimizers and eventually obtained the confusion matrices for each. The best result was gained by the AdaDelta, Gradient Descent, and RMSProp optimizers with 91.3% accuracy for each. Among them, AdaDelta had the most stable and increasing learning approach. As a future extension, to improve accuracy, a large number of data will be used.","PeriodicalId":163774,"journal":{"name":"2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274210","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}

引用次数: 7

Keyword extraction from Tweets using NLP tools for collecting relevant news 使用NLP工具从tweet中提取关键字以收集相关新闻

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313024

Thiruni D. Jayasiriwardene, G. U. Ganegoda

{"title":"Keyword extraction from Tweets using NLP tools for collecting relevant news","authors":"Thiruni D. Jayasiriwardene, G. U. Ganegoda","doi":"10.1109/SCSE49731.2020.9313024","DOIUrl":"https://doi.org/10.1109/SCSE49731.2020.9313024","url":null,"abstract":"Keywords play a major role in representing the gist of a document. Therefore, a lot of Natural Language processing tools have been implemented to identify keywords in both structured and unstructured texts. Text that appears in social media platforms such as twitter is mostly unstructured because of the character limitation. Consequently, a lot of short terms and symbols such as emoticons and URLs are included in tweets. Keyword extraction from grammatically ambiguous text is not easy compared to structured text since it is hard to rely on the linguistic features in unstructured texts. But when it comes to news on twitter, it may contain somewhat structured text than informal text does but it depends on the tweeter, the person who posts the tweet. In this paper, a methodology is proposed to extract keywords from a given tweet to retrieve relevant news that has been posted on twitter, for fake news detection. The intention of extracting keywords is to find more related news efficiently and effectively. For this approach, a corpus that contains tweet texts from different domains is built in order to make this approach more generic instead of making it a domain-specific approach. In fact, the Stanford Core NLP tool kit, Wordnet linguistic database and statistical method are used for extracting keywords from a tweet. For the system evaluation, the Turing test which has human intervention is used. The system was able to acquire an accuracy of 67.6% according to the evaluation conducted.","PeriodicalId":163774,"journal":{"name":"2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133849856","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}

引用次数: 5

Relationships between climatic factors to the paddy yield in the North-Western Province of Sri Lanka 斯里兰卡西北省气候因素与水稻产量的关系

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313047

L. Wickramasinghe, J. Jayasinghe, Upaka S. Rathnayake

{"title":"Relationships between climatic factors to the paddy yield in the North-Western Province of Sri Lanka","authors":"L. Wickramasinghe, J. Jayasinghe, Upaka S. Rathnayake","doi":"10.1109/SCSE49731.2020.9313047","DOIUrl":"https://doi.org/10.1109/SCSE49731.2020.9313047","url":null,"abstract":"Climate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.","PeriodicalId":163774,"journal":{"name":"2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115427182","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}

引用次数: 1

Contextual assistant framework for the Sinhala language 僧伽罗语上下文辅助框架

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313056

D. Dasanayaka, N. Warnajith

{"title":"Contextual assistant framework for the Sinhala language","authors":"D. Dasanayaka, N. Warnajith","doi":"10.1109/SCSE49731.2020.9313056","DOIUrl":"https://doi.org/10.1109/SCSE49731.2020.9313056","url":null,"abstract":"Continuous customer relationship plays an important role in the success of any business milieus in today’s world. Nonetheless, it can be harder to achieve consistent engagement with the customers round the clock and therefore many businesses have paved their focus in using a variety of solutions in overcoming this scenario. Contextual assistants that can have both linear and non-linear conversations with humans implicitly plays a prominent role in such situations. In contrast to resource-rich languages, creating a contextual assistant for resource-poor languages like Sinhala has been difficult mainly due to the unavailability of a rich digital footprint and the complexity of the language. Hence, this research was conducted to propose and implement a novel and common architecture of a contextual assistant framework for the Sinhala language. Here we have used a deep learning Intent Mapping (IM) model to map the consumer response to a predefined “Intent” and a Feature Extraction Mechanism (FEM) to extract related information from the input text. A set of data types for this framework were defined and FEM was trained to identify them efficiently. The IM model gave an accuracy output of 89.67 percent. The results depicted that the implemented system performs with higher accuracy in linear conversations.","PeriodicalId":163774,"journal":{"name":"2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114505675","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}

引用次数: 1

International Research Conference on Smart Computing and Systems Engineering SCSE 2020 Proceedings [Full Conference Proceedings] 智能计算与系统工程国际研究会议SCSE 2020会议文集[会议全文]

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/scse49731.2020.9313027

引用次数: 0

Grammatical error detection and correction model for Sinhala language sentences 僧伽罗语句子语法错误检测与纠错模型

2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) Pub Date : 2020-09-24 DOI: 10.1109/SCSE49731.2020.9313051

H. Pabasara, S. Jayalal

{"title":"Grammatical error detection and correction model for Sinhala language sentences","authors":"H. Pabasara, S. Jayalal","doi":"10.1109/SCSE49731.2020.9313051","DOIUrl":"https://doi.org/10.1109/SCSE49731.2020.9313051","url":null,"abstract":"As the national language of Sri Lanka, the greater part of the exercises at most of all the services are completed in Sinhala whereas it is imperative to guarantee the spelling and syntactic accuracy to convey the ideal significance from the perspective of automated materials with the unavailability of resources even though there are enough amount of available materials as hard copy and books. With the high multifaceted nature of the language, it sets aside extensive effort to physically edit the substance of a composed setting. The necessity to overcome this problem has risen numerous years back. But with the complexity of grammar rules in morphologically lavish Sinhala language, the accuracy of the grammar checkers developed so far has been contrastingly lower and thus, to overcome the issue a novel hybrid approach has been introduced. Spell checked Sinhala active sentences being preprocessed, separated nouns and verbs were analyzed with the help of a resourceful part-of-speech-tagger and a morphological analyzer and alongside the sentences were sent through a pattern recognition mechanism to identify its sentence pattern. Then a decision tree-based algorithm has been used to evaluate the verb with the “subject” and output feedback about the correctness of the sentence. To train this decision tree, a dataset consisting of 800 records which included information about 25 predefined grammar rules in Sinhala was used. Finally, the error correction was provided using a machine learning algorithm-based sentence guessing model for the three possible tenses. Conducted research results paved the way to identify the sentence pattern, grammar rules and finally, suggest corrections for identified incorrect grammatical sentences with an acceptable accuracy rate of 88.6 percent which concluded that the proposed hybrid approach was an accurate approach for detecting and correcting grammatical mistakes in Sinhala text.","PeriodicalId":163774,"journal":{"name":"2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121456430","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}

引用次数: 3