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A Hybrid Model for Music Genre Classification Using LSTM and SVM 基于LSTM和SVM的音乐类型分类混合模型
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530557
Prasenjeet Fulzele, Rajat Singh, Naman Kaushik, Kavita Pandey
{"title":"A Hybrid Model for Music Genre Classification Using LSTM and SVM","authors":"Prasenjeet Fulzele, Rajat Singh, Naman Kaushik, Kavita Pandey","doi":"10.1109/IC3.2018.8530557","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530557","url":null,"abstract":"With today's cutting edge technology and intractable access to voluminous data files via the internet, it is important to meet the computational needs of every user. Machine learning is one such growing branch of artificial intelligence that has made such demands of the users viable. Machine learning models are paving the way for classification techniques such as in music genre classification, and have shown to be efficient in predicting classes to a great extent. To exploit the time dependent nature of the dataset Long Short-Term Memory (LSTM) Neural Network is used for music genre classification and combined with Support Vector Machine (SVM) classifier to enhance its performance. The hybrid model of these two classifiers resulted into an increase in the accuracy of prediction of the individual models. This hybrid model is imposed on GTZAN music dataset and is compared with the results of standalone models of LSTM and SVM. The proposed model exceeded the independent accuracies of the LSTM and SVM classifiers with an accuracy of 89%, reaffirming the efficient utilization of each classifier.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133581668","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: 19
A New Approach for Vehicle Number Plate Detection 一种新的车牌检测方法
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530600
Sarthak Babbar, Saommya Kesarwani, Navroz Dewan, Kartik Shangle, Sanjeev Patel
{"title":"A New Approach for Vehicle Number Plate Detection","authors":"Sarthak Babbar, Saommya Kesarwani, Navroz Dewan, Kartik Shangle, Sanjeev Patel","doi":"10.1109/IC3.2018.8530600","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530600","url":null,"abstract":"Identification of cars and their owners is a tedious and error prone job. The advent of automatic number plate detection can help tackle problems of parking and traffic control. The system is designed using image processing and machine learning. A new system is proposed to improve detection in low light and over exposure conditions. The image of vehicle is captured, which is preprocessed using techniques like grayscale, binarization. The resultant image is passed on for plate localization, for extracting the number plate using CCA (Connected Component Analysis) and ratio analysis. De-noising of number plate is done using various filters. The characters of the number plate are segmented by CCA and ratio analysis as well. Finally, the recognized characters are compared using techniques such as SVC (linear), SVC (poly), SVC (rbf), KNN, Extra Tree Classifier, LR+RF, and SVC+KNN. The proposed techniques help the system to detect well under dim light, over-exposed images and those in which the vehicle is angled.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131384567","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: 13
Gender Identification From Children's Speech 儿童言语中的性别认同
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530666
P. B. Ramteke, Amulya A. Dixit, S. Supanekar, Dr. Nagaraj V. Dharwadkar, S. Koolagudi
{"title":"Gender Identification From Children's Speech","authors":"P. B. Ramteke, Amulya A. Dixit, S. Supanekar, Dr. Nagaraj V. Dharwadkar, S. Koolagudi","doi":"10.1109/IC3.2018.8530666","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530666","url":null,"abstract":"Children's speech can be characterized by higher pitch and format frequencies compared to the adult speech. Gender identification task from children's speech is difficult as there is no significant difference in the acoustic properties of male and female child. Here, an attempt has been made to explore the features efficient in discriminating the gender from children's speech. Different combinations of spectral features such as Mel-frequency cepstral coefficients (MFCCs), ΔMFCCs and ΔΔMFCCs, Formants, Linear predictive cepstral coefficients (LPCCs); Shimmer and Jitter; Prosodic features like pitch and its statistical variations along with Δpitch related features are explored. Features are evaluated using non linear classifiers namely Artificial Neural Network (ANNs), Deep Neural Network (DNNs) and Random Forest (RF). From the results it is observed that the RF achieves an highest accuracy of 84.79% amongst the other classifiers.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133151286","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: 6
Link Prediction Method Using Topological Features and Ensemble Model 基于拓扑特征和集成模型的链路预测方法
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530624
Shruti Pachaury, Nilesh Kumar, Ayush Khanduri, H. Mittal
{"title":"Link Prediction Method Using Topological Features and Ensemble Model","authors":"Shruti Pachaury, Nilesh Kumar, Ayush Khanduri, H. Mittal","doi":"10.1109/IC3.2018.8530624","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530624","url":null,"abstract":"Online social networking has progressively been the new interdisciplinary research area, especially for developing new strategies of investigating these informal networks containing billions of users. However, such networks might not represent real-world connections among people either due to imperfect procurement forms or not yet reflected on the online platform like friends in real-world might not connect with each other online. To predict these unknown connections in the online community is still an open-ended problem. In this paper, a novel link prediction method is proposed to find the missing connections in the social network graphs. The proposed method extracts topological features from the network graph which are used to train an ensemble learning model i.e., random forest classifier. The trained model is used to predict the missing connections. The experimental evaluation is conducted on two networking dataset namely; ‘Facebook networking dataset’ and the ‘Flickr following dataset’ publicly available on Stanford Network Analysis Project (SNAP) and Koblenz Network Collection (KONECT) respectively. The comparison is done with the prediction results on the same features by the state-of-the-art learning models namely; linear support vector machine (LSVM), K-Nearest Neighbours (KNN), AdaBoost, and Gradient Boost. The performance of the considered methods is defined in terms of accuracy, precision, recall, F1-measure, and AUC value. Additionally, the efficiency of the proposed method is validated against the existing link prediction method. The experimental results conclude that the proposed method is accurate than the compared methods in uncovering the hidden links of a social network.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121118491","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
A Recommendation System for Online Purchase Using Feature and Product Ranking 基于特征和产品排名的在线购物推荐系统
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530573
R. Karthik, S. Ganapathy, A. Kannan
{"title":"A Recommendation System for Online Purchase Using Feature and Product Ranking","authors":"R. Karthik, S. Ganapathy, A. Kannan","doi":"10.1109/IC3.2018.8530573","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530573","url":null,"abstract":"Social network occupies an important place and takes a considerable amount of time in people's daily life. It has become so popular that people are sharing a huge amount of data and opinion on social network/review sites, which in turn helps to find interesting insights for organizations/vendors or consumers. In this paper, we propose a new algorithm called Feature Based Product Ranking and Recommendation Algorithm (FBPRRA) for providing suggestions to the customers whose are interested in purchasing good quality products. The proposed algorithm analyzes online products and ranks them according to product reviews. Finally, it recommends the suitable product to the target customers. Experiments have been conducted using online reviews for evaluating the proposed algorithm and found that the proposed recommendation algorithm achieves better prediction accuracy than the exiting classifiers such as Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and K-NN.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115537345","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: 13
Empirical Analysis of Bitcoin Market Volatility Using Supervised Learning Approach 基于监督学习方法的比特币市场波动实证分析
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530636
Hrishikesh Singh, Parul Agarwal
{"title":"Empirical Analysis of Bitcoin Market Volatility Using Supervised Learning Approach","authors":"Hrishikesh Singh, Parul Agarwal","doi":"10.1109/IC3.2018.8530636","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530636","url":null,"abstract":"Crypto currencies are considered as the next model of economics and monetary exchange. In recent years, popular cryptocurrency such as Bitcoin and Ethereum witness an exponential growth in economic sphere. In this paper empirical testing of four conventional machine learning methods is performed to predict the bitcoin prices using last eight years of transactional data. Linear and polynomial regression is implemented using all the features individually. Polynomial regression, Support Vector regression and KNN regression are hyper tuned with grid search logic. Results depicted that KNN regression outperformed others models in attaining mean square error of 0.00021.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128940028","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: 6
Recommending Optimal Tour for Groups Using Ant Colony Optimization 利用蚁群算法为团体推荐最优行程
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530523
Parul Agarwal, Mayank Sourabh, Rishabh Sachdeva, Siddharh Sharma, S. Mehta
{"title":"Recommending Optimal Tour for Groups Using Ant Colony Optimization","authors":"Parul Agarwal, Mayank Sourabh, Rishabh Sachdeva, Siddharh Sharma, S. Mehta","doi":"10.1109/IC3.2018.8530523","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530523","url":null,"abstract":"Tour recommendation is one of the special cases of travelling salesman problem. Many researchers provided near optimal solution to this problem. However, literature shows that tours are mainly recommended to specific persons not to the group of people. This paper provides the different algorithmic approaches to give near optimal tours for the group of people. For such type of problem, it is essential to find the point of interest (POI) of each person in a group. The problem is quite intricate because all people of the group should be satisfied from the tour obtained. Four algorithmic implications are adopted in this work to find optimal tours-exhaustive search, greedy algorithm, dynamic programming and ant colony optimization (ACO). Optimal tour for group not only means shortest length path (total cost) but also satisfaction value of each person from tour obtained. Satisfaction value is a like or dislike of each person in a group from POIs considered in a tour. It was observed that ACO provide better results i.e. better combination of total cost value and satisfaction value as compared to other algorithms.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"897 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120864386","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
Invitation or Bait? Detecting Malicious URLs in Facebook Events 邀请还是诱饵?检测Facebook事件中的恶意url
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530525
Sonu Gupta, Shelly Sachdeva
{"title":"Invitation or Bait? Detecting Malicious URLs in Facebook Events","authors":"Sonu Gupta, Shelly Sachdeva","doi":"10.1109/IC3.2018.8530525","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530525","url":null,"abstract":"With 2.2 billion monthly active users, Facebook is the most popular Online Social Network. Given its huge popularity and diverse features such as pages, events, groups etc., it is potentially the most attractive platform for cybercriminals to launch various attacks. In this paper, we study the role of Facebook Events in disseminating malicious URLs in the network. Here, we focus our analysis on Facebook Events which are created by Facebook Pages. The existing services like Web of Trust (WOT) and other blacklists follow crowdsourcing models. Thus, malicious URLs can only be detected once they are widespread on the network and has done significant damage. Therefore, we train a supervised machine learning model on our labeled dataset to create an efficient classifier for automatic detection of malicious Facebook events, independent of blacklists and third-party reputation services. Our model is able to classify malicious events with 75% accuracy using Support Vector Machine. To the best of our knowledge, this is the first paper to study the presence of malicious URLs on Facebook Events.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125026517","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
Matching Witness' Account with Mugshots for Forensic Applications 将证人的帐户与法医应用的脸部照片相匹配
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530669
Agnitha Mohan, R. Dhir, Hrishikesh Hirashkar, Nagaratna B. Chittaragi, S. Koolagudi
{"title":"Matching Witness' Account with Mugshots for Forensic Applications","authors":"Agnitha Mohan, R. Dhir, Hrishikesh Hirashkar, Nagaratna B. Chittaragi, S. Koolagudi","doi":"10.1109/IC3.2018.8530669","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530669","url":null,"abstract":"This paper proposes a system that can be used by the forensics department to identify and disclose criminal details automatically. The problem of matching the description of a suspect in a crime scene provided by an eye-witness to existing mugshots (mugshots represents photograph taken as someone is arrested) in the police departments criminal database is addressed in this work. Prominent features such as skin colour, size of nose & lips, shape the & size of eyes, and shape of the face are considered for discrimination of individual criminals. The witness fills in the description fields through which, most appropriate images are selected from an existing database. Images are scored on the basis of the degree of closeness to the given description, and most relevant images are displayed first followed by the rest. The classification of images based on explored facial features is done using extreme gradient boosting (XGBoost) supervised an ensemble learning method. Comparatively better performances are observed.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124451663","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: 0
Empirical Investigation of Trends in NoSQL-Based Big-Data Solutions in the Last Decade 近十年来基于nosql的大数据解决方案发展趋势的实证研究
2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530582
Harshit Gujral, Abhinav Sharma, Parmeet Kaur
{"title":"Empirical Investigation of Trends in NoSQL-Based Big-Data Solutions in the Last Decade","authors":"Harshit Gujral, Abhinav Sharma, Parmeet Kaur","doi":"10.1109/IC3.2018.8530582","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530582","url":null,"abstract":"The usage and popularity of NoSQL databases have sharply risen over the past decade due to their ability to handle a huge amount of data by employing scalable architecture, high availability and better performance than traditional relational database systems (RDBMS). In addition to reporting dynamics in NoSQL-database world, this paper focuses on presenting results from the perspective of developers. Stack Overflow provides a comprehensive technical niche with about 15 million technical questions, 8.1 million users and 25 million answers. In this paper, we aim to study variation in yearly trends of 20 NoSQL databases. To reveal the interest of the programmers we have investigated questions-asked and presented an unbiased Normal Interest Score by employing three parameters, first, the number of questions asked, second, mean views on a question and third, the mean score on a question. MongoDB, Cassandra, Redis, and Neo4j emerged as most popular databases in their respective families while NIS of all four of them is decreasing 2015 onwards. Additionally, we have also discussed how real-world events like publications, open-sourcing, mention in critical bills, version-release, acquiring ventures etc affect the interest corresponding to NoSQL databases over Stack Overflow. Results of this work will help database developers, database administrators for database selection, upgradation and maintenance.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134521909","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
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