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Social Media is the next logical marketing arena. Currently, Social networking sites dominate the digital marketing space. In the past years, the World Wide Web (WWW) has become a huge source of user-generated content and opinionative data. Using social media, such as Twitter, facebook, etc, user shares their views, feelings in a convenient way. Social media, where millions of people express their views in their daily interaction, provides their sentiments and opinions about particular thing. A lot of work done earlier analyzes the polarity from text but the accuracy of existing technique is not acceptable because of the lack of feature optimization selection. The concept of feature optimization is used to find out the relevant data according to the sentiment classes. By using the concept of feature optimization technique, the chances of removal of irrelevant data is more and we can achieve better accuracy. In the proposed work, a classification technique named as Artificial Neural Network (ANN) along with genetic algorithm, an optimization algorithm will be used and it can train the large amount of dataset that is optimized by using Genetic Algorithm (GA) approach and can be divided into their groups according to the feature for social sentiment database.

Data mining, Genetic algorithm, ANN,ARM

[1] K. Sarvakar, U. K. Kuchara, “Sentiment Analysis of movie reviews: A new feature-based sentiment classification” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp. 8-12 , 2018.
[2] C. Nanda, M. Dua, “A Survey on Sentiment Analysis” International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.67-70, 2017.
[3] A. Palve, R. D. Sonawane, Amol D. Potgantwar, “Sentiment Analysis of Twitter Streaming Data for Recommendation using Apache Spark” International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp. 99-103, 2017.
[4] S. Jain, “Mining Big Data using Genetic Algorithm” International Research Journal of Engineering and Technology (IRJET 2017), Vol.04, Issue.07, pp.743-747,2017.
[5] S. B. Maind, P Wankar, “Research Paper on Basic of Artificial Neural Network” International Journal on Recent and Innovation Trends in Computing and Communication, Vol.2, Issue.1, pp.96-100, 2014.
[6] N. Sharma, R. Pabreja, U. Yaqub, V. Atluri, S. A. Chun, and J. Vaidya, “Web-based application for sentiment analysis of live tweets,” In the Proceeding of 19th International Conference on Digital Government Research Governance in Data Age, , Delft, Netherlands, pp. 1–2, 2018.
[7] S. M. Jimenez-Zafra, M. T. Martin Valdivia, E. Martinez Camara, and L. A. Urena-Lopez, “Studying the Scope of Negation for Spanish Sentiment Analysis on Twitter,” IEEE Transactions on Affective Computing. , Vol. 14, Issue. 8, pp. 1–14, 2017.
[8] El Alaoui, I., Gahi, Y., Messoussi, R., Chaabi, Y., Todoskoff, A., & Kobi, A, “A novel adaptable approach for sentiment analysis on big social data.” Journal of Big Data, Vol.5(1), pp.1-18, 2018.
[9] C´ and cero N. dos Santos, “Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts,” 25th International Conference on Computing Linguistic, pp. 69–78, 2014.
[10] F. Neri, C. Aliprandi, F. Capeci, M. Cuadros, and T. By, “Sentiment analysis on social media,” In the proceeding of IEEE/ACM International Conference on Advanced Social Networks Analysis Mining, (ASONAM 2012), Birmingham, UK pp. 919–926, 2012.

Gitanjali, Shailja, "An Artificial Neural Network Based Sentiment Analysis System Using Optimization Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1726-1730, 2019.

Machine Translation (MT) is widely considered among the most difficult tasks and applications in the area of Natural Language Processing (NLP) and Computational Linguistics (CL). MT is the method of translating text from source language to target language using computer. The main objective of this proposed research work is to develop Bodo to English machine translation system for enhancing the translation result of Bodo to English Statistical Machine Translation (SMT) System by taking their respective parallel corpus. Here a statistical machine translation engine Moses is used to train statistical models of text translation from source language to a target language. We also used IRSTLM tool to develop the language model and GIZA++ tool to align the words respectively.

Bodo Language, English Language, Machine translation, Moses, Corpus

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[13] Md. Zahurul Islam, “English to Bangla Statistical Machine Translation”, Master Thesis, Universitat des Saarlendes, August 2009.
[14] N.Sharma, P.Bhatia, V.Singh, “English to Hindi Statistical Machine Translation”, June 2011.
[15] Aadil, M. and Asger, M., “English to Kashmiri Transliteration System: A Hybrid Approach”, International Journal of Computer Applications, 162(12), 2017.
[16] Biswajit Brahma, Anup Kr Barman, Shikhar Kr. Sarma, Bhatima Boro, “Corpus Building of Literary Lesser Rich Language-Bodo: Insights and Challenges”, Conference: Proceedings of the 10th Workshop on Asian Language Resources, December 2012.

Maheswar Daimary, Shikhar Kumar Sarma, Mirzanur Rahman, "Bodo To English Statistical Machine Translation System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1731-1736, 2019.

Information retrieval has a motive for obtaining the meaningful information on the basis of user demand. Information retrieval plays a major role in providing the information from huge amount of documents as per the requirements. Now days, the huge amount of data has been spread all over the world. We acquire data from various sources viz; internet, social media etc. some data is created by ourselves. In our system we have lot of documents stored but it is very difficult to address meaningful document or to find the information which relates our document. It is time consuming task to collect the needed information or document from the dataset available with us. In this paper, the focus is done over the information retrieval by constructing ontology framework. TF-IDF will help to find frequency of word present in document which will help to get the weightage of document. Input will be dataset & user document and the output will be documents matching the user document. The threshold is set to retrieve the accurate documents.

Information retrieval, Feature extraction, term frequency& inverse document frequency, ontology

[1] Aizhang Guo, Tao Yang, “Research and Improvement of feature words weight based on TFIDF Algorithm” IEEE 2016.
[2] T.MuthamilSelvan, B.Balamurugan, “Cloud based automated framework for semantic rich ontology construction and similarity computation for E-health applications” 2352-9148, 2016 Elsevier Ltd.
[3] Kaijian Liu and Nora El-Gohary, “Ontology-based sequence labelling for automated information extraction for supporting bridge data analytics” 1877-7058 Elsevier Ltd 2016.
[4] Jie Tao, Amit V. deokar and Omar F. El-Gayar, “An Ontology-based Information Extraction (OBIE) Framework for Analyzing Initial Public Offering (IPO) Prospectus”, 978-1-4799-2504-9/14 IEEE 2014.
[5] Yuefeng Liu and Minyoung Shi, Chunfang Li, “Domain Ontology Concept Extraction Method Based on Text” 978-1-5090-0806-3/16, 2016 IEEE, ICIS 2016.
[6] Chaleerat Thamrongchote and wiwat vatanwood, “Business Process Ontology for Defining User Story” 978-1-5090-0806-3/16, IEEE 2016, ICIS 2016.
[7] Tarek Helmey, Ahmed Al-Nazer, Saeed Al-Bukhitan, Ali Iqbal, “Health, Food and User’s Profile Ontologies for Personalized Information Retrieval” Elsevier B.V 2015.
[8] Ying Qin, “Applying Frequency and Location Information to Keyword Extraction In Single Document” 978-1-4673-1857-0/12 IEEE 2012.
[9] Bernardus Ari Kuncoro and Banbang Heru Iswanto, “TF-IDF Method in Ranking Keywords of Instagram User’s Image Caption” 978-1-4673-6664-9/15 IEEE 2015.
[10] Prafulla Bafna, Dhanya Pramod, Anagha Vaidya, “Document Clustering: TF-IDF” 978-1-4673-9939-5 IEEE 2016.
[11] Amol N. Jamgade, Shivkumar J. Karale, “Ontology Based Information Retrieval System for Academic Library” 978-1-4799-6818-3/15 IEEE 2015.
[12] Aradhana R Patil, Amrita A Manjrekar, “A Novel Method To Summarize and Retrieve Text Documents Using Text Feature Extraction Based on Ontology” 978-1-5090-0774-5/16 IEEE 2016.
[13] Mohamed K. Elhadad, Khaled M. Badran, Gouda I. Salama, “A Novel Approach for Ontology-based Dimensionality Reduction for Web Text Document Classification” IEEE ICIS 2017, Wuhan, China.
[14] Yan Ying, Tan Qingping, Xie Qinzheng, Zeng Ping, Li Panpan “A Graph-based Approach of Automatic Keyphrase Extraction” 1877-0509 ICICT 2017.
[15] Eko Darwiyanto, Ganang Arief Pratama, Sri Widowati, “ Multi Words Quran and Hadith Searching Based on News Using TF-IDF” 978-1-4673-9879-4 IEEE 2016.

Sharvali S. Sarnaik, Ajit S. Patil, "Efficient Retrieval of Relevant Documents by Constructing Ontology Framework," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1737-1740, 2019.

Modern society because of their life style is always prone to imbalanced metabolism disease called diabetes. Early diagnosis of diabetes is major challenge in real life since people don’t check their blood glucose level very often. But if the diabetes remains unattended or is detected at late stage, may lead to severe problem. So, what is important is to predict the diabetes at earliest. For the same reason various researchers are taking efforts by using various data mining techniques for the early prediction of diabetes. The automated prediction system is just one of the outcomes of the efforts taken by the researchers. The proposed system uses convolutional neural network for this kind of classification.

diabetes, Prediction of diabetes, convolution neural network, classification

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[5] D. C. Y. Tharani.S, "Classification using Convolutional Neural Network for Heart and Diabetics Datasets," International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 12, pp. 417-422, december 2016.
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[14] R. Zolfaghari, "Diagnosis of Diabetes in Female Population of Pima Indian Heritage with Ensemble of BP Neural Network and SVM," IJCEM International Journal of Computational Engineering & Management, vol. 15, no. 4, pp. 115-121, July 2012.
[15] D. R. K. S. Manaswini Pradhan, "Predict the onset of diabetes disease using Artificial Neural Network (ANN)," International Journal of Computer Science & Emerging Technologies , vol. 2, no. 2, pp. 303-311, April 2011.
[16] T. Y. Kamer Kayaer, "Medical diagnosis on Pima Indian diabetes using general regression neural networks," Researchgate, january 2003.
[17] R. &. M. M. &. M. K. S. Ramezani, "A novel hybrid intelligent system with missing value imputation for diabetes diagnosis," Alexandria Engineering Journal., April 2017.
[18] D. &. P. S. Choubey, " GA_RBF NN: a classification system for diabetes," International Journal of Biomedical Engineering and Technology, vol. 23, no. 1, pp. 71-91, august 2017.
[19] S. K. P. V. R. Swapna G., "Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals," Procedia Computer Science, vol. 132, pp. 1253-1262, 2018.
[20] R. A. Piyush Samant, "Machine learning techniques for medical diagnosis of diabetes using iris imges," Computer Methods and Programs in Biomedicine, vol. 157, pp. 121-128, 2018.
[21] 2003. [Online]. Available: ] http://ftp.ics.uci.edu/pub/ml-repos/machine-learning databases/pima-indians-diabetes .

Tushar Deshmukh, H.S. Fadewar, Ankur Shukla, "Diagnosis of Diabetes Using Convolutional Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1741-1744, 2019.

The vehicular ad-hoc network (VANET) plays a prominent role in the driver safety through inter-vehicular communication (V2V). Routing is one of the aspects through which vehicle communication have performed through message passing. IEEE 802.11p with the help DSRC supports communication among vehicles (V2V) and in between vehicle to infrastructure (V2I) communication. VANET is basically different from the conventional wireless ad-hoc networks with respect to the speed of the vehicle, fast changes in the topology, fixed movement pattern and frequent disconnection in the links. Thus, developing a routing protocol is a tedious task in the VANET environment. The objective is to verify the behavioral performance analysis of the topological routing protocols in the VANET. The paper consist of the description of the topological routing protocols such as AODV DSDV and AOMDV routing protocols. The simulation is conducted for the topological routing protocols using various scenarios. The parameter analyzed are the average end to end delay, packet delivery ratio, normalized routing load and throughput.

VANET, DSRC, Routing Protocols, AODV, DSDV

[1] S. K. Bhoi and P. M. Khilar, "Vehicular communication: a survey," in IET Networks, vol. 3, no.3, pp. 204-217, September 2014.
[2] Marzak B., Toumi H., Benlahmar E., Talea M., “Performance Analysis of Routing Protocols in Vehicular Ad Hoc Network”, In El- (eds) Advances in Ubiquitous Networking 2. Lecture Notes in Electrical Engineering, vol 397. Springer, Singapore. pp. 31-42, 2017.
[3] Hyun Yu, Sanghyun Ahn and John Yoo, “ A Stable Routing Protocol for Vehicles in Urban Environments”, International Journal of Distributed Sensor Networks volume, 2013.
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[6] Tarapiah, Saed, Aziz, Kahtan, Atalla and Shadi, “Analysis the Performance of Vehicles Ad Hoc Network,” Procedia Computer Science 4th Information Systems International Conference ISICO 2017, Bali Indonesia, 2017.
[7] Yasser Ahmed, M. Zorkany and Kader Abdel Neamat, “Vanet routing protocol for V2V implementation A suitable solution for developing countries,” Cogent Engineering, 2017.
[8] G. Li, L. Boukhatem and J. Wu, "Adaptive Quality-of-Service-Based Routing for Vehicular Ad Hoc Networks With Ant Colony Optimization," IEEE Transactions on Vehicular Technology, vol. 66, no. 4, pp. 3249-3264, April 2017.
[9] M. Hashem Eiza, T. Owens and Q. Ni, "Secure and Robust Multi-Constrained QoS Aware Routing Algorithm for VANETs," in IEEE Transactions on Dependable and Secure Computing, vol. 13, no. 1, pp. 32-45, Jan.-Feb. 1 2016.
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[11] S. A. Ben Mussa, M. Manaf, K. Z. Ghafoor and Z. Doukha, "Simulation tools for vehicular ad hoc networks: A comparison study and future perspectives," 2015 International Conference on Wireless Networks and Mobile Communications (WINCOM), Marrakech, 2015, pp. 1-8.
[12] Martinez, F. J., Toh, C. K., Cano, J.-C., Calafate, C. T. and Manzoni, P. (2011), “A survey and comparative study of simulators for vehicular ad hoc networks (VANETs)”. Wirel. Commun. Mob. Comput., 11: 813–828
[13] V. Bondre and S. Dorle, "Design and performance evaluation of AOMDV routing protocol for VANET," International Conference on Computer, Communication and Control (IC4), pp. 1-4, 2015, Indore.
[14] Marina Mahesh K. and Das Samir R., “Ad hoc on-demand multipath distance vector routing,”WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, vol 6, pp. 969-988,2006.

B.S. Yelure, S.P. Sonavane, "Behavioral Analysis of Routing Protocols in VANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1745-1749, 2019.

The machine learning, as well as data mining techniques, deals with huge datasets. The numbers of dimensions (many features or instances) for these datasets are very large, which reduces performance (accuracy) of classification. The high dimensionality data models generally involve enormous data to be modeled and visualized for knowledge extraction which may require feature selection, classification, and prediction. Because of the high dimensionality of the datasets, it often consists of many redundant and irrelevant features which will grow the classification complexity while degrade the learning algorithm performance. Recent research focuses on improving accuracy by the way of dimension reduction techniques resulting in reducing computing time. So, it leads researchers to easily opt for parallel computing on high-performance computing (HPC) infrastructure. Parallel computing on multi-core and many-core architectures has evidenced to be important when searching for high-performance solutions. The general purpose graphics processing unit (GPGPU) has gained a very important place in the field of high-performance computing as a result of its low cost and massively data processing power. Also, parallel processing techniques achieve better speedup and scaleup. The popular dimensionality reduction methods are reviewed in this paper. These methods are Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Random Projection (RP), Auto-Encoder (AE), Multidimensional scaling (MDS), Non-negative Matrix Factorization (NMF), Locally Linear Embedding (LLE), Extreme Learning Machine (ELM) and Isometric Feature Mapping (Isomap). The objective of this paper is to present parallel computing approaches on multi-core and many-core architectures for solving dimensionality reduction problems in high dimensionality data.

High-performance computing, Parallel computing, Dimensionality reduction, Classification, High-dimensionality data, Graphics processing unit

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[15] J. Chen et al., “A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment", IEEE Transactions on Parallel and Distributed Systems, vol. 28,issue. 4, pp. 919-933, 2017.
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Siddheshwar V. Patil, Dinesh B. Kulkarni, "Parallel Computing Approaches for Dimensionality Reduction in the High-Dimensional Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1750-1755, 2019.

This project mainly focuses on controlling and securing our home or office using machine learning technology. In this system we are implementing a system to monitor our home or office using a security camera system. Most thefts are done in the night and most of the times thieves cover their face during the theft time. So even though the home and office have those security camera systems, we are unable to identify the thief. In this project we use the machine learning technology in the security camera. It identifies each and every object in the visuals by real time. Whenever it identifies a person in the night, the system will send message to the concerned admin and nearby police station. Alarm will be produced as it automatically sends signal to our automation system. This system has the ability to identify the person, animals and all other objects. So system won’t work when it identify an animal in the night.

Machine Learning technology, Security Camera, Identify

[1] Bejammin Danielsson, “Real-time object detection and identification Using machine learning, training a model from a pretrained checkpoint”.
[2] Human Motion Detection and Tracking for Real-Time Security System Pavithra ,Mahanthesh, Stafford Michahial1, Dr. M Shivakumar Asst. Professor, Department of IT1 Prof & Head, Department of ITI. International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 5, Issue 12, December 2016.
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Sam Sebastian, Dipin S Nair, Aiswarya B Nair, Jeswin Elza Varghese, Sithu Ubaid, "Automation of Security System Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1756-1761, 2019.

In most of the countries, Electricity Power is a major concern. Specially for the developing countries like India. For Electricity Power generation from Fossil fuels will not be last long, as well as they cause pollution in environment. There are other factors that can be count for Power generation can be generating the Power from agriculture waste, also heat energy can be stored through various technology for generating power form it when it’s required. In this paper there is literature review of Increasing the Power generation by increasing the efficiency of Biomass plant, thermal plant, Solar plant or by the combination of all these in appropriate way. Possible methods are assumed for two plants. One is combination of solar and Biogas plant, which can be operated on steam as well as gas turbine as it can increase the efficiency by 50%. In another system there is thermal storage system, with the help of molten salt as Solar can provide the energy during day time only, but the thermal storage system it can generate energy when sun radiations are not available.

Solar plant, Biomass plant, efficiency

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[3] Ifegwu Eziyi, Anjaneyula Krothapalli, “Sustainable Rural Development: Solar/ Biomass Hybrid Renewable Energy Systems” In proceeding of Energy procedia, Vol.57, pp. 1492-1501, 2014
[4] T.Srinivas, B.V. Reddy, “Hybrid solar-biomass power plant without energy storage” In Proceeding of Case Studies in Thermal Engineering, Vol.2, pp.75-81, 2014
[5] J. Spelling, B. Laumert, T. Fransson, “Advanced hybrid Tower combined-cycle power plants” In Proceedings of Energy Procedia, SolarPACES 2013
[6] J.H. Peterseim, A. Tadros, S. White, U. Hellwig, J. Landler, Kinneth Galang, “Solar tower-biomass hybrid plant-maximizing plant performance”, In Proceedings of Energy Procedia, SolarPACES 2013
[7] S.Kibaara, S.Chowdhury, SP.Chowdhury, “A Thermal Analysis of Parabolic Trough CSP Biomass Hybrid Power System” In the Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference 2012
[8] Swati Negi, Lini Mathew, “Hybrid Renewable Energy System: A Review”, International Journal of Electronic and Electrical Engineering, Vol.7, pp.535-542, 2014
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Vipanjit Kaur, S. Grover, "Increasing Power generation by increasing efficiency of merged operation of Hybrid Solar and Biomass Plant," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1762-1765, 2019.

Cloud computing, being the most recent emerging paradigm, is a technological advancement that aims at turning the vision of computing utilities into a reality. Simply, it is an approach of making technology available to the users, by the usage of Internet servers for data storage and processing. More specifically, cloud computing offers users benefits such as scalability, availability, reliability and global accessibility. Being a radical mechanism, the major obstacles for massive adoption of cloud computing are security, trust and privacy issues. With some defensive procedures like using a combination of methods that include encryption, authentication, and authorization, users are still concerned about the risks associated with their stored data. In this survey, many efficient and secured claiming techniques are investigated for end users to access the data stored in the cloud. The focus is here much on securing the data residing in cloud and privacy in accessing them. The study will throw lights upon keyword search, indexing, file splitting, encryption, and multi-cloud. Further, various methodologies in the existing system and performance of algorithms together with their pros and cons are discussed. Also, cloud security challenges, privacy and communication issues are considered and addressed here.

Keyword Search, Secure Data, Data Privacy, Cloud Computing, Splitting Techniques, Encryption

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Supriya J., Srusti K.S., Gamana G, S. Sukhaniya Ragani, Raghavendra S., Venugopal K.R., "A Survey on Efficient and Secure Techniques for Storing Sensitive Data on Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1766-1777, 2019.

Artificial intelligence is a field of science which aims to automate the activities that require human intelligence. This has been used since last two decades as a development tool in various fields like forecasting, health care, security and also has significantly improved both manufacturing and service system performance. Since AI and its working lies on large amount of data, an algorithms and data science, users fail to understand and grasp the concepts and lacks the skills needed to work with this technology. It is difficult to identify the cause behind system software/hardware crashes because AI is controlled by machines and algorithms. It requires huge fund to implement the system. But there are some facts that support the adoption of AI such as flexible computing power available on the cloud, availability of ready to use software libraries and data. These changes made it possible for the users to build their own algorithms.

Artificial Intelligence, Data mining, Algorithm, ANN

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Aishath Murshida A, Chaithra B K, Nishmitha B, P B Pallavi, Raghavendra S, Mahesh Prasanna K, "Survey on Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1778-1790, 2019.