Volume-7 , Issue-11 , Nov - 2019 Go Back

Heart disease also refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain (angina) or stroke. This paper presents a model for detecting heart disease using machine learning algorithm. The methodology adopted in this research is Agile Methodology, which follows planning, requirements analysis, designing, coding, testing and documentation in parallel during the stage of production process. In this paper a Heart Dataset was trained using four different machine learning algorithms (K-Nearest Neighbours Classifier, Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier). The algorithm with the best accurate result was used in making predictions. This model was deployed to the web using flask (a python framework), it takes 13 inputs from the user in order to make prediction. The model is implemented using python programming language and flask (a web base framework). This paper uses a Decision Tree Classifier Algorithm and the results obtained from the prediction shows an accuracy of about 98.83%, which is really encouraging.

Heart Disease, Machine Learning, K-Nearest Neighbors, Support Vector machine, Decision Tree, Random Forest

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[6]. C.B.C. Latha, S.C. Jeeva, “Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques”, Informatics in Medicine, Unlocked 16, pp.100203, 2019.

O.E. Taylor, P. S. Ezekiel, F.B. Deedam Okuchaba, "A Model to Detect Heart Disease using Machine Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.1-5, 2019.

Data Preprocessing has become a vital task to be carried out in the Data Mining process. The data becomes the most important resource due to its significance in various domains. However, it is hard to gather every data and saves it in real-time that lead to few missing data. It is not preferable to omit the missing data due to the fact that even a few amount of data acts as a significant part in the outcome. Missing value replacement acts as a main process to handle missing data prior to the prediction of hidden pattern, that exist in the dataset. This paper presents a new, Linear Regression based missing valve replacement in the MLP-RMSprop based classification model to handle missing data. Here, linear regression model is applied to predict the values to replace the missing data, which will help to improve the classification process. Then, multilayer perceptron (MLP) classifier is applied to classify the data which further tuned by the use of root mean square propagation (RMSProp) model. An extensive implementation takes place on three benchmark dataset to showcase the betterment of the presented model. The resultant values from simulation indicated that the projected model offered supreme performance over the other models.

Missing value; Classification; RMSProp; Linear Regression

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[16] Tarkhaneh, O. and Shen, H., 2019. Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search. Heliyon, 5(4), p.e01275.
[17] Sartakhti, J.S., Zangooei, M.H. and Mozafari, K., 2012. Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA). Computer methods and programs in biomedicine, 108(2), pp. 570-579.
[18] K.D. Patel, "Review on Techniques in Natural Language Processing", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.5, pp.1-4, 2019.
[19] Amin Rezaeipanah, Zahra Abshirini, Milad Boshkani Zade, "Solving University Course Timetabling Problem Using Parallel Genetic Algorithm", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.5, pp.5-13, 2019.

G. Suresh, S. Saraswathi, "A Novel Approach for Missing Value Replacement in MLP-RMSProp Based Classification Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.6-19, 2019.

In the recent past, smart speakers and personal assistants like Alexa have been used as evidence in different criminal cases. With more than 6 million Echo devices in 2019 worldwide, there is a good chance for the constantly active devices to record criminal behavior. That is reason enough to take a closer look at the subject. This article discusses different starting points for a forensic investigation of the latest generation of the Echo Dot smart speaker. Therefore, the paper examines three possible aspects for the forensic analysis of the Echo 3 the latest generation of this device. In hardware analysis, the focus is not only on the general examination of the extracted data but also on validating the existing methods for data extraction. A second focus is the cloud-side analysis. Since this type of analysis requires the knowledge of credential data, a client-side analysis will take place too. The last is done because the use of at least one Companion Client is necessary to operate and control Alexa-capable devices. The main findings of this study are presented in this article.

Amazon Echo 3, digital forensics, hardware analysis, IoT, IPA

[1] L. French, “Virtual Case Notes: Not Only Can Alexa Eavesdrop - She Can Also Testify Against You“, Forensic Magazine, May 10, pp.1-3, 2019.
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[3] P. Naik, N. Telkar, A. Patil, ”Smart Home Automation Technique using IoT” , International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), Vol.2, Issue.3, pp.2456-3307, 2017.
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[6] H. Chung, J. Park, S. Lee, “Digital forensic approaches for amazon alexa ecosystem”, In Proceeding of the 17th Annual DFRWS, USA, pp.15-25, 2017.
[7] S. Li, K-K. Choo, W. Qindong, J. William, “IoT Forensics: Amazon Echo as a Use Case”, 2019. doi:10.1109/JIOT.2019.2906946
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[11] M. Kirmani, M.T. Banday, “Digital Forensics in the Context of the Internet of Things”, 2019. doi: 10.4018/978-1-5225-5742-5.ch011
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[13] J.R. Shackleton, “Alexa, amazon assistant or government infor-mant.”, U. Miami Bus. L. Rev., Vol.27, Issue.2, pp.301-227, 2019.
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[18] D. Mishra, “Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams”: International Journal of Scientific Computer Science and Engineering Vol.6, Issue.1, pp.30-36, 2018.

D. Pawlaszczyk, J. Friese, C. Hummert, ""Alexa, tell me …" - A forensic examination of the Amazon Echo Dot 3 rd Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.20-29, 2019.

In this paper we present our project that focuses on developing a Voice enabled Smart Home Assistant that will help find items in a home and also support natural and secure interaction using voice recognition.The software will be helpful for elderly people and those with Alzheimer`s disease to help find items at home. It can also be used by people who often forget where they keep their things as well as large organizations to manage complex inventory, without the friction and multiple steps of keeping inventory updated. The project will be relevant to society, as users will be able to communicate with the Smart Home Assistant in a natural way and security will also be ensured as no unknown users will have access to the software. The software can also be accessed through mobile application.The Voice enabled Smart Home Assistant will employ natural language processing techniques to understand the user’s request to identify the location of the object and report it to the user. The software will be integrated with Amazon Alexa which is a cloud-based voice service and the users can access the software using the Amazon Echo device.

Personal Assistant, Natural Language Processing, Voice Recognition

[1] Hyunji Chung, Sangjin Lee , “Intelligent Virtual Assistant knows your Life”, Computers and Society, Vol. 42, pp. 201–213, 2017.
[2] Abhay Dekate, Chaitanya Kulkarni , Rohan Killedar , “Study of Voice Controlled Personal Assistant Device”, International Journal of Computer Trends and Technology(IJCTT),Vol.42, Issue.1, pp. 52–63, 2016.
[3] Veton Kepuska , “Comparing Speech Recognition Systems(Microsoft API,Google API and CMU Sphinx)”, International Journal of Engineering Research and Application, Vol.07, Issue.3(Part-2), pp. 20-24, 2017.
[4] Ashiq Anjum, “Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics”, Proceeding of Transactions on Cloud Computing, University of Derby, United Kingdom, pp. 125–218.

Sujitha Perumal, Mohammed Saqib Javid, "Voice Enabled Smart Home Assistant for Elderly," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.30-37, 2019.

In recent years the digital data is grown much frequently. Handling and processing of such bulky data are much complex and need the attention of a human. Moreover, the existing techniques and methods are not much suitable to deal with this complex nature of computation. To deal with such a complex nature of computation, the big data analytics played an essential role. In this presented work the unsupervised learning technique namely k-means clustering is implemented initially and their performance is measured. During this to enhance the performance of the system a new modified k-means clustering algorithm is proposed by improving the centroid selection technique and using the RBF kernel. The comparative performance analysis of both the versions of k-means clustering demonstrate the modified k-means clustering is efficient and has the low algorithm run time. Therefore it is a promising approach for analytics, thus it’s a future extension that is also presented in this work.

Big Data, Big Data Analytics, Unsupervised learning, Clustering Algorithm, improvements

[1] R. H. Hariri, E. M. Fredericks, K. M. Bowers, “Uncertainty in big data analytics: survey, opportunities, and challenges”, J Big Data (2019) 6:44, https://doi.org/10.1186/s40537-019-0206-3
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[11] A. B. Ayed, M. B. Halima, A. M. Alimi, “Survey on clustering methods: Towards fuzzy clustering for big data”, 978-1-4799-5934-1/14/$31.00 ©2014 IEEE
[12] X. Cai, F. Nie, H. Huang, “Multi-View K-Means Clustering on Big Data”, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence,
[13] A. Fahad, N. Alshatri, Z. Tari, A. Alamri, I. Khalil, A. Y. Zomaya, S. Foufou, A. Bouras, “A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis”, Vol. 2, No. 3, Sep. 2014, 2168-6750 2014 IEEE
[14] S. S. Chouhan, R. Khatri, “Data Mining based Technique for Natural Event Prediction and Disaster Management”, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.14, April 2016
[15] B. Feizizadeh, M. S. Roodposhti, T. Blaschke, J. Aryal, “Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping”, Arab J Geosci (2017) 10:122, DOI 10.1007/s12517-017-2918-z

Ayush Gupta, Pratik Gite, "Implementation of K-Means Clustering in Big Data Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.38-44, 2019.

This paper presents the comparative performance analysis of multi-level image watermarking using cryptographic algorithm. The proposed technique has been implemented and compared with other image watermarking technique based on DWT-SVD. In both watermarking techniques, the cover image is decomposed into four sub bands (LL, LH, HL and HH) using DWT and thereafter SVD is applied to LL sub band. Both the watermarking techniques have also been compared. Performance of methodology is evaluated using different fidelity parameters .The experimental results show the effectiveness of hybrid image watermarking scheme.

Digital Image Watermarking, DWT, SVD, cryptographic algorithm PSNR, NCC

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[13] Dr. J. Abdul Jaleel, Jisha Mary Thomas , “Guarding Images Using A Symmetric Key Cryptographic Technique: Blowfish Algorithm“, ISSN: 2277-3754 ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 2, August 2013
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[15] P. Gupta, and G Parmar, “Image watermarking using IWT-SVD and its comparative analysis with DWT-SVD”, Proc. IEEE International Conference on Computer, Communications and Electronics (COMPTELIX-2017), pp. 527-531, Manipal University, Jaipur, July, 2017.
[16] R.Tyagi and M. K. Pandey “An adaptive second level hybrid image Watermarking Technique using DWT-SVD in low frequency band’’ Published in IJARCCE, Vol. 6, Issue 1, January 2017.

Pankaj Gautam, Mahendra Kumar Pandey, Sanjay Patsariya, "Comparative Performance Analysis of Multilevel Image Watermarking Using Cryptographic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.45-48, 2019.

With the invent of internet technology and increasing the use of digital technology, the storage system has been into a difficult mode of expansion as the growing data is a big concern for the storage system, typically the images which occupy large scale size of storage. Hence worldwide data Deduplication mechanism had been used enormously to improve the backup storage system. Lots of efforts had done worldwide for identifying the image Deduplication system to improve the storage utilization system. In spite of these continuous efforts the so-called existing system can only manage to remove the images which are the same in texture and size, but it fails to identify the Deduplication of images that may have the same visual perceptions but may have different effects. To solve the above issue of image Deduplication our thesis work proposed the mechanism to identify the duplicated images which are technically the same from the perspective of visual identification but different with some effects. With further improving to remove such duplicate images, we are contributing to improving storage space. Our proposed algorithm based on feature selection and extraction and accuracy optimization will not improve storage space but helps to perform such task quite efficiently and effectively.

Image Deduplication, Feature Extraction, Centroid selection, Storage space, Optimization, DHash, Hamming, SIFT Algorithm

[1] NEMIROVSKIY V.B., STOYANOV A.K. Near duplicate image recognition based on the rank distribution of the brightness clusters cardinality, COMPUTER OPTICS. – 2014. – VOL. 38(4). – P. 811-817.
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[4] Eunji Lee, Jee E. Jang, Taeseok Kim, Hyokyung Bahn, "On-Demand Snapshot: An Efficient Versioning File System for Phase-Change Memory," IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 12, December 2013.
[5] Q. He, Z. Li, X. Zhang, "Data deduplication techniques, "Future Information Technology and Management Engineering (FITME)," vol. I, pp. 430-433, 2010
[6] Maddodi.S, Attigeri G.V, Karunakar. A.K, "Data Deduplication Techniques and Analysis," Emerging Trends in Engineering and Technology (lCETET), pp 664 - 668, IEEE, 2010
[7] N. Mandagere, P. Zhou, M.A. Smith, and S. Uttamchandani."Demystifying data deduplication," In Proceedings of the ACM/IFlP/USENIX Middleware`08 Conference Companion, pages 12-17. ACM, 2008

Rounak A. Samdadia, Nitin Patil, "A Multi Factor Duplicate Image Deduplication System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.49-51, 2019.

Deep learning technology applied to medical imaging may become the most disruptive technology radiology has seen since the advent of digital imaging. Most researchers believe that within next 15 years, deep learning based applications will take over human and not only most of the diagnosis will be performed by intelligent machines but will also help to predict disease, prescribe medicine and guide in treatment. In this case study, Convolutional Neural Network (CNN) has been constructed to determine the nature of bones i.e. whether it is broken or intact. Python is used as a basic language for coding purpose. It can be seen that after 50 epochs the validation accuracy is 96.39 %, it shows the ability of the model to generalize to new data.

Convolutional Neural Network; X-Ray images, Broken bones; Intact bones

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Akshansh Mishra, Priyankan Datta, "Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.52-55, 2019.

Because of a significant increment of pollution in the air, it is required to foresee the pollution of the following dates, months and years. Air pollution is quickly expanding because of different human factors and reasons, such as the generation of synthetic compounds, particulates, pollutants or in-organic materials and other substances which is even the reason for the loss of human lives and even additionally hurts the indigenous habitat like plants and animals, etc. Undoubtedly, air pollution is one of the significant natural problems in metropolitan and urban areas. In this way, Monitoring and safeguarding air quality is one of the most fundamental exercises in numerous modern and urban territories today. Consequently, air quality assessment, observing, and forecast has turned into significant research. The point of this paper is to explore different Machine Learning based strategies especially artificial neural network models for air quality determining in various conditions. This scheme for the future will elaborate on the distributed research results identifying with air quality index and forecast utilizing techniques predicting air quality of a particular area using Neural Networks. Therefore, as of now under this scheme, we will derive the comparative analyses of various neural network Algorithms from past researchers i.e. ANN, MLP, CNN, LSTM, CNN-LSTM, Encoder decoder, and Convolution LSTM. To find the efficiency and effectiveness in the area of air contamination and pollution.

Machine Learning, Artificial Neural Network, Multilayer Perceptron, Convolutional Neural Network, Long Short-Term Memory, Recurrent Neural Network, Encoding and Decoding

[1] Weibo Liua, Zidong Wanga , Xiaohui Liua, Nianyin Zengb, Yurong Liuc,D And Fuad E. Alsaadid ,“A Survey Of Deep Neural Network Architectures And Their Applications”, Brunel University Research Archive(BURA), May 14 – 2018.
[2] Mohammed Kamel Benkaddour, Abdennacer Bounoua, “Feature Extraction And Classification Using Deep Convolutional Neural Networks, Feature Extraction And Classification Using Deep Convolutional Neural Networks”, Laboratory Communication Network & Architecture Multimedia RCAM & DJILLALILIABBES University Sidi Bel Abbes 22000, Algeria. University Kasdi Marbah, FNTIC Faculty, Ouargla 30000, Algeria June, 2019.
[3] Huang, Chiou-Jye & Kuo, Ping-Huan, “Deep CNN-LSTM Model For Particulate Matter (PM2.5) Forecasting In Smart Cities”, Sensors. 18. 2220. 10.3390/S18072220, 2018
[4] Vidushi Chaudhary, Anand Deshbhratar, Vijayanand Kumar, Dibyendu Paul, “Time Series Based LSTM Model To Predict Air Pollutant’s Concentration For Prominent Cities In India”, Harbin Institute Of Technology (Shenzhen) February, 2017
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Ifshita Chaudhary, Shruti Sharma, Preeti Sethi, "Study on Various Machine Learning Techniques for Pollution Forecasting," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.56-63, 2019.

Encryption is the most effective way to achieve data security and converting data to an unrecognizable or encrypted form. Its wide used to secure data sent over wireless networks and the internet. This paper aims at improving the protection and efficiency of image cryptography by employing a highly efficient shuffle based encryption rule and a comparable decryption rule supported random values obtained by using pseudo random number generator .In this paper, a image encryption is projected which has pixels shuffling image division technique. The planned algorithmic program has been examined using multiple analysis ways in and a PSNR value obtained is more than 35 in all cases which shows a good decryption.

Transposition, Error free encryption, PSNR, Cryptography

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Abhishek Kumar Saw, Yogesh Kumar Rathore, "Image Encryption Using Image Division and Suffling Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.64-67, 2019.