Volume-8 , Issue-4 , Apr - 2020 Go Back

In the long history of database communities, lots of research efforts had been done for reducing seek-times and rotational delays caused by mechanical components used in HDD (Hard Disk Drive). As an example of those efforts, some I/O scheduling algorithms were devised for the purpose of efficient services of online video streams being pumped up from HDD storage. To this end, a rate-reservation EDF is recently adopted to be incorporated into the recent platform built on flash storage. In this research, a fixed length of time is chosen as a period unit and the disk bandwidth assumption of each video stream is decided based on that time. The previous rate-reservation EDF algorithm is very suitable for serving a mixture of real-time requests and common requests without deadline. In this paper, we propose a new way that can dynamically compute the varying amounts of free bandwidth arising from more-than-reservation reading, while scheduling video streams according to the rate-reservation EDF algorithm. For this, we devised two data structures that can keep information about workloads and free bandwidth over a certain length of period units. Using scheduling information managed in those data structures, our proposed scheme can efficiently utilize slack times that occur unexpectedly from time to time. Because of the efficient reclamation of slack times, our scheme can improve the actual I/O performance of flash storage that is prepared for Web-based streaming services.

Flash Memory, Video Streaming, I/O Scheduling, Real-time EDF Algorithm

[1] Hofri Micha, “Disk Scheduling: FCFS vs SSTF Revisited," Communications of the ACM, Vol. 23, No 11, pp. 645-653, 1980.
[2] Houssine Chetto and Maryline Chetto, “Some Results of the Earliest Deadline Scheduling Algorithm," IEEE Transactions on Software Engineering, Vol. 15, No. 10, pp. 1261-1269, 1989.
[3] M. Seltzer, P. Chen, and J. Ousterhout, “Disk Scheduling Revisited," in Proc. of the USENIX Winter 1990, pp. 313-324, 1990.
[4] Pengliu Tan, Hai Jin, Minghu Zhang, “A Hybrid Scheduling Scheme for Hard, Soft and Non-Real-time Tasks,” In Proceedings of ISORC, pp. 20-26, 2006.
[5] Kang G. Shin and Yi-Chieh Chang, “A Reservation-Based Algorithm for Scheduling Both Periodic and Aperiodic Real-Time Tasks," IEEE Transactions on Computers, Vol. 44, No. 12, pp. 1405-1419, 1995.
[6] Xin Li, Zhiping Jia, Li Ma, Ruihua Zhang, and Haiyang Wang, “Earliest Deadline Scheduling for Continuous Queries over Data Streams,” In Proceedings of ICESS, pp. 57-64, 2009.
[7] Ray-I Chang, Wei-Kuan Shih, and Ruei-Chuan Chang, “Real-Time Disk Scheduling for Multimedia Applications with Deadline-Modification-Scan Scheme,” Real-Time Systems, Vol.19 , No. 2, pp. 149-168, 2000.
[8] R. K. Abbott and H. Garcia-Molina, “Scheduling I/O Requests with Deadlines: A Performance Evaluation," In Proceedings of the Real-Time Systems Symposium, pp. 113-125, 1990.
[9] E. Balafoutis, M. Paterkakis, and P. Triantallou, “Clustered Scheduling lgorithms for Mixed-Media Disk Workloads in a Multimedia Server," Cluster Computing Journal, Vol. 6, No. 1, pp. 75-86, 2003.
[10] Sungchae Lim, “The Dynamic Sweep Scheme using Slack Time in the Zoned Disk,” In Proceedings of 11st DASFAA, pp. 404-419, 2006.
[11] Mon-Song Chen, Dilip D. Kandlur, and P. Yu, “Optimization of Grouped Sweeping Scheduling (GSS) with Heterogeneous Multimedia Systems," In Proceedings of the ACM Multimedia, 1993.
[12] Seong-Chae Lim, “FlashEDF: An EDF-style Scheduling Scheme for Serving Real-time I/O Requests in Flash Storage,” Vol. 10, No. 3, pp. 26-34, 2018.
[13] Tzu-Jung Huang, Chien-Chung Ho, Po-Chun Huang, Yuan-Hao Chang, Che-Wei Chang, and Tei-Wei Kuo, “Current-aware Scheduling for Flash Storage Devices,” In Proceedings of IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, 2014.
[14] V. Indhumathi, TRP Maximization Technique based Efficient Scheduling in Grid Environment, Internatilal Journal of Scientific Research in Computer Sciences and Engineering, Vol.6, No. 2 , pp. 20-26, 2018.
[15] Vidhi Tiwari and Pratibha Adkar, Implementation of IoT in Home Automation using Android Application, Internatilal Journal of Scientific Research in Computer Sciences and Engineering, Vol.7, No. 2 , pp. 11-16, 2019.
[16] Cheng Ji, Li-Pin Chang, Chao Wu, Liang Shi, and Chun Jason Xue, “An I/O Scheduling Strategy for Embedded Flash Storage Devices With Mapping Cache,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 37, No. 4, 2018.
[17] Jaechun No and Sung-soon Park, “Exploiting the Effect of NAND Flash-Memory SSD on File System Design,” in Proc. of International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. 2012.

Seong-Chae Lim, "Calculation of Free Bandwidth for Rate-reservation EDF Scheduling in Flash Storage," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.1-4, 2020.

Multi-document summarization aims at generating a comprehensive summary of multiple documents related to a common topic without repeatedly conveying the same piece of information while covering the essential information from all the documents. Extractive summarization methods exist to handle Multi-document summarization, while the Abstractive summarization methods are limited to handling single-document summaries. This paper proposes abstractive summarization of multiple documents by extending the state-of-the-art single-document abstractive summarization model Pointer-Generator to generate a multi-document summary. The short abstract summaries generated upon multiple applications of the Pointer-Generator model on individual documents are clustered at the sentence level using Skip-thought embeddings. The representative sentences from each of the clusters constitute the final summary in order to avoid similar sentences while generating the multi-document abstractive summary without loss of information. The proposed methodology is evaluated using the DUC2004 benchmark dataset and observed a gain of 2 to 7 points of ROUGE scores compared to existing state of the art methods.

Multi-Document Summarization, Abstractive, Skip-thought embeddings, ROUGE

[1]. Chen, CL Philip, and Chun-Yang Zhang. "Data-intensive applications, challenges, techniques and technologies: A survey on Big Data." Information sciences 275 (2014): 314-347.
[2]. Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and potential." Health information science and systems 2.1 (2014): 3.
[3]. Andreu-Perez, Javier, et al. "Big data for health." IEEE journal of biomedical and health informatics 19.4 (2015): 1193-1208.
[4]. Singh, Anita Kumari, and Mogalla Shashi. "Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches." International Journal of Computer Sciences and Engineering 7.3 (2019): 950-954.
[5]. Kiros, Ryan, et al. "Skip-thought vectors." Advances in neural information processing systems. 2015.
[6]. Saggion, Horacio, and Thierry Poibeau. "Automatic text summarization: Past, present, and future." Multi-source, multilingual information extraction, and summarization. Springer, Berlin, Heidelberg, 2013. 3-21.
[7]. Chen, Kuan-Yu, et al. "Extractive broadcast News summarization leveraging recurrent neural network language modeling techniques." IEEE Transactions on Audio, Speech, and Language Processing 23.8 (2015): 1322-1334.
[8]. Cheng, Jianpeng, and Mirella Lapata. "Neural summarization by extracting sentences and words." arXiv preprint arXiv:1603.07252 (2016).
[9]. Verma, Sukriti, and Vagisha Nidhi. "Extractive summarization using deep learning." arXiv preprint arXiv:1708.04439 (2017).
[10]. Khan, Atif, and Naomie Salim. "A review of abstractive summarization methods." Journal of Theoretical and Applied Information Technology 59.1 (2014): 64-72.
[11]. Nallapati, Ramesh, et al. "Abstractive text summarization using sequence-to-sequence rnns and beyond." arXiv preprint arXiv:1602.06023 (2016).
[12]. Pasunuru, Ramakanth, Han Guo, and Mohit Bansal. "Towards improving abstractive summarization via entailment generation." Proceedings of the Workshop on New Frontiers in Summarization. 2017.
[13]. Luong, Minh-Thang, et al. "Multi-task sequence to sequence learning." arXiv preprint arXiv:1511.06114 (2015).
[14]. Ling, Jeffrey. Coarse-to-fine attention models for document summarization. Diss. 2017.
[15]. Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence summarization." arXiv preprint arXiv:1509.00685 (2015).
[16]. Shen, Shi-Qi, et al. "Recent advances on neural headline generation." Journal of computer science and technology 32.4 (2017): 768-784.
[17]. See, Abigail, Peter J. Liu, and Christopher D. Manning. "Get to the point: Summarization with Pointer-Generator networks." arXiv preprint arXiv:1704.04368 (2017).
[18]. Singh, Anita Kumari, and Mogalla Shashi. "Vectorization of Text Documents for Identifying Unifiable News Articles." corpora 10.7 (2019).
[19]. Hermann, Karl Moritz, et al. "Teaching machines to read and comprehend." Advances in neural information processing systems. 2015.
[20]. Lin, Chin-Yew. "Rouge: A package for automatic evaluation of summaries." Text summarization branches out. 2004.
[21]. Erkan, Günes, and Dragomir R. Radev. "Lexrank: Graphbased lexical centrality as salience in text summarization." Journal of artificial intelligence research 22 (2004): 457-479. [7].
[22]. Carbonell, Jaime G., and Jade Goldstein. "The use of MMR, diversity-based reranking for reordering documents and producing summaries." SIGIR. Vol. 98. 1998.

Anita Kumari Singh, M. Shashi, "Deep Learning Architecture for Hybrid Multi-Document Abstractive Summarization using Sentence Embeddings," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.5-9, 2020.

Usually, the people are not aware about a disease and the treatments pertaining to it. Also, the symptoms which leads to that disease is unclear and uncertain and if these symptoms are identified by the person, he must go through various steps for getting an appointment with the doctor like making a call to the healthcare facility. It is also a tedious job for the receptionist to manage all these telephonic calls and fix an appointment according to the availability of the doctor. After the diagnosis of a patient there could be a possibility that doctor couldn’t diagnose the patient properly or there could be some inaccuracies in the diagnosis. In this paper we have come up with an exceptional solution to both of the above mentioned problems, that is we have used a medical chatbot under proper guidance for booking an appointment with the doctor and we have also have built an artificial intelligence based model that uses image classification technique to diagnose the reports of the patient which will state the results to which what the patient is suffering from. It uses CNN (convolutional neural networks) for the processing of the image. And this model can detect various respiratory related diseases.

Medical chatbot, Artificial Intelligence, Model, Image classification, Convolutional neural networks

[1] Mrs. Rashmi Dharwadkar, Dr.Mrs. Neeta A. Deshpande "A Medical ChatBot". International Journal of Computer Trends and Technology (IJCTT) V60(1):41-45 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
[2] Z. Lan, G. Zhou, Y. Duan and W. Yan, "AI-Assisted Prediction on Potential Health Risks with Regular Physical Examination Records," 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, 2018, pp. 346-352.
[3] S Suren Makaju, P.W.C Prasad, Abeer Alsadoon, A.K.Singh, A.Elchouemi, “Lung Cancer Detection Using CT Scan Images”, International Journa of Computer Sciences and Engineering, Vol.4, Issue.11, pp.111-117, 2015.
[4] H. A. Shiddieqy, F. I. Hariadi and T. Adiono, "Implementation of deep-learning based image classification on single board computer," 2017 International Symposium on Electronics and Smart Devices (ISESD), Yogyakarta, 2017, pp. 133-137.
[5] Rismiyati and S. Azhari, "Convolutional Neural Network implementation for image-based Salak sortation," 2016 2nd International Conference on Science and Technology-Computer (ICST), Yogyakarta, 2016, pp. 77-82.
[6] C. Huang, S. Ni and G. Chen, "A layer-based structured design of CNN on FPGA," 2017 IEEE 12th International Conference on ASIC (ASICON), Guiyang, 2017, pp. 1037-1040.
[7] S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), Antalya, 2017, pp. 1-6.
[8 ]N. Tajbakhsh et al., “Convolution Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?.” in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299-1312, May 2016.

Rahul Meena, Vighnesh Menon, Vivek Solavande, "Lung Image Classification Using Convolutional Neural Network And Prediction of Different Diseases," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.10-13, 2020.

Deep learning has emerged as a new area in machine learning and is applied to a number of signal and image applications. Although the existing traditional image classification methods have been widely applied in practical problems, such as unsatisfactory effects and weak adaptive ability. The main purpose of the work presented in this paper, is to apply the concept of image feature extraction with AlexNet Convolutional Neural Networks (CNN) in Digital Elevation Map and Topological Map boundary classification of Yangon City in Myanmar. The automated derivation of topographic data from DEMs is faster, less subjective and provides more reproducible measurements than traditional manual techniques applied to topographic maps. Data are acquired from the United States Geological Survey (USGS) database. This study is supposed to handle of geospatial information and production of maps. Geospatial users have to understand the distortion characteristics of each maps. The analysis of this result is revealed that has a good classification accuracy for all the tested maps based on the proposed system.

AlexNet, CNN, Elevation Map, USGS

[1] A. Krizhevsky, et al, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp, 1097-1105.
[2] Bragilevsky L, Baji IV. (2017) “Deep learning for Amazon satellite image analysis.” Communications, Computers and Signal Processing (PACRIM).:1–5
[3] Giacinto G, Roli F. “Design of effective neural network ensembles for image classification purposes”. Image Vision Comput 2001;19(9–10):699–707.
[4] Lu, Dengsheng and Weng, Qihao. (2007) “A survey of image classification methods and techniques for improving classification performance.” International journal of Remote sensing 28(5):823–870
[5] Meng T, Wu C, Jia T, Jiang Y and Jia Z, ‘Recombined convolutional neural networks for recognition of macular disorders in SD-OCT images’, In 2018 37th Chinese control conference (CCC), pp 9362–9367, IEEE.
[6]M. M. R. Khan, et al., "Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository," arXiv preprint arXiv:1809.06186, 2018
[7] Ojala, T., & Pietikäinen, M.; "Texture Classification, Machine Vision and Media Processing Unit", University of Oulu, Finland, Available at.
[8]Y. Kim, "Convolutional neural networks for sentence classification," arXiv preprint arXiv:1408.5882, 2014

Zarli Cho, Khin Myo Kyi, Kyi Thar Oo, "Image Classification based on Feature Extraction with AlexNet Architecture," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.14-18, 2020.

COVID-19 is a new infectious viral disease and it is great challenge to the entire globe. The corona virus COVID-19 is declared as pandemic and it will change the life style of human in our society. The number of infection cases related to COVID-19 is increasing almost exponentially like any other pandemic disease. Till date there is no vaccine or medicine which can prevent from infection of COVID-19. To combat infection from COVID-19 the Governments are trying to reduce the spread of the virus by doing some standard techniques such as lockdown and then testing, tracing, social distancing, quarantining and isolation of citizens. In the present paper the authors have tried to introduce explore the reference sampling to isolate COVID-19 infected patients. Reference sampling will be helpful to collect data and give a proper idea to testing for covid-19 patient.

COVID-19, Pandemic, Sampling, Social

[1] R.Prasad , “ The Pandemic NoteBook , A handy guide from the Hindu on understanding the corona virus pandemic and staying protected against COVID-19”, The Hindu, 2020 page 1-21.
[2] S..Nadeem, “ Coronavirus COVID-19 : Available fee literature Provided by various Companies , Journal and organization around the world” Journal of ongoing Chemical research, vol 5, issue 1, 2020, page- 7-13,
[3] F. Zhang , L Li , H Y Xuan, “Overview of infectious disease transmission models [J].”, Theory and Practice of Systems Engineering, 2011, 31(9):1736-1744.
[4] B. Yang , H.Pei , H .Chen., “. Characterizing and discovering spatiotemporal social contact patterns forhealthcare[J]. “IEEE Transactions on Pattern Analysis & Machine Intelligence, 2016, 39(8): 1532-1546.
[5] Yichi Li, Bowen Wang, Ruiyang Peng, Chen Zhou, Yonglong Zhan, Zhuoxun Liu, Xia Jiang and Bin Zhao1 “ Mathematical Modeling and Epidemic Prediction of COVID-19 and Its Significance to Epidemic Prevention and Control Measures” Remedy Publications LLC. 2020 | Volume 5 | Issue 1 | Article 1052-1061
[6] World health organization ,”Surface sampling of coronavirus disease (COVID-19): A practical “how to” protocol for health care and public health professionals” Version: 1.1 Date: 18 February 2020 , page 1-26
[7] TR Julian, FJ Tamayo, JO Leckie, AB Boehm. (2011), “Comparison of Surface Sampling Methods for Virus Recovery from Fomites. Appl Environ Microbiol.” 77(19): 6918-6925.
[8] H. Taherdoost , “Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research” International Journal of Academic Research in Management (IJARM) Vol. 5, No. 2, 2016, Page: 18-27, ISSN: 2296-1747
[9] S.C. Gupta, V.K.Kapoor “Fundamentals of Mathematical Statistics”Sultan Chand & Sons 2001,India, page 13.1-13.72
[10] A.P.Bainab,M.Jas “Elements of Probability and Statistics, Tata McGraw –Hill Publishing Company Limited, India,Page 279-309
[11] S.K.Mapa, “Introduction to Real Analysis” Sarat book Distributors, india,2000 page 1-20
[12] J.N Sharma, A.R.Vasishtha, “Mathematical Analysis –II “Krishna Prakashan Media (p)Ltd.2007 page 40-60
[13] B.K.Pal, K.Das , “Engineering Mathematics volume-4”,U.N.Dhar & Sons private Ltd. India page 2-42 to 2-73.
[14] Victor Alexander Okhuese, “Mathematical Modeling and Epidemic Prediction of COVID-19 and Its Significance to Epidemic Prevention and Control Measures” Annals of Infectious Diseases & Epidemiology 2020, vol 5 issue 1, article 1052
[15] Parimal Mukhopadhyay, Theory and methods of Survey Sampling , Second Edition , PHI learning.
[16] A. S. Mandloi and V. Choudhary, “ An Efficient Clustering Technique for Deterministically Deployed Wireless Sensor Networks “, International Journal of Scientific Research in Network Security and Communication (ISSN: 2321-3256) , Volume-1, Issue-1, April- 2013 . Page 6-10
[17] S.Dubey, R. Jhaggar , N. Jhariya, A. Thakur, “System for Providing News Associated with Location” , International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639), Vol.5, Issue.3, pp.25-29, June (2017) E-ISSN: 2320-7639

Manoj Kumar Srivastav, Asoke Nath, "A Comprehensive Study on Mathematical Sampling before Testing For COVID-19 Infected Candidates," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.19-24, 2020.

The economy of the country can be calculated based on the circulation of the currency in it. Faux notes circulation is one of the major problems of the various countries .Due to the circulation of the faux note the economy of the countries will be affected drastically. So in order to remove the counterfeit notes from the circulation various methods have been proposed. But there are some drawbacks in the proposed methods. In order to increase the rate of accuracy to determine the faux note the proposed system uses the machine learning with the help of MatLab to increase the rate of accuracy to determine the faux note in the circulation

Faux , Counterfeit, MatLab, Machine Learning, Circulation

[1]. Lee, S., Choi, E., Baek, Y., & Lee, C. (2019). Morphology-based Banknote Fitness Determination. IEEEAccess, 11. doi:10.1109/access.2019.2917514
[2].Ayalew Tessfaw, M. E., Ramani, M. B., & Kebede Bahiru, M. T.(2018). Ethiopian Banknote Recognition and Fake Detection Using Support Vector Machine. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).doi:10.1109/icicct.2018.8473013.
[3]. Achal Kamble , Prof. M. S. Nimbarte.(2018). Design and Implementation of Fake Currency Detection System. International Journal on Future Revolution in Computer Science & Communication Engineering. Volume: 4 Issue: 6.
[4]. Hoang-Thang-vo, Van-dung-hoang.(2018). Hybrid discrimative model for banknote recognition and anti counterfeit. 2018 5th NAFOSTED Conference on Information and Computer Science (NICS).DOI;10.1109/NICS.2018.8606900.
[5]. Ponishjino, P., Antony, K., Kumar, S., & JebaKumar, S. (2017). Bogus currency authorization using HSV techniques. 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). doi:10.1109/iceca.2017.8203667
[6]. Darade, S. R., & Gidveer, G. R. (2016). Automatic recognition of fake Indian currency note. 2016 International Conference on Electrical Power and Energy Systems (ICEPES). doi:10.1109/icepes.2016.7915945
[7]. Rajan, G. V., Panicker, D. M., Chacko, N. E., Mohan, J., & V.K, K. (2018). An Extensive Study on Currency Recognition System Using Image Processing. 2018 Conference on Emerging Devices and Smart Systems (ICEDSS). doi:10.1109/icedss.2018.8544310
[8]. Upadhyaya, A., Shokeen, V., & Srivastava, G. (2018). Analysis of Counterfeit Currency Detection Techniques for Classification Model. 2018 4th International Conference on Computing Communication and Automation (ICCCA). doi:10.1109/ccaa.2018.877770
[9]. Shoeb, A. M., Sayed, H. M., Saleh, N. F., Khafagy, E. I., & Neji, S. (2016). Software system to detect counterfeit Egyptian currency. 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). doi:10.1109/iccicct.2016.7988010
[10]. Dhar, P., Uddin Chowdhury, M. B., & Biswas, T. (2018). Paper Currency Detection System Based on Combined SURF and LBP Features. 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET). doi:10.1109/iciset.2018.8745646
[11]. Er-Hu Zhang, Bo Jiang, Jing-Hong Duan, & Zheng-Zhong Bian. (n.d.). Research on paper currency recognition by neural networks. Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693). oi:10.1109/icmlc.2003.1259870
[12]. Vishnu, R., & Omman, B. (2014). Currency detection using similarity indices method. International Conference for Convergence for Technology 2014. doi:10.1109/i2ct.2014.7092214
[13]. Zhang, Q., & Yan, W. Q. (2018). Currency Detection and Recognition Based on Deep Learning. 2018 15th IEEE International Conference on Advanced Video and Signal BasedSurveillance(AVSS). doi:10.1109/avss.2018.8639124
[14]. Barani, S. (2015). Currency identifier for Indian denominations to aid visually impaired. 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT2015]. doi:10.1109/iccpct.2015.7159392
[15]. Jyothi, C. R., SundaraKrishna, Y. K., & SrinivasaRao, V. (2016). Paper currency recognition for color images based on Artificial Neural Network. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). doi:10.1109/iceeot.2016.775533/

G. Hariharan, D. Elangovan, "Proxy Notes Recognition and Eradication for Betterment of the Society," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.25-27, 2020.

With the growing technologies and the dominance of digital media, the way companies market has changed and businesses are doing all they can to surpass their competitors. With the advancements in technology, as of 2019, 82% of businesses engage in digital marketing. With ever increasing data analysis tools and growth of statistical machine learning as a field, digital marketing channels have seen tremendous growth, and are now considered as an essential part of every company. Though each “company” carries out its analysis in their own way, there are five basic steps involved in digital marketing analysis. This paper presents the steps involved in the process of digital marketing analytics, along with their importance and the most prominent method in each step by exploring popular machine learning tools and proposes a general framework for digital marketing analytics.

digital marketing analytics, digital marketing, machine learning, analytics

[1] M. T. P. M. B. Tiago and J. M. C. Veríssimo, “Digital marketing and social media: Why bother?,” Business Horizons, vol. 57, no. 6, pp. 703–708, Nov. 2014.
[2] H. M. Taiminen and H. Karjaluoto, “The usage of digital marketing channels in SMEs,” Journal of Small Business and Enterprise Development, vol. 22, no. 4, pp. 633–651, Nov. 2015.
[3] P. S. H. Leeflang, P. C. Verhoef, P. Dahlström, and T. Freundt, “Challenges and solutions for marketing in a digital era,” European Management Journal, vol. 32, no. 1, pp. 1–12, Feb. 2014.
[4] V. Shankar, J. J. Inman, M. Mantrala, E. Kelley, and R. Rizley, “Innovations in Shopper Marketing: Current Insights and Future Research Issues,” Journal of Retailing, vol. 87, pp. S29–S42, Jul. 2011.
[5] T. Wuest, D. Weimer, C. Irgens, and K.-D. Thoben, “Machine learning in manufacturing: advantages, challenges, and applications,” Production & Manufacturing Research, vol. 4, no. 1, pp. 23–45, Jan. 2016.
[6] H. Li, “Deep learning for natural language processing: advantages and challenges,” National Science Review, vol. 5, no. 1, pp. 24–26, Sep. 2017.
[7] J. Lin and A. Kolcz, “Large-scale machine learning at Twitter,” in Proceedings of the 2012 international conference on Management of Data - SIGMOD ’12, 2012.
[8] M. Badieh Habib Morgan and M. van Keulen, “Information Extraction for Social Media,” in Proceedings of the Third Workshop on Semantic Web and Information Extraction, 2014.
[9] Adarsh Pradhan, Nabanita Paul, Kakali Das, Rupam Jyoti Bordoloi, Dikhya Baruah, “Survey On Speech Recognition Using Hidden MARKOV Model,” International Journal of Computer Sciences and Engineering, Vol.04, Issue.07, pp.158-161, 2016.
[10] L. M. Badea (Stroie), “Predicting Consumer Behavior with Artificial Neural Networks,” Procedia Economics and Finance, vol. 15, pp. 238–246, 2014.
[11] Yunzhong Liu, Yaping Lin and Zhiping Chen, "Using hidden Markov model for information extraction based on multiple templates," International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003, Beijing, China, 2003, pp. 394-399.
[12] X. Zhao and Y. Ohsawa, “Sentiment Analysis on the Online Reviews Based on Hidden Markov Model,” Journal of Advances in Information Technology, vol. 9, no. 2, pp. 33–38, 2018.
[13] N.-R. Kim, K. Kim, and J.-H. Lee, “Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information,” International Journal Of Fuzzy Logic And Intelligent Systems, vol. 17, no. 4, pp. 329–336, Dec. 2017.
[14] D. Voramontri and L. Klieb, “Impact of Social Media on Consumer Behaviour,” International Journal of Information and Decision Sciences, vol. 11, no. 3, p. 1, 2019.
[15] Seyed Mohammad Hossein Hasheminejad and Mojgan Khorrami, “Data mining techniques for analyzing bank customers: A survey,” IDT, vol. 12, no. 3, pp. 303–321, Dec. 2018.
[16] L. Meyer-Waarden and C. Benavent, “The Impact of Loyalty Programmes on Repeat Purchase Behaviour,” Journal of Marketing Management, vol. 22, no. 1–2, pp. 61–88, Feb. 2006.
[17] Rexzy Tarnando, Yuli Karyanti, “Comparison of Text Classification Algorithms of People Sentiments on Twitter (Case: Transjakarta),” International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.8-12, 2019.
[18] A. Zolnierek and B. Rubacha, “The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task,” in Advances in Soft Computing, Springer Berlin Heidelberg, pp. 329–336.
[19] M. M. A. Grau, M. Tajtakova, and D. A. Aranda, “Machine learning methods for the market segmentation of the performing arts audiences,” International Journal of Business Environment, vol. 2, no. 3, p. 356, 2009.
[20] P. Cal and M. Woźniak, “Parallel Decision Tree for Streaming Data,” in Distributed Computing and Artificial Intelligence, Springer International Publishing, 2013, pp. 27–35.
[21] M. Ramasamy, S. Selvaraj and M. Mayilvaganan, "An empirical analysis of decision tree algorithms," 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2015, pp. 1-4.
[22] D. L. Olson and B. Chae, “Direct marketing decision support through predictive customer response modeling,” Decision Support Systems, vol. 54, no. 1, pp. 443–451, Dec. 2012.
[23] C. Hartmann, P. Varshney, K. Mehrotra and C. Gerberich, "Application of information theory to the construction of efficient decision trees," in IEEE Transactions on Information Theory, vol. 28, no. 4, pp. 565-577, July 1982.
[24] W. Xiaohu, W. Lele, and L. Nianfeng, “An Application of Decision Tree Based on ID3,” Physics Procedia, vol. 25, pp. 1017–1021, 2012.
[25] F. Sciarrone, "Machine Learning and Learning Analytics: Integrating Data with Learning," 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET), Olhao, 2018, pp. 1-5.
[26] I. C. Passos, B. Mwangi, and F. Kapczinski, “Big data analytics and machine learning: 2015 and beyond,” The Lancet Psychiatry, vol. 3, no. 1, pp. 13–15, Jan. 2016.
[27] T. Wuest, D. Weimer, C. Irgens, and K.-D. Thoben, “Machine learning in manufacturing: advantages, challenges, and applications,” Production & Manufacturing Research, vol. 4, no. 1, pp. 23–45, Jan. 2016.
[28] Chen, Chiang, and Storey, “Business Intelligence and Analytics: From Big Data to Big Impact,” MIS Quarterly, vol. 36, no. 4, p. 1165, 2012.
[29] Z. Ge, Z. Song, S. X. Ding and B. Huang, "Data Mining and Analytics in the Process Industry: The Role of Machine Learning," in IEEE Access, vol. 5, pp. 20590-20616, 2017.
[30] Z. Sun, L. Sun, and K. Strang, “Big Data Analytics Services for Enhancing Business Intelligence,” Journal of Computer Information Systems, vol. 58, no. 2, pp. 162–169, Oct. 2016.
[31] S. Poornima and M. Pushpalatha, “A survey of predictive analytics using big data with data mining,” International Journal of Bioinformatics Research and Applications, vol. 14, no. 3, p. 269, 2018.
[32] K. Lepenioti, A. Bousdekis, D. Apostolou, and G. Mentzas, “Prescriptive Analytics: A Survey of Approaches and Methods,” in Business Information Systems Workshops, Springer International Publishing, 2019, pp. 449–460.
[33] T. Itoh, A. Kumar, K. Klein, and J. Kim, “High-dimensional data visualization by interactive construction of low-dimensional parallel coordinate plots,” Journal of Visual Languages & Computing, vol. 43, pp. 1–13, Dec. 2017.
[34] G. Chawla, S. Bamal, and R. Khatana, “Big Data Analytics for Data Visualization: Review of Techniques,” International Journal of Computer Applications, vol. 182, no. 21, pp. 37–40, Oct. 2018.
[35] Yunzhong Liu, Yaping Lin and Zhiping Chen, "Using hidden Markov model for information extraction based on multiple templates," International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003, Beijing, China, 2003, pp. 394-399.
[36] J. Qiu, Z. Lin, and Y. Li, “Predicting customer purchase behavior in the e-commerce context,” Electronic Commerce Research, vol. 15, no. 4, pp. 427–452, Jun. 2015.
[37] Harsh H. Patel, Purvi Prajapati, "Study and Analysis of Decision Tree Based Classification Algorithms", International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.74-78, 2018.

Sanjana Karanam, Rajashree Shettar, "Processes and Techniques in Digital Marketing Analytics," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.28-33, 2020.

Urban built-up area information is required in various applications of land use planning and management. Urban environment is made up with the complex interactions with built up environment and the human communities living within the urban area. The aim of the system is to assess an effective building change detection system that can identify gains and losses of built-up areas in relation to other land cover of Multi-temporal satellite image of Mandalay city in Myanmar. The proposed system apply to combine with gray level histogram features with minimum redundancy maximum relevance (MRMR) approach for built-up change detection system. The experimental analysis revealed that the proposed system combination with histogram features based on MRMR which is more reliable in urban built-up change detection system.

MRMR, Hitogram feature,classification, detection

[1]Liow, Y.-T., and T. Pavlidis. 1990. “Use of Shadows for Extracting Buildings in Aerial Images.” Computer Vision, Graphics, and Image Processing 49: 242–277.
[2]Sirmacek, B., and C. Unsalan. 2009. “Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory.” IEEE Transactions on Geoscience and Remote Sensing 47: 1156–1167.
[3] X. Huang and L. Zhang, “An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 257–272, Jan. 2013.
[4] V. Walter, “Object-based classification of remote sensing data for change detection,” ISPRS J. Photogramm. Remote Sens., vol. 58, no. 3/4,pp. 225–238, Jan. 2004.
[5] T. Celik and K.-K. Ma, “Multitemporal image change detection using undecimated discrete wavelet transform and active contours,” IEEE Trans.Geosci. Remote Sens., vol. 49, no. 2, pp. 706–716, Feb. 2011.
[6] T. Celik, “Multiscale change detection in multitemporal satellite images,”IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 820–824, Oct. 2009.
[7] M. Dalla Mura, J. A. Benediktsson, F. Bovolo, and L. Bruzzone, “An unsupervised technique based on morphological filters for change detection in very high resolution images,” IEEE Geosci. Remote Sens. Lett., vol. 5,no. 3, pp. 433–437, Jul. 2008.
[8] M. Volpi, D. Tuia, F. Bovolo, M. Kanevski, and L. Bruzzone, “Supervised change detection in VHR images using contextual information and support vector machines,” Int. J. Appl. Earth Observ. Geoinf., vol. 20, pp. 77–85, Feb. 2013.
[9]Yawai Tint, Yoshiki Mikami, A Minimum Redundancy Maximum Relevance-based Causal Assessment of Injury Severity, International Journal of Computer Science and Information Security, Vol 15, No. 7, pp. 248-264, July 2017.
[10]Yawai Tint, Yoshiki Mikami, A Minimum Redundancy Maximum Relevance - based Approach for Multivariate Causality Analysis, International Journal of Advanced Computer Science and Applications, Vol 8, No.9, pp. 13-20, 2017.

Kyi Thar Oo, Khin Myo Kyi, Zarli Cho, "Urban Built-up Change Detection with Minimum Redundancy Maximum Relevance Approach," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.34-37, 2020.

Natural Language processing is one of the leading inventions in computer science. Sentiment is one of the sciences in NLP to perform web analysis. Sentiment analysis is opinion or review expressed by someone about something on web. As now a day’s people are depending trusting on online services so the importance of a review is going higher. For selecting a service or product, they need to go through thousands of reviews to understand a service or product quality. They can take proper decision based on these results of classification. Thus, considering the needs and developing attitude in web data mining and increasing dependency of users on reviews. Here we proposed a method and algorithm to classify the data of web reviews. Sentiment analysis is the process to classify positive, negative and neutral reviews and obtain output values that represents how many positive, negative and neutral reviews sentiment expressed. This paper refers a sentence level sentiment analysis.

Natural Language Processing, Sentiment Analysis, web reviews, algorithm, modules, architecture

[1] N.Godbole, M. Srinivasaiah and S. Skiena, “Large-scale sentiment analysis for news and blogs”, ICWSM,07,2007.Godbole et al.
[2] Bing Liu, “Sentiment analysis and Opinion Mining”, ASEEJournals,April 22,2012,PP 30-43.
[3] K.Sarvakar, U.Kuchara, “Sentiment Analysis of movie reviews: A new feature-based sentiment classification”, IJSRCSE, June 2018, vol.6,pp.8-12.
[4] A.Palve,R.Sonawane and A.Potgantwar, “Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark”, IJSRNSC,June 2017,vol. 5,pp.99-103.
[5] A. Levitin, “Design Analysis of Algorithm”, Pearson Publication, New York, pp. 41-94,2011

J.E Rajput, M.B Patil, "An Algorithm to perform Sentiment Analysis of web reviews using C++," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.38-39, 2020.

Today’s education institutes understand the importance of the library with the increase in education standards. Library management system is online system. This system helps in maintaining all daily tasks of library. As the numbers of users are increasing there is a demand for effective library management system that reduces cost of management and saves time of users. This project has facility of student login and a facility of teacher login which are generally not available in manual library management systems. It also has a feature of librarian login through which the librarian can handle the entire system .It also has a facility through which student can view list of issued book to them and its issued date and return date after logging in their accounts. Through feedback students can appeal librarian to add new books and can also give suggestions. This system also authenticates users through email verification.

Library management system, Android Operating System, Firebase, java

[1] A. Noor, S. Hossen, "A Java based University Library Management System", International Journal of Computer Applications, Vol. 180, No.29, pp. 37-45, March 2018.
[2] A. Tripathi, A. Srivastava, "Online Library Management System", IOSR Journal of Engineering, Vol. 2 Issue 2,pp. 180- 186, Feb.2012.
[3] R.Dinesh, S.R.Arun Pravin, M.aravindhan, D.rajeswari, "LIBRARY ACCESS SYSTEM SMARTPHONE APPLICATION USING ANDROID", International Journal of Computer Science and Mobile Computing, Vol.4, Issue.3, pp. 142-149, March- 2015.
[4] M. Inbavalli, A. Chandana, "Integrated Library Management System", International Journal of Computer Sciences and Engineering, Vol.-7, Issue-17, E-ISSN: 2347-2693, May 2019.
[5] V.K. Shukla, S.H. Faizi, A. Sharma, M. Mukhia and T. Bhutia, "Android Based Management System for Library", International Journal of Computer Sciences and Engineering, Volume-4, Issue4, E-ISSN: 2347-2693, 2016.
[6] S. Karanth, J. Castelino, Nireeksha, F. Nazareth, Anvitha K, "An Advanced Library Management System Using Android Device", International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Volume VI, Issue IV, April 2017, ISSN 2278-2540.
[7] A.T. Mary, S.Ramya, S.K. Murthy, Dr.A.Valarmathi, "ENHANCED LIBRARY MANAGEMENT SYSTEM", International Journal of Creative Research Thoughts, Volume 5, Issue 4, ISSN: 2320-2882, October 2017.
[8] N.S.N.Lakshmi Prasanna, B.N.Srinivasa Gupta, "ONLINE LIBRARY MANAGEMENT SYSTEM", Journal of Emerging Technologies and Innovative Research (JETIR), Volume 7, Issue 3, ISSN-2349-5162, March 2020.

S.S. Sawant, H.R. Shirsat, S.S. Dalvi, P.S. Rane, "Library Management System Using Android," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.40-42, 2020.