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• Open Access   Article

  Multimodal Biometric Identification System using Deep Learning

  Bhavya D.N., Chethan H.K.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.1-7, Oct-2020

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  Abstract
  In real world we knows that a multimodal biometric system performs better and overcomes the limitation and gives better classification accuracy when compare to than Unimodal biometric system. This paper proposes a novel multi-modal biometric recognition system based on feature-level fusion and deep learning model. The significance of this paper is, it focuses on the issue of selection of best feature extraction and classification techniques, by investigating different types of feature extraction techniques with different databases of given modality like face, Plamprint and iris. We proposed unimodal biometric recognition using Convolution Neural Network (CNN). Later the results of unimodal recognition used two-layer fusion to build multimodal biometric recognition. Features like Historgram of Gradient, Zernike Moments and Pseudo Zernike Moments are extracted. The performance of proposed multimodal recognition method shows better recognition accuracy than unimodal recognition.

  Key-Words / Index Term
  Multimodal, HOG, Zernike, PZM, CNN

  References
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  Citation
  Bhavya D.N., Chethan H.K., "Multimodal Biometric Identification System using Deep Learning," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.1-7, 2020

• Open Access   Article

  Evaluation of the Frequency Spectrums of an Orthogonal Frequency Division Multiplexing (OFDM) for Signaling Transmission

  U.B. Neto, Y. Iano, D.K. Bonello

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.8-15, Oct-2020

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  Abstract
  The continuous increase in global data traffic, such as audio and video signals, has imposed many improvements for the quality of telecommunication systems, hence several solutions in digital signal processing systems have been proposed in the last decades. In this approach, is presented a review and frequency spectrum simulations of OFDM (Orthogonal Frequency Division Multiplexing) in the four different time domains (16s, 24s, 32s and 48s), respectively. It also simulated six OFM frequency spectrums in MATLAB. A (GUI) interface also shows basic signaling transmission, and performance of the developed algorithm has been examined in terms of frequency, which increases in function of the time domain bands. In general, the results obtained were about a maximum frequency of 3MHz in 50% of the time-domain extraction, and a minimum frequency of -3MHz in 75% of the time-domain extraction, thus the results obtained were satisfactory to show the feasibility of OFDM modulation in a MATLAB environment.

  Key-Words / Index Term
  OFDM modulation; Frequency spectrum; Time-domain; MATLAB simulation; Communication systems

  References
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  Citation
  U.B. Neto, Y. Iano, D.K. Bonello, "Evaluation of the Frequency Spectrums of an Orthogonal Frequency Division Multiplexing (OFDM) for Signaling Transmission," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.8-15, 2020

• Open Access   Article

  Efficient Implementation of Multi-prime RSA using Montgomery Multiplication

  Mohammad Esmaeildoust, Vahid Zarei, Amer Kaabi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.16-19, Oct-2020

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  Abstract
  RSA cryptography is one of the most common algorithm, which exclusively employed in cryptography, digital signature and security systems. By increasing the use of this algorithm, many works are reported to improve the speed of the operation and security levels. Multi-prime RSA is one these improvements over RSA which divides operations over multi prime numbers instead of two in original RSA. In this paper, in order to achieve higher performance, encryption and decryption process of Multi-prime RSA is implemented by using Montgomery multiplication. The implementation results show the noticeable improvement in the speed of the Multi-prime RSA.

  Key-Words / Index Term
  Cryptography, public key, RSA, Montgomery multiplication, Multi-prime RSA

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  Citation
  Mohammad Esmaeildoust, Vahid Zarei, Amer Kaabi, "Efficient Implementation of Multi-prime RSA using Montgomery Multiplication," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.16-19, 2020

• Open Access   Article

  An Efficient E-Learning Simulation Web-Based Tool for a Virtual Lab for Electronics Measurements

  Tamer M. Nassef, Ashraf Aboshosha, Ayman Haggag, Sameh Nessim

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.20-27, Oct-2020

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  Abstract
  Advancement requires efficient technologies help learners to be active and responsive in learning and practicing. Virtual laboratories can be utilized to promote educational and training skills and can be used in the future as the primary instructional instruments. In this paper, we propose a mechanism depends on the use of LabVIEW simulation tool to create a virtual laboratory for the measurement of electrical signals and utilize the use of Local Area Networks to allow for real time interactions between learners and demonstrators, based on team viewer application. The proposed method shows accurate measurement results and an efficient synchronized data manipulation further it improves learners’ skills compared with traditional physical methods.

  Key-Words / Index Term
  E-learning; Ethernet shield; TCP/IP protocol; Arduino UNO; Team viewer

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  Citation
  Tamer M. Nassef, Ashraf Aboshosha, Ayman Haggag, Sameh Nessim, "An Efficient E-Learning Simulation Web-Based Tool for a Virtual Lab for Electronics Measurements," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.20-27, 2020

• Open Access   Article

  Comparative Analysis of Deep Learning Models for Detection of COVID-19 from chest X-Ray Images

  Oluwadare Adepeju Adebisi, John Adedapo Ojo, Olatunde Michael Oni

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.28-35, Oct-2020

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  Abstract
  The coronavirus disease is a viral infectious disease resulting from severe acute respiratory syndrome coronavirus. The new coronavirus, which began in China, in December 2019, has rapidly become pandemic and resulted to over 500000 deaths worldwide. Prompt detection of COVID-19 is necessary to prevent the transmission of COVID-19. In this research, we developed Deep Learning (DL) models for detection of COVID-19 from chest X-ray Images and evaluated the performance of the models by using accuracy, sensitivity and specificity. 401 COVID-19 chest X-ray images were obtained from open access database developed by Dr Cohen while 397 normal and 390 pneumonia chest X-ray images were obtained from Kaggle repository. Modified Alexnet, Googlenet and SqueezeNet were used to classify the chest X-ray images. Transfer learning with Alexnet achieved an overall best performance of 100% accuracy, 100% sensitivity and 100% specificity on binary test dataset and 98.31% accuracy, 98.55% sensitivity and 99.37% specificity on three classes test dataset. The work will provide early detection of COVID-19 and thereby enhance medical decisions, treatments and management procedures.

  Key-Words / Index Term
  Coronavirus; X-ray Images; Deep Learning; Convolutional Neural Network; Transfer Learning

  References
  [1] M. Nicola, Z. Alsafi, C. Sohrabi, A. Kerwan, A. Al Jabir, C. Iosifidis, M. Agha, R. Agha, "The socio economic implications of the coronavirus pandemic (COVID-19) A review”. International journal of surgery (London, England) vol. 78, pp.185–193, 2020
  [2] A. Nath, “Impact of COVID-19 Pandemic Lockdown on Mental Well-Being amongst Individuals in Society,” International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.4, pp.6-9, 2020
  [3] A. Hajjar, A. Ziad, A. Memish, K. McIntosh, “Middle East Respiratory Syndrome Coronavirus (MERS- CoV): A Perpetual Challenge,” Ann Saudi Med, Vol. 33 issue 5, pp.427–436, 2013
  [4] C. Cheng, Susanna K. P. Lau, Y. Woo, Y Yuen, ”Syndrome Coronavirus as an Agent of Emerging and Reemerging Infection’ ’American Society for Microbiology, Vol. 20 issue 4, pp .660-894–436, 2007
  [5] S .Lauer, K. Grantz, F. Jones, Q Zheng, R. Meredith, S. Azman. The Incubation Period of Coronavirus Disease 2019 (COVID- 19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of internal medicine, Vol 172 issue 9, pp. 577–582, 2020
  [6] H. Guo, Y.Zhou , X. Liu , J. Tan, "The impact of the covid-19 epidemic on the utilization of emergency dental Services” , Journal of Dental Sciences, 2020
  [7] S. Chavez,B. Long, A. Koyfman, Y.Liang, "Coronavirus disease (covid-19): A primer for emergency physicians, The American journal of emergency medicine” vol. 6757 issue 20, pp 3018-3019, 2020
  [8] D.Hemdan, M. Shouman, M. Karar, "COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in x-ray images," arXiv preprint arXiv:2003.11055, pp 1-14, 2020
  [9] M. Avci, N. Kozaci, “ Comparison of X-Ray Imaging and
  Computed Tomography Scan in the Evaluation of Knee
  Trauma’’. Medicina , 2019, Vol 55, issue10 , 2019
  [10] Y. Bengio, Y. LeCun,and G. Hinton, Deep Learning". Nature.: vol 7553 issue 521 , 436-444, 2015.
  [11] T. Ozturk, M. Talo, E. Yildirimc, U. Baloglu, O. Yildirim, U. Acharya, " Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-Ray Images” Computers in Biology and Medicine, 121: 10379, 2020
  [12] J.P. Cohen, “ COVID-19 image data collection, 2020
  [13] A. Goldbloom , “ Chest image kaggle data collection’, 2010
  [14] Z. Alom , M. Rahman, S. Nasrin , M. Tarek., K. Asari, "COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches”, 2004.03747,arXiv eess.iv, 2020
  [15] L.Wang, and W. Alexander, "COVID-Net: A Tailored Deep Convolutional Neura Network Design for Detection of COVID-19 Cases from Chest Radiography Images." arXiv preprint arXiv: 2003. 09871, 2020.
  [16] D. Ioannis, I. Apostolopoulos., B. Tzani, "COVID-19: Automatic detection from X-ray images utilizing Transfer Learning with Convolutional Neural Networks,” arXiv:2003.11617 (20013), 2020
  [17] M. Barstugan, U. Ozkaya , S. Ozturk, " Coronavirus (covid-19) classification using CT image by machine learning methods”, arXiv preprint arXiv:2003.09424, 2020
  [18] M.Farooq, A. Hafeez, "Covid-resnet:" A deep learning framework for screening of covid19 from radiographs,” arXiv preprint arXiv:2003.14395 , 2020
  [19] A. Narin, C. Kaya, and Z. Pamuk, "Automatic Detection of Coronavirus Disease (COVID-[19] Using X-ray Images and Deep Convolutional Neural Networks), ” arXiv preprint arXiv:2003.10849, 2020
  [20] J. Zhang, Y. Xie and Y. Chunhua,"COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection,” arXiv:2003.12338v1, 2020
  [21] W. Linda, "COVID?Net. A Tailored Deep Convolutional Neural Network Design for Detection of COVID?19 Cases from Chest Radiography Images,” 2020
  [22] P. Sethy,. S. Behera, " Detection of Coronavirus Disease (COVID-19) Based on Deep Features", doi:10.20944/preprints 20200 3.0300.v1), 2020
  [23] A. Krizhevsky,I. Sutskever , and G. Hinton " ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, pp 1-5, 2020
  [24] O Russakovsky, J. Deng, H. Su, J. Krause, S Satheesh, S. Ma, Z. Huang, ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 2015
  [25] C. Szegedy ,W. Liu, Y. Jia,, P. Sermanet, S. Reed, D. Anguelov, V. Vanhoucke, " Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp1-9, 2015
  [26] N. Iandola, S. Han, W. Moskewicz , K.Ashraf, J.William , K. Keutzer "SqueezeNet: Alexnet level accuracy with 50x fewer parameters and <0.5 MB model size. "Preprint, submitted https://arxiv.org/abs/ 1602.07360, 2016
  [27] N. Japkwowicz, M.Shah, "Evaluating Learning Algorithm: A Classification Perspective,’’Cambridge University press, pp.74-97, 2020
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  Citation
  Oluwadare Adepeju Adebisi, John Adedapo Ojo, Olatunde Michael Oni, "Comparative Analysis of Deep Learning Models for Detection of COVID-19 from chest X-Ray Images," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.28-35, 2020

• Open Access   Article

  Message Security and Parity Bit Recovery

  Khean Ouk, Kimsoung Lim, Sensamnang Ouk

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.36-42, Oct-2020

  Abstract

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  Abstract
  Today is the age of the digital era which uses ICT around the world in daily life. The confrontation of using ICT is security breach. To protect the daily works and daily messages transferring are more secure as needed, we must take countermeasure to prevent the accidental happening to them. One of those countermeasures is using One Time Pad technique to encrypt and decrypt messages unbreakable. This encryption and decryption will highly secure messages. The amount of data is huge to store and need to be recovered as soon as possible. Most people around the world are using IT system such as banking and Microfinance which its data cost billions dollar. If there is a catastrophe occurring to loss data, their businesses will be bankruptcy. One of the most powerful techniques to recover data effectively is a parity bit. The RAID-5 level has ability to recover data effectively when a hard disk among at least 3 hard disks in array failed during the RAID-5 is working because of its parity bit. This information security method is to ensure the confidential, integrity and availability of data.

  Key-Words / Index Term
  encryption, decryption, RAID-5, Parity bit, Cryptography, One Time pad, Cryptosystem, key

  References
  [1]. DavidA.Patterson,Garth Gibson,and Randy H.Katz,“ A Case for Redundant Arrays of Inexpensive Disks (RAID)”, SIGMOD Conferences ,Volume 17, Issue 3,1988.
  [2]. Abiodun Esther Omolara, Aman Jantan, Oludare Isaac Abiodun, Humaira Arshad, “An Enhanced Practical Difficulty of One-Time Pad Algorithm Resolving the Key Management and Distribution Problem”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2018,Hong Kong,2018.
  [3]. E. Yaghoubi & L. A. Bakhtiar & A. Adami & S. M. Hamidi &M. Hosseinzadeh, “All optical OR/AND/XOR gates based on nonlinear directional coupler”, Journal of Optics ,2014.
  [4]. Prof. D. Chandrasekhar Rao, Dr. Amiya Kumar Rath, Dr. M. R. Kabat, “CRYPTOGRAPHY AND NETWORK SECURITY LECTURE NOTES”, Odisha, pp 18.
  [5]. C. E. Shannon,“Communication Theory of Secrecy Systems”, Bell System Technical Journal, vol.28-4,pp 656-715, 1949.
  [6]. Muhammad Iqbal, Muhammad Akbar Syahbana Pane,Andysah Putera Utama Siahaan, “SMS Encryption Using One-Time Pad Cipher”, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 18, Issue 6, PP 54-58,2016.

  Citation
  Khean Ouk, Kimsoung Lim, Sensamnang Ouk, "Message Security and Parity Bit Recovery," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.36-42, 2020

• Open Access   Article

  Histopathologic Cancer Detection Using Convolutional Neural Networks

  Mrunal Chipre, Harshavardhan Bhojakar, Sachin Ghanteppagol, Aditya Patil, Bahubali M. Akiwate

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.43-46, Oct-2020

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  Abstract
  Lung cancer affects one out of ten humans globally. It is diagnosed by detecting the malignancy of the cells of lymph node tissue. Modern medical image processing techniques work on histopathology images, which are captured by a microscope and then analysed by using different algorithms and techniques. Machine learning algorithms are used for processing medical imaging and pathological tools. Manual detection of a cancer cell is a tedious task and involves human error, and hence computer-aided mechanisms are applied to obtain better results as compared with manual pathological detection systems. In deep learning, this is done with convolutional neural network (CNN) by learning features and then categorizing using a fully connected network. Deep learning is generally utilized in the medical imaging field, as it does not require prior expertise in a related field. In this paper, we have trained a convolutional neural network and obtained a prediction accuracy of up to 98%.

  Key-Words / Index Term
  Machine Learning Algorithms, Computer-aided mechanisms, Pathology, Deep Learning, and Convolutional Neural Network

  References
  [1] Sumaiya Dabeer , Maha Mohammed Khan, Shaiful Islam “Cancer diagnosis in histopathological image: CNN based approach” Informatics in Medicine Unlocked 16 (2019)100231.
  [2] Samah AA, Fauzi MFA, Mansor S. Classification of benign and malignant tumors in histopathology images. In: 2017 IEEE international conference on signal and image processing applications, ICSIPA 2017, kuching, Malaysia, september 12-14, 2017; 2017. p. 102–6.
  [3] Han, Z., Wei, B., Zheng, Y. et al. Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. Sci Rep 7, 4172 (2017).
  [4] Adeshina SA, Adedigba AP, Adeniyi AA, Aibinu AM. Breast cancer histopathology image classification with deep convolutional neural networks. In: 2018 14th international conference on electronics computer and computation (ICECCO). IEEE; 2018. p. 206–12.
  [5] Nawaz, M., Sewissy, A. A., and Soliman, T. H.A. (2018). Multi-class breast cancer classification using deep learning convolutional neural network. Int. J. Adv. Compute.Sci. Appl. 9, 316–322.
  [6] F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte ”Breast Cancer Histopathological Image Classification using Convolutional. Neural Networks,” in International Joint Conference on Neural Networks. Brazil, 2017.
  [7] K. Xiao, Z. Wang, T. Xu and T. Wan ”A Deep Learning Method for Detecting and Classifying Breast Cancer Metastasis In Lymph Nodes on Histopathological Images”.Beijing, 2017.
  [8] Chandrasekhar, R.Pavan, P.Bharathi & K.V.Triveni (2019). “HISTOPATHOLOGIC CANCER DETECTION”, IRJCS:: International Research Journal of Computer Science, Volume VI, 102-124.
  [9] J. Sun and A. Binder, "Comparison of deep learning architectures for H&E histopathology images," 2017 IEEE Conference on Big Data and Analytics (ICBDA), Kuching, 2017, pp. 43-48, doi: 10.1109/ICBDAA.2017.8284105.
  [10] Y. Song, J. J. Zou, H. Chang and W. Cai, "Adapting fisher vectors for histopathology image classification," 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017, pp. 600-603, doi: 10.1109/ISBI.2017.7950592.
  [11] N. S. Lele “Image classification using convolutional neural network” IJSRCSE: International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.3, pp.22-26, June(2018)
  [12] Nachiket Kelkar, Niraj Mate, Atharv Kukade, Abhijit Kulkarni, Pradnya Mehta, “Implementation of lung cancer detection &recommendation of oncologist using machine learning” IJCSE: International Journal of Computer Science and Engineering Vol-7, Issue-5, May 2019

  Citation
  Mrunal Chipre, Harshavardhan Bhojakar, Sachin Ghanteppagol, Aditya Patil, Bahubali M. Akiwate, "Histopathologic Cancer Detection Using Convolutional Neural Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.43-46, 2020

• Open Access   Article

  IoT based Water Flood Detection and Early Warning System

  M. Chitra, D. Sadhihskumar, R. Aravindh, M. Murali, R. Vaittilingame

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.47-53, Oct-2020

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  Abstract
  Water flood is the severe and dangerous issue in public. Water flood occurs due to many factors such as heavy rain which causes critical situations. Hence, it is important to investigate water flood in different water bodies like river, sea, ocean etc. This paper proposes water flood detection algorithm in the river with the help of Internet of Things (IoT) technology. At first, a Water Level Detection Sensor, Temperature Sensor and Humidity Sensor are placed near the river to detect the flood in water bodies. The water level detection sensor acts a transmitting unit which is used to detect the water level at the time of the floods. Temperature Sensor and Humidity Sensor are used for measure the live temperature and humidity of the water bodies. Finally the collected information (data) from the water body is transmitted to LCD in order to display on the screen for the end user. When the water level increases certain level in the water body, the sensor detects and sends an alert message (SMS) to the peoples who are nearby the water body. The collected information (data) from the water level sensor and temperature and humidity sensor passed to Thingview Android application in order to find the flow graph level of the water level in the river and temperature, humidity values and sends SMS to the registered contact mobile numbers.

  Key-Words / Index Term
  IoT; Humidity; Temperature; Water Level; SMS, Sensors

  References
  [1]. Zahir, Shahirah Binti, et al. "Smart IoT Flood Monitoring System." Journal of Physics: Conference Series. Vol. 1339. No. 1. IOP Publishing, 2019.
  [2]. Priya, S. Jana, et al. "Flood monitoring and alerting system." International Journal of Computer Engineering & Technology (IJCET) 8.2 (2017): 15.
  [3]. Hughes, Danny, et al. "An intelligent and adaptable grid-based flood monitoring and warning system." Proceedings of the UK eScience All Hands Meeting. Vol. 10. 2006.
  [4]. Fowdur, Tulsi Pawan, et al. "Performance analysis and implementation of an adaptive real-time weather forecasting system." Internet of Things 3 (2018): 12-33.
  [5]. Thielen, Jutta, et al. "The European Flood Alert System-Part 1: Concept and development." Hydrology & Earth System Sciences 13.2 (2009).
  [6]. H. McMillan, J. Freer, F. Pappenberger, T. Krueger, and M. Clark, “Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions,” Hydrological Processes, vol. 24, no. 10, 2010, pp. 1270–1284.
  [7]. F. Pappenberger et al., “Visualizing probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication,” Hydrological Processes, vol. 27, no. 1, 2013, pp. 132– 146.
  [8]. M. Ancona et al., “On the design of an intelligent sensor network for flash flood monitoring, diagnosis and management in urban areas position paper,” Procedia Computer Science, vol. 32, 2014, pp. 941– 946.
  [9]. M. Borga, E. Gaume, J. D. Creutin, and L. Marchi, “Surveying flash floods: gauging the ungauged extremes,” Hydrological processes, vol. 22, no. 18, 2008, p. 3883.
  [10]. E. Gertz and P. Di Justo, Environmental Monitoring with Arduino. O’Reilly,2013.
  [11]. Sadeka Sultana, TawhidaAkand, MrinmoyDey "Design and implementation of Flood detection security system using Node MCU” 2016 9th International Conference on Electrical and Computer Engineering (ICECE), 20-22 Dec. 2016, DOI:10.1109/ICECE.2016.7853980, IEEEXplore.
  [12]. Nikolay N. Bakin,Vasiliy I. Tuyev, Eduard F. Yauk "Arduino IDE" 2011 International Conference and Seminar on Micro/Nanotechnologies and Electron Devices Proceedings, 30 June-4 July 2011, DOI:10.1109/EDM.2011.6006944. ISSN: 1815-3712, IEEEXplore.
  [13]. R. Vajeth, D. Dama,”IoT based flood detection using ultrasonic sensor", 2004 IEEE Africon. 7th Africon Conference in Africa, DOI: 10.1109/AFRICON.2004.1406747,INSPEC Accession Number: 8304644, IEEE Xplore.
  [14]. Carlos M. S. Rodrigues, Bruno S. L. Castro, "A Vision of Internet of Things In industry 4.0 With Esp8266" International Journal of Electronics and Communication Engineering and Technology, January- February 2018, ISSN Print: 0976-6464 and ISSN Online: 0976-6472,IAEME.
  [15]. Ganguly, S. SAR "Paper Battery-A Promising Energy Solution for India" International Journal of Advanced Engineering Research and Studies, E-ISSN2249 – 8974, ResearchPaper.
  [16]. Lee Li ; Liu Gang ,Zeng Han ; Hu Guan , Liu Ning ; Cai Li ,Lin Fuchang, "Design, Construction, and Testing of Switches and Trigger Generator for 1.2-MJ Capacitive Pulsed Power Supply Module" IEEE Transactions on Plasma Science, 09 November 2010, DOI: 10.1109/TPS.2010.2078521, IEEEXplore.
  [17]. Simatupang, Joni Welman, and Faiz Naufal. "Flood Early Warning Detection System Prototype Based on IoT Network." Internetworking Indonesia Journal 11 (2019): 17-22.
  [18]. Souza, Alisson Silva, et al. "A flood warning system to critical region." Procedia Computer Science 109 (2017): 1104-1109.
  [19]. Chowdury, Mohammad Salah Uddin, et al. "IoT based real-time river water quality monitoring system." Procedia Computer Science 155 (2019): 161-168.
  [20]. Udo, Edward N., and Etebong B. Isong. "Flood monitoring and detection system using wireless sensor network." Asian journal of computer and information systems 1.04 (2013).
  [21]. S. Sabeena, M. Jaganathan, “ A vision of Internet of Things”, International Journal of Computer Sciences and Engineering”, vol. 7, issue-2, page no. 492-497, Feb-2019.
  [22]. N.S. Sanguida, M. Sasikala, “A Study on Internet of Things”, International Journal of Computer Sciences and Engineering”, Vol.7, Issue 4, Feb 2019.

  Citation
  M. Chitra, D. Sadhihskumar, R. Aravindh, M. Murali, R. Vaittilingame, "IoT based Water Flood Detection and Early Warning System," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.47-53, 2020

• Open Access   Article

  Manipulation of Email Data Using Machine Learning and Data Visualization

  Vishal Verma, Anurag Sinha

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.54-63, Oct-2020

  Abstract

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  Abstract
  E-mail has become one of the essential economic for all forms of communication in today`s life. The rise in the users of email has drastically increased the data set of the email available on the one tap over the internet. In this paper we will propose an algorithm based on machine learning which will classify the email based on its subject. We have used several machines learning algorithms classifier Such as SVM classifier, neural network classifier. However people mostly prefer email to be as a communication for business and other personal purposes. Application of the emails has been used everywhere in education, corporate, business and so on. With the Rise of the data set of the email it’s generate a Corpus with itself which can be used as a different categorization through which we will classify the email based on its subject matter. The rise in the number of the data sets of the email it brings some more features along with it through which we can extract some features with it and we can implement opinion mining and sentiment analysis and thereby we can extract spam ham detections of the email data out of it. We have used supervised machine learning algorithm for the implementation of the data sets that we have used and converted the unlabeled and unstructured data set of email into the labeled and structured datasets and then we have extracted the features from it. Moreover various public data sets, feature sets, classification techniques, performance measures are examined and use in each in identified application area. In this paper we have used several datasets of email for the subject based classification and we have also proposed algorithm for spam detection for this method we have employed several machine learning algorithm.

  Key-Words / Index Term
  Email classification, spam detection, opinion mining, Machine learning

  References
  [1] Ketan Sarvakar, Urvashi 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] B. Pang, L. Lee, and S. Vaithyanathain,“Thumbs up? Sentiment classification using machine learning techniques,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86, 2002.
  [3] B. Pang and L. Lee, “A sentimental education: sentimental analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 271–278, 2004.
  [4] S.-M. Kim and E. Hovy, “Determining the sentiment of opinions,” in Proceedings of the 20th International Conference on Computational Linguistics, pp. 1367–1373, Association for Computational Linguistics, 2004.
  [5] T. Mullen and N. Collier, “Sentiment analysis using support vector machines with diverse information sources,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 412–418, Barcelona, Spain, 2004.
  [6] J. Wiebe, R. Bruce, M. Martin, T. Wilson, and M. Bell, Learning subjective language,”Computational Linguistics, vol. 30, no. 3, pp. 277–308, 2004.
  [7] C. Zhang, W. Zuo, T. Peng, and F. He, “Sentiment classification for Chinese reviews using machine learning methods based on string kernel,” in Proceedings of the 3rd International Conference on Convergence and Hybrid Information Technology, pp. 909–914, November 2008.
  [8] F. Smadja, H. Tumblin, "Automatic spam detection as a text classification task", in: Proc. of Workshop on Operational Text Classification Systems, 2002.
  [9] Ann Nosseir , Khaled Nagati and Islam Taj-Eddin, “Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 1, March 2013 ISSN (Print): 1694- 0814 | ISSN (Online): 1694-0784.
  [10] R. Kishore Kumar, G. Poonkuzhali, P. Sudhakar,” Comparative Study on Email Spam Classifier using Data Mining Techniques”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2012 Vol I, IMEC2012, March 14- 16,2012, Hong Kong, ISBN: 977-988-19251-1-4.
  [11] Rafiqul Islam and Yang Xiang, member IEEE, “Email Classification Using Data Reduction Method” created June 16, 2010.
  [12] Asmeeta Mali, “Spam Detection Using Baysian with Pattren Discovery”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-3, July 2013.
  [13] Vandana Jaswal, “ Spam Detection System Using Hidden Markov Model”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013 ISSN: 2277 128X.
  [14] Saadat Nazirova, “Survey on Spam Filtering Techniques”, Communications and Network, 2011, 3, 153 160, doi:10.42 36/cn.2011.33019 Published Online August 2011 (http: //www.SciRP.org /journal/cn).
  [15] Neha Singh,”Dendritic Cell Algorithm and Dempster Belief Theory Using Improved Intrusion Detection System “, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013 ISSN: 2277 128X.
  [16] Julie Greensmith, “The Dendritic Cell Algorithm”, Thesis submitted to the University of Nottingham for the degree of Doctor of Philosophy October 2007.
  [17] D. K. Renuka and P. Visalakshi, "Latent Semantic Indexing Based SVM Model for Email Spam Classification," Journal of Scientific & Industrial Research, vol. 73, pp. 437-442, Jul 2014.
  [18] S. Youn, "SPONGY (SPam ONtoloGY): Email classification using two-level dynamic ontology," Scientific World Journal, vol. 2014, 2014.
  [19] Y.Meng, W. Li, and L.F. Kwok, "Enhancing email classification using data reduction and disagreement-based semi-supervised learning," in 2014 1st IEEE International Conference on Communications, ICC 2014, Sydney, NSW, 2014, pp. 622-627.
  [20] N. O. F. Elssied, O. Ibrahim, and W. Abu-Ulbeh, "An improved of spam E-mail classification mechanism using K-means clustering," Journal of Theoretical and Applied Information Technology, vol. 60, pp. 568-580, 2014.
  [21] M. H. Song, "E-Mail Classification based Learning Algorithm Using Support vector machine," in Materials, Mechanical Engineering and Manufacture, Pts 1-3. vol. 268-270, H. Liu, Y. Yang, S. Shen, Z. Zhong, L. Zheng, and P. Feng, Eds., ed StafaZurich: Trans Tech Publications Ltd, 2013, pp. 1844-1848.
  [22] C. Jou, "Spam E-Mail Classification Based on the IFWB Algorithm," in Intelligent Information and Database Systems. vol. 7802, A. Selamat, N. T. Nguyen, and H. Haron, Eds., ed Berlin: Springer-Verlag Berlin, 2013, pp. 314-324.
  [23] Lifan, T. Ma, and H. Xu, "The research on email classification based on q-Gaussian kernel SVM," Journal of Theoretical and Applied Information Technology, vol. 48, pp. 1292-1299, 2013.
  [24] J. R. Mendez, M. Reboiro-Jato, F. Diaz, E. Diaz, and F. FdezRiverola, "Grindstone4Spam: An optimization toolkit for boosting e-mail classification," Journal of Systems and Software, vol. 85, pp. 2909-2920, Dec 2012.
  [25] A. Borg, N. Lavesson, and Ieee, "E-mail Classification using Social Network Information," 2012 Seventh International Conference on Availability, Reliability and Security (Ares), pp. 168-173, 2012.
  [26] N. Perez-Diaz, D. Ruano-Ordas, J. R. Mendez, J. F. Galvez, and F. Fdez-Riverola, "Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification," Applied Soft Computing, vol. 12, pp. 3671-3682, Nov 2012.

  Citation
  Vishal Verma, Anurag Sinha, "Manipulation of Email Data Using Machine Learning and Data Visualization," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.54-63, 2020

• Open Access   Article

  A Survey on –Preserving Deduplication with Auditing Techniques In Cloud Networks- Recent Appraoches

  Basheer P.

  Survey Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.5 , pp.64-69, Oct-2020

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  Citation

  Abstract
  In the current scenario, cloud technologies have attained massive popularity. This cloud technology is used in many applications .In cloud innovation, is the on-request availability of framework assets, information stockpiling and figuring power, without direct dynamic oversight by client. It requires set of equipment and programming components expected to empower distributed computing. The dynamic nature of networks makes them more and more functional and optimal for many applications. Cloud stores your data in data center. This nature of Cloud is more vulnerable to many issues such as ?le deduplication and integrity auditing. In cloud networks, instantaneously needs to be complete these issues urgently in academic world and industry. This is a more important challenging issue, which need more analysis. In past few years, numerous researchers proposed different solutions for ?le deduplication and integrity auditing in Cloud Computing. In this survey, recent approaches and techniques done for Cloud deduplication and auditing is discussed effectively. The review totally presents the issues and deserves of these existing approaches.

  Key-Words / Index Term
  Cloud networks, Cloud storage, deduplication, cloud auditing, security and privacy

  References
  [1] Ali Gholami, Erwin Laure, “Security and privacy issues in cloud computing,”International Journal of Network Security & Its Applications, Vol.8, Issue.1, pp.59-79, 2016.
  [2] Wang, Qian, "Enabling public auditability and data dynamics for storage security in cloud computing." IEEE transactions on parallel and distributed systems ,Vol.22, Issue.5, pp.847-859, 2010.
  [3] H. Hou, J. Yu, and R. Hao,‘‘Cloud storage auditing with deduplication supporting different security levels according to data popularity,’’ J. Netw. Comput. Appl., vol. 134, pp. 26–39,
  2019.
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  [5] R. Di Pietro and A. Sorniotti, ‘‘Boosting ef?ciency and security in proof of ownership for deduplication,’’ in Proc. ASIACCS, Seoul, South Korea, pp. 81–87, 2012.
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  Citation
  Basheer P. , "A Survey on –Preserving Deduplication with Auditing Techniques In Cloud Networks- Recent Appraoches," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.5, pp.64-69, 2020