Volume-9 , Issue-4 , Apr - 2021 Go Back

Yobe State offers free antenatal and postnatal care services to target clients (pregnant women and nursing mothers). This free service was aimed to fulfil the MDGs fifth Goal to reduce maternal and infant mortality rates. However, the service is facing challenges of lack of patronage at the side of the clients. Researches show that, there exists a communication gap between the programme and the clients. Another problem is lack of maternal education from the clients. Some researchers have used ICT methods to solve this type of problems, however, most of the methods applied by the researchers in solving the problems are not cost-effective and skew to favour clients in urban areas. This research used the recent Information and Communication Technologies (ICT) methods to create an integrated platform that comprises Mobile and Web-Based (iMOWBA) to enhance the participation of clients regarding antenatal and postnatal care services. The platform provides different ways of delivering maternal awareness that include Web-based, mobile App, USSD Code and SMS. The technologies used include PHP, MYSQL, JavaScript, HTML and RapidSMS. The platform has increased the ANC services patronage from 46% to 67% of completed rounds.

antenatal, neonatal, postnatal, ICT, pregnancy, Yobe

[1] C. Rooney, “Antenatal care and maternal health, How effective it is?” pp. 1–76, 1993.
[2] M. McDonagh, “Is antenatal care effective in reducing maternal morbidity and mortality?,” Health Policy Plan., vol. 11, no. 1, pp. 1–15, 1996, doi: 10.1093/heapol/11.1.1.
[3] O. D. Somefun and L. Ibisomi, “Determinants of postnatal care non-utilization among women in Nigeria,” BMC Res. Notes, vol. 9, no. 1, pp. 1–11, 2016, doi: 10.1186/s13104-015-1823-3.
[4] K. E. Agho, O. K. Ezeh, F. A. Ogbo, A. I. Enoma, and C. Raynes-greenow, “Factors associated with inadequate receipt of components and use of antenatal care services in Nigeria?: a population-based study,” no. March, pp. 172–181, 2018, doi: 10.1093/inthealth/ihy011.
[5] V. Z. Kuuire et al., “Timing and utilisation of antenatal care service in Nigeria and Malawi,” vol. 1692, no. May, 2017, doi: 10.1080/17441692.2017.1316413.
[6] Y. Dibaba, M. Fantahun, and M. J. Hindin, “The effects of pregnancy intention on the use of antenatal care services: Systematic review and meta-analysis,” Reprod. Health, vol. 10, no. 1, 2013, doi: 10.1186/1742-4755-10-50.
[7] Y. M. Danlami, A. Almu, H. Mairoh, and M. K. Hamza, “Integrating Antenatal and Postnatal Pregnancy Services to Hospital Management System,” Path Sci., vol. 4, no. 10, pp. 1009–1015, 2018, doi: ss10.22178/pos.39-3.
[8] WHO, “WHO technical consultation on postpartum and postnatal care,” World Heal. Organ., pp. 1–56, 2010.
[9] R. E. Rowe, H. Magee, M. A. Quigley, P. Heron, J. Askham, and P. Brocklehurst, “Social and ethnic differences in attendance for antenatal care in England,” Public Health, vol. 122, no. 12, pp. 1363–1372, 2008, doi: 10.1016/j.puhe.2008.05.011.
[10] I. Pezzuto, “Disruptive Innovation and Healthcare: The Case of Nigeria,” SSRN Electron. J., no. April, pp. 48–52, 2019, doi: 10.2139/ssrn.3448392.
[11] O. A. Haruna-ogun, “Health Care for Women International Geographical differentials in uptake of antenatal care services in Nigeria,” vol. 9332, 2018, doi: 10.1080/07399332.2017.1388804.
[12] B. Souley, A. Usman, M. Abdulhamid, B. Zakariyau, and B. Lookman, “Mobile Devices Enable Antenatal Care in Remote Areas of Nigeria,” Br. J. Math. Comput. Sci., vol. 15, no. 5, pp. 1–23, 2016, doi: 10.9734/bjmcs/2016/28418.
[13] G. E. L. van den Berk, M. C. Leoni, G. M. N. Behrens, J. Taljaard, and J. E. Arends, “Improving HIV-related care through eHealth,” Lancet HIV, vol. 7, no. 1, pp. e8–e10, 2020, doi: 10.1016/S2352-3018(19)30348-0.

B.S. Ahmed, M.W. Bara, "An Integrated Mobile and Web Based Approach for Promoting Antenatal and Postnatal Care Patronages and Reduction of Maternal and Neonatal Mortality Rates: A Case Study of Yobe State," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.1-6, 2021.

Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users’ experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users’ personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user’s personality can be accurately predicted through the publicly available information on their Twitter profile. We will describe the type of data collected, our methods of analysis, and the results of predicting personality traits through machine learning. We then discuss the implications this has for social media design, interface design, and broader domains.

personality, user profiles, personalization, cross domains and Twitter

[1] Cantandir, I. Fernandez-Tobiaz, A. Belllogin, "Relating personality types with user preferences in multiple entertainment domains", EMPIRE 1st Workshop on Emotions and Personality in Personalized Services, 2013.
[2] L.R. Goldberg, J.A. Johnson, H.W. Eber, R. Hogan, M.C. Ashton, C.R. Cloninger, "The International personality item pool and the future of public domain personality measures", Journal of Research in Personality, vol. 40, no. 1, pp. 84-96, 2006.
[3] F. Mairesse, M. Walker, M. Mehl, R. Moore, "Using linguistic cues for the automatic recognition of personality in conversation and text", Journal of Artificial Intelligence Research (JAIR), vol. 30, no. 1, pp. 457-500, 2007.
[4] Sucharitha Y, Vijayalata Y, Prasad VK. Analysis of Early Detection of Emerging Patterns from Social Media Networks: A Data Mining Techniques Perspective. InSoft Computing and Signal Processing 2019 (pp. 15-25). Springer, Singapore.
[5] P.T. Costa, R.R. McCrae, "Revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI)", Psychological Assessment Resources, 1992
[6] Sucharitha Y, Prasad VK, Vijayalatha Y. Emergent Events Identification in Micro-Blogging Networks Using Location Sensitivity.
[7] J. Golbeck, C. Robles, K. Turner, "Predicting personality with social media", CHI`11 Extended Abstracts on Human Factors in Computing Systems, pp. 253-262, 2011. Access at ACM
[8] Yadala Sucharitha, Y. Vijayalata and V. Kamakshi Prasad*, “Predicting Election Results from Twitter Using Machine Learning Algorithms”, Recent Advances in Computer Science and Communications (2020) 13: 7. https://doi.org/10.2174/2666255813999200729164142
[9] M. Hall, E. Frank, H. Holmes, P. Pfahringer, P. Reutemann, I.H. Witten, "The WEKA data mining software: an update", SIGKDD Explorations, vol. 11, no. 1, 2009. Access at ACM
[10] Reddy PC, Babu AS. Survey on weather prediction using big data analystics. In2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) 2017 Feb 22 (pp. 1-6). IEEE.
[11] N.M.A. Lestari, I.K.G.D. Putra, A.A.K.A. Cahyawan, "Personality types classification for Indonesian text in partners searching website using Naive Bayes methods", International Journal of Software and Informatics Issues, 2013.
[12] Arifin, K.E. Purnaha, "Classification of emotions in Indonesia text using K-NN method", International Journal of Information and Electronics Engineering, vol. 2, no. 6, 2012
[13] Reddy PC, Sureshbabu A. An applied time series forecasting model for yield prediction of agricultural crop. InInternational Conference on Soft Computing and Signal Processing 2019 Jun 21 (pp. 177-187). Springer, Singapore.
[14] A. Firoj, E.A. Speanov, G. Riccardi, "Personality traits recognition on social network – Facebook", ICWSM Workshop on Personality Classification, 2013
[15] F. Iacobelli, A. Cullota, "Too neurotic not too friendly: structured personality classification on textual data", ICWSM Workshop on Personality Classification, 2013.
[16] Reddy PC, Babu AS. A novel approach to analysis district level long scale seasonal forecasting of monsoon rainfall in Andhra Pradesh and Telangana. International Journal of Advanced Research in Computer Science. 2017 Nov 1;8(9).
[17] F. Celli, F. Pianesi, D. Stillwell, M. Kosinski, "Workshop on computational personality recognition (shared task)", Proceedings of WCPR13 in conjunction with ICWSM-13, 2013.
[18] Daniele Quercia§ Michal Kosinski† David Stillwell? Jon Crowcroft§, Our Twitter Profiles, Our Selves: Predicting Personality with Twitter, Horizon Digital Economy Research grant (EP/G065802/1).
[19] Reddy PC, Sureshbabu A. An Adaptive Model for Forecasting Seasonal Rainfall Using Predictive Analytics. InInternational Journal of Intelligent Engineering and Systems 2019 (pp. 22-32).
[20] Settanni M, Azucar D, Marengo D. Predicting individual characteristics from digital traces on social media: A meta-analysis. Cyberpsychology, Behavior, and Social Networking. 2018 Apr 1;21(4):217-28.
[21] Ozer DJ, Benet-Martinez V. Personality and the prediction of consequential outcomes. Annu. Rev. Psychol.. 2006 Jan 10;57:401-21.
[22] Roberts BW, Kuncel NR, Shiner R, Caspi A, Goldberg LR. The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological science. 2007 Dec;2(4):313-45.
[23] Soto CJ. How replicable are links between personality traits and consequential life outcomes? The Life Outcomes of Personality Replication Project. Psychological Science. 2019 May;30(5):711-27.
[25] Saeb S, Lattie EG, Schueller SM, Kording KP, Mohr DC. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ. 2016 Sep 29;4:e2537.
[26] P Chandrashaker Reddy* and A Suresh Babu, “An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction”, International Journal of Sensors, Wireless Communications and Control (2020) 10: 3.
[27] Ferwerda and M. Schedl, "Enhancing music recommender systems with personality information and emotional states: A proposal", CEUR Workshop Proc., vol. 1181, pp. 36-44, 2014.
[28] Ferwerda, E. Yang, M. Schedl and M. Tkal?i?, "Personality Traits Predict Music Taxonomy Preferences", Proc. 33rd Annu. ACM Conf. Ext. Abstr. Hum. Factors Comput. Syst. - CHI EA ‘15, pp. 2241-2246, 2015.

P Chandra Shaker Reddy, Yadala Sucharitha, G Surya Narayana, "Predicting Personality from Micro-Blogs using Supervised Machine Learning Models," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.7-14, 2021.

Text similarity of text measuring is a challenging task when text is in local languages and large in amount. Text measuring tools are easily available in the market but for regional languages very few tools are available. To figure out we have introduced a text similarity in native languages. In this paper, we are highlighting the Punjabi language where we find out that cosine similarity measures the accuracy of the Punjabi documents with other Punjabi documents. Text in both documents is divided into n-grams and then the common n-grams are found. The text in the documents is subject to pre-processing, which includes tokenization and punctuation removal, followed by stop words removal and stemming. After the preprocessing step, the similarity score is calculated using the cosine similarity. The purpose of doing this is to one step toward highlighting native languages. The features, performance, advantages, and disadvantages of various similarity measures are discussed. In this paper, we provide an efficient evaluation of all these measures and help the researchers to select the best measure according to their requirement.

Semantic similarity, Corpus-based similarity, Knowledge-based similarity, Semantic relatedness

[1] Gentner, Dedre; Markman, Arthur B. (1997). "Structure mapping in analogy and similarity" (PDF). American Psychologist. 52 (1): 45–56. CiteSeerX 10.1.1.87.5696. doi:10.1037/0003-066X.52.1.45. Archived from the original on 2016-03-24.
[2] Greg Aloupis, Thomas Fevens, Stefan Langerman, Tomomi Matsui, Antonio Mesa, Yurai Nunez, and David Rappaport, and Godfried T. Toussaint, "Algorithms for computing geometric measures of melodic similarity," Computer Music Journal, Vol. 30, No. 3, Fall 2006, pp. 67–76
[3] Gentner, Dedre; Markman, Arthur B. (1997). "Structure mapping in analogy and similarity" (PDF). American Psychologist. 52 (1): 45–56. CiteSeerX 10.1.1.87.5696. doi:10.1037/0003-066X.52.1.45. Archived from the original on 2016-03-24.
[4] Balkova, Valentina; Sukhonogov, Andrey; Yablonsky, Sergey (2003). "Russian WordNet From UML-notation to Inter net/Intranet Database Implementation" (PDF). GWC 2004 Proceedings: 31–38. Retrieved 12 March 2017.
[5] Novotný, Vít (2018). Implementation Notes for the Soft Cosine Measure. The 27th ACM International Conference on Information and Knowledge Management. Torun, Italy: Association for Computing Machinery. pp. 1639–1642. arXiv:1808.09407. doi:10.1145/3269206.3269317. ISBN 978-1-4503-6014-2.
[6] Langer, Stefan; Gipp, Bela (2017). "TF-IDuF: A Novel Term-Weighting Scheme for User Modeling based on Users` Personal Document Collections" (PDF). IConference.
[7] Rogers, David J.; Tanimoto, Taffee T. (1960). "A Computer Program for Classifying Plants". Science. 132 (3434): 1115–1118. doi:10.1126/science.132.3434.1115.
[8] A Survey of Encoding Techniques for Reducing Data-Movement Energy", JSA, 2018
[9] Winkler, W. E. (2006). "Overview of Record Linkage and Current Research Directions" (PDF). Research Report Series, RRS.
[10] Andoni, Alexandr; Krauthgamer, Robert; Onak, Krzysztof (2010). Polylogarithmic approximation for edit distance and the asymmetric query complexity. IEEE Symp. Foundations of Computer Science (FOCS). arXiv:1005.4033. Bibcode:2010arXiv1005.4033A. CiteSeerX 10.1.1.208.2079.
[11] Backurs, Arturs; Indyk, Piotr (2015). Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false). Forty-Seventh Annual ACM on Symposium on Theory of Computing (STOC). arXiv:1412.0348. Bibcode:2014arXiv1412.0348B.
[12] Chapman, S. (2006). SimMetrics: a java & c# .net library of similarity metrics, http://sourceforge.net/projects/simmetrics/.
[13] Hall , P. A. V. & Dowling, G. R. (1980) Approximate string matching, Comput. Surveys, 12:381-402.
[14] Peterson, J. L. (1980). Computer programs for detecting and correcting spelling errors, Comm. Assoc. Comput. Mach., 23:676-687.
[15] Jaro, M. A. (1989). Advances in record linkage methodology as applied to the 1985 census of Tampa Florida,Journal of the American Statistical Society,vol. 84, 406, pp 414-420.
[16] Jaro, M. A. (1995). Probabilistic linkage of large public health data file, Statistics in Medicine 14 (5-7), 491-8
[17] Winkler W. E. (1990). String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage,Proceedings of the Section on Survey Research Methods, American Statistical Association, 354–359
[18] Needleman, B. S. & Wunsch, D. C.(1970).A general method applicable to the search for similarities in the amino acid sequence of two proteins",Journal of Molecular Biology48(3): 443–53
[19] Smith, F. T. & Waterman, S. M. (1981).Identification of Common Molecular Subsequences, Journal of Molecular Biology147: 195–197
[20] Alberto, B. , Paolo, R., Eneko A. & Gorka L. (2010). Plagiarism Detection across Distant Language Pairs, In Proceedings of the 23rd International Conference on Computational Linguistics, pages 37–45
[21] Eugene F. K. (1987).Taxicab Geometry , Dover.ISBN0-486-25202-7
[22] Dice, L. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3
[23] Jaccard, P. (1901). Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547-579
[24] Lund, K., Burgess, C. & Atchley, R. A. (1995). Semantic and associative priming in a high-dimensional semantic space.Cognitive Science Proceedings (LEA), 660-665
[25] Lund, K. & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence.Behavior Research Methods, Instruments & Computers, 28(2),203-208
[26] Landauer, T.K. & Dumais, S.T. (1997). A solution to plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge", Psychological Review, 104
[27]https://www.mhrd.gov.in/sites/upload_files/mhrd/files/upload_document/languagebr.pdf

Ramandeep Kaur , Prabhjeet Kaur, "Text Similarity on Native Languages Documents," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.15-19, 2021.

Uncertainty measures form essential constituents of information theory as they provide a sufficient mechanism for determining the quantity of useful information contained in a system. In the present work, the concept of divergence between fuzzy sets are made use of in defining new measures of uncertainty in the framework of fuzzy rough sets. Further, these measures are utilized in developing an algorithm for binary image segmentation of a grey level image. Moreover, the proposed algorithm is implemented using different test images with the help of an OCTAVE program.

Divergence, Image segmentation, Fuzzy Set, Rough set, Uncertainty measure

[1] C. E. Shannon, “A Mathematical Theory of Communication", The Bell System Technical Journal Vol.27, pp. 379 – 423, 1948.
[2] Z. Pawlak, "Rough Sets", International Journal of Computer and Information Sciences, Vol.11, Issue.5, pp.341 - 356, 1982.
[3] L. A. Zadeh, "Fuzzy Sets", Information and Control, Vol.8, Issue.3, pp.338-353, 1965.
[4] A. Nakamura, “Fuzzy Rough Sets”, Note on Multiple-Valued Logic in Japan, Vol.3, pp.1-8, 1988.
[5] A. Radzikowska and E. E. Kerre, "A comparative Study of Fuzzy Rough Sets", Fuzzy Sets and Systems, Vol.126, pp.137-155, 2002.
[6] T. Beaubouef, F. E.. Petry and G. Arora, “Information Theoretic Measures of Uncertainty for Rough Sets and Rough Relational Databases”, Information Sciences, Vol.109, pp.185 – 195, 1998.
[7] J. Y. Liang, K. S. Chin, C. Y. Dang and R. C. M. Yam, "A New Method for Measuring Uncertainty and Fuzziness in Rough Set Theory", International Journal of General Systems Vol.31(4) pp.331- 342, 2002.
[8] J. Y. Liang, J. H. Wang and Y. H. Qian, “A New Measure of Uncertainty Based on Knowledge Granulation for Rough Sets”, Information Sciences Vol.179, pp. 458 – 470, 2009.
[9] J.S. Mi, X. M. Li, H. Y. Zhao and T. Feng, "Information Theoretic Measures of Uncertainty in Generalized Fuzzy Rough Sets", in A. An et al. (eds) RSFDGrC 2007, LNAI 4482, Springer pp. 63 – 70, 2007.
[10] N. Dhanachandra and Y. J. Chanu, "A survey on image segmentation methods using clustering techniques", European Journal of Engineering Research and Science, Vol.2(1), pp 15-20, 2017.
[11] E. E. Kerre and M. Nachtegael(Eds), “Fuzzy Techniques in Image Processing”, Sringer-Verlag, Heidelberg, 2000.
[12] A. E. Hassanien, A. Abraham, J. F. Peters, G. Schaefer, and C. Henry, "Rough sets and near sets in medical imaging: a review", IEEE Transactions on Information Technology in Biomedicine, Vol.13(6), pp.955-968, 2009.
[13] S. S. Begum, D. R. Lakshmi, “A Review of Current Methods in Medical Image Segmentation”, International Journal of Computer Sciences and Engineering, Vol.7(12), pp.67–73, 2019.
[14] N. Senthilkumaran and S. Vaithegi, "Image Segmentation by Using Thresholding Techniques for Medical Images", Computer Science and Engineering: An International Journal, Vol.6(1), pp.1-13, 2016.
[15] J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms” Plenum Press, New York, 2013.
[16] N. Otsu, "A Threshold Selection Algorithm from Gray Level Histograms", IEEE Transactions on Systems, Man and Cybernetics, Vol.9, pp.62 – 66, 1979.
[17] J. Klir and B. Yuan, “Fuzzy Sets and Fuzzy Logic”, Prentice Hall, New Jersey, 1995.
[18] S. Montes, I. Couso, P. Gil and C. Bertoluzza, "Divergence Measure Between Fuzzy Sets", International Journal of Approximate Reasoning, Vol.30, pp.91-105, 2002.
[19] Z. Pawlak, "Rough Sets - Theoretical Aspect of Reasoning About Data", Kluwer Academic Publishers, The Netherlands, 1991.
[20] D. Dubois and H. Prade, "Rough Fuzzy Sets and Fuzzy Rough Sets", International Journal of General Systems, Vol.17, pp.191-209, 1990.
[21] J. S. Mi, Y. Leung and T. Feng, "Generalized Fuzzy Rough Sets Determined by aTriangular Norm", Information Sciences, Vol.178, pp.3203-3213, 2008.
[22] S. Vluymans, L. D`eer, Y. Saeys and C. Cornelis, "Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey", Fundamenta Informaticae, pp.1-34, 2015.
[23] W. Z. Wu, J. S. Mi and W. X. Zhang, "Generalized Fuzzy Rough Sets", Information Sciences, Vol.151, pp.263-282, 2003.
[24] Sheeja T. K. and A. Sunny Kuriakose, "Divergence Based Generalized Fuzzy Rough Sets", International Journal of Computer Sciences and Engineering, Vol.6(6), pp.809 – 815, 2018.
[25] M. J. Wierman, “Measuring Uncertainty in Rough Set Theory”, International Journal of General System Vol.28, pp.283 – 297, 1999.
[26] J. Y. Zhao, Z. Zhang, C. Han, and Z. Zhou, "Complement information entropy for uncertainty measure in fuzzy rough set and its applications", Soft Computing, Vol.19 (7), pp.1997– 2010, 2015.

Sheeja T.K., Sunny Kuriakose A., "A New Uncertainty Measure and Application to Image Processing," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.20-24, 2021.

The IoT-(Internet of Things) is an expeditiously sprouting archetype having prospective to transmute the physical interface amid the folks & organizations. Internet of Things network ambitions to interchange ‘‘things’’ in protected and consistent method over IT-infrastructure. This Internet of Things expertise has originate submission in numerous arenas like healthcare besides privacy concerns, learning & preparation besides resource administration, material dispensation to term a limited. Though, real-world comprehension of the expertise is met copious security which to alleviated for significant effectively placement of IoT expertise. An anticipation method is planned to augment cyber safety of Internet of Things strategies and systems in contradiction of DDoS bouts which devour the band-width in contemporary IOT devices. Subsequently the systems is are wire-less & self-configuring & does not prerequisite an existing setup and partake great volatile bulge engagements, safety develops unique of greatest vigorous dispute to upstretched hooked on the interpretation. Suggested tactic is grounded on examination & inquiries of band-width bouts that predominantly emphasis arranged DDoS and is truthfully a callous encounter remains tough to perceive, besides diminutions recital of system. DDoS embraces collection of assailant bulges besides boards the prey to avert the genuine operators beginning recovering the network services & chattels. Intermission dissuasion method in the IoT strategies are events that is pickled as Supplementary of the invasion recognition scheme to aggressively shield besides avert incursions which are identified through the recognition trials of IDS. The shot which is engendered through IDS subsequently investigating echo of pathological scrutiny is ignoble of recommended process.

IoT, Interruption Deterrence, IT infrastructure, DDoS, pathological investigation

[1] A. Mansour, M. Azab, M. R. M. Rizk and M. Abdelazim, "Biologically-inspired SDN-based Intrusion Detection and Prevention Mechanism for Heterogeneous IoT Networks," 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2018, pp. 1120-1125, doi: 10.1109/IEMCON.2018.8614759.
[2] C. Jiang, J. Kuang and S. Wang, "Home IoT Intrusion Prevention Strategy Based on Edge Computing," 2019 IEEE 2nd International Conference on Electronics and Communication Engineering (ICECE), Xi`an, China, 2019, pp. 94-98, doi: 10.1109/ICECE48499.2019.9058536.
[3] N. Chaabouni, M. Mosbah, A. Zemmari and C. Sauvignac, "A OneM2M Intrusion Detection and Prevention System based on Edge Machine Learning," NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 2020, pp. 1-7, doi: 10.1109/NOMS47738.2020.9110473.
[4] C. Constantinides, S. Shiaeles, B. Ghita and N. Kolokotronis, "A Novel Online Incremental Learning Intrusion Prevention System," 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Canary Islands, Spain, 2019, pp. 1-6, doi: 10.1109/NTMS.2019.8763842.
[5] AHANGER, Tariq Ahamad. Defense Scheme to Protect IoT from Cyber Attacks using AI Principles. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 6, p. 915-926, nov. 2018. ISSN 1841-9844. Available at: . Date accessed: 22 feb. 2021. doi: https://doi.org/10.15837/ijccc.2018.6.3356.
[6] T. A. Ahanger and A. Aljumah, "Internet of Things: A Comprehensive Study of Security Issues and Defense Mechanisms," in IEEE Access, vol. 7, pp. 11020-11028, 2019, doi: 10.1109/ACCESS.2018.2876939.

Shanthi Swaroop M.S., Minavathi, "Enhancing Cyber Security in Modern IOT Using Intrusion Prevention Algorithm for IOT," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.25-29, 2021.

The current education system is getting digitized at a very fast pace. Administrators of Educational institutions have become increasingly concerned about regularity of student attendance and their overall academic performance. In the earlier times, attendance record and performance evaluation was carried out manually like writing down the data using pen and paper. Most of the school’s students and faculty are using iOS, Android and other LMS application based applications. The authors propose to develop application software for the college/university administration. The authors propose to use advanced java and other integrated technologies to develop the application that deals with the all the College/University related work. Administrative data needs to be digitized for the management to get accurate and up-to-date information regarding a student’s academic career.

Java, SpringBoot, Thymeleaf, SQL, GUI, Bootstrap

[1] Faisal hijazi, “E-School – School Management System” May, 2016
[2] Akarsha Sudheer, “A Survey on School Management System on Android and Windows Integrated with Smartcard Readers” International Journal of Innovative Research in Science, Engineering and Technology, issue vol. 4, issue 10, October, 2015
[3] Ms Elizabeth B Kuriakose, “A Cost Effective School Management System for Disadvantaged Schools in the Free State Province Using the Software as a Service (SaaS) delivery model.”, March 2014
[4]Majlinda Fetaji, “Analyses of Factors that Influence the Reliability of e- school Management software System in High Schools in Macedonia” DOI :10.2498/iti.2013.0571
[5] D. Zhibing Liu, Huixia Wang, Hui Zan, “Design and implementation of student information management system.” 2010 International Symposium on intelligence information processing and trusted computing.
[6] S. R.Bharmagoudar, Geeta R.B and S.G. Totad, “Web based student management system.”, Andra Pradesh, vol.2 June 2013.
[7] M.A.Norasiah and A.Norhayti “Intelligent student information system.” 4th International conference on telecommunication technology proceedings. Shah Alam, Malaysia, 0-7803-7773-7/03 2013 IEEE.
[8] Meghan Cosier , Audri Gomez, Aja McKee ,Kimiya Sohrab Maghzi, “Smart phones permitted: How teachers use textmessaging to collaborate”,Springer Science+Business Media New York , 20 October 2013, pp. 347-358, 2013
[9] Kamaruddin, A., Nagalingam, S.V.A.L., Admodisastro, N., Md Rasid, N.S. ,“Parent personal information system to improve parental involvement in children`s learning process in elementary school”, 3rd International Conference on User Science and Engineering (i-USEr), IEEE, pp. 174-179, 2014
[10] Yousuf Hasan, Mustafa Zaidi, Najmi Haider , W.U.Hasan, I.Amin, “Smart Phones Application development using HTML5 and related technologies: A tradeoff between cost and quality”,IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 3, No 3, 2012
[11] Giulia Cavrini , Gina Chianese , Barbara Bocch, Liliana Dozza, “School Climate: Parents’, Students’ And Teachers’ Perceptions”, Procedia - Social and Behavioral Sciences 191, pp. 2044 – 2048, 2015
[12] http://developer.telerik.com/featured/what-is-a-hybrid-mobile-app/
[13]https://developer.mozilla.org/enUS/docs/Web/Guide/HTML/HTML5
[14] http://webdesign.about.com/od/css/
[15] https://en.wikipedia.org/wiki/JavaScript
[16] Susana Juniu, “Computer mediated parent teacher communication”,Actualidades Investigativas en Educación,ISSN 1409-4703, Volume 9, Issue 3, pp. 1-19, 2009
[17] http://teach.com/education-technology/parent-teacher-apps
[18] Deepa V. Jose , Lakshmi Priya C, G. Priyadarshini, Monisha Singh, “Challenges and Issues in Android Application Development- An Overview”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 1, pp. 811-814, 2015
[19] Rong-Jyue Fang , Wen-Jiuh Chiung , Sheng-Jen Yang , Hua- Lin Tsai , Kuo-Cheng Wu , Shue-Tien Juang ,“The effects and limits of teacher- parents communication by Mobile Device-The View of Teachers”, Proceedings of the 6th WSEAS International Conference on Applied Computer Science, 2007
[22] Elka? Hassini , “Student–instructor communication: The role of email”, Computers & Education 47 , pp. 29-40, 2006
[23] Shin Kamada , Takumi Ichimura , Tetsuya Shigeyasu and Yasuhiko Takemoto, “Registration system of cloud campus by using android smart tablet”, SpringerPlus, 2014 [24] Li Ma , Lei Gu and Jin Wang, “Research and Development of Mobile Application for Android Platform”, International Journal of Multimedia and Ubiquitous Engineering, Vol.9, No.4, pp.187-198 , 2014
[25] Li-Hsing Ho, Chang-Liang Hung, Hui-Chun Chen ,“Examining the Acceptance Behavior of Using Mobile Phone Messaging as a Parent- Teacher Medium Based on TAM and UTAUT Models”, Advances in information Sciences and Service Sciences(AISS) 254, Volume4, No.1, 2012
[26] Victor Matos, Rebecca Grasser ,“An experience on multithreading usingAndroid’s Handler Class”, Consortium for Computing Sciences in Colleges,
[27] Kristin Fergis, “The Impact of an Agile Methodology on Software Development Costs”, Project Report, EAS 499 Senior Capstone Project, April, 2012.
[28] Nayan Jyoti Kar, “Adopting Agile Methodologies of Software Development”, SETLabs Briefing, Infosys Technologies, vol. 4 no. 1, pp. 1-9, July-Sep. 2006.
[29] Feature Driven Development and Agile Modelling, http://www.agilemodeling.com/essays/fdd.htm
[30] Robert Imreh, Mahesh S. Raisinghani, “Impact of Agile Software Development on Quality within Information Technology Organizations”, Journal of Emerging Trends in Computing and Information Sciences, Vol. 2, No. 10, pp. 460-475, October 2011
[31] Sharifah Syed-Abdullah & Mike Holcombe & Marian Gheorge, “The Impact of an Agile Methodology on the Well Being of Development Teams”, Empir Software Eng, pp. 143-167, Springer 2006.

S.Satvaya, Shambhavi, R.Goyal, R.Parajuli, S.Jhingran, "Review on SR- Tech Solution," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.30-34, 2021.

Machine learning is one of the prime aspects that is used in various applications of artificial intelligence and is widely used. In the area of machine learning, deep learning is observed to be of great interest to the researchers with the improvement of computer-based data processing. Recent works in this area of deep learning have paved way to the new innovations in science, technology and applied research which has a wide application for identification and classification of images. Such a classification is quite imperative when security is of prime concern. Deep learning is basically an artificial intelligence-machine learning hybrid. This has provided a versatile and precise model that can result in better accuracy. However, theoretical designs and experiments that are existing till date are very complex. So, there is a need to develop techniques that reduce the computational complexity. Deep learning can be used to solve various problems in the study of images and patterns. Image Segmentation is one of such applications. This paper explores the recent work that is carried out in image segmentation using Deep Learning. Many methods that are introduced for image segmentation are based on supervised classification. In general, such methods work well if the training set are representative of the test images in the segment. However, issues can occur in the course of training and test results, due to the impairment in the hardware and the concerned protocols that are existing in various distributions. The weights that are assigned to the features need to be adaptively chosen for proper classification of the segmentation area. This further improves the processing capability of the algorithm so developed.

Deep Learning, Artificial Intelligence, Data Science, Image Segmentation, Supervised Classification

[1] Y. -n. Dong and G. -s. Liang, "Research and Discussion on Image Recognition and Classification Algorithm Based on Deep Learning," 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2019, pp. 274-278, doi: 10.1109/MLBDBI48998.2019.00061.
[2] O. Sharma, "Deep Challenges Associated with Deep Learning," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp. 72-75, doi: 10.1109/COMITCon.2019.8862453.
[3] J. Wu, Yinan Yu, Chang Huang and Kai Yu, "Deep multiple instance learning for image classification and auto-annotation," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3460-3469, doi: 10.1109/CVPR.2015.7298968.
[4] S. Khan, E. Ahmed, M. H. Javed, S. A. A Shah and S. U. Ali, "Transfer Learning of a Neural Network Using Deep Learning to Perform Face Recognition," 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Swat, Pakistan, 2019, pp. 1-5, doi: 10.1109/ICECCE47252.2019.8940754.
[5] L. Khelifi and M. Mignotte, "Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis," in IEEE Access, vol. 8, pp. 126385-126400, 2020, doi: 10.1109/ACCESS.2020.3008036.
[6] H. Ucuzal, ?. YA?AR and C. Çolak, "Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface," 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 2019, pp. 1-5, doi: 10.1109/ISMSIT.2019.8932761.
[7] L. Zhuang and Y. Guan, "Deep Learning for Face Recognition under Complex Illumination Conditions Based on Log-Gabor and LBP," 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, 2019, pp. 1926-1930, doi: 10.1109/ITNEC.2019.8729021.
[8] J. Latif, C. Xiao, A. Imran and S. Tu, "Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review," 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 2019, pp. 1-5, doi: 10.1109/ICOMET.2019.8673502.
[9] Y. Liu, X. Xu and F. Li, "Image Feature Matching Based on Deep Learning," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, 2018, pp. 1752-1756, doi: 10.1109/CompComm.2018.8780936.
[10] I. Karabayir, O. Akbilgic and N. Tas, "A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO)," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2020.2979121.
[11] P. Yin, R. Yuan, Y. Cheng and Q. Wu, "Deep Guidance Network for Biomedical Image Segmentation," in IEEE Access, vol. 8, pp. 116106-116116, 2020, doi: 10.1109/ACCESS.2020.3002835.
[12] A. Van Opbroek, H. C. Achterberg, M. W. Vernooij and M. De Bruijne, "Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning," in IEEE Transactions on Medical Imaging, vol. 38, no. 1, pp. 213-224, Jan. 2019, doi: 10.1109/TMI.2018.2859478.
[13] S. Lei, Z. Shi, X. Wu, B. Pan, X. Xu and H. Hao, "Simultaneous Super-Resolution and Segmentation for Remote Sensing Images," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 3121-3124, doi: 10.1109/IGARSS.2019.8900402.
[14] G. Wang et al., "DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, pp. 1559-1572, 1 July 2019, doi: 10.1109/TPAMI.2018.2840695.
[15] F. Yu et al., "AF-SEG: An Annotation-Free Approach for Image Segmentation by Self-Supervision and Generative Adversarial Network," 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 1503-1507, doi: 10.1109/ISBI45749.2020.9098535.
[16] S. Mishra, P. Liang, A. Czajka, D. Z. Chen and X. S. Hu, "CC-NET: Image Complexity Guided Network Compression for Biomedical Image Segmentation," 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, pp. 57-60, doi: 10.1109/ISBI.2019.8759448
[17] S. M. Sundara and R. Aarthi, "Segmentation and Evaluation of White Blood Cells using Segmentation Algorithms," 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 1143-1146, doi: 10.1109/ICOEI.2019.8862724.
[18] F. Lux and P. Matula, "DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed," 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, pp. 236-239, doi: 10.1109/ISBI.2019.8759594.
[19] C. Lin, Y. Wang, T. Wang and D. Ni, "Segmentation and Recovery of Pathological MR Brain Images Using Transformed Low-Rank and Structured Sparse Decomposition," 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, pp. 1878-1881, doi: 10.1109/ISBI.2019.8759441.
[20] M. Jafari, D. Auer, S. Francis, J. Garibaldi and X. Chen, "DRU-Net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation," 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 1144-1148, doi: 10.1109/ISBI45749.2020.9098391.

Bolla Ramesh, S..Kiran, "Image Segmentation In The Framework of Deep Learning – A Comprehensive Survey," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.35-40, 2021.

The decentralized nature of cloud computing paradigms has resulted class computing which prone to cyber-attacks and intrusions. One of major security matters in cloud is conducting intruder detection approaches for detecting and preventing network intrusions. The aim of this research paper is to review and analyze the research domain of collaborative, cooperative and distributed intrusion detection approaches within cloud environment. The research paper focusses on articles related to the keywords: cooperative, distributed, collaborative and their variations in three major databases, namely ScienceDirect, Springer Nature and the Institute of Electrical and Electronics Engineers’ Xplore. Such databases are sufficiently cover the literature techniques related to the aforementioned keywords. The collected dataset consists of 23 articles, the largest proportion of them focuses on model’s development that leverage collaborative intruder detection approaches, while the rest presents frameworks for intruder detection approaches. This study presents real analyses performed on available work: models, framework limitations and motivations. The study also, specifies the gap of the most state of the art related to cooperative and provides an extensive resource background for researchers who are interested in enhancing the performance of CIDSs within cloud environment. Finally, the paper suggests a new ensemble deep learning based model for improving the performance of proactive multi-cloud cooperative intrusion detection system.

Cloud Computing, Cooperative Intrusion Detection System, Distributed Intrusion Detection System, Collaborative Intrusion Detection System

[1] Adel Abusitta, Martine Bellaiche ?, Michel Dagenais, Talal Halabi, “A deep learning approach for proactive multi-cloud cooperative intrusion detection system”, Future Generation Computer Systems, vol. 98 ,pp. 308–318 2019.
[2] Nurudeen Mahmud Ibrahim, Universiti Teknologi Johor Bahru, Malaysia Anazida Zainal “A Distributed Intrusion Detection Scheme for Cloud Computing”, International Journal of Distributed Systems and Technologies ,vol.11 • Issue 1 pp. 68-82• January-March 2020
[3] Mohamed Idhammada,_, Karim Afdela, Mustapha Belouchb “Distributed Intrusion Detection System for Cloud Environments based on Data Mining technique”, Procedia Computer Science ,vol.127 ,pp. 35–41 ,(2018)
[4] Azuan Ahmad1, Mohd Nazri Kama2, Azri Azmi2, and Norbik Bashah Idris3 “Collaborative Cloud IDS in Detecting Denial of Service by Dendritic Cell Mechanism”, International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 8, No. 4, July 2019
[5] Omar Achbarou(&), My Ahmed El Kiram, Outmane Bourkoukou, and Salim Elbouanani “Multi-agent System-Based Distributed Intrusion Detection System for a Cloud Computing”, International Conference on Model and Data Engineering MEDI 2018: New Trends in Model and Data Engineering , (CCIS, volume 929), pp. 98–107, 2018
[6] Rim Ben Fekih1(&) and Farah Jemili2 “Distributed Architecture of an Intrusion Detection System Based on Cloud Computing and Big Data Techniques”, International conference on the Sciences of Electronics, Technologies of Information and Telecommunications
[7] SETIT 2018: Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1 pp 192-20.
[8] A.A. Ujeniya1*, R.D. Pawar2 , S.A. Sonawane3 , S.B. Shingade4 , S.R. Khonde5, “Hybrid Distributed Intrusion Detection System”, International Journal of Computer Sciences and Engineering pp .232-237 Vol..6, Issue.12, Dec 2018
[9] Mohamed Belaoued1, Abdelouahid Derhab 2, Smaine Mazouzi 3, And Farrukh Aslam Khan 2 “MACoMal: A Multi-Agent Based Collaborative Mechanism for Anti-Malware”, SpecialL Section on Emerging Approaches To Cyber Security, Journal of IEEE Access , VOLUME 8, 2020, Issue.10, pp.66-75, 2012. doi: 10.1109/ACCESS.2020.2966321
[10] Upasana Nagar, Priyadarsi Nanda, Xiangjian He , Zhiyuan(Thomas) Tan “A framework for data security in cloud using collaborative intrusion detection scheme”, Proceedings of the 10th International Conference on Security of Information and Networks Pages 188–193(2017)
[11] Yasir Mehmood; Muhammad Awais Shibli; Ayesha Kanwal; Rahat Masood, “Distributed intrusion detection system using mobile agents in cloud computing environment”, in: 2015 Conference on Information Assurance and Cyber Security (CIACS), Rawalpindi, Pakistan,2015
[12] Hassani Mohamed; Lebbat Adil; Tallal Saida; Medromi Hicham, “A collaborative intrusion detection and Prevention System in Cloud Computing” , Pointe aux Piments, Mauritius,2013
[13] Xiaoguang Han1 , Jigang Sun2 , Wu Qu3, 4*, Xuanxia Yao1, “Distributed malware detection based on binary file features in cloud computing environment”, in 2014The 26th Chinese Control and Decision Conference (2014 CCDC), Changsha, China, pp.438-488
[14] Adel Abusitta, Martine Bellaiche & Michel Dagenais ,“Multi-cloud cooperative intrusion detection system: trust and fairness assurance Annals of Telecommunications, vol. 74, pp.637–653(2019)
[15] A. M. Aleesa1 , B. B. Zaidan2 ,A. A. Zaidan2 Nan M. Sahar1, “Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions” , Neural Computing and Applications vol. 32, pp.9827–9858(2020)
[16] Shaohua Teng; Naiqi Wu; Haibin Zhu; Luyao Teng; Wei Zhang, “SVM-DT-based adaptive and collaborative intrusion detection, IEEE/CAA Journal of Automatica Sinica ,Vol: 5, pp: 108 – 118, Issue: 1, Jan. 2018
[17] Amin Nezarat1 • Yaser Shams2“A game theoretic-based distributed detection method for VM-to-hypervisor attacks in cloud environment”, The Journal of Supercomputing vol: 73, pp 4407–4427,2017
[18] R. Kesavamoorthy & K. Ruba Soundar , “Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system”, Cluster Computing, vol 22, pp 9469–9476(2019)
[19] Omar Achbarou, My Ahmed El Kiram and Salim Elbouanani, “Cloud Security: A Multi Agent Approach Based Intrusion Detection System”, Indian Journal of Science and Technology, , Vol: 10, Issue: 18, pp: 1-6 2017
[20] Weizhi Meng, Wenjuan Li, Laurence T,Yang & Peng Li , “Enhancing challenge-based collaborative intrusion detection networks against insider attacks using blockchain”, International Journal of Information Security volume 19, pages279–290(2020)
[21] Nour Moustafa, Gideon Creech, Elena Sitnikova, Marwa Keshk “Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing”, International Journal of Scientific Research in Network Security and Communication, Vol.1, No.5, pp.1-4, 2013.
[22] Zouhair Chiba,Noreddine Abghour,Khalid Moussaid,Amina El omri,Mohamed Rida, “Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms”, Computers & Security,Vol 86, pp, 291-317,2019
[23] Murat Özalp, Cihan Karakuzu, Ahmet Zengin,, “Distributed Intrusion Detection Systems: A Survey”, Academic Perspective Procedia, Vol, 2, Issue 3, Pp, 400-407,2019
[24] Sahil Garg a,b,?, Kuljeet Kaur a,b, Shalini Batra b, Gagangeet Singh Aujla c,d, Graham Morgan c, Neeraj Kumar b, Albert Y. Zomaya e, Rajiv Ranjan c, “En-ABC: An ensemble artificial bee colony based anomaly detection scheme for cloud environment ”, Journal of Parallel and Distributed Computing Volume 135 , pp 219-233,(2020)
[25] Saadia Ghribi, Amel Meddeb Makhlouf and Faouzi Zarai, “Multi-layer Cooperative Intrusion Detection System for Cloud Environment” In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017) – Vol: 6: pp:36-44 2017
[26] P. Patil1* , T. Bagwan2 , S. Kulkarni3 , C. Lobo4 , S.R. Khonde5, “Multi-Attacks Detection in Distributed System using Machine Learning”, International Journal of Computer Sciences and Engineering, Vol..7, Issue.1, pp. 601-605, Jan 2019
[27] Dr. Manish Kumar1 , Ashish Kumar Singh2, “Distributed Intrusion Detection System using Blockchain and Cloud Computing Infrastructure”,in 2020 International Conference on Trends in Electronics and Informatics (ICOEI)(48184), Tirunelveli, India pp,248-252 2020
[28] Song Deng1 , Ai-Hua Zhou2 , Dong Yue1 , Bin Hu2 , Li-Peng Zhu2 , “Distributed intrusion detection based on hybrid gene expression programming and cloud computing in a cyber physical power system”, IET Control Theory Appl., Vol. 11 Iss. 11, pp. 1822-1829,2017
[29] Osama Alkadi; Nour Moustafa; Benjamin Turnbull; Kim-Kwang Raymond Choo, “A Deep Blockchain Framework-enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks”, IEEE Internet of Things Journal, ,pp, 1 - 1 ,2020
[30] Hamid Reza Ghorbani , Roya Salek Shahrezaie “Toward a Policy-based Distributed Intruison Detection System in Cloud Computing Using Data Mining Approaches” , in 2015 International Congress on Technology, Communication and Knowledge (ICTCK), Mashhad, Iran, pp, 412-419,2015

Montather Ghalib ALi, Fadl Mutaher Ba-Alwi, Ghaleb H. Al-Gaphari, "Survey on an Intrusion Detection Systems Within Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.41-55, 2021.

Next Gen WSN plays an important role in many fields like environment monitoring, transportation security, military, catastrophic area, health, medical, industry so on. However, the most noticeable feature of Next Gen WSN propagates various types of data such as text, image , videos. There are a lot of papers about Next Gen WSN and maximum papers have focused on how to save the energy of WSN. Saving energy in the form of batteries is challenging when integrating security mechanisms. For efficient secure mechanisms we conducted a survey in this paper and tried to find a solution through which we can propose an approach in which secure communication performs with low battery consumption.

WSN; Security; Authentication; Attacks, Next Gen WSN

[1] Akyildiz, Ian F., et al. "A survey on sensor networks." Communications magazine, IEEE 40.8 : 102-114, 2002.
[2] Ling, Chung-Huei, et al. "A Secure and Efficient One-time Password Authentication Scheme for WSN." International Journal of Network Security19.2 : 177-181, 2017.
[3] Tsuji, Takasuke, and Akihiro Shimizu. "One-time password authentication protocol against theft attacks." IEICE transactions on communications 87.3 : 523-529, 2004.
[4] Arampatzis, Th, John Lygeros, and Stamatis Manesis. "A survey of applications of wireless sensors and wireless sensor networks." Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation. IEEE, 2005.
[5] Tsudik, Gene. "Message authentication with one-way hash functions." INFOCOM`92. Eleventh Annual Joint Conference of the IEEE Computer and Communications Societies, IEEE. IEEE, 1992.
[6] Wang, Yong, Garhan Attebury, and Byrav Ramamurthy. "A survey of security issues in wireless sensor networks." (2006).
[7] Dogra, Heena, and Jyoti Kohli. "Secure Data Transmission using Cryptography Techniques in Wireless Sensor Networks: A Survey." Indian Journal of Science and Technology 9.47 (2016).
[8] Lamport, Leslie. "Password authentication with insecure communication."Communications of the ACM 24.11 (1981): 770-772.
[9] SHABANA, K., FIDA, N., KHAN, F., JAN, S., REHMAN, M.. Security issues and attacks in Wireless Sensor Networks. International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), North America, 5, jul. 2016.
[10] F. Büsching and L. Wolf, "The Rebirth of One-Time Pads—Secure Data Transmission from BAN to Sink," in IEEE Internet of Things Journal, vol. 2, no. 1, pp. 63-71, Feb. 2015.
[11] V. P. Singh, M. Hussain and C. K. Raina, "Authentication of base station by HDFS using trust based model in WSN," 2016 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, pp. 1-5, 2016.
[12] G. W. Choi and I. Y. Lee, "A key distribution system for user authentication using pairing-based in a WSN," 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Kuta Bali, Indonesia, 2017, pp. 1-4.
[13] S. S. Abd El dayem, M. R. M. Rizk and M. A. Mokhtar, "An efficient authentication protocol and key establishment in dynamic WSN," 2016 6th International Conference on Information Communication and Management (ICICM), Hatfield, 2016, pp. 178-182.
[14] A. H. Moon, U. Iqbal and G. M. Bhat, "Light weight Authentication Framework for WSN," 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 2016, pp. 3099-3105.
[15] P. Joshi, M. Verma and P. R. Verma, "Secure authentication approach using Diffie-Hellman key exchange algorithm for WSN," 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 2015, pp. 527-532.
[16] P. Banerjee, T. Chatterjee and S. DasBit, "LoENA: Low-overhead encryption based node authentication in WSN," 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, pp. 2126-2132.
[17] Mohamed Hammi, Erwan Livolant, Patrick Bellot, Ahmed Serhrouchni, Pascale Minet. A Lightweight IoT Security Protocol. 1st Cyber Security in Networking Conference (CSNet2017), Oct 2017, Rio de Janeiro, Brazil.
[18] Armando, Alessandro, et al. "The AVISPA tool for the automated validation of internet security protocols and applications." International conference on computer aided verification. Springer, Berlin, Heidelberg, 2005.

Beant Kaur, Ramanjot Kaur, "A Literature Survey on Security Issues in Next Gen WSN," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.56-59, 2021.

In the 21st century IoT plays a major role in all domains The Internet of Things (IoT) is the network of billions of devices, people and services to Interconnect and exchange information and useful data. The IoT applications are highly affirming to increase the level of comfort, efficiency and automations for the user. Due to rapid increase of devices, people, vehicles connecting with the IoT network from anywhere and anytime which causes security and privacy issues.The high level of security and privacy, authentication and recovery from the attacks is required to implement an IoT automated world. All the IoT security threats including DoS, Man-in-the-middle, Tempering, jamming etc. are discussed in the survey

Internet of Things, characteristics of IoT, IoT security, IoT future development. Security; Authentication; Attacks

[1] Ray, Partha Pratim. "A survey of IoT cloud platforms." Future Computing and Informatics Journal 1.1-2 (2016): 35-46.
[2] Tank, Birju, Hardik Upadhyay, and Hirenc Patel. "A survey on IoT privacy issues and mitigation techniques." Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. 2016.
[3] Samie, Farzad, Lars Bauer, and Jörg Henkel. "IoT technologies for embedded computing: A survey." 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS). IEEE, 2016.
[4] Lee, Suk Kyu, Mungyu Bae, and Hwangnam Kim. "Future of IoT networks: A survey." Applied Sciences 7.10 (2017): 1072.
[5] Hassan, Wan Haslina. "Current research on Internet of Things (IoT) security: A survey." Computer networks 148 (2019): 283-294.
[6] Ishaq, Isam, et al. "IETF standardization in the field of the internet of things (IoT): a survey." Journal of Sensor and Actuator Networks 2.2 (2013): 235-287.
[7] Gilchrist, Alasdair. IoT security issues. Walter de Gruyter GmbH & Co KG, 2017.
[8] Shah, Sajjad Hussain, and Ilyas Yaqoob. "A survey: Internet of Things (IOT) technologies, applications and challenges." 2016 IEEE Smart Energy Grid Engineering (SEGE). IEEE, 2016.
[9] Deogirikar, Jyoti, and Amarsinh Vidhate. "Security attacks in IoT: A survey." 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, 2017.
[10] Schurgot, Mary R., David A. Shinberg, and Lloyd G. Greenwald. "Experiments with security and privacy in IoT networks." 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, 2015.
[11] Safdar, Noreen, Hala Asif, and Fatima Farooq. "Energy Use and Human Health Nexus in Pakistan." Review of Economics and Development Studies 6.3 (2020): 661-674.
[12] Gravely, Shannon, et al. "Discussions between health professionals and smokers about nicotine vaping products: Results from the 2016 ITC Four Country Smoking and Vaping Survey." Addiction 114 (2019): 71-85.
[13] Abu-Elkheir, Mervat, Mohammad Hayajneh, and Najah Abu Ali. "Data management for the internet of things: Design primitives and solution." Sensors 13.11 (2013): 15582-15612.
[14] Bohli, Jens-Matthias, et al. "SMARTIE project: Secure IoT data management for smart cities." 2015 International Conference on Recent Advances in Internet of Things (RIoT). IEEE, 2015.
[15] Zhang, PeiYun, MengChu Zhou, and Giancarlo Fortino. "Security and trust issues in fog computing: A survey." Future Generation Computer Systems 88 (2018): 16-27.
[16] Hassija, Vikas, et al. "A survey on IoT security: application areas, security threats, and solution architectures." IEEE Access 7 (2019): 82721-82743.
[17] Bertino, Elisa, and Nayeem Islam. "Botnets and internet of things security." Computer 50.2 (2017): 76-79.
[18] Grammatikis, Panagiotis I. Radoglou, Panagiotis G. Sarigiannidis, and Ioannis D. Moscholios. "Securing the Internet of Things: Challenges, threats and solutions." Internet of Things 5 (2019): 41-70.
[19] Ramotsoela, Daniel, Adnan Abu-Mahfouz, and Gerhard Hancke. "A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study." Sensors 18.8 (2018): 2491.
[20] Abomhara, Mohamed, and Geir M. Køien. "Security and privacy in the Internet of Things: Current status and open issues." 2014 international conference on privacy and security in mobile systems (PRISMS). IEEE, 2014.
[21] Hassija, Vikas, et al. "A survey on IoT security: application areas, security threats, and solution architectures." IEEE Access 7 (2019): 82721-82743.
[22] Cerullo, Gianfranco, et al. "Iot and sensor networks security." Security and Resilience in Intelligent Data-Centric Systems and Communication Networks. Academic Press, 2018. 77-101.
[23] Tang, Xiao, Pinyi Ren, and Zhu Han. "Jamming mitigation via hierarchical security game for iot communications." IEEE Access 6 (2018): 5766-5779.

Kamaljit Kaur, Ramanjot Kaur, "A Literature Survey on Security & Privacy Issues in IoT," International Journal of Computer Sciences and Engineering, Vol.9, Issue.4, pp.60-64, 2021.