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With the exponential growth of Internet of Things, it’s very important that we identify potential domains for the applications of IoT. Starting from smart cities, healthcare, smart agriculture, IoT is about to change all aspects of life. Although the current IoT enabled technologies have become advance in recent years, there are still so many problems that require attention. As IoT uses heterogeneous fields, many research problems may arise. The expansion of IoT devices in almost all areas of our lives, makes it a much focused topic for research. Thus IoT is creating way for new dimensions of research to be carried out. This paper represents recent developments in the field of IoT and talks about research challenges for the same.

Internet of Things, future technologies, smart cities, smart agriculture

[1] M. H. Miraz, M. Ali, P. S. Excell, and R. Picking, “A Review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT)”, in 2015 Internet Technologies and Applications (ITA), pp. 219– 224, Sep. 2015, DOI: 10.1109/ITechA.2015.7317398.
[2] K. K. Patel, S. M. Patel, et al., “Internet of things IOT: definition, characteristics, architecture, enabling technologies, application future challenges,” International journal of engineering science and computing, vol. 6, no. 5, pp. 6122–6131, 2016.
[3] S. V. Zanjal and G. R. Talmale, “Medicine reminder and monitoring system for secure health using IOT,” Procedia Computer Science, vol. 78, pp. 471–476, 2016.
[4] Mano, Y., Faical B. S., Nakamura L., Gomes, P. G. Libralon, R. Meneguete, G. Filho, G. Giancristofaro, G. Pessin, B. Krishnamachari, and Jo Ueyama. 2015. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Computer Communications, 89.90, (178-190). DOI: 10.1016/j.comcom.2016.03.010.
[5] V. Sundareswaran and M. S. null, “Survey on Smart Agriculture Using IoT,” International Journal of Innovative Research in Engineering & Management (IJIREM), vol. 5, no. 2, pp. 62–66, 2018.
[6] S. Rajguru, S. Kinhekar, and S. Pati, “Analysis of internet of things in a smart environment,” International Journal of Enhanced Research in Man-agement and Computer Applications,vol. 4, no. 4, pp. 40–43, 2015.
[7] Z. Alansari, N. B. Anuar, A. Kamsin, S. Soomro, M. R. Belgaum, M. H. Miraz, and J. Alshaer, “Challenges of Internet of Things and Big Data Integration”, in Emerging Technologies in Computing (M. H. Miraz, P. Ex- cell, A. Ware, S. Soomro, and M. Ali, eds.), (Cham), pp. 47–55, Springer International Publishing, 2018, DOI: 10.1007/978-3-31995450-9_4.
[8] M. H. Miraz and M. Ali, “Applications of Blockchain Technology beyond Cryptocurrency”, Annals of Emerging Technologies in Computing (AETiC), vol. 2, no. 1, pp. 1–6, 2018, DOI: 10.33166/AETiC.2018.01.001.
[9] Miraz, M.H., “Blockchain of Things (BCoT): The Fusion of Blockchain and IoT Technologies”, Advanced Applications of Blockchain Technology, Studies in Big Data 60, 2019, DOI: 10.1007/978-981-138775-3_7, https://doi.org/10.1007/978-981-13-8775-3_7.
[10] A. Mazayev, J. A. Martins, and N. Correia, “Interoperability in IoT Through the Semantic Profiling of Objects,” IEEE Access, vol. 6, pp. 19379–19385, 2018.

Raushan Kashypa, "Research Challenges in IoT and its application," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.117-120, 2020.

Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfere with academic achievements yet have likewise long terms consequences beyond academic time. It is widely admitted that between 5% to 10% of the total population is subject to this kind of disability. For assessing such disabilities in early childhood, children have to solve a battery of tests. Human experts score these tests and decide whether the children require specific education strategies based on their marks. The assessment can be lengthy, exorbitant, and emotionally difficult. Dyslexia is a learning disorder characterized by a lack of reading and/or composing skills, trouble in fast word naming and likewise poor in spelling. Dyslexic people have great trouble to read and interpret words or letters. Research work is carried out to order dyslexic from non-dyslexics by different approaches, for example, machine learning, image processing, understanding the cerebrum behaviour through brain science, contemplating the differences in life systems of mind. In recent years, e-learning systems have played an increasingly significant role in higher education and, specifically, in enhancing learning experiences for people who have learning difficulties. However, huge numbers of the people involved in the development and implementation of e-learning instruments overlook the needs of dyslexic students. In this paper, a detailed literature survey is carried on the machine techniques for the prediction of dyslexia students and e-learning for learning and cognitive disabilities.

Machine Learning, dysgraphia, e-learning, brain science, cognitive

[1] Asteriadis, Stylianos, et al. "Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment." Multimedia Tools and Applications 41.3, 469-493, 2009.
[2] Srivastava, Bhavana, and Md Tanwir Uddin Haider. "Personalized assessment model for alphabets learning with learning objects in e-learning environment for dyslexia." Journal of King Saud University-Computer and Information Sciences, 2017.
[3] David, Julie M., and Kannan Balakrishnan. "Machine learning approach for prediction of learning disabilities in school-age children." International Journal of Computer Applications 9.11, 7-12, 2010.
[4] Kroese, Biza Senfert. "Cognitive-behavioural therapy for people with learning disabilities." Behavioural and Cognitive Psychotherapy 26.4, 315-322, 1998.
[5] Polatajko, Helene J., and Noemi Cantin. "Developmental coordination disorder (dyspraxia): an overview of the state of the art." Seminars in pediatric neurology. Vol. 12. No. 4. WB Saunders, 2005.
[6] Cui, Zaixu, et al. "Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach." Human brain mapping 37.4, 1443-1458, 2016.
[7] Ba?tanlar, Yalin, and Mustafa Özuysal. "Introduction to machine learning." miRNomics: MicroRNA Biology and Computational Analysis. Humana Press, Totowa, NJ, 105-128, 2014.
[8] Smola, Alex, and S. V. N. Vishwanathan. "Introduction to machine learning." Cambridge University, UK 32.34 (2008): 2008.
[9] Malav, Anuraj, and Neelu J. Ahuja. "Machine Learning Techniques for Effective Facilitation of Teaching and Learning: A Narrative Review." i-Manager`s Journal on Computer Science 6.2, 42, 2018.
[10] Khan, Rehman Ullah, Julia Lee Ai Cheng, and Oon Yin Bee. "Machine learning and Dyslexia: Diagnostic and classification system (DCS) for kids with learning disabilities." International Journal of Engineering & Technology 7.3.18, 97-100, 2018.
[11] El Hammoumi, Oussama, et al. "Emotion Recognition in E-learning Systems." 2018 6th International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2018.
[12] Chitra, K., and R. Umamaheswari. "Semantically Enchanced Personalised Adaptive E- Learning for General and Dyslexia Learners: An Ontology Based Approach." International Journal of Advanced Networking and Applications 10.1, 3717-3723, 2018.
[13] Rajapakse, Sampath, et al. "ALEXZA: A Mobile Application For Dyslexics Utilizing Artificial Intelligence And Machine Learning Concepts." 2018 3rd International Conference on Information Technology Research (ICITR). IEEE, 2018.
[14] Hamid, Siti Suhaila Abdul, et al. "Dyslexia adaptive learning model: student engagement prediction using machine learning approach." International Conference on Soft Computing and Data Mining. Springer, Cham, 2018.
[15] Rello, Luz, et al. "Screening dyslexia for English using HCI measures and machine learning." Proceedings of the 2018 international conference on digital health. 2018.
[16] Chu, Hui-Chuan, et al. "Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning." Soft Computing 22.9 : 2973-2999, 2018
[17] Cinquin, Pierre-Antoine, Pascal Guitton, and Hélène Sauzéon. "Online e-learning and cognitive disabilities: A systematic review." Computers & Education 130: 152-167, 2019.
[18] Elhammoumi, Oussama, et al. "The Use of NN to Detect Learning Styles of Children with Learning Disabilities in E-Learning System." International Conference on Advanced Intelligent Systems for Sustainable Development. Springer, Cham, 2019.
[19] Atkar, Geeta Bhimrao, and J. Priyadarshini. "Enhancing Readability of Dyslexic Children by Machine Learning Techniques—A Survey." Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Springer, Singapore, 2019.
[20] Jothi Prabha, A., and R. Bhargavi. "Prediction of dyslexia from eye movements using machine learning." IETE Journal of Research (): 1-10, 2019
[21] Chakraborty, Ms Vani. "A SURVEY PAPER ON LEARNING DISABILITY PREDICTION USING MACHINE LEARNING.", International Journal of Information and Computing Science, Volume 6, Issue 5, pp. 481-485. May 2019,
[22] Dcruz, Francis, Vijitashw Tiwari, and Mayur Soni. "Using Machine Learning to Help Students with Learning Disabilities Learn." International Conference on Sustainable Communication Networks and Application. Springer, Cham, 2019.
[23] Hamid, Siti Suhaila Abdul, et al. "Engagement Prediction in the Adaptive Learning Model for Students with Dyslexia." Proceedings of the 4th International Conference on Human-Computer Interaction and User Experience in Indonesia, CHIuXiD`18. 2018.
[24] Rezvani, Z., et al. "Machine learning Classification of Dyslexic Children based on EEG Local Network Features." bioRxiv : 569996, 2019.
[25] Mehigan, T., and I. Pitt. "ENGAGING LEARNERS THROUGH EMOTION IN ARTIFICIALLY INTELLIGENT ENVIRONMENTS." Proceedings of EDULEARN19 Conference 1st-3rd Palma, Mallorca, Spain, pp. 5661-5668. July 2019,
[26] Kaisar, Shahriar. "Developmental dyslexia detection using machine learning techniques: A survey." ICT Express, 2020.
[27] Chakraborty, Ms Vani, and Meentachi Sundaram. "Machine learning algorithms for prediction of dyslexia using eye movement." Journal of Physics:
Conference Series. Vol. 1427. No. 1. IOP Publishing, 2020.
[28] Knoop-van Campen, Carolien AN, Eliane Segers, and Ludo Verhoeven. "Effects of audio support on multimedia learning processes and outcomes in students with dyslexia." Computers & Education 150: 103858, 2020
[29] Trivedi, Mr Viraj, et al. "Detecting the Severity and the Type of Learning Disability with Pattern Extraction Using Machine Learning." dyslexia 16: 18.
[30] Low, Spencer. "Applying Machine Learning to Neuroimaging Data to Identify Predictive Models of Reading Disorder (RD)." 2020.
[31] Usman, Opeyemi Lateef, and Ravie Chandren Muniyandi. "CryptoDL: Predicting Dyslexia Biomarkers from Encrypted Neuroimaging Dataset Using Energy-Efficient Residue Number System and Deep Convolutional Neural Network." Symmetry 12.5: 836, 2020.
[32] Fellman, Daniel, et al. "Predicting visuospatial and verbal working memory by individual differences in e-learning activities." Frontiers in Education. Vol. 5. Frontiers, 2020.
[33] Martinez-Murcia, Francisco J., et al. "EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia." International Journal of Neural Systems : 2050037, 2020
[34] Razzaque, Anjum, and Allam Hamdan. "Internet of things for Learning Styles and Learning Outcomes Improve e-Learning: A Review of Literature." Joint European-US Workshop on Applications of Invariance in Computer Vision. Springer, Cham, 2020
[35] Vinutha K.N and K.S.Sampada “Survey of Automated Recommender System for Web Application”,s International Journal of Computer Science and Engineering Vol.4 , Issue 3, pp. 36-39 , 2016.

V. Kala, S. Vimala, "A Survey on the Machine Learning For E-Learning System and Dyslexia," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.121-126, 2020.

Secure sending is a problematic task because of the partial nature of wireless sensor network properties. This paper provides solution to recognize malicious nodes in wireless sensor networks concluded prevention of black hole attack. It is basically a set of portable hosts associated wirelessly without slightly central management, where respectively node acts as a packet contributor, packet receiver, and a router at the same time. According to the landscape of this system, the active topology and the absence of a central management source some security problems and occurrences, such as the black hole attack, the wormhole attack, and the impression and negation attack. In this survey, we are going to introduce the Black Hole attack security issues and some of the recognition systems used to distinguish the black hole attack. In this kind of attack (black hole attack) the interlopers manipulate the normal performance of the network, by introduc0069ng themselves as the node with the shortest path to the destination. Interlopers can do a malicious behaviour over the network. Our future approach based on a new routing algorithm which educations shortest path in order to avoid malicious node path. Our results demonstrate the success and the effectiveness of our proposed routing procedure.

WSN, HMM, Black Hole, Malicious, Shortest path

[1]. Filippini, Massimo, and Lester C. Hunt. “Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach.” Energy Journal32 (2): 59–80. 2011
[2]. Sohraby, K., Minoli, D., and Znati, T. “Wireless sensor networks: technology, protocols, and applications.” John Wiley and Sons. 2007
[3]. Rawat, P., Singh, K. D., Chaouchi, H., and Bonnin, J. M.“Wireless sensor networks: a survey on recent developments and potential synergies.” The Journal of supercomputing 68(1): 1–48. 2014.
[4]. Kalkha, H., Satori, H., and Satori, K “Performance Evaluation of AODV and LEACH Routing Protocol.” Advances in Information Technology: Theory and Application. .2016.
[5]. Kalkha, H., Satori, H., and Satori, K. ()“A Dynamic Clustering Approach for Maximizing Scalability in Wireless Sensor Network.Transactions on Machine Learning and Artificial Intelligence 2017.
[6]. Akyildiz, I. F., Su, W., S Sankarasubramaniam, Y., and Cayirci, E. “Wireless sensor networks: a survey.” Computer networks, 38(4):393–422. 2002.
[7]. Zia, T., and Zomaya, A. “Security issues in wireless sensor networks.” In Systems and Networks Communications. ICSNC`06. International Conference IEE. 40. 2006
[8]. Sunitha, K., and Chandrakanth, H. “A survey on security attacks in wireless sensor network.” International Journal of Engineering Research and Applications (IJERA), 2(4), 1684–1691. 2012.
[9]. SARVARI, S., et al.: Wireless Local Area Network. “A Comprehensive Review Of Attacks And Metrics.” Journal of Theoretical & Applied Information Technology. 95, no. 13. 2017
[10]. Abraham, A., Falcon, R., and Koeppen, M. “Computational Intelligence in Wireless Sensor Networks: Recent Advances and Future Challenges”. Vol. 676. 2017 Springer .
[11]. Singh, G., and Singh, J. “Prevention of Blackhole Attack in Wireless Sensor Network using IPSec Protocol.” International Journal of Advanced Research in Computer Science, 4(11). 2013
[12]. Saghar, K., Kendall, D., and Bouridane, A. “Application of formal modeling to detect black hole attacks in wireless sensor network routing protocols.” In Applied Sciences and Technology (IBCAST), 2014 11th International Bhurban Conference. IEEE. 191–194. 2014, January
[13]. Wazid, M., Katal, A., Sachan, R. S., Goudar, R. H., and Singh, D. P. (2013, April). “Detection and prevention mechanism for blackhole attack in wireless sensor network.” In Communications and Signal Processing (ICCSP), 2013 International Conference.IEEE. 576–581.
[14]. Gondwal, N., and Diwaker, C.() “Detecting blackhole attack in WSN by check agent using multiple base stations.” American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(2), 149–152. 2013
[15]. Baadache, A., and Belmehdi, A.()“Struggling against simple and cooperative black hole attacks in multi-hop wireless ad hoc networks.” Computer Networks, 73, 173–184. 2014
Irshad Ullah and Shoaib Ur Rehman, “Analysis of Black Hole Attack on Mobile Ad Hoc Networks using Different Manet Routing Protocols” June 2010.
[16]. Sevil Sen, John A. Clark, Juan E.Tapiador, “Security Threats in Mobile Ad Hoc Networks”.
[17]. Tarunpreet Bhatia, A.K.Verma, “Security Issues in MANET: A Survey on Attacks and Defense Mechanism”, International Journal of Advanced Research in Computer Science and Software Engineering, vol.3, issue 6, pp.1382- 1394, 2013.
[18]. Kriti Gupta, Maansi Gujral and Nidhi, “Secure Detection Technique Against Blackhole Attack For Zone Routing Protocol in MANETS”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 6, June 2013.
[19]. Hesiri Weerasinghe and Huirong Fu, Preventing Cooperative Black Hole Attacks in Mobile Ad Hoc Networks: Simulation Implementation and Evaluation, Intenational Journal of Software Engineering and its Application, Vol.2, Issue 3, July 2008.
[20]. Ramaswamy S, Fu H, Sreekantaradhya M, Dixon J, Nygard K, Prevention of Cooperative Black Hole Attack in Wireless Ad Hoc Networks, Paper presented at the International Conference on Wireless Networks, Las Vegas, Nevada, USA, 23-26 June 2003.
[21]. Raj PN, Swadas PB, DPRAODV: A Dynamic Learning System against Blackhole Attack in AODV based MANETs, International Journal of Computer Science Issue, Vol. 2, pp 54-59, 2009.
[22]. Tamilselvan L, Sankaranarayanan V, Prevention of Blackhole Attack in MANET, 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, Sydney, Australia, 27-30 August 2007.
[23]. Yaser khamayseh, Abdulraheem Bader, Wail Mardini, Muneer BaniYasein, in "A New Protocol for Detecting Black Hole Nodes in Adhoc Network, Internalional Journal of COllununication Networks and Infonnation Se curity (IJCNlS), Vol. 3, No. I,April 201
[24]. .Sun B, Guan Y, Chen J, Pooch UW , Detecting Black-hole Attack in Mobile Ad Hoc Networks, 5th European Personal MobileCommunications Conference, Glasgow, United Kingdom, 22-25 April 2003.
[25]. Chethan K C, Shobha rani A, Dr. T G Basavaraju,Scalable Local Route Repair-Hybrid Wireless Mesh Protocol (SLRR-HWMP) for IEEE 802.11, International Journal of Computer Science and Engineering, Volume 8 Issue 5,page no: 173-184
[26]. Adetoye Adeyemo Comparative Analysis of Various Denials Of Service (Dos) Attack Mitigation Techniques, International Journal of Computer Science and Engineering, Volume 8 Issue 4,page no: 162-167

R. Chinthamani, V. Selvi, "Survey of Black Hole Attack Detection Techniques in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.127-132, 2020.

Most block ciphers contain primitive substitution boxes to add required nonlinearity. S-boxes maintain high confusion and resistance to linear and differential attacks. The protection of those ciphers depends on the force of the S-boxes used during the replacement stage. It is difficult to create encrypted, strong S-boxes which fulfill various characteristics like high non-linearity, good avalanche effect, bit-independent requirements, low differential uniformity and linear probability, etc. We proposed in this paper to create an S-box based on optimization of artificial colony bee and chaotic diagram. An initial S-Box is built to customize the algorithm to meet several features. The results of the simulation and comparison with recent proposals suggest that the proposed ABC optimization algorithm performs fairly easily and creates an S-box with a higher degree of security.

ABC optimization Substitution-box Chaotic Logistic map Block ciphers Security

[1]. Menezes, A. J., Oorschot, P. C. V., & Vanstone, S. A. Handbook of applied cryptography. Boca Raton: CRC Press. 1997
[2]. Stinson, D. R. Cryptography: Theory and practice. Boca Raton: CRC Press. 2005
[3]. Schneier, B. Applied cryptography: Protocols algorithms and source code in C. New York: Wiley. 1996
[4]. Knudsen, L. R., & Robshaw, M. The block cipher companion. Berlin: Springer. 2011
[5]. Ozkaynak, F., & Sirma, Y. Designing chaotic S-boxes based on time-delay chaotic system. Nonlinear Dynamics, 74(3), 551–557. 2013
[6]. Cui, L., & Cao, Y. A new S-box structure named Affine-Power-Affine. International Journal of Innovative Computing, Information and Control, 3(3), 751–759. 2007
[7]. Hussain, I., & Shah, T. (2013). Literature survey on nonlinear components and chaotic nonlinear components of block ciphers. Nonlinear Dynamics, 74(4), 869–904.
[8]. Farah, T., Rhouma, R., & Belghith, S. (2017). A novel method for designing S-box based on chaotic map and teaching–learning-based optimization. Nonlinear Dynamics, 88(2), 1059–1074.
[9]. Ahmad, M., Bhatia, D., & Hassan, Y. (2015). A novel ant colony optimization based scheme for substitution box design. Procedia Computer Science, 57, 572–580.
[10]. Guesmi, R., Farah, M. A. B., Kachouri, A., &Samet, M. (2014). A novel design of Chaos based S-boxes using genetic algorithm techniques. In IEEE/ACS 11th international conference on computer systems and applications (AICCSA) (pp. 678–684).
[11]. Wang, Y., Wong, K. W., Li, C., & Li, Y. (2012). A novel method to design S-box based on chaotic map and genetic algorithm. Physics Letters A, 376(6), 827–833.
[12]. Yong, W., & Peng, L. (2012). An Improved method to obtaining S-box based on chaos and genetic algorithm. HKIE Transactions, 19(4), 53–58.
[13]. Clark, J. A., Jacob, J. L., &Stepney, S. (2005). The design of S-boxes by simulated annealing. New Generation Computing, 23(3), 219–231.
[14]. Millan, W. (1998). How to improve the nonlinearity of bijective S-boxes. In Australasian conference on information security and privacy, lecture notes in computer science (Vol. 1438, pp. 181–192).
[15]. Fuller, J., Millan, W., & Dawson, E. (2005). Multi-objective optimisation of bijective S-boxes. New Generation Computing, 23(3), 201–218.
[16]. Laskari, E. C., Meletiou, G. C., &Vrahatis, M. N. (2006). Utilizing evolutionary computation methods for the design of S-boxes.In International conference on computational intelligence and security (pp. 1299–1302).
[17]. May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261(5560), 459–467.
[18]. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06, Erciyes University, Faculty of Engineering, Department of Computer Engineering.
[19]. Tereshko, V. (2000). Reaction–diffusion model of a honeybee colony’s foraging behaviour. In M. Schoenauer (Ed.), Parallel problem solving from nature VI (Vol. 1917, pp. 807–816)., Lecture notes in computer science Berlin: Springer.
[20]. Karaboga, D., &Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.
[21]. Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2014). A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21–57.
[22]. Dawson, M. H., & Tavares, S. E. (1991). An expanded set of S-box design criteria based on information theory and its relation to differential-like attacks. Advances in Cryptology, Lecture Notes in Computer Science, 547, 352–367.
[23]. Braeken, A. (2006). Cryptographic properties of Boolean functions and S-boxes.Ph.D. thesis available at http://homes.esat.kuleuven.be/abraeken/thesisAn.pdf.Accessed 21 May 2017.
[24]. Burnett, L. (2005). Heuristic optimization of boolean functions and substitution boxes for cryptography.Doctoral dissertation, Queensland University of Technology.
[25]. Isa, H., Jamil, N., &Zaba, M. R. (2015). Improved S-box construction from binomial power functions. Malaysian Journal of Mathematical Sciences, 9(S), 21–35.
[26]. Cusick, T. W., &Stanica, P. (2009). Cryptographic Boolean functions and applications. Amsterdam: Elsevier.
[27]. Ding, C., Xiao, G., & Shan, W. (1991). The stability theory of stream ciphers (Vol. 561)., LNCS Berlin: Springer.
[28]. Matsui, M. (1994). Linear cryptanalysis method for DES cipher. In Proceedings of EUROCRYPT’93, lecture notes in computer science (Vol. 765, pp. 386–397).
[29]. Webster, A. F., & Tavares, S. E. (1986). On the design of S-boxes. Advances in Cryptology, Lecture Notes in Computer Science, 218, 523–534.
[30]. Adams, C., & Tavares, S. (1990). The structured design of cryptographically good S-boxes. Journal of Cryptology, 3(1), 27–41.
[31]. Biham, E., & Shamir, A. (1991). Differential cryptanalysis of DES-like cryptosystems. Journal of Cryptology, 4(1), 3–72.
[32]. Sarfraz, M., Hussain, I., & Ali, F. (2016). Construction of S-Box based on Mobius transformation and increasing its confusion creating ability through invertible function.International Journal of Computer Science and Information Security, 14(2), 187–18x.
[33]. Ahmad, M., Mittal, N., Garg, P., & Khan, M. M. (2016). Efficient cryptographic substitution box design using travelling salesman problem and chaos.Perspectives in Science, 8, 465–46.

Manpreet Kaur, Sarabjeet Kaur, "Based on ABC Optimization effective Substitution-Boxes deployed using Chaotic mapping," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.133-140, 2020.

In this paper we have taken various interconnection networks. In order to study properties of those networks we have derived their geometrical patterns from their respective incidence matrices. We have also applied some logical operations on incidence matrices to study various properties of interconnection networks.

Interconnection Network, Topology, Sparse Matrix, Incidence Matrix

[1] B. Brey, “The Intel Microprocessors”, Sixth Edition, Parentice Hall, 2003.
[2] Christian Bischo, “Parallel Computing: Architectures, Algorithms, and Applications”, IOS Press, 2008.
[3] D. Samanta, “Classic Data Sructures” , Prentice Hall of India Pvt. Lt, New Delhi, 2006.
[4] Faizan Nasir, Jamshed Siddiqui, "Comparative Analysis of Cube and Star Based Networks", International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.51-59, 2018.
[5] I. Duff, A. Erisman, J.K. Reid. “Direct methods for sparse matrices,” Oxford University press, Oxford, UK., 1987.
[6] L. Packiaraj, K. Manoj, "Embedding of Circulant Networks into Cycle-of-butterfly", International Journal of Computer Sciences and Engineering, Vol.06, Special Issue.11, pp.264-274, 2018.
[7] Michael J. Quinn, Parallel Computing: Theory and Practice, Tata Mc Graw Hill, 2008.
[8] Parhami. Behrooz and R. Mikhail, "Perfect Difference Networks and related Interconnection Structures for Parallel and Distributed Systems", IEEE transactions on parallel and distributed systems, vol. 16, no. 8, pp. 714-724, August 2005.
[9] Pundir Harikishan, Shivraj. and Sandeep Kumar, "Discreate Mathematics", PragatiPrakashan 2009.
[10] R K Katare and, N.S Chaudhari, ” Some P-RAM Algorithms for Sparse Linear Systems”, Journal of Computer Science 3(12):956-964, 2007.
[11] R K Katare, Sandeep Bharti, Reshma Begum, Pinky Sharma, Mamta Kumari, “Study of Butterfly Patterns of Matrix in Interconnection Network”, International Journal of Scientific & Engineering Research, Volume 7, Issue 12, 320, ISSN 2229-5518, 2016.
[12] R.K. Katare and N.S. Chaudhari, "A Comparative Study of Hypercube and Perfect Difference Network for Parallel and Distributed System and its Application to Sparse Linear System", Varahmihir journal of Computer and Information Sciences Sandipani Academic Ujjain (M P) India, vol. 2, pp. 13-30, 2007.
[13] Rakesh Katare, VECTOR OPERATION ON NODES OF PERFECT DIFERENCE NETWORK USING LOGICAL OPERATORS. International Journal of Advanced Research in Computer Science. 10. 29-39. 10.26483 2019
[14] Rakesh Kumar Katare, Narendra S. Chaudhari:
Study of Topological Property of Interconnection Networks and its Mapping to Sparse Matrix Model. Int. J. Comput. Sci. Appl. 6(1): 26-39 2009.
[15] W. Knight,”Two Heads are Better Than One”, IEEE Review, September 2005.
[16] Y. Saad , M.H. Schultz. “Topological prosperities of Hypercubes,” IEEE Transactions on computers, 37(7),867-872, 1988.

Pinki Sharma, Rakesh Kumar Katare, Reshma Begum, "Study of Topological Properties of Interconnection Networks," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.141-146, 2020.

Blood cancer is one of the types of cancer. Leukemia is one among them. Which was caused due to the abnormal growth of the white blood cells in the bone marrow in the blood. This also affects the functionality of the white blood cells and the red blood cells, platelets. The leukaemia is further divided into four types. In this paper, we are going to discuss and examine the results of the acute lymphoblastic leukemia. In the phase of segmentation procedure, we have taken the edge-based segmentation. Here we will see the results by applying the median filter once and twice with different masks along with different operators. For the process, we have done it in processing tool like Pycharm with python, Opencv package. We have observed the difference between the operators with its functionality, changing the mask values for filtration. The good segmentation leads to the accuracy in classification.

Blood cancer, ALL (Acute Lymphoblastic Leukemia), Image Segmentation, Pycharm

[1] Pooja Deshmukh, prof. C. R. Jadhav, ”A Survey on Detection of Blood Cancer Using White Blood Cells Segmentation”, International Journal of Modern Trends in Engineering And Research(IJMTER), volume02, Issue12, ISSN (print):2393-8161.
[2] Subrajeet Mohapatra, Dipi Patra, Sanghamitra Satpathy, “Unsupervised Blood Microscopic Image Segmentation and Leukemia Detection Using Color Based Clustering”, International journal of Computer information system and industrial Management Application, Vol 4, 2012, pp. 477-485.
[3] Vasundhara Sharma, Sonu Elsa Jacob, Praveen Kumar Sharma, “A Comparative Study of Techniques For Leukemia Detection”, International Journal of Advance Research in Science and Engineering IJARSE, vol.No.4, Special Issue (01), March2015, ISSN-2319-8354(E).
[4] T. Karthikeyan, N. Poornima, Microscopic image segmentation using fuzzy c means for leukemia diagnosis, Leukemia 4(1).
[5] Tejashri G. Patil, V.B. Raskar, “Blood microscopic Image Segmentation & acute leukemia detection” (IJERMT) ISSN: 2278-9359(volume-4, Issue-9).
[6] Manisha, Dr. T. Sethukarasi “Automated Detection of White Blood Cell Cancer Disease Using Classification Techniques-A Survey”, (IRJET), Vol.No:06, Issue 02, FEB 2019. E-ISSN: 2395-0056.
[7] Carolina Reta, Leopoldo Altamirano, Jesus A. Gonzalez, Ranquel Diaz and Jose S. Guichard (2015), “segmentation of bone marrow cells images for morphological classification of acute leukemia”. Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010).
[8] Rejintal, A., Aswini, N. 2016. “Image processing based leukemia cancer cell detection”. IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pages 471–474.
[9] Meghana M.R, Akshatha Prabhu “An Efficient Technique for Identification of Leukemia in Microscopic Blood Samples Using Image Processing”,(IJRPS),ISSN:
0975-7538, year-2019.
[10] Nimesh Patel, Ashutosh Mishra “Automated Leukemia Detection Using Microscopic Images”, procedia computer science 58(2015)635-642.
[11] Shiv Kumar chatarwad, Pratik Bansode, Amar Burade, prof. T.S Chaware, “Blood Cancer Detection Using Image Processing”, (IJARECE)vol.no:7,Issue:5
May 2018, ISSN: 2278-909X.
[12] Chaitali Raje, Jyoti Rangole “Detection of Leukemia in Microscopic Image Using Image Processing”, conference paper. April 2014, ICCSP.2014.6949840.

Afsheen Firdous, Kompella Venkata Ramana, "A Study on Lymphoblastic Leukemia Using Image Processing," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.147-157, 2020.

Object finding is a fast-developing technique in the area of Computer Vision and Machine Learning. Computer vision is one of the principal tasks of deep learning field. Object detection is a technique that identifies the existence of object in an image or video. Object detection can be used in many areas for improving efficiency in the task. The applications for object detection are in home automation, self-driving cars, people counting, agriculture, traffic monitoring, military defence systems, sports, industrial work, robotics, aviation industry and many others. Object detection can be done through various techniques like R-CNN, Fast R-CNN, Faster R-CNN, Single Shot detector (SSD) and YOLO v3. A comparison of these algorithms is done and also their results as well as performance is analysed. The performance and exactness should be utmost important in analysing the algorithms.

Object Detection, Object Finding, R-CNN, Fast RCNN, Faster RCNN, Single Shot Detector, YOLO v3

[1] Y. LeCun, Y. Bengio, G. Hinton, “Deep Learning”, Nature, Vol.521, pp.436-444, 2015.
[2] R. Girshick, J. Donahue, T. Darrell, J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”, IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, pp. 580-587, 2014.
[3] R. Girshick, “Fast R-CNN”, IEEE International Conference on Computer Vision (ICCV), Santiago, USA pp.1440-1448, 2015.
[4] Z. Zhao, X. Wu, S. Xu, P. Zheng, “Object Detection with Deep Learning: A Review”, IEEE Transactions on Neural Networks and Learning Systems, Vol.30, Issue.11, pp.3212-3232, 2019.
[5] S. Ren, K. He, R. Girshick, J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.39, Issue.6, pp.1137-1149, 2017.
[6] J.R.R. Uijlings, K.E.A. van de Sande, T. Gevers and A.W.M. Smeulders, “Selective Search for Object Recognition”, International Journal of Computer Vision, Vol.104, pp.154–171, 2013.
[7] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, A.C. Berg, “SSD: Single Shot Multibox Detector”, Springer, Vol.9905, pp.21-37, 2016.
[8] Z. Shen, Z. Liu, J. Li, Y. Jiang, Y. Chen, X. Xue, “DSOD: Learning Deeply Supervised Object Detectors from Scratch”, IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp.1937-1945, 2017.
[9] S. Zhai, D. Shang, S. Wang, S. Dong, “DF-SSD: An Improved SSD Object Detection Algorithm Based on DenseNet and Feature Fusion”, IEEE Access, Vol.8, pp.24344-24357, 2020.
[10] J. Redmon, A. Farhadi, “YOLO9000: Better, Faster, Stronger”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp.6517-6525, 2017.
[11] J. Redmon, A. Farhadi, “YOLOv3: An Incremental Improvement”, ResearchGate, 2018.
[12] J. Sang, Z. Wu, P. Guo, H. Hu, H. Xiang, Q. Zhang, B. Cai, “An Improved Yolov2 for Vehicle Detection”, Sensors, Vol.18, Issue.12, pp.4272, 2018.
[13] A. Agrawal, A.N. Modi, A. Passos, A. Lavoie, A. Agarwal, A. Shankar, I. Ganichev, J. Levenberg, M. Hong, R. Monga, S. Cai, “Tensor?ow Eager: A Multistage, Python-Embedded DSL for Machine Learning”, Proceedings of Machine Learning and Systems 1 (MLSys 2019), Stanford, California, pp. 178-189, 2019.
[14] J. Y. Lu, C. Ma, L. Li, X.Y. Xing, Y. Zhang, Z.G. Wang. J.W. Xu, “A Vehicle Detection Method for Aerial Image Based on YOLO”, Journal of Computer and Communications, Vol.6, Issue.11, pp.98-107, 2018.
[15] L. Zhao, S. Li, “Object Detection Algorithm Based on Improved YOLOv3”, Electronics, Vol.9, Issue.3, pp.537, 2020.
[16] N. Raviteja, M. Lavanya, S. Sangeetha, “An Overview on Object Detection and Recognition”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.42-45, 2020.
[17] A. Kaur, D. Kaur, “Yolo Deep Learning Model Based Algorithm for Object Detection”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.174-178, 2020.

Anand John, Divyakant Meva, "A Comparative Study of Various Object Detection Algorithms and Performance Analysis," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.158-163, 2020.

Modern applications consist of different types of control devices and sensors that connect to the Internet. These applications are new approved technologies called the Internet of Things. Nowadays, these new technologies have gained a great interest in the field of research because of their existence in several diverse fields and due to the rapid development of these technologies. Communication between these devices generates a large amount of private and sensitive information and data between them. Therefore, maintaining the confidentiality of that data and information in the Internet of Things is of great importance. Mathematical cost (complex mathematical operations) and the number of cycles in traditional cryptographic algorithms leads to a large use of memory and energy waste for devices with limited resources, which makes traditional cipher algorithms inappropriate for Internet of Things devices. A fast and LW algorithm called NLBSIT has been proposed in this regard, which provides the requisite protection and resource constrained confidentiality of data on IoT devices. This algorithm (NLBSIT) uses a 64-bit key to encode 64-bit data, uses simple mathematical operations (XOR, XNOR, shifting, swapping), and uses the features of both the Feistel and SP Network architecture to achieve diffusion and confusion (increasing data security). The FELICS and MATLAB tools are used to simulate the NLBSIT algorithm. To execute this algorithm, various data types are used, such as text and images. The results of the simulation indicate the supremacy of the proposed algorithm in various areas, such as security, efficiency, less cycles (encryption and decryption), and less memory usage.

Lightweight Cryptography LWC; FELICS; RFID tags; IoT Security

[1] Prajakta P. Deshpande, "IoT Based Fleet Management Systems : A Review", International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.436-443, 2019.
[2] Hui, T. K., Sherratt, R. S., & Sánchez, D. D. (2017). Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies. Future Generation Computer Systems, 76, 358-369.
[3] Anjum Sheikh, "IoT Based Security System for Smart Homes", International Journal of Computer Sciences and Engineering, Vol.07, Special Issue.11, pp.35-38, 2019.
[4] Zhou, G., Liu, Z., Shu, W., Bao, T., Mao, L., & Wu, D. (2017). Smart savings on private car pooling based on internet of vehicles. Journal of Intelligent & Fuzzy Systems, 32(5), 3785-3796.
[5] Majumdar, A., Debnath, T., Sood, S. K., & Baishnab, K. L. (2018). Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment. Journal of medical systems, 42(10), 187.
[6] Eisenbarth, T., Kumar, S., Paar, C., Poschmann, A., & Uhsadel, L. (2007). A survey of lightweight-cryptography implementations. IEEE Design & Test of Computers, 24(6), 522-533.
[7] Al-ahdal, A. H., & Deshmukh, N. K. A Systematic Technical Survey Of Lightweight Cryptography On lot Environment.
[8] Al-ahdal, Abdulrazzaq HA, and Nilesh K. Deshmukh, and Galal A. AL-Rummana. "A Robust Lightweight Algorithm for Securing Data in Internet of Things Networks." .Under Publication.
[9] Biswas, A., Majumdar, A., Nath, S., Dutta, A., & Baishnab, K. L. (2020). LRBC: a lightweight block cipher design for resource constrained IoT devices. Journal of Ambient Intelligence and Humanized Computing, 1-15.
[10] Singh, S., Sharma, P. K., Moon, S. Y., & Park, J. H. (2017). Advanced lightweight encryption algorithms for IoT devices: survey, challenges and solutions. Journal of Ambient Intelligence and Humanized Computing, 1-18.
[11] Shirai, T., Shibutani, K., Akishita, T., Moriai, S., & Iwata, T. (2007, March). The 128-bit blockcipher CLEFIA. In International workshop on fast software encryption (pp. 181-195). Springer, Berlin, Heidelberg.
[12] Bogdanov, A., Knudsen, L. R., Leander, G., Paar, C., Poschmann, A., Robshaw, M. J., ... & Vikkelsoe, C. (2007, September). PRESENT: An ultra-lightweight block cipher. In International workshop on cryptographic hardware and embedded systems (pp. 450-466). Springer, Berlin, Heidelberg.
[13] Guo, J., Peyrin, T., Poschmann, A., & Robshaw, M. (2011, September). The LED block cipher. In International workshop on cryptographic hardware and embedded systems (pp. 326-341). Springer, Berlin, Heidelberg.
[14] De Canniere, C., Dunkelman, O., & Kneževi?, M. (2009, September). KATAN and KTANTAN—a family of small and efficient hardware-oriented block ciphers. In International Workshop on Cryptographic Hardware and Embedded Systems (pp. 272-288). Springer, Berlin, Heidelberg.
[15] Shibutani, K., Isobe, T., Hiwatari, H., Mitsuda, A., Akishita, T., & Shirai, T. (2011, September). Piccolo: an ultra-lightweight blockcipher. In International Workshop on Cryptographic Hardware and Embedded Systems (pp. 342-357). Springer, Berlin, Heidelberg.
[16] Borghoff, J., Canteaut, A., Güneysu, T., Kavun, E. B., Knudsen, L. R., Le, G., ... & Rombouts, P. (2012). PRINCE–A Low-latency Block Cipher for Pervasive Computing Applications Full version.
[17] Albrecht, M. R., Driessen, B., Kavun, E. B., Leander, G., Paar, C., & Yalç?n, T. (2014, August). Block ciphers–focus on the linear layer (feat. PRIDE). In Annual Cryptology Conference (pp. 57-76). Springer, Berlin, Heidelberg.
[18] Beaulieu, R., Shors, D., Smith, J., Treatman-Clark, S., Weeks, B., & Wingers, L. (2015, June). The SIMON and SPECK lightweight block ciphers. In Proceedings of the 52nd Annual Design Automation Conference (pp. 1-6).
[19] Wheeler, D. J., & Needham, R. M. (1994, December). TEA, a tiny encryption algorithm. In International Workshop on Fast Software Encryption (pp. 363-366). Springer, Berlin, Heidelberg.
[20] Hong, D., Lee, J. K., Kim, D. C., Kwon, D., Ryu, K. H., & Lee, D. G. (2013, August). LEA: A 128-bit block cipher for fast encryption on common processors. In International Workshop on Information Security Applications (pp. 3-27). Springer, Cham.
[21] Koo, B., Roh, D., Kim, H., Jung, Y., Lee, D. G., & Kwon, D. (2017, November). CHAM: a family of lightweight block ciphers for resource-constrained devices. In International Conference on Information Security and Cryptology (pp. 3-25). Springer, Cham.
[22] Bansod, G., Pisharoty, N., & Patil, A. (2017). BORON: an ultra-lightweight and low power encryption design for pervasive computing. Frontiers of Information Technology & Electronic Engineering, 18(3), 317-331.
[23] Patil, J., Bansod, G., & Kant, K. S. (2017, February). LiCi: A new ultra-lightweight block cipher. In 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI) (pp. 40-45). IEEE.
[24] Zhang, W., Bao, Z., Lin, D., Rijmen, V., Yang, B., & Verbauwhede, I. (2015). RECTANGLE: a bit-slice lightweight block cipher suitable for multiple platforms. Science China Information Sciences, 58(12), 1-15.
[25] Karakoç, F., Demirci, H., & Harmanc?, A. E. (2013, May). ITUbee: a software oriented lightweight block cipher. In International Workshop on Lightweight Cryptography for Security and Privacy (pp. 16-27). Springer, Berlin, Heidelberg.
[26] Yang, G., Zhu, B., Suder, V., Aagaard, M. D., & Gong, G. (2015, September). The simeck family of lightweight block ciphers. In International Workshop on Cryptographic Hardware and Embedded Systems (pp. 307-329). Springer, Berlin, Heidelberg.
[27] Guo, J., Peyrin, T., Poschmann, A., & Robshaw, M. (2011). The LED block cipher. In Cryptographic Hardware and Embedded Systems–CHES 2011 (pp. 326-341). Springer Berlin Heidelberg.
[28] A. H. A. Al-Ahdal, G. A. AL-Rummana, G. N. Shinde, and K. D. Nilesh, “Security Analysis of a Robust Lightweight Algorithm for Securing Data in Internet of Things Networks,” .Under Publication.
[29] Barreto, P. S. L. M., & Rijmen, V. (2000). The Khazad legacy-level block cipher. Primitive submitted to NESSIE, 97, 106.
[30] Usman M, Ahmed I, Aslam MI, Khan S, Shah UA. SIT: a lightweight encryption algorithm for secure internet of things. arXiv preprint arXiv:1704.08688. 2017 Apr 27.
[31] D. Dinu, A. Biryukov, J. Großschädl, D. Khovratovich, Y. L. Corre, L. Perrin, “FELICS – Fair Evaluation of Lightweight Cryptographic Systems”, University of Luxembourg, July 2015.
[32] Biham, E. (1994). New types of cryptanalytic attacks using related keys. Journal of Cryptology, 7(4), 229-246.
[33] A. Webster and S. E. Tavares, “On the design of s-boxes,” in Conference on the Theory and Application of Cryptographic Techniques. Springer, 1985, pp. 523–534.
[34] Kanso, A., & Ghebleh, M. (2018). An efficient lossless secret sharing scheme for medical images. Journal of Visual Communication and Image Representation, 56, 245-255.
[35] Hodeish, M. E., Bukauskas, L., &Humbe, V. T. (2019). A new efficient TKHC-based image sharing scheme over unsecured channel. Journal of King Saud University-Computer and Information Sciences.
[36] Ahmad, M., Doja, M. N., & Beg, M. S. (2018). Security analysis and enhancements of an image cryptosystem based on hyperchaotic system. Journal of King Saud University-Computer and Information Science.
[37] Magalh˜aes, F., Oliveira, H. P., Matos, H., Campilho, A.: HGC 2011 - Hand Geometric Points Detection Competition Database, http://www.fe.up.pt/~hgc2011/.

Abdulrazzaq H. A. Al-Ahdal, Galal A. AL-Rummana, G.N. Shinde, Nilesh K. Deshmukh, "NLBSIT: A New Lightweight Block Cipher Design for Securing Data in IoT Devices," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.164-173, 2020.

In the present age of innovation medicinal field researcher are very much interested in disease classification for the analysis of disease. It is a point of concern on the grounds that real treatment of this disease isn`t found till date. Patients having this ailment must be spared if and just in the event that it is found in beginning period (arrange I and stage II). In the event that it is identified in last stage (arrange III and stage IV) at that point possibility of endurance will be exceptionally less. Machine learning and information mining system will assist medicinal with handling to handle with this issue. Cancer growth has different manifestations, for example, tumor, abnormal bleeding, more weight reduction and so forth. It isn`t vital that a wide range of tumors are harmful. Tumors are fundamentally of two kinds one is benign and the other one is malignant. To give suitable treatment to the patients, side effects must be contemplated appropriately and a programmed expectation framework is required which will characterize the tumor into benevolent or harmful. In the present web world, majority of information is created via web-based networking media or medicinal services sites. From this immense measure of information, side effects can be gotten by utilizing information mining method, which will be further helpful for disease location or classification. This paper makes study of such most recent research study that utilizes on the web and disconnected information for malignant growth arrangement.

Tumors, Machine Learning, binary classification, malicious, gene expression

[1] R. A. Radcliffe, “Gene expression,” in Neurobehavioral Genetics: Methods and Applications, Second Edition, 2006.
[2] D. Oliva, M. Abd Elaziz, and S. Hinojosa, “Image Processing,” in Studies in Computational Intelligence, 2019.
[3] N. Guenther and M. Schonlau, “Support vector machines,” Stata J., 2016.
[4] C. Bulac and A. Bulac, “Decision Trees,” in Advanced Solutions in Power Systems: HVDC, FACTS, and AI Techniques, 2016.
[5] T. G. Clarkson, “Introduction to neural networks,” Neural Netw. World, 1996.
[6] K. R. Koch, Introduction to bayesian statistics. 2007.
[7] P. J. García-Laencina, J. L. Sancho-Gómez, A. R. Figueiras-Vidal, and M. Verleysen, “K nearest neighbours with mutual information for simultaneous classification and missing data imputation,” Neurocomputing, 2009.
[8] O. Fortunato et al., “Assessment of circulating micrornas in plasma of lung cancer patients,” Molecules, 2014.
[9] S. Michiels, S. Koscielny, and C. Hill, “Prediction of cancer outcome with microarrays: A multiple random validation strategy,” Lancet, 2005.
[10] Y. C. Chen, W. C. Ke, and H. W. Chiu, “Risk classification of cancer survival using ANN with gene expression data from multiple laboratories,” Comput. Biol. Med., 2014.
[11] K. Park, A. Ali, D. Kim, Y. An, M. Kim, and H. Shin, “Robust predictive model for evaluating breast cancer survivability,” Eng. Appl. Artif. Intell., 2013.
[12] X. Xu, Y. Zhang, L. Zou, M. Wang, and A. Li, “A gene signature for breast cancer prognosis using support vector machine,” in 2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012, 2012.
[13] O. Gevaert, F. De Smet, D. Timmerman, Y. Moreau, and B. De Moor, “Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks,” in Bioinformatics, 2006.
[14] P. Rosado, P. Lequerica-Fernandez, L. Villallain, I. Pena, F. Sanchez-Lasheras, and J. C. De Vicente, “Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines,” Expert Syst. Appl., 2013.
[15] D. Delen, G. Walker, and A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods,” Artif. Intell. Med., 2005.
[16] J. Kim and H. Shin, “Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data,” J. Am. Med. Informatics Assoc., 2013.
[17] “Pattern Recognition and Machine Learning,” J. Electron. Imaging, 2007.
[18] A. Agah, A. Niknejad, and D. Petrovic, “Introduction to Computational Intelligence Techniques and Areas of Their Applications in Medicine,” in Medical Applications of Artificial Intelligence, 2013.
[19] “Introduction to data mining,” Intell. Syst. Ref. Libr., 2011.
[20] R. Nair and A. Bhagat, “A Life Cycle on Processing Large Dataset - LCPL,” Int. J. Comput. Appl., 2018.
[21] Y. Drier and E. Domany, “Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?,” PLoS One, 2011.
[22] M. Greenacre and R. Primicerio, “Measures of distance between samples: Euclidean,” in Multivariate Analysis of Ecological Data, 2013.
[23] J. A. Nelder and R. W. M. Wedderburn, “Generalized Linear Models,” J. R. Stat. Soc. Ser. A J. R. Stat. Soc. Ser. A (General J. R. Stat. Soc. A, 1972.
[24] Martin, “Two-way ANOVA and ANCOVA,” None, 1000.
[25] R. Berk and J. M. MacDonald, “Overdispersion and poisson regression,” J. Quant. Criminol., 2008.
[26] K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V Karamouzis, and D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction,” CSBJ, vol. 13, pp. 8–17, 2015.
[27] E. Alpayd?n, Introduction to machine learning, vol. 1107. 2014.
[28] D. P. R. G and R. T. Sriramaneni, “Literature Survey on Various Software Cost,” vol. 4, no. Iv, pp. 868–874, 2016.
[29] D. Mladeni?, J. Brank, M. Grobelnik, and I. Natasa Milic-Frayling, “Feature Selection using Linear Classifier Weights: Interaction with Classification Models,” in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004.

Archana Pathak, Nidhi Ruthia, Chetan Agrawal, "Machine Learning Techniques for Cancer Prediction: A Survey," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.174-179, 2020.

The Internet of Things is changing our lives with their increasing multidisciplinary applications. Applications of IoT are growing day by day in the smart healthcare sector. The wide range of applications of IoT includes healthcare services. In our daily life, traffic monitoring, healthcare, security, transport and logistics are major sectors for the study of applications of IoT. In smart healthcare sector, applications of IoT connect smart devices, machines, patients, doctors and sensors to the Internet. Healthcare is becoming a major socio-economic concern when it comes to health expenditure, the need and availability of resources and personal care, especially for the elderly in society. Efficiently and intelligently trends in healthcare are enabling physicians to provide remote monitoring, chronic disease management and elderly care of distant patients, and even care for institutional patients after being connected to the Internet. This article paper examines the various smart healthcare trends that have transformed traditional healthcare systems by makinghealthcare management more efficient through their applications.

IoT, Smart Healthcare, telemedicine, wearable device, smart phone apps

[1] S. Madakam, “Internet of Things: Smart Things “International Journal of Future Computer and Communication”, Vol. 4, No. 4, August 2015
[2] M.S. Dawood, M.J. Margaret, R.Devika,” Review on Applications of Internet of Things (IoT)”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), ISSN: 2278 – 1323, Volume 7, Issue 12,December 2018 .
[3] Soumyalatha, Shruti G Hegde, “Study of IoT: Understanding IoT Architecture”, Applications, Issues and Challenges”, ISSN: 0975-0282, January 2019.
[4] Dr J. Jegathesh Amalraj, S. Banumathi, J. Jereena John,” IOT Sensors and Applications: A Survey”, International Journal Of Scientific and Technology, Research, Volume 8, Issue 08, ISSN 2277-8616, August 2019.
[5] Keyur K Patel, Sunil M Patel,” Internet of Things-IOT: Definition, Characteristics, Architecture, Enabling Technologies, Application & Future Challenges”, IJESC, ISSN 2321 3361, 2016.
[6] Mohammed Baqer M. Kamel, Loay E. George, “Remote Patient Tracking and Monitoring System”, International Journal of Computer Science and Mobile Computing, Vol.2 Issue. 12, pg. 88-94, December 2013.
[7] J. Gubbi, Rajkumar Buyya, S. Marusic, M. Palaniswami, "Internet of Things (IoT): A vision, architectural elements, and future directions." Future Generation Computer Systems 29.7 (2013): 1645-1660, 2013.
[8] A. Ruhanen , M. Hanhikorpi , F. Bertuccelli, A. Colonna, W. Malik , D. Ranasinghe , Tomas Sánchez López ,Matti Tavilampi, ”Sensor-enabled RFID tag handbook”, BRIDGE– (Building Radio frequency Identification solutions for the Global Environment), European Commission contract No: IST-2005-033546,pp.4-6,pp.11- 17, January 2008.
[9] K. Natarajan , B. Prasath, P. Kokila, “Smart Health Care System Using Internet of Things”, Journal of Network Communications and Emerging Technologies (JNCET) , Volume 6, Issue 3, March 2016.
[10] S. M. Riazul Islam, Daehan Kwak; MD. Humaun Kabir, Mahmud Hossain, Kyung-Sup Kwak ,”The Internet of Things for Health Care:A Comprehensive Survey”, IEEE service Volume 3, 2015
[11] S. Sebastian, N. Rachel Jacob, Y. Manmadhan, V.R. Anand, M. J. Jayashree,” Remote Patient Monitoring System” International Journal of Distributed and Parallel Systems (IJDPS) Vol.3, No.5, September 2012.
[12] R. Luanrattana ,K. Win , J. Fulcher, john fulcher, ”Use of Personal Digital Assistants (PDAs) in Medical Education “,20th IEEE International Symposium on Computer-Based Medical Systems CBMS 2007, 20-22 Jun, 307-312.
[13] S. Wilson, R. Laing, “Wearable Technology: Present and Future”, researchgate.net publication- 327542210, 2018.
[14] J. Craig and V. Patterson ,” Introduction to the practice of telemedicine “,Journal of Telemedicine and Telecare 2005; 11: 3–9
[15] M. Al-khafaji, T. Baker, Carl Chalmers, Muhammad Asim, Hoshang Kolivand, Muhammad Fahim, Atif Waraich “Remote health monitoring of elderly through wearable sensors”, Published online: Multimedia Tools and Applications (2019) 78:24681–24706, January 2019.
[16] E. Agu, P. Pedersen, D. Strong, B. Tulu, Qian He, Lei Wang, Y. Li ,“The Smartphone as a Medical Device Assessing enablers, benefits and challenges”, 2013 Workshop on Design Challenges in Mobile Medical Device Systems, 2013.

Mani Shrivastava, Neelam Sahu, "Study of Current Trends in the smart Healthcare Sector using IoT," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.180-186, 2020.