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Cloud computing is growing rapidly, with its server farms evolving at a remarkable rate. This paper tries to elaborate on a community cloud model that tries to use web application to identify maize disease through image matching. The model tries to match uploaded images with the images stored in the file system and if a match is found, the matching image is displayed together with its associated disease. The images were matched based on time, location and images collected from secondary sources to see if the disease can still be identified. All the images matched were successful and the appropriate diseases associated with the images were identified. The load balancing technology was also incorporated into the model mainly for the accessibility of the web application and to avoid overloading of one server. The image matching results were collected and tabulated as shown in chapter four and the results clearly indicated that the community cloud model really helped users to identify possible disease through the web application hosted on the community cloud model servers.

Cloud Computing, Infrastructure-as-a-Service, Web server, Image Matching, Load Balancing

[1] Alhakami, H., Aldabbas, H., & Alwada, T. (2012). COMPARISON BETWEEN CLOUD AND GRID COMPUTING : REVIEW PAPER, 2(4), 1–21.
[2] Al Nuaimi K, Mohamed N, Al Nuaimi M, Al-Jaroodi J 2012 A survey of load balancing in cloud computing: challenges and algorithms In Proceedings - IEEE 2nd Symposium on Network Cloud Computing and Applications, NCCA 2012 p. 137–42
[3] Ahmed, M., & Hossain, M. A. (2014). CLOUD COMPUTING AND SECURITY ISSUES IN THE Cloud. International Journal of Network Security & Its Applications, 6(1), 25–36. http://doi.org/10.5121/ijnsa.2014.6103
[4] Arokia, R., Rajan, P., & Shanmugapriyaa, S. (2013). Evolution of Cloud Storage as a Cloud Computing Infrastructure Service. IOSR Journal of Computer Engineering (IOSRJCE), 1(1), 38–45. Retrieved from http://arxiv.org/abs/1308.1303
[5] Badidi, E. (2013). A Framework for Software-as-a-Service Selection and Provisioning. International Journal of Computer Networks and Communications (IJCNC), 5(3), 12. http://doi.org/10.5121/ijcnc.2013.5314
[6] Basmadjian, R., Meer, H., Lent, R., & Giuliani, G. (2012). Cloud computing and its interest in saving energy: the use case of a private cloud. Journal of Cloud Computing: Advances, Systems, and Applications, 1(1), 5. http://doi.org/10.1186/2192-113X-1-5
[7] Computing, M., Jaiswal, P. R., & Rohankar, A. W. (2014). Infrastructure as a Service : Security Issues in Cloud Computing, 3(3), 707–711.
[8] Dave S, Maheta P 2014 Utilizing round robin concept for load balancing algorithm at virtual machine level in cloud environment Int J Comput Appl [Internet] 94(4) 23–9
[9] Desai T, Prajapati J 2013 A survey of various load balancing techniques and challenges in cloud computing Int J Sci Technol Res [Internet] 2(11) 158–61
[10] Distributed load balancing algorithms for cloud computing 24th IEEE Int Conf Adv Inf Netw Appl Work WAINA [Internet] 551–6
[11] Gopinath P P G, Vasudevan S K 2015 An In-depth analysis and study of load balancing techniques in the cloud computing environment. Procedia Comput Sci [Internet] Elsevier Masson SAS 50 427–32
[12] Gulati, A., & Chopra, R. K. (2013). Dynamic round robin for load balancing in a cloud computing. IJCSMC, 2(6), 274-278.
[13] Haryani N, Jagli D, Sangita O, Dhanamma J, Jagli1 M D, Solanki R, et al. 2014 Dynamic Method for Load Balancing in Cloud Computing Int Conf Circuits, Syst Commun Inf Technol Appl [Internet] 5(4) 336–40

Vincent Mbandu Ochango, Waweru Mwangi, George Okeyo, "Community Cloud Model for Infrastructure –As-A-Service in Learning through Content Sharing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.1-7, 2019.

Nowadays social media is one to express things that are thought and felt by the community. One of the things that’s much talked about is responses from the consumer of products or services. This is very useful for companies to find out the level of satisfaction of their products or services. Twitter is one of the most widely used social media by users. With this fact, it`s really interesting for companies to use the data on Twitter for the company`s progress generally in customer relations. In this study an analysis of public sentiments towards the use of Transjakarta. This study divides community sentiments into three classes, positive, neutral and negative. For data taken from Twitter with the results of research from June to July 2019 by dividing the data into training data and testing data. The amount of training data is 144 tweets and testing data are 36 tweets. Then for the text classification uses 3 algorithms, namely naïve bayes, k-nearest neighbor and logistic regression. Then after the results are obtained, next is to compare the performance levels of three methods by finding the highest f-measurement value using micro average formula. Micro average is chosen because it’s the best for calculating imbalanced datasets. The results show the naïve bayes method has the best f-measurement with 0.861 value. For the next largest f-measurement value is the logistic regression method with an f-measurement value of 0.833, and the last is the k-nearest neighbor method with an f-measurement value of 0.806.

Naïve Bayes, K-Nearest Neighbor, Logistic Regression, F-measurement, Sentiment Analysis, Transjakarta

[1] B. Liu, “Sentiment Analysis and Opinion Mining”, Synthesis Lectures on Human Language Technologies, Vol.5, No.1, pp.1-167, 2012.
[2] M. Trivedi, N. Soni, S. Sharma. S. Nair, “Comparison of Text Classification Alghorithms”, International Journal of Engineering Research & Technology (IJERT), Vol.4, Issue.02, pp.334-336, 2015.
[3] K. Chouksey, A. Ranjan, “Analysis of Indian Election using Random Forest Algorithm”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.50-57, 2019.
[4] B. Sharma, S. Gandotra, U. Sharma, R. Thakur, A. Mahajan, “A Comparative Analysis of Different Machine Learning Classification Algorithms for Predicting Chronic Kidney Disease”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.8-13, 2019.
[5] S.S. Bhadoria, R.K. Patel, “Web Text Content Extraction and Classification using Naïve Bayes Classifier Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.2, Issue.5, pp.1-4, 2014.
[6] K. Sarvakar, U.K. Kuchara, “Sentiment Analysis of movie reviews: A new feature-based sentiment classification”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.8-12, 2018.
[7] M. Bekkar, Dr.H.K Djemaa, Dr.T.A. Alitouche, “Evaluation Measures for Models Assessment over Imbalanced Data Sets”, Journal of Information Engineering and Applications, Vol.3, No.10, pp.27-38, 2013.
[8] Y. Liu, H.T. Loh, A. Sun, “Imbalanced text classification: A term weighting approach”, Expert Systems with Applications, Vol.36, No.1, pp.690-701, 2009.

Rexzy Tarnando, Yuli Karyanti, "Comparison of Text Classification Algorithms of People Sentiments on Twitter (Case: Transjakarta)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.8-12, 2019.

This paper mainly presents face recognition system based on principal component analysis. The goal is to implement the system which is able to distinguish a single face from the larger database. In this research work we are compressing the image using the mathematical tool principal component analysis and then recognize the image from the same data set by the model. First we will describe the basic concepts prevailing with principal component analysis. Then we will see that how principal component can be extracted from a given data set. Then we will go for sampling distribution of Eigen values and Eigen vectors. Then followed by model adequacy test, then we perform our task of image detection. The problem arises when we use high dimensionality space. Because in face or in 3d image, we have different eigen values or vectors and it can’t be fixed due to high dimensions as compared to 2d image. Hence, we use Principal Component Analysis (PCA).

PCA, Eigen values, Eigen vectors, image compression. Dimension reduction

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[11]. W. Yang, C. Sun, L. Zhang, and K. Ricanek, “Laplacian bidirectional pca for face recognition,” Neurocomputing, vol. 74, no. 1, pp. 487–493, 2010.
[12]. M. Al-Amin, “Towards face recognition using eigenface,” International Journal of Advanced Computer Science and Applications, vol. 7, pp 539, 544, 2016.
[13]. Q. Fu, “Research and implementation of pca face recognition algorithm based on matlab,” in MATEC Web of Conferences, vol. 22. 539-544. EDP Sciences, 2015

Saif Ali, Manish Sharma, "Image Compression and Detection Technique Using Principal Component Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.13-16, 2019.

Gradually I.T industry is increasingly relying on a growing quantity of ever-larger software. The fashion of growing technical complexity of the systems united with the requirement for repeatable and predictable process methodologies have driven system developers to establish system development models. With the rising operations of organizations, the need to automate the various activities increased. With this need the concept of system lifecycle models came into survival that emphasized on the need to follow some structured approach towards building new or improved system. At present the software systems cannot be built with mathematical or physical certainty so all software systems are imperfect because they cannot be built with mathematical or physical certainty. The set of processes those proved to be effective and efficient for software development in one organization may or may not be followed in another organization. That is other organization finds another approach for software development more convenient to work with. The software development process is the very complicated thing without any proper step by step generating procedure so to make the software development processes simple and systematic the software development life cycle came in to existence. This is the systematic and structural method of software developing process. The SDLC defines the framework that includes different activities and tasks to be carried out during the software development process. The development lifecycle of software Comprises of four major stages namely Requirement Elicitation, Designing, Coding and Testing. This workflow is a guideline for successful planning, organization and final execution of the software project. There are various software development life cycle models that are used in the software development process heaving their own advantages and disadvantages.

Software Development Life Cycle (SDLC), Models and Comparative Analysis, Activities involved in SDLC models, Model with Different parameter, factors affecting to Chose Process Model, SRS (software requirement specification), SRD (System requirement documentation), Software Process, Risk Analysis, Verification and Validation.

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[5] Ms. Shikha Maheshwari, Prof Dinesh Ch. Jain, “A Comparative Analysis of Different types of Models in Software Development Life Cycle”, IJARCSSE, Volume 2, Issue 5, May 2012 ISSN: 2277 128X
[6] Asmita, Kamlesh, Usha, “Review On Comparative Study Of Software Process Model”, International Journal of Science, Technology & Management,Volume No 04, Special Issue No. 01, March 2015
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[8] K. K. Aggarwal, Yogesh Singh, “Software Engineering” 3rd Edition.
[9] Manzoor Ahmad Rather ,Mr. Vivek Bhatnagar, “A Comparative Study Of Software Development Life Cycle Models”, International Journal of Application or Innovation in Engineering& Management , Volume 4, Issue 10, October 2015
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[11] Manzoor Ahmad Rather, Mr. Vivek Bhatnagar, “A Comparative Study Of Software Development Life Cycle Models“, International Journal of Application or Innovationin Engineering & Management,Volume 4, Issue 10, October 2015
[12] Shubham Dwivedi, “Software Development Life Cycle Models - A Comparative analysis”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 2, February 2016.
[13] Prateek Sharma1, Dhananjaya Singh, “Comparative Study of Various SDLC Models on Different Parameters”, International Journal of Engineering Research ,Volume No.4, Issue No.4
[14] Ratnmala R. Raval,Haresh M. Rathod, “Comparative Study of Various Process Model in Software Development “, International Journal of Computer Applications (0975 – 8887) Volume 82 – No 18, November 2013
[15] Harminder Pal Singh Dhami, “Comparative Study and Analysis of Software Process Models on Various Merits”, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper , Volume 6, Issue 9, September 2016
[16] Roger Pressman, titled “Software Engineering - a practitioner`s approach”.
[17] Hari Krishnan Natarajan, Ram Kumar Somasundaram and Kalpana Lakshmi, “A Comparison between Present and Future Models of Software Engineering”, IJCSI International Journal of Computer Science Issues, Vol.10, Issue.2, 2013. Manzoor
[18] Chetna Sisodiya, Pradeep Sharma, “Scrutiny to Effectiveness of Various Software Process Model”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.88-93, April(2017).
[19] Aanchal, Sonu kumar, “Metrics for Software Components in Object Oriented Environments: A Survey”, International Journal of Scientific Research in Computer Science and Engineering, Volume-1, Issue-2, March-April-2013.

Archana Srivastava, "A Comparative Analysis of Software Development Models Based On Various Parameters," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.17-21, 2019.

The Internet of Things revolutions have made our live stress-free and better by giving us economical services. It always keeps us connected to the embedded system and provides value added services to humans as per their requirements. As per the research, 5 billion devices are already connected with the internet and these numbers will increase to 20 billion in few years. As we know that every cloud has a silver lining, same goes for Internet of Things. IoT devices are becoming a primary object for hacker, it is continuously alluring the attacker or intruder who tries to harness and exploit the devices. Top security concerns of IoT devices are Sensitive information disclosure, Denial of service attack, Tampering of Data and privilege escalation. Internet of Things use cloud servers to exchange information from one device to another device. But if an attacker successfully exploits the vulnerability, it could easily access, read or modify the data while performing the men in middle attack. Moreover malicious user can use this data that can cause potential damage to the targeted organization and can use it for their personal financial gain. This article provides the guidance for best practices to mitigate the DDOS attack and examine their interrelationships.

Internet of Things (IoT), Denial of Service(DOS), Distributed Denial of Service(DDOS), Internet Security, Wireless Security, Spoofing, Secure Routing, IoT Security, Controller, Secure Forwarding Cloud, Device, Sensor, Encrypted session key, Firewall, HTTP/S, Filtering, DNS, BGP, Null routing, BIG-IP server, VoIP or FTP

[1] Constantinos Kolias, Georgios Kambourakis, and Angelos Stavrou “DDoS in the IoT: Mirai and Other Botnets”, 07 July 2017, IEEE, INSPEC Accession Number: 17012613. For Journal
[2] Linux/AES. DDoS: “Router Malware Warning - Reversing an AR March ELF,” MalwareMustDie! Blog, 2014.
[3] Danny Palme, IoT security warning: Cyber-attacks on medical devices could put patients at risk, DOI: 10.1145/2667218, Communications of the ACM 58(4):74-82 • April 2015.
[4] D. Bekerman, “New Mirai Variant Launches 54 Hour DDoS Attack against US College,” blog, Imperva Incapsula, 29 Mar. 2017.
[5] SoniaLaskara, DhirendraMishra “Qualified Vector Match andMerge Algorithm (QVMMA) for DDoS Prevention and Mitigation”. ELSEVIER, India, Volume 79, 2016.
[6] S. Edwards and I. Profetis, “Hajime: Analysis of a Decentralized Internet Worm for IoT Devices,” Rapidity Networks; 16Oct.2016.
[7] Prabhakaran Kasinathan; Claudio Pastrone; Maurizio A.Spirito; Mark Vinkovits, Denial-of-Service detection in 6LoWPAN based Internet of Things.
[8] Georgios Kambourakis, Constantinos Kolias, Angelos Stavrou “The Mirai botnet and the IoT Zombie Armies”,MILCOM- 2017 IEEE Military Communications Conference (MILCOM).
[9] Michele De Donno, Nicola Dragoni, Alberto Giaretta, “Analysis of DDoS-capable IoT malwares”, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).
[10] Natalija Vlajic, Daiwei Zhou “IoT as a Land of Opportunity for DDoS Hackers” C. Kolias et al. Published in: Computer Volume: 51, Issue: 7, July 2018.
[11] M Devendra Prasad1, Prasanta Babu V2, C Amarnath3“Machine Learning DDoS Detection Using Stochastic Gradient Boosting” Volume-7, Issue-4, Page no. 157-166, Apr-2019.
[12] Michele De Donno, 1 Nicola Dragoni, 1, 2 Alberto Giaretta, 2 and Angelo Spognardi3 “DDoS-Capable IoT Malwares: Comparative Analysis and Mirai Investigation” Volume 2018, Article ID 7178164.
[13] Swaroop P T1, Mrs. Chaitra H K “Internet of Things: Smart” College, Volume-4, Special Issue-3, May 2016.

Sapna Rawat, Md Tabrez Nafis, "IoT Devices are Being Weaponized for DDoS Attacks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.22-25, 2019.

Artificial Intelligence (AI) has found various applications in many industries, from development of new alloys to cyber security and healthcare domain. By 2025 it is expected that the market for healthcare artificial intelligence tools will surpass 34 billion dollars. There is no doubt that the application of AI is going to lead to a real digital shift in traditional medical imaging, requiring AI and people to work together to meet the challenges of the medical industry. In our present work, we have tried to determine the hemoglobin level corresponding to Packed Cell Volume (PCV) and Red Blood Cells (RBC) count. In the Artificial Neural Network (ANN) architecture, PCV (%) and RBC count (mill/cumm) are the inputs while hemoglobin (g/dL) is the output. The result obtained is quite promising. Artificial Neural Network (ANN) trained on Rectified Linear Unit (ReLu) activation function showed 97.15% accuracy.

Multiple Myeloma, Artificial Intelligence, Artificial Neural Network, Hemoglobin level

[1] Patel, V.L., Shortliffe, E.H., Stefanelli, M., Szolovits, P., Berthold, M.R., Bellazzi, R. and Abu-Hanna, A., 2009. The coming of age of artificial intelligence in medicine. Artificial intelligence in medicine, 46(1), pp.5-17.
[2] Jaremko, Jacob L. et al. Canadian Association of Radiologists Journal, Volume 70, Issue 2, 107 – 118
[3] Soni, J., Ansari, U., Sharma, D. and Soni, S., 2011. Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), pp.43-48.
[4] Lo, S.C., Lou, S.L., Lin, J.S., Freedman, M.T., Chien, M.V. and Mun, S.K., 1995. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging, 14(4), pp.711-718.

Akshansh Mishra, Mitali Diwan, "Soft computing to determine a Hemoglobin level of an early stage Multiple Myeloma patient by using Rectified Linear Units (ReLu) activation function," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.26-30, 2019.

TXOP is the transmission opportunity scheme given by IEEE 802.11e to enhance the Quality of Service(QoS).This paper observed the TXOP service differentiation for the devices/nodes connected in step network and metrics like throughput is derived. In the current work the simulation is carried out using NS3 and performance of four networks with QoS were estimated by enabling and disabling TXOP and it was observed throughput rate will be increased when TXOP is enabled and decreased when TXOP is disabled.

ADHOC NETWORK, STEP NETWORK, PERFORMANCE METRIC, SIMULATION TOOL, NS3, TXOP

[1]. Yi Shang and Hongchi Shi, “Performance study of localization methods for ad-hoc sensor networks” IEEE International Conference on Mobile Ad-hoc and Sensor Systems, 2004 .
[2] L. Chen and W. Heinzelman, "QoS-aware Routing Based on Bandwidth Estimation for Mobile Ad Hoc Networks," IEEE Journal on Selected Areas of Communication, Special Issue on Wireless Ad Hoc Networks, Vol. 23, No. 3, March 2005.
[3]. S. Marinoni. Performance of Wireless Ad Hoc Routing Protocols - A Simulation Study in Realistic Environments, Master`s thesis, Helsinki University of Technology, May 2005.
[4]. Mohammed ERRITALI, Bouabid El Ouahidi,“ Performance evaluation of ad hoc routing protocols in VANETs”, IJACSA Special Issue on Selected Papers from Third international symposium on Automatic Amazigh processing (SITACAM’ 13) , Special Issue(2):33-40 • July 2013
[5]P. Gupta and P. Kumar. The capacity of wireless networks. IEEE Transactions on Information Theory, 46(2):388–404, March 2000.
[6].Azzedine Boukerche “Performance Evaluation of Routing Protocols for Ad Hoc Wireless Networks” Mobile Networks and Applications August 2004, Volume 9, Issue 4, pp 333–342.
[7] Geetha Jayakumar and Gopinath Ganapathy, “Performance Comparison of Mobile Ad-hoc Network Routing Protocol”, Proc. of IJCSNS International Journal of Computer Science and Network Security, Vol.7, No.11, November 2007.
[8]. Amandeep ,Gurmeet Kaur “Performance analysis of Aodv routing protocol in Manets” International Journal of Engineering Science and Technology (IJEST) ,Vol. 4 No.08 August 2012
[9]. Utpal Barman, Neelpawan Kalita “Performance Analysis of Aodv Routing Protocol in MANET” International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 1, January 2016.
[10]. A. A. Chavana , Prof. D. S. Kuruleb , P. U. Derec “Performance Analysis of AODV and DSDV Routing Protocol in MANET and Modifications in AODV against Black Hole Attack” Procedia Computer Science 7th International Conference on Communication, Computing and Virtualization, vol.79, pp.835 – 844, 2016.
[11] J. Mammen and D. Shah,“ Throughput and Delay in Random Wireless Networks with Restricted Mobility”. IEEE Transactions on Information Theory, 53(3),PP.1108–1116, 2007.
[12]G. Sharma, R. Mazumdar, and N. Shroff,
“ Delay and Capacity Trade- offs in Mobile Ad hoc Networks: a Global Perspective”, IEEE/ACM Transactions on Networking, Vol. 15(5) pp.981–992, 2007.
[13]Tonguz O and Ferrari G., “Adhoc Wireless Networks-A Communication -Theoritic Perspective, Wiley and Sons”, 2009.
[14]Tuteja A, Gujral A, Thalia A, “Comparative Performance Analysis of DSDV, AODV and DSR Routing Protocols in MANET using NS2”, IEEE Comp. Society, 2010, pp. 330-333.
[15]Perkins C.E., “Adhoc Networking”, Chapter-5, Pearson, US 2000.
[16] P. Chouksey,“ Introduction to MANET”, Int. J. Sc. Res. in Network Security and Communication, Volume-4, Issue-2, Apr 2016.

Taskeen Zaidi, Nitya Nand Dwivedi, "Throughput Analysis for Wireless Step Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.31-34, 2019.

There are many techniques used to enhance the network life time each depends upon the number of nodes deployed, a parent node, path followed, energy of the nodes and the forward nodes. The selection of forward node is based on cost equation, the total load to this node is maximum and there is a chance of path loss because the distance between the nodes is in feet which may cause data loss. In this paper we proposed model having a forwarder node, which selection is based on cost function and two fixed data transfer nodes. The purpose is to implement the concept of multilevel, multi-hop and a smart node which divides the load and helps to smooth transfer of data with high stability. The major benefit of this new designed methodology is to increase the network life time by enhancing the throughput, by minimizing the path loss or data loss and maximum packet delivering to the sink.

WBAN, Forward node, cluster Head, Energy consumption, lifetime,Hetrogenous Sensors Network

[1] Nitu Choudhary1*, Deepak Kumar 2, “Multilevel Multi-Hop Technique for more than one Forward Node to increase the Stability of Wireless Body Sensor Networks”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-8, 2019.
[2] Jatinder Singh1*, Navjeet Saini2, Sandeep Kour3,”Enhancement Over Energy Consumption And Network Lifetime Of Wireless Body Area Network: A Review”, International Journal of Computer Sciences and Engineering, Volume-6, Issue-9, 2019.
[3] M. Devapriya1, R. Sudha2, ” A Survey on Wireless Body Sensor Networks for Health care monitoring”, International Journal of Science and Research (IJSR), Volume 3 Issue 9, September 2014.
[4] Arti Sangwan et al,”Wireless Body Sensor Netwroks: a Review”, researchgate, September 2015.
[5] Q. Nadeem et et al, “SIMPLE: Stable increased throughput- multihop protocol for link efficiency in wireless body area networks”, vol1, 2013.
[6] Mehdi Effatparvar1, Mehdi Dehghan2*, Amir Masoud Rahmani1,” Lifetime maximization in wireless body area sensor networks”, Biomedical Research 2017Volume 28 Issue 22, 2017.
[7] Sriyanjana Adhikary et al, “ A New Routing Protocol For Wban To Enhance Energy Consumption And Network Lifetime”.
[8] Sakshi Mehta et al,” Improved Multi-Hop Routing Protocol in Wireless Body Area Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 7, July 2015.
[9] Xiaochen Lai et al,” A Survey of Body Sensor Networks”,mdpi, 2013.
[10] Garth V. Crosby et al, “Wireless Body Area Networks for Healthcare: A Survey”, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.3, No.3, June 2012
[11] Aslam, M., Nadeem Javaid, A. Rahim, U. Nazir, Ayesha Bibi, and Z. A. Khan. "Survey of extended LEACH-Based clustering routing protocols for wireless sensor networks." IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), pp. 1232-1238, 2012.
[12] Li, Hongjuan, Kai Lin, and Keqiu Li. "Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks." Computer Communications, 2011.
[13] Professor Guang-Zhong Yang, “Body Sensor Networks –Research Challenges and Applications” Imperial College London.

Jatinder Singh, Navjeet Saini, Sandeep Kaur, "Load Balancing Technique in Body Area Network by Utilization of Smart Node," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.35-38, 2019.

This paper manages the utilization of Simple Algorithm for identification of range and state of tumor in cerebrum MR images and distinguishes segment of tumor from the given region of tumor. Tumor is an uncontrolled development of tissues in any piece of the body. Tumors are of various kinds and they have various Characteristics and specific treatment. As it is known, mind tumor is inalienably genuine and dangerous in light of its character in the limited space of the intracranial gap (space formed inside the skull). Most Research in created nations demonstrates that the quantity of individuals who have mind tumor have been kicked the bucket because of the reality of off base identification. For the most part, CT sweep or MRI that is coordinated into intracranial gap provides an entire image of cerebrum. Subsequent to exploring an excellent deal factual examination which relies upon on those individuals whose are influenced in cerebrum tumor some huge Risk factors and Symptoms have been found. The improvement of innovation in science day night time endeavours to develop new strategies for treatment. This image is outwardly inspected by way of the doctor for identification and analysis of cerebrum tumor. Anyway this strategy exact decides the specific of stage and size of tumor and distinguishes segment of tumor from the region of tumor. This work utilizes division of cerebrum tumor dependent on the k-implies and fluffy c-implies calculations. This technique permits the division of tumor tissue with exactness and reproducibility similar to manual division.

Magnetic Resonance Imaging (MRI), Brain tumor, Pre-processing, K-means, fuzzy c-means, Thresholding, SVMclassification

[1] Varnish Rajesh , Bharathan Venkat , Vikesh Karan and M. Poonkodi , “Brain Tumor Segmentation and its Area Calculation in Brain MR Images Using K-Mean Clustering and Fuzzy C-Mean Algorithm”, Department of Computer Science and Engineering, SRM University.
[2] Beshiba Wilson and Julia Punitha Malar Dhas, “ An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab”, International Journal of Computer Applications, , Volume 104 – No 15, pp.0975 – 8887, October 2014.
[3] Samarjit Das, “Pattern Recognition using the Fuzzy c-means Technique” International Journal of Energy, Information and Communications, Vol. 4, Issue 1, February 2013.
[4] Samir Kumar Bandhyopadhyay and Tuhin Utsab Paul, “Automatic Segmentation of Brain Tumor from Multiple Images of Brain MRI” International Journal of Application or Innovation in Engineering & Management , (IJAIEM),Volume 2,January 2013.
[5] Ajala Funmilola A, Oke O.A, Adedeji T.O and Alade O.M, Adieus E.A, “Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation”, Journal of Information Engineering and Applications ISSN 2224-5782 (print), No.6, ISSN 2225-0506 (online)Volume 2. 2012.
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[7] A. Meena, “Spatial Fuzzy C-Means PET Image Segmentation of Neurodegenerative Disorder” , A. Meena et.al / Indian Journal of Computer Science and Engineering (IJCSE).
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Priyanka Bangar, Harsha Verma, "An Automatic Segmentation of Brain Tumor from Multiple MRI Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.39-43, 2019.

The healthcare monitoring systems have drawn considerable attentions of the researchers. The prime goal was to develop a reliable patient monitoring system so that the healthcare professionals can monitor their patients, who are either hospitalized or executing their normal daily life activities. In this work we present a mobile device based wireless healthcare monitoring system that can provide real time online information about physiological conditions of a patient. Our proposed system is designed to measure and monitor important physiological data of a patient in order to accurately describe the status of her/his health and fitness. By using the information contained in the text or e-mail message the healthcare professional can provide necessary medical advising. The system mainly consists of sensors (i.e. temperature sensor, gyroscope, accelerometer), location locker (i.e. GPS), microcontroller (i.e. Node MCU), and software (i.e. Embedded C). The patients temperature, no. of steps he/she walks, location, displayed, and stored by our system. Along with above mention parameters, android app will display timing and amount for drinking water and alert about same.

Healthcare, Io, Temprature,heart rate

[1] International Journal of Computer Networks Communications (IJCNC) Vol.7, No.3, May 2015 DOI : 10.5121/ijcnc.2015.7302 13 REAL TIME WIRELESS HEALTH MONITORING APPLICATION USING MOBILE DEVICES Amna Abdullah, Asma Ismael, Aisha Rashid, Ali Abou-ElNour, and Mohammed Tarique
[2] International Journal of Computer Applications (0975 8887) Volume 62 No.6, January 2013 1 Wireless Patient Health Monitoring System Manisha Shelar R.G.P.V. University Department of E T C S.S.S.I.S.T. Sehore Jaykaran Singh R.G.P.V. University Department of E T C S.S.S.I.S.T. Sehore Mukesh Tiwari
[3] A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors Priyanka Kakria,1 N. K. Tripathi,1 and Peerapong Kitipawang, Hindawi Publishing Corporation International Journal of Telemedicine and Applications Volume 2015, Article ID 373474, 11 pages
[4] Real-Time Cloud-Based Health Tracking and Monitoring System in Designed Boundary for Cardiology Patients Aamir Shahzad , 1 Yang Sun Lee,2 Malrey Lee,3 Young-Gab Kim , 1 and Naixue Xiong Hindawi Journal of Sensors Volume 2018, Article ID 3202787, 15 pages
[5] IOT BASED HEALTH CARE MONITORING AND TRACKING SYSTEM USING GPS AND GSM TECHNOLOGIES SARA FATIMA1 , AMENA SAYEED INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VIII /Issue 5 / JUN 2017
[6] Wearable Sensors for Remote Health Monitoring Sumit Majumder 1 , Tapas Mondal 2 and M. Jamal Deen Sensors 2017, 17, 130; doi:10.3390/s17010130 www.mdpi.com/journal/sensors [7] IoT Based Wearable Health Monitoring System B.Srirama Chowdary1 , K.Durgaganga Rao 2
[8] Development of a Heartbeat and Temperature Measuring System for Remote Health Nursing for the Aged in Developing Country Blessed Olalekan Oyebola1 , Ogunlewe Adeyinka Oluremi2 , Toluwani Victor Odueso Science Journal of Circuits, Systems and Signal Processing 2018; 7(1): 34-42
[9] Aashay Gondaliaa , Dhruv Dixitb , Shubham Parasharc , Vijayanand Raghavad , Animesh Senguptae Communicating Author: Vergin Raja Sarobin IoT-based Healthcare Monitoring System for War Soldiers using Machine Learning International Conference on Robotics and Smart Manufacturing (RoSMa2018)
[10] Mr. Ajinkya A. Bandegiri Dr. Pradip C. Bhaskar Real-Time Health Monitoring System: A Review International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 Volume 2 — Issue 1 — Nov-Dec 2017

Rahul Pawar, M.M. Sardeshmukh, Sagar Shinde, "Health Care Monitoring System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.44-48, 2019.