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The current research on IOT deals with how the individual things, devices will enable themselves to see hear and smell, it also deals with how these objects can be connected. But for major applications of our day to day life, just connecting the devices is not enough, the objects need to have the capability of learning, thinking and understanding. In other words, the objects need to think like brain of human being. That is adding cognitive feature to the connected devices. In this work, a detailed survey of the present scenario is done and the challenges ahead for cognitive IoT is discussed.

Cognitive Internet of Things

[1] Amit Sheth, “Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing”, IEEE Computer Society, March/ April 2016
[2] Floriano De Rango, Domenico Barletta, Alessandro Imbrogno, “Energy aware Communication between Smart IoT Monitoring Devices”, 2016 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), July 2016.
[3] Seraphin B. Calo, Maroun Touna, Dinesh C. Verma, Alan Cullen, “Edge Computing Architecture for applying AI to IoT”, IEEE International Conference on Big Data (BIGDATA), 2017.
[4] Shuo Feng, Peyman Setoodeh, and Simon Haykin, “Smart Home: Cognitive Interactive People-Centric Internet of Things”, IEEE Communications magazine, February 2017.
[5] Cory Henson, Amit Sheth, Krishnaprasad Thirunarayan, “Semantic Perception: Converting Sensory Observations to Abstractions”, IEEE Computer Society, March 2012.
[6] Amit Sheth, Pramod Anantharam, Cory Henson, “Semantic, Cognitive, and Perceptual Computing: Paradigms That Shape Human Experience”, IEEE Computer Society, March 2016.
[7] Qihui Wu, Guoru Ding, Yuhua Xu, Shuo Feng, Zhiyong Du, Jinlong Wang, Keping Long, “Cognitive Internet of Things: A New Paradigm Beyond Connection”, IEEE Internet of Things journal, Vol 1, No 2, April 2014.

Vaishak Sundaresh, Surekha K Basavanagowda, T G Basavaraju, "Implementation and Challenges of Cognitive IoT", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.60-64, 2019.

The term “cloud” is very common term and is used in both biological plus technical fields. During rainy reasons cloud plays a very important role in the life of human beings as it stores all molecules for showering the water and humans can preserve the water for future living. In the same way, in technical fields it offers a list of facilities for the user with respect to data such as data storage, security to data, data sharing, and data preservation for the future, updation and maintenance. Cloud is one such platform which allows the user to store the data and application together at one place and it is made available irrespective of the place of access at any time. Cloud can be used by an individual or a company for many purposes such as storing, retrieving and sharing documents, photos, e-mails, etc. The big question which always runs on every researcher’s mind is the need of cloud. The main problem is to find a place to store the huge growing amount of data. According to a survey done by Peter Lyman and HAL R Varian, world produces approximately two Exabyte’s of data every year which means that each individual contribution is around 250 megabytes. So it is possible to fit this much amount of data in any storage medium like papers, magnetic tapes, disks and drives. This paper gives a brief insight into the existing cloud storage techniques which are used by organizations.

IAAS, PAAS, SAAS, GDS-LC, SPMCloud, etc

[1]. Anthony V. Velte, Toby J. Velte, Robert Elsenpeter, “Cloud Computing-A Practical Approach”, McGraw-Hill Companies, in 2010.
[2]. Rajkumar Buyya, James Broberg, Andrzej Goscinski, “Cloud Computing-Principles and Paradigms”, John Wiley & Sons, in 2011.
[3]. Abhishek Parakh, Subhash Kak, “Online data storage using implicit security”, an Elsevier Article Published in May 2009.
[4]. Hongwei Li, Yuanshun Dai, Ling Tian and Haomiao Yang, “Identity-Based Authentication for Cloud Computing”, Springer- Verlag Berlin Heidelberg 2009.
[5]. Hao Jin, Ke Zhou, Hong Jiang, “Dynamic and Public Auditing with Fair Arbitration for Cloud Data”, an Article in IEEE Transactions on Cloud Computing, February 2016.
[6]. Swapnali More, Sangita Chaudhari, “Third Party Public Auditing Scheme for Cloud Storage”, an Article in Procedia Computer Science (Elsivier), December 2016.
[7]. Bibing Hou and Feng Chen, “GDS-LC: A Latency and Cost Aware Client Caching scheme for Cloud Storage”, ACM Transactions on Storage, November 2017.
[8]. Chuan Qin, Jingwei Li and Patrick P.C. Lee, “The Design and Implementation of a Rekeying Aware Encrypted Deduplication Storage System”, ACM Transactions on Storage, February 2017.
[9]. Michael Vrable, Stephen Savage, Geoffrey M. Voelker, “Cumulus: File System Backup to Cloud”, ACM Transactions on Storage, December 2009.
[10]. Alysson Bessani Miguel Correia, Bruno Quaresma, Fernando Andre and Paulo Sousa, “DEPSKY: Dependable and Secure Storage in a Cloud-of-Clouds”, ACM Transactions on Storage, December 2013.
[11]. Luis Angel, D. Bathen and Nikhil D. Dutt, “SPM Cloud: Towards the Single-Chip Embedded ScratchPad Memory-Based Storage Cloud”, ACM Transactions on Design Automation of electronic system, in June 2014.
[12]. Marcus Branden Burger, Christian Cachin and Nikola Knezevic, “Don’t trust the cloud, verify: Integrity and Consistency for object stores”, ACM Transactions on privacy and security, July 2017.
[13]. Hasan Mohamed Ibou, “OpenStack: The Cloud OS”, A Technical Report, October 2013.
[14]. Hui Tian , Yuxiang Chen , Chin-Chen Chang , Hong Jiang , Yongfeng Huang , Yonghong Chen , Jin Liu, “Dynamic-Hash-Table Based Public Auditing for Secure Cloud Storage”, IEEE Transactions on Services Computing, Volume: 10 , Issue: 5 , PP: 701-714,Sept.-2017.
[15]. Jia Yu , Kui Ren , Cong Wang , Vijay Varadharajan, “Enabling Cloud Storage Auditing With Key-Exposure Resistance”, IEEE Transactions on Information Forensics and Security, Volume: 10 , Issue: 6 , PP: 1167-1179, June 2015.
[16]. Bo Mao ; Hong Jiang , Suzhen Wu , Lei Tian, “Leveraging Data Deduplication to Improve the Performance of Primary Storage Systems in the Cloud”, IEEE Transactions on Computers , Volume: 65 , Issue: 6 , PP: 1775 – 1788, June,2016.
[17]. Lan Zhou , Vijay Varadharajan , K. Gopinath, “A Secure Role-Based Cloud Storage System For Encrypted Patient-Centric Health Records”, The Computer Journal ( Volume: 59 , Issue: 11 , Nov. 2016.

Madhura K, Alamelu Mangai J, "Review of Cloud Storage Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.65-68, 2019.

Semantic Web interconnects diverse data sources on the Web, thereby presenting a global database of Web resources. Interlinking is an important activity in establishing semantic links between applications on the World Wide Web. During the interlinking activity, first, link discovery is done to identify the datasets to be linked, and subsequently, link generation is done to generate the matching links, based on appropriate comparator algorithms. The paper presents a literature review on linked open data, focusing on the interlinking activity. Furthermore, the paper presents a novel map-reduce based approach for comprehensively presenting the interlinking algorithms. Lastly, the paper throws some insights on the LOD datasets available in the LOD Cloud 2018.

Semantic Web, Linked open data, Resource description framework, LOD Cloud, Hadoop Map-Reduce

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[10]. W. Beek, L. Rietveld, S Schlobach and F. V. Harmelen, "LOD Laundromat Why the Semantic Web Needs Centralization (Even If We Don’t Like It)”, IEEE Journals and magazines Systems, vol. 20, no. 2, pp. 78-81, 2016. https://ieeexplore.ieee.org/document/7420493
[11]. D2R Server: Accessing databases with SPARQL and as Linked Data. [Online] Available: http://d2rq.org/d2r-server#features
[12]. A. Zaveri et al., ``Quality Assessment for Linked Data: A Survey``, Semantic Web Journal, vol. 7, no. 1, pp. 63‒93, 2015. http://dx.doi.org/10.3233/SW-150175
[13]. https://www.completesoftware.net/2012/02/interlinking-of-data-what-is-it-and-why-is-it-important/
[14]. M. Rani H G et al, “An Investigate Study on the Quality Aspects of Linked Open Data”, ACM ISBN 978-1-4503-6576-5/18/10 DOI: http://doi.org/10.1145/3291064.3291074
[15]. E. Rajabi et al, “Interlinking Educational Data: An Experiment with GLOBE Resources”, 2013 ACM 978-1-4503-2345-1/13/11 http://dx.doi.org/10.1145/2536536.2536588
[16]. W. Leyh et al,”Interlinking Standardized OpenStreetMap Data and Citizen Science Data in the OpenData Cloud”, Springer International Publishing AG 2018 DOI 10.1007/978-3-319-60366-7_9
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[18]. Z. Zhang et al, “An unsupervised data-driven method to discover equivalent relations in large linked datasets”, Semantic Web 8 2017, 197-223, DOI 10.3233/SW-150193.
[19]. J. Debattista et al, “ Luzzu- A Methodology and Framework for Linked Data Quality Assessment”, ACM Journal of Data and Information Quality, Vol. 8, No. 1, Article 4, October 2016.
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[22]. S. Subhashree, R Irny, et al, “Review of Approaches for Linked Data Ontology Enrichment", Springer ICDCIT 2018, LNCS 10722, pp. 27–49, 2018 https://doi.org/10.1007/978-3-319-72344-0_2
[23]. A. Subramanian and S. Srinivasa, “Semantic Integration of Structured Data Powered by Linked Open Data”, ACM. ISBN 978-1-4503-3293-4/15/07 July 13 - 14, 2015, http://dx.doi.org/10.1145/2797115.2797130
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Shweta S A, Shreyas Suresh Rao, "A Survey on Interlinking in Linked Open Data", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.69-74, 2019.

The wireless networks are constrained networks with limited battery backup and memory. The nodes in the Wireless Sensor Network use memory buffers to keep track of the sequence number of the Transport layer segments. This helps to resend the packets during the time of packet loss. The fast retransmission of the lost packets is done by nodes of Wireless Sensor Network with the help of fastest form of memory called as cache memory. This helps to achieve reliability. Understanding the working of cache memory in fulfilling such a great responsibility is a challenge. The survey has been conducted to understand the different types of processors and memories that could be used in the nodes of wireless sensor networks. An overview of optimization methods on cache memory and cache mapping mechanisms to improve the performance of the cache are also studied.

Wireless Sensor Networks (WSN), Cache memory, performance optimization, survey

[1] Waltenegus Dargie and Christian Poellabauer, “Fundamentals of Wireless sensor theory and practice”
[2] Safaa S. Omran and Ibrahim A. Amory, “Design of Two-Dimensional Reconfigurable Cache memory using FPGA”, IEEE 10.1109/ICEDSA.2016.7818502, 2016,
[3] Santana Gil, A. D. Benavides Benitez, J.I., Hernandez Calviño, M. , Herruzo Gómez, E , “Reconfigurable Cache implemented on an FPGA”, International Conference on Reconfigurable Computing, IEEE 10.1109/ReConFig.2010.26, 2010
[4] Dinesh Kumar Gupta, “A Review on Wireless Sensor Networks”, Inter-national Conference on Recent Trends in Applied Sciences with Engineering Applications, ISSN 2224- 610X, Vol.3, No.1, IEEE 10.1109/ICAL.2012.6308240, 2013
[5] Melchizedek I. Alipio and Nestor Michael C. Tiglao,” Analysis of Cache-based Transport Protocol at Congestion in Wireless Sensor Networks”, International Conference on Information Networking (ICOIN), IEEE, 10.1109/ICOIN.2017.7899459, 2017
[6] Bruno Marchil, Antonio Grilo, Mario Nunes, “DTSN: Distributed Transport for Sensor Networks”, 12th IEEE Symposium on Computers and Communications, IEEE, 1-4244-1521-7/0, 2007
[7] Adam Dunkels, Juan Alonso, Thiemo Voigt, “Distributed TCP caching for wireless sensor networks”, SICS Technical Report T2004:06 ISSN 1100-3154 ISRN: SICS-T–2004/06- SE, 2004.
[8] Atif Sharif, Vidyasagar M. Potdar, A. J. D Rathnayaka, “ERCTP: End-to-end Reliable and Congestion aware Transport layer Protocol for heterogeneous WSN”, Scalable Computing: Practice and Experience, vol. 11, no. 4, 2010.
[9] Ahmed Ayadi, Patrick Maill´e, and David,” Improving Distributed TCP Caching for Wireless Sensor Networks”, The 9th IFIP Annual Mediterranean AdHoc Networking Workshop (Med-Hoc-Net), IEEE 10.1109/MEDHOCNET.2010.5546858, 2010.
[10] Su Liu1, Yan Tang*, Yonghua Liu2, “A survey of transport protocol for wireless sensor networks”, 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), IEEE 10.1109/CECNet.2012.6202037, 2012
[11] Fred Stann, John Heidemann, “RMST: reliable data transport in sensor networks”, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, IEEE 10.1109/SNPA.2003.1203361, 2003
[12] Chieh-Yih Wan, Andrew T. Campbell, Member, IEEE, and Lakshman Krishnamurthy, “Pump-slowly, fetch quickly (PSFQ): a reliable transport protocol for sensor networks”, IEEE Journal on Selected Areas in Communications, vol. 23, no. 4, IEEE 10.1109/JSAC.2005.843554 , 2005
[13] Yuhua Liu and Hao Huang, “Multi-path-based Distributed TCP Caching for Wireless Sensor Networks”, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD), IEEE 10.1109/SNPD.2007.297, 2007
[14] Noel Eisley, Li-ShiuanPeh, and _Li Shang, “ In- Network Cache Coherence”, IEEE Computer Architecture Letters , Vol. 5, IEEE Computer Architecture Letters, 2006
[15] Wei Ye, John Heidemann, Deborah Estrin , “An Energy-Efficient MAC Protocol for Wireless Sensor Networks”, Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, IEEE 10.1109/INFCOM.2002.1019408, 2002
[16] Markus Kowarschik and Christian Wei, “An Overview of Cache Optimization Techniquesand Cache Aware Numerical Algorithms”, Deutsche Forschungsgemeinschaft (German Science Foundation), projects Ru 422/7-1,2,3.
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[20] G. Rivera and C.-W. Tseng, “Data Transformations for Eliminating Conflict Misses” In Proc. of the ACM SIGPLAN Conference on Programming Language Design and Implementation, Montreal, Canada, 1998.
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[22] R. Allen and K. Kennedy, “Optimizing Compilers for Modern Architectures” Morgan Kaufmann Publishers, San Francisco, California, USA, 2001.
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Amulya V, Mohan K G, Ramesh Babu H. S, "A Survey on Performance Optimization of Cache Memory in the Individual Nodes of Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.75-80, 2019.

Social media plays a vital role in online news circulation due to its ease of access, low cost, swift diffusion of information. The news or information can be of any topic, propagated in multiple modalities. Because of the huge amount of information exchange through online or through different social media platforms such as Facebook, twitter, differentiating true news from the low quality news that leads to the problem of fake news, which may affect the individual or the growth of the society, has become a challenging task. This calls for identification and filtering of fake news. In this paper our focus is on exploring the existing methods or approaches on content (i.e. text) based fake news identification, existing standard data sets, evaluation metric(s), tools and future scope, which helps the researchers to turn up with different and efficient approaches to identify fake news.

Fake news, Social networks, Multimedia

[1] Kai Shuy, Amy Slivaz, Suhang Wangy, Jiliang Tang, and Huan Liu “ Fake News Detection on Social Media:A Data Mining Perspective”.
[2] Monther Aldwairi, Ali Alwahedi “Detecting Fake News in Social Media Networks”,2018.
[3] Kelly Stahl “ Fake news detection in social media” 15 May 2018.
[4] Sakeena m. Sirajudeen, nur fatihah a. Azmi, adamu i. Abubakar, “online fake news detection algorithm” 2005.
[5] Jiawei Zhang1, Limeng Cui2, Yanjie Fu3, Fisher B. Gouza “Fake News Detection with Deep Diffusive Network Model”.
[6] Sneha Singhania(B), Nigel Fernandez, and Shrisha Rao “3HAN: A Deep Neural Network for Fake News Detection” 2017.
[7] Samir Bajaj “The Pope Has a New Baby!” Fake News Detection Using Deep Learning, 2017.
[8] Shivam B. Parikh and Pradeep K. Atrey “Media-Rich Fake News Detection: A Survey” 2017.
[9] Kai Shu, Deepak Mahudeswaran, and Huan Liu “ FakeNewsTracker: A Tool for Fake News Collection, Detection, and Visualization”.
[10] Ver´onica P´erez-Rosas1, Bennett Kleinberg2, Alexandra Lefevre1 Rada Mihalcea “Automatic Detection of Fake News”.
[11] Greatmoon hoax. https://en.wikipedia.org/wiki/Great_Moon_Hoax. [Online; accessed 25-September-2017].
[12] S. Adali, T. Liu, and M. Magdon-Ismail. Optimal link bombs are uncoordinated. In AIRWeb, 2005.
[13] L. Akoglu, R. Chandy, and C. Faloutsos. Opinion fraud detection in online reviews by network effects. In ICWSM, 2013.
[14] H. Allcott and M. Gentzkow. Social media and fake news in the 2016 election. Journal of Economic Perspectives, 2017.
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[19] K. Chellapilla and D. Chickering. Improving cloaking detection using search query popularity and monetizability. In AIRWeb, 2006.
[20] E. Convey. Porn sneaks way back on web. The Boston Herald, 1996.
[21] H. Allcott, and M. Gentzkow. “Social media and fake news in the 2016 election”. Journal of Economic Perspectives, Vol. 31, No. 2, 2017, pp. 211–236.
[22] V.S. Foster, Vincent S. "The Great Moon Hoax." In Modern Mysteries of the Moon, Springer International Publishing, 2016, pp. 11-44.
[23] v. pickard, "media failures in the age of trump." the political economy of communication vol. 4, no.2, 2017, pp. 118- 122.g. lee.
[24] Bernard e m jones “Exploring the role of punctuation in parsing natural text” .
[25] http://www.journalism.org/2016/05/26/news-use-acrosssocial- media-platforms-2016/
[26] http://www.bbc.com/news/uk-36528256
[27] https://en.wikipedia.org/wiki/Pizzagate conspiracy theory
[28] https://www.buzzfeed.com/craigsilverman/viralfake-election-news-outperformed-real-news-onfacebook?utm term=.nrg0WA1VP0#.gjJyKapW5y
[29] http://time.com/4783932/inside-russia-social-media-waramerica/
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Divya, N. Mehala, "Fake News Detection: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.81-87, 2019.

In current era Internet of Things(IoT) is one of the trending technology. Automation system made up of IoT service reduces the human effort. This paper provides an overview of IoT three layer and five architecture, fundamental elements of IOT, protocols and about issues to be considered while developing IoT system.

Identity-related service, Collaborative aware service, Ubiquition Service

[1] Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, Moussa Ayyash” Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications” Ieee Communication Surveys & Tutorials, Vol. 17, No. 4, Fourth Quarter 2015.
[2] Pallavi Sethi and Smruti R. Sarangi “Internet of Things: Architectures, Protocols, and Applications”. Hindawi Journal of Electrical and Computer Engineering Volume 2017, Article ID 932403
[3] R. Khan, S. U. Khan, R. Zaheer, and S. Khan, “Future Internet: The Internet of Things architecture, possible applications and key challenges,” in Proc. 10th Int. Conf. FIT, 2012, pp. 257–260.
[4] Muhammad Burhan , Rana Asif Rehman , Bilal Khan and Byung-Seo Kim “IoT Elements,Layered Architectures and security issues: A comprehensive survey” Sensors 2018,18, 2796; doi:10.3390/s18092796
[5] Chayan Sarkar, Akshay Uttama Nambi S. N, R. Venkatesha Prasad, Abdur Rahim, Ricardo Neisse “DIAT: A Scalable Distributed Architecture for IoT”. Ieee Internet Of Things Journal, Vol. 2, No. 3, June 2015
[6] D. Yang, F. Liu, and Y. Liang, “A survey of the Internet of Things,” in Proc. 1st ICEBI, 2010, pp. 358–366.
[7] Farzad Samie , Vasileios Tsoutsouras , Lars Bauer , Sotirios Xydis , Dimitrios Soudris , J¨org Henkel “Computation Offloading and Resource Allocation for Low-power IoT Edge Devices“ 978-1-5090-4130-5/16/$31.00 c_2016 IEEE
[8] Venkateswara Raju Konduru , Manjula R. Bharamagoudra “Challenges and Solutions of Interoperability on IoT How far have we come in resolving the IoT interoperability issues” 2017 International Conference On Smart Technology for Smart Nation
[9] Koustav Routh, Tannistha Pal” A survey on technological, business and societal aspects of Internet of Things by Q3, 2017”, 978-1-5090-6785-5/18/$31.00 © 2018 by IEEE
[10] Er. Pooja Yadav, Er. Ankur Mittal, Dr. Hemant Yadav “IoT: Challenges and Issues in Indian Perspective” 978-1-5090-6785-5/18/$31.00 © 2018 by IEEE.
[11] D. Macedo, L. A. Guedes, and I. Silva, “A dependability evaluation for Internet of Things incorporating redundancy aspects,” in Proc. IEEE 11th ICNSC, 2014, pp. 417–422.
[12] I. Silva, R. Leandro, D. Macedo, and L. A. Guedes, “A dependability evaluation tool for the Internet of Things,” Comput. Electr. Eng., vol. 39, no. 7, pp. 2005–2018, Oct. 2013.
[13] T. Elsaleh, A. Gluhak, and K. Moessner, “Service continuity for
subscribers of the mobile real world Internet,” in Proc. IEEE ICC Workshops, 2011, pp. 1–5.
[14] Mirza Abdur Razzaq, Muhammad Ali Qureshi, Sajid Habib Gill, Saleem Ullah ”Security Issues in the Internet of Things (IoT): A Comprehensive Study”,(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 6, 2017.
[15] Cheena Sharma, Dr. Naveen Kumar Gondhi “Communication Protocol Stack for Constrained IoT Systems” 978-1-5090-6785-5/18/$31.00 © 2018 by IEEE.
[16] Luca Mainetti, Luigi Patrono, Antonio Vilei “Evolution of Wireless Sensor Networks towards the IoT:A Survey”.
[17] Shadi Al-Sarawi, Mohammed Anbar , Kamal Alieyan , Mahmood Alzubaidi ”Internet of Things (IoT) Communication Protocols : Review” 2017 8th International Conference on Information Technology (ICIT)
[18] Surapon Kraijak1 , Panwit Tuwanut ”A Survey On Iot Architectures, Protocols, Applications, Security, Privacy, Real-World Implementation And Future Trends”

Meghana N M, Surekha K B, Basavaraju TG, "A Survey on Architecture, Elements, Protocols and Issues of Internet of Things", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.88-92, 2019.

In the recent years, prostate cancer has become the major cause of deaths in the male population around the world. Numerous computer aided techniques such as, Computer Aided Diagnosis (CAD) systems have been designed in order to detect prostate cancer. The CAD systems majorly consist of four stages namely preprocessing, segmentation, feature extraction and finally the classification stages that are interdependent on one another. The CAD systems perform the analysis based on the various screening techniques such as X-Ray, CT scans, TRUS images, MRI scan, and mp-MRI scans. Though the existing CAD systems are considered feasible, the major research challenge is in improving the accuracy, specificity, speed and usability of the existing CAD systems. This paper presents a survey on the various methodologies used for detecting the prostate carcinoma using various types of screening images.

MRI(Magnetic Resonance Imaging), TRUS (Trans rectal Ultrasound), mp-MRI (Multi parametric-Magnetic ResonanceImaging).

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Swetha.P.C, Mohan G Kabadi, Srivinay, "Pre-processing and Classification of Prostate Images for Cancer Detection: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.93-97, 2019.

Health care industry generates huge volumes of data that comprises of complex information pertaining to patients’ data and their medical history. Data mining methodologies have the abilities to discover hidden patterns and associations among the attributes in the medical dataset. Due to Data Mining’s enormous applications, there has been an upsurge in usage of data mining techniques on medical data for discovering useful trends or knowledge patterns that are used in strategic decision-making process or disease diagnosis and treatment process. This paper focuses on need for mining medical data, issues and challenges surrounding Mining Medical Data.

Data Mining, Health care, Knowledge Discovery (KDD), Decision-making

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[2] E. Mercy Beulah, S. Nirmala Sugirtha Rajini, N. Rajkumar “Application Of Data Mining In Healthcare: A Survey”,2016
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[6] Ahmad, Parvez, Saqib Qamar, And Syed Qasim Afser Rizvi. "Techniques Of Data Mining In Healthcare: A Review." International Journal Of Computer Applications 120.15 (2015)..
[7] Dr. C. Sunil Kumar, Dr. A. Govardhan, B. Sunil Srinivas “Data Mining Issues And Challenges In Healthcare Domian, 2014.
[8] Sujatha, R., R. Sumathy, And A. R. Nithya. "A Survey Of Health Care Prediction Using Data Mining." International Journal Of Innovative Research In Science, Engineering And Technology 5.8 (2016): 14538.
[9] Sujatha, R., R. Sumathy, And A. R. Nithya. "A Survey Of Health Care Prediction Using Data Mining." International Journal Of Innovative Research In Science, Engineering And Technology 5.8 (2016): 14538.

Rama Krishna K, K G Mohan, "Mining Medical Data: A Comprehensive Study", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.98-100, 2019.

The usage of cloud is increasing in day to day life which leads to consumption of energy.Cloud Computing with reduced energy consumption has been an important topic for the era of researchers and different computer users of computing systems. Cloud IT is an egressing technology which provides information about communication technologies, proposing new challenges for environmental protection. Green Cloud computing is a component of Green IT. The amount of carbon will be reduced with low energy consumption. This paper outlines the methods to reduce energy consumption in the cloud. To reduce the power in cloud, huge numbers of evaluations and optimizations have been done for successful energy efficiency. There are different methods to implement the cloud with minimum energy consumption like hardware, software and firmware. Hardware method includes reduction of energy in various components of cloud like server, etc. Software includes virtualization techniques, DVFS techniques, etc. Energy has to be reduced to create Eco-friendly environment.

Cloud,optimization,energy efficiency

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Vasantha kumari N, Arul Murugan, "Review of Energy Minimization Strategies for Eco friendly cloud in IT", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.101-104, 2019.

This paper has investigated some of the encryption and compression techniques. The paper has also examined the implication of these techniques for the future of information security. Indeed, the threat environment has combined with corporate networks and made data security more complex. Therefore, an increasing number of data network users, who have turned to the cloud, have strived to secure their information systems beyond conventional virus scanners and firewalls. With an increasing demand for responsive information security systems, various techniques of data encryption and compression have evolved. Some of the devices, platforms, or information systems that have been targeted by these trends include websites, personal databases, and computers. Some of the specific algorithms that have been employed in data encryption include River-Shamir-Adleman (RSA) algorithm (in asymmetric encryption) and include Rivest Cipher (RC6) and Data Encryption Algorithm (DES) (in symmetric encryption). On the other hand, selected algorithms that have been used towards successful lossless data compression include lossless predictive coding, Arithmetic coding, Lempel-Ziv-Welch (LZW) compression, Huffman coding, and Run Length Encoding. These algorithms have gained application to situations involving platforms such as word processing files, tabular numbers, and executable codes. For lossy data compression, some of the algorithms that have gained increasing application include lossy predictive coding, wavelet coding, and transform coding. The implication for the future is that data encryption and compression techniques that will be responsive to the dynamic nature of the threat environment might prove successful in achieving the intended goals of data encryption and compression processes, hence assuring information system security.

Compression,Encryption,RSAalgorithm,RC6,Huffmancoding

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Gowtham Mamidisetti, Ramesh Makala, Ravi Teja K, "Analysis on Encryption and Compression Techniques for Information Security", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.105-112, 2019.