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Internet of things (IoT), is a ground-breaking technology in this current world technology. It is the internet connectivity of physical sensible devices embedded with integrated actuators, circuit technology, software and radars that assist to gather and interchange data. IoT in smart cities interacts with mobile ad - hoc networks (MANET), is solitary compelling approaches to sort out a system as indicated by the system topological changes. MANET is similarly as wireless sensor networks (WSN). Collaboration between WSN and MANET with IoT, makes even more attractive to the operators and economically successful. MANET with the Io connected IoT allows the establishment of an innovative MANET-IoT system and sensor based networks. In this paper, the presented routing solution for MANET-IoT using ad-hoc network protocols and wireless network principles. The presented results of solution investigates a behavioural study of bird grouping that generates an effective methodology to well- organized energy and enhance the network lifetime in the MANET-IoT system. And it is a step forward to an unfailing facility of services over universal future internet infrastructure.

MANET, Internet of Things, Sensor, Clustering, Routing protocol

[1] Tausifa Jan Saleem, “A Detailed Study of Routing in Internet of Things”, International Journal of Engineering Science and Innovative Technology, Vol. 5, Issue. 3, pp. 116-122, 2016.
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[3] Hsu, Chin-Lung; Lin, Judy Chuan- Chuan, "An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives" Computers in Human Behavior, Vol. 62, Issue. C, pp. 516–527, 2016.
[4] Rasa Bruzgiene, Lina Narbutaite and Tomas Adomkus, “MANET Network in Internet of Things System”, INTECH publisher, UK, pp.89-114, 2017.
[5] C. He. “Throughput and Delay in Wireless Ad Hoc Networks.” Final report of EE359 Class Project publisher, Stanford University, 2006.
[6] I. A. Alameri et al. “MANETS and Internet of Things: The Development of a Data Routing Algorithm” Engineering, Technology and Applied Science Research, Vol. 8, Issue. 1, 2604-2608, 2018.
[7] X. Hong, K. Xu, M. Gerla, “Scalable Routing Protocols for Mobile Ad-Hoc Networks” IEEE Network Magazine, Vol.16, Issue. 4, pp 11- 21, 2002.
[8] A. Shrestha, and F. Tekiner, “Investigation of MANET routing protocols for mobility and scalability” Int. Conference on Parallel and Distributed Computing, Higashi Hiroshima, Applications and Technologies, 2009.
[9] V.N. Talooki, and K. Ziarati “Performance comparison of routing protocols for Mobile Ad- Hoc Networks”, Asia Pacific confference, APCC, pp. 1-5, 2006.
[10] Behra Rajesh Umashankar, Rakhi Kumari Purnima, “A Comparative Study of Topology and Position Based Routing Protocols in Mobile Ad Hoc Networks”, International Journal of Advanced Research in Computer Science & Technology, Vol. 2, Issue. 2, Ver. 1, 2014.
[11] O.Vermesan, P.Friess, “Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, River Publisher, 2013.
[12] G. S. Yovanof, G. N. Hazapis, “An Architectural Framework and Enabling Wireless Technologies for Digital Cities & Intelligent Urban Environments”, Wireless Personal Communications, Vol. 49, Issue. 3, pp. 445-463, 2009.
[13] Santar Pal Singh, S.C.Sarma, “A Survey on cluster based routing protocols in wireless sensor networks”, In: International Conference on Advanced Computing Technologies and Applications (ICACTA), Vol. 45, pp. 687-695, 2015.
[14] Tariq Benmansour, Samira Moussaoui, “GMAC: Group Mobility Adaptive Clustering Scheme for Mobile Wireless Sensor Networks” Conference Paper, DOI: 10.1109/ISPS.2011.5898886, 2011.
[15] A. M. E. Ejmaa, S. Subramaniam, Z. A. Zukarnain, and Z. M. Hanapi, ``Neighbour-based dynamic connectivity factor routing protocol for mobile ad hoc network,`` IEEE Access, Vol. 4, pp. 8053_8064, 2016.
[16] A. Taha, R. Alsaqour, M. Uddin, M. Abdelhaq, and T. Saba, ``Energy efficient multipath routing protocol for mobile ad-hoc network using the fitness function,`` IEEE Access, Vol. 5, pp. 10369_10381, 2017.
[17] J. Y. Yu and P. H. J. Chong, ``A survey of clustering schemes for mobile ad hoc networks,`` IEEE Communication Surveys Tuts, Vol. 7, Issue. 1, pp. 32_48, 2005.
[18] Y. Tao, J.Wang, Y.-L.Wang, and T. Sun, ``An enhanced maximum stability weighted clustering algorithm in ad hoc network,`` in Proc. 4th International Conference on Wireless Communication, Network and Mobile Computing, pp. 1-4, 2008.
[19] Y. Tian, R. Hou. An Improved AOMDV Routing Protocol for Internet of Things. In: International Conference on Computational Intelligence and Software Engineering (CiSE), Wuhan. IEEE, pp. 1-4, 2010.
[20] Farooq Aftab, Zhongshan Zhang, and Adeel Ahmad, “Self-Organization Based Clustering in MANETs Using Zone Based Group Mobility”, IEEE Access, Vol. 5, pp. 27464-27476, 2017.
[21] A. Bentaleb, S. Harous, and A. Boubetra, ``A new topology management scheme for large scale mobile ad hoc networks,``, IEEE International Conference on Electronics and Information Technology, pp. 3137, 2015.
[22] J. Sathiamoorthy and B. Ramakrishnan, ``Energy and delay efficient dynamic cluster formation using hybrid AGA with FACO in EAACK MANETs``, Wireless Networks, Vol. 23, Issue. 2, pp. 371385, 2017.
[23] Li-Minn Ang, Kah Phooi, Adamu Murtala Zungeru and Gerald Ijemaru, “Big Sensor Data Systems for Smart Cities”, IEEE Access, pp. 2327 – 4662, 2017.
[24] X.Wang, editor. “Mobile ad-hoc Networks: Protocol design”, INTECH publisher, Rijeka, Croatia, pp 666, 2011.

A. Tamilselvi, E. Ramaraj, "Bio-Enlivened Behavioral Investigation of MANETs in Smart Cities," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.123-129, 2018.

Disease detection frameworks having blend of distinct fields of concerned research are the need of hour as the domain is very challenging and essential for the human race. Its expanding every day and its impact is much bigger than the earlier era. Quality of service(QoS) is an important consideration for designing sophisticated futuristic healthcare systems. One such quality parameter of reliability is consolidated in this disease detection framework incorporating information-centric networking of medical knowledge-base and protein/gene knowledge-base. The proposed framework is designed and implemented keeping in view of research progress of two highly interrelated research areas which are often not used together but can be combined to formulate an efficient disease detection system. Machine learning and deep learning is also incorporated to improve the quality of service parameter in terms of accuracy and reliability of the system. Various ML configurations formed a test bed for the accuracy check at different system settings indicating a perfect mix for a specific detection requirement. Results indicate promising detection rate of 71% combined accuracy and a remarkable 97% accuracy rate for specific single class detection along with the improvement in reliability factor.

ICN, Disease detection, Drug discovery, Machine Learning, Quality of Service

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[9] Lawal Mohammed Ma’aruf, Mariya Garba "Design And Implementation Of An Expert Diet Prescription System," International Journal of Artificial intelligence and Expert System (IJAE), vol. 3, no. 4, pp. 126-134, 2012.
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[13] Yin Zhangy; Long Wangy; Long Huy;Xiaofei Wang,”COMER: ClOud-based MEdicine Recommendation." QSHINE ICST August 18-20 (2014): 1-7.
[14] Burasakorn Yoosooka, Suvi Chomchaiyal, "Medication Recommender System", International Conference on Science and Technology 2015, RMUTT, IEEE, 2015.
[15] Sarath Chandra Janga, Mohan Mallikarjuna Rao Edupuganti, "Systems and Network-Based Approaches for Personalized Medicine" Current Synthetic and Systems Biology 2.3 (2014): 1-5.
[16] Amanpreet Kaur, Sawtantar Singh Khurmi, "A Study Of Cloud Computing Based On Virtualization And Security Threats", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.108-112, 2017
[17] P. Jyotsna, P. Govindarajulu P, “Stage Prediction of Lung Tumor Identification: A Decision Tree Model for Particle Swarm Optimization Algorithm”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1363-1372, 2018.

A. Singh, S. Sharma, R. Singh, G. Singh, A. Kaur, "Quality of Service Enhanced Framework for Disease Detection and Drug Discovery," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.130-136, 2018.

With the entry of huge databases and the resulting prerequisites for excellent machine learning frameworks, new issues emerge and novel feature extraction methods are in demand.Basic feature reduction methods are feature selection and feature extraction. Feature selection find the subset of those prime features in a given initial set and helps in finding optimal solution. Feature extraction method transform original set of features into new subsets which are smaller number of dimensions. Generally features contain information about the target and more features indicate more information and better discrimination power. In this paper we have proposed a novel feature extraction method for feature extraction of maize and paddy dataset. Global thresholding Otsu method is used for segmentation and Error Correcting Output Codes (ECOC) classifier is used for identification of healthy and diseased maize and paddy leaves and found a success rate of 91.32% for paddy leaves and 92.56% for maize leaves. In this experimentation the similarity difference of Gray with Cb Component has given highest accuracy for both data sets.

Disease, ECOC Classifier, Maize, Paddy, Texture Features

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[9] M. Crosier, L D. Griffin, “ Texture classification with a dictionary of basic image features”, In Computer Vision and Pattern Recognition, IEEE Conference. pp. 1-7. 2008.
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[13] L D. Vignolo, D H. Milone & J. Scharcanski, “ Feature selection for face recognition based on multi-objective evolutionary wrappers”, Expert Systems with Applications, Vol.40,Issue.13,pp. 5077-5084, 2013.
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[15] B. Lee, “ A new method for classification of structural textures”, International Journal of Control, Automation, and Systems,Vol. 2,Issue.1,pp. 125-133, 2004.
[16] A. Halimi, A. Roukhe & Ouhamd, “ Defect Detection and Identification in Textile Fabric by SVM Method”, IOSR Journal of Engineering (IOSRJEN) ,Vol. 04, PP.69-77, 2014.
[17] Shreekanth K N , Suresha M , " Identification of Healthy and Diseased Paddy Leaves using Texture Features with ECOC Classifier" , IPASJ INTERNATIONAL JOURNAL OF COMPUTER SCIENCE(IIJCS) , Vol. 6, Issue.2, pp. 034-038, 2018.

T. Harisha Naik, M. Suresha, Shreekanth K. N., "A Novel Feature Extraction Method for Identification of Healthy and Diseased Maize and Paddy Leaves Using ECOC Classifier," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.137-141, 2018.

Social networks are generally represented as graphs (nodes represent users and edges represent their associations). Community in a social network means group of people which are more closely connected to each other as compared to their connection with rest of the people in the network. Clique in a graph is a subgraph such that each node in the subgraph is connected to every other node of this subgraph (complete subgraph). In this way clique is strict version of community. Community detection in social networks has attracted researchers effectively due to its wide range of applications. Cliques, having similar characteristics, prove to be highly applicable in community detection process. There are several community detection techniques in the literature which are developed around cliques. Generally these techniques fall into category of clique percolation methods. Clique percolation is a prominent approach that is based on k-cliques in the graph. This paper represents a detailed discussion of significant k-clique based techniques existing in community detection literature.

Social Graph; Clique, Community Detection, Social Network Analysis

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[13]Tanja Falkowski, Anja Barth and Myra Spiliopoulou, “Studying Community Dynamics with an Incremental graph mining algorithm.”,In Proceedings of the 14th Americas Conference on Information Systems(AMCIS), 2008.
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[15] Jussi M. Kumpula, Mikko Kivela, Kimmo Kaski, and Jari Saramaki, A sequential algorithm for fast clique percolation, arXiv:0805.1449v2 [physics.soc-ph], July 2008.
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[19] Chen Jing ,FU Cai, Zhang Kang, Fang Zhicun,and Han Lansheng, “K-clique Community Detection based on Union-Find.”, IEEE 978-1-4799-4383-8/14 , 2014.
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[21] Shuai Wang, Chao Li, and Daquan Tang, “Service Identifying Based Weapon Systems Architecture Optimization with K-Clique Community Detection Algorithm”, In IEEE 3rd International Conference on Control Science and Systems Engineering , IEEE 978-1-5386-0484-7, pp. 656 – 660 ,2017.
[22] Noha Alduaiji , Amitava Datta , and Jianxin Li, “Influence Propagation Model for Clique-Based Community Detection in Social Networks”, In IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , IEEE 2329-924X , pp. 1-13, 2018.

A. Srivastava, A. Pillai, D. J. Gupta, "Study of Clique Based Community Detection Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.142-149, 2018.

Electronic health records(EHR’s) allows the patient to create his own health information in a hospital and share the information with other doctors in other hospitals.EHR provides security to the personal information of users. For providing privacy and security to EHR’s we use SE(Searchable Encryption) which is a cryptographic technique that allows the user to search for a specific information in encrypted content. In this paper we have used conjunctive keyword search with designated tester and timer enabled proxy re-encryption function which is a time dependent SE scheme. Public key encryption with keyword search (PECK) allows the user to search on encrypted data without decrypting it. Sometimes the patient may want to provide access rights to others , it may be his doctor without revealing his private key. This can be accomplished using proxy re-encryption(PRE). Patients can provide partial access rights to others in a limited time period to perform search operations. The amount of time the third party can search and decrypt the encrypted documents can be controlled. The access rights can be revoked back when the time period expires. This prevents re-encryption of the entire document and generation of keys. A time server is used in the system to generate time tokens for users.

Cloud, Health care, Keyword, Proxy, Delegates

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[6]C. Hu and P. Liu, “An enhanced searchable public key encryptionscheme with a designated tester and its extensions,” J. Comput., vol. 7, no. 3, pp. 716–723, 2012.

Sushma S A., Shubha C, Asha K, "Preserving and Retrieving Health Records using Conjunctive Keyword Search with Designated Tester and Timing Enabled Proxy Re-Encryption in Cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.150-153, 2018.

Software composition is becoming more and more vital as innovation in software engineering shifts from the development of individual components to their reuse and recombination in innovative ways. It is a key topic in computer science and particularly programming language analysis. Software composition is the process of constructing software systems from a set of software components. It aims at improving the reusability, customizability, and maintainability of large software systems. The primary motivation for software composition is reuse. Generally, the composition can be defined as any promising and expressive interaction between the complex software concept and a composition mechanism defines such an interface. The more recently proposed programming approach known as Aspect-Oriented Programming illustrate the concept of modularization i.e. managing software complexity and improving its reusability, understandability, extensibility. It provides an alternative mechanism to solve the code tangling and scattering problems in the implementation of crosscutting concerns using abstraction and composition mechanisms. This work considers different views of software composition and various existing definitions of composition units with the corresponding composition mechanisms. Also, deliberated how software composition is more efficiently reusable in aspect-oriented programming and mentioned the fundamental facts of software composition implementation based on Aspect-Oriented programming paradigm.

Software composition, composition mechanisms, object-oriented programming, aspect-oriented programming, extension,paradigm.

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[47] Jatin Arora, Jagandeep Singh Sidhu and Pavneet Kaur, "Applying Dependency Injection Through AOP Programming to Analyze the Performance of OS", International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.45-50, 2015.
[48] Biswajit Saha, Debaprasad Mukherjee, "Analysis of Applications of Object Orientation to Software Engineering, Data Warehousing and Teaching Methodologies", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.244-248, 2017.
[49] Maushumi Lahon and Uzzal Sharma, "The Intricacies of Software Component Composition", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.111-117, 2015.

P.R. Sarode, R. N. Jugele, "Role of Software Composition in Aspect Oriented Programming," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.154-159, 2018.

Road accidents due to traffic are ever more being acknowledged as major problem for transportation agencies as well as general people. A substantial unexpected result of transportation systems is road accidents with injuries and loss of lives. In this scenario purpose safe driving, specific study of road traffic data is severe to find out elements that are connected to mortal accidents. In this research paper, we determine factors behind road traffic accidents issues solving by data mining algorithms together with data mining algorithms like Density-based spatial clustering of applications with noise and Parallel Frequent mining. We primarily separate the accident locations into k clusters depend on their accident frequency with Density-based spatial clustering of applications with noise algorithm. Next, parallel frequent mining algorithm is apply on these clusters to disclose the association between dissimilar attributes in the traffic accident data for realize the features of these places and analyzing in advance them to spot different factors that affect the road accidents in different locations. The main objective of accident data is to recognize the key issues in the area of road safety. The efficiency of prevention accidents based on consistency of the composed and predictable road accident data using with appropriate methods. Road accident dataset is used and implementation is carried by using Weka tool. The outcomes expose that the combination of Density-based spatial clustering of applications with noise and parallel frequent mining explores the accidents data with patterns and expect future attitude and efficient accord to be taken to decrease accidents.

Accident analysis, Density-based spatial clustering of applications with noise, Road accident dataset, parallel frequent mining, Weka

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Shaik Subhani, "DM Algorithms Based Clustering for Road Accident Data Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.160-167, 2018.

Analytic hierarchy process (AHP) is one of the important multi-criteria decision making algorithms which is used to rank the software requirements on the basis of different criteria like performance, usability, reliability, cost, etc. In the area of software engineering, different methods have been developed to rank the software requirements using AHP like PRGFOREP, GOASREP, etc. Based on our literature review we identify that in software requirements selection (SRS) less attention is given to check the consistency of the “pairwise comparison matrices” (PCM). The ranking values of the software requirements would be consistent only when the PCM would be consistent. Therefore, to address this issue we proposed a method for SRS by generating the different patterns and sub-patterns of the PCM. In our case study, we have generated the 8 patterns and for each pattern we have generated the 64 sub-patterns. As a result, we have generated 512 sub-patterns of PCM and stored the results into a database so that the information stored in the database could be used for requirements analysis. The applicability of the proposed method is explained with the help of a case study.

Software requirements selection, AHP, Pairwise comparison matrices, Types of requirements

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[7]. N. Garg, M. Sadiq, and P. Agarwal, “GOASREP: Goal Oriented Approach for Software Requirements Elicitation and Prioritization using Analytic Hierarchy Process”, International Conference on Frontiers in Intelligent Computing Theory and Applications, AISC, Springer, 2016.
[8]. M. Sadiq, A. Afrin, “Extending AHP-GORE-PSR by Generating Different Patterns of Pairwise Comparison Matrix”, International Conference on Information, Communication and Computing Technology, CCIS, Springer-Verlag, Singapore, 2017.
[9]. S. Khan, C. W. Mohammad, and M. Sadiq, “Generating Patterns and Sub-Patterns of Pairwise Comparison Matrices for the Selection of Software Requirements”, IEEE International Conference on Advances in Computing, Communication, Control, and Networking, 2018.
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[13]. M. Sadiq, J. Ahmad, M. Asim, A. Qureshi , and S. Rani, “ More on Elicitation of Software Requirements and Prioritization using AHP”, In the Proceedings of the IEEE International Conference on Data Storage and Data Engineering pp. 232-236, 2010.
[14]. M. A. Khan, A. Parveen, and M. Sadiq, “A Method for the Selection of Software Development Life Cycle Models Using Analytic Hierarchy Process”, In the Proceedings of the IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques, pp. 539-545, 2014.
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M. Sadiq, S. Khan, C. W. Mohammad, "Software Requirements Selection Using Consistent Pairwise Comparison Matrices of AHP," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.168-175, 2018.

The proposed idea provides information that obscures the idea with the combination of cryptography and steganography. Proposed ideas are important to support verification, confidentiality and fairness. To achieve these effects, the main idea is to use a symmetric cryptography system for privacy and validation security, mainly an incomplete reliability key supported by the steganography method. The proposed idea is primarily dependent on the security with the coding of the emission data is covered in the first phase and the data in the next phase encrypted discharge, so that it ensures a double security for a discharge date. Exhibited steganography idea, image or content as the information first (as a picture) coded and packaged by correlation with minimal image collective size after compressed data were shaken by symmetric cryptographic process using a private key of 128 bits of coded data, this private key splits via a private channel between the sender and the payee, and finally inserts encrypted data into the bitplanes of the title image using the smallest noteworthy piece (LSB) of the standard stereographic procedure. To achieve a high security strategy, the proposed steganography strategy has used random numbering (RAND) which selects an irregular LSB from the envelope. The results shown demonstrate the implementation and feasibility of the proposed proposal for inclusion of the peek flag on sedimentation rate (PSNR), compound and entropy.

Decryption, Encryption, Internet, Steganography, Security etc

[1] Nisha Kundu, Dr. Amadeep Kaur “Secure Approach to Audio Steganography”International Journal of Engineering Trends and Technology (IJETT) , 2017
[2] Sujarani Rajendran, Manivannan Doraipandian “Chaotic Map Based Random Image Steganography Using LSB Technique”, International Journal of Network Security, 2017
[3] Reza tavoli, Maryam bakhshi, Fatemeh salehian, "A New Method for Text Hiding in the Image by Using LSB" (IJACSA) International Journal of Advanced Computer Science and Applications, 2016
[4] Rajalakshmi, Sowjanya.TP2, Hemanthkumar, “Image Steganography using H-LSB Technique for Hiding Image and Text Using Dual encryption method” SSRG International Journal of Electronics and Communication Engineering , 2015
[5] G Prabakaran, R. Bhavani, P.S. Rajeswari, “Multi secure and robustness for medical image based steganography scheme” International Conference on Circuits, Power and Computing Technologies (ICCPCT), 2013
[6] M.K Ramaiya. ; N.Hemrajani, ; , A.K Saxena. “Security improvisation in image steganography using DES” IEEE 3rd International on Advance Computing Conference (IACC), 2013
[7] N. Akhtar, ; P. Johri, ; S Khan, “Enhancing the Security and Quality of LSB Based Image Steganography” 5th International Conference on Computational Intelligence and Communication Networks (CICN), 2013
[8] Amitava Nag, Saswati Ghosh, Sushanta Biswas, Debasree Sarkar, Partha Pratim Sarkar “An Image Steganography Technique using X-Box Mapping” IEEE-International Conference On Advances In Engineering, Science And Management , 2012
[9] RigDas and Themrichon Tuithung ”A Novel Steganography Method for Image Based on Huffman Encoding” IEEE, 2012
[10] Rengarajan Amirtharajan Anushiadevi .R2, Meena .y2, Kalpana. y2 and John Bosco Balaguru “Seeable Visual But Not Sure of It” IEEE-International Conference On Advances In Engineering, Science And Management, 2012
[11] G.Karthigai Seivi, Leon Mariadhasan, K. L. Shunmuganathan “Steganography Using Edge Adaptive Image” IEEE International Conference on Computing, Electronics and Electrical Technologies [ICCEET], 2012
[12] L.Jani Anbarasi and S.Kannan “Secured Secret Color Image Sharing With Steganography” IEEE, 2012
[13] Thomas Leontin Philjon. and Venkateshvara Rao. “Metamorphic Cryptography - A Paradox between Cryptography and Steganography Using Dynamic Encryption” IEEE-International Conference on Recent Trends in Information Technology, ICRTIT , 2011
[14] Ashwak M. AL-Abiachi, Faudziah Ahmad and Ku Ruhana “A Competitive Study of Cryptography Techniques over Block Cipher” UKSim 13th IEEE International Conference on Modelling and Simulation, 2011
[15] Abhishek Gupta, Sandeep Mahapatra and, Karanveer Singh “ Data Hiding in Color Image Using Cryptography with Help of ASK Algorithm”, 2011
[16] Rosziati Ibrahim and Teoh Suk Kuan “Steganography Algorithm to Hide Secret Message inside an Image” Computer Technology and Application, (2011)
[17] Mohit Kulkarni, Maitreyee Phatak, Uma Rathod, Sudhir Prajapati, Mrs. Shivganga Mujgond “Efficient Data Hiding Scheme Using Audio Steganography”, International Research Journal of Engineering and Technology (IRJET), 2016
[18] Manoj Kumar, Naveen Hemrajani and Anil Kishore Saxena “Security Improvisation in Image Steganography using DES” IEEE, 2012
[19] Sesha Pallavi Indrakanti , P.S.Avadhani, Permutation based Image Encryption Technique, International Journal of Computer Applications (0975 – 8887), 2011
[20] Qais H. Alsafasfeh , Aouda A. Arfoa, Image Encryption Based on the General Approach for Multiple Chaotic Systems Journal of Signal and Information Processing, 2011
[21] M.J.Thenmozhi, Dr.T.Menakadevi “A New Secure Image Steganography Using Lsb And Spiht Based Compression Method”, International Journal of Engineering Research & Science (IJOER), 2016
[22] Amnesh Goel, Reji Mathews, Nidhi Chandra “Image Encryption based on Inter Pixel Displacement of RGB Values inside Custom Slices” International Journal of Computer Applications (0975 – 8887) , 2011
[22] Amitava Nag, Jyoti Prakash Singh, Srabani Khan, Saswati Ghosh, Sushanta Biswas, D. Sarkar Partha Pratim Sarkar, Image Encryption Using Affine Transform and XOR Operation ,International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011
[23] Seyed Hossein Kamali, Reza Shakerian, Maysam Hedayati, Mohsen Rahmani “A New Modified Version of Advanced Encryption Standard Based Algorithm for Image Encryption ”2010 IEEE International Conference on Electronics and Information Engineering ICEIE, 2010
[24] ZHANG Yun-peng, ZHAI Zheng-jun, LIU Wei, NIE Xuan, CAO Shui-ping, DAI Wei-di “Digital Image Encryption Algorithm Based on Chaos and Improved DES” ”Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA, 2009
[25] Obaida Mohammad Awad Al-Hazaimeh “Hiding Data in Images Using New Random Technique” IJCSI International Journal of Computer Science Issues, 2012
[26] Nada ElyaTawfiq “Hiding Text within Image Using LSB Replacement” IOSR Journal of Computer Engineering (IOSR-JCE), 2013
[27] Hyder Yahya Atown “Hide and Encryption Fingerprint Image by using LSB and Transposition Pixel by Spiral Method” International Journal of Computer Science and Mobile Computing, 2014
[28] Rajalakshmi, Sowjanya.TP, “Image Steganography using H-LSB Technique for Hiding Image and Text Using Dual encryption method” SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE), 2015
[29] Arun Karthick, Kavitha, Sivakumar, Surender, “A Hybrid Method For Covert Communication Using Steganography And Image Fusion”, International Journal of Advances in Engineering & Technology, May, 2014.
[30] Manpreet Kaur, Vinod Kumar Sharma “Encryption based LSB Steganography Technique for Digital Images and Text Data”, IJCSNS International Journal of Computer Science and Network Security, 2016

R. Sharma, A. Dwivedi, V. Namdeo, "An Approach of LSB- Symmetric Cryptography to Secure Classified Text Content," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.176-182, 2018.

As a signal of more and more distinguished on-line networking, cyberbullying has developed as a big issue harassing kids, adolescents and vernal grown-ups. Machine learning procedures build programmed recognition of harassing messages in web-based social networking doable, and this might build a solid and safe web-based social networking condition. During this important analysis zone, one basic issue is powerful and discriminative numerical portrayal learning of instant messages. During this paper, we tend to propose another portrayal learning strategy to handle this issue. Our technique named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is created by means that of linguistics enlargement of the notable profound learning model stacked denoising autoencoder. The linguistics enlargement includes of linguistics dropout commotion and meagerness limitations, wherever the linguistics dropout clamor is planned in sight of area learning and therefore the word inserting system. Our planned strategy will misuse the hid part structure of tormenting knowledge and soak up a full of life and discriminative portrayal of content.

NLP, cyberbullying, Social Network, Mining, collaboration

[1] A. Agarwal, B. Xie, I. Vovsha, O. Rambow and R. Passonneau, "Sentiment Analysis of Twitter Data," In Proceedings of workshop on languages of social media, 2011. pp. 30-38.
[2] A. Agarwal, F. Biadsy, and K. R. Mckeown. "Contextual phrase-level polarity analysis using lexical affect scoring and syntactic n-grams." Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2009. pp. 24-32.
[3] A. Go, R. Bhayani, L. Huang, “Twitter sentiment classification using distant supervision”, CS224N Project Report, Stanford, 2009. pp. 1-12.
[4] A. Harb, M. Plantié, G. Dray, M. Roche, F. Trousset, and P. Poncelet. "Web Opinion Mining: How to extract opinions from blogs?" In Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology, ACM, 2008. pp. 211-217.
[5] A. Pak, and P. Paroubek “Twitter as a corpus for sentiment analysis and opinion mining,” Proceedings of the seventh International Conference on Language Resource and Evolution (LREC’10), 2010. pp. 19-21.
[6] B. O`Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. "From tweets to polls: Linking text sentiment to public opinion time series." ICWSM 11, no. 122-129 ,2010. pp. 1-2.
[7] B. Pang, L. Lee, and S. Vaithyanathan. "Thumbs up? : sentiment classification using machine learning techniques."Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002. pp. 79-86.
[8] C. Whitelaw, N. Garg, and S. Argamon "Using appraisal groups for sentiment analysis," presented at the Proceedings of the 14thACM international conference on Information and knowledge management, Bremen, Germany, 2005.
[9] D. M. Haughton, J. J. Xu, D. J. Yates, X. Yan “Introduction to Data Analytics and Data Mining for Social Media Minitrack”, 49th Hawaii International Conference on System Sciences, 2016.p. 1414.
[10] D. M. Law, J. D. Shapka, and B. F. Olson. "To control or not to control? Parenting behaviours and adolescent online aggression." Computers in Human Behavior 26.6,2010 pp.1651-1656.
[11] D. Quinn, C. Liming, and M. Maurice "Does age make a difference in the behaviour of online social network users?"Internet of Things (iThings/CPSCom).

Ruksar Fatima, Umme Khadija, "Cyber Bullying Detection on Social Media based on Denoising Auto-Encoder," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.183-187, 2018.