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Breast cancer is the most prevalent cancer among women around the world. However, increased survival is due to the dramatic advances in the screening methods, early diagnosis, and breakthroughs in treatments. Different strategies of breast cancer classification and staging have evolved over the years. Intrinsic (molecular) sub composing is fundamental in clinical preliminaries and well comprehension of the sickness of the disease. To analyze machine learning systems have been utilized to define a set trained with the “bagging” method. Support vector machines (SVM) have been appeared to outflank numerous related methods. However, there have been very few studies focused on examining the classification performances of different classification. The trial comes about demonstrate that SVM classifier can be the better decision for classification, where accuracy of the algorithm is improved by tuning the parameters of the dataset.

Machine Learning, Support Vector Machine(SVM)

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Vikas S, Thimmaraju S N, "“Breast Cancer Diagnosis and Classification Using Support vector machines With Diverse Datasets”," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.442-446, 2019.

This inventory management system keeps all records and transaction details for a particular clinic. User can handle this project by using a single screen through using its different menus and submenus. A clinic inventory has to deal with various fields and sections along with different modules. So, to make it simple so that user can handle this project in a simple way. When the patient will visit on clinic, they will ask to provide their name, address, sex, age and type or problems. As this system will have built in feature that will assign the doctor by taking their type of problem as their input. After entering these fields, user will provide with a registration slip including their name, address, sex, age, name of doctor and chamber number. Each registration form will have unique registration number, arrival time of patient. The doctor will prescribe medicines using patient registration number. For taking medicine user have to give their registration number to the clinic medical shop, where they will get all the medicines.

Clinic Management, Medicine

1. Pro C# 5.0 and .Net 4.5 frame work (By: Andrew Troelsen)
2. C# 6.0 Cookbook (By: Jay Hilyard, Stephen Teihet)
3. Adaptive Code via C#: Agile coding with design patterns and SOLID principles (Developer Reference) (By: Gary McLean Hall)
4. C# Unleased (By: Bart De Smet)
Sites:
http://stackoverflow.com/
https://msdn.microsoft.com/en-us/library/
http://codereview.stackexchange.com/
http://www.c-sharpcorner.com/
http://csharp.net-informations.com/

Pruthviraj M A , "Clinic Management Software," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.447-450, 2019.

Cryptographic algorithms attempt to generate ciphertexts which are as seemingly unrelated to the plain text as possible. We attempt to add a further improvement to existing bit level encryption by incorporating chaos through Game of Life. Chaos is introduced in our algorithm to make the enciphering process even more random. This is made possible by using a reversible encryption process using XOR operation in the encryption algorithm. The algorithm is very bit sensitive i.e. any change in plain text bits will produce a completely different result: cipher text using the same key. The main focus of our algorithm was to introduce randomness in encryption which has been achieved by generating a random matrix consisting of dead and alive cells. The said matrix is used for encryption. The matrix generated is seemingly random depending upon the key given. Thus, achieving the desired target of making the encryption and decryption algorithm even more complex.

Bit level encryption; differential attack; brute force attack; leftshift; rightshift; chaos using game of life matrix; DNAsequence

[1]. Asoke Nath, Soumyadip Ray, Salil Anthony Dhara, Sourav Hazra
“3-DIMENSIONAL BIT LEVEL ENCRYPTION ALGORITHM VERSION-3 (3DBLEA-3)” International Journal of Latest Trends in Engineering and Technology Vol.(10)Issue(2), pp.347-353 May 2018
[2]. Behrouz A. Forouzan, “Cryptography and Network Security”, Special Indian edition 2007, Tata Mc-Graw Hill publishing company limited
[3]. Asoke Nath, Ayan Ghosh, Enakshi Ghosh and Jayisha Saha, ”3-Dimensional Bit Level Encryption Algorithm Ver-2(3DBLEA-2)”, , International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), Vol. 4, page: : 8611-8618 Issue 5, MAY 2017.
[4]. Asoke Nath, Saima Ghosh, MeheboobAlam Mallik, “Symmetric Key Cryptography using Random Key generator” Proceedings of International conference on security and management (SAM-10) held at Las Vegas, USA, July 12-15, 2010, Vol-2, Page: 239-244(2010).

Asoke Nath, Suchandra Datta, Souptik Kumar Majumdar, "Bit Level Encryption Algorithm Using Chaos Version 1.0(BLEAUC-1.0)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.451-456, 2019.

Few decades ago, approach to character recognition was limited to desktop scanner. The usability of such system was limited as they were non portable because of large size. With the advent of technology and portable computing devices such as mobile phone, PDA, iPhone etc. new trends of research has emerged, where Mobile phones are the most commonly used electronic device, eliminating the need for bulky devices like scanners, desktops and laptops. The convergence of powerful processors and high resolution cameras on mobile devices has directed the focus of research to development of mobile applications, where image processing applications such as OCR’s are in demand. This paper present State-of-the-Art survey of Character Recognition systems for mobile devices and summarize some commercially available OCR applications.

Mobile OCR, Document Image Processing, Text Recognition

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[7] R. Smith, “An overview of the Tesseract OCR Engine” IEEE 9th International Conference on Document Analysis and Recognition(ICDAR 2007), Curitiba, Brazil, pp. 629-633, September 2007.
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[11] S. Badla, ”Improving the efficiency of Tesseract OCR Engine”, Master of Science – Thesis report, San José State University, 2014.
[12] A. Chowdhury, A. Foysal and S. Islam, ”Bangla Character Recognition for Android Devices” International Journal of Computer Applications, vol. 136, issue 11, pp. 13-19, February 2016.
[13] Loh Zhi Chang, Zhou Zhi Ying and Steven, ”Robust Pre-processing Techniques for OCR Applications on Mobile Devices”, ACM Proceedings of 6th International Conference on Mobile Technology, Application & Systems, article no. 60, 4 pages, 2009.
[14] A. F. Mollah, N. Majumder, S. Basu and M. Nasipuri, ”Design of an Optical Character Recognition System for Camera based Handheld Devices”, International Journal of Computer Science, vol. 8, issue 4, pp. 283-289, July 2011.
[15]Tuan Nguyen, Don Nguyen, Phu Nguyen,” UIT-ANPR: toward an open framework for automatic number plate recognition on smartphones ”, ACM Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, January 2014.
[16] M. B Gosavi, I. V Pund, H. V Jadhav and S. R Gedam, ” Mobile Application with Optical Character Recognition Using Neural Network”, International Journal of Computer Science and Mobile Computing, vol. 4, issue 1, pp. 483-489, January 2015.
[17] M. V. Chandrashekhar, M. S Kumar, M. B. N. Taj and K. Asha, ”Optical Character Recognition on the Android Operating System for Kannada Characters using Kohonen Neural Network”, International Journal of Advanced Technology in Engineering and Science, vol. 3, special issue 1, pp. 247-251, May 2015.
[18] M. Cutter and R. Manduchi, “Improving the Accessibility of Mobile OCR Apps Via Interactive Modalities”, ACM Transactions on Accessible Computing, Vol. 10, No. 4, Article 11, 27 pages, August 2017.

Ravneet Kaur, Dharam Veer Sharma, "Mobile Based OCR Systems: State-of-the-art Survey for Indian Scripts," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.457-461, 2019.

Significant growth of e-commerce has led to huge number of reviews for a product or service. It provides different aspects of service or a product for the users. Sentiment analysis techniques are used to extract feature and opinion in a concise summary form from the customer reviews. Feature based summarization system uses term frequency and feature opinion learner to generate the summary. Fitness value based binary particle swarm optimization for feature selection is proposed to select the best feature subset. The feature selection in BPSO uses fitness value based on the term frequency and opinion score. In BPSO efficient summary is generated using the multi-objective function based on feature weight score and similarity between term frequency and position. The Recall-Oriented Understanding for Gisting Evaluation (ROUGE) toolkit is used to measure the performance of the Multi objective fitness based BPSO. An experimental result proves that multi-objective FBPSO algorithm improves the feature selection and summary generation accuracy.

Feature selection, Multi-objective, Fitness, Binary Particle Swarm Optimization, Summarization

[1] ArtiBuche, Dr.M.BChandak, Akshay Zadgaonkar, “Opinion Mining and Analysis: A Survey”, Proceedings of the International Journal on Natural Language Computing, Volume 2, No. 3, pp. 39-48, 2013.
[2] Gamgarn Somprasertsri, Pattarachai Lalitrojwong, “Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization”, Journal of Universal Computer Science, Vol.16, No.6, pp. 938-955, 2010.
[3] Dim En Nyaung, Thin Lai Lai Thein, “Feature-Based Summarizing and Ranking from Customer Reviews”, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol: 9, No: 3, 2015.
[4] Li-Ping Jing, Hou-Kuan Huang, Hong-Bo, “Improved Feature Selection Approach TFIDF in Text Mining”, Proceedings of the First International Conference on Machine Learning and Cybernetics, Vol. 2, 2002.
[5] J. Wiebe, E. Riloff, “Creating Subjective and Objective Sentence Classifiers from Unannotated Texts”, In Proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing-10), Vol: 3406, pp. 486-497, 2010.
[6] Florian Wogenstein, J. Drescher, D. Reinel, S. Rill, J. Scheidt, “Evaluation of an Algorithm for Aspect-Based Opinion Mining Using a Lexicon-Based Approach”, WISDOM ’13, Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, Article No. 5, 2013.
[7] Bing Xue, Mengjie Zhang and Will N. Browne, ‘Single Feature Ranking and Binary Particle Swarm Optimisation Based Feature Subset Ranking for Feature Selection’, Proceedings of the Thirty-Fifth Australasian Computer Science Conference, Melbourne, Australia, Vol.122, ACS, pp. 27-36, 2012.
[8] Zhou Z, Liu X, Li P, Shang L, “Feature selection method with Proportionate Fitness based Binary Particle Swarm Optimization”, In: Simulated evolution and learning, pp. 582–592. Springer, New York, 2014.
[9] Ahmed M. Al-Zahrani, Hassan Mathkour, Hassan Abdalla, “PSO-Based Feature Selection for Arabic Text Summarization”, Journal of Universal Computer Science, Vol. 21, No.11, pp. 1454-1469, 2015.
[10] Rasim M. Alguliev, Ramiz M. Aliguliyev, Nijat R. Isazade, “MR&MR-SUM: Maximum Relevance and Minimum Redundancy Document Summarization Model”, International Journal of Information Technology and Decision Making, Vol.12, No.3, pp. 361-393, 2013.
[11] Mohammed Salem Binwahlan , Naomie Salim2 , Ladda Suanmali, “Swarm Based Features Selection for Text Summarization”, International Journal of Computer Science and Network Security, Vol.9, No.1, 2009.
[12] Houda Oufaida, Omar Nouali, Philippe Blache, "Minimum Redundancy and Maximum Relevance for Single and Multi-document Arabic Text Summarization”, Journal of King Saud University – Computer and Information Sciences, Volume 26, Issue 4, pp. 450–461, 2014.
[13] B.Suganya, V.Priya, “Particle Swarm Optimization Based Feature Selection and Summarization of Customer Reviews”, International Conference on Emerging trends in Engineering, Science and Sustainable Technology, pp. 131-135, 2017.
[14] Lin Shang, Zhe Zhou, Xing Liu, “Particle Swarm Optimization-based Feature Selection in Sentiment Classification”, Journal of Soft Computing – A Fusion of Foundations, Methodologies and Applications, Vol.20, Issue.10, pp. 3821-3834, 2016.
[15] Bing Xue, Mengjie Zhang and Will N.Browne, “Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach”, IEEE Transactions on Cybernetics, 43(6), pp. 1656-71, 2012.
[16] Josef Steinberger, Karel Jezek, “Evaluation Measures For Text Summarization”, Computing and Informatics, Vol. 28, pp. 1001–1026, 2009.

B. Suganya, S.C. Lavanya , T. Gowrisankari, "Feature Selection and Summarization of Customer reviews using Fitness based BPSO," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.462-467, 2019.

The data compromise could occur thanks to attacks by different users and nodes inside the cloud. Therefore, high security measures square measure needed to safeguard knowledge inside the cloud. However, the utilized security strategy should additionally take into consideration the improvement of the information retrieval time. In our proposed system propose the Division and Replication of Data in the Cloud for Optimal Performance and Security with NTRU Algorithm that for file storage and data security. In this system consist of two phase they are NTRU Encryption method and FDSCS methodology. In this methodology, we tend to divide a file into fragments, and replicate the fragmented knowledge over the cloud nodes. Each fragment is encrypted by NTRU Encryption Algorithm. Each of the nodes stores solely one fragment of a selected record that ensures that even just in case of an eminent attack, no meaningful information is revealed to the attacker. Moreover, the nodes storing the fragments square measure separated with bound distance by means that of graph T-coloring to ban associate assaulter of dead reckoning the locations of the fragments. Finally our Experimental result shows our proposed method ensures complete security of the data, reduce the data overhead and reduction the encryption and decryption time.

Fragmentation, NTRU algorithm, T-coloring, cloud security, optimization

[1] K. Bilal, S. U. Khan, L. Zhang, H. Li, K. Hayat, S. A. Madani, N. Min-Allah, L. Wang, D. Chen, M. Iqbal, C. Z. Xu, and A.Y.Zomaya,“Quantitative comparisons of the state of the art data center architectures,” Concurrency and Computation: Practice and Experience, Vol. 25, No. 12, 2013, pp. 1771-1783.
[2] Sirisha Aguru, Batteri MadhavaRao, "Data Security In Cloud Computing Using RC6 Encryption and Steganography Algorithms", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.1, pp.6-9, 2019
[3] Poonam Devi , "Attacks on Cloud Data: A Big Security Issue", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.15-18, 2018
[4] Mazhar Ali ; Kashif Bilal ;Samee U. Khan ;Bharadwaj Veeravalli ; Keqin Li ; Albert Y. Zomaya “FDSCS: Division And Replication Of Data In Cloud Foroptimal Performance And Security” Volume: 6 , Issue: 2 , April-June 1 2018
[5] K. Bilal, M. Manzano, S. U. Khan, E. Calle, K. Li, and A. Zomaya, “On the characterization of the structural robustness of data center networks,” IEEE Transactions on Cloud Computing, Vol. 1, No. 1, 2013, pp. 64-77.
[6] Y. Chen, V.Paxson, and R. H. Katz, “Whats new about cloud computing security,” University of California, Berkeley Report No. UCB/EECS-2010-5, Jan. 20, 2010.
[7] K.Hashizume, D. G. Rosado, E. Fernndez-Medina, and E. B. Fernandez, “An analysis of security issues for cloud computing,” Journal of Internet Services and Applications, Vol. 4, No. 1, 2013, pp. 1-13..
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S.Daniel, P. Azeem ul haq, S. Sureshkrishnan, "Fragmentation of Data for Security in Cloud Services," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.468-472, 2019.

In this paper, the use of clustering techniques in the field of precision agriculture has been discussed. Types of clustering techniques discussed are k means clustering, mean shift clustering; Density based spatial clustering of applications with noise (DBSCAN), Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) and Hierarchical clustering. As clustering is a method of identifying similar groups of data in a data set, Clustering has a huge number of uses spread crosswise over different spaces. In data science clustering analysis is used to gain some valuable insights from the data by looking at what groups the data point belongs to which group when clustering algorithm is applied. Few applications of cluster analysis in the field of agriculture are using k means, hierarchical agglomerative clustering approach, pam clustering method and divisive clustering approach to form the clusters based on soil fertility, crop production, irrigation requirements etc.

Precision Agriculture, k_means clustering, Density based spatial clustering of applications with noise(DBSCAN), Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM), Pam clustering, Hierarchial clustering

[1] Mamta Tiwari, Dr.Bharat Misra,”Application of Cluster Analysis in Agriculture”, International Journal of Computer Applications Volume 36– No.4, (0975 – 8887), December 2011.
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[4] Shilpa Mahajan, “Convergence of IT and Data Mining with other technologies”, International Journal of Scientific Research in Computer Science and Engineering, Vol.01, Issue.04, pp.31-37, 2013.
[5] Ms. Shilpa Ankalaki, Jharna Majumdar, “Applications of Clustering Algorithms for Analysis of Agriculture Data”, International Journal of Engineering & Technology, 7, 3, 638-643, 2018.
[6] Dileep Kumar Yadav,” A Comparative Analysis of Clustering Algorithm for Agriculture Data”, International Journal of Current Research Vol. 7, Issue, 07, pp.18361-18364, July, 2015.
[7] Jiawei Han, Micheline Kamber,” Data Mining Concept and Techniques “, 2nd Ed. - Morgan Kaufmann Publishers.
[8] JharnaMajumdar, Sneha Naraseeyappa and Shilpa Ankalaki,” Analysis of agriculture data using data mining techniques: application of big data”, Majumdar et al. J Big Data, 4:20 DOI 10.1186/s40537-017-0077-4, 2017.
[9] Vandana B, S. Sathish Kumar, “ Big Data Analysis through R for Weather Monitoring”, Global Journal of Engineering Science and Researches, ICRTCET-2018, pp 99-106, 2019
[10] Rahmah N, Sitanggang S. Determination of optimal epsilon (Eps) value on DBSCAN algorithm to clustering data on peatland hotspots in Sumatra. IOP conference series: earth and environmental. Science. 2016.

Vandana.B, S. Sathish Kumar, "Cluster Analysis in Precision Agriculture," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.473-477, 2019.

Searching for a Video in World Wide Web has augmented expeditiously as there’s been an explosion of growth in video on social media channels and networks in recent years. At present video search engines use the title, description, and thumbnail of the video for identifying the right one. In this paper, a novel video searching methodology is proposed using the Video indexing method. Video indexing is a technique of preparing an index, based on the content of video for the easy access of frames of interest. Videos are stored along with an index which is created out of video indexing technique. The video searching methodology check the content of index attached with each video to ensure that video is matching with the searching keyword and its relevance ensured, based on the word count of searching keyword in video index. Video captions are generated by the deep learning network model by combining global local (glocal) attention and context cascading mechanisms using VIST-Visual Story Telling dataset. Video Index generator uses Wormhole algorithm, that ensure minimum worst-case time for searching a key with a length of L Also, Video searching methodology extracts the video clip where the frames of interest lies from the original huge sized source video. Hence, searcher can get and download a video clip instead of downloading entire video from the video storage. This reduces the bandwidth requirement and time taken to download the videos.

Video Indexing, Video Searching methodology, VIST- Visual Story Telling dataset

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Jaimon Jacob, Sudeep Ilayidom, V.P. Devassia, "Content Based Video Retrieval System Using Video Indexing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.478-782, 2019.

Intrusion Detection is one of the most effective and widely used implementation against the attacks and threats .Further more attackers keeps on varying their attacking techniques and tools .In this paper we have tried to perform a simulation study to evaluate the performance of varied machine learning classifiers to detect intrusion detection based on KDD 99 cups data set [1] focusing on enhancing the proficiency of Intrusion Detection system (IDS).

DoS- Denial Of Service; U2R-User to root; R2L: Root to local; CIA-Confidentilaity,Integrity,availability ; CM-Confusion Matrix; MLP-Multi Layer perceptron ; NEA-Nearest Cluster Algorithm.; GAU:- Gaussian; K-M: K means algorithm; CPE: - Cost Per example; Pd:Probabilty Detection; FaR:False alrm Rate

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Ashish Puri, Md Tabrez Nafis, "Smart Intrusion Detection Using Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.483-488, 2019.

Satellite imageries are widely available from various sources which can be used for Land use/Land cover analysis. Land use/Land cover analysis is necessary for environmental monitoring, urban planning and natural resource analysis. In this paper, we have used newly created algorithm- Multi Objective Algorithm (MOA) which is the combination of two metaheuristic algorithms for classification of satellite imageries. Classification result was compared with the KNN (K-Nearest Neighbour) algorithm. In this view, satellite imageries of Delhi and Shenyang were used for the experiment purpose. Also accuracy of classification was measured using the error matrix/kappa coefficient and was compared with the KNN classification technique. The classification results of the two major cities indicate a substantial difference in the percentage of overall accuracy and kappa coefficient value.

Classification, Land use/Land Cover, K-Nearest Neighbour, MOA, Accuracy Assessment

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Sanjay Srivas, P. G. Khot, "Performance Evaluation of MOA v/s KNN Classification Schemes: Case Study of Major Cities in the World," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.489-495, 2019.