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Artificial Intelligence (AI) is making its presence felt in diverse areas. One such area which has been invaded by artificial intelligence is brain tumour classification using Artificial Neural Networks because of the complexity in human intervention based approaches. Automated classification reduces the possibility of human errors and reinforces classification at hindsight. The entire process of classification using Artificial Neural Networks (ANN) can be broadly bifurcated into two steps viz. Feature Extraction and Classification. Here, in the proposed paper, a survey on the various mathematical tools required for the feature extraction and classification of brain tumour cases using MRI images is put forth and analyzed. Also previous work and their salient features have been cited.

Artificial Intelligence (AI), Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), adaptive thresholding, binarization

[1] Menzes B.,(2015), “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)”, IEEE Transactions on Medical Imaging, VOL. 34, NO. 10
[2] (Nooshin Nabizadeh and Miroslav Kubat, 2015) Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features, Elsevier, Science Direct, 2015.
[3] R.Lavanyadevi, M.Machakowsalya, J.Nivethitha, A.Niranjil Kumar “Brain Tumor Classification and Segmentation in MRI Images using PNN”, IEEE 2017.
[4] N. Varuna Shree T. N. R. Kumar “Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network” Springer 2018
[5] Dr.Kulhalli K. & Kolge V(2014), “Primary Level Classification of Brain Tumor using PCA and PNN”, International Journal on Recent and Innovation Trends in Computing and Communication Volume: 2 Issue.
[6] Popescu L & Sasu I(2014),“Feature extraction, feature selection and machine learning for image classification: A case study” International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), IEEE.
[7] Sridhar D. & Krishna M(2013), “Brain Tumor Classification U sing Discrete Cosine Transform and Probabilistic Neural Network”, International Conference on Signal Processing, Image Processing and Pattern Recognition [ICSIPR].
[8] Sridhar D. & Krishna M (2013), “Brain Tumor Classification Using Discrete Cosine Transform And Probabilistic Neural Network” International Conference on Signal Processing, Image Processing and Pattern Recognition [ICSIPR] IEEE.
[9] Dahab D.,Ghoniemy S. & Selim G.(2012),“Automated Brain Tumor Detection and Identification Using Image Processing and Probabilistic Neural Network Techniques” International Journal of Image Processing and Visual Communication Volume (Online) 1 , Issue 2.
[10] Hua X.(2012), “Human–computer interactions for converting color images to gray” National Journal on Neurocomputing 85 , Elsevier, PP 1–5.
[11] Kolge V.& Kulhalli K.(2012), “PCA And PNN Assisted Automated Brain Tumor Classification”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), PP: 19-23.
[12] Rao K., Stephen M.,& Phanindra D.(2012), “Classification Based Image Segmentation Approach” International Journal of Computer Science And Technology, Vol. 3, Issue 1.

[13] Othman M. & Basri M.(2011), “Probabilistic Neural NetworkFor Brain Tumor Classification” , Second International Conference on Intelligent Systems, Modelling and Simulation.
[14] Ruikar S., & Doye D.(2010), “ Image Denoising using Wavelet Transform”,International Conference on Mechanical and Electrical Technology (ICMET),IEEE,pp. 509-515.
[15] Fonte P., Silva G.& Quadrado J (2005), “Wind Speed Prediction using Artificial Neural Networks”, 6th WSEAS Int. Conf. on Neural Networks, Lisbon, Portugal, pp. 134-139.
[16] Bajwa I.& Hyder S (2005), “PCA based Image Classification of Single-layered Cloud Types”, IEEE,Interntational Conference on Emerging Technologies.
[17] Mao K.,Tan K.& Ser W(2000), “Probabilistic Neural- Network Structure Determination for Pattern Classification”, IEEE Transactions on Neural Networks, Vol. 11, No. 4.

Ajay Kushwaha, Mahesh Kumar Pawar, Anjana Pandey, "Brain Tumour Classification using Artificial Neural Networks: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.686-690, 2018.

Blockchain is a buzzword in the current technology trends. It is usually coupled with the cryptocurrency terms: Bitcoin and Ethereum. Any applications that can be optimized by decentralization and that needs to be highly secured could opt for this technology. Blockchain technology has already entrenched into the finance and banking domains by its unusual transparency, security, and flexibility, that facilitates the control of transactions in a decentralized manner. Its distributed and peer-authentication features empower it to span across auxiliary domains like Contractual Agreements and Real Property, Smart Grids, Gaming and Entertainment, Government public services and Smart Cities with IOT, reputation systems, and security services. This paper provides a description of key characteristics, architecture, and taxonomy of blockchain technology. Moreover, the paper provides an insight into the popular consensus algorithms, technical challenges, and major application areas. Future trends and signs of progress in the blockchain technology were discussed.

Blockchain, Distributed Ledger, Cryptocurrency

[1] M. Moser, “Anonymity of bitcoin transactions: An analysis of mixing services,” in Proceedings of Munster Bitcoin Conference, M¨unster, Germany, 2013, pp. 17–18.
[2] Zibin Zheng1, Shaoan Xie1, Hongning Dai2, Xiangping Chen4, and Huaimin Wang3,”An Overview of Blockchain Technology:Architecture,Consensus, and Future Trends”,IEEE 6th International Congress On Big Data, pp. 557-564.
[3] E. B. Sasson, A. Chiesa, C. Garman, M. Green, I. Miers, E. Tromer, and M. Virza, “Zerocash: Decentralized anonymous payments from bitcoin,” in Proceedings of 2014 IEEE Symposium on Security and Privacy (SP),San Jose, CA, USA, pp. 459–474, 2014.
[4] S.M . Nasti, S.J. Nasti, R.Bashir, M.A. Butt, “Bitcoin:Surveying First RevolutionaryCryptographic Virtual Currency”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp. 101-103, 2018.
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[6] Tschorsch and B. Scheuermann. Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys & Tutorials, 18(3):464, 2016.
[7] D. Johnson, A. Menezes, and S. Vanstone, “The elliptic curve digital signature algorithm (ecdsa),” International Journal of Information Security, vol. 1, no. 1, pp. 36–63, 2001.
[8] Xu et al. 2017. A Taxonomy of Blockchain-Based Systems for Architecture Design. 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, Sweden, 3-7 April 2017.
[9] Crosby et.al. “BlockChain Technology: Beyond Bitcoin, Applied Innovation Review”, Issue No. 2, June 2016.
[10] D. Kraft, “Difficulty control for blockchain-based consensus systems,” Peer-to-Peer Networking and Applications, vol. 9, no. 2, pp. 397–413, 2016.
[11] C. Decker, R. Wattenhofer, Information propagation in the Bitcoin network. In: Peer-to-Peer Computing (P2P), IEEE Thirteenth International Conference on; p. 1–10,2013.
[12] C. Miguel and L. Barbara, “Practical byzantine fault tolerance,” in Proceedings of the Third Symposium on Operating Systems Design and Implementation, vol. 99, New Orleans, USA, pp. 173–186, 1999.
[13] Peters G.W. Panayi E. 2016. Understanding Modern Banking Ledgers Through Blockchain Technologies: Future of Transaction Processing and Smart Contracts on the Internet of Money , Banking Beyond Banks and Money, Springer Sep 2016, pp. 239-278.
[14] I. Miers, C. Garman, M. Green, and A. D. Rubin, “Zerocoin: Anonymous distributed e-cash from bitcoin,” in Proceedings of IEEE Symposium Security and Privacy (SP), Berkeley, CA, USA, 2013, pp. 397–411.
[15] D.Vandervort “Challenges and Opportunities Associated with a Bitcoin-Based Transaction Rating System” vol. 8438. Springer Berlin; Heidelberg; p. 33–42. 2014.
[16] Zheng, Zibin & Xie, Shaoan & Dai, Hong-Ning & Chen, Xiangping & Wang, Huaimin, “Blockchain Challenges and Opportunities: A Survey, International Journal of Web and Grid Services”, 2017.
[17] Sun et.al. 2016. Blockchain-based sharing services What blockchain technology can contribute to smart cities, Springer
[18] Gurpreet kaur, Manreet Sohal, “IOT Survey: The Phase Changer in Healthcare Industry”, International Journal of Scientific research in Network security and Communication, Vol.6, Issue.2, 34-39, 2018.

N.S. Tinu, "A Survey on Blockchain Technology- Taxonomy, Consensus Algorithms and Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.691-696, 2018.

Sentiment analysis is a fast-growing field which has gained importance in many sectors. It plays a very major role in the field of Natural Language Processing. With the increase in awareness among people and easy access to internet, massive amount of data is available on internet that can be analysed and hence utilized in a constructive manner. This is a survey paper which gives an overview of sentiment analysis along with an insight into the techniques used for sentiment analysis and various applications and challenges in this area.

Sentiment analysis, Machine learning, Lexicon, Classification, Opinion Mining

[1] Seema Kolkur†, Gayatri Dantal and Reena Mahe(2015) , ”Study of different levels for sentiment analysis”, International Journal of Current Engineering and Technology ,Vol.5, Issue 2, pp 768-770, April 2015
[2] Ankush Sharma1, Aakanksha(2014) “A comparative study of sentiments analysis using rule based and support vector machine” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 3, pp 5898-5900, March 2014
[3] Walaa Medhat a,, Ahmed Hassan b, Hoda Korashy(2014) “Sentiment analysis algorithms and applications:A survey”, Ain Shams Engineering Journal Volume 5, Issue 4, pp 1093-1113, December 2014
[4] S. M. Vohra, J. B. Teraiya(2013) ”A comparative study of sentiment analysis techniques”, Journal of information, knowledge and research in Computer engineering, Vol 02, Issue 02, pp 313-317, October 13
[5] Devika M D, Sunitha C, Amal Ganesha(2016), “Sentiment Analysis-A comparative study on different approaches”, Procedia Computer Science 87, pp44 – 49 In Association with Elsevier, Fourth International Conference on Recent Trends in Computer Science & Engineering, Chennai, Tamil Nadu, India, March 2016
[6] Harshali P. Patil1 and Mohammad Atique(2017) “Applications, issues and challenges in sentiment analysis and opinion mining– A User’s perspective”, International Journal of control theory and Application, Vol 10 ,Issue 19, pp 33-43, 2017
[7] Haseena Rahmath P(2014) ”Opinion mining and sentiment analysis -challenges and applications”, International Journal of Application or Innovation in Engineering & Management,Volume 3, Issue 5, pp 401-403, May 2014
[8] U. Aggarwal and G. Aggarwal, “Sentiment Analysis : A Survey”, International Journal of Computer Sciences and Engineering , Volume-5, Issue-5, pp 222-225, May 2017
[9] Uma Aggarwal, Gaurav Aggarwal , “Sentiment Analysis on Demonetization using SVM”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.183-187, 2017.

Deepti G. Aggarwal, "Sentiment Analysis: An insight into Techniques, Application and Challenges," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.697-703, 2018.

This paper comprises of two sections, in the first section study the various energy aware best fit decreasing algorithms like BFD, MBFD, PCA-BFD, and EPOBF, and comparison has been done on the basis of past data. Study shows that PCA-BFD is the best algorithm for energy efficiency. In the second part of this paper PCA-BFD is used for migration purpose. First of all load of all host are evaluated and find the overloaded and under loaded server known as hot-spot node. That node whose load is balanced is considered as non hot-spot node. Virtual machines in hot-spot nodes are sorted in descending order so those high power consumption nodes migrate first. Non hot-spot nodes are sorted in ascending order so low power consumption server are firstly filled.

energy efficiency, PCA-BFD, migration, allocation, load balancing

[1] Beloglazov, A., & Buyya, R, “Energy efficient resource management in virtualized cloud data centers,” Proceedings of the IEEE/ACM international conference on cluster, cloud and grid computing, pp. 826-831,2010
[2] Mustafa,S., et.al., “Performance Evaluation of Energy –aware Best Fit Decreasing Algorithms for Cloud Environments” , International Conference on Data Science and Data Intensive Systems(DSDIS), IEEE, 2015
[3]Quang-Hung, N., Thoai, N., & Son, N. T. (2014). EPOBF: energy efficient allocation of virtual machines in high performance computing cloud. In Transactions on Large-Scale Data-and Knowledge-Centered Systems XVI (pp. 71-86). Springer Berlin Heidelberg.
[4] Mann, Z. Á. (2015). Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms. Acm Computing Surveys (CSUR), 48(1), 11.
[5] Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., & Tenhunen, H, “Utilization prediction aware VM consolidation approach for green cloud computing,” Cloud Computing (CLOUD), IEEE 8th International Conference, pp. 381-388,2015.
[6] Varasteh A, Goudarzi M. Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J. 2015;11(2):772–83.
[7] S. Martello, P. Toth, "Knapsack Problems–Algorithms and Computer Implementations", John Wiley & Sons, 1990
[8] N. Tziritas, C.-Z. Xu, T. Loukopoulos, S. U. Khan, Z. Yu, "Application-aware Workload Consolidation to Minimize both Energy Consumption and Network Load in Cloud Environments", 42nd IEEE International Conference on Parallel Processing (ICPP), 2013

R. Garg, "Optimal Virtual Machine Allocation and Migration Model Based on PCA-BFD," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.704-707, 2018.

Churn prediction is the demanding field today and to stand in the market place or to capture market and for profit maximization churn prediction is very useful. Churn defines the customers switching another company, this is because the market strategy is rapidly changing. Other competitive companies give something new to the customers with low cost. Hence customers change their service provider very fast. Whereas retaining old customers is easy than gaining new customers. Retaining the customers by giving more offers is easy. The goal of this paper is to predict customer churn which will help to retain them. Many organizations feel the data base containing old customer information effectively predicts or generates the outputs. Data mining plays a vital role in churn prediction. Comparative study of the various classification algorithm can be done to give more accurate results.

Churn, weka, decision tree, classification, telecommunication

[1] Thomas Verbraken, Student Member, IEEE, Wouter Verbeke, and Bart Baesens “A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Mode ls” MAY 2013, pp 961
[2] Sam Drazin and Matt Montag “Decision Tree Analysis using Weka”
[3] Georges D. Olle Olle and Shuqin Cai “A Hybrid Churn Prediction Model in Mobile Telecommunication Industry” February 2014, PP 55-62
[4] V. Umayaparvathi, K. Iyakutti “Applications of Data Mining Techniques in Telecom Churn Prediction” March 2012, PP Page 1065-1070
[5]Ms Nisha Saini “Churn Prediction in Telecommunication Industry Using Decision Tree” June 2016
[6] Dr. Mamta Madan, Dr. Meenu Dave, Vani Kapoor Nijhawan “Data Mining for Telecom Customer Churn Management” September 2015, pp. 813-817
[7] Vani Kapoor Nijhawan, Mamta Madan, Meenu Dave ” The Analytical Comparison of ID3 and C4.5 using WEKA” June 2017
[8] Kiran Dahiya, Surbhi Bhatia “Customer Churn Analysis in Telecom Industry” 2015 IEEE
[9] Yong Liu, Yongrui Zhuang “Research Model of Churn Prediction Based on Customer Segmentation and
Misclassification Cost in the Context of Big Data” June 2015
[10] Clement Kirui, Li Hong, Wilson Cheruiyot and Hillary Kirui “Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining” March 2013
[11] Ammar A.Q. Ahmed, Maheswari “Churn prediction on huge telecom data using hybrid firefly based classification” March 2017
[12] Adnan Anjum, Adnan Zeb, Imran Uddin Afridi, Pir Masoom Shah, Saeeda Usman, “ Optimizing Coverage of Churn Prediction in Telecommunication Industry” 2017
[13] HelenTreasaSebastian* and RupaliWagh “ Churn Analysis in Telecommunication using Logistic Regression” March 16, 2017, Pgs. 207-212
[14] Gaganjot Kaur, Amit Chhabra “Improved J48 Classification Algorithm for the Prediction of Diabetes” Volume 98 – No.22, July 2014
[15] P.Rutravigneshwaran” A Study of Intrusion Detection System using Efficient Data Mining Techniques” IJCSE Journal Vol.5 , Issue.6 , pp.5-8, Dec-2017
[16] U.Kaur 1 , M. Mahajan2 , D. Singh” A Comparative Analysis of Trust Models in Cloud Computing”IJCSE Journal
Vol.6,Issue.2,pp.19-23,Apr-2018

S.P.Pund, Dr. S .N. Deshmukh, "Comparative Analysis for Churn Prediction Model in Telecom Industry," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.708-711, 2018.

A Wireless Sensor Network (WSN) consists of spatially distributed autonomous devices using sensors to monitor both environmental or physical conditions. In agriculture, WSNs are used for increasing the performance, high power efficiency and real time monitoring of environment. This paper proposes a real time system in which sensors are used and Arduino to monitor the temperature, humidity and soil moisture and hence presents the data to the users. We have also used solar sun tracker to increase the overall efficiency of the whole system. It uses Light Dependent Resistors (LDR) to sense the sunlight from the sun to convert it into voltage and generates electricity. This system can also be used in the remote areas.

Wireless Sensor Network, Light Dependent Resistor, Precision Agriculture, Threshold

[1] Kristofer O. Flores, Isidro M. Gonzales, Samuel Mathew G. Dumlao, Rosula S.J. Reyes, “Precision Agriculture Monitoring System using Wireless Sensor Network and Raspberry Pi Local Server”, IEEE, 2016.
[2] Anusha P, Dr. Shobha K R, “Design and Implementation of Wireless Sensor Network for Precision Agriculture”, IJSEAS, Volume-1, Issue-4, July 2015.
[3] Manikandan S.V, Jayapriya P., “Precision Agriculture Using Wireless Sensor Network System”, IJECS, Volume-5, Issue-11, Nov. 2016.
[4] Manijeh keshtgary, Amene Deljoo, “An Efficient Wireless Sensor Network for Precision Agriculture”, Canadian Journal on Multimedia and Wireless Networks, vol.3, No.1, January 2011.
[5] G. Sahitya, Dr. N. Balaji, Dr. C.D Naidu, “Prototyping of Wireless Sensor Network for Precision Agriculture”, IJCI, Vol.5, No.4, August 2016.
[6] Prathyusha. K, G. Sowmya Bala, Dr. K. Sreenivasa Ravi, “A Real Time Irrigation Control System for Precision Agriculture Using Wireless Sensor Network in Indian Agricultural Sectors”, IJCSEA, Vol.3, No.4, August 2013.
[7] D.D Chaudhary, S.P. Nayse, L. M. Waghmare, “Application of Wireless Sensor Networks For Greenhouse Parameter Control in Precision Agriculture”, IJWMN, Vol.3, No.1, February 2011.
[8] Herman Sahota, Ratnesh Kumar, Ahmed Kamal, “A WSN for Precision Agriculture and its Performance”, [Online] Wiley Online Library, 2011.
[9] Sukhkirandeep Kaur, R.N Mir, “Impact of Various Performance Parameters on Distributed Protocols in Wireless Sensor Networks”, IJCSE, Volume-6, issue-4, April 2018.

Avalokita Anupam Seth, Ritu Singh, "A Prototype of Wireless Sensor Network Used In Precision Agriculture," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.712-716, 2018.

Due to the advancement of mobile technology and its low cost, mobile telephony has been penetrated at large level. Smartphone users are growing with a rapid pace and the number of new research dimension has been emerged in mobile computing. Mobile Cloud Computing (MCC) is one of the burning areas of research in computer science. it is basically the combination of cloud computing and mobile computing. Availability of high speed internet and enhancement in the battery life of mobile devices, the usage of internet over mobile phone has been increased. In this study, a brief definition of cloud computing, mobile computing and mobile cloud computing is presented. After that a deliberated discussions on MCC architecture, different security levels, and challenges of MCC are covered. At last some applications are discussed where MCC is commonly used.

Cloud Computing, Mobile Computing, Mobile Cloud Computing, Security and Challenges of MCC

[1] Cloud Security Alliances(CSA): Security for critical area of focus in Cloud Computing, http://www.cloudsecurityalliance.org/guidance/csaguide.v3.0.pdf 2011.
[2] Dipali S. Yadav and Kanchan Doke.: Mobile Cloud Computing Issues and Solutions Framework.International Research Journal of Engineering and Technology. Vol.3, pp.1115-1118, (2016).
[3] Mobile Cloud Computing: Architecture, Algorithms and Applications, Debasis Dey, CRC Press, Taylor and Fracise Group, Edition ,2016
[4] http://www.mobilecloudcomputingforum.com/.
[5] White Paper, Mobile Cloud Computing Solution Brief. AEPONA, 2010
[6] R.Buyya, J.Broberg and A.M.Goscinski, Cloud Computing : Principles and Paradigm , John Wiley & Sons, Hoboken, NJ, 2010
[7] K.B.Moses.: Mobile communication evolution. International Journal of Modern Education and Computer Science. Vol.1, pp.25-33, 2014.
[8] Atta ur Rehman Khan, Mazliza Othman, Sajjad Ahmad Madani and Samee Ullah Khan.: A Survey of Mobile Cloud Computing Application Models. IEEE Communications Surveys & Tutorials. Vol.16(1), pp.393-413, 2014.
[9] Prashant Pranav and Naela Rizvi.: Security in Mobile Cloud Computing : A Review. International Journal of Computer Science and Information Technologies. Vol.7(1), pp.34-39 , 2016.
[10] Poonam S.Sharma and Sneha U.Bohra. :Mobile Cloud Computing :Its Challenges and Solutions. International Journal of Computer Science and Mobile Computing. Vol.4, issue 5,, pp.287-293, 2015.
[11] H. T. Dinh, C. Lee, D. Niyato, and P. Wang .:A Survey of Mobile Cloud Computing: Architecture, Applications, and Approaches. Wireless Communications and Mobile Computing, Vol.13, issue 18, pp.1587–1611, 2013
[12] VarshneyU.: Pervasive Healthcare and Wireless Health Monitoring. Journal on Mobile Networks and Applications. Vol.12(2-3), pp.113-127,2007.
[13] Li Z,Wang C, Xu, R.: Computation offloading to save energy on handheld device: a partition scheme. In proceeding of the 2001 international conference on compiler, architecture, and synthesis for embedded system(CASES),.pp.238-246, 2001

Nidhi Rajak, Diwakar Shukla, "Mobile Cloud Computing: Security Levels, Challenges and Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.717-720, 2018.

The computer program that talk to human in natural simple language is called a Chatbot. Chatbots are intelligent, conversational software or software agents that takes input in the form of text and voice. They provide the response of the input in the form of output. The chatbot software technology was developed in the 1960’s. Now the chatbot easier to train and implement. It is an open source code, so its development platform is widely available. It is used to improve customer understanding and supporting learning. These paper presentation analysis about chatbot. The paper discusses about different Chatbots on different technology and uses in different areas.

Rule Of Chatbot Response, Response, AIML

[1] Bayan Abu Shawar, Eric Atwell” A Comparison Between Alice and Elizabeth Chatbot Systems”,Dec 2002,PP.No.2-16
[2] Sameera A. Abdul-Kader,Dr.John Woods,” Survey on Chatbot Design Techniques in Speech Conversation System”,Volume 6,Nov 7 2015,PP.No.1-10
[3] Amine Hallili,” Toward an Ontology-Based Chatbot Endowed with Natural Language Processing and Generation”,Dec 1 2014,PP.No.1-6
[4] M. J. Pereira, and L. Coheur, “Just. Chat-a platform for processing information to be used in chatbots,” 2013S. Willium, “Network Security and Communication”, IEEE Transaction, Vol.31, Issue.4, pp.123-141, 2012.
[5] J. Jia, “The Study of the Application of a Keywords-based Chatbot System on the Teaching of Foreign Languages”, Report of University of Augsburg, Augsburg, , pp.1-36, 2003.
[6] B. P. Kiptonui, “Chatbot Technology: A Possible Means of Unlocking Student Potential to Learn How to Learn, Educational Research”, Vol.4, Issue.2, pp. 218-221, 2013.
[7] S. Ghose, J. J. Barua, “Toward the Implementation of a Topic Specific Dialogue Based Natural Language Chatbot as an Undergraduate Advisor”, International Conference on Informatics, Electronics & Vision, India, pp. 1-5, 2013.
[8] M. Dahiya, “A Tool of Conversation: Chatbot”,IJSCE,Volume 5, E-ISSN: 2347-2693,pp.1-2, May 2017.

Nidhi Singh, Kajol Mittal, "Survey 0n Chatbot Work and Design," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.721-723, 2018.

Every day is greater the number of images obtained to characterize the anatomy and functions of the human body; because of this the automation of the medical image processing has become a practice to improve the diagnosis and treatment of certain diseases. In this study the main areas of application of computer vision to the digital processing of medical images are reviewed. This paper gives the details about the methods of biomedical image processing and after that it also describe about medical imaging modalities. Some of the medical imaging modalities are described in this paper like X-ray imaging, CT, MRI, and ultrasound. The optical modalities like endoscopy, photography and microscopy are also more important in this field. The following steps of image analysis are explained in this paper, feature extraction, segmentation, classification, quantitative measurements and interpretation. It mainly focuses on segmentation of biomedical images, because of its high relevance. Special segmentation methods and techniques have been developed in the medical field.

Medical imaging modalities, Bacterial image analysis, automated image analysis

[1]. Suzuki K, Abe H, MacMahon H, Doi K, “Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)”. IEEE Transactions on Medical Imaging., 2006, vol. 25,no.4, pp. 406-416.
[2]. WeixingWang,Shuguang Wu “A Study on Lung Cancer Detection by Image Processing” international conference on Communications,Circuits and Systems Proceedings, 2006, pp. 371-374.
[3]. Md. FoisalHossain, Mohammad Reza Alsharif “Image Enhancement Based on Logarithmic Transform Coefficient and Adaptive Histogram Equalization”International Conference on Convergence Information Technology, 21-23 November, 2007, pp. 1439 – 1444.
[4]. P.S. Hiremath, Parashuram Bannigidad, International Journal on Biomedical Engineeri ng and Technology (IJBET), Inderscience Publishers Ltd., USA, 2011, Vol. 7, No.3, pp. 257-265.
[5]. Wenhong Li,Yonggang Li,KexueLuo, “Application of Image Processing Technology in Paper Currency Classification System”, IEEE transactions 22-24 Oct. 2008, pp. 1- 5.
[6]. Li Minxia, ZhengMeng “A Study of Automatic Defects Extraction of X-ray Weld Image Based on Computed Radiography System” International Conference on Measuring Technology and Mechatronics Automation - ICMTMA, 2011.
[7]. P.S. Hiremath, Parashuram Bannigidad, “Automated Gram-staining characterization of bacterial cells using colour and cell wall properties Proceedings of the International Conference on Signal and Image processing (ICSIP-2009), Aug. 19-21, 2009, VVIT-Mysore, Karnataka, India, pp. 209-211.
[8]. Kanwal, N. , Girdhar, A. ; Gupta, S “Region Based Adaptive Contrast Enhancement of Medical X-Ray Images”, Bioinformatics and Biomedical Engineering, (ICBBE) 2011 5th International Conference, pp. 1-5.
[9]. Noorhayati Mohamed Noor, Noor Elaiza Abdul Khalid. “Fish Bone Impaction Using Adaptive Histogram Equalization (AHE)” Proceedings of the Second International Conference on Computer Research and Development 2010, IEEE Computer society Washington, pp.163-167.
[10]. Lu Zhang, Dongyue Li, ShuqianLuo , “Information extraction of bone fracture images based on diffraction enhanced imaging” International Conference of Medical Image Analysis and Clinical Application (MIACA) 10-17 June 2010, pp.106 -108.
[11]. Md.FoisalHossain, Mohammad Reza Alsharif, and Katsumi Yamashita “Medical Image Enhancement Based on Nonlinear Technique and Logarithmic Transform Coefficient Histogram Matching”, IEEE/ICME International Conference on Complex Medical Engineering July,2010, pp. 13-15.
[12]. P.S. Hiremath, Parashuram Bannigidad, International Journal on Biomedical Engineeri ng and Technology (IJBET), Inderscience Publishers Ltd., USA, 2011, Vol. 7, No.3, pp. 257-265.
[13]. P.S. Hiremath1, Parashuram Bannigidad2, Soumyashree S.Yelgond3, “ An Improved Automated Method for Identification of Bacterial Cell Morphological Characteristics”, International Journal of Advanced Trends in Computer Science and Engineering, Vol.2 , No.1, Pages : 11-16 (2013)

Shakuntala A. Halemani, "Review on Digital Image Processing in Biomedical Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.724-727, 2018.

Fingerprint classification plays an important role in automatic recognition of fingerprints from a given dataset. It significantly reduces the time taken to map a fingerprint to its nearest match by providing a broad classification of given fingerprint into its relevant class and performing the further search in that class domain only. Various rule-based, model-based and structure-based approaches have been proposed and used to perform such classification. This paper discusses the various mechanism employed to categorize fingerprints into basic classes like arch, whorl, left loop, right loop and tented arch along with the advantages and limitations of each approach. The paper aims to provide a concise study and performance based comparison of various fingerprint classification approaches and the different techniques they use to perform the classification.

Fingerprint Classification, statistical classifiers, rule-based classifiers, neural networks, structural classifiers, hybrid classifiers

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[2] Kücken,M., Newell, AC , “Fingerprint formation”, Journal of theoretical biology, Elsevier,2005.
[3] Germain, R., Califano, A., Colville, S.,"Fingerprint matching using transformation parameter clustering", IEEE Computational Science and Engineering, Vol. 4, No. 4, pp. 42–49, 1997.
[4] Jain, A.K., Prabhakar, S., Hong, L.,"A Multichannel Approach to Fingerprint Classification", Proc. of Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP’98), New Delhi, India, 1998.
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Deepti Goswami, Saurabh Mukherjee, "Comparative Analysis of Fingerprint Classification Algorithms- A review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.728-734, 2018.