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Psychology can be defined as the mental characteristic or attitude of a person, especially those affecting behavior in a context. Every psychological test has an objective and standardized measurement of a sample behavior. Here, sample of behavior refers to an individual’s response on a situation or task which is prescribed or predefined before the task. In order to make the psychological test cost, time, and efficiency effective, we focus on building a Machine Learning classification model which predicts if the student who took the survey belongs to an Introvert category or an Extrovert category. The questions in the survey will focus on an individual features to build a model and questions prepared by the domain expert will carry particular weight, and depending on the answers we will predict which category the person falls under. The performance of the model is predicted by taking Accuracy and confusion matrix into consideration. It is induced for the betterment or ease of analyzing a personality in order to enhance individual’s strength, enhancing professional and personal skills.

psychology, behavior, machinelearning,skills

[1] Kumudavalli M.V., Anagha Shailesh Kulkarni, G. Ambrish, “3D Metric approach to study the factors affecting student’s psychology on Education” International Journal of Computer Sciences and Engineering, Vol.7 , Issue.2, pp.981-984, Feb-2019.
[2] Michael Barkham, Gillian E. Hardy,Mike Startup, “The IIP 32: A short version of the Inventory of Interpersonal Problems”, https://doi.org/10.1111/j.2044-8260.1996.tb01159.x, February 1996
[3] Michael Barkham, Gillian E. Hardy,Mike Startup,” The structure, validity and clinical relevance of the Inventory of Interpersonal Problems”, https://doi.org/10.1111/j.2044-8341.1994.tb01784.x, June 1994.
[4] Jeromy Anglim, Sharon Grant, “Predicting Psychological and Subjective Well-Being from Personality: Incremental Prediction from 30 Facets Over the Big 5”, Journal of Happiness Studies February 2016, Volume 17, Issue 1, pp 59–80
[5] Hans Georg Wolff, Sowon Kim, “The relationship between networking behaviors and the Big Five personality dimensions”, Emerald Group Publishing Limited
[6] Alma M. McCarthy, Thomas N. Garavan , “Developing self‐awareness in the managerial career development process: the value of 360 degree feedback and the MBTI”, MCB UP Ltd
[7] Passmore, Jonathan, “MBTI types and executive coaching”, The Coaching Psychologist, 2006
[8 Dominic B. Dwyer, Peter Falkai, and Nikolaos KoutsoulerisMachine Learning Approaches for Clinical Psychology and Psychiatry Annual Review of Clinical Psychology Vol. 14:91-118 , 2018.

Anagha Shailesh Kulkarni, Kumudavalli M.V, Vanitha, "Student Psychometric Analysis Through Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.1-3, 2019.

Now a day’s smart technology is replacing each and everything in the world. Internet of Things (IoT) has emerged powerfully as a more successful area to express new technologies. Current society is seeing a different flow in the number and domain of devices deployed and used in regular applications, including mobile phones, tablets, wearable devices, and other connected sensing devices, collectively referred to as the IoT. Internet of Things has grown into the lives of human being by allowing a communications between machines, objects and things along with people. The people, software systems and other machines are surrounded with IoT permitted objects which communicate about the present view of things. The world is becoming smarter in all aspect by using IoT technology. IoT features are provided in many of the applications like smart healthcare, smart homes, smart cities, smart energy, waste management, transportation and monitoring type. In IoT technology the physical objects are embedded with RFID, sensors and Internet protocols which allow object to communicate with each other. This paper highlights the application of IoT used in various fields and a review on the concept of IoT related technologies with the advantages and disadvantages that are encountered.

Internet of Things, IoT Applications, Smart Devices

[1] D. Singh, G. Tripathi, Antonio J. Jara, “A survey of Internet of Things: Future Vision, Architecture, Challenges and Services”, IEEE World Forum on Internet of Things, 2014.
[2] D. Yang, F. Liu and Y. Liang, "A survey of the internet of things," in Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI2010), 2010, pp. 358-366.
[3] Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, Michele Zorzi, “Internet of Things for Smart Cities”, IEEE Internet of Things Journal, volume 11 ,2014.
[4] Mari Carmen Domingo, “An overview of the Internet of Things for people with disabilities”, Journal of Network And Computer Applications, vol. 35 pp: 584–596, 2012.
[5] Komal Batool, Muaz A. Niazi, “Self Organized Power Consumption Approximation in the Internet of Things”, IEEE International Conference on Consumer Electronics (ICCE),2015.
[6] Li, S., Da Xu, L., & Zhao, S, “The internet of Things: a survey. Information Systems Frontiers”, 17(2), 243 - 259, 2015.
[7] Q. Wei, S. Zhu, C. Du, “Study on key technologies of Internet of Things perceiving mine”, Procedia Engineering, volume 26 pp. 2326 – 2333, 2011.
[8] K. Sundara Velrani, Dr.G. Geetha, “Sensor Based Healthcare Information System”, IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development, 2016.
[9] Ananda Mohon Ghosh, Debashish Halder, SK Alamgir Hossain, “Remote Human-Health monitoring System Through IoT”, 5th International Conference on Informatics, Electronic and Vision (ICIEV), 2016.
[10] Patel, Shivam, and Yogesh Chauhan. "Heart attack Detection and medical attention using motion sensing Device kinect", International Journal of Scientific and Research Publications 4.1

Aruna Devi.T, Suman Narendra, Musani Poojitha, "IoT Technology and its Applications in Various Fields", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.4-8, 2019.

Autonomous driving technology is an upcoming and fast developing, and a future business booster in the automotive, smart cities, transportation, and other related sectors. The better usage of the concepts such as Artificial Intelligence and Machine learning in the field of automotive industry is a promising one. Certain automobile companies are investing billions of money on research and development of self-driving vehicles. In this paper the author broadly discuss about the autonomous vehicles and their working functionalities, adding to that, the pros and cons of LiDAR technology behind every autonomous vehicles. This paper also throws light on other enabling technology such as GPS, Video Camera, Radar, Position Estimator and Distance Sensors and compare with LiDAR technology.

Autonomous Vehicles, self-driving vehicles, LiDAR, GPS, Video Camera, Position Estimator, Distance sensors

[1] Luke DormehlandStephen Edelstein, 2019, “Sit back, relax, and enjoy a ride through the history of self-driving cars”, Digital Trends.
[2]James M. Anderson, et.al, 2016,”Autonomous Vehicles Technology”, RAND Corporation.
[3]A Todoruț N Cordoș, A Molea1 Mater, 2017, “Current challenges in autonomous driving”, IOP Publishing.
[4]Paul Goodman, 2019,”Advantages and Disadvantages of Driverless Cars”, AxelAddict.
[5]Vatsal Srivastava, 2018, “Core Technologies Used In Self Driving Cars”, Udacity , India.
[6]Cade Metz,2018, “How Driverless Cars See the World Around Them”, New York Times.
[7]James Armstrong, 2018, “How do driverless cars work?” The Telegraph.
[8]Christian wolff,2015,”RaderBasics”,GNU.
[9]Alistail Charlton,2018,”Lidar ,radar and camera”,Gearbrain.
[10]Kerry Taylor-Smith,2018“Lidar used in driverless car”.
[11]2018,”LIDARdiagram”,Texas instruments.
[12]2018,”Robust GPS/GNSS:Driving the future of autonomous vehicles”.
[13]2018,”High precision GPS for Autonomous vehicles”,Novatel.
[14]2018,”Technology and costs”,Google Autonomous vehicles.
[15]2019,”Future autonomous vehicle”,Open access government.
[16]2018,”Advantages and Disadvantages”, Lidar and Radar information.

BhavyaShree P, Sharon Thomas Takri, R. Gurunath, "A Comparative Study of Enabling Technologies for Autonomous Vehicles", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.9-11, 2019.

In the field of cyber security, we have many algorithms that support cryptography. These algorithms are more prone to cyber-attacks and can be cracked easily by hash cracking tools. One of the methods to prevents these attacks is Honey Encryption technique that has gained popularity in recent years. This method provides another layer of protection when the intruder tries to hack an account with bogus data. In this paper, we are efficiently increasing the probability of Honey Encryption by generating more hash keys(password)using python programming approach. Honey encryption helps in minimization of vulnerability. We have designed an algorithm which eventually increases the buffer size for the randomly generated passwords.

Cryptography, Security, Password

[1] Mihir Bellare, Thomas Ristenpart, and Stefano Tessaro. Multi-instance security and its application to password-based cryptography. In Advances in Cryptology–CRYPTO 2012, pages 312–329. Springer Berlin Heidelberg, 2012.
[2] M. Preetha et al, International Journal of Computer Science and Mobile Computing Vol.2 Issue. 6, June- 2013, pg. 126-130
[3] M. Bakker and R. V. D. Jagt, “GPU-based password cracking. Technical report,” Univ. of Amsterdam, 2010
[4] Joseph Jaeger, Thomas Ristenpart, Qiang Tang. Honey Encryption Beyond Message Recovery Security. Presented in EUROCRYPT2016 pages 1 and 2, 2016.
[5] TIOBE Software Index (2011). "TIOBE Programming Community Index Python". 1
[6]"Programming Language Trends - O`Reilly Radar". Radar.oreilly.com. 2 August 2006.
[7] "The RedMonk Programming Language Rankings: January 2011 – ecosystems". Redmonk.com.
[8] H. Bojinov, E. Bursztein, X. Boyen, and D. Boneh. Kamouflage: loss-resistant password management. In ESORICS, pages 286–302, 2010.

Bhoomika R, Gowda Deepa Narsimha, Abhishek V, Bharathi H, "Efficiency Analysis of Honey Encryption Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.12-14, 2019.

Due to the great growth in the usage of computer technologies, image-processing techniques have become one among the most significant as well as rapidly used one in a broad variety of applications, particularly in medical imaging. Medical images are mostly used as radiographic techniques in disease recognition, clinical examinations along with treatment planning. The basic idea of medical image analysis is to develop imaging content. A typical medical imaging system is composed of five major processing phases i.e., image acquisition, pre-processing, segmentation, feature extraction/selection, and classification. Medical scan image usage machines are also constantly as fundamental. With these devices, it is possible to quicken and advance the errand of the examination of the diseases. Here, we have done a study on the present advanced techniques that have been used in various stages of medical image processing along with various medical image tools will be analyzed in a few directions. The essential focus of the assessment is to aggregate and examination on the medical apparatus in order to propose clients of different working systems on what sort of medical image devices to be utilized while investigating different kinds of imaging.

Medical Imaging Tools, Pre-Processing, Segmentation, Feature Extraction, Classification

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[4]. MarjanLaal a "Innovation Process in Medical Imaging” Procedia - Social and Behavioral Sciences 81 (2013) 60 – 64, ELSEVIER.
[5]. Nolden, Marco, et al. The medical imaging interaction toolkit: challenges and advances. International journal of computer assisted radiology and surgery 8.4 (2013): 607-620.
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[9]. Matthias Becker and Nadia Magnenat - Thalmann “Muscle Tissue Labeling of Human Lower Limb in Multi - Channel mDixon MR Imaging: Concepts and Applications”. IEEE / ACM Transactions on Computational Biology and Bioinformatics.
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[12]. Lee, Lay Khoon, Siau Chuin Liew, and Weng Jie Thong. A Review of Image Segmentation Methodologies in Medical Image. Advanced Computer and Communication Engineering Technology. Springer International Publishing, 2015. 1069-1080.
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[18]. A. P. Dhawan, “Image Segmentation and Feature Extraction”, in Principles And Advanced Methods in Medical Imaging And Image Analysis. Singapore: World Scientific Publishing Co. Pte. Ltd, 2008, pp 197-228.
[19]. Semih Ergin, Onur Kilinc, “A new feature extraction framework based on wavelets for breast cancer diagnosis”, Computers in Biology and Medicine 51 (2014) 171–182 Elsevier.
[20]. A. Andrade, J. S. Silva, J. Santos, and P. Belo-soares, “Classifier Approaches for Liver Steatosis using Ultrasound Images,” in Procedia Technology HCIST 2012, pp. 763–770.
[21]. Abhinav Gupta, Bhuvan Gosain and Sunanda Kaushal, A Comparison of Two Algorithms for Automated Stone Detection in Clinical B-Mode ultrasound images of the abdomen. Journal of Clinical Monitoring and Computing (Springer-2010).
[22]. R. Vanithamani, G. Umamaheswari, “Wavelet based De-speckling of Medical Ultrasound Images with Bilateral filter”, TENCON IEEE-2011
[23]. V. P Gladis Pushpa Rathi and Dr. S. Palani, A Novel approach for Feature Extraction and selection on MRI images for brain tumor classification, S Computer Science & Information Technology (CS & IT) 2012.
[24]. R. Tomari, W. Nurshazwani, W. Zakaria, M. Mahadi, A. Jamil, F. Mohd, N. Farhan, and N. Fuad, “Computer Aided System for Red Blood Cell Classification in Blood Smear Image" in Procedia Computer Science ConfPRIDE 2013-2014, pp. 206–213.
[25]. Nailah Afshan, Shaima Qureshi, Syed Mujtiba Hussain Comparative Study of Tumor Detection Algorithms, (Medcom-2014).
[26]. Deepthi Murthy T.S. & G.Sadashivappa. Brain Tumor Segmentation Using Thresholding, Morphological Operations and Extraction of Features of Tumor, (ICAECC-2014)
[27]. Vaishali Kumbhakarna, Vijaya R. Patil, Dr. Seema Kawathekar “Review on Speckle Noise Reduction Techniques for Medical Ultrasound Image Processing”. Int J of Computer Techniques.
[28]. Y. Song, W. Cai, H. Huang, Y. Zhou, Y. Wang, and D. D. Feng, “Locality-constrained Subcluster Representation Ensemble for Lung Image Classification,” Medical Image Analysis, vol. 22, no. 1, pp. 102- 113, 2015.
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[30]. F. Yousefi Rizi, H. Ahmadi Noubari, S. K. Setarehdan Wavelet Based Ultrasound image denoising -Performance Analysis Comparison (EMBS 2011).
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[32]. K. Viswanath, R. Gunasundari, Syed Aathif Hussan, “VLSI Implementation and Analysis of Kidney Stone Detection by Level Set Segmentation and ANN Classification”. ICCC-2015 Elsevier
[33]. A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. Van Riel, M. W. Wille, M. Naqibullah, I. S. Clara, and B. Van Ginneken, “Pulmonary nodule detection in CT images : false positive reduction using multi-view convolutional networks,” IEEE Transactions on Medical Imaging, vol. 35, issue 5, 1160 - 1169, 2016.

S. Chithra, R. Vijayabhanu, "An Analysis of Image Processing Techniques and Tools in Medical Images", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.15-20, 2019.

Every day huge amount of text is generated online in vast quantities about the things happening in the world and in the minds of people. In order to generate meaningful business insights for organizations and analysts, the invaluable text data has to be mined. Extracting insights from unstructured text is costly and time consuming. Businesses use text classification to structure data in a fast and cost-efficient way to enhance decision making and automate processes. Text classification is a process of assigning tags according to the content with broad applications in sentiment analysis, spam detection, topic labeling and intent detection. This paper reviews the various text classification algorithms and their applications.

Text classification, sentiment analysis, tags, spam detection, topic labeling, intent detection

[1] Dang, S., & Ahmad, P.H ,”A Review of Text Mining Techniques Associated with Various Application Areas”, International Journal of Science and Research (IJSR), Vol.4, No.2, pp.2461-2466,2015.
[2] Jiang, S., Pang, G., Wu, M., & Kuang, L,” An improved K-nearest-neighbor algorithm for text categorization. Expert Systems with Applications”, Vol. 39, No. 1, pp. 1503–1509, 2012 https://doi.org/10.1016/j.eswa.2011.08.040271–350.
[3] Jadhav, S., & Channe, H,”Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques”, International Journal of Science and Research (IJSR), Vol. 5, No. 1, pp. 1842–1845, 2016.
[4] Kotsiantis, S. B,”Supervised machine learning: A review of classification techniques”, Informatica, Vol. 31, pp. 249–268, 2007. https://doi.org/10.1115/1.1559160
[5] Patel, B. R., & Rana, K. K,”A Survey on Decision Tree Algorithm For Classification”, International Journal of Engineering Development and Research, Vol. 2, No. 1, pp.1–5, 2014.
[6] Singla, R., Chambayil, B., Khosla, A., & Santosh, J, ”Comparison of SVM and ANN for classification of eye events in EEG”, Journal of Biomedical Science and Engineering, Vol.4 (January), pp. 62–69, 2011. https://doi.org/10.4236/jbise.2011.41008
[7] Shahare, P. D., & Giri, R. N,”Comparative Analysis of Artificial Neural Network and Support Vector Machine Classification for Breast Cancer Detection”, International Research Journal of Engineering & Technology, pp. 2114–2119, 2015.
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G. Jasmine Beulah, "A Review on Text Classification Algorithms and their Applications", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.21-24, 2019.

Security has been a major interest for authentication over networking. In this modern society, mobile devices have a pertained importance with hundreds and millions users. With the help of pins, passwords there are weak authentication mechanisms which allow attackers to access the stored data. By implementing various methods for key exchange cryptographic can solve the problem of security. Identity verification is a developing and mesmerizes much attention. Biometric recognition mainly relates to the automatic recognition of individuals based on their behavioral characteristics. Almost appropriate is based on vein pattern is an approach that uses the vast network of blood vessels that lie underneath a skin. Vein pattern are unique and also difficult to duplicate even twin has an unlike and unique vein structure. Biometric security devices measure unique characteristics of a person, such as voice pattern, the iris or retina pattern of the eye and finger print. In biometrics, it is very difficult for someone to break into a system. Biometric security is a mechanism which is used to prove and provide access to a facility or system based on the automatic and direct verification of a single person’s physical behavior.

Authentication, cryptography, networking

[1]. Kirat Pal Singh Senior Project Fellow Council of Scientific and Industrial Research (CSIR) – Central Scientific Instruments Organization (CSIO) CSIR-CSIO, Ministry of Science & Technology, Chandigarh-160030
[2]. Mohammed Nasir Uddin1, Selina Sharmin2, Abu Hasnat Shohel Ahmed3 and Emrul Hasan4, Shahadot Hossain5 and Muniruzzaman6 Shanto Mariam University of Creative Technology1, 3 ,Uttara University4,5,6 IJCSNS International Journal of Computer Science and Network 16 Security, VOL.11 No.10, October 2011
[3]. SURVEY OF BIOMETRIC RECOGNITION SYSTEMS AND THEIR APPLICATIONS 1 SULOCHANA SONKAMBLE, 2DR. RAVINDRA THOOL, 3BALWANT SONKAMBLE 1Asstt Prof., Department of Information Technology , MMCOE, Pune, India 411052 2Professor, Department of Information Technology, SGGSIE&T,Nanded, India -411017 3Asstt Prof., Department of Computer Engineering, PICT, Pune, India-411043
[4]. Himanshu Srivastava Department of Computer Science & Engineering Roorkee Institute of Technology, Roorkee (U.K.), India IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 1 (Sep. - Oct. 2013), PP 22-29
[5]. Ravi Subban and Dattatreya P. Mankame A Study of Biometric Approach Using Fingerprint Recognition Lecture Notes on Software Engineering, Vol. 1, No. 2, May
[6]. Mohammed Nasir Uddin1, Selina Sharmin2, Abu Hasnat Shohel Ahmed3 and Emrul Hasan4, Shahadot Hossain5 and Muniruzzaman6 Shanto Mariam University of Creative Technology1, 3 ,Uttara University4,5,6 A Survey of Biometrics Security System IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.10, October 2011

Noor Fathima, Pallavi B N, Kavitha, "Biometrics in Network Security", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.25-28, 2019.

Blue eyes technology aims to create computational machines which can sense and understand human feelings and emotions. It uses camera, microphone and various sensors to recognize human actions and emotions and aid disabled people. There are many researches in this area, still they have some limitations. In this paper we have proposed on architecture for blue eyes technology which can identify human emotions through facial features and sensors such as Galvanic skin response sensor, heart pulse sensor, temperature sensor. This paper explores various image processing techniques such as noise removal, segmentation, image enhancement. Supervised classification technique such as artificial neural network with back propagation is used to predict emotions. This research involves many areas like Machine learning, Image processing and IoT.

Human Computer Interaction, Facial Expression detection, Galvanic skin response sensor

[1]. Fu Zhizhong, Lu Lingqiao,Xian Haiying Xuju,”Human Computer International Research And Realization Based On Leg Movement Analysis”, Apperceiving Computer And Intelligence Analysis (ICACIA),2010 International Conference.
[2]. Amir Aly, Adriana Tapus,”Towards an Online Fuzzy Modeling For Human Internal States Detection”,2012 12th Internal Conference On Control, Automation Robotics And Vision Guangzhou, China ,5-7th December 2012(ICARCV2012).
[3]. Mizna Rehman Mizna, et. Al. , “Blue eyes Technology”, Eighth International Conference on Digital Information Management (ICDIM 2013), IEEE, 10-12 sept 2013.
[4]. Jigisha M. Patel ; Nikunj C. Gamit,"A review on feature extraction techniques in Content Based Image Retrieval", International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET),IEEE,2016.Page s: 2259 - 2263.
[5]. Rahmaniansyah Dwi Putri ; Harsa Wara Prabawa ; Yaya Wihardi,"Color and texture features extraction on content-based image retrieval",3rd International Conference on Science in Information Technology (ICSITech)Page s: 711 - 715 .
[6]. Liu, M., Fan, D., Zhang, X., & Gong, X. (2016, November).Human emotion recognition based on galvanic skin response signal feature selection and svm. In Smart City and Systems Engineering (ICSCSE), International Conference on (pp. 157-160).IEEE.
[7]. Koelstra, et. al. Deap: A database for emotion analysis; using physiological signals. IEEE (2012) Transactions on Affective Computing, 3(1), 18-31
[8]. https://www.irjet.net/archives/V5/i4/IRJET-V5I4913.pdf
[9].https://www.mepits.com/project/184/embedded-projects/blue-eyes-technology-monitoring-human-operator-and-intelligence-sensing-system
[10].https://www.slideshare.net/keerthik10/blue-eyes-technology-monitoring-human-operator-and-intelligence-sensing-1
[11]https://www.mepits.com/project/184/embedded-projects/blue-eyes-technology-monitoring-human-operator-and-intelligence-sensing-system
[12]https://researchersclub.wordpress.com/2014/04/04/blue-eye-technology/
[13]https://www.academia.edu/10086667/Blue_Eyes_the_Future_Technology
[14] https://ijrcar.com/Volume_4_Issue_1/v4i108.pdf
[15] https://www.ukessays.com/essays/computer-science/study-of-blue-eye-technology-computer-science-essay.php

T. Kohila Kanagalakshmi, Vibha S, Prathibha K H, "Ascertaining Human Emotions using Blue Eyes Technology", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.29-33, 2019.

Notices, posters, digital panels, social media, and emails are the various means of communication within a campus today. Existence of multiple means leads to confusion and the information to be delivered can be missed or forgotten. To address this issue, we aim to create a steadfast workflow that will enable the individuals pertaining to an institution connect with one an-other, share information and participate in various events in a timely and a smart manner

Smart Campus, IPFS, email, instant messaging, collaboration, document management, digital signatures

[1] A. X. Zhang, M. S. Ackerman, and D. R. Karger, “Mailing lists: Why are they still here, what’s wrong with them, and how can we fix them?” in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, ser. CHI ’15. New York, NY, USA: ACM, 2015, pp.4009–4018. [Online]. Available: http://doi.acm.org/10.1145/2702123.2702194
[2] A. Quan-Haase, “Instant messaging on campus: Use and integration in university students’ everyday communication,” The Information Society, Vol.24, No.2, pp.105–115, 2008. [Online]. Available: https://doi.org/10.1080/01972240701883955
[3] J. P. Dimond, C. Fiesler, B. DiSalvo, J. Pelc, and A. S. Bruckman, “Qualitative data collection technologies: a comparison of instant messaging, email, and phone,” in Proceedings of the 17th ACM international conference on Supporting group work. ACM, 2012, pp. 277–280.
[4] A. J. Flanagin, “IM online: Instant messaging use among college students,” Communication Research Reports, Vol.22, No.3, pp.175– 187, 2005.
[5] R. S. Weisskirch and S. S. Milburn, “Virtual discussion: Understanding college students’ electronic bulletin board use,” The Internet and Higher Education, Vol.6, No.3, pp.215–225, 2003.
[6] M. M. serbinis, d. leibu, e. v. chrapko, and v. pappes, “Internet Document management system and methods,” Jun. 24 2003, us patent 6,584,466.
[7] J. Benet, “IPFS - content addressed, versioned, p2p file system,” 2014.
[8] J. D. Grallo, M. Chalmers, and P. G. Baker, “How do I get a campus ID? the other role of the academic library in student retention and success,” The Reference Librarian, Vol.53, No.2, pp. 182–193, 2012.
[9] M. Collins, “The use of email and electronic bulletin boards in college-level biology,” Journal of Computers in Mathematics and Science Teaching, Vol.17, No.1, pp.75–94, 1998. [Online]. Available: https://www.learntechlib.org/p/15202
[10] S. Alam, M. Kelly, and M. L. Nelson, “Interplanetary wayback: The permanent web archive,” in Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, ser. JCDL ’16. New York, NY, USA: ACM, 2016, pp. 273–274. [Online]. Available: http://doi.acm.org/10.1145/2910896.2925467
[11] M. Stranach, “Emergent tools for teaching and learning: Mattermost, hypothes. is, and appear. in,” 2019.

Kruthika M.R., Adithya Kumar N.S., Abhishek S., Abhigna A., "A Smart Campus Communication System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.34-37, 2019.

Many diseases are arising depending upon the environment, culture, life style, etc. In many Diseases, Multiple types of cancer are increased in various region based on mouth, breast, cervix uteri, ovary, gall bladder, NHL, thyroid, Brain, Stomach, etc. Human of each organ is depending of the cell functionality. Each cell functionality is works our daily foods of sweet, sour, salty, etc. In our country many medical research centers are involved many research areas of Tuberculosis, Cancer, Diabetes, etc. Leading cancer sites data is one of the big tasks to region wise, gender wise, age wise, etc. The study of leading sites of cancer in various region wise, gender wise, age wise are easy to find the avoid the cancer to protect with any immediate solution. Visual analysis of data is easily exploring the details of Cancer leading site and analysis describes to how it is important to save our country for immediate treatment procedure.

Visualization,Cancer, data mining, SPSS

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V. Manikanta, N.G Yethiraj, "Visual analysis of leading Cancer sites using SPSS Software", International Journal of Computer Sciences and Engineering, Vol.07, Issue.09, pp.38-46, 2019.