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A recommendation system is more important and helpful in both research and industry. This paper first examines the method of travel sequence recommendation. The proposed methodology is to design a system based on user’s point of interests. The whole procedure comprise of following: Pages are accessible to the users based on Google API. Based on the point of interest, all the results are retrieved. The proposed methodology is implemented using Google API keys to find places according to user’s point of interests. Three places API used here are place search, place text search and place details API. The technique is tried on self-made database comprising of user information, user’s feedback, country, state and city, spot and spot types. In this website, user can give feedback for the previously visited places

Google API, Point of Interest, Recommendation

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[11] Jie Bao, Yu Zheng, David Wilkie and Mohamed Mokbel “Recommendations in Location-based Social Networks: A Survey, Geoinformatica, 6 February 2015.
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R.H. Joshi, B.D. Deshpande, D.M. Gohane, R.S. Gautam, "An Advanced Intelligent Tourist Guide," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.70-73, 2020.

College Management System is an automated version of student management system. The modules that are managed in this college management system are student personal details, student academic details, student exam result details. These are managed by an administrator. In this college management system the administrator side is designed a web panel with web scraping, where as the student side is designed as Android app. College Management System as categorize into different modules. The following modules are Andhra University Student exam results automation, Certificate Vault designed for both admin and student as web application and Android app, Student complete profile/academic details along with digital study resources.

Automated, Web Panel, Web Scraping, Android App, Digital study resources

[1] Ed Burnette , “Hello android Introducing Google`s Mobile Development Platform”, pp: 30-41, 178-192, ISBN: 10: 1-934356-56-5.
[2] Ashoke Kumar S, “Mastering Firebase for Android Development”, pp: 10-35, 70-75, ISBN: 9781788624251.
[3] Dawn Griffiths, “David Griffiths, Head first Kotlin: a brain-friendly guide”, pp: 300-308, ISBN: 9781491996669.

A. Suraj Kumar, Y. Naga Surya Kiran, P. Sirisha, CH. Srinivas, Sheik Gousia Begum, "Implementation of College Management System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.74-77, 2020.

With a rapidly growing customer base, it has become increasingly difficult for companies to manage and understand customer data, even with the assistance of Customer Relationship Management (CRM) platforms. The vast progress achieved in the field of Data Analytics has made it possible to extract meaningful information from raw customer data efficiently, using which well-informed decisions can be made to enhance customer relationships. This paper discusses the power of Data Analytics and CRM platforms working in unison, known as CRM Analytics. It covers the various applications of CRM Analytics and the key data mining techniques used by business organizations. A comparison between top CRM platforms is carried out and a case study on the latest cloud-based Salesforce CRM platform is presented

CRM, Data Science, Data Analytics, CRM Analytics, Data Mining, Predictive Modeling, Salesforce

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Varun Vijayanand M., Deepamala N., "Survey on Customer Relationship Management Analytics," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.78-85, 2020.

One of the most dominant technologies of today is cloud computing. This dominance is due to its potential to reduce costs while providing the services that are needed to carry out any business more efficiently and effectively. Cloud computing can be deployed universally in very little time. It offers flexibility as well as agility. This paper reviews the features and models of cloud computing. The different models of cloud are discussed in detail and comparison between them is used to understand the functionality and use of each. This paper also provides a comparison between the major cloud computing service providers. The three main cloud providers Amazon, Google, and Microsoft are compared against various features. This helps to understand the services and benefits of each provider. It also includes an overview of the challenges and limitations faced in the field of cloud computing and also the current scope of its application in various industries

Cloud computing, Comparison, Challenges, Applications

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Sukriti Yadav, Rakshith H.V., K. Badari Nath, "A Survey on Cloud Computing Providers and Applications," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.86-93, 2020.

Time series comprise a big portion of real-world data. Time series analysis is one of the most useful tools for researchers and developers. Anomaly detection is one of the key areas of focus in time series. This paper aims to provide a concise and cogent review of the existing research on the topic. In this paper, some widely accepted views and definitions of time series and anomalies are presented. Then, the various methods of anomaly detection in time series are presented, outlining their underlying mechanism, essential features, advantages and drawbacks. Finally, some common trends and observations are summarized, along with a look on the future directions of the research

Time series; anomaly detection; multivariate time series; deep learning; statistics

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[43] Chandola. V, “Anomaly detection for symbolic sequences and time series data”, The University of Minnesota, Minnesota, 2009.
[44] Naoya. T, Takehisa. Y, "Anomaly detection from multivariate time series with sparse representation", Proceeding of IEEE International Conference on Systems, Man and Cybernetics, San Diego, pp.2651-2656, Oct, 2014.
[45] Namrata Ghuse, Pranali Pawar, Amol Potgantwar, "An Improved Approch For Fraud Detection In Health Insurance Using Data Mining Techniques," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.27-33, 2017
[46] R. Satraboyina, GK. Chakravarthi, "Discovery of Ranking Fraud Detection System for Mobile Apps", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.7-10, 2016
[47] Priyanka R.R., Mahesh M., Pallavi S.S., Jayapala G., Pooja M.R., "Crop Protection by an alert Based System using Deep Learning Concept", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.47-49, 2018
[48] N. SelvaKumar, M. Rohini, C. Narmada, M. Yogeshprabhu, "Network Traffic Control Using AI," International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.13-21, 2020

Raghavendran R., Chaitra B.H., "Review of Time Series based Anomaly Detection Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.94-95, 2020.

In the last some years, new methods of communication channels have appeared and become indifferent. These communication channels are the social networking sites which have experienced an exponential growth. During the translation or communication, the problem of polysemy may cause difficulties. Therefore, there is a dire need for sentiment analysis process which can automatically extract and detect the sentiments of data extracted from micro blogging sites. It requires efficient techniques to collect a large amount of social media data and extract meaningful information from them. This paper presents a document level lexicon-based approach to detect the sentiment polarity. So, we focused on pre-processing of data. Instead of removing all polysemy, it includes some polysemous words in the complete procedure of sentiment analysis. We use specific number of polysemy words there but in future we will focus on different words and enhance the accuracy of our documentation.

Sentiment Analysis, polysemy, polarity, wordnet

[1] Udaya Raj Dhungana1, Subarna Shakya, Kabita Baral and Bharat Sharma (2014),” Word Sense Dissambiguation using WSD specific WordNet of Polysemy Words,” in International Journal on Natural Language Computing (IJNLC).
[2] Rice, D. R., & Zorn, C. (2013). Corpus-based dictionaries for sentiment analysis of specialized vocabularies. Proceedings of NDATAD, 98-115.
[3] M. Sinha, M. K. Reddy, P. Bhattacharyya, P. Pandey, and L. Kashyap, “Hindi word sense disambiguation,” Master’s thesis, Indian Institute of Technology Bombay, Mumbai, India, 2004.
[4] U. R. Dhungana and S. Shakya, “Word sense disambiguation in nepali language,” in The Fourth International Conference on Digital Information and Communication Technology and Its Application (DICTAP2014), Bangkok, Thailand, 2014, pp. 46–50.
[5] M. E. Lesk, “Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone,” in SIGDOC Conference, Toronto, Ontario, Canada, 1986.
[6] Kaur, J. (2016). A Review Paper on Twitter Sentiment Analysis Techniques. In International Journal for Research in Applied Science & Engineering Technology (IJRASET) (Vol. 4, No. 2016).
[7] Yih-Jeng LIN, Ming-Shing YU and Chin-Yu LIN (2010), A Combined Approach to the Polysemy Problems in a Chinese to Taiwanese TTS System, National Chung-Hsing University, Taichung. IEEE.
[9] Chauhan, C., & Sehgal, S. (2017, May). Sentiment analysis on product reviews. In Computing, Communication and Automation (ICCCA), 2017 International Conference on (pp. 26-31). IEEE.
[10] Astya, P. (2017, May). Sentiment analysis: Approaches and open issues. In Computing, Communication and Automation (ICCCA), 2017 International Conference on (pp. 154-158). IEEE.
[11] Kolkur, S., Dantal, G., & Mahe, R. (2015). Study of different levels for sentiment analysis. International Journal of Current Engineering and Technology, 5(2), 768Ű770.
[12] Ram, K.S.K., Araballi, S., BR, S., Shobha, G. (2014, December). Sentiment Analysis of Twitter Data. In International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) (Vol. 3 Issue 12, December 2014)
[13] Xiaohui Yu,Yang Liu, Jimmy Xiangji Huang, and Aijun An,,” Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain”, IEEE Trans.On knowledge and data engineering, vol. 24, no. 4, April 2012.
[14] Bhuta, S., Doshi, A., Doshi, U., & Narvekar, M. (2014, February). A review of techniques for sentiment analysis Of Twitter data. In Issues and challenges in intelligent computing techniques (ICICT), 2014 international conference on (pp. 583-591). IEEE.
[15] Mandal, S., & Gupta, S. (2016, December). A Lexicon-based text classification model to analyse and predict sentiments from online reviews. In Computer, Electrical & Communication Engineering (ICCECE), 2016 International Conference on (pp. 1-7). IEEE.
[16] Andrey Kapitanov, Ilona Kapitanova, Vladimir Troyanovskiy, Vladimir Ilyushechkin, Ekaterina Dorogova (2019), Clustering of Word Contexts as a Method of Eliminating Polysemy of Words, National Research University of Electronic Technology, Zelenograd, Moscow, Russia, IEEE.
[17] Mehto, A., & Indras, K. (2016, March). Data mining through sentiment analysis: Lexicon based sentiment analysis model using aspect catalogue. In Colossal Data Analysis and Networking (CDAN), Symposium on (pp. 1-7). IEEE.
[18] Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.
[19] Shahare, F. F. (2017, June). Sentiment analysis for the news data based on the social media. In Intelligent Computing and Control Systems (ICICCS), 2017 International Conference on (pp. 1365-1370). IEEE.
[20] Ramteke, J., Shah, S., Godhia, D., & Shaikh, A. (2016, August). Election result prediction using Twitter sentiment analysis. In Inventive Computation Technologies (ICICT), International Conference on (Vol. 1, pp. 1-5). IEEE.
[21] Jain, A. P., & Dandannavar, P. (2016, July). Application of machine learning techniques to sentiment analysis. In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on (pp. 628-632). IEEE.
[22] Bhoir, P., & Kolte, S. (2015, December). Sentiment analysis of movie reviews using lexicon approach. In Computational Intelligence and Computing Research (ICCIC), 2015 IEEE International Conference on (pp. 1-6). IEEE.

Manisha Malik, Neetu Verma, "Prediction of Polysemous Words in Sentiment Analysis: A Review," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.100-104, 2020.

This project’s motive is to construct a home automation system that uses any mobile device or any electric gadget to control the various home appliances. Smart home automation is very modern and developing field when it uses modern technologies such as Internet of Things (IoT). There are many existing smart home automation systems which includes automatic lights switch on and off, controlling the speed of fan, motion sensors, noise detectors, etc. Most of the electronic appliances which are included in the smart home appliances can be used or manipulated by any user which directly breaches the security of this system. This research mainly deals with the security of the appliances and better performance of the devices installed within the smart home

Wi-Fi, HTTP, POST, GET, Raspberry Pi, Smart Home Automation, Security, Hashing, Encryption, Security

[1] en.wikipedia.org,” home_automation”
[2] w3schools,” http_methods.ref”
[3] tutorialspoint.com, “php_get_post”
[4] watelectronics.com,” know-all-about-raspberry-pi-board- technology”
[5] smarthome.com,” products-you-should-know-about”
[6] iconlabs.com,” security-smart-home”
[7] en.wikipedia.org,” Secure_Hash_Algoithms”

Suraj Rasal, Srijan, Vaibhav Kundu, Linika Labdhi, "Information Delivery in Smart Home Automation Through Security Technique," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.105-109, 2020.

IoT has been a great area of research for providing excellence in area of designing smart cities and intelligent sys-tems. Fuel theft from standing vehicles is a major problem which can be very easily resolved using this technique. In this paper we have proposed a system for detection of fuel theft from vehicle using the concept of IoT as well as wireless sensor networks. The method has shown very good results as compared to other state of the art methods

IoT, Wireless sensor network, smart homes, Internet protocol

[1] Aniket Shinde , Atharva Mane, PurveshSapkale, AmanrajSingh, Jyoti Deshmukh , Prof. FirojMulani ,”Vehicle Fuel Theft Detection Using 89C51, International Journal of Scientific Research and Engineering Development, Vol. 2 ,Issue 2, Mar –Apr 2019.
[2] P. Senthil Raja, B.G.Geetha,” Detection Of Fuel Theft In Heavy Vehicle”, Int J AdvEngg Tech, Vol. 8,Issue 2, April-June,2016, pp.757-765.
[3] M. Saravanan1, T. Krishnapriya, S.R. Lavanya3, P. Karthikeyan, “Fuel Level Indicator For Petrol Bunk Storage Tanks/Oil Industries”, International Research Journal of Engineering and Technology, Vol. 05, Issue: 10, Oct 2018.
[4] Sunil S, Saumya S, “Detection of GSM Based Accident Location, Vehicle Theft and Fuel Theft Using ARM Cortex M-3 Microcon-troller”, International Journal on Future Revolution in Computer Science & Communication Engineering , Vol. 4, Issue-4, April-2018.
[5] Nandini Hiremath , Mrunali Kumbhar1 and Aakriti Singh Pathania, “Embedded system based intelligent digital fuel Gauge”, International Journal of Electronics and Communication (IIJEC), vol. 2,March-April 2016.

P. Sharma, Komal, Akansha, Rajit, Niharika, "Antitheft system for fuel detection using IoT," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.110-116, 2020.

The process of recording/managing attendance has been carried out since many decades. Typical process involves calling out the names of each participant serially which is manually noted down using pen-paper. We propose a secure, parallel, centralised and faster process of attendance management. A mobile application needs to be installed on the stakeholder’s smart phone. The organiser will start the process of attendance which will generate a unique code. The code is shared with the participants (students) which are required to input it on the application. The application will check for the physical presence of the participants using the GPS co-ordinates. The organiser will immediately get the report which is also stored in a central store which can be on premise or on the cloud. This application can also be configured as a SAAS (Software as a Service) offering where multiple organisations can use the same instance of the backend application as the separation is done at the class level

GPS, Magic Code, React Native, Spring Boot, Android Application

[1] Freya. J. Vora, Pooja. L. Yadav, Rhea. P. Rai, Nikita. M. Yadav, Android Based Mobile Attendance System”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 6, Issue 2, February 2016.
[2]Vishwakarma R Ganesh “Android College Management System”, International Journal of Advanced Research In Computer Engineering & Technology (IJARCET) Volume 5, Issue 4, April 2016
[3] Rakhi Joshi1, V. V. Shete2, S. B. Somani3, “Android Based Smart Learning and Attendance Management System”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 6, June 2015
[4] Unnati A. Patel, Dr. Swaminaraya PriyaR.“Development of a Student Attendance Management System Using RFID and Face Recognition: A Review”, International Journal of Advanced Research in Computer Science and Management Studies, Volume 2, Issue 8, August 2014.
[5] Saurabh Kumar Jain, Uma Joshi, BhupeshKumar Sharma, “Attendance Management System” International School of Informatics and Management Jaipur
[6] M. S. Uddin, S. M. Allayear, N. C. Das, and F. A. Talukder, “A Location-Based Time and Attendance System,” Int. J. Comput. Theory Eng., vol. 6, no. 1, pp. 36–38, 2014.
[7] B. Soewito, F. L. Gaol, E. Simanjuntak, and F. E. Gunawan,
“Attendance system on Android smartphone,” ICCEREC 2015 - Int. Conf. Control. Electron. Renew. Energy Commun., pp. 208–211, 2015.
[8] S. Chandrasekaran and D. N. U. Maheswari, “Overview on Location Tracking For Authentication Using Smartphones,” in 2016 10th International Conference on Intelligent Systems and Control (ISCO), 2016, pp. 1–6.
[9] S. Sultana, A. Enayet, and I. J. Mouri, “A Smart, Location-Based Time and Attendance Tracking System using Android Application,” Int. J. Comput. Sci. Eng. Inf. Technol., vol. 5, no. 1, pp. 01–05, 2015.
[10] N. N. Shahade, P. A. Kawade, and S. L. Thombare, “Student Attendance Tracker System in Android,” Int. J. Eng. Appl. Technol. Student, no. C, pp. 119–124, 2013.
[11] M A Muchtar, Seniman, D. Arisandi, and S. Hashanah, “Attendance Fingerprint Identification System Using Arduino and Single Board Computer,” J. Phys. Conf. Ser., vol. 978, 2018.
[12] B. Soewito and E. W. Marciano Simanjuntak, “Efficiency Optimization of Attendance System With GPS and Biometric Method Using Mobile Devices,” Int. J. Commun. Inf. Technol., vol. 8, no. 1, pp. 5–9, 2014.
[13] B. Geetha and F. A. Ahmad, "Attendance System Using a Mobile Device: Face Recognition, GPS or Both?” Int. J. Adv. Electron. Comput. Sci., vol. 3, no. 8, 2016.
[13]S. Badhe, K. Chaudhari, S. Kale, and T. Mane, “Smart Attendance Management System,” in IJCA Proceedings on National Conference on Advancements in Computer & Information Technology, 2016, vol. 7, pp. 213–231.
[14] I. Ahmad et al., “Current technologies and location-based services,” in 2017 Internet Technologies and Applications (ITA), 2017, pp. 299–304.
[15] S. Badhe, K. Chaudhari, S. Kale, and T. Mane, “Smart Attendance Management System,” in IJCA Proceedings on National Conference on Advancements in Computer & Information Technology, 2016, vol. 7, pp. 213–231.

B.K. Sushravya, Swathi B., Kavali Meghana, H. Girisha, "Real-Time Attendance Management System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.117-122, 2020.

CAPTCHA is a security mechanism which is basically used to distinguish between human and bots in automated online systems. Here bot means software robots that are used for brute force attacks or webmail bombarding or DOS attack to the system. CAPTCHA is also useful for protected web services from various types of dynamic attacks every day. Generally, it is seen in online registration form for preventing spam applications. In this paper, we have studied the various types of CAPTCHA like picture CAPTCHA, video CAPTCHA, content CAPTCHA, sound CAPTCHA and their uses in present environments

CAPTCHA; Security; Security by Bot Attack

[1] Korakakis, M., Magkos, E. and Mylonas, P. (2014): Automated CAPTCHA solving: An empirical comparison of selected techniques, Proceedings - 9th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2014, pp. 44–47.
[2] Barbole, B. A. and Surywanshi, S. (2015): A Survey on to Enhance Security Approach Using Discretized Centralization for CAPTCHA as Graphical Password, International Journal of Science and Research (IJSR), 4(11), pp. 826–830.
[3] Yan, J. and Ahmad, A. S. El (2008): A low-cost attack on a microsoft CAPTCHA, Proceedings of the ACM Conference on Computer and Communications Security, pp. 543–554.
[4] Soni, S. and Bonde, P. (2017): E-CAPTCHA : A Two Way Graphical Password based Hard AI Problem, (June), pp. 418–421.
[5] Chew N. and Tygar J. D. (2004): Image Recognition CAPTCHAs, UC Berkeley Computer Science Division technical report.
[6] Fidas, C. A., Avouris, N. M. and Voyiatzis, A. G. (2011): On the necessity of user-friendly CAPTCHA, Conference on Human Factors in Computing Systems - Proceedings, (May 2016), pp. 2623–2626.
[7] Newton, F. and Kouritzin, M. A. (2011): On grey levels in random CAPTCHA generation, Visual Information Processing XX, 8056(c), p. 80560U.
[8] Bursztein, E., Martin, M. and Mitchell, J. C. (2011): Text-based CAPTCHA strengths and weaknesses, Proceedings of the ACM Conference on Computer and Communications Security, 2011, pp. 125–137.
[9] Yan, J. and El Ahmad, A. S. (2008): Usability of CAPTCHAs or usability issues in CAPTCHA design, SOUPS 2008 - Proceedings of the 4th Symposium on Usable Privacy and Security, pp. 44–55.
[10] Siva Nagalakshmi, K., Prakash, P. S. and Prem Kumar, D. S. (2015): Confident Multi-Factor Authentication on web application via CAPTCHA Technologies, International Journal of Computer Engineering in Research Trends, 876(8), pp. 2349–7084.
[11] Jabed, M. and Ranjan, N. (2013): CAPTCHA Based on Human Cognitive Factor, International Journal of Advanced Computer Science and Applications, 4(11), pp. 144–149.
[12] Brodić, D., Amelio, A. and Draganov, I. R. (2017): Statistical Analysis of Dice CAPTCHA Usability, pp. 1–9.
[13] Abdullah Hasan, W. K. (2016): A Survey of Current Research on CAPTCHA, International Journal of Computer Science & Engineering Survey, 7(3), pp. 1–21.
[14] Amelio, A. et al. (2018): The {CAPTCHA} Samples Websit, {ERCIM} News, 2018(112), p. 2018.
[15] Kaur, K. and Behal, S. (2014): CAPTCHA and Its Techniques : A Review, International Journal of Computer Science and Information Technologies (IJCSIT), 5(5), pp. 6341–6344.
[16] Singh, V. and Pal, P. (2014): Survey of different types of CAPTCHA, International Journal of Computer Science and Information Technologies, 5(2), pp. 2242–2245.
[17] Roshanbin, N. (2014): Interweaving Unicode, Color, and Human Interactions to Enhance CAPTCHA Security.
[18] Yan, J. and El Ahmad, A. S. (2016): Usability Analysis of the Specific CAPTCHA Types, International Scientific Conference, pp. II-272–II-277.

Shivank Singh, Rahul Kumar Chawda, "Recent Trends in CAPTCHA Security and Bot Challenges," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.123-127, 2020.