Volume-07 , Special Issue-14 , May - 2019 Go Back

The therapeutic robotsmeets the needs of the market by integrating electronic technology and network functionality. The interactive robot, which features a particular device that contains embedded sensors in each compartment that not only transmits detected signals when users are taking their pills but also displays the messagestatus back to the LCD screen installed on the robot by displaying details such as time, date, message regarding the intake of the pill along with the buzzer that alerts the patience. This studyuses both hardware and software components which forms the embeded system to implement internet of thing (IOT). The module first interacts with the android application through a wifi module hence creating locomotion in the designed therapeutic robot. After receiving the inputs from the sensors,Arduino will send for text display regarding the intake of the pil on the screen and a timely. Therefore, the elderly staying in their home or nursing home institution can savea lot of time by managing their medications via this proposedIOT system. The smart interactive pill box will be crucial for medical care management for a broad spectrum of patients from disabled to people suffering from amnesia, including the elderly.

The Therapeutic robots, Internet of Things, Arduino,embedded sensors, Wi-Fi route, Pill dispensor

[1] Pei-Hsuan Tsai; Tsung-Yen Chen; Chi-Ren Yu; Chi-Sheng Shih; Jane W. S. Liu,” Smart Medication Dispenser: Design, Architecture and Implementation”; 2010 September 27; IEEE; 2011; p. 99 – 110.
[2] Suraj Shinde1, Nitin Bange2, Monika Kumbhar3, Snehal Patil;” Smart Medication Dispenser”; International Journal; 2017 april.
[3] Huai-Kuei Wu; Chi-Ming Wong; Pang-Hsing Liu; Sheng-Po Peng; XunCong Wang; Chih-Hi Lin; Kuan-Hui Tu;” A smart pill box with remind and consumption confirmation functions”; 2015 IEEE 27th October; 2016; Osaka, Japan.
[4] Tsai, P. H., C. S. Shih, and J. W. S. Liu, “Algorithms for scheduling multiple interacting medications,” Institute of Information Science, Academia Sinica, Taiwan, Technical Report TR-IIS- 08-001, April 2008 Pei Hseun Tseui, “Smart Medication Dispenser: “Design, Architecture and implementation”, IEEE journal, Vol-5, March-2011.
[5] Elizabeth Broadbent, Rie Tamagawa, Ngaire Kerse, Brett Knock, Anna Patience, and Bruce MacDonald” Retirement home staff and residents‟ preferences for healthcare robots”, 18th IEEE International Symposium on Robot and Human Interactive Communication, 2009.
[6] A. Sawand, S. Djahel, Z. Zhang, F. Na; Multidisciplinary approaches to achieving efficient and trustworthy eHealth monitoring systems. Commun. China (ICCC); 2014; IEEE/CIC Int. Conf. (2014); p. 187-192.

Shilpa K.A, Twinkle P, Uma G.N, Vedashree C, Vedashree D, "The Therapeutic Robots", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.479-481, 2019.

The major Agenda of smart device for rectifying air quality and respiratory disorder ALERTER using IOT is that the pollution in essential Environment is a major issue now a days. It is being a must to monitor air quality for better and excellent future and well being of all, these is recommend for an air quality monitoring frame work that let us to check and display present air condition in an area or within a fixed boundary and provides an alarm to the end users relating to Asthmas, Nausea, CODP, Respiratory Acidosis etc., with the aim that they can stay away from the zone of respiratory disorders. Frame work uses air sensors to identify closeness of harmful gases observable all around and always convey this info, the sensors being interfaced with Arduino which makes this info and convey it through the app, this makes experts to check air contamination in different places and prevent it. Experts can keep with the object that they can take precautions to manage the issue.

Arduino, IOT, Blynk, Gas Sensors a track of schools, hospitals and other places which demand safety

[1] Riteeka Nayak, Malaya Ranjan Panigrahy, Vivek Kumar Rai, T Appa Rao, “IoT Based Air Pollution Monitoring System”, Imperial Journal of Interdisciplinary Reasearch (UIR), Vol -3, Issue-4, 2017. Fig4. Email and notification alerts
[2] Sarika Deshmukh, Saurabh Surendran, M.P. Sardey, “Air and Sound Pollution Monitoring System using IoT”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol-5, Issue-6, June -2017.
[3] G. Santucci, From Internet of Data to Internet of Things, Paper for “The International Conference on Future Trends of the Internet, 2009”.
[4] International Journal of Wireless & Mobile Networks (IJWMN), “A Wireless Sensor Network Air Pollution Monitoring System” Vol1.2, No.2, May 2010.
[5] C. Pfister, Getting Started with the Internet of Things. Sebastopol, CA: O`Reilly Media Inc., 2011.

Shashi Kumar S M, Damini R, Hema K, Geetha B, "Smart Device for Rectifying Air Quality and Respiratory Threats", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.482-484, 2019.

AdFence is a location based android application that uses geofence technology for product advertisement. The application is based on the geo-fencing technology to deliver location-based services to the users. The application is basically aimed at advertising using the google maps by creating and setting a geo-fence of certain radius. A user can register a geo-fence (area of advertisement) on the map in the application. The geo-fence is characterized by the events in its entry, exit and inside of geofence section. Whenever a user enters or exits a particular geo-fence a series of event or activity triggers can be alerted to the user via the application. The user as an advertiser must set a geofence of a particular radius on the google map in the application. The geo-fence must be provided with the details of its category (Shopping, Restaurants, Medical etc...). The user as a normal user can discover geo-fences on the map during his free roam. The user is expected to set the preference category of interest to be discovered on the map. Once the user enters a particular geo-fence of a selected category a notification of the details of the advertiser and the services offered is displayed on the application. This project is an attempt to improvise and enhance the advertisement system using the geo-fencing technology. The data generated through the application can also be used to study the various factors affecting the market trends, customer survey, advertisement behaviour and user preferences. It can also be used to predict the change in the market trends and customer-retailer interactions in the future and also come up with the solutions to tackle the ever changing markets. This application can also be utilized in an emergency situation. In case of a disaster, hazard situation or road accident, the privileged user (such as a government or verified personnel) is given an option to signal the event on the map as a beacon signal to notify the people about the situation. This helps people to be aware of their surroundings and act accordingly to the situation they encounter.

Geofence, Offers notification, Shops are alerted using GPS

[1]. http://www.androidauthority.com
[2]. http://www.developer.google.com
[3]. https://www.developerr.android.co
[4]. http://www.wikipedia.com
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[6]. http://www.github.com
[7]. https://www.youtube.com/PulsateHQ
[8]. Albright, Brian. “How Geofencing Can Expand The Benefits Of Your Mobile Solution.” Field Technologies Online. 24 May 2013. Web. 5 Dec. 2014.
[9]. Moltz, Barry. “Geofencing: The Latest Tool to Attract Mobile Customers.” OPEN Forum. American Express, 8 Oct. 2013. Web. 5 Dec. 2014.
[10]. U. Bareth, A. K¨upper, and B. Freese, “Geofencing and Background Tracking - The Next Features in LBS,” in Proc. of the 41th Annual Conf. of the Gesellschaft f¨ur Informatik e.V. (INFORMATIK 2011), vol. 192. Berlin, Germany: K¨ollen Druck Verlag GmbH, Oct 2011.
[11]. P. J. Ludford, D. Frankowski, K. Reily, K. Wilms, and L. Terveen, “Because I Carry My Cell Phone Anyway: Functional Location-based Reminder Applications,” in Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, ser. CHI ’06. New York, NY, USA: ACM, 2006, pp. 889–898.
[12]. Sandro Rodriguez Garzon, Bersant Deva Gabriel Pilz, Stefan Medack, “Infrastructure-assisted Geofencing: Proactive Location-based Services with Thin Mobile Clients and Smart Servers,” in 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, March 2015 ,MobileCloud.2015.31

Sudarshan Reddy, M Gowtham, Tharun K, SK Shareef, Gopinath R, "Adfence", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.485-488, 2019.

Individuals and corporate user’s appetite to use email as one of the vital sources of communication. Email has become one of the part and parcel of our lives. Due to globalization, there is an extensive increase in the volume of emails received by a user. A particular user receives about 50-60 emails per day of different categories, for some users it may reach 100 emails. Out of these emails, most of them are not related to user interest. As the volume of emails receive continues to grow, the user has to spend a significant amount of time to process emails. It requires a system to manage these emails and to develop an automated classification system to classify emails into various categories as per the individuals and professional needs such as: academic, business, commercial. This paper presents a comprehensive review of several articles of email classification. The generic framework for email classification is devised and various steps in the framework are discussed in detail. The comparative analysis of various email classification techniques is discussed. The various challenges in the field of email classification are also presented.

E-mail classification, E-mail categorization, Text classification, Preprocessing techniques, Feature extraction and Machine learning techniques

[1] G Mujtaba et al.: Email Classification Research Trends: Review and Open Issues. IEEE Access, Vol. 5, 2017, pp. 9044-9064.
[2] J. D. Brutlag and C. Meek. : Challenges of the email domain for text classification. In: Proc. ICML, 2000, pp. 103–110.
[3] W. W. Cohen. : Learning rules that classify e-mail. In: Proc. AAAI Spring Symp. Mach. Learn. Inf. Access, 1996, p. 25.
[4] Alper Kursat Uysal, Serkan Gunal: The impact of preprocessing on text classification. International Journal Information Processing and Management (50) 2014, pp. 104-112.
[5] Ayca Deniz, Hakan Ezgi Kiziloz. : Effects of various preprocessing techniques to Turkish text categorization using N-Gram features. IEEE 2nd International conference on Computer Science and Engineering (UBMK ‘17), 2017, pp. 655-660.
[6] Julia Proskurnia et al.: Template induction over Unstructured Email corpora. In: Proc. International World Wide Web Conference committee (IW3C2), 2017.
[7] A. Zhang, L. G. Pueyo, J. B. Wendt, M. Najork, and A. Broder. : Email Category Prediction. New York, NY, USA: Association for Computing Machinery (ACM), 2017.
[8] R. Team. : Email statistics report, 2015-2019, The Radicati Group, Inc. Palo Alto, CA, USA, Mar. 2015.
[9] Carlos Adriano Gonc¸alves, Celia Talma Gonc¸ alves, Rui Camacho1, Eugenio Oliveira. : The impact of Pre-Processing on the Classification of MEDLINE Documents. 10th International workshop on pattern recognition in information systems, 2010, pp. 53-61.
[10] Rehab Duwairi, Mohammad Nayef Al-Refai, Natheer Khasawneh. : Feature reduction techniques for Arabic Text classification. Journal of the American Society for Information Science and Technology, 60(11):2347–2352, 2009.
[11] A. A. Alurkar et al.: A proposed data science approach for email spam classification using machine learning techniques. 2017 Internet of Things Business Models, Users, and Networks, Copenhagen, 2017, pp. 1-5.
[12] X. Li, J. Luo and M. Yin. : E-Mail Filtering Based on Analysis of Structural Features and Text Classification. 2010 2nd International Workshop on Intelligent Systems and Applications, Wuhan, 2010, pp. 1-4.
[13] A. Borg and N. Lavesson. : E-mail Classification Using Social Network Information. 2012 Seventh International Conference on Availability, Reliability and Security, Prague, 2012, pp. 168-173.
[14] A. Harisinghaney, A. Dixit, S. Gupta and A. Arora. : Text and image based spam email classification using KNN, Naïve Bayes and Reverse DBSCAN algorithm. 2014 International Conference on Reliability Optimization and Information Technology (ICROIT), Faridabad, 2014, pp. 153-155.
[15] W. Li and W. Meng. : An empirical study on email classification using supervised machine learning in real environments. 2015 IEEE International Conference on Communications (ICC), London, 2015, pp. 7438-7443.
[16] M. K. Chae, A. Alsadoon, P. W. C. Prasad and A. Elchouemi. : Spam filtering email classification (SFECM) using gain and graph mining algorithm. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 2017, pp. 1-7.
[17] S.Smadi, N.Aslam, L.Zhang, R.Alasem, and M.A.Hossain. : Detection of phishing emails using data mining algorithms,’’ in Proc. 9th Int. Conf. Softw., Knowl., Inf. Manage. Appl. (Skima), 2015, pp. 1–8.
[18] M. A. Oveis-Gharan and K. Raahemifar. : Multiple classifications for detecting spam email by novel consultation algorithm. In Proc. IEEE 27th Can. Conf. Elect. Comput. Eng., New York, NY, USA, 2014, pp. 1–5.
[19] R. S. Michalski, J. G. Carbonell, and T. M. Mitchell. : Machine Learning: An Artificial Intelligence Approach. New York, NY, USA: Springer, 2013.
[20] M. Balakumar and V.Vaidehi. : Ontology Based Classification and Categorization of Email. New York, NY, USA: IEEE Press, 2008.
[21] S. R. Gomes et al.,: A comparative approach to email classification using Naive Bayes classifier and hidden Markov model. 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, 2017, pp. 482-487.
[22] Zhao Lu and Jianguo Ding. : An efficient semantic VSM based email categorization method. 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, 2010, pp. V11-525-V11-530.
[23] W. Li, W. Meng, Z. Tan and Y. Xiang. : Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning. 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, Beijing, 2014, pp. 174-181.
[24] S. Garcia, J. Luengo, F. Herrera. Data Preprocessing in Data Mining, Springer, 2015.

Aruna Kumara B, Mallikarjun M Kodabagi, "E-mail Classification System: A Review and Research Challenges", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.489-495, 2019.

Poultry farming is a profitable business, as it produces protein-rich food resources like meat and eggs. However a healthy environment is required for the growth of chicken. To ensure the production of healthy birds, a step was taken to move from manual farming to smart farming. The poultry farm observance system is a solution to constantly monitor the ideal environmental conditions like temperature, humidity and water level. In this paper we have designed an IOT based automated smart poultry farming to monitor the environment and trigger an alarm to control the unfavorable conditions for the growth of the bird.

Raspberry Pi 3B+, MQ -135, DHT11 , IOT, Smart Poultry

[1] So-In C, Poolsanguan S, Rujirakul K. A hybrid mobile environmental and population density management system for good poultry farms. Computers and Electronics in Agriculture. 2014; 109:287–301.
[2] Islam MS, Islam A, Islam MZ, Basher E. Stability analysis of standalone biogas power plants in poultry farms of Bangla Desh. IEEE Transaction on Power System. 2014 Aug.
[3] Junho Bang1, Injae Lee1, Myungjun Noh1, Jonggil Lim1 and Hun Oh2, “Design and Implementation of a Smart Control System for Poultry Breeding`s Optimal LED Environment,” International Journal of Control and Automation Vol.7, No.2 (2014), pp.99-108
[4] Fangwu Dong, Naiqing Zhang, “Wireless Sensor Networks Applied on Environmental Monitoring in Fowl Farm,” HAL Id: hal-01055409 https://hal.inria.fr/hal01055409 Submitted on 12 Aug 2014.
[5] Drishti Kanjilal, Divyata Singh, Rakhi Reddy, Prof Jimmy Mathew, “ Smart Farm: Extending Automation To The Farm Level,” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 3, ISSUE 7, JULY 2014, ISSN 2277-8616
[6] Kumar A. and Hancke, G. P. A Zigbee based mostly Animal Health watching System, Senior Member, IEEE, 2013.
[7] Seung Ho Kim; Jong Mun Jong; Min Tae Hwang; Chang Soon Kang, “Development of an IoT-based atmospheric environment monitoring System.” International Conference on Information and Communication Technology Convergence (ICTC). 2017
[8] Somansh Kumar, Ashish Jasuja,“ Air quality monitoring system based on IoT using Raspberry Pi.”, International Conference on Computing, Communication and Automation (ICCCA), 2017.
[9] Himadri Nath Saha, Nilan Saha, Rohan Ghosh, SayantanRoychoudhury, “Recent trends in implementation of Internet of Things— A review”, IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2016

Vamsi Krishna M, Priyanka S Gowda, Sarika R, Surekha Thota, "Automated Poultry Farming Observance System Using IOT", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.496-498, 2019.

“Indian Sub-continent risk game” is a turn based strategy game having AI players. To give an authentic feel, this game uses a map of ancient India and ancient names for the territories. The dice rolled decides whether a battle is either won or lost. This game can be played against 2 AI players or against human players. The game is developed using Python’s pygame library which has features for basic game development. The user interfaces includes animations like dice being rolled and striking out each other, sound effects for victory and defeat. In turn based games such as Risk it is important to keep the users attention while the AI is playing its turn, to prevent the user from becoming impatient and as such the game incorporates animations and sound effects that are necessary to prevent the game from becoming mundane. The game can be played by users who are as young as 8 to as old as possible. The game has been designed to last for an average of about half an hour.

turn-based-game, strategy-games, probability, dice-rolls, world-domination, Risk, Artificial Intelligence

[1] https://www.thesprucecrafts.com/history-of-risk-412339
[2]Gordon, david, "http://www.cardboardrepublic.com/classics/risk-vs-diplomacy"
[3] "Risk! Rules of Play" . Parker Brothers. 1963. https://www.hasbro.com/common/instruct/Risk1963.PDF
[4] Garrett Robinson’s “The Strategy of Risk” http://web.mit.edu/sp.268/www/risk.pdf
[5]Honary, E. (2007). Total diplomacy: The art of winning Risk. Total Diplomacy.
[6] Osborne, J. A. (2003). Markov chains for the RISK board game revisited. Mathematics magazine, 76(2), 129-135. https://www4.stat.ncsu.edu/~jaosborn/research/RISK.pdf
[7] Wolf, M. (2005). An intelligent artificial player for the game of Risk. Unpublished doctoral dissertation). TU Darmstadt, Knowledge Engineering Group, Darmstadt Germany.
http://www. ke. tu-darmstadt. de/bibtex/topics/single/33.
[8] https://martinsonesson.wordpress.com/2018/01/07/creating-an-ai-for-risk-board-game/
[9]Baris Tan, Markov Chains and the RISK Board Game, this Magazine 70 (1997), 349–357. http://www.ai.rug.nl/~dwedema/projects/Book_review:Games_and_information:_An_introduction_to_Game_Theory_July_2011.pdf
[11] Rasmusen, E., & Blackwell, B. (1994). Games and information. Cambridge, MA, 15.

Ashank Dsouza, Om Prakash, Nitesh Raghavan, Surekha Thota, "Indian Sub-Continent Risk Game", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.499-501, 2019.

In today`s world, location based social networks are mainly used for recommending point of interest. The behaviour of users is mainly arriving at a POI location and these are influenced by their friends and it`s also based on their individual preference. Social influence in social networks can be used by businesses to lure more customers. Depending on the categories of POIs, different target users have a different impact on different categories. This paper selects the POIs which have more influence on the target user by providing a POI score. The main aim is to satisfy target user’s need and to promote business locations (POIs). Based on the score, businesses can look to expand their impact scope.

location based social networks; location promotion; POI location

[1] Li G, Chen S, Feng J, et al. Efficient location-aware influence maximization[C] ACM SIGMOD 2014: 87-98.

[2] Bao J, Zheng Y, Mokbel M F. Location-based and preference-aware recommendation using sparse geo-social networking data[C]. International Conference on Advances in Geographic Information Systems. ACM, 2012:199-208.
[3] Sarwat M, Levandoski J J, Eldawy A, et al. LARS*: An efficient and scalable location-aware recommender system [J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(6): 1384-1399.
[4] Zhu W Y, Peng W C, Chen L J, et al. Modelling user mobility for location promotion in location-based social networks[C] ACM SIGKDD 2015: 1573-1582.
[5] Fei Yu, Zhijun Li, Shouxu Jiang, Shirong Lin, et al. Point-of-interest Recommendation for Location Promotion in Location-based Social Networks. IEEE Conference on Mobile Data Management.

Shantala Devi patil, Aditi Sunil Deshpande, Keerthi Chandra P, Komala M, Madhushree T, "Point-of-interest Recommendation in Location-Based Social Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.502-504, 2019.

Humans are constantly fascinated with auto-operating AI-driven gadgets. The latest trend that is catching the eye of the majority of the tech industry is chatbots. And with so much research and advancement in the field, the programming is winding up more human-like, on top of being automated. The blend of immediate response reaction and consistent connectivity makes them an engaging change to the web applications trend. A chatbot (sometimes referred to as a chatterbot) is a computer program that attempts to simulate the conversation or "chatter" of a human being via text or voice interactions. A user can ask a chatbot a question or make a command, and the chatbot responds or performs the requested action. This work is about implementing a chatbot for education institutions, primarily on mobile and gadgets using google dialog flow. This work helps student enrolment’s and to know more about the university and their offerings. This automatically makes instant messaging their preferred channel of communication, even when it comes to seeking support with an issue, or information about a program they would like to join. The average preference for instant messaging makes it highly beneficial for universities and colleges to create a chatbot to boost interest and enrolments in their course

Agents, Chatbot, Dialogflow, Entities, Intents, Webhook

[1] M. Dahiya, “A Tool of Conversation: Chatbot”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.158-161, 2017.
[2] Jincy Susan Thomas1, Seena Thomas2, “Chatbot Using Gated End-to-End Memory Networks” Vol.5, Issue.3, 2018.
[3] Bhuvnesh Pathak, Umang Garg, Jaya Gupta “Adjútor: An AI based Personal Assistant and Hindi Text Reader” International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 2, 2019 .
[4] https://dzone.com/articles/conversational-interfaces-powered-by-ai-dialogflow
[5] https://dialogflow.com/docs
[6] https://tutorials.botsfloor.com/dialogflow-fulfillment-webhook-tutorial-7cf4ceba0e5e
[7] Sameera A. Abdul-Kader, Dr. John Woods “Survey on Chatbot Design Techniques in Speech Conversation Systems” International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 2, 2019 .

D.S. Nithin, R. H. Vishwanath, "ChatBot Using Google Dialog Flow", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.505-508, 2019.

In this paper, we introduce a multi-factor authentication forverification (2FA) get to control framework for online distributed computing administrations. In particular, in our proposed 2FA access control framework, a characteristic based access control component is actualized with the need of both a client mystery key and a lightweight security gadget. As a client can`t get to the framework on the off chance that they don`t hold both, the component can improve the security of the framework, particularly in those situations where numerous clients share a similar PC for electronic cloud administrations. Likewise, trait based control in the framework additionally empowers the cloud server to limit the entrance to those clients with a similar arrangement of qualities while safeguarding client protection, i.e., the cloud server just realizes that the client satisfies the required predicate, however has no clue on the accurate personality of the client.

Cipher text, Encryption, Distributed storage structures, Security, Cloud computing, Access Control, SEM

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[2] M. H. Au, A. Kapadia, and W. Susilo. Blacr: Ttp-free blacklist able anonymous credentials with reputation. In NDSS. The Internet Society, 2012.
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S. Mayur, M. Harish, R. Tejesh, Vishal Umrao, ShaliniTiwari, "Multi-Factor Authentication", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.509-511, 2019.

Rapid growth of automobile industry has provided us with a faster and cheaper transportation experience than ever. However, this rapid growth has resulted his heavy traffic and parking problems. Everyday hundreds of cars are towed by traffic police due to illegal parking. This problem is more prominent in metropolitan cities where crowd increases exponentially during peak hours. We are providing solution to this parking problem using mobile app. The mobile app will inform users about pre-occupied parking space to save time. The Mobile App can help to simplify the process of vehicle parking in busy streets. The vehicle parking system will use GPS system to provide the user with the nearest parking space to ensure an efficient parking of the vehicle. At the entrance the gates will be installed with RFID readers to provide a human-free interaction which will provide a faster and error-free parking, all of this while providing a safe and reliable parking where the user can park his vehicle without doubting about its safety.

Smart Parking system, RFID Technology and Android

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[3] Leyden, John, "Security takes a back seat on Android in update shambles", 2017.
[4] Prof. Ashwini Gavali, Pooja Kunnure, VarshaPatil, “Smart Parking System Using Android” Vol. 5, Issue 2, pp: (48-52), Month: April -June 2017.
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Mayank Mehiral, Abhishek, Bhavana M, Raghavendra Reddy, "Novel Approach to Smart Parking SystemBy Using RFID", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.512-515, 2019.