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

This paper explains the design, development, implementation and simulation of car rental service in a digital web-page. The design allows the customers to login and go through the service like setting the details of the car, booking the car based on the tariff. It is a user friendly interface and this increases the retention, simplify the vehicles and staff management. This model simplifies the admin work on modifying the data of cars, bookings, transaction details, car’s availability and updating the details of models. Hence, this design reduces the manual paper work and missing of records or data thereby, this model is helpful for both user and in admin perspective. This Car Rental System is being produced for clients so they can book their vehicles from any piece of the city. This application takes data from the clients through filling their subtleties. A client being enlisted in the site has the access to book a vehicle which he requires. The proposed framework is totally incorporated online frameworks. It mechanizes manual methodology in a powerful and proficient way. This computerized framework encourages client and gives to top off the subtleties as per their necessities. It incorporates sort of vehicle they are endeavouring to contract and area. The motivation behind this framework is to build up a site for the general population who can book their vehicles alongside prerequisites from any piece of the city.

Car Rental, Online System, Car Brands, Car Type, Bookings, Booking Management, Registered users

[1] “Simulating different car class upgrades in a car rental company’s operations” – Abdullah A. Alabdulkarim.
[2]“Forecasting Car Rental demand based on Temporal and Spatial Travel pattern” -Shou Lei, Haiquan Wang, Chen Yang, Bowen DU, Runxing Zhong, Runhe Huang.
[3] “Demand responsive mobility as a service” -Jecinta Kamau Asir Ahmed, Anderw Reberio-H, Hironobu Kitaoka, Hiroshi Okajaima, Zahidul Hossein Ripon.
[4] “A new certificate less electronic cash scheme with multiple banks based on group signatures” -Shanping Wang, Zhiqiang Chen, Xiaofeng Wang.
[5] “On the prediction of future vehicle location in free-floating car sharing system” -Simone Formentin, Andera G. Bianchessi and Sergio M. Savaresi.
[6] “Implementation of RVND, VNS, ILS heuristic for the Travelling Car Renter Problem” -Rogerio Ferreira de Moraes, Andre Renato Villela da Silva, Luiz Satoru Ochi and Luis Marti.
[7] “Implementation of RVND, VNS, ILS heuristic for the Travelling Car Renter Problem” -Rogerio Ferreira de Moraes, Andre Renato Villela da Silva, Luiz Satoru Ochi and Luis Marti.
[8] “Optimization Approach to Station Location of car Sharing system” -Jingna Wang, Guowei Hua.
[9] http://www.zipcar.com/
[10] http://www.car2go.com/
[11] http://www.autorentalnews.com/fileviewer

Nirmala .S.Guptha, Gothe Karthik Srinivas, D. Shankar, Channa Keshava V, Chiranth Gowda, "Online Car Rental System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.339-344, 2019.

Recent advances in remote sensing technology have made hyperspectral data with hundreds of narrow contiguous bands more widely available. The hyperspectral data can, therefore, reveal narrow differences in the spectral signatures of land cover classes that appear to be similar when viewed by multispectral sensors. If successfully used, the hyperspectral data can yield higher classification accuracies and more detailed class taxonomies. In this approach, we are using deep learning and neural networks to train a model for classifying land cover using data analytics in hyperspectral imaging.

Hyperspectral imaging, Land cover classification, Deep learning, Tensor flow

[1]. “Hyperspectral Image Analysis using Deep Learning”- a Review Henrik Petersson, David Gustafsson and David Bergström Swedish Defence Research Agency (FOI)Division of C4ISR
[2]. Yushi Chen, Zhouhan Lin, Xing Zhao, Gang Wang, and Yanfeng Gu. “Deep learning-based classification of hyperspectral data”. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 7(6):2094–2107, June 2014
[3]. “Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery” Chein-I Chang, Fellow, IEEE, Chao-Cheng Wu, Member, IEEE, and Ching-Tsorng Tsai
[4]. N. Keshava and J. Mustard, “Spectral unmixing,” IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44–57, Jan. 2002
[5]. M.Winter, “Fast autonomous spectral end-member determination in hyperspectral data,” in Proc. 13th Int. Conf. on Applied Geologic Remote Sensing, vol. 2, Vancouver, pp. 337–344, Apr. 1999.
[6]. J. M. Nascimento and J. M. Bioucas-Dias, “Vertex component analysis: A fast algorithm to unmix hyperspectral data,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898–910, Apr. 2005.
[7]. N. Dobigeon, S. Moussaoui, M. Coulon, J. Y. Tourneret and A. O. Hero, “Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery,” IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4355–4368, Nov. 2009.
[8]. Makantasis, K.; Doulamis, A.; Doulamis, N.; Nikitakis, “A. Tensor-based classification models for hyperspectral data analysis”. IEEE Trans. Geosci. Remote Sens. 2018, 56, 1–15. [CrossRef]
[9]. Zhang, M.; Li, W.; Du, Q. “Diverse region-based CNN for hyperspectral image classification”. IEEE Trans. Image Process. 2018, 27, 2623–2634. [CrossRef]
[10]. http://www.escience.cn/people/feiyunZHU/Dataset_GT.html the website for dataset and ground truth.
[11]. “TensorFlow: A System for Large-Scale Machine Learning” Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean,Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur,Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker,Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng, Google Brain

Sakshi Suman, Sanjana Suman, Santhosh J, Sham Vignesh, K Anitha, "Classification of land cover using Data Analytics for Hyperspectral Imaging", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.345-348, 2019.

The administrative department of any residential college has one major concern, i.e., the safety and security of their students. This is due to the fact that students from all over the country leave their homes to pursue a good education in the college of their choice, leaving their parents anxious about their safety. To ensure the security of hostellers and put their parents’ mind at ease, our team came up with a solution which is to digitizing the entire permission process in such a way that the usage of paper is completely eliminated and the involvement of the warden is minimalized. “Digital REVA” is a paperless venture which uses a website that allows hostellers to seek permission digitally. The request is received as a notification (SMS or e-mail or simple login) by the respective parent who can respond to it in a stipulated time period. Following this, a QR code is generated which represents “Permission Granted” or “Permission Denied” based on the parent’s response. The QR code is scanned at the security gate and the data is stored in a database.

Safety, Security, Hostellers, Warden, Permission, Digitizing, QR Code

[1] Jae Hwa Chang Lumi, “An introduction to using QR codes in scholarly journals”, ‘Application of advanced information technology to scholarly journal publishing’, 12th EASE General Assembly and Conference, Split, Croatia.
[2] Sumit Tiwari, "An Introduction to QR Code Technology”, International Conference on Information Technology (ICIT), Bhuvaneswar, India.
[3] Kunjal B. Mankad and Priti S. Sajja, “Utilization of Web Services for Service Oriented Architecture”, MCA Department, ISTAR, Sardar Patel University, Vallabh Vidyanagar, Gujarat, India.
[4] STM Siregar, MF Syahputra and RF Rahmat, “Human face recognition using eigenface in cloud computing environment”, Published under license by IOP Publishing Ltd., IOP Conference Series: Materials Science and Engineering, Volume 308, conference 1.
[5] G.Ramachandran, Jayanthi S, E.Ravishankar, P.Balaji and S.SivaPrakash, “Applications of Security System”, Dept of Computer Science, VMKV Engineering College & P.K.R College, Tamil Nadu, India.
[6] Kanumuru Rajesh, S.S Waranalatha, K.V. Mounlka Reddy, M. Supraja, “QR Code-Based Real Time Vehicle Tracking in Indoor Parking Structures”, 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India

Alisha Bilquis, Anmol Itnal, Akash Rana, Bindushree DC, "Digital Reva – A Paper-Free Security Solution", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.349-351, 2019.

Agriculture is a cultivation of land and breeding of animals and plants to provide food, fiber, medicinal plants and other products to sustain and enhance life. Agriculture is a backbone of India, Which provides employment opportunities for rural peoples on a large scale in under developed and developing countries. To protect the crops from damage caused by animal as well as divert the animal without any harm. Due to less monitoring on crops led to the destruction in large scale .The proposed system we are developing a detector for safety measures of saving agriculture. Passive infrared radiation sensor detects the change in infrared radiation of warm blooded moving objects in its detection range and offer a warning through buzzer which makes sound due to this the field can be saved from the intruder and this signal is transmitted to GSM and which gives an alert to farmers. Arduino Uno is used to interface with PIR sensor. PIR sensor are excellent devices for wireless sensor networks being low-cost, low power and presenting a small form of factor. The proposed model we present feature extraction and sensor fusion technique that explodes a set of wireless nodes equipped with PIR sensors to track intruder moving into the field. Our approach has reduced computational and memory requirements. Moreover, this method is also designed in such a way that it lets any farmer can use this technique in a convenient way.

PIR sensor, Arduino Uno, Buzzer

[1]Dr.V.VidayDevi,G.MeenaKumari, ”Real-time automation
and monitoring system fro modernized agriculture”, International Journal of review and research in applied sciences and engineering(IJRASE) Vol3 No.1.PP 7-12,2013.
[2]Mustapha, Baharuddin, AladinZayegh, and Rezaul K. Begg. “Ultrasonic and Infrared Sensors Performance In A Wireless Obstacle Detection System” Artificial Intelligence , Modelling and Simulation (AIMS),2013
[3]S.R.Nandurkar,V.R.Thool,R.C.Thool,”design and development of precision agriculture system using wireless sensor network”, IEEE international conference on automation, control, energy and systems(ACES),2014.
[4]G. Merlinsuba,Y M Jagades, SKarthik and E Raj Sampath, “Smart irrigation system through wireless sensor network” ARPN Journal of engineering and applied sciences, vol:10, pp.1, no.17, sep 2015.
[5] NikeshGondchawar, Dr.R.S. Kawitkar, “IoT based smart agriculture”, International Journal of Advanced research in computer and communication engineering , Vol.5, issue 6, June 2016.
[6]A. Tarun “Know About Passive Infrared Sensor (PIR)with projects“[online]. Available:https://www.elprocus.com/passive-infrared-pir-sensor-with-applications/[Accessed Sep 27,2017].
[7] Arttur Frankiewicz; Rafal Cupek. “Smart Passive infrared Sensor – Hardware Platform” Year: 2013-39thAnnual Conference of the IEEE Industrial Electronics Society Pages:7543-7547.
[8] P. Venkateshwari, E. JebithaSteffy, Dr. N Muthukumaran, ‘License Plate cognizance by Ocular Character Perception’, International Research Journal of Engineering and Technology, Vol 5, No. 2, pp.536-542, February 2018.

S Riyaz, S Pallavi, Reema P, R L Nikhilaa, A Ajil, "Passive Infrared (PIR) Sensor for Safe Agriculture", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.352-355, 2019.

Overseeing regularly developing information in the wellbeing segment is a significant enormous issue. Existing framework comprises of an individual who deals with the information and overseeing in records, despite the fact that in numerous clinics which utilize top of the line advances the information is overseen in databases. Here this is overseen by an IT individual who is definitely not a therapeutic expert subsequently there is a probability of blunder while information goes from specialist to the individual; even a touch of mistake can have deadly outcomes. Our answer for this issue is building up the innovation to decrease all the above-expressed issues, disentangling information passage methodology for restorative experts and recovery of the specific information which is spared beforehand. The Impact done by building up this innovation is streamlining the working example and expanding the effectiveness in work and upgrading the time and diminishing the likelihood of mistake, prompting a stage which is quick and dependable

Diagnosing, Machine Learning, Data Management, Data Security, Digitization, Medication

[1]Raghupathi W. Data Mining in Health Care. In: Kudyba S, editor. Healthcare Informatics: Improving Efficiency and Productivity. 2010. pp. 211–223.
[2] What is Patient-centered Health Care? A Review of Definitions and Principles. 2nd ed. London: IAPO; 2007. International Alliance of Patients’ Organizations; pp. 1–34.
[3] Washington, DC: National Academies Press; 2001.Institute of Medicine, Crossing the Quality Chasm-A New Health System for the 21st Century.
[4] 4. Chin R, Lee BY. Economics and patient reported outcomes, Principles and practice of clinical trial medicine. London, Amsterdam, Burlington, San Diego: Elsevier Inc; 2008. pp. 145–66.

Vikas Madhava, Nishanth B, Mohammed Hashir, Mohammed Mazhar, Chaithra N, "Neosis-Diagnosing Tool", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.356-360, 2019.

IOT has enabled us to connect our day to day devices in a network for a sole purpose to exchange data. Today a number of countries have made it mandatory to wear helmet while riding. In this paper, we propose to build a smart helmet system that can be installed on a bike and enforces that the biker first wears the helmet and also the sensors detect alcohol in breath of the biker and bike does not start in case the biker has not worn the helmet or is drunk. The implementation of this system is proposed to be done using NodeMCU which is an open-source firmware and development kit that helps to prototype or build IoT product. We use firmware which runs on the ESP8266 Wi-Fi SoC from Espressif Systems, and hardware which is based on the ESP-12 module. The firmware uses the Lua scripting language. It is based on the eLua project, and built on the Espressif Non-OS SDK for ESP8266.

IOT , Pressure Sensor, Tilt sensor, GPS , Smart Helmet, Wearable Technology

[1] Lakshmi Devi P, Bindushree R, Deekshita N M, Jeevan M, Likhith, “Helmet using GSM and GPS technology for accident detection and reporting system” , (May-2016) , International Journal On Recent And Inovation Trends In Computing And Communication, (Volume-4, Issue-5, May-2016) E-ISSN: 2321-8169
[2] Abhinav Anand, Kumar Harsh, Kushal Kumar, Sourav Gouthi, “Microcontroller based smart wear for driver safety” (April- 2015), International Journal Of Research In Engineering And Technology, E-Issn: 2319-1163, P- Issn: 2321-7308
[3] Dipak Patil, Shruti Shekhawat, “Smart- Helmet”,Advance In Electronic And Electric Engineering, Vol 4, No 5,2014.
[4] Jennifer William, Kaustubh Padwal, Nexon Samuel, Akshay Bawkar, “Intelligent Helmet”, International Journal Of Scientific & Engineering Research(Ijser), Vol 7, Issue 3, March-2016.
[5]Chitte, Salunke, Akshay S., Bhosale Nilesh T., “Smart Helmet And Intelligent Bike System”, International Research Journal Of Engineering And Technology (Irjet), Vol 5, Issue 5, May-2016.
[6] Mohd Khairul, Mohd Rasli, N K Madzhi, “Smart Helmet with Sensors for Accident Prevention”, International Conference on Electrical, Electronics and System Engineering Volume: 978-1-4799-3178-1/13
[7] Ashish Kumar Pardeshi, Hitesh Pahuja, Balwinder singh , “Development of Real Time Helmet based Authentication with Smart Dashboard for Two Wheelers” , The International Symposium on Intelligent Systems Technologies and Applications September 2016
[8] Sethuram Rao, Vishnupriya.S.M, Mirnalini.Y ,Padmapriya.R.S, “The High Security Smart Helmet Using Internet Of Things”, International Journal of Pure and Applied Mathematics Volume 119 No. 12 2018, 14439-14450
[9] Athuljith , Biren Patel , Sourabh Pardeshi, “Intelligent System for helmet detection using raspberry pi” IJSART – Volume 3, Issue 6, 2017

Aishwarya Babu, Anandita Kushwaha, Anu Rawat ,K Aparna, Nimrita Koul, "Smart Helmet Using IoT", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.361-364, 2019.

To forecast the situation of weather at a particular location is a vital application of machine learning. While traditionally this has been done by human experts by identifying patterns in data collected by various measuring instruments, in modern times the machine learning algorithms are used to crunch data and identify patterns which are used for predicting the weather parameters. In this work, we have used neural networks to analyze data from Dark Sky to forecast the climatic conditions.

Machine Leanring, Weather Montoring, Weather Prediction, Dark Sky

[1] Pielke R.A., “A comprehensivemeterological modeling systemRAMS,” Meteorology andAtmosphe-ric Physics,SpringerVerlag Vol. 49, 69-91p,1992.
[2]Lutgens F.K., and Tarbuck E.J., TheAtmospheric, 6th Edn., Prentice Hall,Englewood Cliffs, NJ, 1995.
[3] Siddiqui Khalid J. and Nugen SteveM., Knowledge Based System for
Weather Information Processing andForecas-ting, Department of
Computer Science, SUNY atFredonia, NY 14063, IEEE 1966
[4] Shekhar S. and Huang Y., “DiscoveringSpatial co-location patterns: a summary ofresults,” Proc. Of 7th Int. Symp. on Spatialand Temporal Database, L.A., CA, U.S.A.,236-256p, Jul. 2001
[5] Tung A.K.H., “Efficient mining ofintertransaction association rules”, IEEETrans. on Knowledge and Data Engineering,vol.15(1), 43-56p, Jan./ Feb. 2003
[6]SharmaA., “A Weather Forecasting Systemusing concept of Soft Computing: A newapproach”, PG Research Group SATI,Vidisha(M.P.), India, IEEE 2006
[7] Khalid S., “Towards a Self-ConfigurableWeather Research and Forecasting System”,School of Computing and InformationSciences, Florida International University,Miami FL, 2008
[8]SenduruSrinivasulu, “Extracting SpatialSemantics in Association Rules forWeather Forecasting Image”, ResearchScholar Department of InformationTechnology, Sathyabama UniversityChennai, India IEEE 2010
[9] Wang Y. and Banavar S. “ConvectiveWeather Forecast Accuracy Analysis atcenter and sector levels”, NASA AmesResearch center, Maffett Field, Califonia
[10] Weather.com, http://www.weather.com
[11] AccuWeather.com,http://www.accuweather.com
[12]Anad M. “Prediction and Classification ofThunderstorms using Artificial NeuralNetwork”, International Journal ofEngineering Science and Technology(IJEST), Vol.3 (5) May 2011.

Ronika Surshetty, Satvik Sabharwal, Shreeya Agrawal, Somesh Yadav, Nimrita Koul, "Machine Learning Based Weather Prediction System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.365-368, 2019.

This paper is an attempt to distinguish the blood cells into classify between White Blood Corpuscles (WBC) and Red Blood Corpuscles RBC to further this classification to find the sickle cell detection. The sub categorization of the red blood corpuscles is an important implementation in this paper for the disease classification. The sickle cell anemia is a disease based on RBCs oxygen carrying capability. In order to avoid the misclassification the RBC sub-categorization is carried out. The sickle cell anaemic cells are found using the machine learning algorithms. The convolutional Neural Network based implementation is carried out to find the sickle and non-sickle cell RBCs. The results obtained are found to be satisfactory

Sickle Cell Anaemia, Convolutional Neural Network(CNN), Deep learning Methods

[1] Ruchika Garg , Asha Nigam , Prabhat Agrawal , Ashwini Nigam and Rachna Agrawal “Iron Carboxymaltose: A Safe and Effective Molecule to Combat Anemia in Pregnancy” , International Journal of Current Research and Academic Review ISSN: 2347-3215 Volume 4 Number 2 (February-2016) pp. 124-130
[2] http://www.who.int/mediacentre/factsheets/fs308/en/
[3] https://en.wikipedia.org/wiki/Dacrocyte
[4] https://en.wikipedia.org/wiki/Sickle-cell_disease
[5] http://www.cdc.gov/ncbddd/thalassemia/facts.html
[6] PranatiRakshita, KritiBhowmikb, “Detection of Abnormal Findings in Human RBC in Diagnosing Sickle Cell Anaemia Using Image Processing” , International Conference on Computational Intelligence: Modeling, Techniques and Applications (CIMTA- 2013).
[7] Shashi Bala, Amit Doegar, “Automatic Detection of Sickle cell in Red Blood cell using Watershed Segmentation”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 6, June 2015.
[8] SiddharthSekharBarpanda,Prof. DiptiPatra,“Use of Image Processing Techniques to Automatically Diagnose Sickle-Cell Anemia Present in Red Blood Cells Smear” , Department of Electrical Engineering, National Institute of Technology Rourkela-769008 (ODISHA) May2013.
[9] Aguilar C, Vichinsky E, Neumayr L. “Bone and Joint Disease in Sickle Cell Disease”, HematolOncolClin North Am.; 19(5):929-4, Oct 2005.
[10] MenikaSahu, Amit Kumar Biswas, K. Uma, “Detection of Sickle Cell Anemia in Red Blood Cell: A Review”, International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-2, Issue-3, March 2015. S. Willium, “Network Security and Communication”, IEEE Transaction, Vol.31, Issue.4, pp.123-141, 2012.
[11] Nathaniel Z. Piety ; Sergey S. Shevkoplyas,Paper-Based Diagnostics: Rethinking Conventional Sickle Cell Screening to Improve Access to High-Quality Health Care in Resource-Limited Settings,IEEE Pulse ,Volume: 8 , Issue: 3 , May-June 2017

Shwetha S Patil, Udaya Rani V, "Intelligent Blood Cell Classification Using Machine Learning Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.369-371, 2019.

The human face plays a very important role in expressing a person’s emotion. Likewise, music is considered to be therapeutic and an inevitable mode of entertainment. Computer system with effective facial recognition algorithms can recognize the emotion and classify them into happy, sad, surprise, calm, angry, etc.,. We have developed an emotion based music player which works primarily based on these classifications. The Haar Cascade Classifier is used to classify these emotions based on user’s facial data. The songs are segregated accordingly and played to enhance the user’s mood. The user can also use the buttons (emojis) to select his/her mood. This project also deals with accounting functionalities and also provides mechanisms for privacy and security. This paper describes a very unique model which makes the facial recognition aspect more accurate than ever. Therefore, this application is developed in order to recognize the emotion of a person accurately and play a song based on the user’s mood.

Haar Cascade Classifier, Emotion Recognition, Music Player, Machine Learning, Data Mining

[1]. Anagha S.Dhavalikar and Dr. R. K. Kulkarni, “Face Detection and Facial Expression Recognition System” 2014 International Conference on Electronics and Communication System (ICECS -2014).
[2]. Yong-Hwan Lee , Woori Han and Youngseop Kim, “Emotional Recognition from Facial Expression Analysis using Bezier Curve Fitting” 2013 16th International Conference on Network-Based Information Systems.
[3]. Arto Lehtiniemi and Jukka Holm, “Using Animated Mood Pictures in Music Recommendation”, 2012 16th International Conference on Information Visualisation.
[4]. F. Abdat, C. Maaoui and A. Pruski, “Human-computer interaction using emotion recognition from facial expression”, 2011 UKSim 5th European Symposium on Computer Modelling and Simulation..
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Deepak R, Venkatesh Prasad, Gowri Sai Prabha, Shravanthi R, Andrew Stephen, "Emotion Based Music Player", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.372-375, 2019.

Artificial intelligence and machine learning are the buzz words in the industry as well as for research. The world is moving towards automation and a project in that field is a step closer towards it. The main idea of the project is to make smart door lock using face recognition. The face recognition is developed using artificial intelligence, image processing and machine learning. Based on the face that is recognized by the system it makes a decision based on what it has learnt. It decides whether to unlock the door or not. Machine learning is also used and implemented for the software to work efficiently. With the increase in the data set the efficiency will also increase. The system is shown and made to learn using different machine learning techniques. This project improves the security of homes and also makes it easier for segregation of the guests. Apart from this an app is used to send notifications to home owners so as to take appropriate actions. It is extremely useful as it solves one of the leading problems in the world.

Machine Learning, Android, Artificial Intelligence, Face recognition

[1]Jain, Abhishek, et al. "IoT-Based Smart Doorbell Using Raspberry Pi." International Conference on Advanced Computing Networking and Informatics. Springer, Singapore, 2019.
[2]Sagar, D., and Murthy KR Narasimha. "Development and Simulation Analysis of a Robust Face Recognition Based Smart Locking System." Innovations in Electronics and Communication Engineering. Springer, Singapore, 2019. 3-14.
[3]Chaithanya, J. Krishna, GAE Satish Kumar, and T. Ramasri. "IoT-Based Embedded Smart Lock Control Using Face Recognition System." International Conference on ISMAC in Computational Vision and Bio-Engineering. Springer, Cham, 2018.
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[6]https://internetofthingsagenda.techtarget.com/definition/Internet-of-Things-IoT
[7]https://www.techopedia.com/definition/190/artificial-intelligence-ai

Niketha Mohan Jamakhandi, Harshith M, Jagriti, Priyanka Bharti, "Smart Door Lock using Face Recognition", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.376-379, 2019.