During the course of this project we made an attempt to scale down human labour by creating a system which is able to indicate plant health. Essentially the aim of this project is creating a monitoring system which is able to work on the following principle which indicates the plant health by observing colour of their leaves. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification.The system will note encompassing environmental conditions like temperature, humidity, moisture etc. and it'll be displayed on LCD display and image processing is used for the detection of plant diseases.

Keywords –Disease detection, Image processing, Environmental conditions.

[1]. Ramakrishnan.M, Sahaya Anselin Nisha. A," Groundnut Leaf Disease Detection and Classification by using Back Probagation Algorithm" IEEE ICCSP 2015 conference.
[2]. Sachin D. Khirade, A. B. Patil," Plant Disease Detection Using Image Processing "2015 International Conference on Computing Communication Control and Automation.
[3]. Rajleen Kaur, Dr. Sandeep Singh Kang," An Enhancement in Classifier Support Vector Machine to Improve Plant Disease Detection" 2015 IEEE 3rd InternationalConference on MOOCs, Innovation and Technologyin Education (MITE)
[4]. Ms. Kiran R. Gavhale, Prof. Ujwalla Gawande, Mr. Kamal O. Hajari," Unhealthy Region of Citrus Leaf DetectionUsing Image Processing Techniques"2014 International Conference for Convergence of Technology.
[5]. Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S.Byadgi," Identification and Classification of Fungal disease Affected on Agriculture/Horticulture Crops using Image Processing Techniques" 2014 IEEE International Conference on Computational Intelligence and Computing Research.

The Internet of Things (IOT) is a revolutionary technology that represents the future of computing and communications. Most of the people all over world depend on agriculture. Because of this reason smart IT technologies are needed to migrate from traditional agriculture methods to modern methods. Using modern technologies can control the cost, maintenance and monitoring performance. Precision agriculture sensor monitoring network is used greatly to measure agri-related information like temperature, humidity, water level etc. So with IoT, farmers can remotely monitor their crop and farming equipment and automatic irrigation is done by phones and computers using mobile computing and cloud computing. Recommendation of crop can be done using data mining. Also plant diseases are identified using image processing.

Keywords: Automated Irrigation, Crop Suggestion, Image Processing, Internet of things, Networks Smart Farm.

[1]. Ram Krishna Jha,Santosh Kumar,Kireet Joshi, Rajneesh Pandey" Field Monitoring Using IoT in Agriculture"International Conference on Intelligent Computing,Instrumentation and Control Technologies (ICICICT) – 2017
[2]. Rajalakshmi.P, Mrs.S.Devi Mahalakshmi, "IoT based crop-field monitoring and Irrigation Automation" [3]. Prof C. H. Chavan, Mr.P. V.Karande "Wireless Monitoring of Soil Moisture, Temperature & Humidity Using Zigbee in Agriculture" International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 10 - May 2014.
[4]. Dinkar R Patnaik Patnaikuni " A Comparative Study of Arduino, Raspberry Pi and ESP8266 as IoT Development Board" International Journal of Advanced Research in Computer Science, pp. 2350-2352, Volume 8, No. 5, May-June 2017.
[5]. Mahammad Shareef Mekala, Dr P. Viswanathan "A Survey : Smart Agriculture IoT with Cloud Computing"InternationalconferenceonMicroelectronic Devices, Circuits and Systems (ICMDCS) 10-12 Aug 2017.

Blockchain is offering new opportunities to develop new types of digital services. While research on the topic is still emerging, it has mostly focused on the technical and legal issues instead of taking advantage of this novel concept and creating advanced digital services. We are going to leverage the open source. Blockchain technology to propose a design for a new electronic voting system that could be used in local or national elections. The Blockchain-based system will be secure, reliable, and anonymous, and will help increase the number of voters as well as the trust of people in their governments.

Keywords:Blockchain, Electronic Voting System, e-Voting, I-Voting, iVote.

[1]. D. L. Chaum, "Untraceable Electronic Mail, Return Addresses, and Digital Pseudonyms", Communication of the ACM. Vol. 24(2). (1981), pp. 84-90.
[2]. Madise, Ü. Madise and T. Martens, "E-voting in Estonia 2005. The first practice of country-wide binding Internet voting in the world."Electronic voting,2nd International Workshop, Bregenz, Austria, (2006) August 2-4.
[3]. Trueb Baltic, "Estonian Electronic ID – Card Application Specification Prerequisites to the Smart Card Differentiation to previous Version of EstEID Card Application." http://www.id.ee/public/TBSPEC-EstEID-Chip-App-v3_5-20140327.pdf.
[4]. Cybernetica."Internet Voting Solution."https://cyber.ee/uploads/2013/03/cyber_ivoting_NEW2_A4 _web.pdf

Recently, a large number of cases have been reported about people getting addicted to the use of smartphones. With the ever increasing influence of social media, people spend a lot of time on such apps. Especially young children these days are given smartphones at a very young age. They may not understand what's good for them and what's harmful. In such cases over usage of smartphones leads to various problems such as lack of exercise,social anxiety,etc. In this project, we have tried to implement an application that basically uses two modules- a child module and a parent module. The parent module gets the overall usage analysis of the children, and the parents can decide if they want to put a specific limit on usage of a single app. If the child exceeds the limit,the app gets locked...........

Keywords:Internet addiction, Smartphone addiction, Android application, Activity monitoring,Usage
control, Addiction control..

[1]. S.-I. Chiu "The Relationship between Life Stress and Smartphone Addiction on Taiwanese University Student: A Mediation Model of Learning Self-Efficacy and Social Self-Efficacy" Computers in Human Behavior vol. 34 pp. 49-57 2014.
[2]. Lapointe C. Boudreau-Pinsonneault I. Vaghefi "Is Smartphone Usage Truly Smart? A Qualitative Investigation of IT Addictive Behaviors" Proceedings of the 46th Hawaii International Conference on System Sciences 2013.
[3]. [3]. C. Shin A. K. Dey "Automatically Detecting Problematic Use of Smartphones" Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing pp. 335-344 2013.
[4]. Z. Xu O. Turel Y. Yuan "Online game addiction among adolescents: motivation and prevention factors" European Journal of Information Systems vol. 21 no. 3 pp. 321-340 2012.
[5]. C. Wang M. K. Lee Z. Hua "A theory of social media dependence: Evidence from microblog users" Decision Support Systems vol. 69 pp. 40-49 2015.

Reinforcement learning (RL) has made tremendous achievements, e.g., AlphaGo. Here I list (deep) RL applications in the following categories: computer systems, "science, engineeringandarts", finance, businessmanagement, healthcare, education, energy, transportation, autonomous vehicles, games, robotics, computer vision, and natural language processing (NLP). Games, robotics, computer vision, and NLP are intentionally put at the end, since there are too many recent papers in these areas. I list only a few papers for games and robotics, since they are traditional RL application areas, and many people are familiar with them.

[1]. https://skymind.ai/wiki/deep-reinforcement-learning#define
[2]. https://blog.algorithmia.com/introduction-to-reinforcement-learning/
[3]. https://mc.ai/reinforcement-learning-applications/

Sentiment analysis lets marketers to collect rich data on customer attitudes and opinions in real time. Marketers can also gather feedback on customer reviews as they occur without having to invest in lengthy and costly market research activities. Sentiment analysis results in better targeted marketing, brand-reputation protection and faster detection of opportunities and threats. Sentiment Analysis helps to build a predictive model for analyzing the impact of customer sentiments on product sales.

[1] Changbo Wang, Zhao Xiao, Yuhua Liu, Ynru Xu, Aoying Zhu, and Kang Zhang, "Interpreting the Public Sentiment Variations on Twitter", IEEE transactions on human- machine systems, vol. 43, no. 6, november 2013.
[2] Desheng Dash Wu, Lijuan Zheng, and David L. Olson, "A Decision Support Approach for Online Stock Forum Sentiment Analysis", IEEE transactions on systems, man, and cybernetics: systems, vol. 44, no. 8, august 2014.
[3] Eduard C. Dragut, Hong Wang, Prasad Sistla, Clement Yu and Weiyi Meng, "Polarity Consistency Checking for Domain Independent Sentiment Dictionaries", IEEE transactions on knowledge and data engineering, vol. 22, 2010.

RPA is the technology to manipulate data and to communicate with other digital systems & so on. It is to arrange computer software to act as ROBOT. According to the capabilities of employees , they can boost up the use of robotic process automation. Along with time and money which are two main factors which can be saved by the use of it.RPA "Robots" are same as industrial robots, as it provides improvements in accuracy and increases productivity as well. This transformational process helps people out from dull and repetitive tasks. Large number of industries can use these technologies. The emerge of technologies are based on the artificial intelligence workers. This will help to reduce the barrier to use automated products. People blend how they spend their time like never before.

Keywords: rpa, artificial intelligence, digital transformation.

[1]. https://www.cio.com/article/3083924/innovation/robotic-process-automation-for-healthcare.html
[2]. https://tekclasses.com/course-cat/robotic-process-automation-rpa/
[3]. https://en.wikipedia.org/wiki/Robotic-process-automation http://internetofthingsagenda.techtarget.com/definition/robotic-processautomation
[4]. https://www.lexology.com/
[5]. http://www.expertsystem.com/machine-learning-definition/

A common dilemma of our routine life while relaxing is to decide what content to watch. Sitting in front of TV seems like a futile exercise with no control and no remembrance of content that we consumed. We prefer a smart platform which understands your mood and preferences and not just run on predefined content. We will develop recommender system that recommends movies similar to a given input movie. To create the hybrid model, we accumulated the results of an autoencoder which learns content-based movie embeddings from tag data, and a deep entity embedding neural network which learns collaborative-based movie embeddings from ratings data.

Keywords: Content Filtering, Collaborative Filtering, Deep Learning, Recommender System

[1]. Lund, J., & Ng, Y. (2018). Movie Recommendations Using the Deep Learning Approach. 2018 IEEE International Conference On Information Reuse And Integration (IRI).
[2]. Rahul Katarya, Om Prakash Verma (2016).Effective collaborative movie recommender system using asymmetric user similarity and matrix factorization.International Conference on Computing, Communication and Automation (ICCCA2016).
[3]. Ashish Pal, Prateek Parhi and Manuj Aggarwal(2017), An Improved Content Based Collaborative Filtering Algorithm For Movie Recommendations, Proceedings of 2017 Tenth International Conference on Contemporary Computing ( IC3), 10-12 August 2017, Noida, India.
[4]. Bagher Rahimpour Cami, Hamid Hassanpour, Hoda Mashayekhi(2017), A Content-based Movie Recommender System based on Temporal User Preferences, 2017 3rd Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS).
[5]. Ms. Neeharika Immaneni, Ms. Isha Padmanaban, Ms. Bagavathy Ramasubramanian, Mrs. Rajeswari Sridhar(2017), A Meta-Level Hybridization Approach to Personalized Movie Recommendation.

Heart disease is the top reason for the increasing deaths around the globe. Heart disease detection is carried out by using techniques such as Data Mining. They are widely used in systems for prediction and diagnosis. They discover hidden patterns as well as relationships from the data. The reason behind creating such applications is the leading cause of death due to heart disease. Mostly the systems developed before predict heart disease use data having parameters which are obtained from labs conducted by doing complex tests. Mostly there are no system that predicts heart disease based on risk factors such as age, hypertension, tobacco intake, alcohol consumption, etc. Such common factors are present in heart disease patients. These factors can be used effectively for diagnosis.............

Keywords- Heart Disease, Heart Disease Prediction, Neural Networks, Genetic Algorithm, Risk Factors

[1]. Poirier P, Healthy lifestyle: even if you are doing everything right, extra weight carries an excess risk of acute coronary events. Circulation 117: 3057–3059, 2008.
[2]. Wood , De Backer, Prevention of coronary heart disease in clinical practice: recommendations of the Second Joint Task Force of European and other Societies on Coronary Prevention. Atherosclerosis 140: 199– 270, 1998.
[3]. Andersonc KM, Odell PM, Cardiovascular disease risk profiles. Am Heart J 121: 293–298. 1991.
[4]. Anderson , Odell PM, Wilson PWF, Kannel WB. Cardiovascular disease risk profiles. Am Heart J., 121: 293–298, 1991.
[5]. Kannel WB, An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol 110: 281– 290, 1979.

In this paper, data mining concepts are summarized. Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue.

[1]. Charmi Mehta,"Basics of Data Mining: A Survey Paper", International Journal of Trend in Research and Development, Volume 4(2), ISSN: 2394-9333
[2]. Nikita Jain, Vishal Srivastava, "DATA MINING TECHNIQUES: A SURVEY PAPER", IJRET: International Journal of Research in Enginee ring and Technology eISSN: 2319-1163
[3]. https://www.techopedia.com/definition/1181/data-mining
[4]. https://www.wideskills.com/data-mining-tutorial/data-mining-processes
[5]. http://www.zentut.com/data-mining/data-mining-processes/
[6]. https://www.guru99.com/data-mining-tutorial.html#2

In order to effectively prepare for the day while staying updated with the necessary information and still maintaining a timely schedule is a perplexing task. This mirror provides an efficient way to manage all tasks by combining different services such as weather updates, time, date, news feed and to-do-list. When compared, the mirror will be much more efficient and faster way to gain information than other devices. Face recognition is used to allow access to authorized users and secure the mirror. The news feed uses web servicebased communication to extract data packets offered through various different APIs that are made available by websites. All the required computing power is handled and managed by the raspberry pi. Additionally, the LCD display is used which is raspberry pi and placed behind the two-way mirror. Secondly, add-ons such as an assistant may require a microphone that will be used to control the mirror functionalities through voice, joysticks can be used to interact with the screen and many more.

Keywords- Smart mirror, raspberry pi, Magic mirror

[1]. "SmartReflect: A Modular Smart Mirror Application Platform" by Derrick Gold, David Sollinger, and Indratmo MacEwan University Edmonton, Alberta T5J 4S2
[2]. "Smart mirror for smart life"by Muhammad Mu'izzudeenYusri, Shahreen Kasim, Rohayanti Hassan,2017 IEEE 2017 6th ICT International Student Project Conference (ICT-ISPC)
[3]. "Smart mirror E-health assistant — Posture analyze algorithm proposed model for upright posture" by Biljana Cvetkoska, Ninoslav Marina, DijanaCapeskaBogatinoska, ZhankoMitreski, IEEE EUROCON 2017 -17th International Conference on Smart Technologies.
[4]. "DIY Smart mirror" Sadeta KULOVIC1and Belma RAMIC-BRKIC, University Sarajevo School of Science and Technology.
[5]. "Smart mirror for ambient home environment", M. Anwar Hossain, Abdulmotaleb El Saddik, IEEE Xplore.

Disability of visual text reading has a huge impact on the quality of life for visually disabled people. Visual impairment is now increasing rapidly, especially in these days when information is communicated a lot by text messages rather than voice. In this project, we developed an application that converts an image to text and then to speech. The basic framework of this system is that it captures an image, extracts only the region of interest (i.e. region of the image that contains text) and converts that text to speech, text and removes the background noise.

Keywords- Image to speech, optical character recognition, text to speech, visually impaired.

[1]. PseudoEye - Mobility Assistance for Visually Impaired Using Image Recognition, A.G. Sarika, K. Kirthika, V.Sucharitha, IEEE- 2018.
[2]. Finger-Eye: A Wearable Text Reading Assistive System for the Blind and Visually Impaired, Y. S. Zhiming Liu, IEEE-2016.
[3]. Sensor Fusion for Visually Impaired Navigation in Constrained Spaces, P. W. Chathurika Silva S., IEEE-2006.
[4]. Assistive system for product label detection with voice output for blind users, T. Rubesh Kumar, C. Purnima, 2014.
[5]. A reading aid for blind people using OCR and OpenCV, Mallapa D. Gurav, Shruti S. Salimath, Shruti B. Hatti, Vijayalaxmi I. Baykod, IJSRET-2017.