MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

Crop production problems are common in India which severely effect rural farmers, agriculture sector and the country's economy as a whole. In Crops leaf plays an important role as it gives information about the quantity and quality of agriculture yield in advance depending upon the condition of leaf. In this paper we proposed the system which works on preprocessing, feature extraction of leaf images from plant village dataset followed by convolution neural network for classification of disease and recommending Pesticides using Tensor flow technology. The main two processes that we use in our system is android application with Java Web Services and Deep Learning. We have use Convolution Neural Network with different layers five, four three to train our model and android application as a user interface with JWS for interaction between these systems. Our results show that the highest accuracy achieved for 5-layer model with 95.05% for 15 epochs and highest validation accuracy achieved is for Slayer model with 89.67% for 20 epochs using tensor flow. © 2018 IEEE.

Engineering and Technology

World University root node

Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018

Leaf Disease Detection and Recommendation of Pesticides Using Convolution Neural Network

Abstract—In image processing feature extraction starts from an initial set of measured data and builds derived values as features which are intended as informative and non-redundant. This facilitates the learning and leading to interpretations. Image processing Algorithms are used to detect and differentiate among various desired portions or shapes features of an image. In particular texture, color and shape features of image are used to define similarity among images. Image is a representation of visual information. Objects and its shape are important aspect as image contains information about various useful structures during information analysis. Segmentation of image is one of the necessary task in image processing. It involves generation of various useful parts as set of selected pixels. Such division aims automation in image objects identification and relevant information analysis. For any image shape object is unique. Object detection algorithms typically use extracted features and learning algorithms to recognize instances of an object category. It is commonly used in applications such as image retrieval, security, surveillance and advanced driver assistance systems. During the process of object extraction there is a need to separate image background and foreground in efficient and accurate manner. This paper implements context shape boundary extraction along with color segmentation is done to identify objects and its region color. This combined methodology of image segmentation and context shape description algorithm increases accuracy of detecting useful feature of image. We describe a method for registering pairs of images based on thin-plate spline mappings. This method is scale, translation and rotation invariant.

IEEE International conference on Electrical Electronics, Computers

Improved Color Segmentation Based Context Shape Feature For Image Object Detection

Humans are social animals, and interaction has been one of the major factors for the development of humans. Invention of computer was a benchmark in technological progress achieved by humans. Proper communication technique between humans and electronic appliances has eased out our lives. But due to improper designing of interfaces of systems, nuclear mishaps like Three Mile Island accident have also taken place. To avoid such huge man-made disasters and to facilitate and equip every common man with technology, proper interfacing of technology such that novice as well as experts can use it to reap the full benefits out of any electronic instrument is a must. © 2018, Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

Review on Interface Designing and Human–Computer Interaction

Human action recognition has become vital aspect of video analytics. This study explores methods for the classification of human actions by extracting silhouette of object and then applying feature extraction. The method proposes to integrate HOG feature and PCA feature effectively to form a feature descriptor which is used further to train KNN classifier. HOG gives local shape-oriented variations of the object while PCA gives global information about frequently moving parts of human body. Experiments conducted on Weizmann and KTH datasets show results comparable with existing methods. © 2018, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Silhouette-based human action recognition by embedding HOG and PCA features

Preface

International Journal of Image, Graphics \& Signal Processing

Velocity and Orientation Detection of Dynamic Textures Using Integrated Algorithm.

Researchers have investigated different approaches to maintain the minimum cost and effort in regression testing. Here, test suite reduction is a common technique to decrease the cost of regression testing by removing the redundant test cases from the test suite and then, obtaining a representative set of test cases that still yield a high level of code coverage. Accordingly, here, the authors have developed two various techniques for test suite reduction. In the first technique, ATAP measure is newly developed to find the reduced test suite with the help of greedy search algorithm. In the second technique, DIV-TBAT (DIVersity-based BAT) algorithm is newly devised based on the mechanisms of Boolean logic within BAT algorithm which improve diversity during the search process. The proposed techniques are experimented using eight programs from SIR subject programs and the performance study is conducted using nine different evaluation metrics based on different research questions. The comparative analysis is performed with the existing algorithms like GreedyRatio, GreedyEIrreplaceability, diversity-based genetic algorithm, TBAT, and TAP, to prove the performance improvement over the eight software programs considered.

IET Software

DIV-TBAT Algorithm for Test Suite Reduction in software testing

Driving is a very taxing job as the visual, sensory and motor inputs work in harmony to make driving a successful experience. However, when the harmony is distorted, as in the cases when the driver is frustrated, anxious, the negative mindset can be dangerous as it can lead to potential accidents. Analyzing the emotions of the driver and striving to control them therefore becomes important. The work in this paper focuses on enhancing the current driving system of cars to help reduce the accident rate. It uses analysis of driver emotions via the data obtained from EEG (Electroencephalography) waves and the valence-Arousal models for emotion classification. Additionally, the control of emotions is obtained via the concept of music therapy. The proposed approach uses the music system in the car to automatically respond to changes in the emotional state of the driver. The emotion detection would be relatively real time thus efficiently soothing the driver. Moreover, the automatic adjustment of music in consonance with the mood would reduce the manual interfacing of the knobs in the car, thus saving distraction and reducing the chances of accidents. © 2018 IEEE.

2018 IEEE Punecon

Driving Control Using Emotion Analysis Via EEG

Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection. © 2018, Springer Nature Singapore Pte Ltd.

School of Electronics & Communication Engineering

Intelligent Twitter Spam Detection: A Hybrid Approach

Every day, huge amounts of unstructured text is getting generated. Most of this data is in the form of essays, research papers, patents, scholastic articles, book chapters etc. Many plagiarism softwares are being developed to be used in order to reduce the stealing and plagiarizing of Intellectual Property (IP). Current plagiarism softwares are mainly using string matching algorithms to detect copying of text from another source. The drawback of some of such plagiarism softwares is their inability to detect plagiarism when the structure of the sentence is changed. Replacement of keywords by their synonyms also fails to be detected by these softwares. This paper proposes a new method to detect such plagiarism using semantic knowledge graphs. The method uses Named Entity Recognition as well as semantic similarity between sentences to detect possible cases of plagiarism. The doubtful cases are visualized using semantic Knowledge Graphs for thorough analysis of authenticity. Rules for active and passive voice have also been considered in the proposed methodology. © 2018 IEEE.