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

Engineering and Technology

In the present epoch of technology medical field has become one of the favorite topics of researcher and cancer is among one of them. It is a topic of concern because actual treatment of this disease is not found till date. Patients having this disease can only be saved if and only if it is found in early stage (stage I and stage II). If it is detected in latter stage (stage III and stage IV) then chance of survival will be very less. Machine learning and data mining technique will help medical field to tackle with this problem. Cancer has various symptoms such as tumor, abnormal bleeding, more weight loss etc. It is not necessary that all kinds of tumors are cancerous. Tumors are basically of two types i.e. benign or malignant. To provide appropriate treatment to the patients, symptoms must be studied properly and an automatic prediction system is required which will classify the tumor into benign or malignant. In today's internet world, bulk of data is generated on social media or healthcare websites. From this huge amount of data, symptoms can be fetched by using data mining technique, which will be further useful for cancer detection or classification. This paper makes survey of such latest research study that makes use of online and offline data for cancer classification. © 2017 IEEE.

World University root node

School of Computer Engineering & Technology

Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017

Survey on recent cancer classification systems for cancer diagnosis

Sarcasm is a form of language in which individual convey their message in an implicit way i.e. the opposite of what is implied. Sarcasm detection is the task of predicting sarcasm in text. This is the crucial step in sentiment analysis due to inherently ambiguous nature of sarcasm. With this ambiguity, sarcasm detection has always been a difficult task, even for humans. Therefore sarcasm detection has gained importance in many Natural Language Processing applications. In this paper, we describe approaches, issues, challenges and future scopes in sarcasm detection. © 2017 IEEE.

Literature survey of sarcasm detection

In this paper, we describe our experiment related to using a humanoid robot as a companion for the elderly people. We have analyzed the needs of elderly people and mapped our use cases in accordance with those needs. We have implemented the functionality using IBM Watson and other libraries and performed an experiment at an old age home. Our humanoid robot can detect the user's mood and engage in a friendly conversation. The experiment also indicates that social acceptance ratio of humanoid robot among elderly people is high. © 2018 IEEE.

1st International Conference on Data Science and Analytics, PuneCon 2018 - Proceedings

Humanoid Robot as a Companion for the Senior Citizens

In Wireless Sensor Networks (WSNs), energy consumption is the most challenging issue. The more energy is consumed during captured data transmission than the local data processing at sensor nodes. Because of this reason network lifetime is reduces. Sometimes attacker nodes are also responsible of energy wastage. Data aggregation technique is one of the solutions for this issues of network lifetime and energy loss. In this paper data aggregation technique is used for detecting attack and prevention technique. For collecting the data from the cluster head in the network, system used aggregator Node (AN). If the cluster head (CH) becomes attacker, then AN recover the data from all cluster members, So that the energy in not wasted and packet drop ratio is also minimized. Also, system makes use of the Iterative Filtering (IF) algorithm, which additionally gives trust evaluation to the numerous sources from where the information is aggregated. This IF algorithm is used to detect the Simple attack and Collusion attack on nodes. The proposed system can detect the attack on all nodes including cluster member, cluster head and aggregator node. Additionally this system check data truthfulness on cluster head when data is received from all cluster members. SHAl algorithm is used for integrity checking. Experimental results prove that the proposed system improves the energy utilization, memory management and security of data along with attack detection on CH. © 2017 IEEE.

2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017

Energy Efficient Secured Data Routing Through Aggregation Node in WSN

News media includes print media, broadcast news, and Internet (online newspapers, news blogs, etc.). The proposed system intends to collect news data from such diverse sources, capture the varied perceptions, summarize, and present the news. It involves identifying topic from real-time news extractions, then perform clustering of the news documents based on the topics. Previous approaches, like LDA, identify topics efficiently for long news texts, however, fail to do so in case of short news texts. In short news texts, the issues of acute sparsity and irregularity are prevalent. In this paper, we present a solution for topic modeling, i.e, a word co-occurrence network-based model named WNTM, which works for both long and short news by overcoming its shortcomings. It effectively works without wasting much time and space complexity. Further, we intend to create a news recommendation system, which would recommend news to the user according to user preference. © 2018, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Topic modeling on online news extraction

Image Retrieval systems highly rely on the image signatures stored in database. Constructing Image signatures with optimal size and accurate representation of image is most interesting challenge here. High level image features like object and their characteristic are useful in sentiment image analysis but faces the limitations of domain specific feature vector. Low level image features like color, texture and shape are interesting and useful enough to represent the image in diverse image databases. Targeting to low level image feature: color, the image signatures created are of huge size, as those represents three color planes and their values. Image signatures vary, as the image size varies. Targeting towards creating the optimal image signatures with color feature, to reduce the size of feature vector is possible by considering images in frequency domain. Image transforms converts image in frequency domain, with compressed image data. This data is further reduced form by ignoring the low energy components. This paper discusses the approach to construct image signatures by considering high energy components of transformed image. Further the high energy components are bagged together. Here the intelligent mean-count tree is created based on image information. Performance of Image retrieval is tested using image feature database. © 2018 The Authors. Published by Elsevier Ltd.

Fulltext

Procedia Computer Science

Color Image Retrieval Using Compacted Feature Vector with Mean-Count Tree

Handwritten character recognition of Devanagari script is an area of research in the field of pattern recognition. Feature extraction is crucially significant step in recognition system. In handwritten optical character recognition, the size of feature vectors is very high. By reducing image size, the dimension of feature vectors can be reduced but this also reduces the pixel information. This paper focus on the improvement in performance of recognition system using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In proposed system, first raw features are extracted using three different feature extraction methods: chain coding, edge detection using gradient features and direction feature techniques, which are reduced by LDA and characters are classified using SVM classifier. © 2018 IEEE.

School of Electronics & Communication Engineering

Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018

Recognition of handwritten devanagari characters using linear discriminant analysis

A high precision or surgeon skills are required in some of the precision procedures such as micro-scale minimally invasive surgeries. Robotic manipulators are being explored for carrying out surgeries with minimal invasion, outstanding precision and stability of the tool. Major challenges in developing such systems are motion of the tool with 'mechanical' remote center and preserving surgical feel of the surgeon. This paper presents conceptual design of 2-DOF isosceles trapezoidal remote center motion mechanism. The proposed concepts use mechanically generated RCM. The compact and handy design helps to preserve the 'surgical feel'. The use of compliant links over rigid link mechanism brings the precision in manipulation. Extensive, nonlinear FE analysis demonstrates the accuracy of the RCM. Further the actuation method, sensor scheme and control scheme are presented as an example case. © 2018 IEEE.

School of Mechanical Engineering

2018 3rd International Conference for Convergence in Technology, I2CT 2018

Compliant Remote Center Motion Mechanism for Minimally Invasive Surgical Robots

Kinesics has a decisive role for the mute and deaf for inter personal communication. It necessitates body postures, face expressions and movement of hands Due to the diversity of population, each region has genus of sign language based on its individual rule of grammar. The objective is to manufacture a dynamic gesture of hand identification artifice for Indian kinesics using morphological algorithms. The pithy frames are extricated from the captured video for feature extrication and the interpretation of the sign is displayed on the screen in text format. © 2018 IEEE.

Proceedings of the International Conference on Inventive Research in Computing Applications, ICIRCA 2018

Morphological Based Dynamic Hand Gesture Recognition for Indian Sign Language

In in-network data aggregation in Wireless Sensor Networks (WSN), selection of communication paths and aggregation points are critical network requirements. They enable efficient use of network resources in constrained environments. Base Station (BS) driven routing protocols are prime enablers to implement such network requirements. In Base Station (BS) driven routing protocols the BS proactively finds a path to communicate with each node in the network. However, if a node in the network fails or goes to sleep; the communication path between the BS and some nodes in the network is disabled and these nodes are disconnected from the BS. In this paper, an algorithm to reestablish a communication path between these disconnected nodes and the BS, is proposed. The reestablishment of paths add fault-tolerance; and facilitate retention of connectivity of the nodes in the network to enable communication with the BS. The results demonstrate the merit of the proposed algorithm.