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

The field of Wireless Sensor Network (WSN) is striving for devising ways to minimize energy consumption. Clustering reduces energy consumption and increases scalability along with network lifetime. There is a need to identify appropriate number of clusters to balance traffic in network which is a challenging task for energy efficient WSN. Manually it is difficult to decide number of clusters. Finding optimum number of clusters to minimize energy consumption is the major issue in WSN. Existing algorithms find optimum number of clusters but not optimum transmission range. This paper contributes towards the aforesaid issue by proposing a novel method to find optimum number of clusters and a first attempt to find optimum transmission range. We report a new algorithm, where the number of clusters obtained from proposed method is compared with state-of-the-art methods. Extensive experiments are carried out and result comparison with state-of-the-art approaches demonstrate that our method shows significantly better performance. The analysis reveals that optimum number of clusters obtained by proposed method is less than state-of-the-art method. It is especially suitable for clustering in WSN.

Engineering and Technology

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International Journal Helix, Scientific Explorer

ONCTR: Optimum Number of Clusters and transmission range based clustering in Wireless Sensor Networks

Nowadays, internet is important part of our life. User can explore answer to different queries according to his requirement using internet. The nature of these web resources is dynamic and they are present in huge amount. So it becomes challenge to search quality results of required query efficiently as well as personalized search is also a major challenge in Information retrieval. To handle these challenges, a two-stage framework of web crawler is proposed. In first stage, crawler performs “Reverse searching” that matches user searched query with the URL of link from site database. In second stage, crawler performs “Incremental prioritizing” that matches the searched query content with web document. Then crawler classifies relevant and irrelevant pages according to match frequency of searched keyword and ranks these pages. Proposed crawler performs searching through personalized searching according to user point of interest which is based on profession profile of user. The crawler performs the domain classification which helps user to know the contribution of standard resources of searched query. A separate log file is maintained by crawler considering the issue of searching time. While entering cursor in search box, user will get pre-query result based on past search results. Our objective is to design a Focused Crawler to effectively search the site database and provide quality result to the user.

International Journal of Engineering and Technology (IJET-UAE), ISSN: 2227-524X

Effective approach to crawl web interfaces using a two stage framework of crawler

Many studies demonstrated that the DNA methylation, which occurs in the context of a CpG, has a strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor [1]. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. DNA methylation is one of the most extensively studied epigenetic marks and is known to be implicated in a wide range of biological processes, including chromosome instability, Xchromosome inactivation, cell differentiation, cancer progression and gene regulation [4]. Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. In order to explicitly capture the properties of the data, a deep neural network is used, which composes of several stacked binary restricted Boltzmann machines, to learn the low-dimensional deep features of the DNA methylation data.

International Journal of Advance Research, Ideas and Innovations in Technology

DNA Methylation Data Analytics in Cancer Research

As the population is increasing day by day, the environment should be clean and hygienic. In most of the cities the overflowed garbage bins are creating an unhygienic environment. This will further lead to a rise of different types of diseases. This will degrade the standard of living. To overcome these situations an efficient smart garbage management method has to be developed. In recent decades there is a quick development in the rate of urbanization and therefore there is a need of economical urban improvement designs. Presently utilizing new age innovation and vital approach, the idea of brilliant urban areas is coming up all around the globe. A brilliant city is inadequate without an efficient waste administration framework. This paper depicts the utilization of our model of 'Smart Dustbin' in dealing with the waste accumulation management. The dustbin itself works as a robot, when it is full on command from authorized person it goes to prelearned path (for first time user has to guide towards garbage dumping area) and empties itself. An authorized person gives command from Webpage where dustbin status is updated regularly. © 2018 IEEE.

2018 International Conference on Information , Communication, Engineering and Technology (ICICET)

Smart Dustbin: An Efficient Garbage Management Approach for a Healthy Society

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.

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.