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

World University root node

School of Chemical Engineering

Process Development Division 2015 - Core Programming Area at the 2015 AIChE Annual Meeting

439912 water disinfection using hydrodynamic cavitation: Influence of orifice shape &amp; inception

In recent years, research on Artificial Neural Networks (ANN) prove that it offers several advantages over conventional Computing methods. ANN proves to be most convenient and easy tool for modeling and analysis of non-linear events. This ability of ANN to model non-linear events is important in hydrology to model various hydrological events which are dominantly nonlinear in nature. ANN is important, where the time required to generate solution is critical. It is also capable of Modelling Nonlinear relationship as compared to other Mathematical modeling techniques which model linearity only. In this paper ANN technique along with Fuzzy technique and their interfacing is over-viewed. This paper has two parts-Modelling of Runoff using ANN and Controlling Dam Gates using Fuzzy logic. This ANN modeling is done with the help of MATLAB neural Network tool Box. Back Propagation Algorithm is used to optimize network connection weights. Back Propagation (BP) algorithm evaluates error and back propagates it for more accurate training of ANN. Five river discharge data as inputs are considered and Runoff data as ANN output. Fuzzy Inference System (FIS) simulation is used to automate opening and closing of Dam gates. Predicted Runoff and Water level of Dam is applied as inputs to FIS to control Dam gates. © 2014 Infonomics Society.

School of Electronics & Communication Engineering

International Conference on Information Society, i-Society 2014

Hybrid neuro-fuzzy approach for flood prediction and Dam gate control

Growing dependence of modern society on electrical energy has resulted into increasingly complex nature of electrical power systems. It requires operation of the network closer to their stability limits. This leads to compensated transmission lines. Installing capacitors of fixed or controllable value provides a viable solution to improve power system stability and also to optimize power flow transmission capability of electrical grids transmission capability of power grid, optimize power flow and to improve system stability. However, installations of capacitors results into changing directions and values of fault currents and voltages in the networks. It causes problems for protective relays and fault locators. Distance relay is prone to maloperate due to large variations in voltage and current. Since fault location and classification are important tasks to safe guard electric power systems, as per the literature survey various practical or theoretical schemes are used to attain the reliable protection system for transmission lines equipped with compensation capacitors. This paper highlights merits and demerits of different methodologies to enhance distance protection of compensated transmission lines. © 2015 IEEE.

School of Electrical Engineering

2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015

Recent developments in distance protection of compensated transmission lines

Cloud providers offer computing resources to their customers using either the reservation or on-demand plan. However, reservation plan is cheaper than the on-demand plan since the cost of utilizing the resources is low. Cloud providers provision their resources into virtual machine which are then allocated to the users as per the request for a specific period of time. The problem that arises is related with the efficient management and utilization of the available computing resources. The existing system does not take into account of the user's demand while making provisioning decisions. The proposed scheduler is based on time based scheduling with the main objective of utilizing the idle resources leading to the maximum utilization of the physical resources and with minimum overlapping in the task defined by the user. On the contrary, the implementation of the Time based Scheduler will lead to the increase in CPU utilization and memory consumption. So, the paper also includes a comparative study of the CPU utilization and Memory consumption with different techniques for implementing the scheduler. © 2014 IEEE.

School of Computer Engineering & Technology

Proceedings - 2014 IEEE Global Conference on Wireless Computing and Networking, GCWCN 2014

Efficient time based scheduler for implementing reservation plan in cloud

As data is growing rapidly day by day due to wide usage of social media, this social data will help business analyst as well as researchers to get the feedback about any service or product. Analysis of social media data can be used for many purposes like friend recommendation, product or service recommendation etc. There is need of such algorithms which will classify data accurately and will provide analysis results accurately. Also these algorithms need to scale rapidly with data sets. In this paper, Different phases of social media data mining are identified. We have studied algorithms whose training time remains same though the data size increases. We have implemented decision tree algorithm and compared it with other machine learning algorithms like NaiveBayes, Adaboost etc. It is found that decision tree algorithms gives accurate results as compared to other algorithms. In addition to that, out of different decision tree algorithms random decision tree (RDT) algorithm performs best than other decision tree algorithms like C4.5 or ID3. © 2015 IEEE.

Efficient regression algorithms for classification of social media data

Reliability-based design optimization (RBDO) is an important area in structural optimization. A principal step of the RBDO process is to solve a reliability analysis problem. This problem has been considered in inner loop of double-loop RBDO approaches. Although many algorithms have been developed for solving this problem, there are still some challenges. Existing algorithms do not have good convergence rates and often diverge. There is a need to develop more efficient and stable algorithms that can be used for evaluating all performance functions sufficiently. In this paper, a new method, called “Conjugate Gradient Analysis (CGA) Method”, is proposed to apply in the reliability analysis problems. This method is based on the conjugate gradient method. Some mathematical problems are provided in order to demonstrate the advantages of the proposed method compared with the existing methods. © 2014, Springer-Verlag Berlin Heidelberg.

Structural and Multidisciplinary Optimization

A new reliability analysis method based on the conjugate gradient direction

Offensive Internet chats, particularly the child-exploiting type, tend to follow a documented psychological behavioral pattern. Researchers have identified some important stages in this pattern. The psychological stages broadly include befriending, information exchange, grooming, and approach. Similarities among the posts of a chat play an important role in differentiating as well as in identifying these stages. In this article a novel similarity measure is constructed which gives high Inter-post-similarity among the chat-posts within a particular behavioral stage and low inter-post-similarity across different behavioral stages. A psychological stage corpus-based dictionary is constructed from mining the terms associated with each stage. The dictionary works as a background knowledge-base to support the similarity measure. To find the inter-post similarity a modified sentence similarity measure is used. The proposed measure gives improved recognition of inter-stage and intra-stage similarity among the chat posts compared with other types of similarity measures. The pairwise inter-post similarity is used for clustering chat-posts into the psychological stages. Results of experiments demonstrate that the new clustering method gives better results than some current clustering methods. © 2014 ASIS&T.

Journal of the Association for Information Science and Technology

Constructing an inter-post similarity measure to differentiate the psychological stages in offensive chats

Quality of Service (QoS) optimization is an important design goal in wireless video transmission. The application (APP) layer 802.11e medium access control (MAC) layer, and physical (PHY) layer of the wireless protocol stack can be jointly designed for the exchange of information. This will optimize the performance of wireless network for real-time digital video transmission. This paper proposes an innovative 'QoS-Optimized Adaptive Multi-layer (OQAM)' architecture. It ensures reliable and high-quality video transmission over communication channels. The channel exhibits wide variability in throughput, delay, and packet loss. The simulation is performed using EvalVid and Network Simulator-2. Enhanced Intra Prediction (EnIP) algorithm with H.264/MPEG4 Advanced Video Coding (AVC) is proposed at the APP layer. This H.264/MPEG4 AVC is a non-scalable video enCOder/DECoder (CODEC). Improved MAC Adaptive Retry Limit (IMALr) is proposed as smart-packet drop mechanism at the 802.11e MAC layer. It uses packet overflow drop (Pov) and expired-time packet discard (Pex) algorithm. Enhanced Adaptive Forward Error Correction (EnAFEC) is proposed at the PHY layer. The aforementioned algorithms are jointly considered in the proposed OQAM architecture, which increases the coding efficiency, reduces end-to-end delay and increases reliability of wireless network for real-time Video over Wireless (VoW) transmission. © 2014 Elsevier Inc. All rights reserved.

Journal of Visual Communication and Image Representation

QoS-optimized adaptive multi-layer (OQAM) architecture of wireless network for high quality digital video transmission

The chemical name extraction has a great importance in the biomedical field. Named Entity Recognition is the subtask of information extraction that is used to identify named entities in the given data. There are various dictionary-based, rule-based and machine learning approaches available for Named Entity Recognition. Rule based techniques include hand written rules. In this paper an extensive survey of machine learning models such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Conditional Random Fields (CRFs) etc. that are used to develop NER systems is carried out.

International Journal of Computer Science and Information Technologies

A Survey on Machine Learning Techniques toExtract Chemical Names from Text Documents

Aspect-oriented modeling (AOM) has been developed to modularize crosscutting concerns appropriately in UML models. In software engineering, aspects are concerns that cut across multiple modules. In requirements modeling, we analyze interactions and potential inconsistencies. We use UML to model requirements in a use case driven approach. During requirements specification a structural model of the problem domain is captured with a class diagram. Use cases refined by activities are the join points to compose crosscutting concerns. Graph transformation systems provide analysis support for detecting potential conflicts and dependencies between rule-based transformations.