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 emotions, set in simple words are what people feel. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and helps in understanding behavioral aspect of human. Human faces provide various information about emotions. As per psychological researcher, a person expresses his emotions less by verbal talk and more by non-verbal body posture and gestures. Emotion recognition or Affective Computing (AC) being the AI related area imparts intelligence to computers in recognizing human emotions. Emotion recognition is proved a popular research area topic in few decades. The aim of this paper is to report an illustrative and comprehensive study of most popular emotion recognition methods, which are generally used in emotion recognition problems. We are motivated by the lack of detailed study of all possible techniques implementations in available literature. This paper provides an up-to-date comprehensive survey of techniques available for emotion recognition.

Engineering and Technology

International Journal of Computer Science and Information Security (IJCSIS)

A Comprehensive Survey on Techniques forFacial Emotion Recognition

Wireless location finding is one of the key technologies for wireless sensor network. GPS is the technology used but it can be used for the outdoor locations. When we deal with the indoor location GPS does not work. Indoor locations include buildings like supermarkets, big malls, parking spaces, universities, etc. In this area the accuracy of the GPS location is greatly reduced. Location showed on the map in not correct when the GPS is used under the indoor environments. But for the indoor localization it requires the higher accuracy and the special purpose GPS is not feasible for the current view. And also when GPS is used in the mobile device it consumes a lot of the mobile battery to run the application which causes the drainage of the mobile battery within some hours. So to find out the accurate location for indoor environment we use the RSSI-based trilateral localization algorithm. The algorithm has the low cost and the algorihm does not require any additional hardware support and moreover the algorithm is easy to understand. The algorithm consumes very less battery as compared the battery consumption of the GPS. Hence this algorithm has become the mainstream localization algorithm in the wireless sensor network. With the development of the wireless sensor network and the smart devices the WIFI access points are also increasing. The mobile smart device detects three or more known WIFI hotdpot position. And using the values from the WIFI routers it calculates the current location of the mobile device. Under the indoor environment and can navigate to the destination using the navigation function and also can enable the low consumption of the smart mobile battery for the tracking purpose.

Indoor Wireless Tracking Using Wi-Fi

International Journal of Innovative Research in Computer  and Communication Engineering

A Survey on Opinion Mining

It has been observed that, the use of wireless sensor network platforms have procured vast popularity over last few years. The wireless sensor networks are being widely used in agriculture, home security, health care monitoring and traffic management systems. Usage of these systems has increased rapidly. An organization or institute can make use of the data obtained from various such sensor network systems for their analysis. Data obtained from such networks can be misused by any person if the systems are not well secured. Thus misuse of valuable information raises many problems for organization or institute. So to tackle this problem, we need to secure the sensor network systems from vulnerable attacks. In this paper, we propose a new novel approach for securing wireless sensor networks with different layers of security along with data encryption / decryption strategies. As there is a huge concern regarding privacy and integrity of data, this approach will help the organizations to secure their information and make underlying wireless networks more secure and reliable. This paper also brings focus on design and implementation of reliable secure sensor network for the health care monitoring in medical field. The architecture will allow smooth and efficient interconnection of patient's body area network and stationary wireless networks. It presents native solutions for event management, time synchronization and security. © 2016 IEEE.

World University root node

Proceedings - International Conference on Global Trends in Signal Processing, Information Computing and Communication, ICGTSPICC 2016

Novel approach towards securing wireless sensor network systems for health care

This paper introduces a smart street light controlling system to boost energy efficiency of the city. Now a days people are so busy that they rarely find the time to switch OFF the light when it has no use. This results in consumption of lot of energy. This paper proposes the system where street light changed to ON state in the evening before sun sets and they are switched off in the morning after sunrise when there is enough light on the street. This leads to reduce of energy consumption. In this system the movement of vehicle and human is detected on highways to switch on a chunk of street light ahead of it and switch off the trailing lights. This is achieved by processing the image of the object and sending control message to the street light block. Additional feature of the system such as using a suitable sensors of the detection of failed street light and then sending the SMS to control authority using GSM mode to take appropriate action regarding failure. © 2017 IEEE.

Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017

Survey on energy efficient smart street light system

Several vendors opt for Cloud based website hosting nowadays, as it is very trustworthy, easily scalable and reasonable hosting solution than traditional website hosting in various aspects. The most common attacks carried out currently against web application are SQL injection. Cross Site Scripting (XSS), Distributed Denial of Service (DDoS) and Cross Site Request Forgery (CSRF). Distributed Denial-of-service (DDoS) attack is one of the major web security attacks to the cloud hosted websites. Hence, this paper mainly concentrates upon soft computing techniques used so far against such attack. The most straightforward way to carry out the attack is through botnet. The malicious HTTP traffic generated by an attacker imitates to be genuine and hence go undetected. Nowadays, different soft computing techniques are used as a tool to detect malicious traffic. This paper presents a review of these techniques implemented so far that differentiate between genuine and malicious traffic. Economic Denial of Sustainability (EDoS) is a special sort o f DDoS attack that exploits cloud's pay-as-you-go model. The attacker generates anomalous HTTP requests that cause up scaling of the architecture and this puts financial burden over website owner. This paper tries to realize whether existing techniques can be used for defending EDoS attack and it also points out research directions in this field. © 2017 IEEE.

Proceedings of the 7th International Conference Confluence 2017 on Cloud Computing, Data Science and Engineering

A survey: Cloud hosted website protection using soft computing techniques

The research contribution presents data reporting using the adaptive dynamic pruning and data aggregation algorithms for query based wireless sensor network called as APDA. The aim of APDA is to reduce the network communication overheads through dynamic sub-network pruning to save the energy, minimize the unnecessary extra non relevant data transmissions. We construct tree rooted at base station BS. Mainly, proposed work includes three algorithms: 1) network formation using MST, 2) data aggregation using normalization and 3) pruning technique with multi-cast communication. Aggregation tree includes dominator nodes (DN) which perform certain operations like data aggregation, multicast communication and pruning the sub network using normalized values in the range of 0 to 1 and at lower layer i.e. Inferior nodes (IN) sense the requested information and reports to its upstream DN nodes. The objective of APDA is to prolong the network lifetime and reduce query latency while reducing the communication runtime overheads. The solutions are well demonstrated through extensive simulations to prove the validity of proposed approaches to minimize the query response time and to get in time delivery of requested data.

APDA: Adaptive pruning &amp; data aggregation algorithms for query based wireless sensor networks

Various statistical methods such as co-occurrence matrix, local binary patterns and spectral approaches such as Gabor filters have been used for generating global features for image classification. However, global image features fail to distinguish between local variations within an image. Bag-of-visual-words (BoVW) model do capture local variations in an image, but typically do not consider spatial relationships between the visual words. Here, a novel image representation 'Pixel N-grams', inspired from the character N-gram concept in text retrieval has been applied for texture classification purpose. Texture is an important property for image classification. Experiments on the benchmark texture database (UIUC) demonstrates that the overall classification accuracy resulting from Pixel N-gram approach (89.5%) is comparable with that achieved using BoVW approach (84.4%) with the added advantage of simplicity and reduced computational cost.