Summary form only given, as follows. Since the year 2016, International Conference on Recent Trends and Challenges in computational Models (ICRTCCM) is being conducted by the Department of Computer Science and Engineering of University College of Engineering Tindivanam, Melpakkam, Tindivanam, Tamilnadu, India. The first International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM'16) was conducted from 11th-13th Feb 2016. ICRTCCM'16 provided an opportunity for young research scholars, delegates and students from academia as well as industries to interact and share their experience and knowledge in technology and its applications. ICRTCCM'16 had notable keynote speeches, panel discussions and paper presentations. ICRTCCM'16 conference received a total of 173 papers, out of which 86 manuscripts have been accepted. The acceptance ratio was 49.71. All the accepted papers were published in one of the following journals: AJBAS - Australian Journal of Basic and Applied Sciences; ANAS – Advances in Natural and Applied Science; IJCAR - International Journal of computing Academic Research; IJCTA – International Journal of Control Theory and Applications; WASJ – World Applied Science Journal; and MEJSR – Middle East Journal of Scientific Research.

The future healthcare systems' growth and development have greater dependence on Wireless Body Area Network (WBAN). Notwithstanding, the utilization of untrustworthy methods has made it defenseless against various attacks. These attacks can be kept under check by utilizing different security procedures. For this reason a security protocol named Se-Small is utilized. The procedure includes signing the primary packet by the base station before it is transmitted. Subsequently this sign is transmitted to the rest of the data item from which hash value is produced. This procedure reduces overhead and transmission delay in the system. The wireless network essentially utilizes three distinct structures for packet transmission. They incorporate - Tree, Star and Linear chain. This paper uncovers the utilization of flawlessly displayed and distributed structure where the three essential structures have been assembled for productive transmission of packets in WBAN. For this reason Fish-Bone structure is utilized. Failure in any of the cluster head has likewise been compensated by giving a substitute component. Mobile node is utilized here to gather the information from master heads. For proficient transmission buffer length of mobile node is kept high.

This paper mainly focuses on low power based amplifier (LNA) design. These types of amplifier design are suitable for low power wireless applications. Here different types of low noise amplifier designs are considered for designing a front end receiver. LNA is used for impedance matching, amplify weak signal and eliminate the unwanted noise signal. Selecting proper circuit element, this delivers better output and prevent short circuitry. Noise cancellation technique, which eliminates unwanted signals are present in the incoming signal. Common gate, resistive parallel feedback and cascading feedback network are designed for low power. Transient analysis and AC analysis are used for calculating the power consumption and the bandwidth respectively. Using current reuse circuit, this enables the generation of necessary current to the design. Cadence analog library is used for designing a low noise amplifier in 180nm technology.

Grouping the nodes and form a cluster with less energy consumption by that maximizing the life time is an challenging task in WSNs. The two important steps in clustering are Cluster formation and Cluster Head (CH) selection. The novel and efficient clustering called Clustering using Eigen Values (CEV) is proposed in this paper with the increased lifetime of the sensor nodes using the spectral graph theory. This work uses the Laplacian matrix of spectral theory for clustering. The Eigen values of Laplacian Matrix and its corresponding eigenvector are used to group the nodes of WSN. CH is selected using fuzzy logic and constraints on energy and distance. This work is evaluated and compared with LEACH and HEED for performance comparison. The results obtained in this work show that the proposed work yields better performance when compared to other existing cluster based techniques based on the parameters such as network lifetime and energy consumption.

Emotion recognition has become an important topic in natural language processing. Usually words labeled with their emotion is the starting place to find the emotion of a text,but it is very essential that context must also be considered. It is not a simple task to capture the overall emotion of a Text, as words are mutually influence their emotion related interpretation.There is a lot of word-based dictionaries available for emotion recognition from the text. However, commonsense knowledge bases are very less. We used ConceptNet ( commonsense knowledge) for concept mining from poems. In this paper we proposed a concept identification method by using ConceptNet knowledge base to depict the concept of poems.This system gives a precision value of 71% and inter-rater agreement of 48%, depicting its moderate agreement with Human Expert interpretation.

Video Summarization plays a vital role in the internet user's life, especially for those searching for user specified video of interest for a long time. In order to provide support for users in terms of searching and retrieving video content, it is necessary to segment the video into shots and extract representative frame of each shot which acts as a summary of the video. So, in this paper, an approach for shot boundary detection and keyframe extraction is proposed. To provide a better summarization of video, Local Binary Pattern (LBP) based feature extraction for detecting video shots is performed in order to identify objects motion information. Based on local maxima analysis, an efficient keyframe, which contains salient information of a shot is proposed. The performance of the proposed work is evaluated using evaluation metrics like Precision, Recall, and F1-Measure.