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 Electronics & Communication Engineering

Hand gesture is a natural interaction method, and hand gesture recognition is familiar in human–computer interaction. Yet, the variations, as well as the complexity of hand gestures such as self-structural characteristics, views, and illuminations, made hand gesture recognition as a challenging task. Nowadays, the human–computer interaction area enhancement leads to putting interest in the dynamic hand gesture segmentation based on the gesture recognition system. Apart from the lengthy clinical success, dynamic hand gesture segmentation through webcam vision seems challenging due to the light effects, partial occlusion, and complicated environment. Hence, to segment the entire hand gesture region and enhance the segmentation accuracy, this paper develops an improved segmentation and deep learning-based strategy for dynamic hand gesture recognition. The data is gathered from the ISL benchmark dataset that consists of both static as well as dynamic images. The initial process of the proposed model is the pre-processing, which is being performed by grey scale conversion and histogram equalization. Further, the segmentation of gestures is done by the novel Adaptive Hough Transform (AHT), where the theta angle is tuned. Once the segmentation of gestures is done, the optimized Deep Convolutional Neural Network (Deep CNN) is used for gesture recognition. The learning rate, epoch count, and hidden neurons are tuned by the same heuristic concept. As the main contribution, the segmentation and classification are enhanced by the hybridization of Electric Fish Optimization (EFO), and Whale Optimization Algorithm (WOA) called Electric Fish-based Whale Optimization Algorithm (E-WOA). The training of optimized Deep CNN is handled by Dynamic Time Warping (DTW) for avoiding redundant frames, thus enhancing the performance of dynamic hand gestures. Quantitative measurement is accomplished for evaluating hand gesture segmentation and recognition, which portrays the superior behaviour of the proposed model.

Engineering and Technology

Multimedia Tools & Applications

Adaptive hough transform with optimized deep learning followed by dynamic time warping for hand gesture recognition

Bone Age Assessment (BAA) is a frequent clinical procedure used to evaluate endocrinology, genetics, and development issues in children. BAA is commonly used because it is simple, with minimum exposure to radiation and multiple ossification centers for the maturity evaluation. The two techniques by which bone age is estimated are the 'Greulich Pyle (GP) method and the Tanner Whitehouse (TW) method.' Whichever method is used, each technique faces the limitation of intra- and inter-operator variability. Therefore these methods need to be automated. We have suggested an existing segmentation approach to segment each bone or 'region of interest (ROI)' from the hand wrist x-ray using u-net architecture. The proposed approach has been trained on 48 images and tested on 24 X-ray hand images to produce an average Dice coefficient of 0.88 ± 0.095. Thus, the suggested technique can offer an approach to segment x-ray images with annotated regions ROIs accurately. © 2021 IEEE.

World University root node

International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

Segmentation Using CNN for BAA using Modified TW3 method

2021 2nd Global Conference for Advancement in Technology (GCAT)

A Comparative Analysis of Speckle Noise Removal in SAR Images

Pedestrian Intent Detection using Skeleton-based Prediction for Road Safety

The grape cluster identification and its segmentation for the sake of total weight prediction task of wine yard shows the need of segmentation atomization with better accuracy. The challenge of grape cluster segmentation is considered to provide solutions using deep neural network models such as YOLO v3, Mask RCNN, U-net. This paper contributes in terms of the modified U-net model for the segmentation of grape clusters using training and testing strategy for the validation of the results. The results are obtained for the accuracy of the classification of pixels as part of grape cluster or outside of clusters and comparative results show improvement in segmentation using modified U-net. The accuracy, precision and recall analysis is performed and the comparatively proposed model shows better results

INFORMATION TECHNOLOGY IN INDUSTRY

COMPARATIVE ANALYSIS OF DEEP LEARNING ARCHITECTURES FOR GRAPE CLUSTER INSTANCE SEGMENTATION

In today's era of communication, sharing of information is unavoidable. Analysis of security and reliability while communicating is essential. Information may get altered, copied and copyright can be infringed. Previously, digital watermarking and different encryption techniques like AES, DES, HASH and MAC were used as a primary security technique for information hiding. But, in all these methods, there are some vulnerabilities, because of which information may be hacked or changed while transmission. In addition to the security of the information, there should also be an error-free transmission of data. This review paper discusses various image encryption techniques which are using joint error correction and encryption methods. Based on the literature review, a novel method for joint encryption and encoding is suggested. Low-Density Parity-Check (LDPC) code, a highly recommended technique in 5G, is used for reliability. Chaotic sequence based on a logistic map is used for security. Proposed technique also uses the S-Box of AES algorithm for enhancing the security obtained by combining LDPC and Chaotic sequence. This paper aims to provide a general overview of image encryption techniques. Based on the study, a new system for joint encryption and encoding using LDPC, Chaotic map and AES S-Box is suggested. © 2021 IEEE.

Proceedings of IEEE International Conference on Signal Processing,Computing and Control

Review of Joint Encoding and Encryption for Image Transmission using Chaotic Map, LDPC and AES Encryption

Human activity recognition framework is facing many challenges and issues that are promoting the development of a new activity recognition system to improve accuracy under realistic conditions. Therefore, efficient human activity detection is proposed in this paper. At first, the input video is converted into frames then the keyframes are selected using a structural similarity measure. Next, the local and global features are selected from the video frames. Finally, the selected features are fed to the classifier for identifying human activity. In the proposed method, the traditional deep learning algorithm is improved by applying optimization techniques. Adaptive Monarch Butterfly Optimization algorithm (AMBO) is used to improve the performance of the traditional deep learning algorithm. Monarch Butterfly Optimization Algorithm (MBO) is a population-based natural inspired algorithm. It mimics the foraging and the social behavior of the butterflies. The optimal parameters are selected for activity detection. The proposed method is computed based on accuracy, sensitivity, and specificity and implemented using the MATLAB platform. The experimental results show activity recognition accuracy improved to 96%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine Vision and Augmented Intelligence—Theory and Applications: Select …

Hybrid Features Enabled Adaptive Butterfly Based Deep Learning Approach for Human Activity Recognition

Technological advancements in the military sector&nbsp; have been phenomenal in the last vicennial. According to an article&nbsp; referred, the total annual global military expenditure rose to $&nbsp; 1.917 trillion in 2019. The advent of wireless data communication&nbsp; and technologies like Artificial Intelligence and IoT, have&nbsp; transformed and augmented the lives of common civilians. The&nbsp; Air-force and the Naval-force have experienced major&nbsp; transformation in terms of technology. On the other hand, this&nbsp; kind of technology has not yet reached the Land-force department.&nbsp; The military vest as well as the helmet are amongst the most&nbsp; crucial pieces of safety equipment that have undergone no&nbsp; significant upgrade since the last semi-centennial. The main goal&nbsp; of this study is to build and implement a smart system consisting&nbsp; of a smart vest and a smart helmet using sensors, modules, and&nbsp; advanced wireless communication technology to improve the&nbsp; utility and functionality. &nbsp;

Proceedings - International Conference on Communication, Information and Computing Technology, ICCICT 2021

IoT based Smart Vest and Helmet for Defence Sector

The development in ocean exploration and observation make the demand for moving object detection in underwater increasingly urgent. The moving object detection in underwater with dynamic or moving background is the challenging and difficult task for the researchers. In dynamic environment, foreground object as well as background of the scene both are in motion condition, thus it is very difficult to detect foreground moving object in underwater medium. In proposed work, background is categorized into three types i.e. static, moderate dynamic and high dynamic backgrounds. The GMG algorithm invented by Andrew B. Godbehere, Akihiro Matsukawa, Ken Goldberg is implemented to detect moving object and compare the results for the three background categories. The sensitivity and accuracy parameter values are considered for the comparison of the results. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Advances in Intelligent Systems and Computing

Underwater Moving Object Detection Using GMG

Of late, the research world has been vigorously involved in, inventing strategy and techniques to improve the spontaneity of Human Computer Interaction (HCI). Gesture recognition is one of the most probable techniques in this area. The eventual aim here is to introduce an intelligent system for hand gesture recognition in both static and dynamic area, which is still a challenging point due to the lag of valuable beneficial methods. The main intent of this paper is to implement an efficient hand gesture recognition model considering both static and dynamic datasets for Indian Sign Languages (ISL). In static type, images are taken for processing, whereas video frames are used for processing the dynamic type. The proposed recognition model involves five main steps "(a) Image pre-processing, (b) gesture segmentation, (c) Feature extraction, (d) Optimal Feature Selection, and (e) Recognition". In the pre-processing phase, greyscale conversion and histogram equalization are performed. The pre-processed image is subjected to the segmentation process, where the Active Contour model and Canny Edge Detection is implemented. In the feature extraction phase, both the contour image, and the edge detected image is deployed, in which Histogram of Oriented Gradients (HOG) features are extracted from the contour image, and Edge Oriented Histogram (EOH) features are extracted from edge detected images. To reduce the dimension of HOG, and EOH features, Principle Component Analysis (PCA) is applied. Further, the region props features are extracted for both contour and edge detected image. Finally, all these features are summed, and the optimal feature selection process performs here to select the unique feature giving different information with less correlation. Finally, the recognition classifier called Neural Network (NN) is adopted, where the new training algorithm is used to update network weight. Dynamic Time Warping (DTW) method helps to remove the repeated frames in the video and to reduce the time consumption of testing. In both feature selection and classification, a hybrid algorithm Deer Hunting-based Grey Wolf Optimization (DH-GWO) is used for selecting the features and weight update in NN as well. Hence, the integration of a hybrid meta-heuristic algorithm is highly efficient for recognizing the characters for images and words for videos with high recognition accuracy. © 2020 Elsevier Inc.