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

Proposed a smart doorbell system using webservers for making life easy and secure. In the proposed model, we are using Raspberry Pi for managing different types of interfaces and communications. We have used HTTP (Hypertext Transfer Protocol) for communication purpose. We have also used different types of software, including device software, control services, data derives and core services making this model efficient and salable. The overall aim of this paper is to make appropriate use of the technology for the needy and coming generations to come. In the proposed paper Histogram of Oriented Gradients (HOG) algorithm is used for Face Recognition.

School of Electronics & Communication Engineering

Engineering and Technology

Journal of Xidian University

Smart Doorbell Using web server IoT And AI

Nowadays, dynamic hand gesture recognition has become a complicated work in the recognition of pattern and the communities with consideration of computer vision. This paper tempts to frame an algorithmic analysis on proposed “static and dynamic-oriented hand gesture recognition”. Moreover, the recognition of static and dynamic models is improved by Dynamic Time Warping (DTW) model. In the phase called pre-processing, “grey scale conversion and histogram equalization” is used, whereas, in segmentation, “Active Contour model, and canny edge detection” is employed. Moreover, the significant features are extracted depending on the “Histogram of Oriented Gradients (HOG), and Edge Oriented Histogram (EOH)”, and the feature dimension is reduced by Principle Component Analysis (PCA). The optimal feature selection technique is adopted by the novel “Deer Hunting-based Grey Wolf Optimization (DH-GWO)”. Moreover, the “significant frames in the video” are eliminated by the DTW pattern. Finally, the characters and words are exactly recognized by Neural Network (NN), using the DH-GWO training. Here, the analysis is carried out by varying the random number e from 0.5 to 3.0 of the proposed DH-GWO.

2022 International Conference for Advancement in Technology (ICONAT)

Algorithmic Analysis on Deer Hunting-based Grey Wolf for Dynamic Time Wrapping-based Hand Gesture Recognition

Accidents and road crashes occurring due to dissociation of driver from his consciousness is one of the hot topics researchers have been contemplating to curtail, from the past few years. This dissociation wanes the driver's ability to concentrate on the road while driving and promotes occurrence of mishaps. There has been a challenge to non-intrusively detect drowsiness and fatigue-level of drivers in real-time environments and concomitantly make it computationally faster and less complex. The paper proposes a faster real-time driver inactiveness detection system capable of alerting drivers of their drowsiness by inspecting and scrutinizing the state of eyes and mouth and follows a non-obtrusive approach. The system utilizes eyes to monitor the drooping nature of eyes and examines the mouth's current state to detect any yawning activity performed. Initially, it uses Haar transform to extract facial features and strong classifiers for detecting faces by incorporating Adaboost algorithm. Next, shape predictors are used to locate the key points for obtaining position of mouth and eyes within the face area. States of the mouth and eyes are continuously monitored with the help of the metrics viz. Eye Aspect Ratio (EAR) and Mouth Yawn Width respectively and are used to alert the driver through voice-enabled commands followed by a warning alarm sound to trigger them thereby aiding in regaining their consciousness back and focusing on their driving. The algorithm used in this system is tested under different lighting conditions and against different possible situations and accurate results in terms of successful detection rates are obtained. © 2022 IEEE.

World University root node

2022 6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

Real-Time Voice Enabled Drivers' Safety Monitoring System Using Image Processing

Identification of the crop is an important aspect for preventing the losses of the agricultural product. The study of crop diseases means studying the visually observable patterns observed on the crop. Disease detection on crops is very unfavourable for sustainable agriculture. It is very difficult to monitor the crop diseases manually. It requires an enormous amount of work, professionals in the crop diseases, and requires extreme processing time. Hence, image processing is used for the detection of crop diseases. The proposed system involves five important steps: a) image capturing, b) image pre-processing, c) image segmentation, d) feature extraction and e) classification. Finally, features are trained with the machine learning algorithm and tested on unknown images. Results are being calculated based on qualitative and quantitative analysis. In qualitative analysis, pre-processing and segmentation methods are operated on the whole data set. In quantitative analysis, the accuracy of all machine learning algorithms is computed. This paper discussed the methods used for the detection of crop diseases using their leaf image. This paper also discussed some segmentation and feature extraction algorithms used in crop disease detection. The classification is done using Gradient Boosting Classifiers with the highest accuracy.

International Research Journal of Engineering and Technology (IRJET)

AI-BASED CROP IDENTIFICATION, DISEASE RECOGNITION AND PESTICIDE SUGGESTION

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.

Multimedia Tools & Applications

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

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.

Computer Science Review

Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with Dynamic Time Warping

Kinesics has a decisive role for the mute and deaf for inter personal communication. It necessitates body postures, face expressions and movement of hands Due to the diversity of population, each region has genus of sign language based on its individual rule of grammar. The objective is to manufacture a dynamic gesture of hand identification artifice for Indian kinesics using morphological algorithms. The pithy frames are extricated from the captured video for feature extrication and the interpretation of the sign is displayed on the screen in text format. © 2018 IEEE.