scholarly journals Deep Fuzzy Multi-Object Categorization in Scene

Object Categorization is the process of, identifying and labelling the various distinct Classes (Categories), in the given input image. The Deep Fuzzy Multi-Object Categorization (DFMOC) model, combines the learning capability of Convolution Neural Networks (CNN) and the uncertainty-managing ability of Fuzzy system, for carrying out the categorization task. This work starts with Background Elimination process for ensuring the image clarity, followed by Fuzzification and Fuzzy Entropy computation. Simple fuzzy sets are to be framed, by employing Fuzzy C-Means (FCM) algorithm, for fuzzification of the input image. Thresholding Block is incorporated, for determining the clusters . The Fuzzy Entropy Computation (FEC) is done, to minimize the Fuzziness rate of the acquired input and consequently, the layers of CNN are trained in accordance with that. Caltech-101 Dataset is been utilized for analysis. Average Precision Rate of Categorization (APRC), along with other metrics namely Time taken and Error Rate, shows that DFMOC model performs better than other models

2020 ◽  
Vol 14 ◽  
Author(s):  
Vasu Mehra ◽  
Dhiraj Pandey ◽  
Aayush Rastogi ◽  
Aditya Singh ◽  
Harsh Preet Singh

Background:: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Objective:: Developing a system for sign language recognition becomes essential for deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. Methods:: The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models which have been trained by using Tensor Flow and Keras library. Result:: The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. Conclusion:: It’s the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. Proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in identification of different gestures.


1978 ◽  
Vol 14 (3) ◽  
pp. 373-387
Author(s):  
David Hartman

Hope is a category of transcedence, by means of which a man does not permit what he senses and experiences to be the sole criterion of what is possible. It is the belief or the conviction that present reality (what I see) does not exhaust the potentialities of the given data. Hope opens the present to the future; it enables a man to look ahead, to break the fixity of what he observes, and to perceive the world as open-textured. The categories of possibility and of transcendence interweave a closely stitched fabric - hope says that tomorrow can be better than today.


2018 ◽  
Vol 50 ◽  
pp. 01028
Author(s):  
Darya A. Aripova ◽  
Irina S. Bashmakova

The given research deals with the problem of phraseologism formation and further terminologization of such word-combinations as: Dutch slice-hip roof, Dymaxion House, Inhoff tank, dragon summer, cyclopean concrete, dragon tie, horse shoe curve, double Roman tile, etc. It is noted that object categorization takes place in man’s consciousness. Once being used as a fixed word-combination in professional text, the lexical unit may preserve the formed holistic meaning and can be transferred to the category “phraseologism”. The three obligatory identification requirements for word-combination to be transferred to the category “phraseologism” have been defined. The phraseologism functions in the scientific and technical text have been determined.


2022 ◽  
Vol 10 (1) ◽  
pp. 0-0

Developing a system for sign language recognition becomes essential for the deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in the exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of a human-computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models that have been trained by using TensorFlow and Keras library. The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV


Author(s):  
Chen-Sen Ouyang

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.


2019 ◽  
Vol 9 (15) ◽  
pp. 3011 ◽  
Author(s):  
Wahyu Rahmaniar ◽  
Wen-June Wang

Calcaneus fractures often occur because of accidents during exercise or activities. In general, the detection of the calcaneus fracture is still carried out manually through CT image observation, and as a result, there is a lack of precision in the analysis. This paper proposes a computer-aid method for the calcaneal fracture detection to acquire a faster and more detailed observation. First, the anatomical plane orientation of the tarsal bone in the input image is selected to determine the location of the calcaneus. Then, several fragments of the calcaneus image are detected and marked by color segmentation. The Sanders system is used to classify fractures in transverse and coronal images into four types, based on the number of fragments. In the sagittal image, fractures are classified into three types based on the involvement of the fracture area. The experimental results show that the proposed method achieves a high precision rate of 86%, with a fast computational performance of 133 frames per second (fps), used to analyze the severity of injury to the calcaneus. The results in the test image are validated based on the assessment and evaluation carried out by the physician on the reference datasets.


Author(s):  
Yu Fan ◽  
Lin Li

In this paper, a new vibration reduction approach by means of symmetric piezoelectric network is proposed, combining energy harvesting and vibration reduction. The system could be constructed by several individual structures with identical mechanical parameters, such as blades of rotor machinery. Two basic forms of network-connection are studied, in which dissipation of both mechanical and electric field is considered. Dynamic models are established by the Lumped Parameter approach and Kirchhoff’s Circuit Theorem, and the normalizing process is used to make the models more general. Subsequently, the modal information and harmonic response of piezoelectric networks with an arbitrary number of components are obtained. Based on the dynamic characteristics of piezoelectric networks, the mechanism of vibration-suppression behavior of such systems is studied. Design guidelines of these vibration reduction systems are established via parameter studies. Eventually, the optimized parameters of each network-connection form are obtained analytically. It is shown that the symmetric piezoelectric network can suppress the response of the given frequency to zero, and also perform better than pure passive piezoelectric shunts in resonant frequency band.


2020 ◽  
Vol 11 (4) ◽  
pp. 84-111
Author(s):  
Abu Sadat Mohammed Yasin ◽  
Md. Majharul Haque ◽  
Md. Nasim Adnan ◽  
Sonia Rahnuma ◽  
Anowar Hossain ◽  
...  

An autonomous robot is now an internationally discussed topic to ease the life of humans. Localization and movement are two rudimentary necessities of the autonomous robots before accomplishing any job. So, many researchers have proposed methods of localization using external tools like network connectivity, global positioning system (GPS), etc. However, if these tools are lost, either the movement will be paused, or the robot will be derailed from the actual mission. In these circumstances, the authors propose an approach to localize an autonomous robot in a specific area using the given set of images without external help. The image database has been prepared and kept in the internal memory of robot so that image matching can be done quickly. The localization method has been accomplished using three algorithms: (1) SURF, (2) ICP-BP, and (3) EMD. In the evaluation, SURF has been found better than ICP-BP and EMD in terms of accuracy and elapsed time. The authors believe that the proposed method will add value to other methods using some external tools even when those tools are unavailable.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Guohua Cao ◽  
Dan Shan

The aims of this paper are to use a birandom variable to denote the stock return selected by some recurring technical patterns and to study the effect of exit strategy on optimal portfolio selection with birandom returns. Firstly, we propose a new method to estimate the stock return and use birandom distribution to denote the final stock return which can reflect the features of technical patterns and investors' heterogeneity simultaneously; secondly, we build a birandom safety-first model and design a hybrid intelligent algorithm to help investors make decisions; finally, we innovatively study the effect of exit strategy on the given birandom safety-first model. The results indicate that (1) the exit strategy affects the proportion of portfolio, (2) the performance of taking the exit strategy is better than when the exit strategy is not taken, if the stop-loss point and the stop-profit point are appropriately set, and (3) the investor using the exit strategy become conservative.


Author(s):  
FATEMA N. JULIA ◽  
KHAN M. IFTEKHARUDDIN ◽  
ATIQ U. ISLAM

Dialog act (DA) classification is useful to understand the intentions of a human speaker. An effective classification of DA can be exploited for realistic implementation of expert systems. In this work, we investigate DA classification using both acoustic and discourse information for HCRC MapTask data. We extract several different acoustic features and exploit these features using a Hidden Markov Model (HMM) network to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features. To classify discourse information, we exploit two classifiers such as a HMM and Support Vector Machine (SVM). We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify 12 different DAs in MapTask data. We obtain 65.2% and 55.4% DA classification rates using acoustic and discourse information, respectively. Furthermore, we obtain combined accuracy of 68.6% for DA classification using both acoustic and discourse information. These accuracy rates of DA classification are either comparable or better than previously reported results for the same data set. For average precision and recall, we obtain accuracy rates of 74.89% and 69.83%, respectively. Therefore, we obtain much better precision and recall rates for most of the classified DAs when compared to existing works on the same HCRC MapTask data set.


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