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A HYBRID APPROACH FOR DEEP LEARNING BASED FINGER VEIN BIOMETRICS TEMPLATE SECURITY
ГИБРИДНЫЙ ПОДХОД К БЕЗОПАСНОСТИ ШАБЛОНОВ БИОМЕТРИЧЕСКИХ ДАННЫХ ВЕН ПАЛЬЦА НА ОСНОВЕ ГЛУБОКОГО ОБУЧЕНИЯ

Shendre Shivam, Shubhangi Sapkal
2020 IZVESTIYA SFedU ENGINEERING SCIENCES  
Biometric, template security, hybrid, binary decision diagram (BDD), fuzzy commitment scheme, deep learning and machine learning Introduction.  ...  In this paper we have discussed a hybrid method for finger vein biometric recognition based on deep learning approach using BDD and fuzzy commitment schemes.  ...  A novel biometric tem-plate protection algorithm using the binary decision diagram (BDD) [4] for deep learning based finger vein biometric systems.  ... 
doi:10.18522/2311-3103-2020-3-173-183 fatcat:l6fd5i7v3rhu5kxa2afzrwhr2a

Speech Emotion Recognition: A Review Paper

2021 International Journal for Research in Applied Science and Engineering Technology  
Artificial Neural Network (ANN), and K-nearest neighbour (KNN) are some of the techniques used to distinguish various emotions from human expression.  ...  We examined the fundamentals of a speech emotion recognition system and explored various pre-processing, feature extraction, and classification techniques for the system in this paper.  ...  KNN can be used both for classification as well as regression [12] [16] . 3) Pattern Recognition Neural Network (PRNN): Pattern recognition is the method of using machine learning data to identify regularities  ... 
doi:10.22214/ijraset.2021.33656 fatcat:tznu42bhvrd5tb7wguzqbqm6be

Assessing Effectiveness of Exercised Variants of Machine Learning Techniques

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Thus, the machine learning scheme is incorporated with deep learning and artificial intelligence technology.  ...  However, there are different schemes (perception based, instance-based and logic based) to provide an effective classification, prediction, and data recognition in terms of characterizing the features  ...  Keras is user-friendly, modular, and extensible and allows fast experimentation with deep neural networks.  ... 
doi:10.35940/ijitee.d1781.029420 fatcat:3dig3j6ja5hovmazntsm3ldt3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TIP 2021 3167-3178 F Face recognition 2D-LCoLBP: A Learning Two-Dimensional Co-Occurrence Local Binary Pattern for Image Recognition.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Fruits and Vegetables Freshness Categorization Using Deep Learning

Labiba Gillani Fahad, Syed Fahad Tahir, Usama Rasheed, Hafsa Saqib, Mehdi Hassan, Hani Alquhayz
2022 Computers Materials & Continua  
The recognition and categorization of fruits and vegetables are performed through two deep learning models: Visual Geometry Group (VGG-16) and You Only Look Once (YOLO), and their results are compared.  ...  We gathered a dataset comprising of 60K images of 11 fruits and vegetables, each is further divided into three categories of freshness, using hand-held cameras.  ...  Local Binary Patterns (LBP) are used for feature extraction and Principal Component Analysis (PCA) is used for dimensionality reduction.  ... 
doi:10.32604/cmc.2022.023357 fatcat:wde46o7nu5h55j7xqcu42hdbhy

Deep Multi-Classifier Learning for Medical Data Sets
English

Rosaida Rosly, Mokhairi Makhtar, Mohd Khalid Awang, Hasni Hassan, Ahmad Nazari Mohd Rose
2020 International Journal of Engineering Trends and Technoloy  
Then, a combination at classification level between these classifiers using deep learning approach was applied to get the highest accuracy and see which the most suitable Deep Multi-classifier Learning  ...  This paper presents a comparison among the different classifier such as Sequential Minimal Optimization (SMO) , decision tree (J48) , random forests (RFs), Naïve Bayes (NB) and Instance Based for K-Nearest  ...  ACKNOWLEDGEMENT This research was supported by Research Management and Innovation Centre, Universiti Sultan Zainal Abidin.  ... 
doi:10.14445/22315381/cati1p201 fatcat:olx2t4yoonbptkxi7evvtzvygq

Identification and Analysis of Neurodegenerative Diseases with Twin Layered CNN Using Gait Dynamics

2022 International Journal of Intelligent Engineering and Systems  
The classification of each disease group is made with the selected features by the Random forest and Multi SVM machine learning approach, which gives the accuracy of 99.89%, and 97.06% in classification  ...  This study identifies the gait dynamics of Neurodegenerative disease (NDD) patients using a deep convolution neural network (CNN) approach.  ...  Visualization, supervision and project administration was done by Pushpa Rani Mariathangam and Joseph Emerson Raja.  ... 
doi:10.22266/ijies2022.0430.07 fatcat:sawi4gyyj5bxrac6n42l3mx5zy

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems.  ...  This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.  ...  Such domains include logic programming, expert system, pattern recognition, machine learning (ML) and reinforcement learning [6] .  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

Facial Expression Recognition Using Python Using CNN Model

Akash Kumar, Athira B. Nair, Swarnaprabha Jena, Debaraj Rana, Subrat Kumar Pradhan
2021 Current Journal of Applied Science and Technology  
We evaluate our proposed method with the dataset which we used and the recall of angry, fear, happy, neutral, sad, and surprise is 60%, 31%, 84%, 22%, 57% and 58% respectively and the f1-score is 51% 35%  ...  Each day has a number of instances and all instances include numerous amounts of communication. Every communication expressed with emotion tells us about the state of the person.  ...  It was developed to make implementing deep learning models as fast and easy as possible for research and development.  ... 
doi:10.9734/cjast/2021/v40i2031459 fatcat:lly5wg3bgjce3asvkm2wb65wee

Applying big data based deep learning system to intrusion detection

Wei Zhong, Ning Yu, Chunyu Ai
2020 Big Data Mining and Analytics  
Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection.  ...  Particularly, the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples.  ...  Deep learning has produced better results than shallow learning models in the fields of computer vision, speech recognition, automatic machine translation, and finance [10, 11] .  ... 
doi:10.26599/bdma.2020.9020003 fatcat:cqbejohorjcahn34vzhvtpxtta

Deep Learning Techniques to Address Issues in Data Quality and Data Variety

2019 International journal of recent technology and engineering  
A major use of Deep Learning is processing, learning and training from the huge amounts of unsupervised data, analyze patterns from the data and can be used for large Datasets in which the raw data is  ...  In this paper, Deep Learning techniques for addressing Data of different variety/formats is analyzed, enabling fast and full processing and integration of large amounts of different variety of information  ...  A major use of Deep Learning is processing, learning and training from the huge amounts of unsupervised data, analyze patterns from the data and can be used for large Datasets in which the raw data is  ... 
doi:10.35940/ijrte.d9018.118419 fatcat:4tmbdz3gqzdvxgxwioczfv5daq

Recent Advances in Deep Learning Techniques for Face Recognition [article]

Md. Tahmid Hasan Fuad, Awal Ahmed Fime, Delowar Sikder, Md. Akil Raihan Iftee, Jakaria Rabbi, Mabrook S. Al-rakhami, Abdu Gumae, Ovishake Sen, Mohtasim Fuad, Md. Nazrul Islam
2021 arXiv   pre-print
In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques.  ...  Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation.  ...  Figure 9 describes the basic Reinforcement Learning state diagram. The combination of Deep Learning and Reinforcement Learning is used mainly in face recognition.  ... 
arXiv:2103.10492v1 fatcat:h526swzntjgmlcjmwnuidqg44u

Machine Vision based Fruit Classification and Grading - A Review

Sapan Naik, Bankim Patel
2017 International Journal of Computer Applications  
Multi resolution Grayscale and Rotation Invariant Texture Classification with Local Binary Patterns, 2002.  ...  International Deep Learning / Convolutional Neural Networks For the image classification and recognition tasks, development in deep learning and convolution neural network (CNN) have been proved to be  ... 
doi:10.5120/ijca2017914937 fatcat:aoy6mq7zrrdltogwbg27o3dicy

Classification of Human Whole-Body Motion using Hidden Markov Models [article]

Matthias Plappert
2016 arXiv   pre-print
These features are then used to perform the multi-label classification using two different approaches. The first approach simply transforms the multi-label problem into a multi-class problem.  ...  This bachelor's thesis presents different approaches to solve the multi-label classification problem using Hidden Markov Models (HMMs).  ...  In this case, the decision maker uses multiple binary classifiers internally to learn a mapping from the likelihoods of the HMMs to the multi-label prediction using the binary relevance method.  ... 
arXiv:1605.01569v1 fatcat:p7r2rnvqhratvlhvna22vc6p7q

APPLICATION OF DEEP LEARNING IN HEALTH INFORMATICS: A REVIEW

Vinit Mehta, Noopur Shrivastava
2021 International Journal of Technical Research & Science  
Finally the limitations and challenges of deep learning in the field of health informatics have been discussed.  ...  The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.  ...  CONCLUSION Deep learning has gained a central position in recent years in machine learning and pattern recognition.  ... 
doi:10.30780/specialissue-icrdet-2021/002 fatcat:gsv76fv4qbe5fh5g3va5da2izu
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