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Model Compression for Non-Sequential and Sequential Visual pattern recognition Networks ― A Hybrid Approach 2

Model Compression for Non-Sequential and Sequential Visual P...

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2nd international conference on Signal and Information Processing, IConSIP 2022

作者： Bhalgaonkar, Seema Munot, Mousami Anuse, Alwin SCTR's Pune Institute of Computer Technology PES's Mcoe SPPU Dept of E&tc Pune India SCTR's Pune Institute of Computer Technology SPPU Dept of E&tc Pune India School of Ece Dr. Vishwanath Karad MIT-WPU Pune411038 India

ISBN: (纸本)9781728168852

Visual pattern recognition Networks (VPRNs) delivers remarkable performance using Deep Neural Networks (DNN). Various DNN architectures are available such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variants. Continuous emergence of novel architectures, availability of big data and enormous computing powers keeps this area flourishing. However, such DNN based VPRN models are plunged with enormous computational complexities, intense memory requirements, and high power consumptions which restricts its use in edgeAI environments. For instance, a typical pre-trained VGG-16 model needs 500MB memory, has over 138 million parameters and involves 15.5 billion Floating Point Operations (FLOPs). Such overheads demand for compression of VPRN models while retaining its performance. In this regard, various compression methods have been reported in literature. This paper presents experimentation on hybrid compression method which combines pruning and quantization to achieve higher compression ratio for nonsequential as well as sequential DNN based VPRNs. © 2022 IEEE.

关键词： Deep neural networks

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3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

3rd International Conference on Soft Computing and its Engin...

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3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

ISBN: (纸本)9783031057663

The proceedings contain 33 papers. The special focus in this conference is on soft computing and its Engineering Applications. The topics include: Sentiment Analysis of Twitter Data Using Machine Learning Approaches;mining Spatio-Temporal Sequential patterns Using MapReduce Approach;a Split-Then-Join Lightweight Hybrid Majority Vote Classifier;identification of Barriers in Adoption of IoT: Commercial Complexes in India;a Dynamically Adapting Framework for Stock Price Prediction;evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances;database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison;microstructure Image Classification of Metals Using Texture Features and Machine Learning;segregation of Areca Nuts Using Three Band Photometry and Deep Neural Network;Early Diagnosis of Alzheimer’s Disease from MRI Images Using Scattering Wavelet Transforms (SWT);constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential pattern Mining;KTSVidRec: A Knowledge-Based Topic Centric Semantically Compliant Approach for Video Recommendation on the Web;intelligent Facial Expression Evaluation to Assess Mental Health Through Deep Learning;intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities;crop Disease Prediction Using Multiple Linear Regression Modelling;radial Basis Function Network Based Intelligent Scheme for software Quality Prediction;generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite Images;KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation;weighted Hybrid Recommendation System Using Singular Value Decomposition and Cosine Similarity;CT/MRI 3D Fusion for Cerebral System Analysis;text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems;arabic Cyberbullying Detection from Imbalanced Dataset Using Machine Learning;in

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MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation

MART: Masked Affective RepresenTation Learning via Masked Te...

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conference on Computer Vision and pattern recognition (CVPR)

作者： Zhicheng Zhang Pancheng Zhao Eunil Park Jufeng Yang VCIP & TMCC & DISSec College of Computer Science Nankai University Nankai International Advanced Research Institute (SHENZHEN. FUTIAN) College of Computing Sungkyunkwan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Limited training data is a long-standing problem for video emotion analysis (VEA). Existing works leverage the power of large-scale image datasets for transferring while failing to extract the temporal correlation of affective cues in the video. Inspired by psychology research and empirical theory, we verify that the degree of emotion may vary in different segments of the video, thus introducing the sen-timent complementary and emotion intrinsic among temporal segments. We propose an MAE-style method for learning robust affective representation of videos via masking, termed MART. First, we extract the affective cues of the lexicon and verify the extracted one by computing its matching score with video content, in terms of sentiment and emotion scores alongside the temporal dimension. Then, with the verified cues, we propose masked affective modeling to re-cover temporal emotion distribution. We present temporal affective complementary learning that pulls the complementary part and pushes the intrinsic one of masked multimodal features, where the constraint is set with cross-modal attention among features to mask the video and recover the degree of emotion among segments. Extensive experiments on five benchmarks show the superiority of our method in video sentiment analysis, video emotion recognition, multimodal sentiment analysis, and multimodal emotion recognition.

关键词： Representation learning Sentiment analysis Image segmentation Emotion recognition Correlation Training data Psychology

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Detecting Theft over pattern Change recognition and Locating Stolen Micro Mobility Vehicles in Uncovered Signals Areas

Detecting Theft over Pattern Change Recognition and Locating...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Majd Zaki Baik Mohammed M. Alqahtani Ameen Banjar Department of Computer Science College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Property theft, including vehicle theft, has become a significant concern globally. Technological advancements are providing smart solutions to address issues like vehicle theft. This research aims to enhance indoor localization by developing a solution for asset tracking. The emergence of micro-mobility vehicles, such as e-scooters and e-bikes, presents an opportunity to promote sustainable urban transportation. However, micro-mobility businesses—direct-to-consumer companies and sharing platforms—face significant challenges in fleet management and theft prevention. The present study proposes a “Bluetooth Low Energy (BLE) beacon” solution to help these businesses protect their fleets, even in worst-case scenarios. The research objectives are twofold: to track stolen micro-mobility vehicles and to trace lost assets accurately, using a long transmission range of up to 500 meters. The proposed approach enables operators to accurately locate stolen scooters through indoor mapping, leveraging the BLE beacon's extended transmission range and a battery life of up to 20 years. Research designs consist of artifacts such as constructs, models, methods, and instantiations created to achieve the goals of scientific inquiry into the design. The artifact represents a comprehensive framework for improving asset tracing in challenging environments.

关键词： Meters Location awareness Prevention and mitigation Computational modeling Motorcycles Transportation Companies pattern recognition Information technology Business

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Hybrid soft computing based Approach for Ageing in Face recognition

Hybrid Soft Computing based Approach for Ageing in Face Reco...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Dr. Jitendra Kumar Chaudhary M. Sameena Nazeer Rajesh Singh Devvret Verma Amit B. Kasar Megha Dhotay School of Computing Graphic Era Hill University Bhimtal Campus Bhowali Uttrakhand Department of ECE Ashoka Women's Engineering College Affiliated to JNTUA Ananthapuramu Kurnool Andhra Pradesh Arcadia Grant Chandanwari Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Engineering Sciences International Institute of Information Technology Savitribai Phule Pune University Hinjawadi Pune Maharashtra Computer Engineering MIT School of Polytechnic Pune Maharashtra India

Proposed Approach is a collection of methods for handling a certain class of complicated issues that call for the usage of intelligent software computers. For computational complexity issues, the merger of pattern recognition with soft Context of online like Rnns has demonstrated a bright future, giving birth to strategies and systems with great performance and speed. Utilization of this fusion approach greatly improved the quality of detecting faces in a face - recognition workflow. DCNs are used in our hybrid algorithm, which combines CNN and LSTM, and they focus on the idea of optimizing the face's deep features. Results from our suggested system's testing upon that CACD datasets are quite encouraging and supportive

关键词： Computers Training Measurement Learning systems System testing Face recognition software

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A Garbage Classification Method Based on Improved YOLOv5

A Garbage Classification Method Based on Improved YOLOv5

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2022 international conference on Networks, Communications and Information Technology, CNCIT 2022

作者： Yan, Xiaobo Yang, Yuan Feng, Le Wang, Lin Tan, Mian Guizhou Minzu Unversity Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province. Guiyang China College of Date Science and Information Engineering. Guizhou Minzu Unversity Guiyang China

ISBN: (数字)9781665452960

ISBN: (纸本)9781665452960

With the development of national economy, people's daily garbage is increasing day by day. Relying on manpower to sort garbage is a heavy workload and low efficiency. In this paper, an automatic garbage classification system model based on computer vision is proposed to solve the above problems. Firstly, the YOLOv5 object detection algorithm is improved, and abandoning small object detection to obtain faster detection speed. Secondly, shortcut the complex network layer in the YOLOv5 algorithm framework to speed up the recognition speed while ensuring the recognition accuracy. Finally, the traditional loss function is used to improve the detection speed of large objects in raspberry pie. We collect various types of garbage data set, trains the model on the improved algorithm and tests it by embedding raspberry pie. The experimental results show that the proposed algorithm framework has the characteristics of fast speed and high precision, which can complete the garbage classification at the source of garbage. © 2022 IEEE.

关键词： Network layers

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3rd international conference on Emerging Electrical Energy, Electronics and computing Technologies 2021

3rd International Conference on Emerging Electrical Energy, ...

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3rd international conference on Emerging Electrical Energy, Electronics and computing Technologies 2021, ICE4CT 2021

The proceedings contain 92 papers. The topics discussed include: transformer health index by prediction artificial neural networks diagnostic techniques;energy efficient ant colony system for packet routing in wireless sensor network;evaluation of two-part rain attenuation model at Ku-band for tropical and equatorial regions;performance of restricted fault and bias differential protection against earth fault on a transformer;design and implementation of an automatic speed control system of vehicles for avoiding road accidents in Bangladesh;real time retinal optic disc segmentation via guided filter and discrete wavelet transform;air plasma sterilizer using a parallel dielectric barrier discharge;design and characterization of screen-printed piezoresistive cantilever for gas sensor application;effect of build parameters on process energy consumption and material usage in fused deposition modelling method;and channels selection for pattern recognition of five fingers motor imagery electroencephalography signals.

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A Fine-grained Human Gait recognition Algorithm Based on Unordered Video Sequences

A Fine-grained Human Gait Recognition Algorithm Based on Uno...

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Information and Communication Technology (ICTech), international conference of

作者： Dongsheng Yang Ying Bai Hongliang Wang Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Gait recognition is an effective and unique biometric recognition pattern, and the performance of the algorithm mainly depends on its ability to extract and distinguish features from spatial temporal sequences. To improve the robustness of fine-grained human gait recognition algorithms, this paper proposes a deep gait recognition algorithm based on unordered sequences. Firstly, this article expands the Casia-C dataset to a new large-scale human gait behavior dataset; Secondly, multi-scale serialized feature extraction is performed on shallow to deep features. The improved Gaitset model enhances the mining of fine-grained sequence level features, while preserving the temporal input independence of the original Gaitset model, achieving fast, flexible, and effective gait recognition tasks. Finally, this article designs and implements the overall process from video capture, human contour segmentation and preprocessing to gait behavior recognition. The experimental results show that the proposed algorithm significantly improves accuracy and robustness in different walking states on the most widely used dataset in the field of gait recognition, CASIA-B (Institute of Automation, Chinese Academy of Sciences) and the expanded CASIA-C dataset.

关键词： Legged locomotion Accuracy Image recognition Video sequences Feature extraction Robustness Behavioral sciences pattern recognition Information and communication technology Gait recognition

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pattern-Driven Privacy: Columnar Binary Transformation for Robust Iris recognition

Pattern-Driven Privacy: Columnar Binary Transformation for R...

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international Research conference on Smart computing and Systems Engineering (SCSE)

作者： Wiraj Udara Wickramaarachchi Dongdong Zhao Junwei Zhou Jianwen Xiang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Department of Information and Communication Technology Rajarata University of Sri Lanka Mihintale Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

Iris is a distinct and strong biometric feature that is being employed as a method of authentication all over the world. Irreversibility, Renewability, and Unlinkability are the most critical aspects required to ensure the privacy of biometrics. Keeping recognition performance at a high level while providing privacy is a critical challenge that many researchers try to overcome. Information distortion through transforming original iris data into another form of information can be used to ensure privacy features. Therefore, this paper proposes a novel approach for iris authentication that ensures privacy features and high recognition performance. The binary representation of the existing iris template is transposed to another new binary representation by computing it column-wise. The Genuine Acceptance Rate (GAR) depicted for the proposed method is 97.11%, while the False Acceptance Rate (FAR) is 0.01%. Compared to the existing research works, the proposed method demonstrates an Equal Error Rate (EER) of 0.59%. The theoretical and experimental proof with explanation is stated in the paper to confirm the privacy aspects.

关键词： Privacy Data privacy Error analysis Databases Authentication Systems engineering and theory Distortion

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Mean AR-Local Binary pattern For Facial Expressions Classification Based on KNN Classifier

Mean AR-Local Binary Pattern For Facial Expressions Classifi...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Narubonia Srilatha Lokeswara Reddy Computer Science and Engineering JNTUA Ananthapur India Computer Science and Engineering K.S.R.M College of Engineering (Autonomous) Kadapa India

According to its significance, facial expression recognition (FER) has reached an incredibly prominent position in the field of image processing, beginning in the 1990s and continuing to the present day. Face recognition, feature extraction, and face categorization are the three common steps in FER. In contrast to the various FER systems described for recognizing just part of face expressions, the system presented in this study can identify all eight fundamental facial emotions (neutral, pleased, angry, contempt, surprise, sad, fear, and disgust). We employ the Japanese Female Facial Expression (JAFFE) and the Extended Cohn-Kanade (CK+) datasets to verify our results. The presented methods LBP patterns in this work namely, Asymmetric Region LBP(AR-LBP), Mean Asymmetric Region LBP (Mean AR-LBP), We used GLCM feature extractor technique for feature selection. For the given input image, The GLCM calculates different features factors named Contrast, Correlation, and Homogeneity, Entropy, Mean, variance, and dissimilarity which were results to a feature vector. The findings of the given approach, which employed K-Nearest Neighbor (KNN) to categories facial emotions, are then contrasted with those of the standard LBP pattern. The recognition rate utilizing the provided technique is 96% for the CK + data set and 97% for the JAFFE data set, as shown by the testing results.

关键词： Emotion recognition Image recognition Correlation Face recognition Feature extraction Entropy Standards

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