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

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

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international conference on Signal and Information Processing (IConSIP)

作者： Seema Bhalgaonkar Mousami Munot Alwin Anuse Dept of E&TC SCTR's Pune Institute of Computer Technology & PES's MCOE SPPU Pune India Dept of E&TC SCTR's Pune Institute of Computer Technology SPPU Pune India School of ECE Dr. Vishwanath Karad MIT-WPU Pune India

ISBN: (纸本)9781728168869

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.

关键词： Deep learning Visualization Quantization (signal) Recurrent neural networks Power demand Computational modeling Memory management

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Analysis and modeling of contextual activity pattern for identifying lifestyle disorders 12

Analysis and modeling of contextual activity pattern for ide...

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12th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2021

作者： Sakhare, Apeksha V. Sharma, Ashish K. Computer Science and Engineering G H Raisoni University MH Amravati India Computer Science and Engineering G H Raisoni College of Engineering MH Nagpur India School of Engineering and Technology G H Raisoni University MH Amravati India

Nowadays with the help of various government health records and surveys, it has been found that the root cause of the various severe diseases (non-communicable) very frequently occur in the early age of humans and adolescents has not even aware of any symptoms or signs. This type of disease has a root cause of human irregular and very unhealthy, unorganized lifestyle and diet pattern. From the data analysis done on health information collected by various government organizations, it has been found that 309889 adolescents between ages 12-18 (53.8 female genders) from approx. 70 countries have been suffering from this non-communicable disease. Medical departments or health care organizations are producing a very wide amount of health-based data every day which be used for identifying or recognizing or predicting this disease which has no early signs or symptoms during some of its growing stages. This hidden data of healthcare organizations will be useful for taking decisions effectively for affected person treatment and recovery procedure. Here the main challenge is how to extract the hidden data from the collected health records, there some sort of data mining and machine learning technique is required. The expected outcome of our research work is that if the disease is detected and identified at its early stage it can be cured very effectively and easily with more accuracy and thus reducing the risk factor of life of a patient and the cost of treatment required to cure the disease. Early recognition of disease will also increase the percentage rate of cure from a disease of a patient and also decreases the risk of loss of life of a patient. This research paper provides a brief introduction of lifestyle Health problems and its cause. Our proposed research work includes a review of various techniques used by different research scholars, a working block diagram with a brief description of the diagram, and at the end, we have concluded with future work and the scope of

关键词： Diseases

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soft Pseudo-labeling Semi-Supervised Learning Applied to Fine-Grained Visual Classification 25th

Soft Pseudo-labeling Semi-Supervised Learning Applied to Fin...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Mugnai, Daniele Pernici, Federico Turchini, Francesco Del Bimbo, Alberto University of Florence Florence Italy

ISBN: (纸本)9783030687984

Pseudo-labeling is a simple and well known strategy in Semi-Supervised Learning with neural networks. The method is equivalent to entropy minimization as the overlap of class probability distribution can be reduced minimizing the entropy for unlabeled data. In this paper we review the relationship between the two methods and evaluate their performance on Fine-Grained Visual Classification datasets. We include also the recent released iNaturalist-Aves that is specifically designed for Semi-Supervised Learning. Experimental results show that although in some cases supervised learning may still have better performance than the semi-supervised methods, Semi Supervised Learning shows effective results. Specifically, we observed that entropy-minimization slightly outperforms a recent proposed method based on pseudo-labeling. © 2021, Springer Nature Switzerland AG.

关键词： Entropy

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Epilepsy Detection Through EEG Signal Classification Using Machine Learning Methods

Epilepsy Detection Through EEG Signal Classification Using M...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Vivek Deshpande P. Gajendran Varun Ojha Latha B Ishika Soni Arunkumar D T Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

In this exertion demonstrates the application of a deep learning algorithm to the analysis of focal and non-focal EEG data for the finding of epilepsy. This research presents a strategy for automating the diagnosis of epilepsy by making use of a soft computing resource. By using the ANFIS classification technique, focal and non-focal samples may be distinguished. Structures computation, features classification, and features transformation are the three pillars upon which this technique rests. For an electroencephalogram (EEG) data, the Approximate and Directional bands are produced when the suggested NSCT is used. NSDF and Non-Subsampled Pyramid Filter are the two main components of this NSCT. These fall into two categories: low pass, which includes power frequencies, and high pass, which includes high frequency noise. To do this, we construct directed subbands and reroute the high-pass signal using NSDF. Step two of decomposition involves creating high and low frequency sub bands by passing the low pass sub band signal via the Non-Sub Sampled Pixel Frequency (NSPF). The directed sub bands that are created are achieved by retransmitting the high-frequency sub band signal via NSDF. Using the statistical properties obtained from these sub-bands, the ANFIS classification method divides the overall EEG signal into focal and non-focal samples. Applying the proposed method to a collection of EEG signal extraction measurements allows for validation.

关键词： Deep learning Analytical models Time-frequency analysis Sensitivity Noise Epilepsy pattern classification Brain modeling Electroencephalography Data models

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2020 7th international conference on soft computing and Machine Intelligence, ISCMI 2020

2020 7th International Conference on Soft Computing and Mach...

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7th international conference on soft computing and Machine Intelligence, ISCMI 2020

ISBN: (纸本)9781728175591

The proceedings contain 52 papers. The topics discussed include: evaluation of deep learning based human expression recognition on noisy images;NichePSO and the merging subswarm problem;melt-pool defects classification for additive manufactured components in aerospace use-case;a multiscale approach to detect the semantics of locomotion without positioning information;a comprehensive diagnostic tool for skin cancer using a multifaceted computer vision approach;comparison of swarm and graph algorithms for solving travelling salesman problems;and GPQ: greedy partial quantization of convolutional neural networks inspired by submodular optimization.

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An Efficient Convolutional Neural Network Approach for Facial recognition 10

An Efficient Convolutional Neural Network Approach for Facia...

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10th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Mangal, Aayushi Malik, Himanshu Aggarwal, Garima Amity Univ ASET Dept Comp Sci Noida UP India

ISBN: (纸本)9781728127910

Data security being the main concern now a days, has faced a lot of threat in terms of breaching of information which requires immediate attention. Biometrics have served a long-run for this purpose which is a part of Deep Learning. In the recent past, face recognition has become a very important tool for safety and security purposes. This paper presents the application of face recognition technique, making use of Convolutional Neural Network (CNN) with Python and a comparison is drawn between the other techniques such as Principal Component Analysis (PCA), Local Binary pattern (LBP) and K Nearest Neighbour (KNN). Unlike conventional methods, the proposed scheme uses four Convolutional layers with ReLu layers, four pooling layers, a fully connected layer and a softmax Loss Layer to normalize the probability distribution. The dataset consists of 1500 images with different facial expressions and the model is trained and tested in order to acquire an accuracy using CNN method. Experimental results show that the proposed Neural Network scored an accuracy of 96.96%.

关键词： Facial recognition Deep Learning Face Database Convolutional Neural Networks K Nearest Neighbour Principal Component Analysis Local Binary pattern Histogram

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Exploration of Emotional pattern recognition and Emotional Model Construction Based on Big Data 9th

Exploration of Emotional Pattern Recognition and Emotional M...

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9th international conference on Frontier computing, FC 2019

作者： Xu, Zhiqiang He, Jialiang Liu, Yongzhi Sichuan University of Media and Communications Chengdu China College of Information and Communication Engineering Dalian Nationalities University Dalian China

ISBN: (纸本)9789811532498

Based on the brief introduction of the definition and correlation of big data and affective computing, this paper illustrates the methods of affective pattern recognition by taking face recognition, speech recognition, language recognition and bio-physiological recognition as examples, and then puts forward the viewpoint that the research hotspot of affective recognition under the background of big data has shifted from single mode to multi-mode fusion. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data

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FineCTI: A Framework for Mining Fine-grained Cyber Threat Information from Twitter Using NER Model

FineCTI: A Framework for Mining Fine-grained Cyber Threat In...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chunyan Ma Jun Jiang Kai Zhang Zhengwei Jiang Peian Yang Xuren Wang Huamin Feng Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Information Engineering College Capital Normal University Beijing China Beijing Electronic Science and Technology Institute Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

To timely respond to cyber threats related to a specific IT infrastructure called fine-grained (e.g., Windows or Linux), security analysts need to require timely and comprehensive threat information. Twitter, as a vital source of real-time threat information, provides abundant but overwhelming information due to the increased data sources. Automatically mining and summarizing fine-grained threat information from Twitter can help security analysts maintain the infrastructure’s security. Most existing studies focus on classification, which carries less threat information. Some works use clustering based on text similarity relying on the embedding of text obtained from pre-trained models, which cannot be applied to short text, resulting in noisy clusters. Several works build topic models. However, the incoherent topic keywords are difficult to understand and analyze. To overcome these challenges, we design a FineCTI framework to mine the threat information related to the specific infrastructure on Twitter and generate a detailed threat information summary that is machine-readable and human-readable, efficiently reducing information overload. FineCTI optimizes the feature extraction part based on the named entity recognition model and performs clustering based on features extracted, thus effectively reducing the influence of sparsity of tweets on the clustering result and with the V-measure score improved by 7%. The cluster analysis results show that we can mine the fine-grained threats up to 15 days before the official disclosure date.

关键词： Privacy Social networking (online) soft sensors Linux Blogs Feature extraction Real-time systems

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Grading Problem-Solving for Clustering Students' Score Using Dynamic Programming Procedure in The Context of Dynamic Time Warping

Grading Problem-Solving for Clustering Students' Score Using...

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international conference on Informatics and computing (ICIC)

作者： Mochamad Nizar Palefi Ma'ady Tabina Shafa Nabila Syahda Muhammad Nasrullah Anindya Salwa Salsabila Ully Asfari Hawwin Mardhiana Department of Information Systems Institut Teknologi Telkom Surabaya Surabaya Indonesia

ISBN: (纸本)9798350345728

Clustering is the solution for grouping a plenty of data when there is no knowledge of the classes. Clustering is typically a well-known algorithm in the origins of machine learning or artificial intelligence. However, artificial intelligence is, unfortunately, relatively hard to understand for some reasons, hence, followed by the rise of explainable artificial intelligence research. In this paper, we propose an alternative approach to grouping problem-solving by using a dynamic programming procedure in the context of dynamic time warping. We conduct a cluster analysis in stagewise optimization with the following four steps; (1) define an optimal value function, (2) formulate recurrence relation, (3) set the boundary condition, and (4) answer is given by. We also demonstrate a numerical example along with its table-filling solution in the case of grading students' scores into $k$ clusters. The results conclude that dynamic time warping algorithm may have preferences to be an alternative solution for certain machine learning tasks beyond traditional unsupervised methods instead.

关键词： Machine learning algorithms Heuristic algorithms Clustering algorithms Machine learning Programming Dynamic programming pattern recognition

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An Efficient pattern recognition Based Method for Drug-Drug Interaction Diagnosis 1

An Efficient Pattern Recognition Based Method for Drug-Drug ...

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1st international conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Javed, Rabia Saba, Tanzila Humdullah, Salman Mohd Jamail, Nor Shahida Javed Awan, Mazhar Lahore College for Women University Computer Science Department Lahore54300 Pakistan Prince Sultan University Computer and Information Science Riyadh11586 Saudi Arabia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru81310 Malaysia University of Management and Technology Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9780738131771

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier. © 2021 IEEE.

关键词： Diagnosis

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