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An Ensemble of CapsNet and KSVM Using Feature Fusion: application to COVID-19 Detection 3

An Ensemble of CapsNet and KSVM Using Feature Fusion: Applic...

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Aidoo, Evans Fiasam, Linda Delali Mensah, Acheampong Edward University of Electronic Science & Technology of China School of Information & Software Engineering Chengdu China University of Electronic Science & Technology of China School of Information & Software Engineering Network and Data Security Key Lab of Sichuan Province Chengdu China Zhejiang Gongshang University School of Computer & Information Engineering Hangzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural Networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

关键词： COVID-19

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Multi-classification on the diagnosis of 4 early stages of Alzheimer's Disease by Transfer learning Models 3

Multi-classification on the diagnosis of 4 early stages of A...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Geng, Longling Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781510663473

Nowadays, deep learning models are widely used in medical diagnosis, with advantages of higher accuracy and better capability of analysing complex medical images over traditional check. This work compares 7 commonly used CNN models - VGG16, ResNet50, InceptionV3, DenseNet-121, Xception, Inception-Resnet V2, and MobilenetV3-small - on the diagnosis of the four early stages of Alzheimer's Disease - Normal control (NC), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). Models are initially adjusted with a dense layer of 4 outputs on the multi-classification problem and deployed in various overfitting prevention setting. Also, by using transfer learning and fine-tuning technique, the training accuracy and testing accuracy of the 7 models have both improved by over 30%. Among all, DenseNet121 has the overall best performance of 95.48% testing accuracy, 0.92 F1-score and second smallest number of parameters of 7,041,604, with a good portability on mobile computing devices, likely due to the reuse of feature maps to extract more accurate features. Inception-ResNet-v2 is adopted when only considering accuracy and reliability and due to its largest number of parameters, Inception-ResNet-v2 is more suggested to be used on computer medical application. VGG16 has the highest accuracy of 96.11% and a small number of parameters of 14,716,740, however, it is currently not suitable for applying to real-life application since its F1-score of 0.8943 and precision of 0.8266, indicating numerous false cases. © 2023 SPIE.

关键词： Medical applications

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Empirical Study of Multi-class Weed Classification Using Deep learning Network Through Transfer learning 1

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Gourisaria, Mahendra Kumar Sahoo, Vishal Kumar Sahoo, Biswajit Sarangi, Partha Pratim Singh, Vijander School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur302034 India Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU—Norwegian University of Science and Technology Alesund Norway

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

In agriculture, weed infestation presents various challenges to quality farming. They (weed) compete for resources and hinder a crop’s growth. To combat weed infestation on cropland, farmers use expensive fertilizer which uses toxic chemicals that can have lowered crop yield and quality. Fertilizer application is labor-intensive if done manually, automation is prohibitively expensive and resource-intensive. With the rise in popularity of deep neural networks especially CNN for image classification, it is an excellent candidate to automate weed classification in real-time and potentially automate weed removal using robots/UAVs cost-effectively. The paper adopts dual training approach, one from scratch and the other using transfer learning on 17,509 weed images with nine different classes of weed on five CNN architecture. Various preprocessing techniques are employed to further increase the accuracy of the models. ResNet50V2 achieved the highest classification accuracy of 93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Cost effectiveness

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Benchmarking Neural Networks-Based Approaches for Predicting Visual Perception of User Interfaces 3rd

Benchmarking Neural Networks-Based Approaches for Predicting...

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Bakaev, Maxim Heil, Sebastian Chirkov, Leonid Gaedke, Martin Novosibirsk State Tech Univ Novosibirsk Russia Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9783031056437;9783031056420

Deep learning techniques have become the mainstream and unquestioned standard in many fields, e.g. convolutional neural networks (CNN) for image analysis and recognition tasks. As testing and validation of graphical user interfaces (GUIs) is increasingly relying on computer vision, CNN models that predict such subjective and informal dimensions of user experience as aesthetic or complexity perception start to achieve decent accuracy. They however require huge amounts of human-labeled training data, which are costly or unavailable in the field of Human-computer Interaction (HCI). More traditional approaches rely on manually engineered features that are extracted from UI images with domain-specific algorithms and are used in "traditional" machine learning models, such as feedforward artificial neural networks (ANN) that generally need fewer data. In our paper, we compare the prediction quality of CNN (a modified GoogLeNet architecture) and ANN models to predict visual perception per Aesthetics, Complexity, and Orderliness scales for about 2700 web UIs assessed by 137 users. Our results suggest that the ANN architecture produces smaller Mean Squared Error (MSE) for the training dataset size (N) available in our study, but that CNN should become superior with N > 2912. We also propose the regression model that can help HCI researchers to foretell MSE in their ML experiments.

关键词： User Interfaces Aesthetics Visual Complexity Deep learning

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Model of Information System for application of Blockchain Technologies 3

Model of Information System for Application of Blockchain Te...

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2021 3rd international conference on computer, Communications and Mechatronics Engineering, CCME 2021

作者： Ishchukova, Evgeniya Romanenko, Kirill Salmanov, Vyacheslav Department of Information Security Southern Federal University Russia

This article describes the current security problems of blockchain networks, identifying the least explored area. The concept of an information system for investigating blockchain incidents is presented. An example of stealing bitcoins from a cryptocurrency wallet was taken as input. This example contains a fairly large number of blocks and transactions, and everything needs to be studied. An overview of the existing vulnerabilities in the Ethereum blockchain platform is presented. A brief description of the basic principles of operation of two largest cryptocurrency platforms - Bitcoin and Ether - is given. Examples of possible incidents that are widespread among users of blockchain systems are considered. The architecture of the designed solutions is described. The first experimental results obtained on the basis of the implementation of the designed algorithms are presented. It is concluded that iterating over a large number of transactions takes a lot of time;in the future, it is planned to apply machine learning methods to analyze such a large data array. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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Siamese network tracking algorithm based on reinforcement learning 3

Siamese network tracking algorithm based on reinforcement le...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Ji, Qingbo Qi, Yufei Liu, Hang Hou, Changbo Gong, Jianyang School of Information and Communication Engineering Harbin Engineering University Heilongjiang Harbin150001 China Key Laboratory of Advanced Ship Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Heilongjiang Harbin150001 China China Telecom Heilongjiang Branch Heilongjiang Harbin150001 China

ISBN: (纸本)9781510663473

In the field of computer vision, object tracking is a hot research topic. At present, the coordinate box of most algorithms is vertical box. However, in practical application, the frame selection area of the object is expanded due to the rotation of the object, which makes it impossible to obtain the real motion angle of the object. To solve this problem, this paper proposes a Siamese network tracking algorithm (SiamRotate) based on rotation box reinforcement learning. In order to predict the rotation Angle of the object, firstly, we processes the VOT2013 and VOT2014 datasets to increase the angle richness, and used the processed datasets to train the angle decision-making network through the combination of supervised training and reinforcement learning. Based on the SiamRPN network, the tracking network obtains the feature response through the deep cross-correlation operation of the feature map of the two branches through SiamRPN, and then processes the feature response map through the angle decision network to predict the object rotation angle. The performance indexes of the proposed method reached 0.62(A), 0.21(R), 0.425(EAO) on VOT2016, and 0.61(A), 0.27(R), 0.399(EAO) on VOT2018, and the tracking speed reaches 138fps. Finally, comparative experiments show that the algorithm has good performance in speed and accuracy. © 2023 SPIE.

关键词： Forecasting

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

ISBN: (纸本)9783030957100

The proceedings contain 54 papers. The special focus in this conference is on Artificial Intelligence and Speech Technology. The topics include: Feature Extraction and Sentiment Analysis Using machine learning;a Neural Network Based Approach for Text-Level Sentiment Analysis Using Sentiment Lexicons;cross Linguistic Acoustic Phonetic Study of Punjabi and Indian English;prashn: University Voice Assistant;spectrogram Analysis and Text Conversion of Sound Signal for Query Generation to Give Input to Audio Input Device;a Contrastive View of Vowel Phoneme Assessment of Hindi, Indian English and American English Speakers;Text-Based Analysis of COVID-19 Comments Using Natural Language Processing;indian Languages Requirements for String Search/comparison on Web;dictionary Vectorized Hashing of Emotional Recognition of Text in Mutual Conversation;Speaker Independent Accent Based Speech Recognition for Malayalam Isolated Words: An LSTM-RNN Approach;Comparative Analysis of NLP Text Embedding Techniques with Neural Network Layered Architecture on Online Movie Reviews;current State of Speech Emotion Dataset-National and international Level;context-Aware Emoji Prediction Using Deep learning;development of ManiTo: A Manipuri Tonal Contrast Dataset;deep Neural Networks for Spoken Language Identification in Short Utterances;a Lightweight Deep learning Approach for Diabetic Retinopathy Classification;machine Hearing a Cognitive Service for Aiding Clinical Diagnosis;critical Insights on Cancer Detection Using Deep learning;OHF: An Ontology Based Framework for Healthcare;enhancing the Deep learning-Based Breast Tumor Classification Using Multiple Imaging Modalities: A Conceptual Model;a Review on Speech Synthesis Based on machine learning;EEG Based Stress Classification in Response to Stress Stimulus;hindi Phoneme Recognition - A Review.

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A Review of Healthcare Recommendation Systems Using Several Categories of Filtering and machine learning-Based Methods 3

A Review of Healthcare Recommendation Systems Using Several ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Kumar, Pardeep Kumar, Ankit Baba Mastnath University Asthal Bohar Rohtak India

ISBN: (纸本)9781665462006

Health-care Recommendation Systems (RSs) is a famous application of AI (artificial intelligence) that involves the investigators worldwide. Several ML methods are utilized to develop health-care RSs. Selecting the best ML method to give customers a service/product is an interesting task in health-care RSs. Currently, it is observed in the buying pattern of individuals from in-shop to online the outcome in accessibility of OI (online information) which is exponentially improved day by day. In shopping scenarios, these RSs must be capable of advising reliable solutions to the customers. The medical health-care RSs have to manage a large quantity of data by cleaning the reliable data depending on the data analysis, generated on the user behaviour made by the customers during their online terms. RSs can suggest suitable items to customers based on their interests and existing favourites, leading to improved sales. This paper described the overview of health-care RS, phases of RSs such as IC (information collection), learning, and recommender or predictor. This paper presents an overview of three main methods used to construct health-care RSs that are content-based, collaborative filtering (CF)-based and hybrid (HB)-based filtering methods and discuss several ML-based methods used in health-care RSs with their performance comparison. The ML-based methods include;MFM (matrix factorization model), CNN, MLP, SVD, etc. © 2022 IEEE.

关键词： Health care

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A comparative research on the effectiveness of two mobile applications for vocabulary learning: BaiCiZhan and Bubeidanci 3

A comparative research on the effectiveness of two mobile ap...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Zhang, Peirao Department of English Language Education The Education University of Hong Kong Hong Kong

ISBN: (纸本)9781510661240

In recent years, with the development of science and technology, more and more language teachers and researchers pay attention to the application of mobile devices in language learning. However, few studies have explored the effectiveness of specific vocabulary learning applications in the Chinese context and made suggestions for students' choices. This research involves 16 participants using two popular Apps named BaiCiZhan and Bubeidanci respectively. They were required to do a pre-test, two immediate post-tests and a delayed post-test, to compare the difference in the effect of vocabulary retention of the two apps. The results showed that although both apps significantly improved the retention effect immediately after learning and one week after learning, there was no difference between the two apps. In addition, interviews were conducted to investigate the participants' experience of using the two apps and combining the vocabulary learning strategies used in two apps. This study concluded that the learning style preference of learners could be seen as a factor, and the two most important vocabulary learning strategies respectively used by the two apps which are using pictures and using various context sentences both have their own advantages and disadvantages, which makes no difference in the final result of word retention between the two apps. © 2022 SPIE.

关键词： learning systems

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CorefPM: Coreference Resolution as Linguistic Prompt Prediction and machine Reading Comprehension 22

CorefPM: Coreference Resolution as Linguistic Prompt Predict...

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3rd international conference on Control, Robotics and Intelligent System, CCRIS 2022

作者： Zhao, Jianxuan Wang, Huazhen Zhao, Yifei College of Computer Science and Technology Huaqiao University Xiamen361021 China College of Preparatory Education Huaqiao University Xiamen361021 China

ISBN: (纸本)9781450396851

Coreference resolution is a key challenge in natural language processing and is considered as a good test of machine intelligence. Recent coreference resolution prefers to supervised methods due to its superior performance. But supervised methods suffer from data scarcity and limited generalizability. In this paper, we present CorefPM, an unsupervised approach for the coreference resolution task. We formulate the task as following steps: (1) We generate the candidate mention set by linguistic knowledge and score them by prompt prediction method.(2) We obtain another mention with its confidence score by machine reading comprehension(MRC) model.(3) We output final resolution result by comparing candidate mention and MRC mention. Experiments demonstrate that our approach significantly outperforms the baseline of few-shot and zero-shot in ZeroCLUE, although performs worse on CLUEWSC 2020 dataset than supervised learning baselines. Our experiment results suggested that linguistic knowledge is valuable and can improve the result of coreference resolution task. © 2022 ACM.

关键词： Supervised learning

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