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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

computer Systems science & engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

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Tsinghua science and Technology 2022年第5期27卷 843-854页

作者： Huiling Zhang Min Hao Hao Wu Hing-Fung Ting Yihong Tang Wenhui Xi Yanjie Wei Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China School of Software Engineering University of Science and Technology of ChinaHefei 230051China Department of Computer Science The University of Hong KongHong Kong 999077China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure ***,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant ***,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party *** this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial *** have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical *** proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

关键词： multi-sequence alignment residue-residue contact prediction feature extraction statistical information Deep Learning(DL) high computational efficiency

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Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs 37

Scalable Edge Blocking Algorithms for Defending Active Direc...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Guo, Mingyu Ward, Max Neumann, Aneta Neumann, Frank Nguyen, Hung School of Computer and Mathematical Sciences University of Adelaide Australia School of Physics Maths and Computing Computer Science and Software Engineering University of Western Australia Australia Department of Molecular and Cellular Biology Harvard University CambridgeMA United States

ISBN: (纸本)9781577358800

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack path with the maximum success rate. The defender can block a limited number of edges (i.e., revoke accesses) from a set of blockable edges, limited by budget. The defender's aim is to minimize the attacker's success rate. We exploit the tree-likeness of practical AD graphs to design scalable algorithms. We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. For graphs with small tree widths, we propose a tree decomposition based dynamic program. We then propose a general method for converting tree decomposition based dynamic programs to reinforcement learning environments, which leads to an anytime algorithm that scales better, but loses the optimality guarantee. For graphs with small numbers of non-splitting paths (a parameter we invent specifically for AD graphs), we propose a kernelization technique that significantly downsizes the model, which is then solved via mixed-integer programming. Experimentally, our algorithms scale to handle synthetic AD graphs with tens of thousands of nodes. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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Differentially private locality sensitive hashing based federated recommender system

Differentially private locality sensitive hashing based fede...

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作者： Hu, Hongsheng Dobbie, Gillian Salcic, Zoran Liu, Meng Zhang, Jianbing Lyu, Lingjuan Zhang, Xuyun Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Computer Science The University of Auckland Auckland New Zealand Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand School of Mechanical Electrical and Information Engineering Shandong University Weihai Shandong China Department of Computer Science and Technology Nanjing University Nanjing China Department of Computer Science National University of Singapore Singapore Department of Computing Macquarie University Sydney Australia

Recommender systems are important applications in big data analytics because accurate recommendation items or high-valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine-grained data for training. In the current big data era, data often exists in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy-preserving recommender system is of paramount importance. Existing privacy-preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we conduct privacy analysis on the existing locality sensitive hashing (LSH) approach based privacy-preserving recommender system and show how an attacker can retrieve user's information under such a recommender system. Given such privacy risks, we propose differentially private LSH approach to build recommender system that can offer differential privacy guarantees for users. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing privacy preservation of users' data in contributing parties. Extensive experiments on real-world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets. © 2021 John Wiley & Sons, Ltd.

关键词： Budget control

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A Comprehensive Approach to Identify Stroke based on Real-time Medical Data 1

A Comprehensive Approach to Identify Stroke based on Real-ti...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Maragatharajan, M. Shanmugapriya, S. Sudhan, M.B. Deeptha, R. Paramasivam, Sankar Muthu Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Srm Institute of Science and Technology Department of Computer Science and Engineering RamapuramTamilnadu Chennai India Srm Institute of Science and Technology Department of Information Technology RamapuramTamilnadu Chennai India Palo Alto Networks Inc Distinguished Software Engineer 3000 Tannery Way Santa ClaraCA United States

ISBN: (纸本)9798350372748

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person's risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology's rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model's performance using evaluation measures including accuracy, precision, and recall. The model's insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health. © 2024 IEEE.

关键词： Forecasting

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A Twin Dual-Arm Robot Testbed to Collect the Human Demonstration Data for Imitation Learning 13

A Twin Dual-Arm Robot Testbed to Collect the Human Demonstra...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kim, Juhyung Heo, Sungwon Kim, Songwoo Jung, Ho-Jin Yoon, Han Ul Yonsei University Division of Computer and Telecomm. Engineering Mirae Campus Wonju Korea Republic of Graduate School Yonsei University Department of Computer Science Wonju Korea Republic of Yonsei University Division of Software Mirae Campus Wonju Korea Republic of

ISBN: (纸本)9798350373332

Collecting consistent human demonstrations has been a problematic issue in the field of a learning from demonstration. This paper presents a twin dual-arm robot testbed to collect human demonstration data while performing manual tasks in the activities of daily livings such as opening/closing a window, opening-up/closing-down a sauce bottle cap, etc. The testbed consists of the two sets of a dual-arm robot;for each robot, one-side arm has 6-DoF with a crab-gripper as its hand. Two dual-arm robots are mounted at the front and back of a table-type station. A human operator performs actions to complete a given task with the one at the back;the human operator's actions, meanwhile, are duplicated by the other robot at the front simultaneously. The experimental results show the testbed's potential to gather consistent human demonstration data for training a machine learning agent to manipulate the dual-arm robot under an imitation learning framework. © 2024 IEEE.

关键词： Bottles

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Learning Discriminative Representation for Few-Shot Knowledge Graph Completion 7

Learning Discriminative Representation for Few-Shot Knowledg...

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7th International Conference on Intelligent Information Processing, ICIIP 2022

作者： Wu, Yanping Tian, Ling Hui, Bei Wang, Zhaokun Song, Lexuan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (纸本)9781450396714

Few-Shot Knowledge Graph Completion (FKGC) aims to infer the missing facts of a relation with only few corresponding known instances. To accomplish this task, one prominent prerequisite is to fully explore and exploit useful information from these supporting instances. However, many existing methods usually treat the support set as a group of independent contents, and fail to explicitly model the interactions among instances. They also majorly focus on positive instances in the support set while overlooking negative ones. Generally, much useful information in the support set has not been well applied yet. Therefore, this work proposes a novel model FSDR to fully capture the fertile information within supporting sets. Extensive experiments on two benchmark datasets demonstrate our model is indeed effective and outperforms state-of-the-art FKGC approaches. © 2022 ACM.

关键词： Knowledge graph

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A Graph Neural Network-based Code Recommendation Method for Smart Contract Development

A Graph Neural Network-based Code Recommendation Method for ...

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2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Tang, Xiuwen Gan, Jiazhen Jiang, Zigui Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Zhuhai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

Smart contracts can be considered as a service in the blockchain system and have been applied in many fields, covering financial products, online games, real estate, transportation and logistics. However, smart contract technology is still in its infancy. Development task is facing many difficulties and challenges, thus providing a set of new or improved development aids for the smart contract ecosystem is an urgent problem that needs to be solved. This paper proposes a smart contract code recommendation method based on graph neural network, which aims to facilitate the development of smart contracts and help developers realize smart contracts faster and more securely. Experimental results show that this method is better than the existing model of smart contract code recommendation in terms of accuracy. © 2022 IEEE.

关键词： Smart contract

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QAIC: Quality-assured image crowdsourcing via blockchain and deep learning 26

QAIC: Quality-assured image crowdsourcing via blockchain and...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Baowei Yuan, Yi Li, Bin Dai, Changyu Wu, Yufeng Zheng, Weiqian Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Ministry of Education Engineering Research Center of Digital Forensics Nanjing China Nanjing China

ISBN: (纸本)9798350331684

Recently, image crowdsourcing, a new trading mode, has been proposed to bridge the gap between the excess photos generated by intelligent devices and the great demand for images. However, traditional crowdsourcing methods often rely on centralized platforms, which risk data leakage and a single point of failure (SPOF). Moreover, due to the subjectivity of image quality assessment and the complexity of image data structure, image quality is difficult to control for traditional crowdsourcing frameworks without exposing data privacy. In this work, we propose a blockchain-based image crowdsourcing framework named QAIC to address these issues. Within the framework of QAIC, the transaction information is stored using a multichain structure, and the transaction process is implemented using smart contracts. We design an image selection and pricing mechanism for QAIC, where high-quality image sets can be spontaneously selected, and each image can be dynamically priced based on distortion degree and content relevance. Finally, to accurately obtain image quality, we design a dual output neural network model to evaluate the image quality, where a lightweight architecture is adopted, and piecewise outputs are designed to protect image privacy and reduce the on-chain computational cost Extensive analysis and experiments demonstrate that the quality of transaction data and reasonable pricing can be ensured using the QAIC without compromising image privacy. © 2023 IEEE.

关键词： Blockchain

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An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease 4

An Efficient Deep Learning Model based Diagnosis System for ...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Ikram Ullah, Muhammad Ullah, Inam Ihtesham, Muhammad Junaid, Hazrat Yousafzai, Spogmay Sardar, Fouzia Department of Computer Software Engineering Uet Mardan Pakistan China University of Geosciences School of Automation Wuhan430074 China Dalian Jiaotong University School of Software Dalian116028 China Abdul Wali Khan University Department of Computer Science Maradan Pakistan University of Malakand Department of Computer Science and Information Technology Dir Lower Pakistan University of Malakand Department of Zoology Pakistan

ISBN: (纸本)9798350335316

Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable. © 2023 IEEE.

关键词： Convolutional neural networks

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