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International Conference on Computer Supported Cooperative Work in Design

作者： Zhijun Li Zhenyu Yang Yiwen Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, aspect sentiment quadruple prediction (ASQP) has become a popular task in the field of aspect-based sentiment analysis. When modeling comment statements, it is often necessary to consider structural information between sentiment labels. Existing approaches categorize these labels by encoding text and dependency tree structures separately and then incorporating their representations, but this approach fails to effectively capture the structural information between sentiment labels. To address this problem, we propose a method for ASQP called Opinion-Tree-guided Contrastive Learning (OTCL), which embeds structural information directly into the text instead of modeling it separately. Specifically, OTCL utilizes the opinion tree structure to guide the construction of positive samples of the input text during training. By associating the input text with its positive samples, the text encoder can learn to generate textual representations associated with the opinion tree structure. We validate the effectiveness of our method on two commonly used datasets.

关键词： Training Sentiment analysis Federated learning Contrastive learning Predictive models Encoding Task analysis

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MCAF: Multi-Headed Causal Attention Fusion Approach for Multimodal Sentiment Analysis

MCAF: Multi-Headed Causal Attention Fusion Approach for Mult...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qingao Wang Xingang Wang Jinxiao Yang Yurun Bi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multimodal sentiment analysis(MSA) aims to synthesize multiple pieces of information, including textual, visual, and auditory information, to more accurately infer sentiment polarity. Due to the existence of complex and diverse affective causal relationships between different modalities, they interact with each other and constitute a multidimensional expression of human emotions. In the face of unbalanced data, i.e., when there are significant differences in the number of samples between different categories, the attention mechanism of the model tends to focus on spurious correlations in the training data, seriously impairing the model’s generalization ability and leading to bias, unbalanced prediction, and false correlation identification problems. To address the above problems, this study proposes a new multimodal fusion method based on the "within-sample sampling" and "cross-sample sampling" front door adjustment methods in causal inference strategies: the multi-head causal attention fusion method (MCAF). This method aims to capture potential causal relationships between different modalities through different attention heads, helping the model focus more on key causal features and avoid paying too much attention to false correlations in the training data. This helps improve the generalization performance of the model, make the decision-making process of the model more interpretable, and enhance the accuracy and robustness of the model for multimodal sentiment analysis tasks, making it more suitable for practical application scenarios.

关键词： Analytical models Sentiment analysis Visualization Correlation Neural networks Training data Predictive models

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kai Lu Jing Zhao Lichao Ding Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method.

关键词： Training Uncertainty Soft sensors Noise Training data Knowledge graphs Data models Stability analysis Reliability Thermal stability

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Health Data Analytics Using IoT: Transforming Healthcare Management and Treatment Strategies for Improved Patient Outcomes

Health Data Analytics Using IoT: Transforming Healthcare Man...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Dinh Cong Tuan Paravthy Unnikrishnan Bala Dhandayuthapani M. Vennila M. E Choi Sang Long Deepali Rani Sahoo Faculty of Communication Dai Nam University Vietnam Department of Striroga and Prasuti Tantra (Gynaecology and Obstetrics) Amrita School of Ayurveda Amrita Vishwa Vidyapeetham Amritapuri Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Oman Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Faculty of Business Raffles University Malaysia Symbiosis Law School Noida Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The integration of Web of Things (IoT) innovation within the healthcare segment has revolutionized conventional healthcare administration and treatment procedures, driving progressed quiet results. This paper looks at the transformative effect of well-being information analytics utilizing IoT in upgrading healthcare administrations, optimizing understanding care, and advancing preventive measures. The paper analyzes the key components and applications of IoT in healthcare, emphasizing its part in information collection, real-time observation, and examination of understanding imperative signs, action levels, and pharmaceutical adherence. With the help of IoT-enabled gadgets such as wearables, sensors, and keen checking frameworks, healthcare experts can assemble comprehensive, real-time persistent information, encouraging early discovery and opportune mediation for basic well-being issues. Besides, the utilization of IoT in well-being information analytics empowers the improvement of personalized treatment plans custom-fitted to a person's persistent needs, cultivating a patient-centric approach to healthcare conveyance. By leveraging machine learning calculations and prescient analytics, healthcare suppliers can expect potential well-being complications, in this manner minimizing the chance of antagonistic occasions and clinic readmissions. In addition, the execution of IoT-based healthcare arrangements advances further understanding and observing, empowering healthcare suppliers to offer ceaseless, personalized care past conventional healthcare settings.

关键词： Technological innovation Data analysis Medical services Machine learning Real-time systems Internet of Things Wearable devices Mediation Wearable sensors Pharmaceuticals

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Optimizing wastewater treatment plant operations: a machine learning approach for energy consumption and climate dynamics analysis

Optimizing wastewater treatment plant operations: a machine ...

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8th IET Smart Cities Symposium (SCS 2024)

作者： Mahmoud Elshabrawy Faris H. Rizk Ahmed Mohamed Zaki Marwa M. Eid Ali E. Takieldeen Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg 24060 Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University 11961 Shaqra Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

ISBN: (数字)9781837243105

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved.

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Artificial Intelligence-Driven Lung Cancer Detection Model Using Chest Radiography Images

SSRN

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SSRN 2023年

作者： Malebary, Sharaf J. Abulfaraj, Anas W. Dutta, Ashit Kumar Sait, Abdul Rahaman Wahab Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Archives and Communication King Faisal University Al Ahsa Hofuf31982 Saudi Arabia

In recent times, medical imaging techniques are widely applied for detecting lung cancer (LC) in the earlier stages. However, there is a lack of an effective LC detection model using chest radiography (CXR) images. The developers face challenges in extracting key features from the CXR images due to their complexities. There is a demand for a framework for handling the complexities of CXR images and generating insight to assist clinicians and radiologists to make a decision. Therefore, the researchers intend to build an LC detection framework using the CXR images. This study proposes an artificial intelligence-driven LC detection model using improved crow search optimization (ICSO) feature selection and the ResNest model. A Cauchy mutation is introduced in the ICSO for updating the position of the image features. The ResNest model’s hyper-parameters are tuned using the Harris Hawks optimization algorithm. To evaluate the proposed model’s performance, the researcher obtained normal and abnormal CXR images from seven healthcare centers across India. The dataset comprises 789 normal and 843 abnormal CXR images. In addition, the researchers employed baseline models for the comparative evaluation. The comparative analysis findings highlighted that the proposed framework obtained an average accuracy, F1-measure, MCC, and Kappa of 98.3, 97.9, 96.4, and 95.6. The area under the receiver operating characteristics and precision-recall curves of 0.99 and 0.98 outlines the significance of the proposed model in detecting LC from the CXR images. The model can assist clinicians and radiologists in making effective decisions and identifying the LC in the earlier stages. © 2023, The Authors. All rights reserved.

关键词： Diseases

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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A comprehensive approach to mitigate cyberattacks in power systems using an AI-based detection algorithm and the unit commitment corrective action

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Electric Power Systems Research 2025年 247卷

作者： Yuanrong, Jin Alahmari, Saad Sulaiman, Riza Hassan, Ali Hassan Mohammed, Jamal Alwakeel, Ahmed M. Rasheed, Saim Sichuan Vocational College of Information Technology Lizhou District Sichuan Guangyuan China Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Universiti Kebangsaan Malaysia Bangi43600 Malaysia College of Computer and Cyber Sciences University of Prince Mugrin Madinah41499 Saudi Arabia IT Department University of Prince Mugrin Madinah41499 Saudi Arabia Faculty of Computers & Information Technology University of Tabuk Tabuk71491 Saudi Arabia Research Center University of Tabuk Tabuk71491 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University PO Box 80221 Jeddah21589 Saudi Arabia

In smart power systems, cyber-attacks like False Data Injection (FDI) attacks present a significant threat to grid stability and optimal performance operation. This paper proposes an AI-driven approach to detecting anomalous behavior in the system and mitigating cyberattacks. Specifically, it addresses some common attacks known as Load Redistribution (LR) attacks. The proposed detection methodology leverages a deep learning model based on Variational AutoEncoders (VAE) and utilizes anomaly detection to identify cyberattacks and evaluate their severities. A corrective action using Security Constraint Unit Commitment (SCUC) is introduced to counteract worst-case scenarios. Finally, the effectiveness of the proposed approach is demonstrated through a case study on the IEEE 118-bus system. © 2025

关键词： Deep learning

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Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition

Bio-inspired Swarm Intelligence: a Flocking Project With Gro...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Song Tianbo Hu Weijun Cai Jiangfeng Liu Weijia Yuan Quan He Kun Ira A. Fulton Schools of Engineering Arizona State University Tempe USA Faculty of Applied Mathematics and Control Processes Saint Petersburg State University Saint Petersburg Russia Department of Chemistry Lomonosov Moscow State University Moscow Russia Allen School of Engineering & Computing Trine University Angola USA Faculty of Engineering The Open University of China Hangzhou China College of Computing Illinois Institute of Technology Chicago USA

With the help of the adaptive gain control algorithm and the flocking SWARM algorithm with adversarial agents, the project described in this article makes the entire system communication-free while maintaining the same functionality and performance. It outlines the entirety of this biologically inspired swarm intelligence's thought process, from inception to conclusion (Flocking algorithm). The entire project is dependable and practically impenetrable under a variety of conditions. The neighbor observation for group conjecture is then strengthened by the use of the adaptive gain control technique and a partial Kalman filter. After the system has been stabilized, use the built-in camera of the SWARM drone to introduce the Object Recognition method in computer vision. As a result, the system enters the real world from the simulation level.

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Classification of epilepsy period based on combination feature extraction methods and spiking swarm intelligent optimization algorithm

Classification of epilepsy period based on combination featu...

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作者： Duan, Lijuan Lian, Zhaoyang Chen, Juncheng Qiao, Yuanhua Miao, Jun Li, Mingai Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing University of Technology Beijing China Applied Sciences Beijing University of Technology Beijing China School of Computer Science Beijing Information Science and Technology University Beijing China

Epilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time-frequency features and principal component features of epilepsy. The time-frequency features are obtained by WPT or STFT-PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance. © 2020 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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