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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Wang, Tianhao Zhang, Mingyue Shang, Lin Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350345445

Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing. © 2023 IEEE.

关键词： Image reconstruction

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Task Scheduling in Fog Computing: Parameters, Simulators and Open Challenges 6

Task Scheduling in Fog Computing: Parameters, Simulators and...

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Saad, Muhammad Qureshi, Rehan Inam Rehman, Aqeel Ur Sir Syed University of Engineering and Technology Software Engineering Department Karachi Pakistan UIT University Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350333718

As Internet of Things (IoT) is evolving very rapidly which is producing massive amount of data and normally all this data is shifted towards the cloud for further execution which puts burden on the cloud and doesn't fulfill QoS requirements. In order to overcome this problem computing facility were extended towards the edge of the network and the concept is known as Fog computing. Due to heterogeneity of fog nodes and dynamic user requirements, task scheduling in fog computing has become the big challenge. This paper covers the literature review which classify the task scheduling algorithm in terms of approaches, it highlights the parameters mostly used, it also covers the analysis among the available simulation tools and highlight the open issues and challenges which will help the researchers for their problem formulation. © 2023 IEEE.

关键词： Fog computing

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Metaverse virtual social center for elderly communication in time of social distancing

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 68-80页

作者： Hui LIANG Jiupeng LI Yi WANG Junjun PAN Yazhou ZHANG Xiaohang DONG Department of Software Engineering Zhengzhou University of Light IndustryZhengzhou 450002China General Hospital of Pingmei Shenma Group Pingdingshan 467000China Department of Computer Science and Technology Beihang UniversityBeijing 100191China

Background The lack of social activities among the elderly due to physical limitations can result in loneliness and depression. The spread of COVID-19 has made it difficult for the elderly to conduct stable social activities, increasing feelings of loneliness. The metaverse is a virtual world that mirrors reality. This allows the elderly to overcome the constraints of reality and perform social activities stably and continuously, providing new ideas for alleviating loneliness. Methods By analyzing their needs, a virtual social center framework for the elderly was proposed in this study. In addition, a prototype system was designed according to this framework. The elderly can socialize in virtual reality with metaverse-related technologies and human–computer interaction tools. Additionally, a test was conducted jointly with the chief physician of the geriatric rehabilitation department of a tertiary hospital. Results The results demonstrated that the mental state of the elderly who had used the virtual social center was significantly better than that of those who had not used it. Conclusions Thus, virtual social centers can help relieve loneliness and depression among the elderly with increasing global epidemics and an aging society. Hence,they have promotional value.

关键词： Elderly Loneliness and depression Metaverse Virtual social center HCI

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AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network 39

AutoAC: Towards Automated Attribute Completion for Heterogen...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Zhu, Guanghui Zhu, Zhennan Wang, Wenjie Xu, Zhuoer Yuan, Chunfeng Huang, Yihua Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350322279

Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and more attention. However, the existing work mainly focuses on the design of novel GNN models, while ignoring another important issue that also has a large impact on the model performance, namely the missing attributes of some node types. The handcrafted attribute completion requires huge expert experience and domain knowledge. Also, considering the differences in semantic characteristics between nodes, the attribute completion should be fine-grained, i.e., the attribute completion operation should be node-specific. Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes. To address the above challenges, we propose a differentiable attribute completion framework called AutoAC for automated completion operation search in heterogeneous GNNs. We first propose an expressive completion operation search space, including topology-dependent and topology-independent completion operations. Then, we propose a continuous relaxation schema and further propose a differentiable completion algorithm where the completion operation search is formulated as a bi-level joint optimization problem. To improve the search efficiency, we leverage two optimization techniques: discrete constraints and auxiliary unsupervised graph node clustering. Extensive experimental results on real-world datasets reveal that AutoAC outperforms the SOTA handcrafted heterogeneous GNNs and the existing attribute completion method. © 2023 IEEE.

关键词： Graph neural networks

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Group Recommendation Method Based on Graph Neural Network

Group Recommendation Method Based on Graph Neural Network

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Lu, Mengye Zhu, Peng Miao, Chun Wang, Youjian Wang, Jiacun Nanjing University of Science and Technology School of Economics and Management Nanjing China Monmouth University Department of Computer Science and Software Engineering NJ United States

ISBN: (纸本)9798350376739

Group activities are becoming more and more common on the Internet in the big data environment. Which makes many scholars focus on how to recommend items or activities to a group. However, conventional recommendation systems grapple with issues like data sparsity and the cold start problem, which limits the relevance and accuracy of recommendation. Therefore, this paper conducts an in-depth exploration of group recommendation methods utilizing graph neural networks and introduces a novel recommendation framework aimed at enhancing the efficacy of group recommendations. Firstly, this paper discusses how to construct an effective graph structure to encode the social interaction and individual characteristics of users. Then, an improved graph neural network architecture is proposed. Finally, extensive experimental evaluation is carried out on actual datasets, and the findings demonstrate that our framework markedly enhances the accuracy of recommendations. © 2024 IEEE.

关键词： Graph neural network Group recommendation Recommendation system

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Emotion-Driven Predictive Modeling of Airline User Reviews: A Comparative Analysis of ML Models 15th

Emotion-Driven Predictive Modeling of Airline User Reviews: ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Jihad, Kamal H. Baker, Mohammed Rashad Mahmood, Ozlam Abdulhakeem Umar, Shahla Uthman Parameshachari, B.D. College of Science University of Kirkuk Kirkuk Iraq Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Department of Information Technology College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Department of ECE Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9783031810824

This paper investigates the role of emotion-driven predictive modeling in airline user reviews and its importance in the context of the highly competitive aviation industry. The study utilizes the NRCLex emotion analysis and assesses the efficacy of three Machine Learning (ML) models, namely Random Forest (RF), Decision Tree (DT), and Multilayer Perceptron (MLP), in predicting passenger sentiments. Our findings indicate that the MLP model outperforms the other models, achieving the highest accuracy and F1-score. This research focuses on understanding passenger sentiment in airline user reviews and its significance for service quality and customer satisfaction in the aviation industry. Using NRCLex and comparative ML analysis, our findings emphasize the MLP's potential for sentiment analysis, benefiting airlines and industry stakeholders by enhancing the passenger experience and customer loyalty while addressing predictive accuracy and model complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Emotion Recognition

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Predictive Modeling of House Prices;A Regression Approach 18

Predictive Modeling of House Prices;A Regression Approach

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Saeed, Natasha Saeed, Ahsan Bashir, Mariam Caceres-Najarro, Lismer Andres National University of Sciences and Technology Department of Computer & Software Engineering Islamabad Pakistan National Excellence Institute Department of Computer Science Islamabad Pakistan Korea Institute of Energy Technology Naju-si Korea Republic of

ISBN: (纸本)9798350328172

Housing prices have always been an important issue among the business community. The sudden increase in property prices has an impact on economic cycles as well as our means of living. However, understanding several features that affect residential real estate prices is still challenging. Traditional techniques have been unable to choose essential features for precise predictions, which frequently leads to reduced prediction accuracy. In this research work, we utilized machine learning techniques to increase prediction accuracy and get data-driven insights into property evaluation. This study compares Random Forest Regressor and the Decision Tree Regressor using an extensive dataset of Bangalore that includes nine property features. The findings show that our models achieved an optimal accuracy level and provide insights into the variances in model performance, assisting policymakers in choosing the most suitable approaches to get accurate house prices across various market circumstances. © 2023 IEEE.

关键词： Decision trees

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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

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computer Systems Science & Engineering 2023年第8期46卷 1533-1547页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Civil Engineering Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop *** agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental *** control has become one of the significant problems in the agricultural *** traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same *** more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed *** may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable *** study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision *** HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision *** image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise *** addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature *** detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection *** experimental results of the HHOGCN-WD technique are investigated under the benchmark *** results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

关键词： Weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning

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Learning Age of Information-Oriented Energy-Efficient Scheduling with Non-Orthogonal Multiple Access 13

Learning Age of Information-Oriented Energy-Efficient Schedu...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Li, Jinhua Cao, Yi Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

ISBN: (纸本)9798350355079

We propose a deep Q-network (DQN)-based user scheduling scheme aimed at low network-wide age of information (AoI) in a non-orthogonal multiple access (NOMA) uplink status update system. In our system, end users can harvest energy from wireless signals received on the radio-frequency band to extend the lifespan of their batteries. Given the battery replacement cost in many practical scenarios, our objective is to design a scheduling scheme to maintain a low AoI while efficiently utilizing energy harvesting and reducing the number of battery replacements. Considering the instantaneous AoI, remaining energy, and channel condition of end users, we model the user scheduling problem as a Markov decision process (MDP), which is solved by DQN. In particular, the choice of transmission power to save energy while enabling a high NOMA decoding rate is incorporated in the design of DQN. Experiments indicate that our DQN-based scheduling scheme achieves a low average AoI of the system with significantly fewer battery replacements compared to various benchmarking schemes. © 2024 IEEE.

关键词： Markov processes

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An Applied Artificial Intelligence Aided Technique for Effective Classification of Breast Cancer

An Applied Artificial Intelligence Aided Technique for Effec...

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Waqar, Mishal Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Nawaz, Ali University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Mubarak Al-Abdullah Department of Computer Science Kuwait GULF University for Sciences and Technology Al-Abdullah Department of Computer Science Kuwait

ISBN: (纸本)9781665476010

Among Women, Breast cancer is one of the maximum occurring diseases. Many women die every year because of breast cancer globally. Early prediction and diagnosis of this disease can prevent death in the end. The survival rate increases on detecting breast cancer early as better treatment can be provided. Development in prediction and diagnosis is necessary for the life of people. A higher amount of accuracy in the prediction of breast cancer is necessary for the treatment aspects and also for the survivability of patients. It is apparent that there are different techniques available in breast cancer detection but machine learning algorithms can bring a large contribution to the process of prediction and early diagnosis of breast cancer. In this study, we use a Wisconsin dataset which was collected from a scientific dataset of 569 breast cancer. Out of 569 patients, 63% were diagnosed with benign and 37% were diagnosed with malignant cancer. The benign tumor grows slowly and does not spread. We apply five machine learning algorithms to this dataset and train a model for predicting malignant and benign tissues (BCs). Algorithms are K-Nearest neighbor, Support vector machine, Decision tree, Deep learning, and Random-forest respectively. The effectiveness of these algorithms is evaluated in terms of accuracy, F measure, confusion matrix, and specificity. By comparing the results deep learning classifier gives the highest accuracy and outclass all the other classifiers by attaining an accuracy of 9S.l3%. SVM gives an accuracy of 97.66% whereas KNN gives an accuracy of 95.79% etc. © 2023 IEEE.

关键词： Forecasting

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