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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lai, Jing Luo, Wenlang College of Software Engineering Jiangxi University of Science and Technology Jiangxi Nanchang China School of Electronics and Information Engineering Jinggangshan University Jiangxi Ji’an China

ISBN: (纸本)9798400710865

In the rapidly evolving business environment, discrete enterprises are facing fierce market competition and changing market demand challenges. To maintain their market competitiveness, these enterprises must ensure the precision and real-time update of their product data. Orderly product data management is one of the key elements to achieve this goal. Version control, as an efficient data management strategy, plays a crucial role in discrete enterprises. By implementing version control, enterprises can trace the evolution of product data, enhance the efficiency of team members’ collaboration, and reduce the risks of data errors or confusion. This paper takes a specific discrete manufacturing enterprise as the research object and deeply explores the actual problems faced by the enterprise, namely, how to effectively manage and organize the specific product data of the enterprise. To address this challenge, this paper proposes a customized solution, namely, developing a PDM (product data management) system specifically for the enterprise based on the bsdiff algorithm, and implementing fine-grained permission control using the Shiro framework. This system not only helps enterprises achieve orderly data management but also enhances data accuracy and traceability, thereby improving the overall operational efficiency and product quality of the enterprise. © 2024 Copyright held by the owner/author(s).

关键词： Data accuracy

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Urdu Sentiment Analysis Using Deep Learning 18

Urdu Sentiment Analysis Using Deep Learning

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Nasir, Imtisall Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Balochistan University of Information Technology Engineering and Management Science Department of Computer Science Quetta Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Computer Engineering Quetta Pakistan Pakistan Television Corporation Department of Engineering Lahore Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Software Engineering Quetta Pakistan

ISBN: (纸本)9798331508692

Sentiment Analysis is a deep text analysis to determine the expressions from the written text, The Urdu language holds significant importance due to the usage of these social media platforms and content generated at these platform in Urdu. Effective sentiment analysis in Urdu can provide valuable information about social trends, customer feedback, and public opinion on different sets of topics in the Urdu context. This study uses an deep learning (ANN) Artificial Neural Network to predict and classify the sentiments in the Urdu language, labeling them into positive, negative, and neutral sentiments. To achieve the research goals, advanced preprocessing techniques were utilized such as removing stop words, emojis, emails, and characters, removing symbols, performing lemmatization to expand the meaning of text, tokenized each word by using Lexicon-based approach and ngram. The dataset are tweets dependent and have 1,140,021 tweets. These steps ensure that data is standardized and cleaned to be split into training and testing. The datasets were converted into sequence and categorized to be input into the ANN model. The ANN model was trained and achieved an accuracy of 90%, precision of 98%, and f1-score of 97%. These high values indicate that the model accurately predicted the sentiments in the Urdu text. However, during experimentation limitations were discovered due to the complexities of Urdu text, difficult script patterns, and linguistic problems that were not solved by the preprocessing technique. During analysis, the limitation was discovered due to the limited number of preprocessing techniques and tools for Urdu text. However, the study highlights the potential of neural networks, especially ANN in Urdu sentiment analysis. This study contributed significantly to Urdu natural language processing by providing robust techniques and identifying limitations to work on it as a research problem. © 2024 IEEE.

关键词： Natural language processing systems

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Multiscale Spectral–Spatial Capsule Neural Network for Hyperspectral Image Classification 1

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Weiye Xu, Yuanping Xu, Zhijie Kong, Chao Niu, Xuemei Huang, Jian School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom School of Management Chengdu University of Information Technology Chengdu China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

Deep learning models have recently shown good performance in the hyperspectral remote sensing image classification tasks. In particular, a capsule network (CapsNet) was introduced as a powerful alternative to convolutional neural networks (CNNs). The CapsNet adopts vector neuron and encode the spatial relationship of features in an image, which exhibits encouraging performance. Motivated by CapsNet, this paper presents a novel capsule network based on multiscale spectral–spatial features to improve the performance for hyperspectral images (HSIs) classification. First, multi-scale features are extracted from the hyperspectral data in 1D spectral, 2D spatial and 3D spatial-spectral cubes, and their primary capsule neurons are constructed separately. Then, a multi-head attention mechanism is introduced to model the association of all primary capsule neurons from multiple dimensions to efficiently extract finer-grained multi-scale spatial-spectral information. Finally, the multiscale capsule neurons are updated using a dynamic routing agreement to obtain more discriminative high-level capsule features thus improving the classification accuracy. Experimental results on two commonly used HSIs datasets (Indian Pines, Pavia University) demonstrate that the proposed model can achieve better performance compared with other state-of-the art deep-learning-based approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neurons

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An Approach for Early Detection of Schizophrenia 15

An Approach for Early Detection of Schizophrenia

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Paranagama, Sachin Uditha, Isiwara Daundasekara, Pamodya Hettiarachchi, Shehani Weerasinghe, Lokesha Kasthurirathna, Dharshana Sri Lanka Institue Of Information Technology Faculty Of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350370249

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Controlling update distance and enhancing fair trainable prototypes in federated learning under data and model heterogeneity

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Defence technology(防务技术) 2025年第5期47卷 15-31页

作者： Kangning Yin Zhen Ding Xinhui Ji Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Institute of Public Security Kash Institute of Electronics and Information IndustryKash 844000China Shanghai New Energy Vehicle Public Data Collection and Monitoring Research Center Shanghai 200241China

Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat *** prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model *** methods allow for the sharing of only class representatives between heterogeneous clients while maintaining ***,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization *** address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous ***,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive *** experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.

关键词： Heterogeneous federated learning Model heterogeneity Data heterogeneity Contrastive learning

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Research on the Relationship Between New Energy Vehicles and Dual Carbon in Yangtze River Delta Based on GRA-LSTM Model 7

Research on the Relationship Between New Energy Vehicles and...

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7th IEEE Advanced information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Zhou, Jinxuan Deng, Liang Tong, Yi Gao, Yan Wuhan Business University Department of Information Engineering Hubei China Data Science and Big Data Technology China Internet of Things Engineering China Software Engineering Big Data Technology

ISBN: (纸本)9798350339161

New energy automobile industry plays an important role in building a green, low-carbon and recycling industrial system. In this paper, the prediction simulation training and prediction accuracy comparison study are carried out with the help of the newly constructed GRA-LSTM model, Biological Neural Network model and the first-order one-variable gray GM (1, 1) model. The LSTM model is created and trained by Learning Rate Decay function. The Learning Rate Decay callback function is set, and the learning rate is gradually reduced during the training process to carry out simulation training, and finally the trained model is used to make predictions, and the time for the Yangtze River Delta region to reach carbon peak is 2028, and the time to reach carbon neutrality is 2060, and at the same time this paper analyzes and finds out that the higher the new energy automobile market ownership is, the shorter the time for carbon peak and carbon neutrality will be.2. © 2024 IEEE.

关键词： Forecasting

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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Computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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Look AI - An Intelligent System for Socialization of Visually Impaired 6

Look AI - An Intelligent System for Socialization of Visuall...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Mendis, G.L.M.M. Deshan, W.M.Y. Bandara, H.M.G.M. Gunethilake, K.C. Wijendra, Dinuka Krishara, Jenny Sri Lanka Institute of Information Technology Department of Software Engineering Colombo Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9781665491259

It has found that 30% of the people among the visually impaired people are having a significant number of depressive symptoms whereas, the prevalence of depression in blind people was reported to be 33%, or nearly double the rate of the general population. Therefore, social activities among visually impaired people should increase. This research paper provides a unique system for assisting visually impaired individuals in navigating indoor environments heavily relies on the use of artificial intelligence, particularly deep learning and computer vision techniques. By leveraging these advanced technologies, the system can analyze and process large amounts of visual data in real-time, accurately identifying and locating various objects in the environment, including faces, doors, stairs, cross walks, traffic lights, potholes and other obstacles. The system then provides real-time feedback to the user via an audio interface, enabling them to navigate indoor and outdoor environments safely and independently. The use of artificial intelligence in this system is particularly significant, as it enables the system to adapt and improve over time based on user feedback and additional data. That means, the system can continuously improve its performance and accuracy, ultimately resulting in a more effective and reliable tool for visually impaired individuals. The authors have also evaluated the system in a real-world setting, demonstrating its effectiveness in assisting visually impaired individuals with indoor navigation. Overall, the proposed system has the potential to significantly enhance the quality of life for visually impaired individuals, showcasing the transformative power of artificial intelligence in addressing real-world challenges. © 2023 IEEE.

关键词： Recurrent neural networks

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Fake News Classification: Past, Current, and Future

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Computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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