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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Issam Jebreen Shadi Nashwan Ahmad Al-Qerem Ali Mohd Ali Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarka University Jordan College of Engineering Technology Al-Balqa Applied University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

With the rapid evolution of artificial intelligence (AI) and computer vision, assistive technologies have become increasingly adept at supporting individuals with special needs. This paper provides a comprehensive examination of two critical areas: AI-powered visual assistance systems for the visually impaired and sign language recognition and generation frameworks for the Deaf and hard-of-hearing communities. We review the latest research trends, algorithms, and tools, highlighting emerging deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers in both domains. We then propose a unified framework that leverages edge computing, multimodal learning, and advanced human-computer interaction (HCI) techniques to address challenges such as real-time object recognition, complex gesture capture, data scarcity, and cultural variations in sign languages. We further detail an experimental design and set of results that demonstrate the feasibility and benefits of these approaches, followed by a discussion of ethical considerations, limitations, and future directions. Our goal is to illustrate a path toward more inclusive AI systems that offer robust solutions for accessibility, ultimately bridging communication gaps and fostering independence for individuals with special needs [1]–[11].

关键词： Sign language computer vision Visualization Ethics Visual impairment Assistive technologies Transformers Real-time systems Object recognition Artificial intelligence

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A Clustering-guided Contrastive Fusion for Multi-view Representation Learning

arXiv

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arXiv 2022年

作者： Ke, Guanzhou Chao, Guoqing Wang, Xiaoli Xu, Chenyang Zhu, Yongqi Yu, Yang The Institute of Data Science and Intelligent Decision Support Beijing Jiaotong University Beijing100080 China The School of Computer Science and Technology Harbin Institute of Technology Weihai264209 China The School of Computer Science and Engineering Nanjing University of Science And Technology Nanjing210000 China The Faculty of Intelligent Manufacturing Wuyi University Jiangmen529000 China

Multi-view representation learning aims to extract comprehensive information from multiple sources. It has achieved significant success in applications such as video understanding and 3D rendering. However, how to improve the robustness and generalization of multi-view representations from unsupervised and incomplete scenarios remains an open question in this field. In this study, we discovered a positive correlation between the semantic distance of multi-view representations and the tolerance for data corruption. Moreover, we found that the information ratio of consistency and complementarity significantly impacts the performance of discriminative and generative tasks related to multi-view representations. Based on these observations, we propose an end-to-end CLustering-guided cOntrastiVE fusioN (CLOVEN) method, which enhances the robustness and generalization of multi-view representations simultaneously. To balance consistency and complementarity, we design an asymmetric contrastive fusion module. The module first combines all view-specific representations into a comprehensive representation through a scaling fusion layer. Then, the information of the comprehensive representation and view-specific representations is aligned via contrastive learning loss function, resulting in a view-common representation that includes both consistent and complementary information. We prevent the module from learning suboptimal solutions by not allowing information alignment between view-specific representations. We design a clustering-guided module that encourages the aggregation of semantically similar views. This action reduces the semantic distance of the view-common representation. We quantitatively and qualitatively evaluate CLOVEN on five datasets, demonstrating its superiority over 13 other competitive multi-view learning methods in terms of clustering and classification performance. In the data-corrupted scenario, our proposed method resists noise interference better than

关键词： Semantics

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dataDecon: data Cleansing Tools for Large Language Model with Efficient Decontamination Techniques

DataDecon: Data Cleansing Tools for Large Language Model wit...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Sumeth Yuenyong Norapat Buppodom Koravich Sangkaew Konthee Boonmeeprakob Prachya Boonkwan Jillaphat Jaroenkantasima Pitikorn Khlaisamniang Anuruth Lertpiya Apivadee Piyatumrong Peerawat Rojratchadakorn Thaweewat Rugsujarit Teerapol Saengsukhiran Kriangkrai Saetan Isada Sukprapa Thanachot Thavornmongkol Nucharee Thongthungwong Patteera Triamamornwooth Chanon Utupon Kobkrit Viriyayudhakorn Phoochit Witchutanon Sadit Wongprayon Thepchai Supnithi Department of Computer Engineering Faculty of Engineering Mahidol University National Electronics and Computer Technology Center Pathumthani Thailand Artificial Intelligence Asssociation of Thailand Big Data Institute (Public Organization) Bangkok Department of Electrical Engineering Faculty of Engineering Kasetsart University Looloo Technology Faculty of Engineering Kasetsart University Kasikorn Labs Kasikorn Business Technology Group Faculty of Science King Mongkut’s Institute of Technology Ladkrabang School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Next AI Solutions Department of Biomedical Engineering Faculty of Engineering Mahidol University Department of Computer Science University of Liverpool Department of Mechanical Engineering King Mongkut’s University of Technology North Bangkok Artificial Intelligence Entrepreneur Association of Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University

ISBN: (数字)9798331509910

ISBN: (纸本)9798331509927

Large language models (LLMs) play an important role in modern NLP technology as they are versatile for a wide array of NLP tasks. However, constructing an LLM is challenging due to concealed construction pipelines, the lack of cleansed datasets, and hyperparameter settings, making it almost irreproducible. This paper presents an efficient pipeline for constructing an LLM tailored to a low-to-medium-sourced language with a high level of data contamination and tools to cleanse the dataset. Following our pipeline, we constructed OpenThaiGPT, an LLM for Thai, with only open-sourced datasets such as CC100, OSCAR, and mC4, and achieved the state-of-the-art accuracies on our downstream tasks. Here, we disclosed the data statistics and all hyperparameter settings for reproducibility.

关键词： Accuracy Large language models Decontamination Pipelines data visualization data models Natural language processing Reproducibility of results Arrays Contamination

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Flavours of Convolution for Unsupervised Aspect Extraction and Aspect-based Sentiment Analysis 7

Flavours of Convolution for Unsupervised Aspect Extraction a...

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7th Workshop on Natural Language for Artificial Intelligence, NL4AI 2023

作者： Chatterjee, Soumick Prakash, Sowmya Nürnberger, Andreas Genomics Research Centre Human Technopole Viale Rita Levi-Montalcini 1 Milan20157 Italy Faculty of Computer Science Otto von Guericke University Magdeburg Universitätspl. 2 Magdeburg39106 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Universitätspl. 2 Magdeburg39106 Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Sentiment analysis, the task of understanding the underlying sentiment from a given data, is frequently used in various fields, from market research to recommender systems. To analyse different sentiments for different aspects present in a given sentence, aspect-based sentiment analysis (ABSA) approaches have been proposed. The task of ABSA can be divided into two subtasks: aspect extraction (AE) and aspect-based polarity detection (ABPD). Most of the state-of-the-art approaches are based on some form of neural networks using convolutional layers. Recently, flavours of convolution, like extremely separated convolution layer (XSepConv) - which can reduce computational cost along with the parameter size of large kernels and depth-wise over-parameterised convolutional layer (DOConv) - which can improve the training efficiency, have been proposed. They have shown their superior capabilities when it comes to image-related tasks, but they have not been explored for textual tasks like ABSA. This paper performs unsupervised AE and weakly supervised ABSA using those specialised convolutional layers. It could be shown that using such layers instead of traditional convolutional layers can significantly improve the performance of the model. © 2023 Copyright for this paper by its authors.

关键词： Extraction

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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Engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System 25

Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV ...

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25th International computer science and Engineering Conference, ICSEC 2021

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Maitrichit, Nagorn Sornlertlamvanich, Virach Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand Faculty of Data Science Tokyo Japan University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Thailand has faced unrest in recent years, as have other countries around the world. The continuation of present trends means a tendency for an increase in both crimes against people and property. Nowadays, CCTV technology is widely used as surveillance and monitoring tools to help keep people safe. However, most of them still rely primarily on police personnel to inspect the displays. A weapon detection system can reduce the screen-reading workload of police officers with a limited workforce. The integration of weapon detection with CCTV cameras has a role to play in solving the problem. To develop the weapon detection system, the datasets used in this research were collected from 2 public datasets: ARMAS Weapon detection dataset and IMFDB Weapon detection system. The object detection method was used from TensorFlow Object Detection API using 1) SSD MobileNet-V1, 2) EfficientDet-D0 and 3) Faster R-CNN Inception Resnet-V2. For all experimental results, the object detection model is the Faster R-CNN Inception V2 using dataset 1, ARMAS Weapon detection dataset, with the highest mAP of 0.540 with the Average Precision with 0.5 IoU and 0.75 IoU at 0.793 and 0.627, respectively. © 2021 IEEE.

关键词： Object detection

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Advances in Glaucoma Detection Through Machine Learning Technology

Advances in Glaucoma Detection Through Machine Learning Tech...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Rushikesh Burle Sanskruti Gaurkhede Leelkanth Dewangan Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Computer science And Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Artificial Intelligence and Data science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

In the event that glaucoma is not treated promptly, blindness may become permanent. It ranks second among the factors that might lead to blindness in the eyes. One example of a recent imaging technology that looks within the eye is the fundus camera. Glaucoma is diagnosed using a variety of techniques, such as the retinal nerve fibre layer analyzer, The Topcon image net technique and optical coherence tomography. Despite its expensive cost and the dearth of available research in this field, the optic cup to disc ratio is used to diagnose glaucoma. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The beaker slowly gets better and inhabits most of the judgement as the glaucoma becomes worse. If glaucoma is not pickled trendy a timely manner, blindness may become irreversible. It is the second most common cause of ocular blindness. A contemporary imaging tool called the fundus camera is utilised to view within the eye. Among the methods used to diagnose glaucoma include the retinal nerve fibre layer analyzer, optical coherence tomography, and the Topcon image net approach. Despite its high cost and paucity of research, glaucoma is diagnosed using the optic cup to discus relation. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The cup expands as glaucoma progresses until it finally occupies the majority of the eye.

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Maize Plant Disease Prediction of UAV Images for Precision Agriculture Using Fusion of Multimodal

Maize Plant Disease Prediction of UAV Images for Precision A...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Vinay Mishra Nenavath Srinivas Naik Santosh Kumar Saeed Hamood Alsamhi Abdu Saif Edward Curry Department of Computer Science and Engineering International Institute of Information Technology (IIIT)-Naya Raipur Chhattisgarh India Insight Centre for Data Analytics University of Galway Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

The agriculture sector is vital to a country’s economic and productive development. With technological advancements, we can identify plant diseases in their early stages, similar to how human diseases are diagnosed, and avoid significant financial loss. For example, a new technique has been developed to detect diseases in maize crops, a widely used raw product in the food industry and precision agriculture. Furthermore, farmers ensure the fields do not remain wet for extended periods to prevent fungus infection. Currently, machine learning and deep learning approaches are being utilized, making agriculture one of the most researched topics in AI. This study proposes a novel framework for predicting maize plant disease using a fusion of multi-deep learning models. The proposed framework uses a four-stage pipeline for detecting disease in maize plants by fusion of Xception, DenseNet-121, and ensembling of Xception and DenseNet-121 models to achieve excellent results on the OSF drone image dataset, which consists of 1821 high-resolution images of maize plants taken from unmanned aerial vehicles (UAVs). The accuracy of our proposed approach is 96.95%, which is highly effective, considering the challenges posed by drone images.

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BookChain: A Secure Library Book Storing and Sharing in Academic Institutions using Blockchain Technology

BookChain: A Secure Library Book Storing and Sharing in Acad...

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International Conference on computer Communication and Informatics (ICCCI)

作者： P. Chinnasamy Anupriya Elumalai Ramesh Kumar Ayyasamy S.P. Kavya S. Dhanasekaran Ajmeera Kiran Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India School of Computer Science and Engineering VIT – AP University Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Kalasalingam Academy of Research and Education Deemed to be University Srivilliputtur Tamilnadu India

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book’s interacting entities on blockchain because every consumer could retrace the book’s renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants.

关键词： Wireless communication Productivity Smart contracts Force Interference Decentralized applications Libraries

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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