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IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Abdulla Jaafar Desmal Mohamed Alsaeed Suraya Hamid Ali Hussain Zulait College of Computer Studies University of Technology Bahrain Salmabad Bahrain Department of Information System Universiti Malaya Kuala Lumpur Malaysia College of Information Technology University of Bahrain Sakhair Bahrain

This paper explores the benefits, challenges, and implications of integrating social media platforms into mobile government and examines its impact on enhancing citizen engagement and improving service delivery. Social media can incorporate citizen-government interactions by enabling the realtime communication, fostering transparency, and promoting public participation. Government agencies can leverage social media platforms to reach citizens directly, with different demographic characteristics, disseminate information efficiently, and get valuable feedback. Different successful use cases discussed, where social media has been employed in mobile government initiatives, such as during emergencies cases, policy formulation, and service promotion. Using the systematic Literature Review, the results show one key benefit of leveraging social media in mobile government platforms is enhanced citizen engagement and increase the user satisfaction. Social media creates interactive spaces where citizens can voice opinions, express concerns, and participate in decision-making processes. It facilitates the formation of virtual communities and encourages collaboration between the user and the service provider.

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Exploring Religions and Cross-Cultural Sensitivities in Conversational AI

Exploring Religions and Cross-Cultural Sensitivities in Conv...

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Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Mahfuzur Rahman Shuvo Rahul Debnath Nobanul Hasan Rabeya Nazara Fyaz Nafin Rahman Md Jahid Alam Riad Prosenjit Roy Management Information System International American University California United States Computer Science Prairie View A&M University Texas United States Computer Information Systems Prairie View A&M University Texas United States Master of Science in Information Technology (MSIT) Washington University of Science and Technology Vienna United States

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

With the fast growth of conversational AI, it is very important that these systems can handle religion and cultural sensitivity correctly. New, cutting-edge models like BERT, mBERT (multilingual BERT), and XLM-RoBERTa are very good at many types of natural language processing. But not a lot is known about how well they talk and act about their religious views. The dataset primarily contains content related to Christianity, Islam, and Hinduism, reflecting their prevalence in online discussions and AI training corpora. AIs need to learn how to deal with harsh words, social nuances, and touchy topics where they can talk about religion. Artificial intelligence systems could give answers that don't seem to be legit or different meaning which are very sensitive. This is a big ethical *** look at how well BERT, mBERT, and XLM-RoBERTa can handle religious and cultural situations in this study. We focus on how well they do across languages using the PAWS-X dataset. PAWS-X is a good tool for testing cross-lingual transfer because it has goals for finding paraphrases in a number of different languages. This lets us see how well these models understand complex meanings in a variety of linguistic and cultural settings. We use the dataset to test how well the model works in talks about Hinduism, Islam, and Christianity, which are very sensitive to cultural and religious *** see how well these models can understand underlying cultural differences and come up with suitable responses, we test them on religious sentence pairs as part of our methodology. We test both their language skills and their ability to stay sensitive when religious or doctrinal information is present. We also look into their cross-lingual transfer skills to see how well they work in different religious meaning. BERT, mBERT, and XLM-RoBERTa perform well in basic tasks but struggle with religious and cultural nuances. BERT and mBERT handle multiple languages effectively but often falter in uncle

关键词： Training Ethics Analytical models Sensitivity Conversational artificial intelligence Switches Complexity theory Cultural differences Context modeling Testing

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DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Dual-Teacher Semi-Supervised Network for Kidney Ultrasound Image Segmentation

DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Du...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yang Luo Jun Liu Mengqian Ding School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Accurate segmentation of kidney ultrasound (KUS) images is of great significance for the diagnosis of kidney diseases. However, KUS images have challenges such as low contrast, large size variations, and acoustic shadow interference. In addition, obtaining labeled data is a major challenge in the field of medical image segmentation. This paper proposes a dual-branch hybrid CNN Transformer combined with a dual teacher semi-supervised segmentation network (DHCT-DT) based on the MT model. The reliability of guidance can be improved between two teachers by constructing consistency regularization constraints. The U-Net architecture is employed between the main teacher and student model. The auxiliary teacher model adopts a dual-branch hybrid CNN Transformer architecture. The multi-scale channel fusion attention mechanism (MSCF) is introduced to capture the feature correlation information of KUS images at different scales and channels. Two different training methods can combine their advantages to fully extract local and global information from images. To demonstrate the applicability of the proposed method, experiments were conducted on two datasets: a KUS dataset and a breast ultrasound (BUS) dataset. Both datasets demonstrated the most competitive segmentation performance. For example, in a semi-supervised experiment using only 10% labeled data in the BUS dataset, the Dice coefficient, Jaccard coefficient, HD, and ASSD reached 82.28%, 76,36%, 13.36mm, and 3.71mm.

关键词： Training Image segmentation Ultrasonic imaging Attention mechanisms Accuracy Predictive models Network architecture Transformers Reliability Kidney

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Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 283-300页

作者： Hala J.Alshahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

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Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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Tensor MIMO Perfect-Equalizer Design Using New Projection-Based Method and Approximation-Error Analysis

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IEEE systems Journal 2024年第1期18卷 668-679页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Chungli32003 Taiwan National Yang Ming Chiao Tung University Network and System Division Information Technology Service Center Hsinchu30010 Taiwan

As real environments are quite complex, multiple-input-multiple-output (MIMO) channels should be characterized by the multidimensional-multirelational models. Tensor linear algebra has been emerging as a powerful and promising tool for such MIMO channel modeling and estimation. However, to the best of our knowledge, there hardly exists any algebraic framework of MIMO channel equalization when the channel is modeled as an arbitrary-dimensional transfer tensor (a tensor whose entries are all rational z-functions). In this work, we first propose a new method based on the projection, namely, 'projection-based method,' to determine whether a given transfer tensor is a full-rank tensor or not and carry out the inversion of an invertible transfer tensor. Our idea is employing the projection subspace of the system transfer tensor to simplify the pertinent inversion process. Second, we design the finite-impulse-response (FIR) realization to approximate the inverse of an arbitrary transfer tensor such that all entries of the approximated inverse transfer tensor are finite-degree polynomials of z. Third, we also analyze the computational and memory complexities required by our proposed new projection-based method for checking the full-rank condition of a given transfer tensor and for undertaking the tensor inversion. Our developed new theories and analysis in this work can be applied to determine the FIR model-orders of an approximated inverse MIMO transfer tensor often encountered in signal processing, control, communications, etc. © 2007-2012 IEEE.

关键词： FIR filters

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Anaesthesia Monitoring: From Conventional to AI-Enabled Data Acquisition Methods 20

Anaesthesia Monitoring: From Conventional to AI-Enabled Data...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan University of Limerick Electronic and Computer Engineering Limerick Ireland School of Electronics Peking University Ministry of Education Engineering Research Center on Mobile Digital Hospital System Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9781665463973

It is widely recognized that the timely acquisition and presentation of accurate physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Collecting, storing, synchronizing, integrating, evaluating, and retrieving pertinent information from patient data remains very difficult due to limited computer power, the need for specialized software, inconsistency between monitoring devices, and inadequate data storage. This paper introduces the general steps of data acquisition in anaesthesia and summarizes some data acquisition systems. It also introduces some applications of the burgeoning field of artificial intelligence (AI) to data acquisition, illustrating how AI techniques may be used to advantage to assist the anaesthesiologist in monitoring and interpreting such data. © 2023 IEEE.

关键词： Data acquisition

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Feature Selection in Text Classification:Identifying Spurious Words with Causal Inference Methods

Feature Selection in Text Classification:Identifying Spuriou...

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作者： Zixuan Zhao Hengzhuang Li Jinxuan Chen Yang Li Jiayun Song Cheriton School of Computer Science University of Waterloo School of Computer Science and Technology Huazhong University of Science & Technology Department of Information Management and Information System Central South University School of Art and Sciences The Ohio State University

As has been scrutinized by many,non-causal model may contain spurious correlations that act like shortcuts during the prediction phase,undermining cross-domain *** can be caused by biased training data that contains spurious words with neutral meanings yet can induce the model to predict *** on this assumption,we propose a series of methods to detect these spurious words before feeding the model with the training *** used advanced causal inference methods which are arising novas in recent studies,such as propensity score matching and inverse propensity score weighting to facilitate the feature selection before *** experimented with multiple approaches to estimate propensity scores and got profound *** further experimented with BERT model to evaluate the effectiveness of feature selection and find that the model performance with indomain and out-of-domain testing samples is boosted after we remove the spurious words detected by our methods in the training data.

关键词： Sentiment Analysis Cross-domain Accuracy Kernel Method PSM IPW BERT

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Adaptive Fake Audio Detection with Low-Rank Model Squeezing

Adaptive Fake Audio Detection with Low-Rank Model Squeezing

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Zhang, Xiaohui Yi, Jiangyan Tao, Jianhua Wang, Chenglong Xu, Le Fu, Ruibo State Key Laboratory of Multimodal Artificial Intelligence System Institute of Automation Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China University of Science and Technology of China China Department of Automation Tsinghua University China School of Artificial Intelligence University of Chinese Academy of Sciences China

The rapid advancement of spoofing algorithms necessitates the development of robust detection methods capable of accurately identifying emerging fake audio. Traditional approaches, such as finetuning on new datasets containing these novel spoofing algorithms, are computationally intensive and pose a risk of impairing the acquired knowledge of known fake audio types. To address these challenges, this paper proposes an innovative approach that mitigates the limitations associated with finetuning. We introduce the concept of training low-rank adaptation matrices tailored specifically to the newly emerging fake audio types. During the inference stage, these adaptation matrices are combined with the existing model to generate the final prediction output. Extensive experimentation is conducted to evaluate the efficacy of the proposed method. The results demonstrate that our approach effectively preserves the prediction accuracy of the existing model for known fake audio types. Furthermore, our approach offers several advantages, including reduced storage memory requirements and lower equal error rates compared to conventional finetuning methods, particularly on specific spoofing algorithms. © 2023 CEUR-WS. All rights reserved.

关键词： Fake detection

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Detection of Student Sleepiness Using U-Net and Conv-LSTM Methods to Improve Learning Quality

Detection of Student Sleepiness Using U-Net and Conv-LSTM Me...

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technology, Engineering, and Computing Applications (ICTECA), International Conference on

作者： Ben Rahman Septi Andryana Aris Gunaryati Teddy Mantoro Department of Information System Faculty of Information and Communication Technology Universitas Nasional Jakarta Indonesia Department of Information Technology Faculty of Information and Communication Technology Universitas Nasional Jakarta Indonesia Department of Computer Science Faculty of Science and Technology Sampoerna University Jakarta Selatan Jakarta Indonesia

In the context of monitoring student engagement during learning sessions, the ability to accurately detect sleepiness can be a valuable asset. Early detection of sleepiness is critical to evaluate the need for intervention to ensure effective learning outcomes. Prolonged student sleepiness can negatively impact learning effectiveness and may result in academic challenges. This research used a semantic segmentation network to monitor and detect the level of sleepiness in students during learning sessions by modifying the U-Net architecture, which is enhanced with Convolutional Long Short-Term Memory (Conv-LSTM) blocks. Conv-LSTM blocks enable efficient feature sharing between network layers and improve accuracy in detecting sleepiness. In this study, experiments were carried out and the proposed method for monitoring students in the classroom was evaluated. The results of this research showed that the percentage of students who made movements detected in the video and extracted in image detection was 92% accurate and not sleepy.

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