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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Haodi Zhang Wenxi Huang Chenyu Xu Zhengbang Yang Hongxin Zhou Fengtian Qi Chen Zhang Kaishun Wu School of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University

Knowledge graphs (KGs), either constructed automatically from texts or collected manually from crowdsourcing workers, may contain uncertainty. The uncertainty may propagate into the knowledge graph embedding and downstream tasks, which is potentially harmful, especially for those confidencesensitive applications such as medical diagnostic suggestion. Crowdsourcing workers with domain knowledge can help improve the data quality of knowledge graphs, by knowledge checking. However, due to the large scale of knowledge graphs and the limitation of adequate crowdsourcing workers, it is unrealistic to check all triplets in a knowledge graph to improve the data quality. Therefore, in this paper, we propose a crowdsourcing framework that efficiently improves the confidence of knowledge graphs with limited budget. We instantiate the framework in the medical domain and conduct a series of experiments with realworld medical data. We deploy the framework for knowledge graph embedding UKGE and corresponding downstream tasks. The experimental results show that the proposed method efficiently improves the quality of the knowledge graphs, and hence improves the performance of probabilistic knowledge graph embedding in the downstream tasks.

关键词： Crowdsourcing Uncertainty Data integrity Distributed databases Knowledge graphs Probabilistic logic Medical diagnosis

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Sinhala Sign Language Translation through Immersive 3D Avatars and Adaptive Learning

Sinhala Sign Language Translation through Immersive 3D Avata...

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Advancements in computing (ICAC), International Conference on

作者： B.G.J Gamage R. P. S. D Paranagama R M S H Ranaweera A.V.R. Dilshan Ishara Weerathunga Dharshana Kasthurirathna Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research paper addresses the challenge of bridging the communication gap between sign language users and non-users through a comprehensive study on sign language translation. The focus is on Sinhala Sign Language (SSL), and the research problem encompasses various innovative components. These include the conversion of Sinhala Sign Language (SSL) into text, emotion recognition and representation, personalized sign language learning, and accurate identification and translation of sign language gestures. By leveraging machine learning, deep learning, 3D modeling, and image/video processing techniques, the system enhances accessibility, communication, and education for the deaf and hard-of-hearing community. The outcomes of this research encompass an accurate and visually appealing sign language translation system, personalized learning interfaces, and the integration of emotions into sign language communication. The research contributes by providing insights into SSL grammar and lexicon, developing a tailored sign language learning curriculum, implementing reinforcement learning algorithms for personalized feedback, utilizing 3D components for enhanced visual quality, and optimizing deployments and models for improved performance. The proposed system has significant implications for improved accessibility, education, and inclusivity, fostering effective communication and understanding between sign language users and non-users.

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An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

An Efficient Classification Techniques for Brain Tumor Using...

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作者： Badshah, Shah Hussain Farhatullah khan, Gul Zaman Hassan, Muhammad Abul Junaid, Hazrat Sohail, Muhammad Mahbob, Muhammad Awais Ahamad, Izaz Ullah, Nadeem Department of Computing and Technology Abasyn University Peshawar Pakistan School of Automation China University of Geosciences Wuhan430074 China Department of Computer Software Engineering University of Engineering and Technology Mardan Pakistan Department of Computer Science and Information Technology University of Malakand Dir Lower Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science University of Engineering and Technology Taxila Pakistan Department of Information Engineering and Computer Science University of Trento Trento38122 Italy

Today, brain tumor is a very dangerous disease that can even cause death. There are many ways to classify Brain MRI images of tumors. Various aspects of current research have limitations;some methods are accurate but take a long time to compute while some algorithms are fast, but with low accuracy. Consequently, a lot of effort is needed in this area. In this paper, our major goal is to reduce computation time and improve the classification accuracy for brain MRI images. This research proposes four phases: preprocessing, feature extraction, feature selection, and classification. During pre-processing, the median filter was used to reduce noise in the images, and the grayscale image size was reduced to further limit the image for efficient use. After that the Gray images are then given as inputs to the Feature extraction phase to select a limited feature from the image namely, texture features, shape features, smoothing features. The images features obtained in the feature’s extraction phase are still large, and we cannot simply feed them to machine learning models for processing due to computational constraints. So, we need to analyze it and pick out some interesting elements from the images. The statistical features namely, variances, mean, correlation are used in the feature selection phase to select more significant features to reduce the size of the features and reduce computational time. These features are combined and labeled in a file to train Machine Learning (ML) algorithms. We used two types of machine learning algorithms in the classification phase: classifier and evolutionary algorithm. The selected classifier algorithms results are combined in a file and we named it S-K-E-L Classifier and it gives us acceptable results in terms of accuracy and time. Similarly, an evolutionary algorithm named P-S-D-G Classifier also gives us acceptable results in terms of accuracy and time. Lastly, we combine both S-K-E-L Classifier and P-S-D-G Classifier in a file with

关键词： Classification (of information)

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Text Recognition for Station Equipment Troubleshooting Based on BERT-BiLSTM-CRF Models

Text Recognition for Station Equipment Troubleshooting Based...

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2023 China Automation Congress, CAC 2023

作者： Bai, Wei Wang, Xiaosu Liang, Bo Yang, Guoyuan Zhi, Hanyu Qiao, Yinwei Institute of Computing Technology China Academy of Railway Sciences Co. Ltd. Beijing China Postgraduate Department China Academy of Railway Sciences Beijing China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350303759

The excessive length and high semantic complexity of station equipment fault handling report texts are addressed to the extent that effective text recognition is difficult to achieve. A station equipment fault handling dataset is formulated and a text recognition system for station fault reports based on the BERT-BiLSTM-CRF model is designed. The method first preprocesses the data using the BERT pre-trained language model to generate word vectors, which are then fed into BiLSTM to obtain the embedded contextual information, which is passed through the CRF model to obtain the global optimal solution, and finally outputs the textual results of the station equipment fault handling. The experimental results show that the model outperforms the other five comparative models by more than 2% in terms of precision, recall, and F1 value (checking accuracy) for intelligent recognition over the five entities predicted by the other five comparative models. This brings a new direction of thinking for the analysis of equipment troubleshooting texts in railway stations. © 2023 IEEE.

关键词： Semantics

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Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets

arXiv

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arXiv 2023年

作者： Koufakou, Anna Garciga, Jairo Paul, Adam Morelli, Joseph Frank, Christopher Department of Computing and Software Engineering Florida Gulf Coast University Fort MyersFL United States

Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented in the related literature, and we explore the performance of traditional as well as state-of-the-art deep learning models in the presence of different characteristics in the data. We also explore the use of data augmentation in order to improve performance. Our experimental work shows that state-of-the-art models such as RoBERTa perform the best for all cases. We also provide observations and discussion that highlight the complexity of emotion classification in these datasets and test out the applicability of the models to actual social media posts we collected and labeled. © 2023, CC0.

关键词： Emotion Recognition

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A Blockchain based Framework for Verifying Drug Authenticity

A Blockchain based Framework for Verifying Drug Authenticity

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Information Technology (ACIT)

作者： Soomaiya Hamid Narmeen Zakaria Bawany Kiran Fatima Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Karachi Pakistan

Counterfeit drugs are fake medicines that are potentially harmful for health. Safeguarding the integrity of pharmaceutical distribution plays a crucial role in preventing the circulation of counterfeit drugs. Traditional drug supply chain systems have no effective mechanism to ensure the traceability and integrity of the drug supply chain process. Various ICT-based solutions have been adopted previously and numerous regulations were passed by drug regulatory authorities to ensure interoperable and traceable drug distribution process. However, these solutions could not guarantee a tamper-proof history of supply chain process. Blockchain technology has gained traction in many diverse fields including real-estate, identity management, digital voting, healthcare, etc. Therefore, we have proposed a decentralized framework to insure the authenticity and traceability of the drugs. Moreover, this study presents an overview of how blockchain technology can be employed to counter the fake medicines circulation by embedding the traceability in drug supply chain process. Comparison of various blockchain-based solutions and open research challenges in this regard are also presented.

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Deep Learning for Violence Detection in Surveillance: The Role of Transfer Learning and Pre-Trained Models

Deep Learning for Violence Detection in Surveillance: The Ro...

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Information Technology (ACIT)

作者： Anum Ilyas Surayya Obaid Narmeen Zakaria Bawany Department of Computer Science and Software Engineering Center for Computing Research Jinah University for Women Karachi Pakistan

In recent years, the use of CCTV footage for proactive crime prevention has surged, particularly in public places like airports, train stations, and malls. However, the efficacy of these surveillance systems becomes questionable for their substantial reliance on human resources which may lead to erroneous or delayed responses. This research takes a comprehensive approach for violence detection which was previously limited to binary classification, by categorizing violence into four distinct classes: abuse, arson, assault, and fight. It employs transfer learning combined with computer vision techniques for violence detection and classification in video footages. The study compares the performance of four pre-trained neural networks, namely DenseNet121, VGG16, MobileNet, and Xception. The dataset created for this research is compiled from three datasets available on Kaggle. The results reveal that the Xception model performed comparatively better achieving the highest AUC score of 98.39%, while the VGG16 model attained the lowest AUC score of 96%. In addition to the AUC score, precision, recall, and fl-score are employed as performance metrics. Transfer learning with convolutional neural networks (CNN) significantly reduced computational requirements and time. Automating the detection and categorization of violent behavior through the employed approach has the potential to reduce the risk of fatalities and injuries in public areas. It also improves the speed and accuracy of threat detection, enabling swift preventive actions by authorities.

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Evaluating Propagation Models for IIoT in Underground Mining: an Experimental Comparative Study in Underground Coal Mines

Evaluating Propagation Models for IIoT in Underground Mining...

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IEEE ANDESCON

作者： Fabian Medina Oscar Montañez Cristian Suancha Eduardo Avendaño Sandra Céspedes Systems and Computing Engineering school UPTC Sogamoso Colombia Electronic Engineering school UPTC Sogamoso Colombia Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

Planning the Industrial Internet of Things (IIoT) systems for underground mining is critical in guaranteeing communication between nodes (sensor/actuator) and ensuring the effectiveness of their functionalities. Therefore, it is necessary to understand how electromagnetic waves disperse in under- ground environments through models that attempt to reproduce their behavior. This understanding is critical to designing and simulating efficient communications for IIoT systems requiring reliable and continuous data transmission. This paper evaluates two propagation models for underground mining tunnels and emphasizes the analysis of the materials that make up the side walls, the floor, and the roof. The analysis of the models is compared with experimental measurements of received power in the 433 MHz band made in coal mines in Colombia. The results, which have direct practical implications, show that the True Rays model generates a behavior similar to that observed in the experimental measurements, with determination coefficients above 74%. At the same time, we assessed the impact of the standard deviation of surface roughness on the accuracy of the received power predictions in the True Rays model.

关键词： Analytical models Accuracy Predictive models Surface roughness Coal mining Rough surfaces Standards Floors Tuning Industrial Internet of Things

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Efficient Certificateless Authenticated Key Agreement for Blockchain-Enabled Internet of Medical Things

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Computers, Materials & Continua 2023年第4期75卷 2043-2059页

作者： Chaoyang Li Yanbu Guo Mianxiong Dong Gang Xu Xiu-Bo Chen Jian Li Kaoru Ota College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450002China Department of Sciences and Informatics Muroran Institution of TechnologyMuroran050-8585Japan School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing100876China

Internet of Medical Things(IoMT)plays an essential role in collecting and managing personal medical *** recent years,blockchain technology has put power in traditional IoMT systems for data sharing between different medical institutions and improved the utilization of medical ***,some problems in the information transfer process between wireless medical devices and mobile medical apps,such as information leakage and privacy *** paper first designs a cross-device key agreement model for blockchain-enabled *** model can establish a key agreement mechanism for secure medical data ***,a certificateless authenticated key agreement(KA)protocol has been proposed to strengthen the information transfer security in the cross-device key agreement *** proposed KA protocol only requires one exchange of messages between the two parties,which can improve the protocol execution ***,any unauthorized tampering of the transmitted signed message sent by the sender can be detected by the receiver,so this can guarantee the success of the establishment of a session key between the strange *** blockchain ledger can ensure that the medical data cannot be tampered with,and the certificateless mechanism can weaken the key escrow ***,the security proof and performance analysis are given,which show that the proposed model and KA protocol are more secure and efficient than other schemes in similar literature.

关键词： Certificateless key agreement authentication blockchain internet of medical things

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud computing System for Diabetes Prediction Enabled by Ensemble Machine Learning 15

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

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15th IEEE International Conference on Cloud computing Technology and Science, CloudCom 2024

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering Al-Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331507589

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction. © 2024 IEEE.

关键词： Electronic health record

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