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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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Computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj Hafiz Tayyab Rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

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MSFR: Stance Detection Based on Multi-Aspect Semantic Feature Representation via Hierarchical Contrastive Learning

MSFR: Stance Detection Based on Multi-Aspect Semantic Featur...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xuechen Zhao Lei Tian Feng Xie Bin Zhou Haiyang Wang Hongzhou Wu Liqun Gao School of Computer National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Zero-shot stance detection aims to determine the stance of previously unseen targets during the inference phase. Achieving effective feature alignment from seen targets to unseen targets is crucial for zero-shot stance detection. In this paper, we propose MSFR, a hierarchical contrastive learning framework, which consists of two core components: inter-aspect contrastive learning for distinguishing aspect-level features and intra-aspect contrastive learning for capturing attribute-level features. Specifically, inter-aspect contrastive learning first maps the global features of an utterance to multiple aspects that influence semantic expression (referred to as aspect-level feature differentiation). This process facilitates the alignment of semantic features across different factors of seen and unseen targets. Intra-aspect contrastive learning enhances the distinguishability of features within the same aspect (referred to as attribute-level feature differentiation) and improves the model’s fine-grained generalization capability. Experimental results demonstrate the superior performance of our model compared to competing baseline models.

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Structural Reliability of a Multipath Routing Network with Reconfigurations when Switching Routes

Structural Reliability of a Multipath Routing Network with R...

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2022 International Russian Automation Conference, RusAutoCon 2022

作者： Bogatyrev, Vladimir Anatolievich Le, Anh Tu Abramova, Evgenia Alexandrovna Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia University Itmo Faculty of Software Engineering and Computer Technology Saint Petersburg Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The reliability of data transmission is one of the most important factors in building network models. The need for timely delivery of packets to the user leads to the search for new methods to improve the configuration of network structures. A study of the possibility to improve the structural reliability of multipath routing networks in the presence of request sources with full and non-full connection to a set of network paths has been conducted. Improving the reliability and fault tolerance of multipath routing networks is based on a configuration that allows new routes to be formed as a result of using segments of existing paths that have remained connected after failures. The investigated possibility of increasing the reliability of a multipath network is achieved by optimizing the placement of route switch nodes. © 2022 IEEE.

关键词： Network routing

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Deep Reinforcement Learning for Dynamic Demand in Shared Bicycle Scheduling Problem 9

Deep Reinforcement Learning for Dynamic Demand in Shared Bic...

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9th International Conference on Big Data and Information Analytics, BigDIA 2023

作者： Jiao, Zixiao Xiao, Wenhua Xin, Hailu Liu, Daqian Yan, Hui Chen, Chao National University of Defense Technology Laboratory for Big Data and Decision Changsha China Academy of Military Sciences Beijing China National University of Defense Technology College of Systems Engineering Changsha China Liaoning Technical University Software College Liaoning China Central South University School of Traffic and Transportation Engineering Changsha China

ISBN: (纸本)9798350330076

In contemporary shared bicycle scheduling systems, a common occurrence is a severe imbalance between regions, with one area suffering from a dearth of bicycles while another area is burdened with an excess. The task of balancing the distribution of vehicles can be abstracted as The Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPSPDTW). The primary objective is to efficiently schedule vehicles between multiple sites to minimize the overall transportation cost. However, in practical scheduling scenarios, the demand at each site undergoes dynamic fluctuations over time, thereby presenting a formidable challenge for problem-solving. Hence, this study employs a deep reinforcement learning algorithm equipped with a pointer network, constructing a computational environment capable of accommodating the dynamic nature of demand and lifting the constraints on the number of scheduled vehicles. Furthermore, the integration of a node masking mechanism enables the acquisition of high-quality scheduling strategies for addressing the shared bicycle scheduling predicament. Two distinct scheduling strategies are proposed, one prioritizing cost optimization and the other emphasizing user demand fulfillment. Empirical findings substantiate that the proposed model surpasses other conventional algorithms in terms of performance efficacy and computational efficiency. © 2023 IEEE.

关键词： Reinforcement learning

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Analysis of converting -circuit into -circuit

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EPJ Quantum technology 2025年第1期12卷 1-14页

作者： Luo, Qing-bin Ding, Lang Yang, Guo-wu Li, Xiao-yu College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

A $\mathfrak{C^{*}}$ -circuit, which was proposed in Asiacypt 2022 by Huang and Sun (Advances in cryptology – ASIACRYPT 2022, pp. 614–644, 2022), can directly perform calculations with the existing quantum states, thereby reducing the use of quantum resources in quantum logic synthesis. We theoretically prove how to convert a $\mathfrak{C^{0}}$ -circuit into the corresponding $\mathfrak{C^{*}}$ -circuit through two lemmas and one theorem. The first lemma proves the interchangeability of CNOT gates and NOT gates by using the equivalence of quantum circuits. The second lemma proves that adding CNOT gates to the front of a quantum circuit whose initial states are all $|0\rangle $ s will not change the output states of the circuit. The theorem is used to describe what kind of $\mathfrak{C^{0}}$ -circuit can be transformed into $\mathfrak{C^{*}}$ -circuit, and the correctness of this transformation is proved. Our work will provide a theoretical basis for converting $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit. Then applying the theoretical analysis results to the multiplication over $\text{GF}(2^{8})$ , the constructed quantum circuit needs 27 Toffoli gates and 118 CNOT gates, which is 15 fewer Toffoli gates and 43 CNOT gates than the current best result. This shows that the method of constructing quantum circuits by using the conversion of $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit is very efficient.

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Efficient Cutting of Submarine Pipelines by Linear-Shaped Charge 30th

Efficient Cutting of Submarine Pipelines by Linear-Shaped Ch...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Zhang, Shenhe Wang, Longkan Yang, Shuxin Zong, Zhi Zhang, Zhifan State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian116024 China China Ship Research and Development Academy Beijing100192 China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai200240 China

ISBN: (纸本)9783031687747

Directional explosive cutting method has been applied to the cutting, maintenance and even destruction of submarine pipelines because of the high cutting speed and low risk to adjacent components. This paper develops a three-dimensional numerical model for the damage effects on the pipe induced by the Linear-Shaped Charge (LSC). The explosive is 50 kg TNT. The internal diameter of the pipe is 871 mm, and the thickness of the steel wall is 34 mm. The standoff distance is set to 1.0 times the height of the LSC and an air cavity is added in front of the liner for better jet formation effect. The pipe is filled with air, and the bottom of the pipeline is anchored to the seabed. Due to the symmetry of the numerical model, a quarter of the numerical model is established in order to improve the calculating efficiency. The FSI (Fluid-Structure Interaction) is taken into consideration. Based on structured-arbitrary Lagrange Euler (S-ALE) method, the damage process of the pipeline under the coupling load of the jet penetration, hydrostatic pressure, shock wave and bubble is investigated. According to the numerical simulation results, the pipe is immediately destroyed under the combined effect of the load. The results indicate that it is possible to effectively cut the pipelines by using a linear-shaped charge technique. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Submarine pipelines

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Predicting the Decision-Making Performance Based on Self-attention and Long-Short Term Memory Network 23rd

Predicting the Decision-Making Performance Based on Self-att...

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23rd International Symposium on Knowledge and systems Sciences, KSS 2024

作者： Yuan, Erbiao Yang, Guangfei Zhou, Yuhe Liu, Lian Institute of Systems Engineering Dalian University of Technology Dalian116024 China Operation Software and Simulation Research Institute Dalian Naval Academy Dalian116021 China

ISBN: (纸本)9789819601776

Decision-making requires a combination of intuition, perception and deep thinking skills, which is crucial in personal work and life. Improving decision-making performance is quite beneficial for enhancing people’s competitiveness. This paper predicts the decision-making performance and further analyzes the impact of indoor environment on it. Unlike previous studies, which were mostly empirical, our paper discusses the decision-making performance from a data-driven perspective. We collect data using Chinese chess game and indoor environmental sensors as experimental tools, and propose a hybrid model SA–LSTM, which integrates self-attention (SA) mechanism and long-short term memory (LSTM) network. The results demonstrate that our model can accurately predict the decision-making performance. Compared with other five models, SA–LSTM has lower error and better goodness of fit. Specifically, the root mean square error and mean absolute error of our model are reduced by 11% and 14% compared with the second best performing model LSTM. In addition, SA–LSTM model shows greater robustness. Further interpretability analysis suggests the impact of environmental features on the prediction of decision-making performance, and the SA mechanism enables a deeper exploration of their relationship, thereby emphasizing the significance of environmental variables and enhancing prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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Stroke Prediction Using the Trust Evaluation with Data Leakage Avoiding

Stroke Prediction Using the Trust Evaluation with Data Leaka...

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Ye, Xiaoyan Xu, Wenchao Ye, Xiaoying Long, Dan Yin, Qiuyang Huang, Binhua Software Engineering Institute of Guangzhou Guangzhou China Neusoft Institute Guangdong Foshan China The Affiliated Medical Hospital Hunan Traditional Chinese Medical College Hunan Province Directly Affiliated TCM Hospital Changsha China Shenzhen Institute of Advanced Technology Shenzhen China

Early detection of the severe disease - stroke is a key step toward effective treatment. Stroke disease data is imbalanced and normally contains the majority of negative cases (without stroke) and the minority of positive cases (stroke). Previous work has used SMOTE to deal with imbalanced data, but most researchers have implemented it for the entire dataset, which means the "answer"was silently "be told"and saved in the entire data, causing data leakage. Moreover, the previous work uses accuracy only as the metrics make the result less guaranteed. We propose a method using the SMOTE applied to the training set only and apply 13 machine learning classifiers for predicting stroke. We combine the AUC with accuracy as evaluation metrics in the stroke prediction task, which can elevate the confidence level of the assessment of results. The experiment shows that misused SMOTE and standardization can cause data leakage and the combined metrics can evaluate models with higher trustworthiness. We conclude that using our method can avoid data leakage and assess the model with higher trustworthiness. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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