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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Harini, V.K. Bhavadharini, R.M. Upreti, Charvi Alex, Libin Schoo of Computer Science and Engineering Vellore Institute of Technology Chennai India Schoo of Computer Science and Engineering Vellore Institute of technology India

ISBN: (纸本)9798350374711

As per a 2023 publication, addressing mental health problems in India holds immense significance due to the scale of human value impact involved, particularly given the country's population size. It is crucial to recognize that mental health issues affect a significant portion of the population and can lead to severe consequences if left unaddressed. With this paper, we aim to contribute to the advancement of mental healthcare provision by developing a task-oriented, closed-domain chatbot. This chatbot will serve as a valuable tool for individuals seeking support and information regarding mental health issues. The paper compares existing models which use various embeddings and transformers and focuses on the development of a task-oriented, closed-domain chatbot using a retrieval-based approach with text data. The maximum accuracy of 91.48% was achieved in the LSTM model. © 2024 IEEE.

关键词： bag-of-words chatbot closed domain mental health RoBERTa task oriented model word2vec

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Mental Health Chatbot

Mental Health Chatbot

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Students Conference on engineering and Systems (SCES)

作者： Harini V K Bhavadharini R M Charvi Upreti Libin Alex Schoo of Computer Science and Engineering Vellore Institute of Technology Chennai India Schoo of Computer Science and Engineering Vellore Institute of technology City Country

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

关键词： Training Accuracy Mental health Medical services Chatbots Transformers Data models

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 4

Customer Segment Classification Prediction in the Australian...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luo, Yiming Zhang, Rui Wang, Fei Wei, Tao Business Schoo The University of Sydney Sydney Australia Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China School of Chemical and Environmental Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment. © 2023 ACM.

关键词： Customer satisfaction

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Spatiotemporal Feature Extraction and Fusion for Longitudinal Alzheimer’s Disease Diagnosis 17th

Spatiotemporal Feature Extraction and Fusion for Longitudina...

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17th International Conference on Brain Informatics, BI 2024

作者： Guan, Zhenghua Yang, Peng Lei, Haijun Yang, Bao Song, Xuegang Dong, Lei Yan, Xueqin Wei, Cuimei Liang, Chunhua Xiao, Xiaohua Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen University Medical schoo1 Shenzhen518060 China Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen Second People’s Hospital Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789819632961

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. The brain structure of AD patients progressively atrophies over time. Longitudinal data can capture the changes in imaging over time, providing important information for early detection and diagnosis of the disease, which is not achievable with images from a single time point. We propose a novel longitudinal AD diagnosis model that leverages deformation field data obtained by registering images from adjacent time points. Each pixel in the deformation field represents the displacement vector of the corresponding pixel between two scans, reflecting information about brain tissue atrophy. By effectively extracting both temporal and spatial features from the longitudinal data, our model enhances early AD diagnosis. The input data for the model consists of processed sMRI images and deformation fields calculated between each pair of time points in the longitudinal sMRI data. The model is composed of three modules: Convolutional Long Short-Term Memory (CLSTM), Gate Fusion Long Short-Term Memory (GFLSTM), and an attention module. CLSTM extracts temporal features while preserving spatial features by maintaining the 3D shape of the features, GFLSTM achieves spatiotemporal feature fusion within a cascaded network, and the attention module further enhances feature representation. Our proposed method was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and experiments demonstrate that it outperforms several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Don't worry about mistakes! Glass Segmentation Network via Mistake Correction

arXiv

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arXiv 2023年

作者： Zheng, Chengyu Li, Peng Zhang, Xiao-Ping Lu, Xuequan Wei, Mingqiang The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada The Schoo of Information Technology Deakin University VIC Australia

Recall one time when we were in an unfamiliar mall. We might mistakenly think that there exists or does not exist a piece of glass in front of us. Such mistakes will remind us to walk more safely and freely at the same or a similar place next time. To absorb the human mistake correction wisdom, we propose a novel glass segmentation network to detect transparent glass, dubbed GlassSegNet. Motivated by this human behavior, GlassSegNet utilizes two key stages: the identification stage (IS) and the correction stage (CS). The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information. The CS then progressively refines the coarse prediction by correcting mistake regions based on gained experience. Extensive experiments show clear improvements of our GlassSegNet over thirty-four state-of-the-art methods on three benchmark datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Glass

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 23

Customer Segment Classification Prediction in the Australian...

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Proceedings of the 2023 4th International Conference on Machine Learning and computer Application

作者： Yiming Luo Rui Zhang Fei Wang Tao Wei Business Schoo The University of Sydney Austialia Computer and information sciences program Harrisburg University of Science and Technology United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College China School of Chemical and Environmental Engineering Sichuan University of Science & Engineering China

ISBN: (纸本)9798400709449

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A Traceable Privacy-Preserving Authentication Protocol for VANETs Based on Proxy Re-signature

A Traceable Privacy-Preserving Authentication Protocol for V...

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2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

作者： Zhong Chen Yong Deng Jianbin Hu Tao Yang Wei Xin Hu Xiong YonggangWang School of Electronics Engineering and Computer Science Peking University Beijing P.R. China School of Electronics Engineering and Computer Science Peking University Beijing P.R. China Schoo Schoolof Electronics Engineering and Computer Science Peking University Beijing P.R. China

In this paper, based on a single hop proxy resignature in the standard model, we introduce a traceable privacy-preserving communication protocol for VANETs. The proposed protocol has some appealing features: The Trusted Authority (TA) designates the Roadside Units (RSUs) translating signatures computed by the On-Board Units (OBUs) into one that is valid as for TA's public key. As a result, the potential danger that vehicles could be traced by the signatures on messages can be well deleted, and attacks are thwarted by using an endorsement mechanism based on signatures. The security analysis shows that the protocol can achieve good conditional privacy target of VANETs.

关键词： vehicular ad-hoc networks proxy re-signature privacy-preserving

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Design of computer Control System for Pressure Regulator of Air Pump Switch

Design of Computer Control System for Pressure Regulator of ...

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The 5th International Conference on computer science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE'10)

作者： Wang Hong Zhao Yan Schoo Schooll of Computer Science and Technology Hubei University of Economy Wuhan Hubei Province 43 School of Mechantronic Engineering Wuhan University of Technology Wuhan Hubei ProvinceChina

A regulator of the air pump switch is developed in order to realize the automation of regulating pressure of the air pump *** equipment adopts pneumatic device and stepping motor as the power transmission and actuator; and realizes the automatic control of the equipment work process based on the industry control computer. The utilization of this system can improve as ten times as the productivity of the regulate system.

关键词： industry control computer air pump switch automatic regulating pressure

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AN EFFICIENT ALGORITHM BASED ON TIME DECAY MODEL FOR MINING MAXIMAL FREQUENT ITEMSETS

AN EFFICIENT ALGORITHM BASED ON TIME DECAY MODEL FOR MINING ...

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： LI-BO WANG HUI-LING HE GUO-YAN HUANG CHANG-ZHEN HU JIA-DONG REN College of Information Science and Engineering YanShan University QinHuangDao 066004 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Information Science and Engineering YanShan University QinHuangDao 066004 China Schoo

Mining maximal frequent itemsets is an active research area in data stream mining. A new algorithm, called MFI-TD (mine maximal frequent itemsets based on time decay model) is proposed for mining maximum frequent itemsets. A new data structure, called PW-tree ( Point based Window-tree ) is introduced to store each transaction for the current window, and the final node of the path which denotes a maximum frequent itemset is pointed by the DP ( domain pointer). Then according to the data structure, the MFI-TD gradually reduces the weight of historical transaction supporting number, and deletes the obsolete and infrequent itemset branches in PW-tree by using of time decay model. Thus MFI-TD decreases the space complexity and reduces maintenance cost of PW-tree. Experimental results show that MFI-TD has better space efficiency and result accuracy than DSM-MFI algorithm.

关键词： Data stream Mazimal frequent itemsets Time decay model

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A Compounded Genetic and Simulated Annealing Algorithm for the Closest String Problem

A Compounded Genetic and Simulated Annealing Algorithm for t...

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The 2nd International Conference on Bioinformatics and Biomedical engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

作者： Xiaolan LIU Mauch Holger Zhifeng HAO Guangchao WU School of Computer Science & Engineering South China University of Technology Guangzhou China Schoo Natural Sciences Collegium Eckerd College Florida USA School of Mathematical Sciences South China University of Technology

The closest string problem is an NP-hard problem, which arises in computational molecular biology and coding theory. Its task is to find a string that minimizes maximum Hamming distance to a given set of strings. In this paper, a compounded genetic and simulated annealing algorithm (CGSA) which combines the merits of genetic algorithms and simulated annealing is presented to solve CSP. An adapting two-point crossover operator and a heuristic gene mutation operator designed by us are used in CGSA. In addition, by analyzing the optimal solution's structural features some rules are designed to pretreat the data, which reduces the problem size. We report computational results which show that the CGSA is capable of finding good solutions in a reasonable amount of time.

关键词： Closest String Problem Genetic Algorithm Simulated Annealing Computational Biology Hybrid Methods

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