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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Guo, Jianxiong Zhu, Zhehao Gao, Yucen Gao, Xiaofeng Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031496134

Clustering is a basic technology in data mining, and similarity measurement plays a crucial role in it. The existing clustering algorithms, especially those for social networks, pay more attention to users’ properties while ignoring the global measurement across social relationships. In this paper, a new clustering algorithm is proposed, which not only considers the distance of users’ properties but also considers users’ social influence. Social influence can be further divided into mutual influence and self influence. With mutual influence, we can deal with users’ interests and measure their similarities by introducing areas and activities, thus better weighing the influence between them in an indirect way. Separately, we formulate a new propagation model, PR-Threshold++, by merging the PageRank algorithm and Linear Threshold model, to model the self influence. Based on that, we design a novel similarity by exploiting users’ distance, mutual influence, and self influence. Finally, we adjust K-medoids according to our similarity and use real-world datasets to evaluate their performance in intensive simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data mining

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Crop Disease Recognition and Classification: A Deep Dive into Machine Learning Techniques - A Survey 1st

Crop Disease Recognition and Classification: A Deep Dive int...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Sangar, G. Rajasekar, V. Department of Computer Science and Engineering Faculty of Engineering and Technology SRMIST Vadapalani Chennai India

ISBN: (纸本)9783031823886

In the changing farming business, crop diseases and other challenges highlight the need for early detection and effective disease control to ensure global food sustainability. Deep Learning (DL) and Machine Learning (ML) are vital to plant leaf sickness identification, as this survey shows. Deep learning models like CNNs can automatically detect complex patterns in plant leaf photos, enabling reliable disease diagnosis. Mean- while, machine learning models like Support Vector Machines extract important elements from image data, providing valuable insights. Transfer learning with pre-trained models like VGG and ResNet and data augmentation improve model generalization. Deep learning (DL) models’ accuracy, precision, recall, and F1 score measure their ability to identify healthy and unhealthy plants. Deep learning (DL) and machine learning (ML) methods are used to diagnose plant leaf diseases in this survey. This study aims to synthesize knowledge, identify trends and tendencies, and suggest precision agriculture research directions. This survey seeks to advance sustainable crop management by understanding early and accurate disease detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images 26

Explainable AI-Based Humerus Fracture Detection and Classifi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Barua, Koushick Mahmud, Tanjim Barua, Anik Sharmen, Nahed Basnin, Nanziba Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Hossain, Sazzad Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Maa-O-Shishu Hospital Medical College Dept. of Obstetrics and Gynecology Chittagong4100 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Chittagong4202 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden Department of Computer Science and Engineering Dhaka1207 Bangladesh

ISBN: (纸本)9798350359015

The human skeletal framework relies heavily on bones, and one such crucial component is the 'Humerus.' Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available 'MURA' dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)"and "Ensemble-2 (DenseNet121 and DenseNet169),"which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model's predictions, providing valuable insights for medical practitioners. © 2023 IEEE.

关键词： Convolutional neural network Explainable AI (XAI) Humerus X-ray diagnosis

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Systematic comparison between a research-grade EEG device and a consumer-grade BCI device for predicting consumer preference using an ML framework

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Multimedia Tools and Applications 2025年 1-19页

作者： Ishtiaque, Farhan Miya, Mohammad Tohidul Islam Mashrur, Fazla Rabbi Rahman, Khandoker Mahmudur Vaidyanathan, Ravi Anwar, Syed Ferhat Sarker, Farhana Tat, Huam Hon Hamid, Abu Bakar Abdul Mamun, Khondaker A. AIMS Lab IIRIC UIU Dhaka Bangladesh School of Business and Economics United International University Dhaka Bangladesh Department of Biomedical Engineering University of Rochester RochesterNY United States Department of Mechanical Engineering Imperial College London London United Kingdom Office of the Vice Chancellor BRAC University Dhaka Bangladesh and Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Faculty of Business City University of Macau Taipa China Faculty of Business Information & Human Sciences Infrastructure University Kuala Lumpur Kuala Lumpur Malaysia Department of CSE United International University Dhaka Bangladesh

Evaluation of marketing stimuli such as static advertisements, video advertisements, promotions, etc. is an important part of marketing research. Traditionally, the evaluation is done through large surveys, focus group discussions, questioner-based surveys, etc. However, these methods are time-consuming and expensive. Here, neuromarketing offers a solution that can improve marketing research. Recently, several EEG-based neuromarketing studies proved the potential of neuromarketing as a marketing research tool. However, most of these researchers used research-grade EEG devices. Research-grade EEG devices are bulky and require high setup time. This is why, despite much successful research, the technology is yet to be implemented on a day-to-day basis. On the other hand, consumer-grade BCI devices are cheap, wearable, and offer little to no setup time. So, it is ideal for day-to-day implementation. However, consumer-grade BCI lacks in terms of signal quality and available channel number, and it has not been widely tested in neuromarketing. So, in this study, we compared a research-grade EEG device and a consumer-grade BCI device for consumers’ preference prediction using a machine learning framework. We designed an experiment to record participants' EEG data while they were shown marketing stimuli. Their response has been taken as affective attitude (AA) purchase intention (PI). After that, binary classification was performed. We achieved 80.71% and 82.07% accuracy for predicting purchase intention and affective attitude using the research-grade EEG device, whereas it was 78.31% and 81.23% using the consumer-grade BCI device. These findings prove that consumer-grade BCI can be a viable option for day-to-day usage in neuromarketing. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sales

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A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

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Intelligent Automation & Soft Computing 2023年第7期37卷 17-32页

作者： Aoqi Xu Khalid A.Alattas Nasreen Kausar Ardashir Mohammadzadeh Ebru Ozbilge Tonguc Cagin School of Economics Fujian Normal UniversityFuzhou350007China Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenler34220IstanbulTurkey Multidisciplinary Center for Infrastructure Engineering Shenyang University of TechnologyShenyang110870China American University of the Middle East Department of Mathematics&Statistics54200EgailaKuwait

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identiﬁ*** main gap is the weakness of current methods *** *** primary objective of this study is to present a more robust method against *** paper proposes a new deep learning scheme for modeling and identiﬁcation *** suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level *** the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanﬁlter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is *** designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data *** various ana-lyses and comparisons,the better efﬁciency of the proposed fuzzy modeling scheme is veriﬁ*** main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

关键词： Modeling computational intelligence fuzzy logic systems modeling identiﬁcation deep learning type-3 fuzzy systems optimization

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Using wearable sensors to study the brain-heart interactions during attentional and resting states

Using wearable sensors to study the brain-heart interactions...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lee, Jia-Jeng Chen, Chun-Chuan Wu, Eric Hsiao-Kuang Yeh, Shih-Ching Wang, Wei-Jen National Central University Laboratory of Computational Neuroscience Department of Biomedical Sciences and Engineering Taoyuan Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

Wearable sensors have a significant increase in both research and commercialization as a kind of consumer electronics. In the field of healthy science, wearable sensors provide affordable solutions for massive screening or long tern monitoring, for instance, wearable dry electroencephalography (EEG) for brain and electrocardiogram (ECG) for heart. Human brain and heart are the most important organs and the targets for healthy monitoring. Given that brain and heart sometimes have comorbidities of each other as they are reciprocally connected, it is important to monitor their relations. However, only a few studies addressed the relationships between them under healthy states. This study aims to examine the brain-heart interactions(BHI) under different states, including two different resting states and one attentional state using wearable sensors. Twenty subject were recruited and performed a memory task in a virtual supermarket. Five-channel dry EEG and two-channel ECG were acquired before, during, and after the task. Pearson correlation was employed to analyze the relations between EEG features and heart rate variability (HRV). Two sample t test and machine learning method were employed to analysis the difference between states. We found that higher frequency oscillations in the brain were used to communicate with the heart during the pre-rest state and task state respectively, then switched to lower frequency for resetting the BHI after task. Moreover, the combination of EEG and ECG features can best distinguish the pre and post resting states. Our findings suggest that BHI are dynamic and state-dependent and studying the BHI can provide better understanding of the states in the body to aid the diagnosis and/or treatment for diseases affected both brain and heart. © 2023 IEEE.

关键词： Electroencephalography

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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Unitary Operator Based Quantum Secure Access Structure for Medical Image Sharing

Unitary Operator Based Quantum Secure Access Structure for M...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Padulkar, Digambar Abraham, Jibi School of Computational Sciences Coep Technological University Department of Computer Science & Engineering Pune India Baramati Savitribai Phule Pune University Vpkbiet Pune India

ISBN: (纸本)9798350350821

Ensuring the secure storage and sharing of medical records in the cloud is increasingly crucial due to vulnerabilities in classical encryption methods, such as key manipulation and cloud collusion. This paper introduces a novel quantum-based approach using Quantum Fourier Transform (QFT) and Unitary operators. We designed three quantum circuits for different image sizes: one for the Unitary operator, one for encryption, and one for decryption. DICOM images are first transformed into quantum images with the NEQR algorithm, then encrypted using a Unitary operator, which serves as the encryption key. This key is securely transmitted via quantum teleportation, enhancing protection against classical and quantum attacks. Extensive simulations show the method's effectiveness and robustness in safeguarding medical data during cloud storage and transmission. This makes it a promising solution for secure medical image sharing in quantum communication networks. © 2024 IEEE.

关键词： Quantum communication

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Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 75-89页

作者： Aijuan Song Guohua Wu Witold Pedrycz Ling Wang School of Traffic and Transportation Engineering Central South UniversityChangsha 410075China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Systems Research Institute Polish Academy of SciencesWarsaw 01447Poland Department of Automation Tsinghua UniversityBeijing 100084China

Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple *** algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one ***,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used *** contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve *** VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation *** enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the *** test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,*** results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared ***,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

关键词： Evolutionary algorithm(EA) nonlinear equations systems(ENSs) problem domain knowledge variable reduction strategy(VRS)

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Few-shot Action Recognition with Video Transformer 17

Few-shot Action Recognition with Video Transformer

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17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023

作者： Aikyn, Nartay Abu, Assanali Zhaksylyk, Tomiris Tu, Nguyen Anh Nazarbayev University Department of Computer Science School of Engineering and Digital Sciences 53 Kabanbay Batyr Ave Astana010000 Kazakhstan

ISBN: (纸本)9798350370911

This paper proposes a novel few-shot action recognition framework that integrates the Transformer-based feature backbone into meta-learning. The proposed method includes pre-training the Video Transformer and utilizing metric-based meta-learning with the ProtoNet algorithm. Extensive experiments on benchmark datasets demonstrate that our approach achieves remarkable performance, surpassing baseline models and obtaining competitive results compared to state-of-the-art models. Additionally, we investigate the impact of supervised and self-supervised learning on video representation and evaluate the transferability of the learned representations in cross-domain scenarios. Our approach suggests a promising direction for exploring the combination of meta-learning with Video Transformer in the context of few-shot learning tasks, potentially contributing to the field of action recognition in various domains. © 2023 IEEE.

关键词： Benchmarking

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