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5th international conference on Intelligent computing and Control Systems, ICICCS 2021

作者： Jayant, Deepanshu Bhat, Aruna Delhi Technological University Department of Computer Science New Delhi India

ISBN: (纸本)9781665412728

This paper represents the different techniques used for the study of robust facial recognition. The need of facial recognition is expanding very rapidly in current technologies as it is possible to identify a humane face features through different digital mediums. Although, facial recognition is not done achieved easily in the real-world situation as most of the time the human face goes through various occlusions making hard for the system to complete the identification process. The sole purpose of this article is to compare the techniques used and identify the most accurate and efficient one from all of them. Robust facial recognition is a task of doing recognition under uncertain environments and features. The main aim of the robust facial recognition is to analyze and identify the face images where the picture proposes difficult viewpoints, angles, poses, illumination, noises and expressions. The techniques studied for this article are Local Binary patterns, HOG features, Occlusion-adaptive Deep Networks (ODN), Robust Principal Component Analysis (RPCA), Progressive Convolutional Neural Network and Region Attention Networks (RAN). In this article, a table is created summarizing the experiments and results of the techniques studied. © 2021 IEEE.

关键词： Local binary pattern

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Systematic Review: Image Processing Based Dorsal Vein pattern Biometric Authentication System

Systematic Review: Image Processing Based Dorsal Vein Patter...

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international conference on computing, Engineering, and Design (ICCED)

作者： Fajar M Syam Rian Maulana Yusup Samsudin Muchtar Ali Setyo Yudono Electrical Engineering Nusa Putra University Sukabumi Indonesia

Biometrics leverages unique biological or behavioral traits for person or object identification, characterized by traits such as uniqueness, indivisibility, immutability, and resilience against loss or theft. Evolving biometric systems include facial recognition, iris scanning, fingerprint recognition, palm line recognition, and dorsal hand vein recognition. Dorsal hand vein identification, concealed beneath the skin, provides a highly secure biometric modality resistant to forgery, tampering, or damage. This systematic literature review offers an overview of dorsal hand vein image processing for biometric authentication systems, covering recent advancements in image processing, including concept clarification, data acquisition, pre-processing, segmentation, feature extraction, and identification phases. The review spans research articles from 2018 to 2023, sourced from reputable outlets such as 208 MDPI journals, Google Scholar, IEEE Xplore, Google Books, and other scholarly repositories. Stringent article selection, guided by predefined keywords, ensures research quality. Findings emphasize the necessity of a camera and infrared LED for dorsal hand vein data acquisition to build an effective identification system. Pre-processing involves Region of Interest (ROI) selection, contrast enhancement, and intensity normalization. Segmentation employs morphological operations, filter banks, and adaptive thresholding. Feature extraction encompasses statistical features and the Gray Level Co-occurrence Matrix (GLCM). Convolutional Neural Networks (CNNs) play a pivotal role in identification, featuring layers such as Conv1_1, Conv1_2, batch normalization, max-pooling, and dropout layers.

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On the Role of LLM to Forecast the Next Pandemic

On the Role of LLM to Forecast the Next Pandemic

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Tommaso Ruga Eugenio Vocaturo Ester Zumpano CNR-NANOTEC DIMES University of Calabria Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Large Language Models (LLMs) have emerged as powerful tools in medical applications, demonstrating signifi-cant potential for analyzing vast clinical datasets, identifying patterns, and facilitating early detection of health anomalies. These capabilities are particularly relevant for detecting both individual disease onset and potential pandemic scenarios at a population level. The integration of LLMs into medical workflows could revolutionize how data-driven insights are harnessed for pandemic prediction and management, although implementing these technologies requires careful consideration of critical issues, particularly data privacy and security. This paper presents a comprehensive examination of current research at the intersection of LLMs and pandemic response, analyzing the literature from both bibliometric and medical perspectives. Through the selection of 849 publications across major databases, we provide an overview of the current state of research in this domain, identify emerging patterns from a clinical standpoint, and evaluate potential implications for future pandemic prediction. Our findings reveal significant trends in the application of LLMs to pandemic-related challenges, highlighting both opportunities and critical areas requiring further investigation, particularly in mental health impacts and early warning systems. The analysis reveals that while LLMs show promise in early detection and pattern recognition, challenges remain in data privacy, model interpretability, and the integration of diverse data sources. This research serves as a foundation for understanding how LLMs can be effectively deployed in pandemic prediction and management, while acknowledging the complexities and ethical considerations inherent in such applications.

关键词： Data privacy Pandemics Reviews soft sensors Large language models Mental health Medical services Predictive models Market research pattern recognition

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soft computing and Machine Learning in Dam Engineering

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WATER 2023年第5期15卷 917-917页

作者： Hariri-Ardebili, Mohammad Amin Salazar, Fernando Pourkamali-Anaraki, Farhad Mazza, Guido Mata, Juan Univ Colorado Boulder Dept Civil Environm & Architectural Engn Boulder CO 80309 USA Univ Maryland Coll Comp Math & Nat Sci College Pk MD 20742 USA Int Ctr Numer Methods Engn CIMNE P Gral Martinez Campos 41 Madrid 28010 Spain Univ Colorado Denver Dept Math & Stat Sci Denver CO 80204 USA Italian Natl Comm Large Dams I-2300198 Rome Italy Natl Lab Civil Engn LNEC Ave Brasil 101 P-1700066 Lisbon Portugal

Dams have played a vital role in human civilization for thousands of years, providing vital resources such as water and electricity, and performing important functions such as flood control. The scale and complexity of dam projects have increased in recent years, making their safety evaluation even more challenging. Therefore, it is crucial that dam engineers consider all potential risks and take appropriate measures to ensure the safety and stability of these structures [1]. The nature and existence of dams are highly coupled with concepts such as population growth, climate change, global warming, and water security [2]. According to the international Commission on Large Dams’s (ICOLD) [3] most recent update in April 2020, there are about 58,700 registered large dams in the world. Figure 1 illustrates the global distribution of these large *** computing is a collection of techniques in computer science that aim to provide solutions to problems that are difficult or impossible to solve using traditional, “hard” methods of computation. soft computing encompasses various computational techniques that are designed to replicate human-like problem-solving behavior. It includes a variety of techniques such as fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning, which are used to solve problems that are too complicated for traditional, rule-based approaches [4]. soft computing has a wide range of applications in engineering, including control systems, signal processing, pattern recognition, and optimization. For example, in signal processing, neural networks and genetic algorithms can be used to improve the accuracy of signal classification and feature extraction. In optimization, genetic algorithms and probabilistic reasoning can be used to solve complex optimization *** warning systems for dam failure [15];Real-time monitoring and control of dam systems [16];Predictive maintenance and condition monitoring of dam structures [17,1

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Scalable Federated Learning for Fingerprint recognition Algorithm

Scalable Federated Learning for Fingerprint Recognition Algo...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chenzhuo Wang Yanrong Lu Athanasios V. Vasilakos School of Computer Science and Technology Civil Aviation University of China Tianjin China School of Safety Science and Engineering Civil Aviation University of China Tianjin China Center for AI Research University of Agder Grimstad Norway

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Fingerprint recognition technology is widely used in various terminal devices and serves as a powerful and effective method for authentication. Existing research relies on centralized training models based on datasets, overlooking the privacy and heterogeneity of the data itself, resulting in the leakage of user information and decreased recognition accuracy. In order to solve the problem of data security and privacy protection, this paper proposes a federated learning-based architecture called FedFR(Federated Learning-Fingerprint recognition). The parameters from each endpoint are iteratively aggregated through federated learning to improve the performance of the global model under privacy constraints. Moreover, to solve the client-side unfairness issue in traditional federated learning caused by randomly selecting aggregation weights, a client selection method based on reservoir sampling is proposed, increasing the diversity of data distribution. Using the real-world databses, the effectiveness of FedFR is compared and analyzed through simulation experiments. The results show that FedFR exhibits good performance in terms of privacy protection levels, evaluation accuracy, and scalability. Distinct from traditional fingerprint recognition algorithms, FedFR improves the security and scalability of the model from the data source, providing a reference for the application of federated learning in biometric technology.

关键词： Training Privacy Data privacy Federated learning Scalability soft sensors Fingerprint recognition

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Optimizing Human Motion Posture Detection and Humanized computing for Smart Health Applications

Optimizing Human Motion Posture Detection and Humanized Comp...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Weiwei Wang Jingshan Zhang School of Sports Medicine and Rehabilitation North Sichuan Medical College Nanchong Sichuan China Department of Sports Humanities and Social Sciences Sichuan Sports College Chengdu Sichuan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Existing human motion detection technologies have problems such as insufficient accuracy and poor real-time performance, which makes it difficult to meet the needs of health management. This paper proposes a human motion posture detection and humanized calculation method based on multi-sensor fusion and LSTM. This paper first deploys IMU sensors at key parts of the human body, and uses visual sensors to capture the three-dimensional posture information of human motion, and obtains physiological data such as heart rate and number of steps through wearable devices such as smart bracelets. Secondly, the IMU data is denoised and calibrated to eliminate sensor errors. Next, the fused data is trained using a long short-term memory network (LSTM). The rationality of the motion posture is then analyzed, and the user's exercise intensity and health status are evaluated based on the motion data and physiological data. In terms of posture recognition accuracy, the average value of this method is 92.61 %. In terms of abnormal posture detection rate, the detection rate of some data points of this method is close to 100%. At the same time, the maximum recognition time of this method is only 789 milliseconds, and the minimum recognition time is as low as 503 milliseconds, which is better than the traditional method. The optimized human motion posture detection and humanized calculation method proposed in this paper realizes high-precision detection of human motion and intelligent health management through multi-source data fusion, LSTM construction, human kinematics analysis and personalized health assessment.

关键词： Training Accuracy soft sensors Data integration Real-time systems Physiology Data models Long short term memory Intelligent sensors Wearable sensors

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VLNet: A Multi-task Network for Joint Vehicle and Lane Detection 12th

VLNet: A Multi-task Network for Joint Vehicle and Lane Det...

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12th international conference on Image and Graphics, ICIG 2023

作者： Feng, Aiqi Liu, Haodong Xu, Tianyang Zhang, Donglin Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9783031463075

Visual perception is a crucial component of autonomous driving. Fully understanding the road environment is conducive to the safe driving of vehicles. However, most existing methods are usually unable to simultaneously accomplish multiple visual perception tasks, limiting the promotion and utilization. To this end, we propose VLNet: a novel and effective multi-task road environment perception network, which can perform vehicle and lane detection simultaneously. Specifically, VLNet consists of a shared feature extraction network and two detection branches for specific tasks. The vehicle detection branch uses a multi-scale method to detect vehicles, and the lane detection branch outputs instance-level segmentation map of lanes. Considering the correlation between vehicles and lanes, we leverage the semantic clues of the lane branch to assist vehicle detection. Compared with single-task networks, the proposed method reduces memory and computing costs effectively. Finally, experiments on the challenging Boxy vehicle detection dataset and the LLAMAS lane detection dataset demonstrate the efficacy of the developed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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ANSAN: Utilizing Inception V3 for Glaucoma Detection

ANSAN: Utilizing Inception V3 for Glaucoma Detection

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international conference on computing and Networking Technology (ICCNT)

作者： K R Hanish G Sukantha Velan Avadhani Bindu Senthil Kumar Thangavel K Somasundaram Sathyan Parthasaradhi Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India Sathyan Eye Hospital and Coimbatore Glaucoma Foundation Ramanathapuram Coimbatore India Department of Mathematics and Computer Science Ashland University Ohio USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The progressive deterioration of the optic nerve, which results in irreversible vision loss if left untreated, is the hallmark of a group of eye illnesses known as glaucoma. High intraocular pressure (IOP) is a significant risk factor, but normal IOP is not always necessary for the development of normal-tension glaucoma (NTG). Even so, glaucoma can occur in the absence of high blood pressure. Convolutional neural networks (CNNs) [6], in particular, are deep learning models that have recently demonstrated potential in the diagnosis of glaucoma. These models are capable of detecting minute patterns suggestive of glaucoma by examining massive collections of retinal fundus images. The efficacy of the Inception V3 model [20] in automated glaucoma diagnosis was demonstrated by its 88 % accuracy in separating glaucomatous from non-glaucomatous images after it was trained with a large amount of data and enhanced methodologies.

关键词： Glaucoma Training Accuracy Computational modeling Receivers Predictive models Retina Data models pattern recognition Reliability

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User Activity recognition and pattern Prediction Using Call Data Mining

User Activity Recognition and Pattern Prediction Using Call ...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Nitish Kumar Ojha Preetish Ranjan Dept. Of CSE Amity University Noida India Dept. Of CSE Amity University Patna Bihar India

The story of technology is certainly as old as humanity itself. With the advancement of time, human society has been instrumental in communication technology. Technology has worked in two ways, as a supporting tool for crime as well as a helping tool to identify and investigate the e at the same time. In the last decade, India has seen rapid growth in the field of mobile communication as it is continuously increasing at an exponential rate, the availability of cheap data and availability of economical rate of calling encourages users to make calls easily and this continuous calling within a certain group creates a network considering relationship a parameter. The use of existing models of rapid data mining on mobile phone call logs / call data records (CDR)establishes a new dimension in the field of a user's social behavior. Meanwhile, it makes a possible methodology of suspect detection based on certain attributes i.e., cell phone conversation ratio, calling pattern among users, terrain-basedfacts, etc. In this paper, key areas which have been covered are “Machine's perception towards prediction of complex social systems and strange behavior of cell phone user” based on data mining techniques applied on call details of cell phone users.

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A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2022

作者： Sun, Xiao An, Chengyang Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Head and neck (H &N) cancer is one of the most prevalent cancers [1]. In its treatment and prognosis analysis, tumors and metastatic lymph nodes may play an important role but their manual segmentations are time-consuming and laborious. In this paper, we propose a coarse-to-fine ensembling framework to segment the H &N tumor and metastatic lymph nodes automatically from Positron Emission Tomography (PET) and Computed Tomography (CT) images. The framework consists of three steps. The first step is to locate the head region in CT images. The second step is a coarse segmentation, to locate the tumor and lymph region of interest (ROI) from the head region. The last step is a fine segmentation, to get the final precise predictions of tumors and metastatic lymph nodes, where we proposed a ensembling refinement model. This framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[2, 3] as team SJTU426. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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