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Finger Vein Authentication Based on Wavelet Scattering Networks

Computers, Materials & Continua 2022年第8期72卷 3369-3383页

作者： Amjad Rehman Majid Harouni Maedeh Omidiravesh Suliman Mohamed Fati Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering Dolatabad BranchIslamic Azad UniversityIsfahanIran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityIsfahanIran MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two *** biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been *** of the challenges in biometrics is physical *** of finger vein is of the biometrics least exposed to physical *** methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification *** paper presents a novel method of scattering wavelet-based identity *** wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural *** distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier *** improves classification and *** proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).In addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission *** best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.

关键词： Biometrics finger veins wavelet scattering vein authentication disaster risk reduction

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AI-based Catalytic Performance Prediction for CO2 Electrochemical Reduction using Ionic Liquids

AI-based Catalytic Performance Prediction for CO2 Electroche...

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Annual Conference of Industrial Electronics Society

作者： Rabeea Jaffari Sulafa Abdalmageed Saadaldeen Mohammed Lakshitha Gunasekara Software Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Department of Energy Technology Aalborg University Aalborg Denmark Centre for Data Analytics and Cognition La Trobe University Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Recently, ionic liquids (ILs) have garnered remarkable attention as electrolytes for CO 2 electrochemical reduction (CO 2 ER) due to their unique properties viz. thermal and chemical stability, good CO 2 solubility, and their ability to reduce the reaction overpotential. The catalytic performance of ILs in CO 2 ER has been explored via experimental methods which have limitations, given the unclear understanding of the complex reaction mechanisms. Recently, Artificial Intelligence (AI) methods have gained increased attention across diverse applications including chemical engineering. These methods play a pivotal role in extracting insights, understanding patterns, and mitigating uncertainty within datasets. Hence, in this study, we investigate two categories of AI models (ML and DL) for predicting the CO 2 ER catalytic performance using ILs via Gibbs free energy and capacity. For this task, we formulate a novel dataset (CO 2 ERIL) from 90 ILs in two formats using: (1) 30 electronic and geometric IL properties, (2) IL chemical structures in SMILES data format. The dataset versions are formulated using the Conductor-like Screening Model for Realistic Solvents (COSMO-RS) and TURBOMOLE software. The prediction results of the AI models on the two dataset versions depict a similar trend in the predicted target variables signifying that the dataset format does not really affect the model performance. Moreover, the best ML model outperforms the DL model for one target variable, Free energy predictions, but lags in the other target variable, capacity predictions. Our study is novel in terms of AI methodology as well as the CO 2 ER dataset. Our research outcomes will contribute to the more effective selection of ILs for CO 2 ER catalysis.

关键词： Solvents Liquids Uncertainty Predictive models Market research Stability analysis Software Artificial intelligence Thermal stability Chemicals

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Classification of soybean response to off-target dicamba exposure using UAV-based imagery and machine learning methods

Classification of soybean response to off-target dicamba exp...

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2022 ASABE Annual International Meeting

作者： Sarkar, Shagor Tian, Caio Canella Vieira Tian, Fengkai Zhou, Jing Zhou, Jianfeng Chen, Pengyin Division of Plant Science & Technology ColumbiaMO United States Data Science and Analytics College of Engineering University of Missouri ColumbiaMO65211 United States Biological Systems Engineering University of Wisconsin-Madison MadisonWI United States

Though dicamba tolerant soybean genotypes are widely adopted by the world, but non-resistant soybean genotypes are still in fields and are subjected to exposure to dicamba as off-target and are damage reported by farmers from the past couple of years. Non-resistant soybean genotypes are vulnerable to dicamba, and severity of damage depends on growth stage, dosage, frequency, duration of exposure, and potential genetic background. From the past few years, development of crop high-throughput phenotyping has shown potential in assessing crop traits and response in different stages of life cycle. The goal of this study was to develop a UAV imagery-based classification system to help breeders to precisely and quickly select resistive genotypes that are exposed to off-target dicamba damage. An RGB (red-green-blue) camera was used to collect imagery data of 240 soybean breeding lines at the reproductive stages from five different fields. Dicamba damage scores were visually assessed by the breeders and are obtained on the same day or next day of image acquisition. Dicamba scores were grouped into three classes namely class 1, class 2, and class 3. Seven image features such as canopy coverage, color hue, color saturation, triangular greenness index (TGI), green leaf index (GLI), entropy, and contrast were extracted to classify the dicamba scores that are associated with the image features. ANOVA result of each image feature shows that damage of dicamba varies significantly among fields than the variation among three classes of dicamba score. Extreme gradient boosting (XGBoost) model were developed to classify three classes of dicamba scores using image features. Classification model were developed using standardized image feature data and an overall classification accuracy of 0.75 were obtained from the model. Whereas, class 1 showed higher accuracy of 0.91, and 0.83 were obtained for both class 2 and 3. Furthermore, class 2 resulted higher kappa, precision, recall, sensitiv

关键词： Entropy

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Template-Order Driven Feature Integration With Generative Models for Aspect Sentiment Triplet Extraction

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2275-2285页

作者： Jiazhou Chen Ruiqiang Guo College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei Normal University Shijiazhuang China

Aspect-based sentiment analysis (ABSA), which explores the nuanced sentiments individuals express toward specific services or products, has shown significant potential in practical applications. Recently, the aspect sentiment triplet extraction field has witnessed significant advancements through the prowess of generative models. However, existing methods face critical limitations: (1) fixed-order decoding (e.g., aspect term $\rightarrow$ opinion term $\rightarrow$ sentiment polarity) ignores interdependencies between elements; (2) token-by-token generation fails to model term boundaries, which struggles with multi-word terms or sentences containing multiple triplets. To address these challenges, we propose a Template-Order driven Feature Integration (TOFI) framework, which integrates two novel modules: Template-Order Prompt (TOP) and Feature Progressive Sequence Labeling (FPSL). TOP employs various orders of sentiment elements to prompt the model to generate sentiment tuples from different perspectives, each using a different element order. It then aggregates the sentiment tuples that appear across all orders. FPSL leverages sequence labeling to integrate the rich semantic information of aspect terms and opinion terms into the model, enhancing its ability to capture semantic nuances. Furthermore, the TOFI framework introduces unified multi-domain training, prefixing domain identifiers to inputs for multi-task learning across datasets. Extensive experiments on multiple public benchmarks demonstrate that the proposed framework consistently surpassed representative baseline models.

关键词： Labeling Decoding Training Feature extraction Speech processing Semantics Sentiment analysis Pipelines data mining Benchmark testing

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Unleashing the Potential of Model Bias for Generalized Category Discovery 39

Unleashing the Potential of Model Bias for Generalized Categ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： An, Wenbin Lin, Haonan Nie, Jiahao Tian, Feng Shi, Wenkai Wu, Yaqiang Wang, Qianying Chen, Ping Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics China Nanyang Technological University Singapore Lenovo Research University of Massachusetts Boston United States

ISBN: (纸本)157735897X

Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlabeled data, leveraging another labeled dataset containing only known categories. The primary challenges stem from model bias induced by pre-training on only known categories and the lack of precise supervision for novel ones, leading to category bias towards known categories and category confusion among different novel categories, which hinders models’ ability to identify novel categories effectively. To address these challenges, we propose a novel framework named Self-Debiasing Calibration (SDC). Unlike prior methods that regard model bias towards known categories as an obstacle to novel category identification, SDC provides a novel insight into unleashing the potential of the bias to facilitate novel category learning. Specifically, we utilize the biased pre-trained model to guide the subsequent learning process on unlabeled data. The output of the biased model serves two key purposes. First, it provides an accurate modeling of category bias, which can be utilized to measure the degree of bias and debias the output of the current training model. Second, it offers valuable insights for distinguishing different novel categories by transferring knowledge between similar categories. Based on these insights, SDC dynamically adjusts the output logits of the current training model using the output of the biased model. This approach produces less biased logits to effectively address the issue of category bias towards known categories, and generates more accurate pseudo labels for unlabeled data, thereby mitigating category confusion for novel categories. Experiments on three benchmark datasets show that SDC outperforms SOTA methods, especially in the identification of novel categories. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Multi-task learning in under-resourced Dravidian languages

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Journal of data, Information and Management 2022年第2期4卷 137-165页

作者： Hande, Adeep Hegde, Siddhanth U. Chakravarthi, Bharathi Raja Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India University Visvesvaraya College of Engineering Bangalore University Bangalore India Insight SFI Research Centre for Data Analytics National University of Ireland Galway Galway Ireland

It is challenging to obtain extensive annotated data for under-resourced languages, so we investigate whether it is beneficial to train models using multi-task learning. Sentiment analysis and offensive language identification share similar discourse properties. The selection of these tasks is motivated by the lack of large labelled data for user-generated code-mixed datasets. This paper works with code-mixed YouTube comments for Tamil, Malayalam, and Kannada languages. Our framework is applicable to other sequence classification problems irrespective to the size of the datasets. Experiments show that our multi-task learning model can achieve high results compared to single-task learning while reducing the time and space constraints required to train the models on individual tasks. Analysis of fine-tuned models indicates the preference of multi-task learning over single task learning resulting in a higher weighted F1 score on all three languages. We apply two multi-task learning approaches to three Dravidian languages, Kannada, Malayalam, and Tamil. Maximum scores on Kannada and Malayalam were achieved by mBERT subjected to cross entropy loss and with an approach of hard parameter sharing. Best scores on Tamil was achieved by DistilBERT subjected to cross entropy loss with soft parameter sharing as the architecture type. For the tasks of sentiment analysis and offensive language identification, the best performing model scored a weighted F1-Score of (66.8%, 90.5%), (59%, 70%) and (62.1%,75.3%) for Kannada, Malayalam and Tamil on sentiment analysis and offensive language identification respectively. © The Author(s) 2022.

关键词： Code-mixing Dravidian languages Multi-task learning Offensive language identification Sentiment analysis Under-resourced languages

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Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer’s Disease

Leveraging Multimodal Models for Enhanced Neuroimaging Diagn...

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IEEE International Conference on Big data

作者： Francesco Chiumento Mingming Liu School of Electronic Engineering Dublin City University Dublin 9 Ireland Insight Research Ireland Centre for Data Analytics Dublin City University Dublin 9 Ireland

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The rapid advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have shown great potential in medical diagnostics, particularly in radiology, where datasets such as X-rays are paired with human-generated diagnostic reports. However, a significant research gap exists in the neuroimaging field, especially for conditions such as Alzheimer’s disease, due to the lack of comprehensive diagnostic reports that can be utilized for model fine-tuning. This paper addresses this gap by generating synthetic diagnostic reports using GPT-4o-mini on structured data from the OASIS-4 dataset, which comprises 663 patients. Using the synthetic reports as ground truth for training and validation, we then generated neurological reports directly from the images in the dataset leveraging the pre-trained BiomedCLIP and T5 models. Our proposed method achieved a BLEU-4 score of 0.1827, ROUGE-L score of 0.3719, and METEOR score of 0.4163, revealing its potential in generating clinically relevant and accurate diagnostic reports.

关键词： Neuroimaging Training Biological system modeling Large language models X-rays Radiology data models Meteors Medical diagnosis Alzheimer's disease

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JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences through Multi-Task Learning

JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shih-Wei Guo Ya-Chun Wen Jui-Ling Yu Yao-Chung Fan Department of Computer Science and Engineering National Chung Hsing University Taichang Taiwan Department of Japanese Language and Literature Providence University Taichang Taiwan Department of Data Science and Big Data Analytics Providence University Taichang Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of Japanese language processing, the correct use and understanding of Keigo (honorific language) presents unique challenges due to its complex nature and context-dependent usage. This study introduces JH-Ranker, a novel multitask learning framework designed to enhance the recognition and understanding of Keigo in Japanese sentences. We are employing a multi-task learning approach by integrating Keigo Level, Contextual Field, and the Classification of Keigo Forms. This integration aims to foster a comprehensive understanding of Keigo usage from small data, leveraging the synergy between multiple tasks to achieve a deeper understanding with limited resources. Utilizing the BERT-base-japanese pre-trained model, JH-Ranker takes into account various Keigo Forms and contextual scenarios, going beyond mere syntactic analysis. Our methodology also emphasizes the importance of considering both the form and context of Keigo, addressing challenges such as label imbalance and overfitting in multi-label classification tasks. The experimental results demonstrate a significant improvement in Keigo recognition capabilities over existing baseline models, particularly in discerning the subtle nuances of Keigo Levels and Forms. This study contributes to the realm of natural language processing by presenting a refined and contextually sensitive method for Japanese Keigo. It demonstrates the language model’s adaptability and understanding in dealing with intricate honorific language scenarios.

关键词： Adaptation models Analytical models Neural networks Syntactics Multitasking Natural language processing Context modeling

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Probabilistic Gain Control in a Multi-Speaker Setting Using EEG-Based Auditory Attention Decoding

Probabilistic Gain Control in a Multi-Speaker Setting Using ...

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European Signal Processing Conference (EUSIPCO)

作者： Nicolas Heintz Simon Geirnaert Iris Van De Ryck Tom Francart Alexander Bertrand Department of Electrical Engineering (ESAT) KU Leuven STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium Department of Neurosciences KU Leuven ExpORL Belgium

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Current hearing aids have difficulties focusing on the correct speaker in complex environments where multiple people are talking simultaneously. This is because hearing aids do not know to which speaker the user aims to listen. A promising solution is to use so-called auditory attention decoding (AAD) algorithms, which infer the at-tended speaker based on brain activity recorded with, e.g., elec-troencephalography (EEG). AAD models decode on a window-to-window basis to which person a subject wishes to listen. However, only limited research has been done on how these AAD decisions can be converted into a gain control system that controls the volume of each competing speaker in a hearing aid. Existing gain control systems are either difficult to tune, unstable and/or not designed for use in situations with more than two competing speakers. We therefore propose a novel general purpose gain control system that can be easily used on any AAD model and in scenarios with an arbitrary number of speakers. We demonstrate that the gain control system is stable, even for AAD algorithms with very low accuracies, and even in scenarios with more than 2 speakers.

关键词： Accuracy Signal processing algorithms Oral communication Switches Signal processing Hearing aids Brain modeling Stability analysis Decoding Gain control

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Epilepsy Detection Grand Challenge 48

Epilepsy Detection Grand Challenge

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chatzichristos, Christos Bhagubai, Miguel Van Paesschen, Wim De Vos, Maarten Un. Hospital Leuven Lab. for Epilepsy Research KU Leuven & Department of Neurology Belgium Signal Processing and Data Analytics KU Leuven STADIUS Center for Dynamical Systems Department of Electrical Engineering Leuven Belgium KU Leuven Department Development and Regeneration Leuven Belgium

ISBN: (纸本)9781728163277

In this paper, we describe the epilepsy detection grand challenge, in association with ICASSP 2023. The challenge was centered on seizure detection using wearable behind-the-ear EEG. Two separate tasks were set for the participants: 1) obtain the best overall performance in seizure detection;2) systematically engineer the data in a data-centric task to optimize a given AI model for seizure detection. © 2023 IEEE.

关键词： Neurology

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