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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Shashidhara, H.S. Kalbag, Saish K Mathias, Tanya Srinivasa, K.G. Niranjanamurthy, M. Affiliated to Visvesvaraya Technological University M S Ramaiah Institute of Techology and Engineering Department of Informaition Science and Engineering Belgaum Bangalore India International Institute of Information Technology Department of Data Science and Artififial Intelligece Chhattisgarh Naya Raipur India Affiliated to Visvesvaraya Technological University B M S Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Belgaum Bangalore India

ISBN: (纸本)9798350306927

Parkinson's disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson's disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson's disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson's disease by analyzing hand-drawn spirals and waves in photographs. © 2023 IEEE.

关键词： Diagnosis

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Predicting Indian Sign Language Gestures Using Deep learning and machine learning Techniques

Predicting Indian Sign Language Gestures Using Deep Learning...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Bhushan Fulkar Rushikesh Burle Pramod Mahale Utkrsha Pacaharaney Department of Artificial intelligence And Data science Datta Meghe institute of Higher Education and Research Wardha India Department of Artificial Intelligence And Machine Learning Datta Meghe institute of Higher Education and Research(DU) Wardha Maharashtra Department of Artificial Intelligence And Machine Learning Datta Meghe institute of Higher Education and Research Wardha India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Indian Sign Language is the language used by people with disabilities in India. Unfortunately, the general public's ignorance of sign language makes it difficult for the especially abled and the general public to communicate. We are releasing a strategy to reduce this difference. We have adopted two distinct strategies to deal with this problem. first by employing the depth+RGB data acquired with a Microsoft Kinect to make gesture predictions in real-time. To segment the hand region from the RGB-D camera data, we used affine transformation and 3D reconstruction to map the RGB and depth information. Convolutional neural networks were trained with inputs that were segmented hand images or videos. With 50 static Indian Sign Language hand gestures used for training, a 97.18% classification accuracy was achieved on test data. Furthermore, this model achieved a 97.71 % classification accuracy when we transferred learning American Sign Language. A convolutional kernel and an LSP were used to train twenty dynamic gestures. This model's classification accuracy was 98.88%. Nevertheless, as soon as we implemented this system, we found that the methodology was flawed. Essentially, it is absurd to need to carry around both a bulky Microsoft Kinect and a device capable of performing the computation required for human-to-person interaction. We attempted to address this issue by using semantic segmentation of the hands. U-Net and ResNet 101 were combined.

关键词： Training Sign language Accuracy Three-dimensional displays Webcams Speech recognition Auditory system Speech enhancement Real-time systems Videos

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Feature Importance: A Closer Look at Shapley Values and LOCO

arXiv

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arXiv 2023年

作者： Verdinelli, Isabella Wasserman, Larry Department of Statistics and Data Science Carnegie Mellon University PittsburghPA United States Machine Learning Department Carnegie Mellon University PA United States

There is much interest lately in explainability in statistics and machine learning. One aspect of explainability is to quantify the importance of various features (or covariates). Two popular methods for defining variable importance are LOCO (Leave Out COvariates) and Shapley Values. We take a look at the properties of these methods and their advantages and disadvantages. We are particularly interested in the effect of correlation between features which can obscure interpretability. Contrary to some claims, Shapley values do not eliminate feature correlation. We critique the game theoretic axioms for Shapley values and suggest some new axioms. We propose new, more statistically oriented axioms for feature importance and some measures that satisfy these axioms. However, correcting for correlation is a Faustian bargain: removing the effect of correlation creates other forms of bias. Ultimately, we recommend a slightly modified version of LOCO. We briefly consider how to modify Shapley values to better address feature correlation. © 2023, CC BY.

关键词： Game theory

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Deep learning-based Semantic Search Techniques for Enhancing Product Matching in E-commerce

Deep Learning-based Semantic Search Techniques for Enhancing...

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Computing and machine Intelligence (ICMI), International Conference on

作者： Fatima Aamir Raheimeen Sherafgan Tehreem Arbab Akhtar Jamil Fazeel Nadeem Bhatti Alaa Ali Hameed Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Data and Machine Learning Lead Upstart Commerce Islamabad Pakistan Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Searching is the process of information retrieval utilizing specific criteria or keywords. Integrating search function-alities on e-commerce platforms enables users to efficiently locate exactly what they are searching for through keyword matching. Beyond conventional keyword matching, semantic search involves aligning products with customer queries by capturing the essence of the queries, thereby retrieving semantically related products from the pertinent catalog. Semantic search enhances the e-commerce shopping experience by allowing platforms to tailor responses to user preferences through an in-depth understanding of search intents. Challenges such as morphological variations, spelling errors, and the interpretation of synonyms, antonyms, and hypernyms are addressed through deep learning models de-signed for semantic query-product matching. This study conducts a comparative analysis of various semantic search methodologies and assesses their efficacy, incorporating deep learning strate-gies for query auto-completion and spelling corrections. The evaluation employs sentence transformer models to determine the optimal approach for semantic searching, gauged by nDCG, MRR, and MAP metrics. LSTM, BART, and n-gram models are also examined for auto-completion capabilities. The research analyzes the Amazon Shopping Queries dataset and the Upstart Commerce catalog datasets.

关键词： Deep learning Measurement Training Semantic search Transformers Information retrieval Electronic commerce

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Pass: Peer-Agreement Based Sample Selection for Training with Instance-Dependent Noisy Labels

SSRN

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SSRN 2024年

作者： Garg, Arpit Nguyen, Cuong Felix, Rafael Do, Thanh-Toan Carneiro, Gustavo Australian Institute for Machine Learning University of Adelaide Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Department of Data Science and AI Monash University Australia

The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples, yet our empirical observations reveal their limitations, especially for labels with instance dependent noise (IDN). An important characteristic of IDN is the difficulty to distinguish the clean-label samples that lie near the decision boundary (i.e., the hard samples) from the noisy-label samples. We, therefore, propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem. Utilising a trio of classifiers, PASS employs consensus-driven peer-based agreement of two models to select the samples to train the remaining model. PASS is easily integrated into existing LNL models, enabling the improvement of the detection accuracy of noisy- and clean-label samples, which increases the classification accuracy across various LNL benchmarks. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust learning

arXiv

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arXiv 2025年

作者： Garg, Arpit Nguyen, Cuong Felix, Rafael Liu, Yuyuan Do, Thanh-Toan Carneiro, Gustavo Australian Institute for Machine Learning University of Adelaide Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Department of Engineering Science University of Oxford United Kingdom Department of Data Science and AI Monash University Australia

Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets often contain a mix of in-distribution (ID) and out-of-distribution (OOD) instance-dependent label noise, a challenge that is rarely addressed simultaneously by existing methods and is further compounded by the lack of comprehensive benchmarking datasets. Furthermore, even though current noisy-label learning approaches attempt to find noisy-label samples during training, these methods do not aim to estimate ID and OOD noise rates to promote their effectiveness in the selection of such noisy-label samples, and they are often represented by inefficient multi-stage learning algorithms. We propose the Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise (AEON) approach to address these research gaps. AEON is an efficient one-stage noisy-label learning methodology that dynamically estimates instance-dependent ID and OOD label noise rates to enhance robustness to complex noise settings. Additionally, we introduce a new benchmark reflecting real-world ID and OOD noise scenarios. Experiments demonstrate that AEON achieves state-of-the-art performance on both synthetic and real-world datasets*. © 2025, CC BY-NC-ND.

关键词： Contrastive learning

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： K Sangeetha Vs Balaji M Manju S Renuka Devi P Velayutham Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline; this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis.

关键词： Emotion recognition Accuracy Face recognition Pipelines Real-time systems Multilingual Interviews Random forests Standards Testing

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CBAM-SAUNet: A novel attention U-Net for effective segmentation of corner cases 46

CBAM-SAUNet: A novel attention U-Net for effective segmentat...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Rajamani, Srividya Tirunellai Rajamani, Kumar Angeline, J. Karthika, R. Schuller, Björn W. Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany Department of Artificial Intelligence Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Germany Munich Data Science Institute Germany Munich Center for Machine Learning GLAM-the Group on Language Audio & Music Imperial College London London United Kingdom

ISBN: (纸本)9798350371499

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM-SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)'s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM-SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset. © 2024 IEEE.

关键词： Image segmentation

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Contrastive learning for Adapting Language Model to Sequential Recommendation 24

Contrastive Learning for Adapting Language Model to Sequenti...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Liang, Fei-Yao Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks. © 2024 IEEE.

关键词： Contrastive learning

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Cross-Store Next-Basket Recommendation 24

Cross-Store Next-Basket Recommendation

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Ma, Liang-Chen Li, Ya Mai, Zi-Feng Liang, Fei-Yao Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model. © 2024 IEEE.

关键词： Graph embeddings

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