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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Arshad, Iram Bharatwaj K R, Roopesh Alsamhi, Saeed Hamood Curry, Edward SRI. Engineering TUS: Midlands Midwest Athlone Westmeath Ireland CeADAR University College Dublin Dublin Ireland Insight Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9798350305333

This paper introduces a novel, cost-effective method for enhancing human-robot collaboration in Industry 4.0 manufacturing using the OpenCV AI Kit-Lite (OAK-D-Lite). Our vision-based system employs deep learning models for realtime human detection and dynamic adjustment of robot operations, ensuring safety without needing costly Lidar sensors. We further present an autonomous system utilizing You Only Look Once (YOLO) v5 that accurately identifies, classifies, and handles workpieces, effectively reducing human intervention. We demonstrate the solutions using a Lego Mindstorm kit-based robotic arm, highlighting the flexibility and adaptability of our approach. Our research contributes significantly to safer and economically efficient AI integration in industrial settings, marking a step forward for Industry 4.0 manufacturing. © 2023 IEEE.

关键词： Industry 4.0

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NLP-Based Analysis of Annual Reports: Asset Volatility Prediction and Portfolio Strategy Application 32

NLP-Based Analysis of Annual Reports: Asset Volatility Predi...

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32nd Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Li, Xiao Xu, Yang Yang, Linyi Zhang, Yue Dong, Ruihai Insight Centre for Data Analytics School of Computer Science University College Dublin Ireland School of Economics and Management Beihang University Beijing China Zhejiang University Zhejiang China School of Engineering Westlake University Zhejiang China

Leveraging recent developments in natural language processing (NLP), we constructed a prediction model using corporate financial annual reports to forecast the stock volatility indicator Beta (β), by analyzing risk discussions. The predicted Beta values were used to construct investment portfolios, whose market performance was then evaluated. Our research demonstrates that the Hierarchical Transformer-based model effectively captures complex risk information from annual reports, leading to improved returns in portfolio simulations. Our motivation arises from the need to better understand and process long, unstructured financial texts like annual reports, which contain crucial yet nuanced risk factors. By utilizing the hierarchical model, we aim to overcome traditional models’ limitations in handling such long documents, thereby improving the model’s understanding of both sentence-level and document-level contexts. The results highlight the potential of deep learning, particularly hierarchical models, in financial text prediction, and provide a novel perspective on asset management strategies. Compared to the S&P 500 benchmark, portfolios constructed using the predicted Beta values from our model achieved an average return increase of 21% over the same period. © 2024 Copyright for this paper by its authors.

关键词： Prediction models

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Pretrained Sentence Embedding and Semantic Sentence Similarity Language Model for Text Classification in NLP 3

Pretrained Sentence Embedding and Semantic Sentence Similari...

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3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Valli Mayil, V. Ratha Jeyalakshmi, T. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Jain University School of Sciences Department of Data Science & Analytics Karnataka Banglore India

ISBN: (纸本)9798350320749

A key task in the field of Natural Language Processing (NLP) is determining semantic similarity between text sentences. Sentence pair modeling, Textual similarity and language modeling are few important tasks in NLP. Traditional machine learning algorithms require an enormous quantity of training data, but it is a timeconsuming process. Pre-trained models can be modified for a variety of downstream applications since they use methods for generically learning the characteristics of neural network topologies and language representations. Bidirectional Encoder Representations from Transformers- BERT & GPT are the popular architectures in NLP which enable to use minimal fine-tuning effort to produce effective results. In this work a fine-tuned BERT model that is suitable for semantic sentence similarity which predicts the entailment, neutral and contradictory categories of sentence pairs is presented. The fine-tuning feature promotes the training phase of model whichis widely effective across different types of semantic similarity models. The performance analysis of our system shows that the fine-tuned model reduces the number of neurons in the neural network there by reducing storage and time spent in expensive training task to create deep learning model. © 2023 IEEE.

关键词： Semantics

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Addressing the Challenge of Missing Medical data in Healthcare analytics: A Focus on Machine Learning Predictions for ICU Length of Stay

Addressing the Challenge of Missing Medical Data in Healthca...

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2024 Intermountain engineering, Technology and Computing, IETC 2024

作者： Karankot, Mahmad Isaq Marceau, Max Glenn, Ethan M. Fowers, Rylan P. Hedges, David M. Sheehey, Bonnie Whitaker, Bradley M. Electrical and Computer Engineering Montana State University BozemanMT United States SelectHealth Analytics/Data Science Department MurrayUT United States History and Philosophy Montana State University BozemanMT United States

ISBN: (纸本)9798350373073

This research investigates the impact of missing data on the performance of machine learning algorithms, with a particular focus on the MIMIC-IV dataset. This project aims to investigate the extent to which missing data negatively impacts the training of machine learning algorithms, and whether demographic groups with a higher proportion of missing data (i.e.,ethnicity) have lower predictive accuracy. Using advanced machine learning and data analysis techniques, our results highlight important considerations related to missing data in medical datasets and provide useful insights for improving predictive modeling and decision support systems in clinical practice offers. © 2024 IEEE.

关键词： Singular value decomposition

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NSMD-NAS: Retinal Image Segmentation with Neural Architecture Search and Non-Subsampled Multiscale Decomposition 13

NSMD-NAS: Retinal Image Segmentation with Neural Architectur...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhang, Hanyu Tang, Lixin Song, Xiangman Xu, Te Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shen Yang110819 China Northeastern University Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shen Yang110819 China

ISBN: (纸本)9798350308365

Accurate segmentation of the microvascular component of the retinal fundus image is of great significance for the clinical diagnosis. Currently, deep learning-based methods are often employed for retinal fundus image segmentation. However, most deep learning models manually designed by experts are not suitable for task-specific applications. Therefore, we propose a novel retinal image segmentation method that integrates a neural architecture search framework with a non-subsampled multiscale decomposition (NSMD-NAS) technique. Specifically, the feature extraction layer includes the convolutional layer and the transformer layer. In addition, the non-subsampling filter bank is embedded into the convolutional and the transformer modules respectively to generate a new network module for exploring multiscale, multi-frequency, and multi-directional information. Unlike previous wavelet-based network modules, non-subsampling network modules have translation invariance. Subsequently, the components of the network are specified by the elaborated encoding strategy, and then the network with the highest adaptation is searched by a genetic evolutionary algorithm (GA) to finally obtain the best network structure. Experimental results demonstrate the effectiveness of each component in the proposed model on the public benchmark datasets. © 2024 IEEE.

关键词： Convolution

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Improving Security in Vehicle Cloud Technologies

Improving Security in Vehicle Cloud Technologies

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Dey, Kunal Kumar, Ritesh Balaji, K.A. Department of Data Analytics and Mathematical Sciences Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

Recently, the development of vehicle computer technology has changed the conventional idea of a car from a simple means of transportation to an intelligent, safe smart vehicle. Additionally, cloud computing has been effortlessly incorporated into the Intelligent Transportation System, also known as ITS, features, which include traffic accident avoidance and live traffic information providing. ITS services have grown to include effective traffic management, accident avoidance, and convenience services in the context of Vehicular Cloud Computing (VCC). Be that as it may, the ongoing arrangement of vehicle benefits for the most part centers on utilizing information from in-vehicle sensors and misses the mark with regards to collaborating with outside foundation. Moreover, the utilization of Bluetooth networks in VCC settings makes security gambles with that could uncover private data held in vehicle sensors, including the personality of the driver and installment data during exchanges. We present the Virtual Computing Center Service-oriented Privacy Framework (VCC-SSF) to settle these limitations and security issues present in VCC-based administrations. As well as ensuring the smooth conveyance of viable and easy to use VCC administrations, this total engineering puts security first. New elements are incorporated, for example, precautionary mishap the executives benefits and further developed installment organization. The VCC-SSF additionally has solid security components that incorporate confirmation, validation, access control, encryption, honesty, secrecy affirmation, and protection assurances for both client private data and in-vehicle information. © 2023 IEEE.

关键词： data integrity encryption Intelligent Transportation System (ITS) payment services and accident management security Vehicle cloud technologies Vehicular Cloud Computing (VCC)

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VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Code-Mixed Spanish Social Media Comments 3

VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Cod...

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3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Kumaresan, Prasanna Kumar Ponnusamy, Kishore Kumar Shanmugavadivel, Kogilavani Navaneethakrishnan, Subalalitha Chinnaudayar Priyadharshini, Ruba Chakravarthi, Bharathi Raja Insight SFI Research Centre for Data Analytics University of Galway Ireland Guru Nanak College Tamil Nadu Chennai India Kongu Engineering College Tamil Nadu India Department of Computer Science & Engineering SRM Institute of Science and Technology Tamil Nadu India Gandhigram Rural Institute-Deemed to be University Tamil Nadu India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland

ISBN: (纸本)9789544520847

Our research aims to address the task of detecting homophobia and transphobia in social media code-mixed comments written in Spanish. Code-mixed text in social media often violates strict grammar rules and incorporates non-native scripts, posing challenges for identification. To tackle this problem, we perform pre-processing by removing unnecessary content and establishing a baseline for detecting homophobia and transphobia. Furthermore, we explore the effectiveness of various traditional machine-learning models with feature extraction and pre-trained transformer model techniques. Our best configurations achieve weighted F1 scores of 0.86 on the test set and 0.86 on the development set for Spanish, demonstrating promising results in detecting instances of homophobia and transphobia in code-mixed comments. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer with XAI Techniques

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IEEE Access 2025年 13卷 96317-96336页

作者： Haque, Fariha Asif Hasan, Mohammad Siddique, Md. Abu Ismail Roy, Tonmoy Kanti Shaha, Tonmoy Islam, Yamina Paul, Avijit Chowdhury, Muhammad E. H. Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh Utah State University Department of Data Analytics & Information Systems Old Main Hill Logan 84322 (435) 797-1000 UT United States Chittagong University of Engineering Technology Electrical Electronic Engineering Bangladesh International Islamic University Chittagong Department of Computer Science & Engineering Chittagong 4318 Bangladesh Qatar University Department of Electrical Engineering Doha 2713 Qatar

In the field of medical imaging, deep learning (DL) techniques have made significant contributions to the detection and classification of various cancers. Identifying the precise regions in medical images containing cancerous cells plays a crucial role in the diagnostic process. Early and accurate cancer detection is essential for effective treatment and improved patient outcomes. However, manual diagnosis is labor-intensive, requiring the specialized expertise of radiologists, and the increasing number of cancer cases presents challenges in processing large volumes of image data efficiently. To address these challenges, an end-to-end concatenated Convolutional Neural Network (CNN) attention model has been proposed for automatic lung cancer classification. This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. This model achieves impressive performance, with an accuracy of 99.54%, precision of 99.31%, recall of 99.95%, F1-score of 99.66%, and an AUC of 99.97%. These results demonstrate that this approach not only surpasses existing methods but also provides a highly accurate and interpretable solution. By reducing the need for extensive manual intervention, this model enables faster and more reliable lung cancer diagnosis, paving the way for timely and effective treatments. © 2013 IEEE.

关键词： CLAHE convolutional neural network explainable AI gradient-weighted class activation mapping Gradio Lung cancer Shapley additive explanation

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Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

Analysing the Twitter Sentiments in COVID-19 Using Machine L...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Katarya, Rahul Nath, G Amar Singhal, Divit Shukla, Aryaman Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Engineering New Delhi India

ISBN: (纸本)9781665495295

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.

关键词： Coronavirus

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eCeLLM: generalizing large language models for E-commerce from large-scale, high-quality instruction data 24

eCeLLM: generalizing large language models for E-commerce fr...

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Proceedings of the 41st International Conference on Machine Learning

作者： Bo Peng Xinyi Ling Ziru Chen Huan Sun Xia Ning Department of Computer Science and Engineering The Ohio State University Department of Computer Science and Engineering and Translational Data Analytics Institute The Ohio State University Department of Computer Science and Engineering and Translational Data Analytics Institute and Department of Biomedical Informatics The Ohio State University

With tremendous efforts in developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in generalist modeling and out-of-domain generalizability in many fields. Toward fully unleashing their power for e-commerce, in this paper, we construct ECInstruct, the first open-sourced, large-scale, and high-quality benchmark instruction dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive experiments and evaluation demonstrate that eCeLLM models substantially outperform baseline models, including the most advanced GPT-4, and the state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM exhibits excellent generalizability to out-of-domain settings, including unseen products and unseen instructions, highlighting its superiority as a generalist e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce. ECInstruct and eCeLLM models are publicly accessible through https://***/eCeLLM/.

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