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12th International Conference on Learning Representations, ICLR 2024

作者： Si, Wenwen Park, Sangdon Lee, Insup Dobriban, Edgar Bastani, Osbert Department of Computer & Information Science University of Pennsylvania United States Graduate School of AI and Computer Science & Engineering POSTECH Korea Republic of Department of Statistics and Data Science University of Pennsylvania United States

Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift, which is precisely when reliable uncertainty quantification can be most useful. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting, where the probabilities of labels can differ between the source and target distributions. Our algorithm relies on constructing confidence intervals for importance weights by propagating uncertainty through a Gaussian elimination algorithm. We evaluate our approach on four datasets: the CIFAR-10 and ChestX-Ray image datasets, the tabular CDC Heart Dataset, and the AGNews text dataset. Our algorithm satisfies the PAC guarantee while producing smaller prediction set sizes compared to several baselines. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Forecasting

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Effect of Signal Conditioning and Evoked-Potential Based Representation on Stability and Distinctiveness of EEG Brain Signatures 12

Effect of Signal Conditioning and Evoked-Potential Based Rep...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Oztemel, Muhammed Esad Soysal, Omer Muhammet Louisiana State University Division of Electrical and Computer Engineering United States Southeastern Louisiana University Computer Science United States

ISBN: (纸本)9798350330366

Biometric patterns have been used for authentication purposes for more than decades. Although traditional biometric measurements including fingerprints, facial recognition, and sound recognition are often used for authentication purposes, there is ongoing concern about the risks associated with their repeatability and susceptibility to theft. The utilization of electroencephalography (EEG) has promising potential thanks to the distinctive patterns extracted from brain signals. The main goal of this study is to explore the effectiveness of signal conditioning and evoked potentials on uniqueness and permanence of individuals' EEG signals for authentication purpose. We utilized the Laplacian of Gaussian as a signal conditioning operator. In addition, three signifying functions were explored in obtaining evoked potential time-domain representation from an individual EEG signal. Different stimuli protocols were employed for comparison. A time-series and a statistical technique were utilized for uniqueness and permanence analysis. © 2024 IEEE.

关键词： Authentication

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A Comparative Study of Deep Learning Models for Guava Leaf Disease Detection 3

A Comparative Study of Deep Learning Models for Guava Leaf D...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Thangaraj, Rajasekaran Mohan, M. Moulik, M. Logeshwari, A. Loganathan, T. Koushika, P. Centre for IoT and AI Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494007

Plant diseases are regarded as one of the major variables affecting food production and reducing production losses. Early identification and disease diagnosis of fruit crops are essential for the food industry to increase production. The development of the healthy fruit sector is highly dependent on plant diseases. Guava fruit is a good source of fiber, calcium, pectins, and vitamin C. Guava disease has become a major issue for guava production, which has a negative impact on farmers' socioeconomic growth. Infections that harm guava plants and fruits pose the greatest obstacles, although they can be avoided by early identification. Deep learning has recently been widely used to automatically identify a variety of diseases that cannot be seen with the human eye. In this study, convolution neural models are suggested to use leaf images to detect guava disease. The presented models can be used to categorize the main guava leaf diseases, including Canker, Dot, Healthy, Mummification and Rust. The kaggle dataset is used to gather images of guava with the disease Canker, Dot, Healthy, Mummification and Rust. In our work, CNN models like DenseNet121, DenseNet169, InceptionV3, and Xception are used to categorize the diseases that affect guava leaves. The experimental findings indicate that, with an accuracy of 96.12%, the DenseNet169 model outperformed other models. When the model's effectiveness is assessed using performance metrics like precision, recall, and F1 score, the results demonstrate that DenseNet169 gives excellent results. © 2023 IEEE.

关键词： Convolutional neural networks

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Breaking Barriers: A Paradigm Shift in Technology Accessibility for Individuals with Physical Disabilities 39

Breaking Barriers: A Paradigm Shift in Technology Accessibil...

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39th Annual AAai Conference on Artificial Intelligence, AAai 2025

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)157735897X

Individuals living with disabilities often face challenges in their daily lives, from managing physical tasks to coping with emotional needs. It is imperative to provide them with personalized, courteous, and empathetic support that can address their unique needs. To bridge this gap, we propose an Empathetic Disability Support System (EDiSS), designed to offer personalized support tailored with correct politeness and empathetic strategies as per individual users' OCEAN traits, gender, and age. To train EDiSS, first, a specialized personalized disability support dialogue dataset (PDCARE) is created encompassing a wide spectrum of disabilities, such as Spinal Cord Injuries, Neurological Disorders, Orthopedic Disabilities, etc, and support areas like Physical Therapy Exercises, Pain Management, Emotional Support, etc. EDiSS employs a reinforcement learning-based dialogue model with a novel reward function. It adapts its tone and content based on the user's persona, gender, and age to provide respectful and empathetic assistance across various aspects of daily living. Our experiments and evaluation demonstrate the effectiveness of EDiSS in improving the quality of life of individuals with disabilities, marking a significant advancement in leveraging technology to provide much-needed support and assistance in their daily challenges. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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Machine Learning Algorithm-based Prediction Models for Adolescent Suicide Attempt

Machine Learning Algorithm-based Prediction Models for Adole...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Jung, Jinwoo Kang, Seulmi Go, Myeonghwan Lee, Sieon Cho, Seokheon Department of Computer Science and Engineering Konkuk University Seoul Korea Republic of Department of Computer Science and Engineering Yonsei University Seoul Korea Republic of Department of AI Computer Engineering Kyonggi University Suwon Korea Republic of Qualcomm Institute University of California La Jolla San Diego United States

ISBN: (纸本)9798331521165

This paper presents a system aimed at preventing the serious social issue of adolescent suicide by identifying at-risk adolescents early and providing appropriate support. To achieve this, we propose machine learning models based on data from the Korea Youth Risk Behavior Web-Based Survey (KYRBWS), targeting South Korean adolescents and considering various factors to predict suicide attempts. The models incorporate variables such as adolescents' behavioral patterns, emotional states, and family and social support systems. We applied Random Forest for feature selection to identify important features from the original dataset. The core algorithms include Logistic Regression, Random Forest, and XGBoost. To ensure robust performance, we employed K-fold cross-validation. Additionally, to address the challenge of data imbalance, we applied various sampling techniques to develop reliable, high-performance models for predicting suicide attempts. By detecting adolescents at high risk of suicide attempts, we aim to contribute to suicide prevention efforts and promote adolescent health and well-being. © 2025 IEEE.

关键词： Health risks

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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BiGRU-ANN based hybrid architecture for intensified classification tasks with explainable ai

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4211-4221页

作者： Chakraborty, Sovon Talukder, Muhammad Borhan Uddin Hasan, Mohammad Mehadi Noor, Jannatun Uddin, Jia Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh AI and Big Data Department Woosong University Daejeon South Korea

Artificial Intelligence (ai) is increasingly being employed in critical decision-making processes such as medical diagnosis, credit approval, criminal justice, and many more. However, many ai models exploit complex algorithms that are difficult for humans to see through, which can lead to concerns about accountability, bias, and the ability to trust the outcomes. With the increasing demand for ai systems to be transparent, interpretable, and reliable, the field of Explainable ai (Xai) has gained attention of the researchers. This paper presents a robust hybrid architecture that combines Bidirectional Gated Recurrent Units (BiGRU) and Artificial Neural Networks (ANN) for the classification of texts and sentiment analysis. Interpretable Model Agnostic Explanation (LIME) has been employed with our proposed model to enhance confidence in the outcomes. The proposed architecture is found to be effective for sentiment analysis from texts, and classifying images containing handwrit- ten characters. It leverages the BiGRU to model the sequential dependencies in the data, while the ANN is used for the final classification. Evaluations on both Bengali and English datasets show that the proposed architecture outperforms state-of-the-art models in various performance metrics, providing meaningful and interpretable explanations for its predictions. The model can be used in systems that require the architectures to be computationally less demanding, yet a decent accuracy is secured. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： ANN BiGRU Explainable ai LIME Sentiment analysis

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Analysis of Bigmart Outlets sales using Big Data Analytics Method 2

Analysis of Bigmart Outlets sales using Big Data Analytics M...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAaiC 2023

作者： Ramasami, Meaga Varsha Thangaraj, Rajasekaran Kumar, S Manoj Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Centre for IoT and Ai Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

Sales prediction is an important problem for businesses. A sales forecast can guide a company's production, pricing, and promotion strategies, and can help mitigate risks associated with under or overproduction. Traditional methods of sales prediction utilized statistical techniques like regression analysis or time series analysis, but these methods have certain limitations, including the need for large and clean data sets. This research work focuses on exploring the patterns and trends present in a large-scale retail dataset through exploratory data analysis (EDA). The primary objective of this research is to uncover insights that can enhance the understanding of customer behaviour, identify opportunities to improve business processes and inform data-driven decisions. The data consists of daily sales records for 1560 products of ten BigMart Outlets. This study explores several preprocessing steps, such as handling missing values and outliers, scaling and normalization, and feature engineering. The findings also revealed that the top-selling items were bought by a diverse range of customer demographics, indicating that marketing campaigns should target a broader audience. Furthermore, the study identified several unusual transactions that required further investigation. © 2023 IEEE.

关键词： Sales

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Failure Analysis of IoT-based Smart Agriculture System: Towards Sustainable Food Security 5

Failure Analysis of IoT-based Smart Agriculture System: Towa...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Md Mokhlesur Abdulhamid, Alhassan Kabir, Sohag Gope, Prosanta Military Institute of Science and Technology Department of Computer Science and Engineering Bangladesh School of Computer Science Ai and Electronics University of Bradford United Kingdom University of Sheffield Department of Computer Science United Kingdom

ISBN: (纸本)9798350394290

Internet of Things (IoT)-based smart agriculture systems are increasingly being used to improve agricultural yield. IoT devices used for agricultural monitoring are often deployed in outdoor environments in remote areas. Due to the exposure to harsh environments and the nature of deployment, sensors and other devices are susceptible to an increased rate of failure, which can take a system to unsafe and dangerous states. Failure of a smart agriculture system can cause significant harm to nature and people and reduce agricultural production. To address the concerns associated with the failure of the system, it is necessary to understand how the failures of the components of a system can contribute to causing the overall system failure. This paper adopts Fault Tree Analysis, a widely used framework for failure behaviour analysis in other safety-critical domains, to demonstrate the qualitative failure analysis of smart irrigation systems based on the components' failure. © 2023 IEEE.

关键词： Internet of things

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