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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Ibrahim Mohammad Alreshidi Irene Moulitsas Karl W. Jenkins Centre for Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre (DARTeC) UK Centre for Computational Engineering Sciences Cranfield University UK

ISBN: (纸本)9781450396943

Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. These findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs.

关键词： machine learning EEG preprocessing Mental states classification

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics

Enhancing Interpretability: The Role of Explainable AI in He...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Nikita Zade Meher Langote Prateek Verma Department of Artificial Intelligence & Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare.

关键词： Renewable energy sources Explainable AI Scalability Closed box Medical services Predictive models Regulation Artificial intelligence Prognostics and health management Cancer

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Automatic Human Life Rescue Analysis Model with Incorporation of Deep learning Algorithm

Automatic Human Life Rescue Analysis Model with Incorporatio...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： V.K.G. Kalaiselvi Prithikhaa S Divya Darshini J S Bharath Reddy Anandkumar B Shanmugasundaram Hariharan Department of Information Technology Sri Sairam Engineering College Tamilnadu India Department of CSE KG Reddy College of Engineering and Technology Telangana India Department of Artificial Intelligence and Machine Learning Vardhaman College of Engineering India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

In today's fast-paced world, every passing moment can mean the difference between life and death. To address this critical issue, we have developed an innovative application designed to provide rapid emergency medical response. Ambulance services play a pivotal role in emergency healthcare by offering vital transportation and care. However, limited access to ambulance contact information often hampers patients' ability to secure timely assistance during emergencies. With the widespread adoption of smartphones and mobile applications, the proposed solution integrates React Native with a Convolutional Neural Network (CNN) to create an advanced mobile app for law enforcement, capable of real-time car accident detection. Utilizing datasets from Kaggle, images are resized to optimize CNN training. Through transfer learning, the CNN architecture effectively extracts spatial features, enabling accurate accident prediction, validated by robust evaluation metrics such as precision and recall. The React Native app facilitates swift notifications to law enforcement, utilizing cutting-edge deep learning technology to enhance road safety and improve emergency response efficiency. This project aims to streamline emergency services, minimizing response and transport times, thereby saving lives and fostering safer communities.

关键词： Deep learning Training Law enforcement Medical services Emergency services Road safety Mobile applications Convolutional neural networks Time factors Accidents

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On convergence of federated averaging langevin dynamics 24

On convergence of federated averaging langevin dynamics

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Wei Deng Qian Zhang Yi-An Ma Zhao Song Guang Lin Machine Learning Research Morgan Stanley Department of Statistics Purdue University West Lafayette Halicioglu Data Science Institute University of California San Diego Adobe Research Department of Mathematics and School of Mechanical Engineering Purdue University

We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions with distributed clients. In particular, we generalize beyond normal posterior distributions and consider a general class of models. We develop theoretical guarantees for FA-LD for strongly log-concave distributions with non-i.i.d data and study how the injected noise and the stochastic-gradient noise, the heterogeneity of data, and the varying learning rates affect the convergence. Such an analysis sheds light on the optimal choice of local updates to minimize the communication cost. Important to our approach is that the communication efficiency does not deteriorate with the injected noise in the Langevin algorithms. In addition, we examine in our FA-LD algorithm both independent and correlated noise used over different clients. We observe that there is a tradeoff between the pairs among communication, accuracy, and data privacy. As local devices may become inactive in federated networks, we also show convergence results based on different averaging schemes where only partial device updates are available. In such a case, we discover an additional bias that does not decay to zero.

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A Comprehensive Review of Mojo: A High-Performance Programming Language

A Comprehensive Review of Mojo: A High-Performance Programmi...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

As artificial intelligence continues to advance at an unprecedented pace, the selection of programming languages significantly affects development processes, workflows, and outcomes. Mojo, a novel programming language, has gained attention through its integration of Python-like syntax, Rust-inspired safety features, and superior performance characteristics. This paper evaluates Mojo across four critical dimensions: distinctive features, performance metrics, ecosystem maturity, and practical applications, while examining potential adoption barriers. Positioned at the intersection of high-level programming simplicity and low-level performance optimization, Mojo demonstrates particular utility for artificial intelligence developers requiring both flexibility and computational efficiency. Through analysis of prominent libraries, including Endia, Lightbug HTTP, Basalt, and ***, this study examines Mojo's capabilities and limitations. By presenting a comparative analysis of Mojo's features, emerging ecosystem, and applicability to artificial intelligence development, this research addresses the current literature gap and provides substantive insights into this emerging programming language.

关键词： Reviews Ecosystems Syntactics Programming Libraries Real-time systems Safety Artificial intelligence Optimization Python

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Stochastic Large-scale machine learning Algorithms with Distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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IEEE International Conference on Big data

作者： Biyi Fang Diego Klabjan Truong Vo Dept. of Engineering and Applied Mathematics Northwestern University Evanston IL USA Dept. of Industrial Engineering and Management Sciences Northwestern University Evanston IL USA Dept. of Machine Learning and Data Science Northwestern University Evanston IL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm.

关键词： machine learning algorithms Computational modeling Stochastic processes Optimization methods machine learning Predictive models Prediction algorithms Sparks Distributed computing Convergence

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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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Exploring Deep Neural Networks for Accurate Diabetic Retinopathy Prediction

Exploring Deep Neural Networks for Accurate Diabetic Retinop...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kujani, T. Arivubrakan, P. Raju, K. Sankar, P. Mani Reddy, B. Surekha Bhavana, E. Computer Science And Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Data Science Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India

ISBN: (纸本)9798350352689

Diabetic Retinopathy (DR) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a Densely connected Convolutional Network (DenseNet-201) for the efficient detection of DR. This model is specifically trained to classify fundus images into six distinct categories: no DR, mild, moderate, and severe, proliferative DR, and hard exudates. The datasets utilized in this study include the publicly available Diabetic Retinopathy Detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including data Collection, Preprocessing, Augmentation, and Modeling. During the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing DenseNet-201 architecture selected as the model's backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a Deep Neural Network (DNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting DR. The primary objective of this research is to develop a reliable and automated system for Diabetic Retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes. © 2024 IEEE.

关键词： Deep neural networks

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Disease and Medications Text Visualization Using Scattertext

Disease and Medications Text Visualization Using Scattertext

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Rama Krishna, K. Sandhya, Kaipa Praveen Gujjar, J. Devadas, Raghavendra M. Hiremani, Vani Sapna, R. Impact college of Engineering and Applied Sciences Department of Artificial Intelligence and Machine Learning Bengaluru India Impact college of Engineering and applied sciences Department of Data Science Bengaluru India Faculty of Management Studies Bengaluru India Department of Information Technology Manipal India University Pune India

ISBN: (纸本)9798331527495

Before text data can be analysed and visualised, it must be thoroughly cleaned due to its messy nature. data visualizations use the data to tell an engaging and simple-to-read story. That is what the Scattertext tool does. Scatter text allows which terms stand out in your corpora and how their frequency varies across categories. The dataset used in this paper includes reviews of drugs. This dataset offers a useful window into how people discuss their thoughts and experiences using various drugs. The drug names, user ratings, and the actual written reviews are among the details included in the dataset. This dataset consists of drug reviews collected from various sources, hence expressing a wide range of opinions and feelings. Each row in this dataset represents the experience a user had with a given medication and hence allows us to explore those emotions that are associated with any particular set of drugs. Understanding opinion expressed in the review about drugs might go a long way in helping decision-making processes in both healthcare and pharmaceuticals. The scatter plot distinguishes between labelled points and unlabelled ones by using a custom algorithm for Scattertext. By hovering their cursor over the labelled areas done client-side, users can interactively see which terms correspond to which points. © 2025 IEEE.

关键词： data visualization

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A Comparative Study on Multi-modal Fusion for Automated Lung Disease Diagnostics

A Comparative Study on Multi-modal Fusion for Automated Lung...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Sachin Kumar Pradeep Kumar Mallick Olga Vorfolomeyeva Big Data and Machine Learning Lab South Ural State University Chelyabinsk Russia School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Faculty of Arts and Culture West Kazakhstan State University Uralsk Kazakhstan

High-quality X-rays are now available to diagnose lung diseases with the help of radiologists. However, the diagnostic process is time consuming and depends on specialist availability in medical institutions. Patient information may include chest X-rays of varying quality, medical test results, doctors’ notes and prescriptions, and medication details, among others. In this study, we present a model for classifying pulmonary diseases using multimodal data from patient clinical studies and radiographic images. Various methods were used to generate artificial samples for both images and tabular data on the laboratory study results during data preparation. We also proposed a method for establishing a correspondence between the generated modals. The late fusion architecture of the proposed multimodal model was implemented. We conducted experiments on pulmonary data-set with two modalities. Results shows that an increase in accuracy and other parameters were observed for multimodal data fusion using our model in comparison with image only modality and clinical data only modality. It strengthen the fact that multimodality provides more insight for the fusion model to learn and provide a more precise diagnosis than single modality.

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