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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Yamini, J. Kanimozhi, T. Jenefa, A. Bagyalakshmi, K. Aarthi, K. Dhivya, P. Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Machine learning Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Ppg Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Mathematics Coimbatore India Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of 95.65% with a loss of 0.18 without overfitting and underfitting. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Approach To Healthcare Chatbots Using SpaCy NLP And Naive Bayes Algorithm

A Comprehensive Approach To Healthcare Chatbots Using SpaCy ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Shruthi G Sinchana K C Soumya C P Sugnyan S Abbigeri Department of CSE Center for Data Science and Applied Machine Learning (CDSAML) PES University Bengaluru India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Imagine a world where medical assistance is just a conversation away, where artificial intelligence helps navigate your health concerns with precision, by the intersection of machine learning and healthcare, our chatbot emerges as a powerful ally for patients everywhere. This research work focuses on developing a healthcare chatbot which provides diagnosis of patients by taking user-input symptoms, suggesting medications and also hospitals in a particular region. Both speech and text options are introduced to interact with the user. Firstly, the user is prompted to enter the symptoms faced by them, the symptoms are extracted from user input sentences using SpaCy NLP. These extracted symptoms are fed to a Multinomial Naïve Bayes Classifier for disease prediction. Additionally, chatbot also provides the required medications and the hospitals in the region from the data stored in the CSV and PDF files. The user interface of the chatbot is built using Flask. SQL is leveraged for user account management and to store the diagnosis history of the user. The dataset used for training is a CSV file consisting of 132 unique symptoms, 42 unique diseases and a total of 4,920 combinations of symptoms and associated diseases. The dataset is pre-processed in the form of 1s and 0s to make it suitable for our Multinomial Naive Bayes model which achieved a test accuracy of 95% which is better than the existing models having an accuracy of around 93%.

关键词： Training Structured Query Language Accuracy Hospitals Navigation machine learning User interfaces Chatbots Medical diagnostic imaging Diseases

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High-pressure phases of liquid silane studied using first-principles molecular dynamics simulations

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Physical Review B 2024年第2期110卷 024106-024106页

作者： Y. H. Badjo B. Boates B. C. Saha S. A. Bonev Department of Physics Physical and Life Sciences Directorate Physics Division Machine Learning and Data Science Cash App Block San Francisco California 94110 USA

The structural and electronic transformations of liquid silane as a function of pressure are studied using first-principles molecular dynamics simulations. We present results for two isotherms, 1000 K and 2000 K, in the pressure range from ambient up to 265 GPa. The phases of dense liquid silane bear some similarities with its crystalline phases but there are significant differences as well. Notably, we find the emergence of a mixed polymeric-molecular H2 phase with strong temperature dependence and metallization in the liquid occurring at significantly lower pressure than in the solid. We report dc conductivity, which reaches higher values, and has a different pressure dependence and physical origin than recent analysis of experimental measurements has suggested.

关键词： Electronic structure High-pressure studies Liquid-liquid phase transition Structural properties Liquids Density functional calculations Molecular dynamics

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ECTSum: A New Benchmark dataset For Bullet Point Summarization of Long Earnings Call Transcripts

ECTSum: A New Benchmark Dataset For Bullet Point Summarizati...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Mukherjee, Rajdeep Bohra, Abhinav Banerjee, Akash Sharma, Soumya Hegde, Manjunath Shaikh, Afreen Shrivastava, Shivani Dasgupta, Koustuv Ganguly, Niloy Ghosh, Saptarshi Goyal, Pawan Department of Computer Science and Engineering IIT Kharagpur India Goldman Sachs Data Science and Machine Learning Group India Leibniz University of Hannover Germany

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, discussing facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and experts-written short telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarization methods across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple yet effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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Harmonizing Precision Agriculture: Augmented Insights into Plant Disease Detection Using Deep learning

Harmonizing Precision Agriculture: Augmented Insights into P...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Chauhan, Shweta Kumar, Rahul Kumar, Bibek MITS College of Engineering Department of Artificial Intelligence and Machine Learning Gwalior474005 India GLA University ECE Department Uttar Pradesh Mathura281406 India School of Data Science Symbiosis University of Applied Sciences Madhya Pradesh Indore453112 India

ISBN: (纸本)9798350364088

This research addresses the critical issue of accurate plant disease identification, recognizing its paramount significance in preserving agricultural yields and safeguarding natural ecosystems. Leveraging the power of deep learning, specifically Convolutional Neural Networks (CNNs), our study focuses on developing a robust image classification model capable of precisely detecting various plant diseases. However, the challenge of limited training data necessitated the implementation of diverse data augmentation techniques to enrich the dataset. The research involves a meticulous exploration of different augmentation strategies, including rotation, flipping, zooming, translation, shearing, and cropping. The acquired dataset comprises 25,000 images categorized into 38 classes, with 20,000 designated for training and 5,000 for validation. The CNN model, with convolutional, pooling, dense, dropout, and flatten layers, exhibits a remarkable accuracy of 93.5% and 95% on the valid and training dataset, reflecting its adeptness in learning intricate patterns. Furthermore, the study reviews recent advancements in plant disease identification, including innovative models like PPLC-Net, Deep CNN, SG2-ADA, and HQIA. These breakthroughs underscore the continuous evolution of techniques to address challenges such as imbalanced datasets, computational demands, and overfitting. The research concludes with recommendations for future exploration, emphasizing the potential refinement of data augmentation techniques, hyperparameter tuning, and broader disease dataset acquisition. The outcomes presented here contribute to the growing body of knowledge in precision agriculture and pave the way for further advancements in plant disease detection methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Sequential Kernelized Stein Discrepancy

arXiv

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arXiv 2024年

作者： Martinez-Taboada, Diego Ramdas, Aaditya Department of Statistics & Data Science Carnegie Mellon University United States Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University United States

We present a sequential version of the kernelized Stein discrepancy, which allows for conducting goodness-of-fit tests for unnormalized densities that are continuously monitored and adaptively stopped. That is, the sample size need not be fixed prior to data collection;the practitioner can choose whether to stop the test or continue to gather evidence at any time while controlling the false discovery rate. In stark contrast to related literature, we do not impose uniform boundedness on the Stein kernel. Instead, we exploit the potential boundedness of the Stein kernel at arbitrary point evaluations to define test martingales, that give way to the subsequent novel sequential tests. We prove the validity of the test, as well as an asymptotic lower bound for the logarithmic growth of the wealth process under the alternative. We further illustrate the empirical performance of the test with a variety of distributions, including restricted Boltzmann machines. Copyright © 2024, The Authors. All rights reserved.

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UNDERSTANDING THE GENERALIZATION OF ADAM IN learning NEURAL NETWORKS WITH PROPER REGULARIZATION 11

UNDERSTANDING THE GENERALIZATION OF ADAM IN LEARNING NEURAL ...

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11th International Conference on learning Representations, ICLR 2023

作者： Zou, Difan Cao, Yuan Li, Yuanzhi Gu, Quanquan Department of Computer Science Institute of Data Science The University of Hong Kong Hong Kong Department of Statistics & Actuarial Science The University of Hong Kong Hong Kong Machine Learning Department Carnegie Mellon University United States Department of Computer Science University of California Los Angeles United States

Adaptive gradient methods such as Adam have gained increasing popularity in deep learning optimization. However, it has been observed in many deep learning applications such as image classification, Adam can converge to a different solution with a worse test error compared to (stochastic) gradient descent, even with a fine-tuned regularization. In this paper, we provide a theoretical explanation for this phenomenon: we show that in the nonconvex setting of learning over-parameterized two-layer convolutional neural networks starting from the same random initialization, for a class of data distributions (inspired from image data), Adam and gradient descent (GD) can converge to different global solutions of the training objective with provably different generalization errors, even with weight decay regularization. In contrast, we show that if the training objective is convex, and the weight decay regularization is employed, any optimization algorithms including Adam and GD will converge to the same solution if the training is successful. This suggests that the generalization gap between Adam and SGD in the presence of weight decay regularization is closely tied to the nonconvex landscape of deep learning optimization, which cannot be covered by the recent neural tangent kernel (NTK) based analysis. © 2023 11th International Conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Gradient methods

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The Future of Driving: A Review on the Combination of Artificial Intelligence and Autonomous Vehicles 2

The Future of Driving: A Review on the Combination of Artifi...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Zade, Nikita Gawande, Mayur Verma, Prateek Gundewar, Swapnil Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability. © 2024 IEEE.

关键词： Automotive industry

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Review of machine learning and Deep learning algorithms for Personality traits classification 2

Review of Machine Learning and Deep Learning algorithms for ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bhadane, Satish N. Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits"It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of pe

关键词： Adversarial machine learning

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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

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Journal of machine learning Research 2023年第1期24卷 1-11页

作者： Hedström, Anna Weber, Leander Bareeva, Dilyara Krakowczyk, Daniel Motzkus, Franz Samek, Wojciech Lapuschkin, Sebastian Höhne, Marina M.-C. Understandable Machine Intelligence Lab TU Berlin Berlin10587 Germany Department of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin10587 Germany Department of Computer Science University of Potsdam Potsdam14476 Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus—a comprehensive, evaluation toolkit in Python that includes a growing, wellorganised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/). © 2023 Anna Hedström, Leander Weber, Dilyara Bareeva, Daniel Krakowczyk, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M.-C. Höhne.

关键词： Open systems

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