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arXiv 2021年

作者： Chakraborty, Tanmay Trehan, Utkarsh Zuluaga, Maria A. MALIS - Machine Learning and Intelligent Systems Department of Data Science and Engineering EURECOM Biot France

Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatial-spectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods. The code is publicly available in https://***/tanmay-ty/SpectralNET. © 2021, CC BY.

关键词： Convolution

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An Improved Uniform Convergence Bound with Fat-Shattering Dimension

SSRN

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SSRN 2023年

作者： Colomboni, Roberto Esposito, Emmanuel Paudice, Andrea Department of Computer Science Università degli Studi di Milano Via Giovanni Celoria 18 Milan20131 Italy Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Via Enrico Melen 83 Genoa16152 Italy

The fat-shattering dimension characterizes the uniform convergence property of real-valued functions. The state-of-the-art upper bounds feature a multiplicative squared logarithmic factor on the sample complexity, leaving an open gap with the existing lower bound. We provide an improved uniform convergence bound that closes this gap. © 2023, The Authors. All rights reserved.

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The Role of Technology in Modern Education

The Role of Technology in Modern Education

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

作者： Priya Kate Utkarsha Pacharaney Abhay Tale Aditya Barhate Nishad Sheikh Chinmaya Khadse Department of Computer Science and Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

It can be seen that technology has played a big role in today’s education through the use of smart boards and AI. Some of the advantages are that it affords students better access and opportunity for participation and brings more flexibility and efficiency to the teaching process and allows students to experience learning in new ways. However, there are also disadvantages such as digital divides, screen time, privacy concerns, and technical limitations. After highlighting the implications, the article analyses potential developments such as AI tutors and 5G connectivity as potential frontier technologies for the next era of learning technologies. Concisely, technology is an enhancement resource that should be embraced and complement quality teaching. The appropriate policy and continuous enhancement are the two crucial strategies for the education systems to implement technology to foster students with competencies required in the twenty-first century. Further research is needed regarding such matters as time spent in front of screens, media education, and implementation of new technologies in schools.

关键词： Training Industries Privacy Education Medical services learning (artificial intelligence) Media Stakeholders Security Digital divide

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Non-Invasive Suicide Risk Prediction Through Speech Analysis

Non-Invasive Suicide Risk Prediction Through Speech Analysis

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E-Health and Bioengineering Conference (EHB)

作者： Shahin Amiriparian Maurice Gerczuk Justina Lutz Wolfgang Strube Irina Papazova Alkomiet Hasan Alexander Kathan Björn W. Schuller Chair of Health Informatics TUM University Hospital Munich Germany Munich Center for Machine Learning (MCML) Germany Department of Psychiatry Psychotherapy and Psychosomatics Faculty of Medicine University of Augsburg Germany German Center for Mental Health Munich Germany Munich Data Science Institute (MDSI) Germany

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

The delayed access to specialized psychiatric assessments and care for patients at risk of suicidal tendencies in emergency departments creates a notable gap in timely intervention, hindering the provision of adequate mental health support during critical situations. To address this, we present a non-invasive, speech-based approach for automatic suicide risk assessment. For our study, we collected a novel speech recording dataset from 20 patients. We extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. We proceed by conducting a binary classification to assess suicide risk in a leave-one-subject-out fashion. Our most effective speech model achieves a balanced accuracy of 66.2%. Moreover, we show that integrating our speech model with a series of patients' metadata, such as the history of suicide attempts or access to firearms, improves the overall result. The metadata integration yields a balanced accuracy of 94.4%, marking an absolute improvement of 28.2 %, demonstrating the efficacy of our proposed approaches for automatic suicide risk assessment in emergency medicine.

关键词： Accuracy Speech analysis Emergency medicine Mental health Metadata Predictive models Feature extraction Recording Risk management History

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Deep Reinforcement learning In Autonomous Cars To Preventing Road Accident

Deep Reinforcement Learning In Autonomous Cars To Preventing...

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

作者： Rushikesh Thote Aditya Barhate Abhay Tale Prateek Verma Department of Computer Science and Design 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 Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies.

关键词： Road accidents Reviews Decision making Transportation Transforms Deep reinforcement learning Road safety Safety Reliability Stress

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Smart Healthcare: Harnessing the Power of machine learning for Predictive Analysis

Smart Healthcare: Harnessing the Power of Machine Learning f...

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Engineering and Technology (PICET), Parul International Conference on

作者： Aditya Barhate Praveen Kumar Prateek Verma Nayan Jikar Abhay Tale Vaibhav Hikre Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of machine learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry.

关键词： Ethics Solid modeling Medical services machine learning Radiology Supercomputers Real-time systems Internet of Things Predictive analytics Biomedical imaging

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： M Revathi P Kalaiarasi S Jeevitha G Akilandasowmya Artificial Intelligence & Data Science St. Joseph's Institute of Technology Chennai India Department of Machine Learning Kalasalingam University Krishnankoil India Information Technology Jerusalem College of Engineering Chennai India Computer and Communication Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy.

关键词： Support vector machines Image segmentation Accuracy machine learning algorithms Brain tumors Biological systems Intelligent systems Diseases

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The effect of differential victim crime reporting on predictive policing systems

arXiv

引用

arXiv 2021年

作者： Akpinar, Nil-Jana De-Arteaga, Maria Chouldechova, Alexandra Department of Statistics and Data Science & Machine Learning Department Carnegie Mellon University United States Information Risk and Operations Management Department McCombs School of Business University of Texas at Austin United States Heinz College Department of Statistics and Data Science Carnegie Mellon University United States

Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are commonly known as hot spot predictive policing. These systems predict where future crime is likely to concentrate such that police can allocate patrols to these areas and deter crime before it occurs. Previous research on fairness in predictive policing has concentrated on the feedback loops which occur when models are trained on discovered crime data, but has limited implications for models trained on victim crime reporting data. We demonstrate how differential victim crime reporting rates across geographical areas can lead to outcome disparities in common crime hot spot prediction models. Our analysis is based on a simulation1 patterned after district-level victimization and crime reporting survey data for Bogotá, Colombia. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas. This may lead to misallocations both in the form of over-policing and under-policing. © 2021, CC BY-NC-SA.

关键词： Crime

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Pattern Discovery in an EEG database of Depression Patients: Preliminary Results

Pattern Discovery in an EEG Database of Depression Patients:...

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International Conference on Measurement

作者： Kateřina Hlaváčková-Schindler Christina Pacher Claudia Plant Mykola Lazarenko Milan Paluš Jaroslav Hlinka Aditi Kathpalia Martin Brunovský Data Mining and Machine Learning Research Group Faculty of Computer Science University of Vienna Vienna Austria Department of Complex Systems Institute of Computer Science Czech Academy of Sciences Prague Czechia Clinical Research Programme National Institute of Mental Health Klecany Czechia

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient’s brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants" ongoing at the University of Vienna, the Czech Academy of sciences and the National Institute of Mental Health in the Czech Republic.

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Prior-Dualgan: Rain Rendering from Coarse to Fine

SSRN

引用

SSRN 2023年

作者： Hu, Mingdi Yang, Jingbing Yu, Jianxun Jing, Bingyi School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Hanjiang Machine Tool Co. LTD Hanzhong723003 China Department of Statistics & Data Science Southern University of Science and Technology Shenzhen518055 China

The success of deep neural networks (DNN) in deraining has led to increased research in rain rendering. In this paper, we introduce a novel Prior-DualGAN algorithm to synthesize diverse and realistic rainy/non-rainy image pairs to improve DNN training for deraining. More precisely, the rain streak prior is first generated using essential rain streak attributes;then more realistic and diverse rain streak patterns are rendered by the first generator;finally, the second generator naturally fuses the background and generated rain streaks to produce the final rainy images. Our method has two main advantages: (1) the rain streak prior enables the network to incorporate physical prior knowledge, accelerating network convergence;(2) our dual GAN approach gradually improves the naturalness and diversity of synthesized rainy images from rain streak synthesis to rainy image synthesis. We evaluate deraining algorithms using our generated rain-augmented Rain100L, Rain14000, and Rain-Vehicle dataset, and show significant performance improvements. The source code will be released shortly after article's acceptance. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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