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International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa)

作者： Felipe Pagginelli Patricio Paul Catala Felix Krahmer Dept. of Mathematics TU Munich Garching Germany IBMI Helmholtz Munich Neuherberg Germany Dept. of Mathematics&Munich Data Science Institute TU Munich and Munich Center for Machine Learning Garching

ISBN: (数字)9798350365504

ISBN: (纸本)9798350365511

Recent works put forth the Unlimited Sensing Framework (USF), a novel approach to analog-to-digital conversion for high dynamic range sensing. It addresses the saturation phenomenon that commonly arises when physical measurements exceed the dynamic range of a sensor, yielding permanent loss of the input data. However, the USF still has some limitations when dealing with random noise. In the present paper, we propose a novel iterative method to tackle unlimited sensing in a noisy setting. In one step, our approach applies local transformations of the range to remove strong artifacts caused by the noise on local subdivisions of the domain. In the following step, the signal is then approximated via a least squares method. These two types of steps are then alternated. We illustrate the performances of our algorithm in high noise regime.

关键词： Multimodal sensors Conferences Noise Imaging Radar imaging Loss measurement High dynamic range Noise measurement Iterative methods Least mean squares methods

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Real-Time GPS Tracking and Health Monitoring for Animals Using IoT and Mobile Application 2

Real-Time GPS Tracking and Health Monitoring for Animals Usi...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Usha, V. Karagatla, Sandeep Gowr, P. Sheela Jegathambal, P.M.G. Lizy, A. Sathya, V. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of CSE Chennai India Chennai India Vels Institute of Science Technology & Advanced Studies Chennai India Institute of Science and Technology Dept. of Artificial Intelligence and Machine Learning Chennai India Velammal Engineering College Dept of Information Technology Chennai India

ISBN: (纸本)9798331505745

The Internet of Things (IoT) grounded solution of Real Time Global Positioning System (GPS) tracking with Health Monitoring of Animals for Owners to track their animals like cows, buffaloes, dogs etc. This GPS module and a heartbeat sensor are attached to the animals in order to be able to follow them closely. This data is sent to a mobile app, and the owner can see their heart on the map as well as monitor its pulse. Based on analysis of such heart rate variations, the system determines when it is seeing an abnormal activity related to e.g. stress (increased heart rate) or potential health risks (no sign of a heartbeat), which could be interpreted as fear or death. Each animal has an initial heart rate level that is set, and when this rate exceeds it, the system sounds an alert to indicate danger. This method is especially helpful for keeping an eye on animals in urban areas, areas close to forests, and areas with high rates of theft and loss. The project demonstrates the successful execution of a reliable, real-time animal monitoring system with practical applications in both urban and rural environments. © 2025 IEEE.

关键词： Animal Safety GPS Tracking Heartbeat Sensor LoRa device Mobile Application Real-time Monitoring

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Early Detection of Black Fungus Using Deep learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Internet of Animal Health Things (IoAHT) Framework for Clinical Mastitis Detection in Dairy Cows

Internet of Animal Health Things (IoAHT) Framework for Clini...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Ankitha K Venugopala P S Harish Kunder Ankitha Shetty Dept. of Artificial Intelligence and Data Science NMAMIT Nitte Karkala India Dept. of Artificial Intelligence and Machine Learning Alvas Institute of Engineering and Technology Moodbidri India

The Earth serves as a shared habitat not just for humans but also for an array of animal species and organisms. Throughout history, animals have played a crucial role in preserving the natural ecosystem balance and meeting diverse human needs. They contribute significantly to various sectors, such as Agriculture, Dairy, Poultry, and animal husbandry, thus bolstering a country's economy. Human responsibility lies in ensuring the well-being of these animals, which is overseen by Veterinary science. In India, a major milk and milk product producer, the dairy industry actively contributes to the nation's economy. Among the primary milk sources-cows, buffaloes, goats, and sheep-cows hold a revered place in different faiths. Particularly in the dairy industry, cows are vital for milk production. However, monitoring cow health poses challenges. Clinical Mastitis, an inflammation affecting cows' mammary glands, hampers milk production. Automatic milking machines are commonly used, but identifying and segregating mastitis-infected cows from healthy ones remains difficult. The contagious nature of the disease increases the risk of widespread infection among cows. To combat this issue, we propose a solution called the Internet of Animal Health Things (IoAHT), based on the Internet of Things (IoT). IoAHT employs IoT technology for animal health, aiming to accurately identify cases of clinical mastitis in cows. The implementation of IoAHT has the potential to revolutionize health monitoring in the dairy industry, safeguarding cow well-being and ensuring sustainable milk production.

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics 38

Probabilistic Decomposed Linear Dynamical Systems for Robust...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024 Neural information processing systems foundation. All rights reserved.

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Seokhan Lee Bonhwa Ku Hanseok Ko Dept. Computer Science and Engineering Korea University Seoul Republic of Korea Machine Learning Big Data Institute Korea University Seoul Republic of Korea Dept. Electrical and Computer Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines.

关键词： learning systems Large language models Diversity reception Signal processing algorithms Reinforcement learning Signal processing Acoustics Safety Speech processing

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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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A Security Analysis Model for IoT-ecosystem Using machine learning-(ML) Approach

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Recent Advances in Computer science and Communications 2024年第6期17卷 39-47页

作者： Pradeep Kumar, N.S. Kantipudi, M. V. V. Prasad Praveen, N. Suresh, S. Aluvalu, Rajanikanth Jagtap, Jayant Dept.of ECE S.E.A College of Engineering and Technology Bangalore India Pune412115 India Dept. Of Electrical Engineering University of Technology and Applied Sciences Ibra Oman Dept.of CSE Balaji Institute of Technology & Science Warangal India Dept.of IT Chaitanya Bharathi Institute of Technology Hyderabad500075 India Dept.of AIML NIMS Institute of Computing Artificial Intelligence and Machine Learning NIMS University Rajasthan Jaipur India

Introduction: The attacks on IoT systems are increasing as the devices and communication networks are progressively integrated. If no attacks are found in IoT for a long time, it will affect the availability of services that can result in data leaks and can create a significant impact on the associated costs and quality of services. Therefore, the attacks and security vulnerability in the IoT ecosystem must be detected to provide robust security and defensive mechanisms for real-time applications. Method: This paper proposes an analytical design of an intelligent attack detection framework using multiple machine learning techniques to provide cost-effective and efficient security analysis services in the IoT ecosystem. Result: The performance validation of the proposed framework is carried out by multiple performance indicators. Conclusion: The simulation outcome exhibits the effectiveness of the proposed system in terms of accuracy and F1-score for the detection of various types of attacking scenarios. © 2024 Bentham science Publishers.

关键词： Ecosystems

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a data science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： data science

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Unveiling the Vision: A Comprehensive Review of Computer Vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

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Advances in data Engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Meena Laad Ratan Maurya Najeeb Saiyed Dept. of Applied Science Symbiosis Institute of Technology Pune Maharashtra Dept. of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Maharashtra

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

In the era of rapid technological advancement, Computer Vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and machine learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer Vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer Vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer Vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer Vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. Applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer Vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer Vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring.

关键词： Deep learning Computer vision Reviews Smart cities Surveillance Precision medicine Urban planning

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