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arXiv 2023年

作者： Conde, Pedro Lopes, Rui L. Premebida, Cristiano Institute os Systems and Robotics Department Of Electrical and Computer Engineering University of Coimbra Portugal Critical Software S.A. Portugal

The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a growing demand for highly reliable models in many domains, making the problem of uncertainty calibration pivotal when considering the future of deep learning. This is especially true when considering object detection systems, that are commonly present in safety-critical applications such as autonomous driving, robotics and medical diagnosis. For this reason, this work presents a novel theoretical and practical framework to evaluate object detection systems in the context of uncertainty calibration. This encompasses a new comprehensive formulation of this concept through distinct formal definitions, and also three novel evaluation metrics derived from such theoretical foundation. The robustness of the proposed uncertainty calibration metrics is shown through a series of representative experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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AI-Assist tool to enhance the reliability and accuracy of automated speech and writing support for students with disabilities in learning languages 11

AI-Assist tool to enhance the reliability and accuracy of au...

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11th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Surana, D. Amruta Devika, Mrs K. Prema, S. Dhannaseth, Nilesh H. Gopinath, N. Tamilarasan, P. Department of Computer Engineering Sinhgad Institute of Technology Lonavala410401 India Computer Scienceand Business Systems Sri Manakula Vinayagar Engineering College Pudhucherrry India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India St. Vincent Pallotti College of Engineering and Technology Maharashtra Nagpur441108 India Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of English and Foreign Languages SRM College of engineering and Technology SRMIST Kattankulathur Chennai India

ISBN: (纸本)9798350360059

Innovative educational technology have made it possible to communicate with children who have special needs (SEN) in new ways. Artificial intelligence (A.I.)-based approaches have been among the most effective during the past ten years. The efficient application of artificial intelligence tools is seen as a means of improving SEN students' quality of life. As a result, the establishment of systems for both diagnosis and intervention calls for the adoption of A.I. techniques. Today, people from all socioeconomic backgrounds and walks of life concur on the value of education. Everyone needs to have a top-notch education in order to advance and succeed in today's society of information. It is now feasible to communicate with children with special needs (SEN) in new ways thanks to innovative educational technology. In the past ten years, artificial intelligence (A.I.)-based strategies have been among the most successful. The effective use of artificial intelligence tools is viewed as a way to enhance the quality of life for SEN students. As a result, the use of A.I. techniques is necessary for the creation of systems for both diagnosis and intervention. Today, people from all socioeconomic levels and walks of life agree that education is important. To advance and be successful in today's information culture, everyone needs to have a top-notch education. Then, it will display the statement that goes with that specific term. The learner will then get it in both text and audio versions after that. This makes it simple to assist any student with a disability in having good communication in a foreign language. ©2023 IEEE.

关键词： Students

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Quality of Service (QoS)-Driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital Technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Fitness Approximation through Machine Learning

arXiv

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arXiv 2023年

作者： Tzruia, Itai Halperin, Tomer Sipper, Moshe Elyasaf, Achiya The Software and Information Systems Engineering Department Ben-Gurion University of the Negev Israel The Computer Science Department Ben-Gurion University of the Negev Israel

We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, through dynamic adaptation to the evolutionary state. Maintaining a dataset of sampled individuals along with their actual fitness scores, we continually update a fitness-approximation ML model throughout an evolutionary run. We compare different methods for: 1) switching between actual and approximate fitness, 2) sampling the population, and 3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than that of the fully run GA-depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators-where fitness computation is costly-our approach is generic and can be easily applied to many different domains. © 2023, CC BY.

关键词： Genetic algorithms

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

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Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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CASolarNet: Channel Attention EfficientNet-based Model for Solar Panel Dust Detection

CASolarNet: Channel Attention EfficientNet-based Model for S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Hussein M. A. Mohammed Abdulfattah E. Ba Alawi Energy and Cooling Systems Department Turk Telekom Ankara Turkey Mechatronics and Robotics Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The accumulation of dust on solar panels dramatically reduces the efficiency of solar energy systems, an issue that is often overlooked in both urban and rural settings. This work proposes a novel solution to this widespread problem of Solar Dust Detection. Utilizing EfficientNet, which is known for its balanced approach to model depth, width, and resolution, our proposed model accurately identifies dust on solar panels. The suggested model (CASolarNet) employs Channel Attention, EfficientNet, and focal loss to enhance the performance of detecting dust on solar cells. By dynamically modifying feature weights, channel attention techniques enhance dust detection by emphasizing the locations of dust in an image. This capability is crucial for maintaining optimal energy production and extending the lifespan of solar cells. Our model has been extensively tested and validated, resulting in a high accuracy rate of more than 98%. The obtained performance demonstrates the model's effectiveness in various environmental conditions, establishing a new standard in solar maintenance technology. The use of the proposed CASolarNet model has several advantages, including increased energy efficiency, lower maintenance costs, and greater sustainability of solar power systems.

关键词： Image resolution Photovoltaic cells Solar energy Production Feature extraction Maintenance Power systems

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Intelligent System to Maximize Customer Experience in Restaurants

Intelligent System to Maximize Customer Experience in Restau...

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Advancements in Computing (ICAC), International Conference on

作者： R.U.A Jayalal Y.E Nimeshika G.N.N Samarasekara G.R.C.I Wijerathna Narmada Gamage Poojani Gunathilake Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

With technological improvements in the restaurant industry, competition is fierce, and a restaurant's success depends on providing excellent and luxury customer satisfaction. The food industry has had to continuously advance with technology in order to adequately satisfy the wants of people leading increasingly luxurious lives. While some restaurants still rely on manpower, the majority are eager to advance with technology in order to improve the standard of their offerings and enhance their customers' experiences. In order to optimize customer satisfaction and experience, this research project intends to investigate the potential of intelligence in the restaurant business. This research has explored intelligent systems such as digital self-service menus for ordering food, augmented reality (AR) visualization systems for customers, the observation of chefs in the kitchen, and the usage of robots to serve meals. This research paper analyzes the advantages of automating restaurant operations and how to do so to enhance customer satisfaction. Overall, this study has the potential to transform the restaurant industry and increase customer satisfaction.

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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Chaotification as a Method for the Treatment of Uncertainty

Chaotification as a Method for the Treatment of Uncertainty

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2021 International Conference on Data Science and Its Applications, ICoDSA 2021

作者： Benderskaya, Elena N. Great St. Petersburg Polytechnic University Computer Systems and Software Engineering Department St. Petersburg Russia

ISBN: (纸本)9781665443036

Chaotification of the systems is considered as a method for the implementation of the 'order-chaos' boundary computations. A natural phenomenon of the adaptation mechanism provided by self-organization is used for the treatment of uncertainty. Under consideration is the uncertainty dealing with the object model stricture or the object's environment model stricture and the uncertainty dealing with the object model parameters or the object's environment model parameters. Due to the nonlinear dynamic, it is possible to obtain a universal and adequate structure that resolves the complex problem dealing with uncertainty. The proposed approach is based on the idea of the original expansion of the solution space with additional dimensions. The possibilities of the synergetics and chaos theory for problem-solving in the area of computer Science and systems Science are discussed. The potential of chaotic systems to uncertainty resolution by self-organization and synchronization is demonstrated by the example of clustering in the condition of full uncertainty. An illustrative example of the proposed approach to solve the clustering problems without information about the number of clusters and information about specific data topology is presented. A comparison of the classical approaches and the proposed is given. Also, the features of the proposed approach and the further work are considered. © 2021 IEEE.

关键词： Chaotic systems

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