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The Extended RoSA Conceptual Architecture for Managing Consumer-Grade Social Robot Surrogates

The Extended RoSA Conceptual Architecture for Managing Consu...

引用

IEEE Games Media Entertainment (GEM)

作者： Jens-Martin Loebel Curtis L. Gittens Dept. of Economics Magdeburg-Stendal University of Applied Sciences Magdeburg Germany Dept. of Computer Science Mathematics and Physics The University of the West Indies Cave Hill Campus Bridgetown Barbados

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

This paper introduces the Extended Robot Surrogate Architecture (eRoSA), a novel framework tailored for managing consumer-grade social robots used in entertainment and companion scenarios, including elder care. By extending the Robot Surrogate Architecture, eRoSA enhances the operational effectiveness of robotic surrogates by integrating advanced modules for state and personality management, allowing for dynamic and context-sensitive interactions across both healthcare and leisure domains. The architecture features a Hardware Abstraction Layer and a ReST Communication Module, which facilitate scalable, modular interactions across diverse robotic platforms, ranging from caregiving to entertainment applications. Challenges addressed include the complexity of integrating diverse robotic systems, ensuring real-time performance, and maintaining interaction quality. By emphasizing psychosocial interactions and modular adaptability, eRoSA aims to significantly improve the utility and acceptance of social robots in varied real-world settings, outlining a path for future empirical research and practical refinement.

关键词： Social robots Entertainment industry Medical services Media Real-time systems Hardware Distance measurement

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Detection of Cancer Cells Using Convolutional Neural Networks (CNNs)

Detection of Cancer Cells Using Convolutional Neural Network...

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Computing and Data Analytics (ICCDA), International Conference on

作者： Md. Ghouse Mohiuddin D. Laxmi Sreenivasa Reddy Aijaz Ahmed Dept. of Computer Science University PG College Palamamuru University Mahabubnagar Telangana State India Dept. of IT Chaitanya Bharthi Institute of Technology - CBIT Hyderabad Telangana State India Dept. of IT University of Technology & Applied Science Shinas Sultante of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The fastest spreading dieses among the cancer dieses throughout the world is the Breast cancer. Identifying it at the prior stage will be helpful for effective treatment. Convolutional Neural Networks (CNNs) is an efficient technique which has demonstrated significant ability in classifying and recognizing patterns in medical images. In this research paper we have explored the application of deep learning models for detecting cancer cells from histopathological images, and focused on various architectures, training methods, and assessment measures. Here we specifically evaluated the functioning of four well-known models of CNN comprising AlexNet, VGG16, InceptionV3 and, ResNet50 by employing the dataset from BreakHis for binary classification of the histopathological images into Malignant and Benign classes. Each model is fine-tuned and trained through transfer learning. Here we used cross-entropy loss with Adam optimizer for binary classification, through which the learning rate is adaptively adjusted, and facilitating efficient handling of large datasets. In this research work for training our models we applied Stratified k-fold cross-validation approach for ensuring stability and generalizability. The effectiveness and performance of the models are assessed with the help of Accuracy, Precision, & Recall, and F1-Score. Comparative analysis indicates that ResNet50 outperforms the other models in detecting cancer cells. Finally, we discussed the potencies and weakness of the applied models for offering valuable perceptions into their efficacy for medical image classification tasks.

关键词： Training Adaptation models Analytical models Accuracy Transfer learning Breast cancer Convolutional neural networks Biological cells Medical diagnostic imaging Residual neural networks

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A GAN-LSTM Architecture for ECG Anomaly Detection

A GAN-LSTM Architecture for ECG Anomaly Detection

引用

Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Soumia OUBADI Naçima MELLAL Rachida BOUCHOUAREB Soumia ZERTAL dept. Computer science Research Laboratory on Computer Science's and Complex System University of Oum El Bouaghi Algeria dept. Networks and telecommunication Research Laboratory of Artificial Intelligence and Autonomous Objects Institute of Applied Science & Technology University of Oum El Bouaghi Algeria dept. Computer science Research Laboratory of Artificial Intelligence and Autonomous Objects University of Oum El Bouaghi Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Cardiovascular diseases (CVDs) continue to be a primary cause of global mortality, requiring prompt detection and care to avert serious consequences. Electrocardiograms (ECGs) are vital diagnostic instruments for cardiovascular disorders, yet traditional diagnostic methods encounter considerable obstacles, such as class imbalance and substantial heterogeneity in the available data. This study introduces a novel ECG classification technique utilizing GAN and LSTM. Generative Adversarial Networks (GANs) are employed to produce additional synthetic ECG samples, addressing class imbalance in the training dataset. The suggested design integrates LSTM units into the GAN generator to collect and maintain temporal signal patterns, while anomaly detection is performed using a CNN-LSTM model. Experimental findings indicate a significant enhancement in classification performance, achieving an accuracy of up to 94.74%. The findings indicate that the proposed GAN-LSTM model significantly improves ECG anomaly detection and classification, offering substantial potential for the rapid and precise identification of arrhythmias.

关键词： Training Instruments Mortality computer architecture Electrocardiography Generative adversarial networks Real-time systems Convolutional neural networks Anomaly detection Long short term memory

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AI-Powered Dermatology: Achieving Dermatologist-Grade Skin Cancer Classification

AI-Powered Dermatology: Achieving Dermatologist-Grade Skin C...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Priyanka Kaushik Yash Chopra Amar Kajla Minakshi Poonia Akram Khan Dhruv Yadav Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Eng. Math’s & Computing Madhav Institute of Technology & Science Gwalior India Dept. of Computer Science Madhav University Abu Road India

In the realm of dermatology, the accurate diagnosis of skin cancer has long been a challenging endeavor. This paper introduces a cutting-edge solution for achieving dermatologist-grade skin cancer classification through the power of artificial intelligence (AI). Departing from traditional methods that necessitate laborious manual feature extraction and domain-specific preprocessing, our system adopts a deep neural network architecture, specifically Google Net Inception v3 CNN, fine-tuned using a vast and diverse clinical image dataset. Dataset comprises 135,550 images meticulously organized within a structured taxonomy encompassing 2,055 distinct disease categories. To unlock the full potential of fine-grained classification, An innovative algorithm is introduced to facilitate precise identification of various skin diseases. This research underscores the transformative potential of AI-powered dermatology in the realm of early skin cancer detection. By achieving dermatologist-level accuracy, this approach has the capacity to significantly impact public health outcomes, particularly in regions where skin cancer is a prevalent concern.

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Detecting Ransomware Encryption with File Signatures and Machine Learning Models

Detecting Ransomware Encryption with File Signatures and Mac...

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Irish Signals and Systems Conference (ISSC)

作者： Michael Duignan Michael Schukat Enda Barrett Dept. of Computer Science & Applied Physics Atlantic Technological University Galway Ireland Dept. of Information Technology University of Galway Galway Ireland

This study presents an analysis of the use of machine learning models in the identification and classification of ransomware encrypted files, differentiating them from standard encrypted or compressed files, and non-encrypted files (referred to as goodware). The study utilized a robust dataset of approximately 159,897 files, categorized into goodware, Chaos, Conti, and Xorist strains, and applied five machine learning models: Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor, Naive Bayes, and Classification and Regression Trees to this dataset. The models were trained using an array of data points, including file headers and footers, entropy, Chi Squared, and file extensions. The analysis revealed high accuracy rates of between 97% and 100% in distinguishing ransomware encrypted files from other file types, demonstrating the importance of file extensions as a key determinant in this process. The study also draws attention to the increasing prevalence and complexity of ransomware strains, specifically those which do not alter file extensions, thereby posing additional challenges to identification and classification efforts. The research suggests further investigation and study into a wider array of ransomware strains and a more extensive range of file types. Special emphasis is recommended on strains that do not modify file extensions, as understanding these could significantly enhance the efficiency and effectiveness of machine learning models in ransomware detection.

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IoT and Machine Learning based Valve Stiction Estimation in Process Industry

IoT and Machine Learning based Valve Stiction Estimation in ...

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Control Instrumentation System Conference (CISCON)

作者： Vimal Kumar V Ragesh G. K Aparna George Priya S Dept. of Computer Science Indian Institute of Information Technology Kottayam Pala Kerala India Dept. of Applied Electronics & Instrumentation Rajagiri School of Engineering & Technology Ernakulam Kerala India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

Stiction in control valves represents an undesirable nonlinear phenomenon. Product quality and process safety frequently suffer due to the occurrence of valve stiction within process control systems utilizing control valves as their final control elements. This paper introduces a straightforward and pragmatic Machine Learning technique, supported by Internet of Things (IoT) technology, for identifying and assessing the initiation of stiction in all pertinent control loops on a periodic basis. Furthermore, this paper suggests an IoT-based innovative approach to facilitate collaboration among industrial experts across multiple process plants, enabling them to explore new avenues for preventive maintenance concerning stiction in control valves.

关键词： Training Maximum likelihood estimation Neural networks Process control Machine learning Valves Control systems Safety Internet of Things Preventive maintenance

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Efficient Atrophy Mapping: A Single-Step U-Net Approach for Rapid Brain Change Estimation

Efficient Atrophy Mapping: A Single-Step U-Net Approach for ...

引用

Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Riccardo Raciti Alessia Rondinella Lemuel Puglisi Francesco Guarnera Daniele Ravì Sebastiano Battiato Dept. of Math and Computer Science University of Catania Catania Italy School of Physics Engineering and CS University of Hertfordshire Hatfield UK

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

The estimation of brain atrophy is crucial for eval-uating brain diseases and analyzing neurodegeneration. Existing methods for computing atrophy maps often suffer from lengthy processing times due to the computational cost of multi-step processing. In this work, we propose a novel technique for atrophy map calculation using a single U-net-based architecture. This approach consolidates multiple traditional medical imaging processing steps into a single process, aiming to accelerate the computational time required. Specifically, our method estimates structural changes by generating a flow map from two longitudinal Magnetic Resonance Imaging (MRI) scans of the same subject. We trained and evaluated our system on a dataset comprising 2000 Tl-weighted MRI scans sourced from two different public datasets on Alzheimer's Disease. Experimental results demonstrate a considerable reduction in execution time while maintaining atrophy mapping performance comparable to state-of-the-art solutions. We believe that our pipeline could significantly benefit clinical applications for measuring brain atrophy, especially in scenarios requiring the evaluation of large cohorts, such as clinical trials. Our code is freely available at https://***/Efficient Atrophy Mapping: A Single-Step U-net Approach for Rapid Brain Change Estimation.

关键词： Atrophy Codes Extended reality Magnetic resonance imaging Pipelines Estimation Neural engineering computer architecture Metrology Computational efficiency

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Joint Channel Estimation and Symbol Detection in Overloaded MIMO Using ADMM

Joint Channel Estimation and Symbol Detection in Overloaded ...

引用

IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Swati Bhattacharya K.V.S. Hari Yonina C. Eldar Dept. of Electrical Communication Engineering Indian Institute of Science India Faculty of Math and Computer Science Weizmann Institute of Science Israel

This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for overloaded multiple-input multiple-output (MIMO) wireless communication systems, with the number of receive antennas being less than or equal to the number of transmit antennas. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) markedly improves the symbol detection performance by yielding 12-16 dB gain in signal-to-noise ratio (SNR) for a bit error rate (BER) of 10 −3 over state-of-the-art JED using Alternating Minimization (JED-AM). This gain in BER for the proposed JED-ADMM is also accompanied by a significant reduction in computational complexity (1/4 times) as compared to JED-AM.

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PAC-Bayes-Chernoff bounds for unbounded losses 24

PAC-Bayes-Chernoff bounds for unbounded losses

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ioar Casado Luis A. Ortega Aritz Pérez Andrés R. Masegosa Machine Learning Group Basque Center for Applied Mathematics (BCAM) Machine Learning Group Computer Science Dept. - EPS. Universidad Autónoma de Madrid Department of Computer Science Aalborg University

ISBN: (纸本)9798331314385

We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cramér-Chernoff bounds to the PAC-Bayesian setting. The proof technique relies on controlling the tails of certain random variables involving the Cramér transform of the loss. Our approach naturally leverages properties of Cramér-Chernoff bounds, such as exact optimization of the free parameter in many PAC-Bayes bounds. We highlight several applications of the main theorem. Firstly, we show that our bound recovers and generalizes previous results. Additionally, our approach allows working with richer assumptions that result in more informative and potentially tighter bounds. In this direction, we provide a general bound under a new model-dependent assumption from which we obtain bounds based on parameter norms and log-Sobolev inequalities. Notably, many of these bounds can be minimized to obtain distributions beyond the Gibbs posterior and provide novel theoretical coverage to existing regularization techniques.

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A Low-Cost Brine Level Measurement System for Farmers Through NB-IoT Technology

A Low-Cost Brine Level Measurement System for Farmers Throug...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Wang, Dao-Jiun Fanjiang, Yong-Yi Hung, Chi-Huang Hsiao, Yao-Yun Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Lee-Ming Institute of Technology Dept. of Information Technology New Taipei City Taiwan Lee-Ming Institute of Technology Dept. of Digital Multimedia New Taipei City Taiwan

ISBN: (纸本)9781665441544

Because of the usual changes in weather. In the past, farmers had to constantly check whether their crops were kept in a good growing environment. This research uses the Internet of Things technology to measure the water level of the pickled cabbage barrel using an ultrasonic module, lens and microcontroller (MCU). In addition to using a non-contact ultrasonic module, a lens is also used to capture the image of the pickled cabbage barrel, so that farmers can understand the situation remotely without leaving home. If the brine level is abnormal, farmers can be notified via instant messaging software. © 2022 IEEE.

关键词： Internet of things

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