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6th Social Robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Salvini, Pericle Pattinson, Jo-Ann Kunze, Lars Winfield, Alan Jirotka, Marina Department of Computer Science University of Oxford United Kingdom Faculty of Environment University of Leeds United Kingdom School of Engineering University of the West of England United Kingdom

ISBN: (纸本)9781643685670

AI-enhanced reasoning enables robots to create detailed accounts of their own situated behaviour as well as the behaviour of other people. This capability is currently employed by robot designers to achieve transparency, trust, and enhance robot social and communicative capabilities. Furthermore, robots may be designed to resemble humans both in their physical appearance and their behaviour. This approach is intended to facilitate more effective interactions with people. In this article we identify and examine some of the ethical, social and legal implications of these capabilities for the investigation of robot accidents. We consider two aspects in particular. The first of these is the role of robots as subjects in a testimony regarding an incident in which they are directly or indirectly involved. This can be described as a case of robots acting as witnesses. The second aspect is the role of robots as objects in a human testimony. This can be described as a case of robots being witnessed. © 2025 IOS Press. All rights reserved.

关键词： Social robots

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Superpixel-Based Sparse Labeling for Efficient and Certain Medical Image Annotation 27th

Superpixel-Based Sparse Labeling for Efficient and Certain M...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rezaei, Somayeh Jiang, Xiaoyi Department of Information Engineering University of Padova Padua Italy Faculty of Mathematics and Computer Science University of Münster Einsteinstrasse 62 Münster48149 Germany

ISBN: (纸本)9783031781971

Supervised deep learning crucially depends on large amount of high-quality annotation data. While labeling for classification and grading tasks is rather efficient to achieve, labeling for segmentation is much more difficult and time-consuming, characterized by pixel-based dense annotation in practice. This approach suffers from two fundamental disadvantages: 1) Lack of efficiency because of the large number of pixels on region boundaries (other parts of an image can be easily labeled) and more importantly the need of precise positioning of boundary pixels. 2) Lack of certainty. The area around the boundaries is in fact the part of an image, where even medical experts are often uncertain and may make non-precise annotations, resulting in varying annotations by different experts (the serious problem of inter-observer variability). To overcome these disadvantages, we propose superpixel-based annotation instead of pixels. Importantly, we do not require to label in the area of boundaries with high uncertainty for medical experts. We automatically fill the unlabeled area (boundary gap). In addition to heuristic rules we also study the random walker. Experiments were conducted with three different medical segmentation tasks and two network models. Despite the easy-to-make sparse annotation we are able to achieve segmentation results that are comparable or even superior to those obtained by using pixel-based dense annotation. In addition to the high efficiency, our approach substantially reduces the inter-observer variability as a positive side effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Superpixels

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Impact of defects on the spectral characteristics of PureB avalanche-mode light emitting diodes 29

Impact of defects on the spectral characteristics of PureB a...

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Light-Emitting Devices, Materials, and Applications XXIX 2025

作者： Jelavić, Eva Nanver, Lis K. Knežević, Tihomir Ruder Boskovic Institute Division of Materials Physics Laboratory for Semiconductors Zagreb Croatia University of Twente Faculty of Electrical Engineering Mathematics & Computer Science Enschede Netherlands

ISBN: (纸本)9781510685208

Single photon avalanche diodes (SPADs) fabricated in PureB silicon technology offer exceptional versatility, functioning both as light emitting diodes and detectors sensitive down to a single photon. In PureB technology, nanometer-thin layers of boron are deposited on the silicon surface, creating defect-free ultra-shallow junctions. In this paper, PureB diodes with a junction depth of a few tens of nanometers are studied as avalanche-mode light-emitting diodes (AMLEDs) with the advantage that optical absorption was minimal. They were systematically analyzed to investigate the impact of defects on their optical and electrical characteristics. Bulk defects were unintentionally introduced by contamination during the processing of the n+ buried layer. These defects cause the increase in dark currents and the formation of light-emitting spots, observable in light emission spectra taken at reverse biases below the breakdown voltage. An increase in relative intensity for wavelengths from 450 nm to 550 nm was observed in spectral characteristics of light emitting spots compared to the light emission of the active-region breakdown. Optical and electrical characterization was conducted on diodes with different geometry, implantation doses in the multiplication region, and distance to an implicit guard ring to examine the impact of these parameters on the light emission spectrum. Low-temperature electrical characteristics and light emission patterns of the devices were obtained by measurements at cryogenic temperatures. © 2025 SPIE.

关键词： Particle beams

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Document Summarizer: A Machine Learning approach to PDF summarization

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Procedia computer science 2025年 252卷 1025-1036页

作者： Rama Krishna Peddarapu Sudagoni Anogna M N S Sri Harshini J Sireesha K Guna Rathna Department of Computer Science and Engineering“ Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today’s professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts.

关键词： K Means Clustering Abstractive Extractive Sentence Transformer Embeddings

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD Dataset 11

Comparative Analysis of Machine Learning Techniques for Arab...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Attar, Hani Sharrab, Yousef Smadi, Saja Jbaily, Safa Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Isra University Department of Data Science and Artificial Intelligence Amman Jordan Yarmouk University Dept. Computer Science Irbid Jordan

ISBN: (纸本)9798331509293

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. Data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles Dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency;indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications. © 2025 IEEE.

关键词： Logistic regression

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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Whale Optimization Algorithm-Based Feature Selection Combined with the XGBoost Model for Breast Cancer Detection: A Comparative Study 9th

Whale Optimization Algorithm-Based Feature Selection Combine...

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9th International Conference on Smart City Applications, SCA 2024

作者： Benghazouani, Salsabila Nouh, Said Zakrani, Abdelali Department of Mathematics and Computer Science Faculty of Sciences Ben M’Sik Hassan II University Casablanca Morocco Department of Computer Science Engineering ENSAM Hassan II University Casablanca Morocco

ISBN: (纸本)9783031886522

Breast cancer has emerged as a significant cause of female mortality globally, underscoring the critical need for early detection and diagnosis. Machine Learning, as a cutting-edge technology, holds immense promise in addressing healthcare challenges, including breast cancer detection. Despite advancements in technology, accurately discerning malignant breast masses from benign ones remains a complex and challenging task in medical research. Feature selection techniques play a vital role in this endeavor, as they aim to identify the most pertinent and informative features from complex datasets, thus improving the accuracy and reliability of models for detecting breast cancer. Numerous recent studies have emphasized the efficacy of metaheuristic algorithms in optimizing various problems across different domains, with particular focus on the Whale Optimization Algorithm (WOA). This paper focuses on evaluating the effectiveness of different feature selection (FS) approaches, such as WOA-based Feature selection, Recursive Feature Elimination, and SelectFromModel, in conjunction with diverse machine learning approaches for detecting breast cancer. The study assesses methods, including Support Vector Machine, XGBoost, Decision Tree, and Logistic regression using performance evaluation measures including, recall, F1-score, accuracy, area under the curve (AUC), precision, and feature removal percentage. Notably, FS based WOA combined with XGBoost model demonstrated outstanding performance measures, with an accuracy rate of 99.4%, an F1-score of 99.4%, 100.0% precision, 98.8% recall, and a 99.4% AUC. These findings contribute valuable insights into enhancing breast cancer detection accuracy and facilitating early intervention strategies, thereby potentially improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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5G Technology and Impact on Modern engineering 4

5G Technology and Impact on Modern Engineering

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kate, Priya Pacharney, Utkarsha Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Department of Computer Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

5G technology represents a progressive leap in Wi-Fi conversation, providing unheard-of pace, connectivity, and capacity. Its deployment has profound implications for modern engineering, impacting industries such as telecommunications, healthcare, transportation, and clever towns. With speeds up to one hundred instances quicker than 4G and latency as little as 1 millisecond, 5G permits real-time facts trade, fostering improvements like independent vehicles, far-flung surgeries, and the Internet of Things (IoT). Currently, Engineers are challenged to design infrastructure that will assist the dense community of gadgets related to 5G, necessitating advancements in antenna design, power performance, and statistics security. The era also quickens the adoption of part computing, reducing reliance on centralised facts centres and ensuring quicker processing near the date supply. The transformative impact of 5G extends beyond connectivity. It reshapes the layout and capability of present-day engineering structures, emphasising adaptability, resilience, and scalability. As 5G continues to conform, its function in driving innovation and redefining engineering paradigms becomes increasingly glaring. © 2025 IEEE.

关键词： 5G mobile communication systems

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Translator Chatbot in Cloud Environment Using Amazon Translate and Amazon Lex 8th

Translator Chatbot in Cloud Environment Using Amazon Transla...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Prakash, Vijay Sachdeva, Vasu Bhatia, Sunpreet Garg, Lalit Faculty of ICT University of Malta Msida Malta Department of Computer Science and Engineering Graphic Era Hill University Dehradun India School of Computer Sciences University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819738588

In today’s globalized world, people from different parts of the world often communicate with each other for various reasons, such as business, travel, or personal interactions. However, language differences can create a significant hurdle in effective communication, leading to misunderstandings and even communication breakdowns. Machine translation technology has been developed to automatically translate text from one language to another to address this issue. However, it can still be challenging for people to communicate effectively using machine translation technology, especially if they are unfamiliar or have questions about the translations. This paper addresses solving the problem of the language barrier that often exists between people who speak different languages. This paper uses Amazon Translate and Amazon Lex services to create a translator chatbot in the cloud environment. The chatbot aims to assist users in translating text from one language to another. The chatbot is built using Amazon Lex, a conversational interface for building chatbots, and Amazon Translate, a neural machine translation service. The paper involves setting up the necessary AWS services and configuring them to work together. This includes setting up the Amazon Lex bot, creating the required intents and utterances, and integrating the Amazon Translate service for language translation. The experimental analysis provides accurate and high-quality translations in real time, improving communication across language barriers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bot (Internet)

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Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2025年第2期12卷 1-11页

作者： Nguyen, Thi-Tuyet-Hai Hung, Tran Cong Son, Nguyen Hong Hanh, Tan Duy, Tran Trung Tu, Lam-Thanh Faculty of Information Technology Posts and Telecommunications Institute of Technology Viet Nam School of Computer Science & Engineering Saigon International University Ho Chi Minh City Viet Nam Faculty of Telecommunications Posts and Telecommunications Institute of Technology Viet Nam Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory. © (2025), (European Alliance for Innovation). All rights reserved.

关键词： Coverage probability deep learning energy harvesting long range power beacon

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