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2nd International Conference on informatics, ICI 2023

作者： Ravindar, B. Naik, Darshana A. Jain, Neetu Lourens, Melanie Tiwari, Mohit Kunekar, Pankaj SR University School of Sciences Telangana Warangal India Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Institute of Management and Research New Delhi India Durban University of Technology Faculty of Management Sciences South Africa Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350343830

Numerous businesses, particularly education, are being rapidly transformed by artificial intelligence (AI). AI is being utilised in school management to enhance student results, the learning experience, and administrative chores. This study intends to investigate the use of AI in educational management, comprising advantages and disadvantages. The study effort examines the literature on AI in school management using a systematic review method. According to the research, AI has several benefits, such as increased student engagement, personalised instruction, and cost effectiveness. But AI also presents several difficulties, including ethical issues, possible biases, and the necessity to reskill the workforce. The study finds that AI may significantly enhance educational management, but its application must be done so carefully and cautiously. © 2023 IEEE.

关键词： Students

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Meta-learning in Healthcare: A Survey

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SN computer Science 2024年第6期5卷 791页

作者： Rafiei, Alireza Moore, Ronald Jahromi, Sina Hajati, Farshid Kamaleswaran, Rishikesan Department of Computer Science Emory University Atlanta GA United States Department of Mechatronics Engineering University of Tehran Tehran Iran School of Science and Technology University of New England Armidale NSW Australia Department of Biomedical Informatics Emory University School of Medicine Atlanta GA United States Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA United States

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model’s capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. These unique characteristics position meta-learning as a suitable choice for developing influential solutions in various healthcare contexts, where the available data is often insufficient, and the data collection methodologies are different. This survey discusses meta-learning broad applications in the healthcare domain to provide insight into how and where it can address critical healthcare challenges. We first describe the theoretical foundations and pivotal methods of meta-learning. We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies. Finally, we highlight the current challenges in meta-learning research, discuss the potential solutions, and provide future perspectives on meta-learning in healthcare. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automated medicine Clinical decision support system Few-shot learning Healthcare Meta-leaning Multi-task learning

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms 7

Efficient Resource Management in IoTs Using Evolutionary and...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Gupta, Keshav Dev Cengiz, Korhan Awasthi, Charu Ramini, Siva Akash Chatterjee, Shyamapriya Pavithra, M. Poornima College of Engineering Department of Computer Science Engineering Jaipur India University of Fujairah College of Information Technology United Arab Emirates Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Hradec Kralove500 03 Czech Republic JSS Academy of Technical Education Department of Information Technology Noida India Andhra Pradesh Amaravati India Narula Institute of Technology Department of Information Technology West Bengal Kolkata India Jansons Institute of Technology Karumathampatti Department of Computer Science and Engineering Coimbatore641659 India

ISBN: (纸本)9798350383065

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems. © 2023 IEEE.

关键词： Swarm intelligence

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Cultivate Smart and Healthy Ageing 9th

Cultivate Smart and Healthy Ageing

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9th EAI International Conference on IoT Technologies for HealthCare, HealthyIoT 2022

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Markakis, Evangelos Mastorakis, George Batalla, Jordi Mongay Pallis, Evangelos Dobre, Ciprian Department of Computer Science University of Nicosia and University of Nicosia Research Foundation Nicosia Cyprus Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Department of Management Science and Technology Hellenic Mediterranean University Agios Nikolaos Crete Greece Warsaw University of Technology Warsaw Poland Department of Industrial Design and Production Engineering School of Engineering University of West Attica Athens Greece National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (纸本)9783031286629

The essential criterion for developing an intelligent age-friendly environment is evaluating and monitoring the provision of services. Our research aims to allow the deployment of a broad range of digital healthcare solutions interconnected in an IoT network ecosystem. Through their interoperability, we will be able to cultivate age-friendly smart homes for older adults. Thus, we will enable interpreted living and wellbeing. The intelligent integrations of digital solutions will allow the acquisition and evaluation of health-related data. Standardization, interoperability and scalability of the integration will increase efficiency in healthcare, improving older adults’ Quality of Life (QoL). Also, interpreting their needs will allow key stakeholders to optimize the Quality of Service (QoS). The proposed Internet of Health (IoH) framework will encapsulate sustainable and affordable innovative solutions and their benefits for a comfortable, meaningful, and independent life in an intelligent environment. Therefore, the frame mediated by edge computing, in-home and community settings will be able to interact with healthcare networks contributing to hospitalizations and institutional care. © 2023, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Internet of things

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A federated learning attack method based on edge collaboration via cloud

A federated learning attack method based on edge collaborati...

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作者： Yang, Jie Baker, Thar Gill, Sukhpal Singh Yang, Xiaochuan Han, Weifeng Li, Yuanzhang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computer Science Beijing Institute of Technology Beijing China School of Mechanical Manufacturing and Automation Engineer of China Railway Tunnel Bureau Group Co. Ltd Henan Zhengzhou China

Federated learning (FL) is widely used in edge-cloud collaborative training due to its distributed architecture and privacy-preserving properties without sharing local data. FLTrust, the most state-of-the-art FL defense method, is a federated learning defense system with trust guidance. However, we found that FLTrust is not very robust. Therefore, in the edge collaboration scenario, we mainly study the poisoning attack on the FLTrust defense system. Due to the aggregation rule, FLTrust, with trust guidance, the model updates of participants with a significant deviation from the root gradient direction will be eliminated, which makes the poisoning effect on the global model not obvious. To solve this problem, under the premise of not being deleted by the FLTrust aggregation rules, we construct malicious model updates that deviate from the trust gradient to the greatest extent to achieve model poisoning attacks. First, we utilize the rotation of high-dimensional vectors around axes to construct malicious vectors with fixed orientations. Second, the malicious vector is constructed by the gradient inversion method to achieve an efficient and fast attack. Finally, a method of optimizing random noise is used to construct a malicious vector with a fixed direction. Experimental results show that our attack method reduces the model accuracy by 20%, severely undermining the usability of the model. Attacks are also successful hundreds of times faster than the FLTrust adaptive attack method. © 2022 John Wiley & Sons Ltd.

关键词： Vectors

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Practical big data techniques for end-to-end machine learning deployment: a comprehensive review

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Discover Data 2025年第1期3卷 1-18页

作者： El-Sayed, Ahmed Abougabal, Mohamed Lazem, Shaimaa Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Informatics Research Institute City of Scientific Research and Technological Applications New Borg El-Arab Egypt

In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms have been proposed to tackle these issues, there is still a need for a conceptual guide that synthesizes state-of-the-art tools and techniques across the full machine learning (ML) workflow. In this survey, we consolidate research on big data infrastructures, distributed processing frameworks, and ML methods, mapping out an end-to-end conceptual framework that can serve as a reference architecture for end-to-end deployment. Specifically, we discuss key aspects of big data analytics (storage solutions, platforms like Hadoop and Spark, NoSQL databases), preprocessing approaches (dimensionality reduction, instance selection, noise handling), ML algorithms (supervised, unsupervised, and emerging deep learning paradigms), and deployment considerations (monitoring, continuous integration, and versioning). By gathering and integrating these elements, our survey provides a comprehensive overview of existing solutions, clarifies the design choices developers must consider, and identifies research gaps that remain to be addressed.

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power Systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA Science and technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Deterministic Sensitivity Oracles for Diameter, Eccentricities and All Pairs Distances 49

Deterministic Sensitivity Oracles for Diameter, Eccentriciti...

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49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022

作者： Bilò, Davide Choudhary, Keerti Cohen, Sarel Friedrich, Tobias Schirneck, Martin Department of Information Engineering Computer Science and Mathematics University of L'Aquila Italy Department of Computer Science and Engineering Indian Institute of Technology Delhi India School of Computer Science The Academic College of Tel Aviv-Yaffo Israel Hasso Plattner Institute University of Potsdam Germany

ISBN: (纸本)9783959772358

We construct data structures for extremal and pairwise distances in directed graphs in the presence of transient edge failures. Henzinger et al. [ITCS 2017] initiated the study of fault-tolerant (sensitivity) oracles for the diameter and vertex eccentricities. We extend this with a special focus on space efficiency. We present several new data structures, among them the first fault-tolerant eccentricity oracle for dual failures in subcubic space. We further prove lower bounds that show limits to approximation vs. space and diameter vs. space trade-offs for fault-tolerant oracles. They highlight key differences between data structures for undirected and directed graphs. Initially, our oracles are randomized leaning on a sampling technique frequently used in sensitivity analysis. Building on the work of Alon, Chechik, and Cohen [ICALP 2019] as well as Karthik and Parter [SODA 2021], we develop a hierarchical framework to derandomize fault-tolerant data structures. We first apply it to our own diameter and eccentricity oracles and then show its versatility by derandomizing algorithms from the literature: the distance sensitivity oracle of Ren [JCSS 2022] and the Single-Source Replacement Path algorithm of Chechik and Magen [ICALP 2020]. This way, we obtain the first deterministic distance sensitivity oracle with subcubic preprocessing time. © Davide Bilò, Keerti Choudhary, Sarel Cohen, Tobias Friedrich, and Martin Schirneck;licensed under Creative Commons License CC-BY 4.0

关键词： Directed graphs

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Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 257-271页

作者： Christopher Wiedeman Peter Lorraine Ge Wang Richard Do Amber Simpson Jacob Peoples Bruno De Man Department of Electrical and Computer Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA GE Research-Healthcare NiskayunaNY 12309USA Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Radiology Memorial Sloan Kettering Cancer CenterNew YorkNY 10065USA Biomedical Computing and Informatics Queen’s UniversityKingstonON K7L 3N6Canada

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver *** tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction *** hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural *** kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver *** pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using *** of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency *** data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal *** the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to ***,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical *** results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is *** work should investigate the informationpreserving kernels in datasets with clinical labels.

关键词： Radiomics Deep learning Computed tomography Colorectal liver metastases Virtual clinical trials Image reconstruction

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Novel Noise Filter Techniques and Dynamic Ensemble Selection for Classification

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Recent Advances in computer Science and Communications 2022年第1期15卷 48-59页

作者： Nguyen, Thi Ngoc A. Nhu, Quynh P. Solanki, Vijender K. School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam CMC Institute of Science and Technology 11 Duy Tan Cau Giay Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: Ensemble selection is one of the most researched topics for ensemble learning. researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensemble system classifiers. Dynamic Ensemble Selection (DES) is one of the most effective techniques in classification problems. DES systems select the most appropriate classifiers from the candidate classifier pool. Ensemble models that balance diversity and accuracy in the training process improve performance than the whole classifiers. Objective: In this paper, novel techniques are proposed by combining Noise Filter (NF) and Dynamic Ensemble System (DES) to have better predictive accuracy. In other words, a noise filter and DES make the data cleaner and DES improves the performance of classification. Methods: The proposed NF-DES model, which was demonstrated on twelve datasets, especially has three credit scoring datasets and a performance measure accuracy. Results: The results show that our proposed model is better than other models. Conclusion: The novel noise filer and dynamic ensemble learning with the aim to improve the classification ability are presented. To improve the performance of classification, noise filter with dynamic ensemble learning makes the noise data toward the correct class. Then, novel dynamic ensemble learning chooses the appropriate subset classifiers in the pool of base classifiers. © 2022 Bentham Science Publishers.

关键词： Support vector machines

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