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FedUA: An Uncertainty-Aware Distillation-Based Federated Learning Scheme for Image Classification

Information (Switzerland) 2023年第4期14卷 234页

作者： Lee, Shao-Ming Wu, Ja-Ling Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei10617 Taiwan

Recently, federated learning (FL) has gradually become an important research topic in machine learning and information theory. FL emphasizes that clients jointly engage in solving learning tasks. In addition to data security issues, fundamental challenges in this type of learning include the imbalance and non-IID among clients’ data and the unreliable connections between devices due to limited communication bandwidths. The above issues are intractable to FL. This study starts from the uncertainty analysis of deep neural networks (DNNs) to evaluate the effectiveness of FL, and proposes a new architecture for model aggregation. Our scheme improves FL’s performance by applying knowledge distillation and the DNN’s uncertainty quantification methods. A series of experiments on the image classification task confirms that our proposed model aggregation scheme can effectively solve the problem of non-IID data, especially when affordable transmission costs are limited. © 2023 by the authors.

关键词： Distillation

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Blockchain in Environmental Sustainability Measures: a Survey

arXiv

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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

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Improving energy efficiency in internet of things using artificial bee colony algorithm

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Recent Patents on engineering 2021年第2期15卷 161-168页

作者： Sivaram, Murugan Porkodi, V. Mohammed, Amin Salih Karuppusamy, S. Anbu Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Networking Lebanese French University Kurdistan Region Erbil Iraq Department of Electronics & Communication Engineering Excel Engineering College Namakkal Tamilnadu India

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities to reduce the constraint demands of the current security protocols. Objective: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem among IoT devices and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, the energy efficiency in WSNs has been an important concern. Methods: In this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial at-tempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability. © 2021 Bentham Science Publishers.

关键词： Energy efficiency

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Convolutional Neural Network Model based Deep Learning Approach for Osteoporosis Fracture Detection

Convolutional Neural Network Model based Deep Learning Appro...

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networking and Communications (ICNWC), International Conference on

作者： R. Dhanalakshmi M. Thenmozhi Swati Saxena Hemalatha Mahalingam School of Computer science and Engineering Vellore Institute of Technology Chennai India Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Computer science and Engineering KCG College of Technology Chennai India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Kingdom of Saudi Arabia

Osteoporosis is a bone ailment which takes place because of minimum bone physique, damaging of micro-structure of bone, more over an excessive vulnerability to breakage. The main fitness difficulty throughout the globe is Osteoporosis, particularly in aged people. It may create spinal or hip breakages which can result in morbidity and burden. Hence the diagnosis of Osteoporosis at early stages and forecasting the existence of the fracture is highly essential. However automated analysis and diagnosis of osteoporosis from virtual radiographs could be very difficult as they have little variations. The proposed approach in this work uses high-dimensional textured function representations calculated from radiography pictures to distinguish healthy from osteoporotic issues. CNN helps to identify osteoporosis using structural MRI measurements of bone with high accuracy

关键词： Training Osteoporosis Hospitals Switches Bones Data models Real-time systems

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Data-Driven Multiobjective Optimization for Massive MIMO and Hyperdensification Empowered 5G Planning under Realistic Network Environment

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Zeleke, Seifu G. Haile, Beneyam B. Bekele, Ephrem T. Mutafungwa, Edward Hämäläinen, Jyri School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Department of Communications and Networking School of Electrical Engineering Aalto University Espoo02150 Finland

To accommodate the increasing data rate demand, the fifth-generation (5G) cellular network came up with new technological advancements including massive multiple-input multiple-output (massive MIMO) and hyperdensification which can significantly boost network capacity. On the other hand, the introduction of these technologies along with their heterogeneity brings a challenge in terms of network operators' need to identify a cost-effective optimal deployment approach which is hardly entertained by the legacy planning and optimization method. Hence, to leverage the core benefits of those technologies in a cost-effective manner, we need a holistic planning framework that takes into account their coverage, capacity and cost impact, and realistic spatiotemporal distribution of users. In this work, we present a data-driven multiobjective optimization planning framework that can be used not only for small cells but also for massive MIMO. The planning framework is illustrated using a 5G planning case study for a service area in Addis Ababa, Ethiopia, considering its realistic network data that is collected from the network management system and different potential identified deployment options. Ray tracing is employed to compute propagation, and users and demands are distributed based on the realistic network data. A two-stage optimization and a joint optimization are applied to identify points that provide optimal network performance. Simulation results reveal that the planning method provides Pareto points for different deployment options that can significantly improve the performance of the existing network while reducing the total network cost. © 2023 Seifu G. Zeleke et al.

关键词： Multiobjective optimization

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Charged particles

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Implementation and Comparison of SiC and GaN switches for EV Fast Recharging Systems

Implementation and Comparison of SiC and GaN switches for EV...

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2023 IEEE Asia Meeting on Environment and Electrical engineering, EEE-AM 2023

作者： Scire, Daniele Garraffa, Giovanni Lullo, Giuseppe Vitale, Gianpaolo Cusumano, Pasquale Calabretta, Michele Alberto Messina, Angelo Costantino, Giuseppe Busacca, Alessandro Sferlazza, Antonino University of Palermo Department of Engineering Palermo Italy University of Enna Kore Faculty of Engineering and Architecture Enna Italy National Research Council of Italy Institute for High Performance Computer and Networking Palermo Italy STMicroelectronics Adg Power and Discretes R&d Division Catania Italy STMicroelectronics R&d and Public Affairs Catania Italy

ISBN: (纸本)9798350381061

Wide bandgap material-based devices allow faster switching frequency and exhibit smaller losses than traditional Si devices;nevertheless, a complete understanding of the functioning of these new devices remains poorly understood. A fast battery charger for electric vehicles based on a converter employing SiC and GaN devices is here reported Besides, these two technologies are experimentally compared, in the same layout, to highlights their performance in terms of electrical dynamic and electromagnetic compatibility. © 2023 IEEE.

关键词： Silicon carbide

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3D Drone Path Planning Algorithms with Collision Avoidance: Collision Resolution vs. Prevention

3D Drone Path Planning Algorithms with Collision Avoidance: ...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Rutuja Shivgan Jorge Medina Ziqian Dong Roberto Rojas–Cessa Department of Electrical and Computer Engineering New York Institute of Technology New York NY Department of Electrical and Computer Engineering Networking Research Laboratory Helen and John C. Hartmann New Jersey Institute of Technology Newark NJ

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Because of their limited flight range, multiple drones are often deployed simultaneously to perform complex tasks. The flight path is planned for each drone to follow to complete the job before task execution. However, multi-drone path planning places drones at risk of in-flight collisions. To overcome this problem, we model the multi-drone path planning problem as a multi-vehicle routing problem that maximizes job coverage subject to collision-free paths. We propose three 3D collision-free path planning algorithms, namely, XTRACT, 3DETACH, and ASCEND. XTRACT and 3DETACH provide collision-free paths by setting partial paths at different altitudes while ASCEND prevents intersecting paths at the planning phase by selecting different altitudes. We limit the search processes to two altitudes to demonstrate sufficiency with the lowest height complexity. Through exhaustive evaluations, we compare the performance of the proposed schemes and show the trade-offs between resolving and preventing collisions from path planning. We identify the best-performing strategy by using a profit model to evaluate the plethora of applicable performance metrics.

关键词： Space vehicles Solid modeling Three-dimensional displays Vehicle routing Path planning Orbits Planning

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Intelligent Video Surveillance for Weapon Detection: A Novel Approach for Low-Cost, Effective Monitoring in Low-Light Environments

Intelligent Video Surveillance for Weapon Detection: A Novel...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： R Mahaveerakannan Balamanigandan R P. Subramanian Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Networking Institute of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

These days, no public safety system is complete without security and video surveillance cameras. The systems still need human intervention in many places to identify potentially dangerous scenarios. Delays in discovering occurrences or unforeseen dangers can occur when security services are understaffed, endangering the public. Using intelligence-based techniques, this project aims to create a cost-effective solution for analysing surveillance videos in real-time and detecting firearms in various scenarios. The rising annual incidence of crimes involving guns and other hazardous weapons is an issue that the police are finding increasingly difficult to address, as is evident from a number of statistics. Many areas, particularly those without gun prohibitions, have a high rate of crimes committed with knives or firearms. It is essential for public safety that crimes are detected early. Using video monitoring to identify the presence of potentially lethal weapons like knives and firearms is one approach to avoiding these kinds of incidents. Now, intervention and monitoring are also necessary for control and monitoring. Presented here is a video demonstration of a tracking system that is well-suited for controlling and monitoring targets. There has been a plethora of recent study into the use of deep learning approaches for weapon detection in video surveillance. But the detection of weapons in low-light or nighttime situations has received very little research. To fill this void, this research suggests a new method for weapon detection that is optimised for low-light settings. For this reason, an improved YOLOv7 model is used for weapon detection. We extended YOLOv7's feature extraction capabilities by integrating the CBAM attention method. To further concentrate smaller locations, we augmented the network's backbone with an SPPF+ layer. Lastly, the YOLOv7 model was updated to include decoupled heads, which allow for more efficient data extraction from arrays. More pre

关键词： Head Law enforcement Weapons Video surveillance Feature extraction Public security Security Reliability Monitoring Optimization

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Early Detection of Heart Disease Using Deep Learning Model 8

Early Detection of Heart Disease Using Deep Learning Model

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8th International Conference on Smart Structures and Systems, ICSSS 2022

作者： Deepika, R. Srikaanth, P. Balaji Pitchai, R. Srm Institute of Science and Technology Kattankulathur Technology School of Computing Tamilnadu India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9781665497619

Identifying and detecting the early signs of diseases is difficult due to asymptomatic conditions. Detecting the signs early gives the patient a higher chance of surviving. There are several technologies to identify the heart diseases for timely treatment. So far, different techniques of machine learning is used to predict heart abnormalities. Machine learning models trained by large amounts of data in the healthcare field contain a lot of medical images that enable the medical professionals to identify even minimal changes with a high level of accuracy. This paper presents the deep learning technique used to predict the heart abnormalities. Deep learning technique helps in processing the image data through various tests which include electrocardiogram, echocardiogram etc., that detects the abnormalities in the heart. © 2022 IEEE.

关键词： Heart Deep learning Medical services Feature extraction Data models Intelligent structures Medical diagnostic imaging

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