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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Colombo, Cristian Giovanni Longo, Michela Nocera, Leonardo Ota, Ryosuke Zaninelli, Dario Politecnico di Milano Department of Energy Milan Italy Politecnico di Milano Milan Italy Tokyo Metropolitan University Faculty of Systems Design Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350395914

With new stringent policies affecting the transport and generation sectors, new technologies have emerged to drive the electric and technological transition. One of these is the smart road, a highly sensorised system to enable the integration of new vehicles and safety technologies. In this paper, the implementation of a smart road is simulated, first from an energy point of view and then from an economic point of view, by identifying the power demand of components and their cost. Next, the energetic core of the smart road is implemented. The energy core consists of a PV plant capable of powering the infrastructure. In this paper a simulation to test the proposed methodology is implemented. In the case study, a 280.05 kW smart road is powered by an optimally dimensioned PV plant. The production guaranteed by the 283.8 kWp plant will be 389.49 MW h/year and requires 57232. Furthermore, the 234.57 x20AC investment for the plant will represent less than 10% of the total investment, highlighting how renewable integration can be an economic sustainable purpose. Finally, another method to return the investment as quickly as possible will be proposed. © 2024 IEEE.

关键词： PV' Renewable Integration Road Infrastructure Smart Road

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A systematic approach for data acquisition for tool modelling and analysis of wear-dependent tool deflections during milling Inconel 718 17

A systematic approach for data acquisition for tool modellin...

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17th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2023

作者： Potthoff, Nils Neumann, Louisa Wiederkehr, Petra Virtual Machining Computer Science XIV TU Dortmund University Otto-Hahn-Str. 12 Dortmund44227 Germany Faculty of Mechanical Engineering TU Dortmund University Dortmund44227 Germany

Nickel-based superalloys are commonly used for components in the aerospace industry. Due to their favorable mechanical properties, such as high strength and corrosion resistance, these materials are suitable for use under extreme temperature conditions. However, the poor machinability results in high process forces and high tool wear, and, therefore, large tool displacements, so that high manufacturing tolerances are often difficult to achieve. Simulation systems can be used to optimize the process. The data acquisition for the development of the models required is challenging. In the following, a method for in-situ digitization of the tool is presented. Furthermore, the occurring tool deflections during the machining of Inconel718 are analyzed for different wear conditions to serve as basis for the parameterization of simulation models. © 2024 Elsevier B.V.. All rights reserved.

关键词： Corrosion resistance

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Identification of Texts Using ML Algorithm for The Accurate Recognition

Identification of Texts Using ML Algorithm for The Accurate ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pasha, M Jahir Kajendran, K. Deshmukh, Shrikala Kumar, G. Sreekanth, N. Pund, Sachin G.Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Panimalar Engineering College Dept. of Computer Science and Engineering Chennai India Amity University Mumbai India Bharati Vidyapeeth College of Engineering Pune India Srm Institute of Science and Technology Faculty of Management Kattankulathur India Mallareddy Engineering College for Women Hyderabad India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

A Comparative evaluation of device mastering Algorithms for textual content classification textual content category is an vital assignment in herbal language processing, where the aim is to routinely assign a label or category to a given text file. With the increasing quantity of text facts available in diverse domain names, the need for efficient and accurate text class algorithms has turn out to be extra vital. In this paper, we gift a comparative analysis of machine mastering algorithms for text category. We experiment with one of a kind algorithms, such as aid Vector Machines, Naive Bayes, Random Forests, and Multilayer Perceptron, on numerous benchmark datasets. Our results reveal that the choice of set of rules considerably affects the overall performance of textual content classification, and there may be no unmarried high-quality algorithm that works well for all datasets. We also provide insights into the strengths and weaknesses of each set of rules and talk their applicability in specific eventualities. Our findings can guide researchers and practitioners in selecting the maximum suitable system gaining knowledge of algorithm for their text category duties. Textual content classification, device learning, assist vector machines, naive bays, random forests, multilayer perceptron, and comparative analysis. © 2023 IEEE.

关键词： Multilayer neural networks

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Dual objective bandit for best channel selection in hybrid band wireless systems

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4115-4125页

作者： Hashima, Sherief M. Fouda, Mostafa Hatano, Kohei Kasban, Hany Mohamed, Ehab Mahmoud Fukuoka819-0395 Japan Engineering Department Nuclear Research center Egyptian Atomic Energy Authority Inshas Cairo13759 Egypt Electrical and Computer Engineering Department College of Science and Engineering Idaho State University PocatelloID83209 United States Faculty of Arts and Science Kyushu University Fukuoka819-0395 Japan Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Wadi Addwasir13759 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

This paper manipulates a creative online learning solution for optimal band/channel assignment in hybrid radio frequency and visible light communication (RF/VLC) systems. Typically, the multiband transmitter (Tx) has no prior information about different channel characteristics including their payoffs (achievable data rate) and energy consumption outcome. Practically, Tx has to choose the best reward arm/band with the lowest energy consumption to prolong its limited battery capacity. Hence, we envision a lightweight cost-aware multi-armed bandit (CA-MAB) as a proper realistic solution to the cumbersome and slowly convergent ordinary band assignment methods, where the transmitter/player intends not only to maximize his cumulative payoff (achievable data rate) but also to mitigate his cost (battery consumption due to the utilized band). Therefore, we propose a dual objective MAB scheme to manage such problem intelligently. Numerical simulations indicate that proposed method outperforms naive MAB versions, including Thompson sampling (TS), Upper Confidence bound (UCB), and traditional hybrid band selection (HBA) approaches, correspondingly. Especially, our proposed algorithm delivers 99% of the optimal performance concerning energy consumption, achievable data rate, and convergence speed. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Millimeter waves

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A Comprehensive Analysis on Machine Learning Based Methods for Lung Cancer Level Classification

SSRN

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SSRN 2025年

作者： Farshchiha, Shayli Asoudeh, Salman Kuhshuri, Maryam Shavali Eisaei, Mehrshad Azadi, Mohamadreza Hesaraki, Saba Department of Computer Science Shamsipour Technical College Tehran Iran Islamic Azad University Science and Research Branch Faculty of Mechanics Electrical Power and Computer Iran University of Birjand Iran Isfahan University of Technology Department of Electrical and Computer Engineering Faculty of Engineering Isfahan Iran Faculty of Computer College of Technical and Engineering University of Velayat Iran University of Isfahan Department of Electrical Engineering Faculty of Engineering Isfahan Iran University of Mazandaran Mazandaran Iran

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model performance, taking into account minimum child weight and learning rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM, Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted. Furthermore, the correlation between features and targets is examined using the deep neural network (DNN) model and thus their capability in detecting complex patterns is established. It is argued that several ML models can be capable of classifying lung cancer stages with great accuracy. In spite of the complexity of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic Regression excel with superior performance. The models perform better than the others in lung cancer prediction on the complete set of comparative metrics like accuracy, precision, recall, and F-1 score. © 2025, The Authors. All rights reserved.

关键词： Lung cancer

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DSTNet: Distinguishing Source and Target Areas for Image Copy-Move Forgery Detection 27th

DSTNet: Distinguishing Source and Target Areas for Image Cop...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Kaiqi Yuan, Xiaochen Huang, Guoheng Liu, Kun School of Cyberspace Security Shandong University of Political Science and Law Jinan China Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9783031783111

In copy-move forgery detection, most relevant studies concern locating the copy-move areas without the distinction of source and target regions. This paper proposes an end-to-end network, DSTNet, to identify the source and target based on consistency detection between the copy-move region and the non-copy-move region. The DSTNet is composed of two stages, the Pre-processing stage and the Discrimination stage. Pre-processing Stage extracts internal information of copy-move and non-copy-move areas and conducts a series of operations to meet the requirements of network input. Discrimination stage allows multiple patches for input and classifies the input patches. Specifically, the Pre-processing stage, contains the Copy-move Patches Selection (CM Patches Selection) and Genuine Patches Selection, can select pairs of copy-move and none copy-move patches. We train the proposed DSTNet on two large synthetic datasets and use the public datasets CASIA and Comofod for evaluation. The experiment shows that our method achieves excellent results. Particularly, we achieve a 5.4% higher F1 based on ground-truth of copy-move mask (GT-CM) on CASIA dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large datasets

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Network Time Synchronization as a Quantum Physical Layer Service 28

Network Time Synchronization as a Quantum Physical Layer Ser...

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28th European Wireless Conference, EW 2023

作者： Nunavath, Nikhitha Garbugli, Andrea Nande, Swaraj Shekhar Foschini, Luca Bassoli, Riccardo Fitzek, Frank H.P. The Deutsche Telekom Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany research group Technische Universität Dresden Dresden Germany The Department of Computer Science and Engineering University of Bologna Italy

ISBN: (纸本)9783800762262

In the context of 6G architecture development, the concept of a softwarized (orchestration) continuum is a key pillar. Nevertheless, achieving complete softwarization of network functionalities, tasks, and operations presents inherent challenges, leading to critical trade-offs and limitations. This article explores a novel approach to address these issues by integrating quantum technologies and the Physical Layer Service Integration (PLSI) paradigm. Specifically, we propose the formulation and analysis of network synchronization as a quantum PLSI problem. Our study evaluates synchronization time offset in both conventional Precision Time Protocol (PTP) and quantum-based approaches within the network. We investigate the impact of various network conditions on the precision of PTP synchronization, ranging from nanoseconds under ideal circumstances to microseconds when utilizing virtual network devices. Further, we perform a simulation to generate frequency-entangled photon pairs to access nonlocal temporal correlations and calculate the time offsets. Our findings reveal that entanglement-based PLSI for network synchronization achieves precision at the picosecond level. These results emphasises the high precision achievable by interpreting the network synchronisation problem in the perspective of PLSI and not as a service of the softwarized continuum. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Quantum communication

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A Resource-Efficient Federated Learning Framework for Intrusion Detection in IoMT Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Fahim-Ul-Islam, Md. Chakrabarty, Amitabha Alam, Md. Golam Rabiul Maidin, Siti Sarah Brac University Department of Computer Science and Engineering Bangladesh INTI International University Faculty of Data Science and Information Technology Nilai Malaysia

The emergence of medical sensors in Smart Healthcare Systems (SHS) has enabled the development of sophisticated Internet of Medical Things (IoMT) networks which is crucial for tracking vital physiological parameters in consumer electronics. These networks are progressively incorporated into consumer devices allowing users to monitor their health parameters effortlessly. Nonetheless, they encounter considerable security and privacy issues stemming from weaknesses in data transfer. Despite the potential of Intrusion Detection Systems (IDS), there is a critical need for a real-time, highly precise attack detection system optimized for the edge-centric Internet of Medical Things (IoMT) environment in consumer sensor devices. Therefore, we introduce the federated kolmogorov-arnold network (FedIoMT), an advanced federated learning framework that utilizes meta-learning along with an advanced clustering method to achieve robust model aggregation. Our FedIoMT acts as both an adaptive personalized updating mechanism and a shared global classifier. In addition, we propose a novel intrusion detection model that uses the kolmogorov-arnold convolutional network (KANConvNet) as its local classifier, which improves scalability and interpretability in the FL framework updates. Our FedIoMT model demonstrates superior performance compared to other existing FL-based methods, achieving validation accuracies of 99.38%, 99.23%, 99.26%, and 99.2% across four benchmark IoMT datasets under the IID setting. FedIoMT utilizes significantly more floating point operations per second (FLOP) counts up to 2.8 times more than other models and also excels in memory efficiency, consuming up to 93% less peak memory across devices compared to the alternatives. We thoroughly evaluated FedIoMT on embedded consumer devices, demonstrating its resource-efficient characteristics. © 2025 IEEE.

关键词： Network intrusion

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Analyzing and Developing the Security of DNS-Based Authentication in Wireless Sensor Network 3

Analyzing and Developing the Security of DNS-Based Authentic...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sharma, Umesh Chand Kumar, Jambi Ratna Raja Kumari, Meenakshi Anandhasilambarasan, D. Latha, B. Jadhav, Prranjali Mangalayatan University Department of Computer Science Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Analyzing and developing the safety of DNS-based total Authentication in Wireless Sensor Networks (WSNs) is a critical research vicinity that is being actively pursued. While preceding work has blanketed authentication strategies to relax the communications between a node and its base station, our studies focus on analyzing the authentication schemes used to set up a comfy verbal exchange in a wireless Sensor network among nodes. We look at the assets of Symmetric and asymmetric Authentication Protocols for WSNs and examine their effectiveness in terms of their authentication effectiveness and overhead. We also discuss how they can be incorporated with current authentication schemes used for WSNs. We then gift a singular protocol that uses DNS-based Authentication to safely set up a verbal exchange in a Wi-Fi Sensor network. Ultimately, we put in force and tested our protocol and the degree of its overall performance in simulated surroundings. Our experiments' results show that our protocol gives an effective authentication answer for WSNs. © 2023 IEEE.

关键词： authentication effectiveness exchange experiments schemes

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Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

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computers, Materials & Continua 2022年第2期70卷 2097-2111页

作者： R.H.Aswathy P.Suresh Mohamed Yacin Sikkandar S.Abdel-Khalek Hesham Alhumyani Rashid A.Saeed Romany F.Mansour Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare *** gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile *** Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the *** current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as *** steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and ***,the IoT gadgets are utilized in the collection of a patient’s health *** proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed *** application of FPA helps in tuning the DNN parameters for better classification *** simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD *** results were examined under different *** simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.

关键词： Deep learning chronic kidney disease IoT cloud computing feature selection classification

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