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Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN

Measurement: Sensors 2024年 35卷

作者： Anto Pravin, R. Murugan, K. Thiripurasundari, C. Ranjith Christodoss, Prasanna Puviarasi, R. Abdul Lathif, Syed Ismail Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu India Department of Electronics and Communication Engineering KSK College of Engineering and Technology Tamil Nadu India Department of Computing Mathematics and Physics Messiah University Mechanicsburg PA United States Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness. © 2024 The Authors

关键词： Internet of things

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. The main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. The study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. The ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R2 score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. This significant R2 score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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A Novel Delegated Proof of Work Consensus Protocol

A Novel Delegated Proof of Work Consensus Protocol

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2021 International Conference on Artificial Intelligence for Cyber Security systems and Privacy, AI-CSP 2021

作者： Kara, Mostefa Laouid, Abdelkader Bounceur, Ahcene Lalem, Farid Alshaikh, Muath Kebache, Romaissa Sayah, Zaoui El Oued University LIAP Laboratory El Oued39000 Algeria University of Western Brittany UBO Lab-STICC UMR CNRS Brest6285 France Saudi Electronic University College of Computing and Informatics Computer Science Department Riyadh11673 Saudi Arabia

ISBN: (纸本)9781665467148

Blockchain is a new technology for storing and transmitting information evolving without centralized control, it uses peer-to-peer networks and allows the constitution of replicated and distributed registers, these registers are secured thanks to cryptography by the linking of blocks to each other. As part of solving the challenges that exist to develop this technology, we present this article introducing significant improvements to this efficient mechanism. These improvements consist of proposing a consensus algorithm called Delegated Proof of Work (DPoW). Thanks to this algorithm, we offer a significant reduction in the very large amount of energy currently consumed in the mining process, we reduce the domination of an element with great computing power, we reinforce security against the 51 % attack, reducing its percentage to 67 %, and we also reduce the influence of false identities created within the network (Sybil Attack). On the other hand, we propose a consensus model for permissioned blockchain, this model is based on the encryption of random values and offers total security to reach a consensus. © 2021 IEEE.

关键词： Blockchain

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High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Junyi Li, Ziao Liu, Bangli Cai, Haibin Saada, Mohamad Meng, Qinggang Robot Science and Engineering Northeastern University Liaoning Shenyang110819 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Foshan Graduate School of Innovation Northeastern University Guangdong Foshan528311 China Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom

Recently, the significant achievements have been made in skeleton-based human action recognition with the emergence of graph convolutional networks (GCNs). However, the state-of-the-art (SOTA) models used for this task focus on constructing more complex higher-order connections between joint nodes to describe skeleton information, which leads to complex inference processes and high computational costs. To address the slow inference speed caused by overly complex model structures, we introduce reparameterization and over-parameterization techniques to GCNs and propose two novel high-performance inference GCNs, namely HPI-GCN-RP and HPI-GCN-OP. After the completion of model training, model parameters are fixed. HPI-GCN-RP adopts re-parameterization technique to transform high-performance training model into fast inference model through linear transformations, which achieves a higher inference speed with competitive model performance. HPI-GCN-OP further utilizes over-parameterization technique to achieve higher performance improvement by introducing additional inference parameters, albeit with slightly decreased inference speed. The experimental results on the two skeleton-based action recognition datasets demonstrate the effectiveness of our approach. Our HPI-GCN-OP achieves performance comparable to the current state-of-the-art (SOTA) models, with inference speeds five times faster. Specifically, our HPI-GCN-OP achieves an accuracy of 93% on the cross-subject split of the NTU-RGB+D 60 dataset, and 90.1% on the cross-subject benchmark of the NTU-RGB+D 120 dataset. Code is available at ***/lizaowo/HPI-GCN. Copyright © 2023, The Authors. All rights reserved.

关键词： Musculoskeletal system

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H observer-based control for fuzzy Hadamard fractional-order systems

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Computational and Applied Mathematics 2025年第4期44卷 1-14页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad El Hajjaji, Ahmed Ben Makhlouf, Abdellatif Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

While the $$H_\infty $$ observer-based control has found widespread application in the literature for integer-order systems, researchers have shown less interest in addressing the same issue within the fractional-order framework. In this context, this study delves into the application of $$H_\infty $$ observer-based control for the Hadamard fractional-order system (HFOS) described by the Takagi–Sugeno fuzzy models (TSFM). Using Lyapunov approach and by employing a matrix decoupling technique, LMI based conditions ensuring the existence of an observer and controller, are proposed. To minimize the impact of disturbances on the controlled output, $$H_\infty $$ optimization technique is used. The validity of our approach is substantiated through an example, underscoring the robustness and reliability of our proposed findings.

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Hyperdimensional spin-orbit microlaser

Hyperdimensional spin-orbit microlaser

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zhang, Zhifeng Zhao, Haoqi Wu, Shuang Wu, Tianwei Qiao, Xingdu Gao, Zihe Agarwal, Ritesh Longhi, Stefano Litchinitser, Natalia M. Ge, Li Feng, Liang Department of Materials Science and Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Dipartimento di Fisica Politecmco di Milano Piazza L. da Vinci 32 MilanoI-20133 Italy Institute de Fisica Interdisciplinar y Sistemas Complejos Palma deMallorca Spain Department of Electrical and Computer Engineering Department of Physics Duke University DurhamNC27708 United States Department of Engineering Science and Physics College of Staten Island CUNY Staten IslandNY10314 United States The Graduate Center CUNY New YorkNY10016 United States

ISBN: (纸本)9781957171258

Despite the rapid development of photonic devices and systems, on-chip information technologies are mostly limited to two-level systems due to the lack of sufficient reconfigurability. Here, we demonstrate a high-dimensional, spin-orbit microlaser for chip-scale flexible generation and manipulation of arbitrary four-level states. Two microcavities coupled through a non-Hermitian synthetic gauge field are designed to emit spin-orbit-coupled states of light with six degrees of freedom (DOFs). The vectorial state of the emitted laser beam in free space can be mapped on a Bloch hypersphere defining an SU(4) symmetry, demonstrating dynamical generation and reconfiguration of high-dimensional superposition states with high fidelity. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Photonic devices

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A Machine Learning Approach for Analyzing Texture and Morphometry Markers of Glioblastoma TERT Promoter Mutations in DAPI Fluorescence Images

A Machine Learning Approach for Analyzing Texture and Morpho...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Afsari, Fatemeh Naglah, Ahmed Gupta, Akshita Lohaan, Harshit Onubogu, Ugoma Janiszewska, Michalina Sarder, Pinaki Division of Nephrology Hypertension and Renal Transplantation-Quantitative Health Section Department of Medicine College of Medicine University of Florida GainesvilleFL32603 United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL32611 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Department of Molecular Medicine The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology JupiterFL33458 United States

ISBN: (纸本)9781510686045

Advanced molecular and imaging techniques, such as STAR-FISH, are frequently required to identify mutations in tissue specimens to properly diagnose Glioblastoma (GBM), an aggressive type of brain tumor. Mutations to the telomerase reverse transcriptase promoter (TERTp) are common in cancer and present in approximately 80% of all GBM tumors. We hypothesize that machine learning can stratify cells by their TERTp mutation status using DAPI fluorescence images since the STAR-FISH technique can be costly and inaccessible for some healthcare facilities. In this study, we present a machine learning-based approach to recognize nuclei harboring TERTp mutations solely based on the DAPI fluorescence images of GBM tissues. The proposed pipeline integrates a convolutional neural network (CNN)-based deep learning model to segment cells, extract hand-engineered features, and feed them into fully connected (FC) layers within the neural network or other machine learning classifiers to identify TERTp mutant cells. This approach involves extracting DAPI features, including both morphological and textural image features, followed by an ensemble of machine learning modules to predict TERTp mutant cells. We evaluate our approach on a cohort of 18 patients (70 pairs of DAPI and STAR-FISH processed images). Our experimental results show that the FC layers within the neural network achieve the highest AUC, F1-Score, and recall of 0.77, 0.69, and 0.86, respectively, with the integrated CNN model achieving a Dice score of 0.83, accurately segmenting cells. Our work is one of the earliest attempts that we are aware of to use machine-learning models for TERTp mutation identification using DAPI fluorescence images. Our results indicate the possibility of improved GBM diagnosis accuracy. To improve patient diagnosis and treatment planning for GBM, future research aims will expand the cohort and enhance the models to provide more precise and diagnostically valuable models for the interpretation o

关键词： Image segmentation

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Racial Bias During "Black Lives Matter": How Social Movement Reshapes Airbnb Host Performance 42

Racial Bias During "Black Lives Matter": How Social Movement...

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42nd International Conference on Information systems: Building Sustainability and Resilience with IS: A Call for Action, ICIS 2021 TREOs

作者： Xu, Tong Lu, Angela Mishra, Abhay Song, Linqi Liu, Zengyan Department of Information Systems City University of Hong Kong Kowloon Tong Hong Kong Ivy College of Business Iowa State University AmesIA50011 United States Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong

ISBN: (纸本)9781713893608

Racial bias is an important issue and has been observed on sharing economy (SE) platforms. However, few studies examine how racial bias changes during large-scale social movement. We examine this issue with five million Airbnb host records in the Unites States during the Black Lives Matter (BLM), one of the largest social movements since the Civil Rights era. We find that racial bias evolves during the BLM movement, affecting host performance differentially. Critically, host performance varies by supply-side contextual heterogeneity, such as racial composition in neighborhoods. The results highlight the dynamic process of racial bias in the sharing economy, which continues to be reshaped by social movement such as the BLM as well as local environment. The paper contributes to an understanding of racial bias as a dynamic process, especially during race-related social movement that spills over to online platforms and affects user behavior. © 2021 42nd International Conference on Information systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

关键词： Sustainable development

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Blockchain as a decentralized communication tool for sustainable development 2

Blockchain as a decentralized communication tool for sustain...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Hatamleh, Amer Aseh, Khairi Altrad, Abdullah Amayreh, Khalid Thaher Hilles, Shadi Mohamed, Rajina R. Ravindran Pathmanathan, P. Aledinat, Lowai Saleh UniSZA Faculty of Informatics and Computing Malaysia Irbid National University Faculty of Financial and Business Science Information System Department Irbid Jordan International Institute of Applied Science Swiss School of Management Netherlands Al Madinah International University Faculty of Computer and Info. Tech Malaysia Irbid National University Faculty of Financial and Business Science Administration Department Irbid Jordan Istanbul Okan University Department of Software Engineering Turkey Universiti Tenaga Nasional College of Computing Dan Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia

ISBN: (纸本)9781665432221

This research introduces the distributed ledger technology (blockchain) in terms of sustainable development in the future. This study is based on private, public and global communications in various industries. The growing demands for secure and decentralized transactions around the world demonstrate the need for Blockchain technology as a medium of operations and communications. The blockchain system is completely decentralized and allows users to exchange messages in an efficient and secure manner. The highlight of the paper is the anticipated future use of blockchain networks as a communication tool in every business and digital economy for a safe and sustainable development, while presenting its advantages and limitations that have been confirmed based on the experiences of various experts. Moreover, the article provides recommendations for the use of blockchain to greatly complicate its identification in future communications. The paper also concludes that the use of the blockchain system could be beneficial and enhance future communications and digital technologies. © 2021 IEEE.

关键词： Blockchain

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An Intelligent Voice-Recognition Wheelchair System for Disabled Persons

An Intelligent Voice-Recognition Wheelchair System for Disab...

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Sustainable Computing and Smart systems (ICSCSS), International Conference on

作者： A.V. Kalpana V. Venkataramanan G. Charulatha G Geetha Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering D.J Sanghvi College of Engineering Mumbai Maharashtra India Department of Electronics and Communication Engineering Peri Institute of Technology Chennai Tamil Nadu India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India

This study illustrates the creation of an intelligent voice-recognition wheelchair for disabled persons who cannot manually man-oeuvre their wheelchairs. Using voice recognition, the patient operates the wheelchair, and a GPS device included in the wheelchair can track the patient’s position and communicate the data to a Smartphone. To carry out the people’s orders, the voice module V3 records and can recognize the patient’s voice. This module converts the voice commands into letters, subsequently transmitted to the Wi-Fi module to operate the wheelchair. The Wi-Fi module sends commands to the motor drive for the wheels. In addition, three levels of motor speed adjustment are available: low, medium, and high. This device also utilizes an Infrared sensor to automatically identify obstacles, allowing the patient’s family members to know exactly where they are. Disabled People can be used this device, which simultaneously provides a voice-controlled wheelchair, obstacle detection, motor speed control, and patient GPS tracking via mobile phone; it will be a successful solution for disabled people across the world.

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