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Nonvolatile electrical switching of optical and valleytronic properties of interlayer excitons

Light(Science & Applications) 2022年第2期11卷 221-229页

作者： Tong Ye Yongzhuo Li Junze Li Hongzhi Shen Junwen Ren Cun-Zheng Ning Dehui Li School of Optical and Electronic Information Huazhong University of Science and Technology430074 WuhanChina Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology430074 WuhanChina Department of Electronic Engineering Tsinghua University100084 BeijingChina Frontier Science Center for Quantum Information 100084 BeijingChina School of Electrical Computerand Energy EngineeringArizona State UniversityTempeAZ 85287USA

Long-lived interlayer excitons(IXs)in van der Waals heterostructures(HSs)stacked by monolayer transition metal dichalcogenides(TMDs)carry valley-polarized information and thus could find promising applications in valleytronic *** manipulation approaches for valley polarization of IXs are mainly limited in electrical field/doping,magnetic field or twist-angle ***,we demonstrate an electrochemical-doping method,which is efficient,in-situ and *** find the emission characteristics of IXs in WS2/WSe2 HSs exhibit a large excitonic/valley-polarized hysteresis upon cyclic-voltage sweeping,which is ascribed to the chemical-doping of O2/H2O redox couple trapped between WSe2 and *** advantage of the large hysteresis,a nonvolatile valley-addressable memory is successfully *** valley-polarized information can be non-volatilely switched by electrical gating with retention time exceeding 60 *** findings open up an avenue for nonvolatile valley-addressable memory and could stimulate more investigations on valleytronic devices.

关键词： nonvolatile valley electrical

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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A Secure Federated Learning Framework using Blockchain and Differential Privacy 9

A Secure Federated Learning Framework using Blockchain and D...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Firdaus, Muhammad Larasati, Harashta Tatimma Rhee, Kyung-Hyune Pukyong National University Department of Artificial Intelligence Convergence Korea Republic of Pusan National University Department of Computer Engineering Korea Republic of Institut Teknologi Bandung School of Electrical Engineering and Informatics Indonesia

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Federated learning (FL) has considerably emerged as a promising solution to enhance user privacy and data security by enabling collaboratively multi-party model learning without exchanging confidential data. Nevertheless, most existing FL approaches still rely on a central server to obtain a global model by collecting all uploaded models from participants, which may lead to several threats from malicious participants and even expose participant privacy. Therefore, to tackle these problems, we proposed a secure FL framework by empowering blockchain to replace the centralized aggregator sever and utilize Differential Privacy (DP) to address various attacks, e.g., membership inference attacks, during the collaborative FL model training process. The proposed framework has been implemented through two scenarios, i.e., blockchain-based FL to form a decentralized system and DP-based FL to construct the randomized privacy protection using the IBM DP Library. © 2022 IEEE.

关键词： Blockchain

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Statistical and Deep Machine Learning Techniques to Forecast Cryptocurrency Volatility 22nd

Statistical and Deep Machine Learning Techniques to Forecast...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Cebrián-Hernández, Ángeles Jiménez-Rodríguez, Enrique Tallón-Ballesteros, Antonio J. Department of Applied Economics Seville University Seville Spain Department of Financial Economics Pablo de Olavide University Seville Spain Department of Electronic Computer Systems and Automatic Engineering University of Huelva Huelva Spain

ISBN: (纸本)9783031274084

This paper studies cryptocurrency volatility forecast covering a state-of-the-art review as well empirical comparison through supervised learning. This research has two main objectives. The first objective is the usage of artificial intelligence in the field of predicting the volatility of cryptocurrencies, in particular bitcoin. In this work, supervised machine learning algorithms from two different perspectives, a statistical one and a deep learning one, are compared to predict bitcoin volatility using economic-financial variables as additional information in the models. The second objective is to compare the fit of artificial intelligence models with traditional econometric models such as Multivariate GARCH (traditional generalized heteroscedasticity conditional autoregressive model (M-GARCH). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Deep Learning-Based Automated System for Multi-Classification of Plant Diseases in Real-Time Environments

Deep Learning-Based Automated System for Multi-Classificatio...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： K.B Aruna S Prema V Sureka L Sudha V Sasikala Computer Science and Engineering Department S.A. Engineering College Chennai Computer Science and Engineering Department Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Electronic and Communication Engineering Sri Sairam Engineering College

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Agriculture provides food for the world's expanding population. Several developing nations are going through this period, and agriculture has been developed to fulfil the rising food demand. While agriculture seeks to feed the world's rapidly growing population, plant disease reduces the amount and quality of food, fiber, and biofuel crops. Although losses can be severe, they normally account for 42% of the yield of the six most important food crops. Managing illnesses costs farmers sometimes without enough professional assistance, which has unfavorable outcomes including pollution and subpar disease control. Deep learning algorithms are helping farmers all around the world identify and categories the illness. In order to identify disease in plant leaves, it employs techniques like Convolutional Neural Networks (CNN). This research discussed the preset and user specified models that were employed to identify diseases in plant leaves. We are transmitting the Driver circuit to power the pump motors to Arduino Uno using UART protocols. Determine the different soil types using an NPK sensor. At the end, we are using Arduino to control the motor model with a server motor.

关键词： Plant diseases Sociology Crops Soil Motors Agriculture Telecommunication computing

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Bearing-only Localization Based on an Array Network with Distributed ADMM

Bearing-only Localization Based on an Array Network with Dis...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Guanxi Sun Wei Liu School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The two-dimensional target localization problem for a network of distributed sensor sub-arrays (with a sub-array at each receiver) is investigated in the compressive sensing framework based on group sparsity, and the well-known alternating direction method of multipliers (ADMM) method is applied to derive a distributed implementation algorithm. It does not require transmission of the impinging signals from each sub-array to a central processor, while the whole optimisation process is realised in a distributed way. Simulation results show that the proposed algorithm under certain conditions performs better than the existing group sparsity based localization method in terms of localization accuracy, speed and computational complexity.

关键词： Location awareness Simulation Receivers Convex functions Sensors Sparse matrices Computational complexity Optimization Signal to noise ratio Sensor arrays

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Advanced Computational Modeling for Brain Tumor Detection:Enhancing Segmentation Accuracy Using ICA-Ⅰ and ICA-Ⅱ Techniques

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computer Modeling in engineering & Sciences 2025年第4期143卷 255-287页

作者： Abdullah A Asiri Toufique A.Soomro Ahmed Ali Faisal Bin Ubaid Muhammad Irfan Khlood M.Mehdar Magbool Alelyani Mohammed S.Alshuhri Ahmad Joman Alghamdi Sultan Alamri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Artificial Intelligence and Cyber Futures Institute Charles UniversityBathurstNSW 2795Australia Department of Electronic Engineering The University of LarkanoLarkana75660Pakistan Electrical Engineering Department Sukkur IBA UniversitySukkur65200Pakistan Computer Science Department Sukkur IBA UniversitySukkur65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Department of Radiological Sciences College of Applied Medical ScienceKing Khalid UniversityGuraigerAbha62521Saudi Arabia Radiology andMedical Imaging Department College of AppliedMedical SciencesPrince Sattam bin Abdulaziz UniversityKharj11942Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesTaif UniversityTaif21944Saudi Arabia

Global mortality rates are greatly impacted by malignancies of the brain and nervous ***,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human *** address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and ***2 is utilized for image preprocessing and optimization,enhancing MRI consistency and *** Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise *** on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of ***,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 *** integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor *** research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.

关键词： Brain image segmentation MR brain enhancement independent component analysis brain tumor

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios

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Journal of electronic Science and Technology 2025年第2期23卷

作者： Kutpanova, Zarina Kadhim, Mustafa Zheng, Xu Zhakiyev, Nurkhat Department of Computer Engineering Astana IT University Astana010000 Kazakhstan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Department of Computational and Data Science Astana IT University Astana010000 Kazakhstan

Unmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payloads away from target locations. The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations. The civil defense department must balance coverage, accurate landing, and flight safety while considering battery power and capability. Deep Q-network (DQN) models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces. Earlier strategies focused on advanced DQNs for UAV path planning in different configurations, but rarely addressed non-cooperative scenarios and disaster environments. This paper introduces a new DQN framework to tackle challenges in disaster environments. It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return. A new DQN model is developed, which incorporates the battery life, safe flying distance between UAVs, and remaining delivery points to encode surrounding hazards into the state space and Q-networks. Additionally, a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings. The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance. © 2025 The Authors

关键词： Unmanned aerial vehicles (UAV)

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Improving Imbalanced Data Classification Through Integrated Data-Level and Algorithm-Level Techniques 6

Improving Imbalanced Data Classification Through Integrated ...

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6th International Conference on Contemporary Computing and informatics, IC3I 2023

作者： El Hajjami, Salma Amelal, Hicham El-Hajjami, Abdelkarim Kaushik, Keshav Khan, Inam Ullah Ibn Zohr University Faculty of Science LabSIV Department of Computer Science Agadir Morocco INP-ENSEEIHT University of Toulouse Department of Computer Science and Telecommunications France School of Computer Sciences University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Electronic Engineering Isra University Pakistan

ISBN: (纸本)9798350304480

Imbalanced class distribution is common issue in machine learning and data mining. It affects various applications like fraud detection, medical diagnosis, and network intrusion detection. The mentioned problem occurs when the majority class greatly outnumbers the minority class, hindering learning systems designed for balanced distribution. Furthermore, presence of overlapping instances between two classes exacerbates problem. This paper introduces a novel method that integrates ensemble and cost-sensitive techniques at algorithm-level with OSBNR approach at data-level to address this challenge. Furtherly, suggested technique is compared with conventional resampling methods, in conjunction with three ensemble algorithms and three cost-sensitive algorithms, to determine optimal combination. Simulation presents OSBNR is having optimal results in terms of processing time and evaluation metrics like G-mean, AUC-ROC, TPR-Recall, precision, Fl-score and FPR. PyOSBNR is simulated as python index package which can easily handle class imbalance in the presence of behavioral noise. © 2023 IEEE.

关键词： Cost-sensitive methods Ensemble methods Fraud detection Imbalanced class distribution Machine learning OSBNR PyOSBNR

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