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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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Evaluation of Deep Neural Operator Models Toward Ocean Forecasting

Evaluation of Deep Neural Operator Models Toward Ocean Forec...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Rajagopal, Ellery Babu, Anantha N. S. Ryu, Tony Haley, Patrick J. Mirabito, Chris Lermusiaux, Pierre F. J. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States Center for Computational Science and Engineering Massachusetts Institute of Technology Department of Mechanical Engineering CambridgeMA United States

ISBN: (纸本)9798218142186

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications. © 2023 The Marine technology Society (MTS).

关键词： Flow of fluids

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Performance-Guaranteed Solutions for Multi-Agent Optimal Coverage Problems using Submodularity, Curvature, and Greedy Algorithms 63

Performance-Guaranteed Solutions for Multi-Agent Optimal Cov...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Welikala, Shirantha Cassandras, Christos G. Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Boston University Division of Systems Engineering and Center for Information and Systems Engineering BrooklineMA02446 United States

ISBN: (纸本)9798350316339

We consider a class of multi-agent optimal coverage problems in which the goal is to determine the optimal placement of a group of agents in a given mission space so that they maximize a coverage objective that represents a blend of individual and collaborative event detection capabilities. This class of problems is extremely challenging due to the non-convex nature of the mission space and of the coverage objective. With this motivation, greedy algorithms are often used as means of getting feasible coverage solutions efficiently. Even though such greedy solutions are suboptimal, the submodularity (diminishing returns) property of the coverage objective can be exploited to provide performance bound guarantees. Moreover, we show that improved performance bound guarantees (beyond the standard (1-1 / e) performance bound) can be established using various curvature measures of the coverage problem. In particular, we provide a brief review of all existing popular applicable curvature measures, including a recent curvature measure that we proposed, and discuss their effectiveness and computational complexity, in the context of optimal coverage problems. We also propose novel computationally efficient techniques to estimate some curvature measures. Finally, we provide several numerical results to support our findings and propose several potential future research directions. © 2024 IEEE.

关键词： Optimal detection

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A Self-Balanced Five-Level Buck PFC EV Rectifier 49

A Self-Balanced Five-Level Buck PFC EV Rectifier

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Agarwal, Ritika Jain, Anekant Gupta, Krishna Kumar Jain, Sanjay K. Bhatnagar, Pallavee Ibanez, Federico Martin Nikolovski, Srete Thapar Institute of Engineering & Technology Department of Electrical and Instrumentation Engineering Patiala India Manipal Institute of Technology Mahe Department of Mechatronics Manipal India Center for Energy Science and Technology Skolkovo Institute of Science and Technology Moscow Russia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9798350331820

This paper introduces a new self-balancing buck PFC Multilevel Rectifier based on a five-level Switched Capacitors (SCs) architecture. The proposed design offers a broad range of output voltage, enabling operation with various loads and accommodating both single and double/dual output modes. Additionally, it ensures effective load voltage balancing, even when the both loads have different values. The rectifier functions in buck operation mode and provides extensive output voltage regulation, making it suitable for simultaneous electric vehicle (EV) battery charging. To evaluate the performance of proposed rectifier this paper explores the operating principle, modulation strategy, closed-loop control, and experimental validation with same and different load variation scenarios. © 2023 IEEE.

关键词： Rectifying circuits

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Resilient, Federated Large Language Models over Wireless Networks: Why the PHY Matters

Resilient, Federated Large Language Models over Wireless Net...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Andrei, Vlad C. Djuhera, Aladin Li, Xinyang Mönich, Ullrich J. Saad, Walid Boche, Holger Virginia Tech Electrical and Computer Engineering Department ArlingtonVA United States Technical University of Munich Munich Germany Munich Center for Quantum Science and Technology Germany

ISBN: (纸本)9798350351255

In this paper, the problem of training large language models (LLMs) in split federated learning over real-world wireless networks is investigated. In the considered system, the embedding layers of an LLM are first computed at a client and then trans-mitted over a wireless MIMO-OFDM link to a server instance for further processing, continuing the forward- and initiating the backpropagation of the training to the originating client. Due to channel impairments and adversarial attacks, the server needs to compute the model losses and gradients using corrupted parameters such as embeddings in LLMs. The computation of the corresponding model losses is rigorously characterized using such perturbed embeddings and a direct connection to the communication mean-squared error (MSE) for models beyond simple neural networks is established. Subsequently, the communication errors are modeled as part of the training process, and a method to design beamforming, scheduling and power allocation is proposed, ensuring high task performance and model convergence even in the case of worst-case jamming. Results on two natural language processing tasks using different LLM architectures confirm the validity of the theoretical analysis and prove the effectiveness of the proposed wireless system design in terms of accuracy and F1 score. © 2024 IEEE.

关键词： Resource allocation

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Prediction of Adsorption and Desorption Isotherms for Atmospheric Water Harvesting 21

Prediction of Adsorption and Desorption Isotherms for Atmosp...

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21st International Learning and technology Conference, L and T 2024

作者： Kabbaj, Narjisse El-Amin, Mohamed F. Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Nat. Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study improves the mathematical modeling for the potential of atmospheric water harvesting (AWH) using desiccant materials. This research is crucial in highlighting the sustainable water management strategies of AWH systems in converting atmospheric moisture into a vital water source. The primary focus of our research methodology was the analytical derivation of sorption isotherms, essential for the hygrothermal simulation of desiccant materials. This was accomplished through the application of two established models, namely, the Guggenheim-Anderson-de Boer (GAB) and Van Genuchten (VG). Experimental data on various anhydrous salts from existing literature have been used. An in-depth comparative analysis of these models reveals that the VG model aligns more closely with the experimental data, thus asserting its superiority in enhancing the selection and efficiency of desiccant materials in AWH systems. By confirming the VG model's superiority in accurately modeling sorption isotherms, our research not only improves the model of AWH systems but also, importantly, contributes to the development of advanced water harvesting technologies. © 2024 IEEE.

关键词： Water conservation

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HERA: A Novel Heuristic Resource Allocator for Multi-SDM Multi-User Settings

HERA: A Novel Heuristic Resource Allocator for Multi-SDM Mul...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Antoniadis, Christos Katsalis, Kostas Tyrovolas, Dimitrios Tegos, Sotiris A. Ioannidis, Sotiris Diamantoulakis, Panagiotis D. Karagiannidis, George K. Liaskos, Christos DOCOMO Euro-Labs Munich Germany Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Technical University of Crete Dept. of Electrical and Computer Engineering Chania Greece Artificial Intelligence & Cyber Systems Research Center Lebanon University of Ioannina Computer Science Engineering Department Ioannina Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece

ISBN: (纸本)9798331507428

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software-defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future. © 2024 IEEE.

关键词： Resource allocation

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Manipulating Skyrmion Motion on a Nanotrack with Varied Material Parameters and Tilted Spin Currents

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Chinese Physics Letters 2023年第9期40卷 107-112页

作者：罗佳 Jia-Hao Guo 侯云鹤王君林徐永兵周艳 Philip Wing Tat Pong 赵国平 College of Physics and Electronic Engineering Sichuan Normal UniversityChengdu 610068China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina School of Integrated Circuits Guangdong University of TechnologyGuangzhou 510006China School of Physics Engineering and TechnologyUniversity of YorkYorkYO105DDUnited Kingdom School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNJ 07102USA Center for Magnetism and Spintronics Sichuan Normal UniversityChengdu 610068China

Magnetic skyrmions are topological quasiparticles with nanoscale size and high mobility,which have potential applications in information storage and spintronic *** manipulation of skyrmion’s dynamics in the track is an important topic due to the skyrmion Hall effect,which can deviate the skyrmions from the preferred *** propose a new model based on the ferromagnetic skyrmion,where the skyrmion velocity can be well controlled by adjusting the direction of the *** this design,we can avoid the annihilation of the skyrmion induced by the skyrmion Hall effect,which is confirmed by our micromagnetic simulation based on Mumax^(3).In the meantime,we increase the average velocity of the skyrmion by varying the intrinsic material parameters in the track,where the simulations agree well with our analytical results based on the Thiele ***,we give a phase diagram of the output of the skyrmion in the T-type track,which provides some practical ways for design of logic gates by manipulating crystalline anisotropy through the electrical control.

关键词： equation annihilation topological

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