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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 Probabilistic Public-Key Encryption with Ensuring Data Integrity in Cloud computing 4

A Probabilistic Public-Key Encryption with Ensuring Data Int...

引用

4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kara, Mostefa Karampidis, Konstantinos Papadourakis, Giorgos Laouid, Abdelkader Alshaikh, Muath University of El Oued LIAP Laboratory El Oued El Oued39000 Algeria Hellenic Mediterranean University Department of Electrical and Computer Engineering Greece College of Computing and Informatics Saudi Electronic University Computer Science Department Riyadh11673 Saudi Arabia

ISBN: (纸本)9798350300918

Researchers are working hard to face various challenges in the fast-moving field of network security and data exchange. When it comes to encryption techniques, efforts vary to address multiple issues such as computational speed, data size requirements, application scope, etc. that are driven by the purposes of collecting and sharing sensitive data that must be stored and processed securely. This paper proposes an efficient asymmetric cryptosystem that bases on the multiplication of plaintext by a variable public key. The origin of this key is constant, but for each encryption, a random number must be added to it. Therefore, a new public key each time is generated;in other words, a simulation of a one-time pad system. For each message, the sender generates a new secret twin of the public key;The receiver can decrypt all messages encrypted by different twins with a single private key. The proposed probabilistic scheme is linear coding and hence is lightweight, easy to execute, and practical in many areas. We consider the cloud to be an untrusted part that tries to disclose data when decoded by systems, the proposed method provides the additive homomorphic property and ensures data integrity when a homomorphic operation is performed by the cloud;thus, we realize verifiable partial homomorphic encryption. © 2023 IEEE.

关键词： Network security

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DFD-Net:lung cancer detection from denoised CT scan image using deep learning

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Frontiers of computer science 2021年第2期15卷 119-131页

作者： Worku J.SORI Jiang FENG Arero W.GODANA Shaohui LIU Demissie J.GELMECHA School of Electrical Engineering and Computing Department of Computer Science and EngineeringAdama Science and Technology UniversityAdama1888Ethiopia Faculty of Natural Computational Science Department of Mathematics Bule Hora UniversityBule HoraHagere Maryam144Ethiopia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518052China School of Electrical Engineering and Computing Department of Electronics and Communications EngineeringAdama Science and Technology UniversityAdama1888Ethiopia

The availability of pulmonary nodules in CT scan image of lung does not completely specify *** noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every *** this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this *** introduced model is composed of denoising and detection part in an end to end ***,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing ***,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is *** two paths focus on the joint integration of local and global *** this end,each path employs different receptive field size which aids to model local and global *** further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative ***,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels *** found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and *** intensive experimental results achieved competitive results.

关键词： medical image discriminant correlation analysis features fusion image detection denoising

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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Automatic Detection and Characterization of Human Veins Using Infra-red Image Processing

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Journal of computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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Enhancing the Performance of E-Mode AlGaN/GaN HEMTs With Recessed Gates Through Low-Damage Neutral Beam Etching and Post-Metallization Annealing

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 150-155页

作者： Chen, Yi-Ho Ohori, Daisuke Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan Tohoku University Institute of Fluid Science Sendai980-8577 Japan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Pioneer Semiconductor Innovation Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan

This study investigated the electrical properties of AlGaN/GaN high-electron-mobility transistors (HEMTs) with varied recess depths under the gate electrode. We demonstrated a recess depth of approximately 6 nm, which was achieved through neutral beam etching (NBE) technique with a low etch rate of 1.8 nm/min, resulting in device enhancement-mode (E-mode) behavior with threshold voltage (Vth) of 0.49 V. The effects of post-metallization annealing (PMA) on the device performance were also examined. The results revealed that PMA treatment improves the DC characteristics of the devices, including maximum drain current (IDMAX), transconductance (gm), subthreshold swing (SS), on-off ratio, and off-state leakage current, with maximum enhancement percentage of 18.3% for IDMAX, 3758% for on-off ratio, and 54.3% for SS. Moreover, this study compared the recess depths of metal-insulator-semiconductor high-electron-mobility transistors (MIS-HEMTs) with the SiN dielectric layer. The results showed that MIS-HEMTs exhibit more negative Vth values, which can be attributed to the controlled surface states achieved through passivation. © 2020 IEEE.

关键词： Etching

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An AoI-Guaranteed Sensor Data Collection Strategy for RIS-assisted UAV Communication System

An AoI-Guaranteed Sensor Data Collection Strategy for RIS-as...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Yang, Bowen Yu, Yao Li, Jianqi Xu, Tongze Deng, Der-Jiunn Chin, Hui-Hsin Li, Yonghui Northeastern University School of Computer Science and Engineering Shenyang China National Changhua University of Education Department of Computer Science and Information Engineering China Overseas Chinese University Department of Information Technology China University of Sydney School of Electrical and Information Engineering Sydney Australia Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350345384

The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data's AoI due to its high maneuverability. However, the UAV's high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Maneuverability

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Liu, Yuchen Li, Gaolei Yin, Changchuan Quek, Tony Q.S. Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing100876 China Singapore University of Technology and Design 487372 Singapore University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350351255

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method. © 2024 IEEE.

关键词： Gaussian distribution

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

arXiv

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arXiv 2025年

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2025, CC BY-NC-ND.

关键词： Edge computing

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