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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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Reduced Sensor Control Approach for Active Power Decoupling Circuit in PV Microinverter Application

Reduced Sensor Control Approach for Active Power Decoupling ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Shen, Yidi Zakzewski, Daniel Hasnain, Arafat Resalayyan, Rakesh Khaligh, Alireza University of Maryland Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD20742 United States

ISBN: (纸本)9798350316445

Active power decoupling (APD) circuit is of great importance in single-phase photovoltaic (PV) inverter systems for eliminating the fluctuating power drawn from the PV panel. The control of APD circuit is the key to the power decoupling performance and maximum power point tracking (MPPT) efficiency. This paper proposes a control approach for parallel boost type APD circuit with a single sensor, reducing the count of sensors compared to the conventional APD control approach. Operation of the APD circuit with control approach utilizing only the readings from the decoupling capacitor voltage sensor is analyzed in this work. Specifically, controller design for the APD circuit with reduced sensor applicable to a 40 V, 400 W microinverter is presented and experimentally verified with a GaN-based prototype. © 2023 IEEE.

关键词： Active Power Decoupling Microinverter PV Reduced Sensor

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE 5

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Liang, Yiyuan Jia, Hong Wang, Licai Huang, Yangchen Yang, Zhe Big Data Research and Development Center North China Institute of Computing Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350385557

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions. © 2024 IEEE.

关键词： document-level event extraction relation augment Universal Information Extraction

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Scalable Data Profiling for Quality Analytics Extraction 13th

Scalable Data Profiling for Quality Analytics Extraction

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Nikolakopoulos, Anastasios Chondrogiannis, Efthymios Karanastasis, Efstathios Osa, María José López Aroca, Jordi Arjona Kefalogiannis, Michalis Apostolopoulou, Vasiliki Deligeorgi, Efstathia Siopidis, Vasileios Varvarigou, Theodora School of Electrical and Computer Engineering Institute of Communication and Computer Systems National Technical University of Athens Athens Greece Bilbao Spain Valencia Spain OTE Group of Companies Athens Greece Centre for Research and Technology Hellas Marousi Greece

ISBN: (纸本)9783031632266

In today’s modern society, data play an integral role in the development global industry, since they have become a valuable asset for companies, institutions, governments, and others. At the same time, data generated daily, at a global scale, require significant resources to pre-process, filter and store. When it comes to acquiring such stored data, it is essential to understand which dataset fits to the needs of the user beforehand. One particularly important factor is the quality of a dataset, which could be determined based on a series of quality related attributes generated by it. Such attributes constitute "Profiling", the process of obtaining information from a data sample, related to the complete dataset’s quality. However, in the era of Big Data, the ability to apply profiling techniques in complete large datasets should also be considered, in order to obtain complete quality insights. This paper attempts to provide a solution for this consideration by presenting "DaQuE", a scalable framework for efficient profiling and quality analytics extraction in complete datasets of all volumes. © IFIP International Federation for Information Processing 2024.

关键词： Extraction

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Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling

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Complex System Modeling and Simulation 2023年第2期3卷 83-101页

作者： Zhen Shu Aijuan Song Guohua Wu Witold Pedrycz College of Systems Engineering National University of Defense TechnologyChangsha 410073China School of Traffic and Transportation Engineering Central South UniversityChangsha 410075China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canadaand also with the Systems Research InstitutePolish Academy of SciencesWarsaw 01447Poland

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property ***-world emergency material scheduling(EMS)problems are typically large-scale and possess complex *** evolutionary algorithm(EA)is one of the effective methods for solving EMS ***,the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality *** handle the above challenges,we apply the idea of a variable reduction strategy(VRS)to an EMS problem,which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS ***,we define an emergency material allocation and route scheduling model,and a variable neighborhood search and NSGA-II hybrid algorithm(VNS-NSGAII)is designed to solve the ***,we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality ***,we integrate VRS with VNS-NSGAII to solve the reduced EMS *** prove the effectiveness of VRS on VNS-NSAGII,we construct two test cases,where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support *** results show that VRS can improve the performance of the standard VNS-NSGAII,enabling better optimization efficiency and a higher-quality solution.

关键词： emergency material scheduling evolutionary algorithm variable reduction strategy

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Quantum Resnet18 for Classifying and Predicting the Maize Leaf Diseases

Quantum Resnet18 for Classifying and Predicting the Maize Le...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Kujani, T. Bhuvanya, R. Sathya, T. Arivubrakan, P. Usha, V. Thangaselvi, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Tamilnadu Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798350354539

The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including Leaf Blight, Grey Leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops. © 2024 IEEE.

关键词： Crops

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Optimizing Requirements Prioritization: Majority Voting Goal-Based Approach with Vertical Binary Search

Optimizing Requirements Prioritization: Majority Voting Goal...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Brahmam, Madala Guru Anand R, Vijay Grover, Veena Gochhait, Saikat School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India School of Management Niet Greater Noida India Symbiosis Institute of Digital and Telecom Management Symbiosis International Deemed University Neurosciences Research Institute India

ISBN: (纸本)9798350372229

There is a growing need for firms, organizations, and industries to prioritize requirements, which emphasizes the need for an efficient method to satisfy customers. When combined with the Vertical Binary Search approach, the Majority Voting Goal Based (MVGB) prioritization strategy provides a complete solution for arranging needs in order of importance. In this paper, the MVGB and Vertical Binary Search technique are explained in detail, along with a 4-step methodical approach that is in line with the principles of Binary Search. Stakeholder decisions and their allocated votes for each requirement are the basis for the approach, which yields computed values. The superiority of MVGB in terms of speed, fault tolerance, reliability, and other crucial criteria is revealed by a comparative analysis of the approach against alternative demand prioritizing methods, particularly Multi-voting and Binary Search. © 2024 IEEE.

关键词： 4 step method Majority Voting Goal Based Multi-voting and Binary Search method Requirement prioritization

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Circumnavigation of UAVs integrating QP-Based CLF and CBF in a Cooperative Path-Following Framework 63

Circumnavigation of UAVs integrating QP-Based CLF and CBF in...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anand, Pallov Aguiar, A. Pedro Sujit, P.B. Universidade Do Porto SYSTEC-ARISE Research Center for Systems and Technologies Faculdade de Engenharia Porto Portugal Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal462060 India

ISBN: (纸本)9798350316339

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in various applications often requiring coordinated operations of multiple UAVs. Circular formation control has garnered significant attention due to its diverse relevance in many situations. In this paper, we propose a Quadratic Programming (QP) based Control Lyapunov Function (CLF) and Control Barrier Function (CBF) framework for circumnavigating stationary and moving targets for single and multiple UAVs while avoiding collision with a known obstacle. By integrating CLF and CBF constraints in a cooperative path-following framework, our approach offers robustness and scalability for complex multi-agent systems, and ensures asymptotic tracking of the target while maintaining closed-loop stability of the error dynamics and safety margins around the obstacle. Through theoretical analysis and simulation studies, we demonstrate the effectiveness and versatility of our proposed framework in challenging environments. © 2024 IEEE.

关键词： Multi agent systems

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A Reliable Federated Learning Server Rotation Algorithm in IoV

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IEEE Internet of Things Journal 2025年第11期12卷 15501-15513页

作者： Cai, Xuelian Yang, Junyi Chang, Tianyu Fu, Yuchuan Yu, F. Richard Cheng, Nan Li, Changle Hui, Yilong The State Key Laboratory of Integrated Services Networks Xidian University Shaanxi 710071 China Research Institute of Smart Transportation Xidian University Shaanxi Xi’an710071 China The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Federated Learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this paper proposes a reliable Rotating Server Federated Learning (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this paper proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL. © 2014 IEEE.

关键词： Federated learning

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