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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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Micron-resolved quantum precision measurement of magnetic field at the Tesla level

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Chinese Physics B 2024年第12期33卷 95-99页

作者： Si-Han An Shi-Yu Ge Wen-Tao Lu Guo-Bin Chen Sheng-Kai Xia Ai-Qing Chen Cheng-Kun Wang Lin-Yan Yu Zhi-Qiang Zhang Yang Wang Gui-Jin Tang Hua-Fu Cheng Guan-Xiang Du College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Portland Institute Nanjing University of Posts and TelecommunicationsNanjing 210023China The School of Mechanical and Electrical Engineering Suqian CollegeSuqian 223800China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Yichang Testing Technique R&D Institute Yichang 443003China

We develop a quantum precision measurement method for magnetic field at the Tesla level by utilizing a fiber diamond *** to our system is a micron-sized fiber diamond probe positioned on the surface of a coplanar waveguide made of nonmagnetic *** with a nuclear magnetic resonance magnetometer,this probe demonstrates a broad magnetic field range from 10 mT to 1.5 T with a nonlinear error better than 0.0028%under a standard magnetic field generator and stability better than 0.0012%at a 1.5 T magnetic ***,we demonstrate quantitative mapping of the vector magnetic field on the surface of a permanent magnet using the diamond magnetometer.

关键词： nitrogen-vacancy center fiber diamond magnetometer precision measurement

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Fundus Image Classification via an Integrated Deep Learning Model and Random Forest for Glaucoma Diagnostics 2nd

Fundus Image Classification via an Integrated Deep Learnin...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zeng, Haotian Cong, Jiheng Hong, Hantong Yu, Zihan Zhang, Fan Li, Guojie School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China Hebei University of Environmental Engineering Qinhuangdao China School of Electrical Engineering Electronics and Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9789819639489

Glaucoma is a leading cause of irreversible blindness, often worsened by delayed diagnosis. Traditional diagnostic methods have limitations in early detection and require significant expertise. Recent advancements in machine learning and deep learning have shown promise in enhancing the diagnostic accuracy and efficiency of glaucoma detection using fundus imagery. This study presents a four-stage method for glaucoma classification. Fundus images are processed using VGG16 and ViT for feature extraction, capturing both local and global features. These features are then fused and reduced in dimensionality using Principal Component Analysis. Finally, the reduced features are classified using a Random Forest classifier. The integrated feature fusion model demonstrates significant improvements in diagnostic performance, achieving higher accuracy, specificity, and sensitivity in distinguishing between non-referable and referable glaucoma classes compared to traditional methods. Specifically, our model achieved an accuracy of 94.2%, an F1 score of 94.2%, a sensitivity of 94.44%, and a specificity of 94.0%. The use of both CNNs and ViTs for feature extraction leverages their strengths, resulting in a more effective diagnostic tool. The combination of CNNs, ViTs, and Random Forest classifiers, along with advanced data augmentation techniques, shows substantial potential for early glaucoma detection and ongoing monitoring. This approach addresses the limitations of current machine learning models and enhances diagnostic accuracy and efficiency, making it a promising tool for clinical settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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Dual-Layer Based Microgrid Protection Using Voltage Synchrophasors

Dual-Layer Based Microgrid Protection Using Voltage Synchrop...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Menezes, Thiago S. Coury, Denis V. Fernandes, Ricardo A.S. Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Department of Electrical Engineering Federal University of São Carlos São Carlos Brazil

ISBN: (纸本)9798350396782

Distribution networks have been facing many changes in recent years, mainly considering their integration with microgrids. Their combined use has several operational challenges, such as power quality issues, system instability, and protection problems. Considering the decreased costs of phasor measurement units (PMUs), many protection schemes have been proposed using these devices. Thus, this paper presents a dual-layer protection for microgrids based on the RMS value of voltage measurements. The first layer uses conventional overcurrent relays to detect faults. In case of failure or delayed operation of these relays, a second layer device should operate, which utilizes the voltage from strategically allocated PMUs. In this context, if necessary, the second layer device sends a remote trip signal. The proposed method was evaluated for all fault types, considering different microgrid operation modes and power injection from distributed generators. Overall, the dual-layer protection significantly improved the microgrid protection reliability, regardless of its operation mode. © 2023 IEEE.

关键词： Fault detection microgrid protection synchrophasor undervoltage

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Impedance Emulation Control of Wave Energy Converters

Impedance Emulation Control of Wave Energy Converters

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Annual Conference of Industrial Electronics Society

作者： Pranav Chandran InYong Kim Ted Brekken B. Johnson Chandra Family Department of Electrical and Computer Engineering University of Texas at Austin Austin TX School of Electrical Engineering and Computer Science Corvallis OR

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Modeling and control of wave energy conversion (WEC) systems for maximum power extraction is challenging due to complex multiphysics that include fluids, mechanics, and machine drives. To uncover an intuitive model that clearly depicts WEC system operation, we utilize a force-current equivalent circuit framework which then enables us to design an impedance emulation control strategy. To provide context for our framework, we focus on a standard reference model-3 (RM3) point absorber device coupled to a permanent magnet synchronous generator. After the proposed extremum seeking controller is computed, we experimentally validate its performance on a platform that consists of two back-to-back connected inverters that emulate the WEC system.

关键词： Performance evaluation Systems operation Emulation Synchronous generators Permanent magnets Inverters Impedance Wave energy conversion Integrated circuit modeling Standards

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ArcText: A Unified Text Approach to Describing Convolutional Neural network Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第4期3卷 526-540页

作者： Sun, Yanan Yen, Gary G. Xue, Bing Zhang, Mengjie Lv, Jiancheng Sichuan University College of Computer Science Chengdu610065 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

The superiority of convolutional neural networks (CNNs) largely relies on their architectures that are usually manually crafted with extensive human expertise. Unfortunately, such kind of domain knowledge is not necessarily owned by every interested user. Data mining on existing CNNs can discover useful patterns and fundamental comments from their architectures, providing researchers with strong prior knowledge to design effective CNN architectures when they have no expertise in CNNs. There are various state-of-the-art data mining algorithms at hand, while there is only rare work on mining CNN architectures. One of the main reasons is the gap between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized to feed to a data mining algorithm. In this article, we propose a unified approach, named ArcText, to describing CNN architectures based on text. Particularly, four different units and an ordering method have been elaborately designed in ArcText, to uniquely describe the same CNN architecture with sufficient information. Also, the resulted description can be exactly converted back to the corresponding CNN architecture. ArcText bridges the gap between CNN architectures and data mining researchers, and has the potential to be utilized to wider scenarios. © 2020 IEEE.

关键词： Data mining

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Efficient Permutation and Diffusion Model based on TCMT for Image Encryption 2

Efficient Permutation and Diffusion Model based on TCMT for ...

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2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Sandhu, Mandeep Kaur Singh, Surender Kaur, Manjit Chandigarh University Computer Science and Engineering Punjab Gharuan India Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Korea Republic of

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Chaos contains unique properties that are well-suited to the cryptography area. Due to their simplicity and chaotic nature, for security-related challenges, one-dimensional chaotic maps are often used, as opposed to multidimensional chaotic maps, which can be difficult to create and evaluate. Classic one-dimensional chaotic maps, on the other hand, have a limited spectrum of chaotic behavior. We present novel one-dimensional chaotic maps in this study, including a combination Tent-Cubic map (TC). To provide a wide spectrum of chaotic behavior, we proposed a 1-dimensional chaotic map by mixing classical and basic one-dimensional chaotic maps. Afterward, an efficient image encryption scheme named (TCMT) is proposed. It consists of two modules, Tent-Cubic map and Mersenne-permutation Twister's technique are used in each module to perform permutations and diffusion. It has low correlation coefficients, a huge keyspace, and the histogram is uniformly distributed, as well as resistance to different assaults, according to the simulation results. © 2022 IEEE.

关键词： Cryptography

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Certain Investigation of Filtered-OFDM in Visible Light Communications system 7

Certain Investigation of Filtered-OFDM in Visible Light Comm...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Rashid Hujijo, Hayder S. Ilyas, Muhammad Qasim, Abdullah Ali Ullah, Ubaid Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey Dept. of Computer Engineering Altinbas University Istanbul Turkey Dept. of Tech. Communications Engineering Al-Furat Al-Awsat Technical University Najaf Iraq Network and Communication Engineering Al Ain University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665464086

Filtered-OFDM scheme recently considers a promising element for Visible Light Communication (VLC) systems. Converting the F-OFDM bipolar signal to a nonnegative signal and being compatible with the Intensity Modulation and Direct Detection (IM/DD) is a crucial issue for researchers. To achieve a unipolar signal, many techniques have been proposed. In this paper, a Direct Current-bias (DC-bias) technique is employed to acquire a signal unipolarity and evaluate the efficiency of the F-OFDM system. The simulation findings offer that when the DC bias increased, the peak-to-average-power ratio (PAPR) increased even when using different modulation orders. In contrast, comparing different modulation orders, F-OFDM shows better bit error rate (BER) performance, especially in low-order modulation schemes. © 2023 IEEE.

关键词： Quadrature amplitude modulation

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Advanced Deep Learning Approaches for Real-Time Anomaly Detection in IoT Environments 7

Advanced Deep Learning Approaches for Real-Time Anomaly Dete...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Goyal, Himanshu Rai Husain, Saif O. Dixit, Krishna Kant Boob, Nandini Shirish Reddy, B. Ravali Kumar, Jaysheel Sharma, Smita Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Amity University Amity School of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

The proliferation of Internet of Things (IoT) systems has led to the generation of vast amounts of data, increasing the need for effective anomaly detection mechanisms to ensure system reliability and security. Traditional methods often fall short due to the high dimensionality and dynamic nature of IoT data. This paper presents a deep learning-based approach for real-time anomaly detection in IoT systems, leveraging the power of neural networks to identify patterns and anomalies within complex datasets. By employing deep learning algorithms such as Convolutional Neural networks (CNNs) and Long Short-Term Memory (LSTM) networks, this framework can efficiently detect abnormal behaviors in real-time, offering a significant improvement over conventional methods. The proposed system is trained on labeled datasets collected from IoT devices, allowing it to distinguish between normal and anomalous activity. Furthermore, the model adapts to new data, enhancing its detection capabilities over time. Experimental results demonstrate that this approach achieves high accuracy and low false-positive rates, making it suitable for deployment in real-world IoT environments. The framework's scalability and adaptability highlight its potential for enhancing the security and reliability of IoT networks, particularly in critical applications such as smart cities, healthcare, and industrial IoT. © 2024 IEEE.

关键词： Convolutional neural networks

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Toward Efficient Coordination of Power Distribution network and Electric Vehicles: Deep Reinforcement Learning With Robust Reward Function

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8164-8176页

作者： Peiyue Li Sheng Chen Zhinong Wei Qiuwei Wu Guoqiang Sun Jiahui Jin School of Electrical and Power Engineering Hohai University Nanjing China School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast U.K.

The rapid increase in electric vehicle (EV) usage has led to an urgent need for coordinating EV charging activities with power distribution networks (PDNs) to accommodate the resulting redistributed electrical demand and implement greater operational flexibility for PDNs. However, current efforts relying on the implementation of EV charging price strategies to influence the decision-making activities of EV users at charging stations face challenges arising from the uncertainty of EV charging behavior and data privacy concerns. This work addresses these issues by developing a robust reinforcement learning approach that requires no personal EV user information to determine charging schedules and pricing strategies at charging stations, while the uncertainty in EV behavior is addressed by applying a robust reward function. The loss function is relaxed using Holder’s and Cauchy-Schwarz inequalities, which yields an upper bound for the loss caused by the worst case scenario and therefore enhances the computational efficiency of the solution process. Numerical results demonstrate that the proposed method contributes to balancing the PDN load and improving the utilization of fast charging stations (FCSs). Compared to deterministic and traditional robust methods, the proposed method reduces computation time in uncertain environments while ensuring moderate revenue of FCSs.

关键词： Pricing Partial discharges Transportation Electric vehicle charging Distributed power generation Costs Reactive power Power transmission lines Electrification Voltage

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