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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

computer Modeling in engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Intrusion Detection Tool for Residential Consumers Equipped with Smart Meters

Intrusion Detection Tool for Residential Consumers Equipped ...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Sharma, Akshat Saini, Vikash Kumar Kumar, Rajesh Al-Sumaiti, Ameena S. Heydarian-Forushani, Ehsan Mugneeram Bangur Memorial University Jodhpur India Malaviya National Institute of Technology Center for Energy and Environment Jaipur India Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Malaviya National Institute of Technology Department of Electrical Engineering Jaipur India Qom University of Technology Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9781665471640

Residential load forecasting holds significant importance in the supervision of contemporary power grids, aiding utilities in efficiently allocating resources to meet the surging electricity demand. However, the proliferation of smart meters and IoT devices introduces new challenges to the power grid, particularly concerning the security and integrity of load forecasting systems. Intrusion attacks targeting load signature data can distort predictions, potentially leading to power supply disruptions and substantial economic repercussions. This research introduces a robust residential load forecasting tool based on a secure deep learning algorithm. The system demonstrates a swift ability to detect attacks and differentiate between manipulated data from cyber-attackers and authentic data. The initial step involves identifying intrusions within the dataset, utilizing a decision tree classifier that relies on features extracted through explainable artificial intelligence. This process achieves a commendable 96.6% accuracy in intrusion classification. Subsequently, the identified intrusions are counteracted via a meticulously designed clipper-clamper data filter, effectively neutralizing threats and restoring the original data. The restoration's efficacy is assessed using the KS test, validating the data's recovery. The regenerated data is then utilized as input for deep-learning models in load forecasting, with the CNN-GRU-ATTENTION model emerging as the most effective performer compared to other alternatives. © 2023 IEEE.

关键词： Attention mechanism Cyber-security Deep learning False data injection Residential load forecasting

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Cyber Twin technology for AI-Driven Real-Time Software Security in IoT Ecosystems 4

Cyber Twin Technology for AI-Driven Real-Time Software Secur...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Dhasmana, Gulshan Ravi, K.S. Nayana Yadav, M. Desai, Ishan Shankar, Rajmani Oracle Cloud Engineering Data Center NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Aj Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

As Internet of Things (IoT) ecosystems grow more complex, ensuring real-time security has become a major challenge. Traditional security approaches are insufficient for handling dynamic and interconnected IoT networks, which are increasingly targeted by sophisticated cyber-attacks. To address these issues, new methodologies that combine real-time monitoring and adaptive security mechanisms are needed. Cyber Twin technology, an innovative extension of digital twin technology, presents a promising solution by creating AI-driven digital replicas of IoT devices and software systems for continuous security monitoring and management. This paper introduces a Cyber Twin technology Framework for AI-driven real-time software security in IoT ecosystems. The framework employs advanced AI models, including Convolutional Neural Networks (CNNs) for anomaly detection and Generative Adversarial Networks (GANs) for synthetic data generation to simulate potential attack scenarios. A dynamic reinforcement learning module is integrated to optimize threat response strategies based on evolving threat patterns. By creating real-time digital replicas of IoT components, the Cyber Twin framework continuously monitors device behaviors, identifies anomalies, and autonomously initiates mitigation actions. The system is evaluated in a simulated IoT environment with over 500 interconnected devices. Experimental results demonstrate that the Cyber Twin framework achieved a 99.2% detection accuracy in identifying cyber threats, with a false positive rate of 1.3%. The dynamic response module reduced incident response time by 35% compared to traditional methods, enhancing the system's ability to neutralize potential threats in real-time. The use of GAN-based synthetic data also enabled proactive defense strategies, reducing attack success rates by 40% during testing. The Cyber Twin technology Framework provides a robust solution for real-time software security in complex IoT ecosystems. By leveraging A

关键词： Generative adversarial networks

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Capacity Bounds for Identification With Effective Secrecy

Capacity Bounds for Identification With Effective Secrecy

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Rosenberger, Johannes Ibrahim, Abdalla Bash, Boulat A. Deppe, Christian Ferrara, Roberto Pereg, Uzi Technical University of Munich Tum School of Computation Information and Technology Germany University of Arizona Electrical and Computer Engineering Department United States Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Helen Diller Quantum Center Israel

ISBN: (纸本)9781665475549

An upper bound to the identification capacity of discrete memoryless wiretap channels is derived under the requirement of semantic effective secrecy, combining semantic secrecy and stealth constraints. A previously established lower bound is improved by applying it to a prefix channel, formed by concatenating an auxiliary channel and the actual channel. The bounds are tight if the legitimate channel is more capable than the eavesdropper's channel. An illustrative example is provided for a wiretap channel that is composed of a point-to-point channel, and a parallel, reversely degraded wiretap channel. A comparison with results for message transmission and for identification with only secrecy constraint is provided. © 2023 IEEE.

关键词： Semantics

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Three Gossiping Protocols in Three-Dimensional Underwater optical Cellular Network

Three Gossiping Protocols in Three-Dimensional Underwater Op...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Wang, Yuan Zhang, Tianyi Tian, Jiahan Zhang, Junwei Xie, Yitong Zhang, Fei Zhang, Guowu Wang, Gaoxuan Hong, Xiaojian Fei, Chao He, Sailing National Engineering Research Center for Optical Instruments College of Optical Science and Engineering Ningbo Innovation Center Zhejiang University China The Hong Kong Polytechnic University Photonics Research Institute Department of Electronic and Information Engineering Hong Kong School of Electrical Engineering Royal Institute of Technology Department of Electromagnetic Engineering Stockholm Sweden

ISBN: (纸本)9798350312614

In this paper, three different low-complexity sector-based Gossiping routing protocols, namely Gossiping with probabilistic selection (Gossiping-PS), Gossiping with visibility priority (Gossiping-VP), and Gossiping with energy priority (Gossiping-EP), are evaluated through the three-dimensional underwater optical cellular network (UOCN). Comprehensive performance comparisons are made among the above three routing protocols in terms of the average hop, end-to-end delay, network lifetime, packet-loss rate, and energy utilization. Numerical analysis shows that Gossiping-PS significantly outperforms the other two schemes, while Gossiping-Vpand Gossiping-EP behave even worse than the standard Gossiping routing protocol under some circumstances, which is owing to the fact that the Greedy algorithm makes the best choice for the current moment instead of taking the global optimality into consideration. © 2023 IEEE.

关键词： LED-based communication routing protocol underwater optical cellular network (UOCN)

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Enhancing Modulation Bandwidth of Parallel Micro-LED Arrays for Visible Light Communication

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第6期46卷 944-947页

作者： Guo, Wen-An Wan, Ming-He Liu, Shi-Biao Liu, Zhong Lu, Ting-Wei Chen, Guo-Long Lu, Yi-Jun Kuo, Hao-Chung Wu, Ting-Zhu Chen, Zhong Xiamen University National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen361005 China National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics Graduate Institute of Electro-Optical Engineering Hsinchu30010 Taiwan Hon Hai Research Institute Semiconductor Research Center Taipei11492 Taiwan

In this letter, parallel micro-LED arrays with numbers of 1×1, 1×2, 2×2 and 2×3 were fabricated and the p-electrode patterns of arrays were optimized. The results show that both the light output power (LOP) and modulation bandwidth increase with the parallel array numbers. Moreover, optimizing the p-electrode pattern can effectively facilitate current spreading, mitigate thermal crowding, and enhance the LOP. Under combined with the LOP and thermal effect, the 2 × 2 ring-electrode parallel array achieved the highest modulation bandwidth of 172 MHz and maximum LOP of 5.70 mW, which were improved by 17.28% and 13.98%, respectively. This work provides a reference to design micro-LED chip for high bandwidth. © 1980-2012 IEEE.

关键词： Bandwidth

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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Design of high-reflectivity dynamic switchable metasurface holography

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optical and Quantum Electronics 2024年第10期56卷 1-13页

作者： Yang, Zehao Wang, Yunfei Zhu, Qiaofen Xi, Sixing Zhang, Lei Hu, Dan Zhang, Yan School of Mathematics and Physics Science and Engineering Hebei Computational Optical Imaging and Photoelectric Detection Technology Innovation Center Hebei International Joint Research Center for Computational Optical Imaging and Intelligent Sensing Hebei University of Engineering Handan056038 China School of Mechanical and Electrical Engineering Chuzhou University Chuzhou239000 China Department of Physics Beijing Key Laboratory of Metamaterials and Devices Key Laboratory of Terahertz Optoelectronics Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Ministry of Education Beijing100048 China

In the field of holographic imaging, high-reflectivity metasurfaces have become a crucial technology for achieving high efficiency and high-resolution image reconstruction. This study utilizes the dynamic tunability of magnesium (Mg) to numerically design an optical metasurface capable of displaying multiple holographic images in different states, achieving a reflectivity of up to 82%. In its normal state, the metasurface uses the resonance phase modulation of the metallic Mg structure to display images. Upon the introduction of hydrogen, the Mg transitions to its dielectric state, and a separate gold (Au) structure is used to modulate and display different images. The core contribution of this research lies in the application of a high-reflectivity metal layer, which enhances the imaging effects post-magnesium switching and improves the quality of image display. This work provides a new solution for dynamic optical displays and information storage technologies, thereby expanding the application scope of optical metasurfaces. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Holographic displays

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Fast calculations of vector electromagnetics in 3d periodic structures based on multiple scattering theory and broadband green’s function

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Progress in Electromagnetics Research 2024年 179卷 19-36页

作者： Tsang, Leung Liao, Tien-Hao Tan, Shurun Xu, Xiaolan Bai, Xuyang Gao, Ruoxing Radiation Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States National Taipei University of Technology Taipei10608 Taiwan Zhejiang University/University of Illinois Urbana-Champaign Institute Zhejiang University Zhejiang Haining314400 China State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

We have developed a fast method of using Multiple Scattering Theory-Broadband Green’s Function (BBGF-MST) for band field calculations. In this paper, we successfully extended the method to the vector electromagnetic case of 3D periodic structures. In the MST-BBGF approach, the broadband transformation to vector spherical waves for 3D is derived using the Broadband Green’s function. The band eigenvalue problem is expressed in terms of the single scatterer T matrix which is independent of the periodic lattice nor the Bloch vector. For the first five bands, the dimension of the KKR eigen equation is merely 6, as 6 vector spherical waves are utilized for the scattered waves. We make extensive comparisons of the results with the commercial software COMSOL in both accuracy and computation efficiency. The CPU requirement on a standard laptop for the MST-BBGF method is merely 0.309 seconds for the first 5 bands. The MST-BBGF method is accurate and is at least two orders of magnitude faster than commercial software COMSOL. In the band field calculations, we employ the approach of extended coefficient to use the low order eigenvector of 6 to extend to 240 vector spherical wave coefficients without the need of recalculating the eigenvalue nor the eigenvector of the KKR equation. The extended coefficients approach gives accurate band field solutions for the entire (0, 0, 0) cell. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Periodic structures

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Efficient Acoustic-optical Fiber Coupling with Wire Horn Structure

Efficient Acoustic-Optical Fiber Coupling with Wire Horn Str...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Wang, Ruixuan Theis, Logan Homa, Daniel Wang, Anbo Pickrell, Gary Center for Photonics Technology The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Center for Photonics Technology Department of Material Science and Engineering Virginia Tech BlacksburgVA United States

This paper reports on a wire horn structure to enhance acoustic-optical fiber coupling. Compared with the conventional schemes, it was proved to enhance the ultrasonic wave peak-to-peak amplitude coupling efficiency b... 详细信息

ISBN: (纸本)9781957171296

关键词： Coupling efficiency Fiber Bragg gratings Fiber optic sensors Mode conversion optical fibers Tunable lasers

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