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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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SSVEP Based BCI control of an Exoskeleton 18th

SSVEP Based BCI Control of an Exoskeleton

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18th International Conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Sibișanu, Remus Constantin Olar, Marius Leonard Roșca, Sebastian Daniel Leba, Monica Computer and System Control Engineering Department University of Petrosani 20 Universitatii Petrosani332006 Romania

ISBN: (纸本)9783031816840

This paper explores recent innovation in the field of robotic teleoperation, presenting a state-of-the-art system for a robotic arm, configurable as an exoskeleton or prosthetic limb. Based on noninvasive neural headset technology, the system captures movement intentions directly from the user’s neural activity. By precisely interpreting neural signals and translating them into robotic arm movements, the system bridges the gap between human intention and robotic action. Its uniqueness lies in its ability to interpret the subtleties of human intent through non-invasive means, providing intuitive and fluid control over the robotic arm. The system is distinguished by the efficient use of rich neural activity, thus improving usability and opening up new possibilities for people with mobility impairments. The paper analyzes technical complexities, detailing the functionality of neural headsets, signal decoding algorithms, and the command formation process for the robotic arm. It also presents empirical evidence of the effectiveness of the system and discusses possible applications and future improvements. This research not only contributes to robotic teleoperation, but also paves the way for more human and empathetic interactions with machines in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Exoskeleton (Robotics)

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Crowd E-Home—A Crowd-Based Collaborative Decoration Platform

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International Journal of Crowd Science 2022年第3期6卷 135-141页

作者： Ge, Rui Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

With the rapid development of Chinese urbanization and industrialization level, the Chinese real estate transaction area continues to grow, and the country's decoration industry is expanding. Due to the rapid expansion, the decoration industry has failed to establish a good industry order, and the current decoration industry has many problems, such as opaque information and low industrial efficiency. Traditional and Internet decoration companies have failed to solve these problems effectively. The purpose of this study is to build a new collaborative decoration platform to solve the shortcomings of the decoration industry and promote its healthy development. A crowd-based collaborative decoration platform called Crowd E-home is proposed. Based on the Building Information Modeling (BIM) of houses, the platform constructs collaborative intelligent digital models, which have the characteristics of evolution, coordination, multifaceted, and initiative. These models act as intermediate coordinators to complete the collaborative division of labor. This platform plays a role in matching multiparty transactions among consumers, designers, construction teams, material suppliers, and supervisors to solve the problem of the huge coordination costs of traditional decoration companies. It also breaks the space-time limit of consumer selection during the decoration process, improves the decision-making power of consumers, and meets consumer needs for personalized services. The collaborative concept of the platform and the application of BIM optimize the existing decoration industry chain, which transforms the traditional chain model into a star-shaped decoration closed-loop ecosystem. © The author(s) 2022.

关键词： Decision making

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Leakage Localization of Gas Pipelines Based on Modal Acoustic Emission and Cross-power Spectrum Peak Optimization

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IEEE Sensors Journal 2025年第11期25卷 19982-19992页

作者： Zhang, Wenbiao Gong, Kezheng Cui, Xiwang North China Electric Power University School of Control and Computer Engineering Beijing102206 China Beijing Information Science and Technology University School of Instrument Science and OptoElectronics Engineering Beijing100192 China

Leakage accidents in natural gas pipelines bring huge property losses and pose serious safety risks. Therefore, faster and more accurate leakage localization is of great significance. In this paper, a new method based on modal acoustic emission (MAE) and peak optimization of cross-power spectrum is proposed to enhance the performance of leakage localization. When a pipeline leakage occurs, the sensors installed on the pipelines receive the acoustic emission (AE) signals, which have various modes. Instead of extracting a single mode through complex algorithms, the proposed method directly extracts the cross-power spectrum of a specified mode by choosing the appropriate window function and window parameters to locate the leakage with velocity information from dispersion curves. Gaussian, Hamming and Blackman window functions are compared in the extraction of the cross-power spectrum of L(0,1) mode, among which the Blackman window function is found to be the most effective. The artificial fish swarm algorithm combined with the iteration criterion for peak optimization is used to obtain suitable window parameters. Finally, the localization error of the above method is less than 5.2%. In addition, the performance of the iteration criterion for peak optimization of cross-power spectrum combined with other intelligent swarm algorithms are investigated. The results show that the maximum errors of other intelligent methods are still less than 9%, demonstrating the great compatibility of the proposed method. © 2001-2012 IEEE.

关键词： Acoustic emissions

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AdaLightLog: Enhancing Application Logs Anomaly Detection via Adaptive Federating Learning 19th

AdaLightLog: Enhancing Application Logs Anomaly Detection vi...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Menegatti, Danilo De Santis, Emanuele Felli, Stefano Giuseppi, Alessandro Department of Computer Control and Management Engineering ‘Antonio Ruberti’ Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031842597

The role of anomaly detection systems in Critical Infrastructures (CIs) is critical due to the complexity of CIs and their control systems, which are usually implemented by computer-based controllers that constantly produce logs of their activities. Moreover, many CIs, located in different locations or even belonging to different companies, may share similar application software for controlling the CIs themselves. The goal of this work is to use such logs to perform automatic anomaly detection in a federated learning (FL) paradigm, which ensures that no data is exchanged between sites to train the anomaly detection models, but each learning agent learns on its own data, leveraging the knowledge acquired by the other agents. Our proposed approach - AdaLightLog - which implements a modified FL paradigm with adaptive loss functions at local servers side and weighted averaging of local server models, so to differentiate the quality of the different local servers’ models in the global averaging, is tested against state-of-the-art methods and shows an improvement in performance in terms of accuracy, precision and recall with respect to the standard FL implementation (FedAvg). Furthermore, a comparison between different metrics for the adaptive loss functions and the dynamic weights is presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs 46

User Input Search - Custom Motion Estimation Algorithm Optim...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Benjak, J. Hofman, D. Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia

ISBN: (纸本)9789532331042

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data - the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested. © 2023 MIPRO Croatian Society.

关键词： Real time systems

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Optimal therapy for HIV infection containment and virions inhibition

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Infectious Disease Modelling 2024年第1期9卷 10-26页

作者： Paolo Di Giamberardino Daniela Iacoviello Muhammad Zubair Dept.of Computer Control and Management EngineeringSapienzaUniversity of RomeVia Ariosto 2500185RomeItaly

Prevention and early diagnosis are the best and most effective ways for defeating *** is still no vaccine,but treatments with antiretroviral drugs are now available which,in many cases,allow the infection to become ***,research has highlighted side effects of these drugs and the fact that a flare-up of the infection occurs if the therapy is *** recent years,the presence of virus reserves located in various parts of the body,including the brain,has been *** possibility of controlling the infection of healthy cells and of interrupting the proliferation of virions inside the brain has been studied,proposing optimal control strategies.

关键词： Epidemic modeling HIV-AIDS Optimal control

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Radiomics-based classification in imbalanced datasets: Complexity or interpretability

Radiomics-based classification in imbalanced datasets: Compl...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Boesso, Simone Farina, Lorenzo Petti, Manuela Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350386226

Radiomics represents a specialized branch of medical imaging where quantitative features are extracted from images. Performing a classification using radiomics means solving two common problems: the imbalanced setting, and the large number of features that would increase the risk of overfitting. Moreover, since its main application and impact are in clinical field, there is the need of interpretable models for explaining their results. The aim of this study is to compare two modelling approaches: one based on a logistic regression model, known for its simplicity and interpretability, and RUSBoost, an ensemble method designed to handle class imbalance with potentially higher complexity, in order to answer the question whether higher complexity and lower interpretability are justified when dealing with radiomics data. Additionally, due to the large literature suggesting it, we analyze the impact of a feature selection step applied to these two classifiers. Test performances measured across 20 repeated splits on two datasets show how the RUSBoost approach is able to capture more detailed patterns of the data but this is highly dependent on the dataset at hand. © 2024 IEEE.

关键词： Logistic regression

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A Crowd Equivalence-Based Massive Member Model Generation Method for Crowd Science Simulations

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International Journal of Crowd Science 2022年第1期6卷 23-33页

作者： Xing, Aoqiang Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support studies about crowd phenomena. As modelbased scientific activities, crowd science simulations take extra efforts on member models, which reflect individuals who own characteristics such as heterogeneity, large scale, and multiplicate connections. Unfortunately, collecting enormous members is difficult in reality. How to generate tremendous crowd equivalent member models according to real members is an urgent problem to be solved. A crowd equivalence-based massive member model generation method is proposed. Member model generation is accomplished according to the following steps. The first step is the member metamodel definition, which provides patterns and member model data elements for member model definition. The second step is member model definition, which defines types, quantities, and attributes of member models for member model generation. The third step is crowd network definition and generation, which defines and generates an equivalent large-scale crowd network according to the numerical characteristics of existing networks. On the basis of the structure of the large-scale crowd network, connections among member models are well established and regarded as social relationships among real members. The last step is member model generation. Based on the previous steps, it generates types, attributes, and connections among member models. According to the quality-time model of crowd intelligence level measurement, a crowd-oriented equivalence for crowd networks is derived on the basis of numerical characteristics. A massive member model generation tool is developed according to the proposed method. The member models generated by this tool possess multiplicate connections and attributes, which satisfy the requirements of crowd science simulations well. T

关键词： Data models Numerical models Level measurement Task analysis Standards

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COVID-19 Spread Simulation in a Crowd Intelligence Network

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International Journal of Crowd Science 2022年第3期6卷 117-127页

作者： Shan, Linzhi Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

In this paper, the Crowd Intelligence Network Model is applied to the simulation of epidemic spread. This model combines the multi-layer coupling network model and the two-stage feedback member model to study the epidemic spread mechanisms under multiple-scene intervention. First, this paper establishes a multi-layer coupled network structure based on the characteristic of Social Network, Information Network, and Monitor Network, namely, the Crowd Intelligence Network structure. Then, based on this structure, the digital-self model, which has a multiple-scene effect and two-stage feedback structure, is designed. It has an emotional state and infection state quantified by using attitude and self-protection levels. This paper uses the attitude level and self-protection level to quantify individual emotions and immune levels, and discusses the impact of individual emotions on epidemic prevention and control. Finally, the availability of the Crowd Intelligence Network Model on the epidemic spread is verified by comparing the simulation trend with the actual spread trend of COVID-19. © The author(s) 2022.

关键词： COVID-19

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