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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Kierzkowski, A. Kisiel, T. Mardeusz, E. Ryczyński, J. Department of Technical Systems Operation and Maintenance Faculty of Mechanical Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9798350386097

The security screening system is inextricably linked to the human factor. In the current systems at airports, it is not verified that an operator marks an alarm haphazardly when he evaluates an X-ray scan image of items carried in air transportation. This condition can distort the actual value of screening operators' evaluation indicators. The article presents the preliminary results of ongoing research on the implementation of eye-tracking technology for evaluating the performance of airport screening operators. The research focuses on using eye-tracking technology to track eye focus points. The results of the research indicate that there is an error when pointing the eye to a reference location. The results presented in the article are a prelude to further work on estimating the level of error that should have been considered in the security screening system. © 2024 IEEE.

关键词： Airport security

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Assessment of the Cybersecurity Efficiency of G20 Countries Using Data Envelopment Analysis 17

Assessment of the Cybersecurity Efficiency of G20 Countries ...

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17th International Conference on Information Security and Cryptology, ISCTurkiye 2024

作者： Kupeli, Mihrac Sogut, Esra Çankiri Karatekin University Faculty of Science Department of Statistic Çankiri Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798331533649

With the rapid advancement of digital transformation, cybersecurity has emerged as a strategic priority for nations worldwide. Ensuring the secure participation of individuals, businesses, and governments in digital environments requires robust cybersecurity measures. However, measuring cybersecurity poses significant challenges due to the complexity of the issue and the limited availability of reliable data. This study addresses these challenges by employing Data Envelopment Analysis (DEA) to assess and rank the cybersecurity performance of G20 countries, utilizing existing cybersecurity indices such as the Global Cybersecurity Index (GCI) and the National Cyber Security Index (NCSI). The results indicate that Indonesia, France, India, and Japan exhibit efficient cybersecurity performance, while Mexico demonstrates the weakest performance among the G20 nations. This research provides valuable insights for policymakers and stakeholders to develop strategies aimed at enhancing national cybersecurity capabilities. © 2024 IEEE.

关键词： Cybersecurity

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Enhancing Physical-Layer Security in Full-Duplex Cooperative Wireless Networks over α-μ Fading Channels 7

Enhancing Physical-Layer Security in Full-Duplex Cooperative...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Saddouq, Ayam Al-Mistarihi, Mamoun F. Khoderi, Mahmoud A. Darabkh, Khalid A. Jordan University of Science and Technology Faculty of Engineering Electrical Engineering Department P.O. Box 3030 Irbid Jordan The University of Jordan School of Engineering Computer Engineering Department Amman11942 Jordan

ISBN: (纸本)9798350367027

Cooperative communication is an emerging method that allows devices with a single antenna to share their antennas and assist other nodes in transmitting signals. This leads to enhanced spatial diversity, lower power consumption, and increased reliability of signal reception. This study uses full-duplex mode and α-μ fading channels and derives the secrecy outage probability for a single relay to assess the system's secrecy performance and examine how utilizing jamming signals affects system security. Theoretical results for the derived expression are given. © 2024 IEEE.

关键词： Cooperative communication

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Sub-zero soil CO2 respiration in biostimulated hydrocarbon-contaminated cold-climate soil can be linked to the soil-freezing characteristic curve

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Environmental science and Pollution Research 2025年第4期32卷 1783-1804页

作者： Nayeema, Tasnim Lee, Aslan Hwanhwi Richter, Amy Ng, Kelvin Tsun Wai Chang, Wonjae Department of Civil Geological and Environmental Engineering College of Engineering University of Saskatchewan 57 Campus Drive Engineering Building SaskatoonSKS7N 5A9 Canada Faculty of Engineering and Applied Science Environmental Systems Engineering University of Regina 3737 Wascana Parkway ReginaSKS4S 0A2 Canada

Extending unfrozen water availability is critical for stress-tolerant bioremediation of contaminated soils in cold climates. This study employs the soil-freezing characteristic curves (SFCCs) of biostimulated, hydrocarbon-contaminated cold-climate soils to efficiently address the coupled effects of unfrozen water retention and freezing soil temperature on sub-zero soil respiration activity. Freezing-induced soil respiration experiments were conducted under the site-relevant freezing regime, programmed from 4 to − 10 °C at a seasonal soil-freezing rate of − 1 °C/day. The effects of unfrozen water retention on extending soil respiration activity emerged at the onset of soil-freezing. The unfrozen water effect became significant below 0 °C (correlation r = 0.83–0.94) and comparable to the temperature effect (correlation r = 0.82–0.90), successfully demonstrating the coupled effects on sub-zero respiration activity. Soil CO2 respiration modelling based on the temperature dependency only (Arrhenius and Q10 models) did not accurately describe sub-zero respiration activity associated with increased unfrozen water retention in treated contaminated soils. The shifted SFCCs of the treated soils, expressed as a function of soil temperature (T) and unfrozen water content (θ), served as a key framework for efficiently developing the sub-zero respiration model (SFCC-RESP). The developed SFCC-RESP model closely approximated the changes in soil respiration rates influenced by T and θ in the treated soils (R2 = 0.94–0.98) and described the abrupt decrease and subsequent stabilization in CO2 production during the transition to the deeply frozen soil phase. The SFCC-RESP model integrated with soil thermal models (TEMP/W) can be used to produce spatial distributions of T, θ, and CO2 production in the treated soil matrix, providing a tool to approximate the abundance of unfrozen habitable niches when developing cold-tolerant bioremediation strategies. © The Author(s), under exclusive li

关键词： Soil pollution

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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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Change Vector Change Indices Method for Unsupervised Remote Sensing Change Detection

Change Vector Change Indices Method for Unsupervised Remote ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Sujith Kumar, A. Sundaram, Venkatesan Meenakshi Prabhavathy, P. National Institute of Technology Department of Computer Science and Engineering Puducherry India School of Computer Science Engineeering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331540685

Unsupervised Remote sensing change detection is very important because it addresses the problem of scarcity in the data availability in training. Some of the researchers are successful in finding the change regions but are not able to classify what changes happened in those regions. Nearly every study faces challenges in generalizing the fluctuations occurring during both the growing and non-growing seasons. The current study utilizes a novel architecture consisting of two parts. The first part involves proposing change regions through the change vector calculation method and applying Otsu's Thresholding on the calculated change vectors, while the second part entails classifying these proposed change regions into vegetation change, building change, and non-change regions. non-change region classification process aims to eliminate false changes identified by the change vector analysis and Otsu's Thresholding. In the second stage, NDVI, NDWI, and NDMI indices are compared and the resulting change mask obtained is subjected for element-wise multiplication operation with the change mask obtained in the first stage. The study addressed the challenge of generalizing short-term changes, such as those occurring during the growing and non-growing seasons, by computing the median of pixels over a time range encompassing both types of changes. Additionally, the method was fine-tuned using NIT Puducherry data from two time periods. Subsequently, the entire Karaikal district was considered to examine the changes comprehensively. The present study outperformed different state of the art methods like CVI_OTSU, PCI_Kmeans, Continuous Change Detection and Classification (CCDC). The present study got better results with an accuracy of 86.07 and kappa coefficient of 0.7898. © 2024 IEEE.

关键词： Change detection

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Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis

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IEEE Journal of Biomedical and Health Informatics 2024年第7期28卷 4084-4093页

作者： Dorjsembe, Zolnamar Pao, Hsing-Kuo Odonchimed, Sodtavilan Xiao, Furen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan University of Tokyo Faculty of Engineering Tokyo113-8654 Japan College of Medicine Institute of Medical Device and Imaging National Taiwan University Taipei100 Taiwan

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 dice score of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of the proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. © 2013 IEEE.

关键词： Semantics

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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

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computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India 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

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

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computer systems science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning

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Neural Computing and Applications 2024年第33期36卷 20723-20750页

作者： Abdel-salam, Mahmoud Kumar, Neeraj Mahajan, Shubham Faculty of Computer and Information Science Mansoura University Mansoura Egypt Department of CSE Thapar Institute of Engineering and Technology Deemed to Be University Patiala147004 India Delhi-NCR India

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. © The Author(s) 2024.

关键词： Support vector regression

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