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Graph Models of Neurodynamics to Support Oscillatory Associative Memories

Graph Models of Neurodynamics to Support Oscillatory Associa...

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international Joint conference on Neural Networks

作者： Gabriel P. Andrade Miklos Ruszinko Robert Kozma College of Information and Computer Sciences University of Massachusetts Amherst 140 Governors Drive Amherst MA USA Alfréd Rényi Institute of Mathematics Hungarian Academy of Sciences 13-15 Reáltanoda utca Budapest Hungary

recent advances in brain imaging techniques require the development of advanced models of brain networks and graphs. Previous work on percolation on lattices and random graphs demonstrated emergent dynamical regimes, including zero- and non-zero fixed points, and limit cycle oscillations. Here we introduce graph processes using lattices with excitatory and inhibitory nodes, and study conditions leading to spatio-temporal oscillations. Rigorous mathematical analysis provides insights on the possible dynamics and, of particular concern to this work, conditions producing cycles with very long periods. A systematic parameter study demonstrates the presence of phase transitions between various regimes, including oscillations with emergent metastable patterns. We studied the impact of external stimuli on the dynamic patterns, which can be used for encoding and recall in robust associative memories.

关键词： Firing Associative memory Fires Computational modeling Numerical models Mathematical model Brain

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Cervical Cancer Vascular Segmentation in 3d Power doppler Ultrasound Using Advanced deep Learning Models

Cervical Cancer Vascular Segmentation in 3D Power Doppler Ul...

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IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Mihaela Miron Anisia-Luiza Culea-Florescu Bogdan Ioan Ştefãnescu Department of Computer Science and Information Technology University “Dunărea de Jos” Galati Romania Department of Medicine University “Dunârea de Jos” Galati Romania

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

Cervical cancer remains a significant global health challenge. Precise segmentation of vascular anatomy in 3d Power doppler ultrasound (3d - PdU) images is crucial for accurate diagnosis, effective treatment planning and continuous monitoring of the disease. recent advances in deep learning have significantly enhanced the precision of segmentations for assessing tumor aggressiveness in various types of tumors. However, these advancements are still limited and face challenges when applied to cervical cancer vascular anatomy. Among deep learning models, U-Net excels in pixel-level image analysis, allowing for detailed mapping of vascular structures, which is vital for improving the management and outcomes of cervical cancer patients. This study proposes a framework with two stages: first, extracting the ground-truth masks of the vascular regions from cervical cancer 3d-PdU images, and second, evaluating the performance of three deep learning models in segmenting the vascular regions within these images. After the first stage is validated by gynecological clinicians, three models are evaluated: a U-Net, a U-Net variant with VGG16 backbone, and a U-Net variant with denseNet121 backbone. The U-Net variant with denseNet121 backbone achieved the best results on the test dataset, with a pixel accuracy of 97.08%, an Intersection over Union (IoU) of 90.63%, and a dice coefficient of 95.08%.

关键词： deep learning Image segmentation Solid modeling Accuracy Ultrasonic imaging Three-dimensional displays Image color analysis doppler effect Cervical cancer Tumors

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AMoEBA Image Segmentation: modeling of Individual Voronoi Tessellations

AMoEBA Image Segmentation: Modeling of Individual Voronoi Te...

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Congress on Evolutionary Computation

作者： N.G. Johnson B. Karthikeyan d.A. Ashlock K.M. Bryden Department of Mechanical Engineering Iowa State University Ames IA USA Department of Mathematics and Statistics University of Guelph Guelph ONT Canada

recent advancements in digital imaging systems have provided means for greater definition in photography. These technologies have brought high-resolution pictures that show greater clarity and allow for a 'deeper' analysis of information. This occurs through decreased distortion that may have otherwise resulted in features becoming 'blurred' or 'hidden'. However, this strength is accompanied by a weakness due to the large amount of data needed to display the otherwise non-apparent information; in turn creating massive file sizes which produce troubling or infeasible computation times during image storage and transfer. Therefore methods for handling these giant image files could be of great use in image processing and compression if an adequate amount of information can be maintained. during analysis the degree to which information must be replicated exactly depends on the purpose of the final product and the capability of computational resources. This work uses Graph Based Evolutionary Algorithms (GBEAs) to segment images into balanced-weight Voronoi tessellations which are then modeled by least squares surface fitting techniques using the Adaptive modeling by Evolving Blocks Algorithm (AMoEBA). This twice optimized process yields new techniques of preserving image quality during image analysis and complex decision making. Methods in this paper begin by breaking an image into numerous components using balanced-weight Voronoi tessellations that are optimized to conform to features and detail in an image. These features are represented by surfaces through the strength and efficiency of the AMoEBA algorithm to find optimal representations that can differ from one tessellation to another.

关键词： Image segmentation Information analysis Image storage Surface fitting digital images Photography Computer displays Image processing Image coding Evolutionary computation

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Unmasking Manipulations: recent advances in Image Tampering detection

Unmasking Manipulations: Recent Advances in Image Tampering ...

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IoT, Communication and Automation Technology (ICICAT), international conference on

作者： Chiyyedu Manasa dheeraj d deepthi V S Dept. of CSE DSCE Dayanada Sagar College of Engineering Bengaluru India Dept. of DT Atria Center for AI & DC Bengaluru India Dept. of CSE-CY DSCE Dayanada Sagar College of Engineering Bengaluru India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

This work aims at exploring the cutting-edge developments in image tampering detection, to unveil the latest techniques and methodologies employed to identify and expose digital image manipulations. The paper begins by reviewing the evolving landscape of image tampering and the growing need for advanced detection mechanisms in an era of sophisticated editing *** paper delves into pixel-based analysis, frequency-based analysis, statistical approaches, and the integration of deep learning in image tampering detection. Highlighting the strengths and limitations of each method, the review provides insights into how these approaches contribute to the ongoing battle against image ***, the role of metadata examination, watermarking, and steganalysis is discussed in the context of enhancing the robustness of tampering detection. The paper addresses challenges posed by the rapid evolution of tampering techniques and the emergence of adversarial attacks, emphasizing the importance of adaptive and resilient detection strategies. Real-world applications of image tampering detection, ranging from criminal investigations to journalistic ethics, are explored to demonstrate the practical significance of the discussed advancements. The paper concludes by proposing avenues for future research, emphasizing the need for interdisciplinary collaboration and the development of standardized benchmarks to evaluate the effectiveness of emerging detection methods.

关键词： deep learning Training Accuracy Watermarking Metadata Feature extraction Forgery Blockchains Convolutional neural networks Medical diagnostic imaging

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Securing IoT Environments from Botnets: An Advanced Intrusion detection Framework Using TJO-Based Feature Selection an d Tree Growth Algorithm-Enhanced LSTM

Securing IoT Environments from Botnets: An Advanced Intrusio...

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recent advances in Science and Engineering Technology (ICRASET), international conference on

作者： Ramya Vani Rayala Chandrakanth Reddy Borra Piyush Kumar Pareek Srinivas Cheekati University of the Cumberlands United States Department of AIML Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Concerns about cyberattacks are on the rise in the modern digital era, particularly with the expansion of the Internet of Things (IoT). Protecting IoT environments from harmful behaviour requires cybersecurity intrusion detection solutions. The ever-changing nature of infections and the proliferation of attack routes make botnet identification a formidable challenge. Numerous network devices have been targeted by botnet assaults, resulting in significant losses across several industries, due to the fast development of the IoT. Threats posed by botnets to network security are real, and deep learning models have demonstrated promise in efficiently detecting botnet activity in data collected from network traffic. Because of their remarkable capacity to autonomously discover intricate patterns and characteristics inside massive datasets. Unfortunately, the increasingly diverse nature of IoT ecosystems is rendering ineffective the traditional network-level intrusion detection solutions that rely on pre-defined rule sets. A framework to address this issue is presented in this study. When determining if a dataset is suitable for use in transfer learning, our suggested methodology encourages using that dataset as the source domain. The attributes that are most relevant are selected from the pre-processed data using Tom and Jerry Optimiser (TJO). Next, the prediction procedure makes use of Optimised Long-Short Term Memory (LSTM), with Tree Growth Algorithm (TGA) taking LSTM fine-tuning into account. The purpose of this selection procedure is to ascertain whether or not the suggested model is suitable for implementation, providing the optimal course of action in such cases. By selecting an appropriate source domain data set, our evaluation shows that the suggested framework achieves the best accuracy.

关键词： deep learning Training Botnet Biological system modeling Transfer learning Intrusion detection Telecommunication traffic Predictive models Prediction algorithms Long short term memory

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Integrating Remote Sensing Technologies for Real-Time disaster Management, Mitigation, and decision Support Systems

Integrating Remote Sensing Technologies for Real-Time Disast...

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recent advances in Science and Engineering Technology (ICRASET), international conference on

作者： M. Balamurugan M. Sathesh Kannan Ramakrishnan M. Raja K. Kalaiarasi Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department ECE Saveetha Engineering College Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

This study provides a detailed understanding of methodologies on how to apply remote sensing technologies, predictive modeling, dSS in disaster management and mitigation. Using satellite images, aerial maps, and remote sensors, the system gathers data for real-time monitoring of, and real-time assessment of the probability of, natural disasters including hurricanes, earthquakes, floods, and wildfire. disaster prediction has utilized Random Forest, Support Vector Machine (SVM) and Neural Networks models where accuracy was ranging from ${8 7 \%}$. $8 \%$ and $93.5 \%$. This insight was made possible by the dSS that helped in decision making processes, particularly response to disasters and distribution of resources, optimal evacuation plans. Satellite and drone data provided features for real-time monitoring proving the ability for swift detection, which improved the response effectiveness. The optical, radar, and thermal imaging technologies reached the accuracy of over $94 \%$ in post-earthquake damage assessment, which helped in targeting the rescue operations effectively. The developed system is also highly extensible and can be scaled depending on the chosen picture of future disasters, as well as updated as new solutions in field of remote sensing and machine learning appear. The results provide evidence of the effectiveness of the offered system to improve the corresponding disaster preparedness, response, and recovery for further enabling better practices regarding disaster management and fewer associated consequences.

关键词： decision support systems Support vector machines Accuracy Satellites disasters Prevention and mitigation Predictive models Real-time systems Remote sensing drones

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Nanotechnology in Biomedical Image Processing Using Ensemble deep Learning Approach

Nanotechnology in Biomedical Image Processing Using Ensemble...

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advances in Computation, Communication and Information Technology (ICAICCIT), international conference on

作者： Ancysophia M Thomas S Esakkiammal N. Jayanthi P Kamalarajan G. Yamini Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu India Department of Chemistry St. Mother Theresa Engineering College Vagaikulam Thoothukudi Tamil Nadu India Institute of Management Nirma University Gota Tragad Ahmedabad India Department of Physics R. M. K. College of Engineering and Technology Tamil Nadu India Department of Chemistry R.M.D. Engineering College Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Correct identification of the most recent case of pneumonia fever determines successful therapy and management of the condition. Computable tomography (CT) scans can rapidly and precisely classify and evaluate cases of pneumonia fever. Almost every hospital has chest CT scanning available, which lets one rapidly classify pneumonia fever sufferers. Considering the great availability of chest CT imaging, this seems reasonable. The chest CT-based pneumonia fever categorization requires a lot of time with fast spreading infections. Medical staff workers should be able to gain from automated CT scan analysis considering their busy schedules. In the framework of this work, we build a feature extraction and classification system leveraging CT scan inputs. Part of the framework’s training and testing courses is teaching the classifier—in charge of creating a model that exactly classifies entering CT images. This module among others makes up the framework. The classifier in this work is convolutional neural network (CNN) variants. The performance of the present model is evaluated by the simulation; the results show that the suggested method achieves better accuracy than any other classifications.

关键词： Training Schedules Pneumonia Accuracy Computed tomography Biological system modeling Training data Feature extraction Convolutional neural networks Testing

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Research on MRI Image Super-Resolution Reconstruction Algorithm Based on Information distillation

Research on MRI Image Super-Resolution Reconstruction Algori...

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data Science and Computer Application (ICdSCA), IEEE international conference on

作者： Huazheng Zhu Ling Tang Yuanyuan Jia Yaping Liu Jun Zhang Aiping Jiang Zicheng Nie School of Computer Science and Engineering Chongqing University of Science & Technology Chongqing China School of Digital Economy Chongqing Vocational College of Art and Engineering Chongqing China College of Medical Informatics Chongqing Medical University Chongqing China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

In recent years, deep learning has demonstrated powerful capabilities in computer vision tasks and injected new energy into MRI image super-resolution reconstruction algorithms. However, existing MRI super-resolution methods based on 2d and 3d CNNs require interpolation to upsample low-resolution images, which increases the computational burden. To address this, this paper proposes an MRI image super-resolution algorithm based on information distillation, called dASId-SR. The algorithm uses information distillation to reduce network parameters and computational complexity while extracting deep features, and applies sub-pixel convolution for direct reconstruction and upsampling, thereby reducing the model's complexity. The core innovations include constructing a Mixed Information distillation Module (M-IMdB), introducing a differentiable architecture search algorithm in the deep feature extraction module, and implementing dense connections between modules to enhance feature utilization efficiency. Experimental results show that on the Kirby21 and ALVIN-adult datasets, with a reconstruction factor of 3, the proposed method achieves peak signal-to-noise ratios (PSNR) of 40.21dB and 37.56dB, respectively, outperforming other networks by 0.02dB to 2.68dB and 0.02dB to 1.41dB. Moreover, the model's parameter size is only 0.45MB, demonstrating that the proposed algorithm can generate high-quality MRI images while maintaining a low parameter count.

关键词： PSNR Convolution Magnetic resonance imaging Computational modeling Superresolution Computer architecture Reconstruction algorithms Feature extraction Computational complexity Image reconstruction

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dIGILOG: A digital-analog SiPM towards 10 ps prompt-photon tagging in TOF-PET

DIGILOG: A digital-analog SiPM towards 10 ps prompt-photon t...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： S. Gundacker C. Bruschini A. Gola K. Herweg S. Merzi A. Muntean V. Nadig L. Parellada-Monreal G. Paternoster M. Penna M. Ruzzarin E. Charbon V. Schulz Physics of Molecular Imaging Systems (PMI) RWTH Aachen University Aachen Germany Advanced Quantum Architecture Laboratory (Aqua) EPFL Neuchatel Switzerland Fondazione Bruno Kessler (FBK) Trento Italy

Functional imaging techniques like positron emission tomography (PET) are an essential tool in an aging society. despite impressive advances in microelectronics, photodetectors and scintillation materials, PET is still awaiting a breakthrough in terms of reduced cost and increased performance. Large potential is seen in ultraprecise time-of-flight (TOF), aiming at coincidence time resolutions (CTRs) better than 30 ps. However, state-of-the-art TOF-PET systems are still far away from this goal, achieving typical CTRs of 214 ps (FWHM). Several proposals have been put forth, whereas the most promising is to use prompt photon emission, e.g. Cherenkov radiation in BGO crystals, which are cheap to produce, thus contributing to drastic cost cutting. However, Cherenkov detection is challenging due to its limited photon yield, which in turn requires a very high photon detection efficiency (PdE), low dark count rate (dCR) and extremely fast and innovative electronic readout schemes. recent analog silicon photomultipliers (aSiPMs) meet the first two targets, but not the *** the digilog project we envisage to unite the best of these two worlds, combining high PdE, low dCR and an exceptional SPTR. To reach this goal, we will segment state-of-the-art aSiPMs into smaller clusters, called µSiPMs. A balanced segmentation of the electronic readout will make it possible to efficiently detect the first scintillation and Cherenkov photons, with a manageable granularity at system level. The µSiPM signals will feature photon-density time walk correction and photon counting. We envision to create 3d-stacked sensors where the electronics will be housed in a CMOS bottom-tier and the µSiPMs in the top-tier chip. Preliminary measurements on first µSiPM test-structures already reached PdE and dCR close to their commercial counterparts, while an SPTR of 25 ps FWHM, close to our sub-20 ps goal, has been achieved.

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Clustering Based Segmentation with 1d-CNN Model for Grape Fruit disease detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Piyush Kumar Pareek Ramya I M Jagadeesh B N LeenaShruthi H M Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

There has been a lot of focus on agricultural image dispensation research in the previous years. Computers employ image processing procedures to do picture analysis. recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision.

关键词： Computers Image segmentation Computational modeling Pipelines Crops Real-time systems Convolutional neural networks

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