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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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Energy Efficient Behavior Modelling for Demand Side Recommender System in Solar Microgrid Applications Using Multi Agent Reinforcement Learning Model

SSRN

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SSRN 2022年

作者： Onile, Abiodun Emmanuel Belikov, Juri Levron, Yoash Petlenkov, Eduard Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Electricity consumers are often faced with challenges relating the choice of optimal energy saving plan, increasing integration of transient’s renewable sources of energy promises tantalizing solution which also constitutes emerging stability challenges for the electricity Grid. Demand side management using Battery storage as grid balancing measures are crucial towards extending physical limits of existing electricity grid. On the one hand, battery storage is assuming a critical role in decarbonization efforts partly due to its tendencies to store excess electricity obtained via renewable energy sources thus enabling unlimited potentials for seasonal storage. On the other hand, while solving the identified problem, other problems relating consumer behavior towards adoption of energy/battery efficiency technologies, consumers comfort-feedback and control loop integration exist. In this study, we presented electricity energy storage (EES) technologies which are embedded into the grid and further enhanced with the use of Reinforcement learning control and recommendation system technologies for improving the grid management, the grid reliability and sustainability, mitigated operational issues, improved BESS utilization and attain self-consumption goals. The result of the study shows that operating the BESS using the multi-agent RL control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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A New U-Net Based License Plate Enhancement Model in Night and Day Images 5th

A New U-Net Based License Plate Enhancement Model in Night a...

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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Chowdhury, Pinaki Nath Shivakumara, Palaiahnakote Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Gjøvik Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030414030

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection. © Springer Nature Switzerland AG 2020.

关键词： Pixels

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Designing planar cubic B-spline curves with monotonic curvature for curve interpolation

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Computational Visual Media 2020年第3期6卷 349-354页

作者： Aizeng Wang Chuan He Fei Hou Zhanchuan Cai Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology&Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

Monotonic curvature plays an important role in industrial design and styling of curves with aesthetic shapes,e.g.,in automobile and aircraft design[1].Used in conventional parametric CAD/CAM systems,general B-splines are not adequate for aesthetic *** for the straight line and circle,monotonic curvature distribution,associated with pleasing shape,is very difficult to achieve.

关键词： curvature monotonic curves

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A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation 5th

A New Forged Handwriting Detection Method Based on Fourier S...

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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Kundu, Sayani Shivakumara, Palaiahnakote Grouver, Anaica Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030414030

Use of handwriting words for person identification in contrast to biometric features is gaining importance in the field of forensic applications. As a result, forging handwriting is a part of crime applications and hence is challenging for the researchers. This paper presents a new work for detecting forged handwriting words because width and amplitude of spectral distributions have the ability to exhibit unique properties for forged handwriting words compared to blurred, noisy and normal handwriting words. The proposed method studies spectral density and variation of input handwriting images through clustering of high and low frequency coefficients. The extracted features, which are invariant to rotation and scaling, are passed to a neural network classifier for the classification for forged handwriting words from other types of handwriting words (like blurred, noisy and normal handwriting words). Experimental results on our own dataset, which consists of four handwriting word classes, and two benchmark datasets, namely, caption and scene text classification and forged IMEI number dataset, show that the proposed method outperforms the existing methods in terms of classification rate. © Springer Nature Switzerland AG 2020.

关键词： Spectral density

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Bpel Process Anti-Patterns Detection Using Multi-Objective Evolutionary Search

SSRN

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SSRN 2022年

作者： Daaji, Marwa Ouni, Ali Gammoudi, Mohamed Mohsen Bouktif, Salah Mkaouer, Mohamed Wiem Faculty of Sciences University of Tunis El Manar Tunisia ETS Montreal University of Quebec MontrealQC Canada Higher institute of Multimedia Art University of Manouba Tunisia Department of Computer Science and Software Engineering CIT UAE University Rochester Institute of Technology RochesterNY United States

Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artefact, such processes are frequently changed to add new or modify existing functionalities or adapting to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns. The presence of these anti-patterns often leads to a regression of software quality. In this context, prior studies showed that detecting such anti-patterns is difficult and time-consuming, especially with the increasing complexity of business processes. In this paper, we introduce an automated approach to detect BPEL process anti-patterns using Multi-Objective Genetic Programming (MOGP). Our approach consists in exploiting real-world instances of service-based business process anti-patterns to learn detection rules based on combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains. In this study, we consider a variety of BPEL process anti-patterns types such asdata flow and portability anti-patterns. © 2022, The Authors. All rights reserved.

关键词： Genetic programming

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Local gradient difference features for classification of 2D-3D natural scene text images 25

Local gradient difference features for classification of 2D-...

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25th International Conference on Pattern Recognition, ICPR 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Raghavendra, Ramachandra Lu, Tong Pal, Umapada Lopresti, Daniel Anuar, Nor Badrul Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9781728188089

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification. © 2020 IEEE

关键词： Image classification

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A Construction Drawing Primitive Extraction System Based on CNN

A Construction Drawing Primitive Extraction System Based on ...

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第十八届分布式计算及其应用国际学术研讨会

作者： Houhou Song Lingyu Hu Wanli Feng Quanyin Zhu Liangsheng Zhu Wenwen Bian Bin Wu Faculty of Computer & Software Engineering Huaiyin Institute of Technology

In order to solve problem of huge manpower demand in the construction industry and heavy drawing work,a construction primitive extraction system was *** system relieved pressure of the artificial building review and improved the reviewing efficiency of the reviewing *** system uses a Convolutional Neural Network(CNN) to extract drawing primitives using the Visual Geometry Group(VGG) network training *** component analysis algorithm improves feature comparison efficiency by reducing the dimension of the *** show that the practicability of the system can meet the requirements of general construction primitive extraction.

关键词： Construction drawing Convolutional Neural Network Primitive Extraction

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DRCNN-IDS Approach for Intelligent Intrusion Detection System

DRCNN-IDS Approach for Intelligent Intrusion Detection Syste...

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2020 International Conference on Computing and Information technology, ICCIT 2020

作者： Manikandan, V. Gowsic, K. Prince, T. Umamaheswari, R. Ibrahim, Banar Fareed Sampathkumar, A. Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq KSR Institute for Engineering and Technology Department of Information Technology Namakkal India Arba Minch University Arba Minch Institute of Technology \Faculty of Computing and Software Engineering Ethiopia Gnanamani College of Technology Department of CSE Namakkal India Cyprus International University Computer Engineering Department Cyprus VIT Bhopal University Department of Computer Science Engineering Bhopal India

ISBN: (纸本)9781728126807

In recent times, deep learning technique turns to be an interesting topic, which is extensively utilized to construct an Intrusion Detection System (IDS) for identifying and classifying web attacks at network level in a periodic manner. Moreover, numerous challenges as malicious attack change its characteristics constantly. There are diverse malware datasets that are available publicly. In this investigation, Convolutional Neural Network (CNN), which a kind of Deep learning is explored along with dimensionality reduction for Intrusion Detection (DRCNN-IDS). This approach is used to identify and classify unpredictable attacks in networks. Due to the constantly changing nature of attacks, evaluation of benchmark dataset is also essential. A conceptual evaluation of this investigation with DRCNN-IDS is compared with Machine learning approaches. The irrelevant features of attacks are eliminated for reducing the network dimensionality for the computation of accuracy. The reduced features are extracted automatically by the anticipated CNN model, which is a preliminary advantage of using deep learning concepts. This work utilizes KDD cup 99' dataset for accuracy computation. Simulation was carried out in MATLAB environment and the performance is compared with prevailing Machine Learning approaches. The anticipated model shows enhanced performance in terms of accuracy. Comparison is made with DNN, RNN, RF, SVM, Naive Bayes and so on. Accuracy attained with simulation is 96%. © 2020 IEEE.

关键词： Intrusion detection

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Design and Fabrication of a Low-Loss Microstrip Lowpass-Bandpass Diplexer for WiMAX Applications

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China Communications 2020年第6期17卷 109-120页

作者： Leila Nouri Salah Yahya Abbas Rezaei Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Faculty of Electrical–Electronic Engineering Duy Tan UniversityDa Nang 550000Vietnam Department of Communication and Computer Engineering Cihan University-ErbilErbilKurdistan RegionIraq Department of Software Engineering Faculty of EngineeringKoya UniversityKoya KOY45Kurdistan RegionIraq Department of Electrical Engineering Kermanshah University of TechnologyKermanshahIran

This work presents a compact lowpass-bandpass microstrip diplexer with a novel *** consists of a lowpass filter integrated with a bandpass filter via a simple compact *** proposed bandpass filter consists of four rectangular patch cells and some thin *** step impedance structures,with a radial cell,are applied to achieve a lowpass frequency *** lowpass channel of the introduced diplexer has 2.64 GHz cut-off frequency,whereas,the bandpass channel center frequency is 3.73 GHz for WiMAX applications and covers the frequencies3.31 GHz to 4 *** addition to having novel structures,both filters have other advantages in terms of high return loss,low insertion loss and high *** presented microstrip diplexer has the compact size of 29 mm×13.8 mm×0.762 mm,calculated at 2.64 *** obtained insertion losses are 0.20 dB(for the first channel)and 0.25 dB(for the second channel),which make the proposed diplexer suitable for energy *** stopband properties of both bandpass and lowpass filters are improved by creating several transmission *** comparison results show that the lowest insertion losses,the minimum gap between channels,good return losses,and good isolation are obtained.

关键词： compact diplexer lowpass-bandpass low-loss microstrip selectivity

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