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International Conference on VLSI Design

作者： Kritanta Saha Pritha Banerjee Susmita Sur-Kolay Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering University of Calcutta Kolkata India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Given n points in the 2D plane which are the pins of a net, a Rectilinear Steiner Minimum Tree (RSMT) which consists only of vertical and horizontal line segments is the shortest network to interconnect the pins along with a few extra (Steiner) points. An RSMT for a net has a minimum total wirelength in Manhattan distance and acts as a backbone to many state-of-the-art global routers. However, finding an RSMT is NPcomplete. In this work, we propose a method for constructing an RSMT with an image-to-image translation using a conditional generative adversarial network (cGAN) based model. Images of RSMTs with optimal wirelength for randomly generated nets form our training dataset. Experimental results show that, after training, our model generates RSMTs with $\leq 0.348\%$ wirelength error on average compared with the optimal solution for nets with degrees up to 50. This is the first successful attempt to solve the RSMT construction problem as an image-to-image translation problem using a cGAN based model.

关键词： Training Steiner trees Translation Computational modeling Machine learning Very large scale integration Generative adversarial networks Generators Pins Testing

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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency departments 1

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Shibu, N.V. Sujatha, M. Suresh, M. Xavier Sindhuja, K. Raj, T. Saju Srinivasan, C. SRM Institute of Science and Technology Department of Computing Technologies School of Computing Kattankulathur Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University Department of Physics School of Advanced Sciences and Languages Madhya Pradesh Bhopal India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. Results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology. © 2024 IEEE.

关键词： Data-driven triage Emergency medicine Healthcare efficiency Healthcare optimization Real-time decision support Resource management

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Denoising Diffusion Models for High-Resolution Microscopy Image Restoration

Denoising Diffusion Models for High-Resolution Microscopy Im...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Pamela Osuna-Vargas Maren H. Wehrheim Lucas Zinz Johanna Rahm Ashwin Balakrishnan Alexandra Kaminer Mike Heilemann Matthias Kaschube Frankfurt Institute for Advanced Studies Department of Computer Science and Mathematics Goethe University Frankfurt Institute of Physical and Theoretical Chemistry Goethe University Frankfurt

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluo-rophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability. Our code and datasets are available at https://githubcom/kaschube-lab/ddpm_highres_microscopy, https://***/10.5281/zenodo.14178277, https://***/10.5281/zenodo.14215837.

关键词： Training Image resolution Microscopy Noise reduction Stochastic processes Diffusion models Probabilistic logic Reliability Signal resolution Signal to noise ratio

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Enhancing Early Diagnosis of Autism Spectrum Disorder Using a Bayesian-Optimized Random Forest Algorithm

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Procedia computer science 2025年 258卷 1576-1585页

作者： Tina Babu Rekha R Nair Kishore S Sheila Mahapatra Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte University (Deemed to be University) Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD.

关键词： Autism Spectrum Disorder (ASD) Machine Learning Bayesian Optimization Random Forest Early Diagnosis Predictive Modeling

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

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computers, Materials & Continua 2022年第7期72卷 1357-1372页

作者： Nguyen A.Tuan D.Akila Souvik Pal Bikramjit Sarkar Thien Khai Tran G.Mothilal Nehru Dac-Nhuong Le Faculty of Information Technology Ho Chi Minh City University of Foreign Languages-Information TechnologyHo Chi Minh City70000Vietnam Department of Information Technology Vels Institute of ScienceTechnology&Advanced StudiesChennai600117India Department of Computer Science and Engineering Sister Nivedita UniversityKolkata700156India Department of Computer Science and Engineering JIS College of EngineeringNadia741235India Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

This article presents a new scheme for dynamic data optimization in IoT(Internet of Things)-assisted sensor *** various components of IoT assisted cloud platform are *** addition,a new architecture for IoT assisted sensor networks is ***,a model for data optimization in IoT assisted sensor networks is proposed.A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization(MIDDO)algorithm for IoT assisted sensor network is proposed in this *** proposed algorithm considers every node data and utilized membership function for the optimized data *** proposed framework is compared with two stage optimization,dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio,coverage ratio and sensing *** optimization was performed based on the availability of cloud resource,sensor energy,data flow volume and the centroid of each *** was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%,reliability ratio of 94.74%,coverage ratio of 85.75%and sensing error of 0.154.

关键词： Data optimization IoT sensor network reliability membership sensor energy

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Metrocycle: Designing a Browser-Based Road Safety Education App for Motorcycle and Bicycle Riders in Metro Manila, Philippines 4th

Metrocycle: Designing a Browser-Based Road Safety Education ...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Batrina, Jan Paul Gonzaga, Chase Rina, Brill Julian Juayong, Richelle Ann Gaspay, Sandy Mae Cabarle, Francis George Department of Computer Science University of the Philippines Diliman Quezon City Philippines Institute of Civil Engineering University of the Philippines Diliman Quezon City Philippines National Center of Transportation Studies University of the Philippines Diliman Quezon City Philippines Research Group on Natural Computing Department of Computer Science and Artificial Intelligence SCORE Lab I3US Universidad de Sevilla Seville Spain

ISBN: (纸本)9783031733437

With the recent increase in the use of bicycles and motorcycles as a main mode of transportation in the Philippines, there is also an increasing need to address the extremely high rate of fatalities and injuries from these vehicles. Previous research shows that these road crashes are usually attributed to driving behavior and human error. While other researches have already explored road safety education (RSE) apps to help drivers practice safe driving, most of these are tailored for 4-wheeled vehicle drivers and do not reflect the driving environment for motorcycle and bicycle drivers in Metro Manila. Most of the more fully-featured training apps are also only limited to either PC or mobile only. To address these limitations, this research aims to design and develop Metrocycle - a browser-based RSE driving application for motorcycle and bicycle users. Metrocycle seeks to effectively engage and accurately inform motorcycle and bicycle users of proper driving behavior and standard road safety practices specific to Metro Manila. The application will be designed through reviewing government published RSE guidelines, addressing limitations of current RSE applications and consultation with motorcycle and bicycle safety domain experts in the Philippines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motorcycles

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MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers

MPEC: Manifold-Preserved EEG Classification via an Ensemble ...

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International CSI computer Conference, CSICC

作者： Shermin Shahbazi Mohammad–Reza Nasiri Majid Ramezani Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Accurate classification of EEG signals is crucial for brain-computer interfaces (BCls) and neuroprosthetic applications, yet many existing methods fail to account for the non-Euclidean, manifold structure of EEG data, resulting in suboptimal performance. Preserving this manifold information is essential to capture the true geometry of EEG signals, but traditional classification techniques largely overlook this need. To this end, we propose MPEC (Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers), that introduces two key innovations: (1) a feature engineering phase that combines covariance matrices and Radial Basis Function (RBF) kernels to capture both linear and non-linear relationships among EEG channels, and (2) a clustering phase that employs a modified K-means algorithm tailored for the Riemannian manifold space, ensuring local geometric sensitivity. Ensembling multiple clustering-based classifiers, MPEC achieves superior results, validated by significant improvements on the BCI Competition IV dataset 2a.

关键词： Manifolds Geometry Support vector machines Technological innovation Accuracy Sensitivity Brain modeling Electroencephalography Brain-computer interfaces Reliability

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Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

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computers, Materials & Continua 2022年第9期72卷 5301-5314页

作者： Muhamad Erza Aminanto Ibnu Rifqi Purbomukti Harry Chandra Kwangjo Kim School of Strategic and Global Studies Universitas IndonesiaDepok16424Indonesia National Institute of Information and Communications Technology(NICT) Koganei184-8795Japan Department of Electrical Engineering and Information Technology Universitas Gadjah MadaYogyakarta55281Indonesia School of Computing Korea Advanced Institute of Science and Technology(KAIST)Daejeon34141Korea

Internet of Things(IoT)networks leverage wireless communication protocols,which adversaries can *** attacks,injection attacks,and flooding are several examples of different attacks existing in Wi-Fi *** Detection System(IDS)became one solution to distinguish those attacks from benign *** learning techniques have been intensively utilized to classify the ***,the main issue of utilizing deep learning models is projecting the data,notably tabular data,into an *** study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network(CNN)models,*** define the particular sequence of placing the attribute values in a grid that would be captured as an *** the most important subset of attributes and EfficientNet,we aim for an accurate and lightweight IDS module deployed in IoT *** examine the proposed model using the Wi-Fi attacks dataset,called the AWID2 *** achieve the best performance by a 99.91%F1 score and 0.11%false-positive *** addition,our proposed model achieved comparable results with other statistical machine learning models,which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy.

关键词： Intrusion detection impersonation attack convolutional neural network anomaly detection

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Design and development of intelligent well logging interpretation system 41

Design and development of intelligent well logging interpret...

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第41届中国控制会议

作者： Saifei Ma Jin Tang Zerui Li Kun Li Wenjun Lv School of Computer Science and Technology Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Key Laboratory of Multimodal Cognitive Computing Anhui University Institute of Advanced Technology University of Science and Technology of China Department of Automation University of Science and Technology of China

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,the traditional well logging interpretation method manually operated by experts has shown some problems,such as low efficiency and poor results,facing the challenges of high dimension,diversity and complexity of current logging *** the progress of artificial intelligence,its effectiveness and efficiency in logging interpretation have gradually attracted researchers' ***,the uncertainty of logging process necessitates the preprocess of collected logging data before employing the artificial intelligence ***,an intelligent well logging interpretation system is designed and developed in this *** the logging data anomalies may include data repetition,null value,etc.,a specific preprocessing module is designed in the system to tackle the data anomalies,so as to ensure that the data input into the algorithms is correct and *** governance can effectively enhance the effectiveness of data,so it is used to guide the design and implementation of logging interpretation ***,user mode and debug mode are designed to solve the problem of cross functional conflict in data governance.

关键词： System Development Logging Interpretation Data Governance Artificial Intelligence

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