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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Karthigaiveni Madhubala. C G. Avinash E. Saranya P. Rohini G. Yogapriya Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India School of Environment and Architecture Design SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science Engineering Sri Eshwar College of Engineering Chennai India Department of Computer Science and Engineering Saranathan College of Engineering Trichy Tamilnadu Chettinad School of Architecture Chettinad Academy of Research and Education Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases.

关键词： Deep learning Productivity Plant diseases Image segmentation Accuracy Computational modeling Transfer learning Crops Agriculture Robustness

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City models: past, present and future prospects

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Frontiers of Urban and Rural Planning 2025年第1期3卷 1-24页

作者： Ritter, Helge Herzog, Otthein Rothermel, Kurt Cohn, Anthony G. Wu, Zhiqiang Faculty of Technology Bielefeld University Bielefeld Germany College of Architecture and Urban Planning Tongji University Shanghai People’s Republic of China FB 03 University of Bremen Bremen Germany Institute of Parallel and Distributed Systems University of Stuttgart Stuttgart Germany School of Computer Science University of Leeds Leeds UK The Alan Turing Institute London UK

This paper attempts to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes that define a city’s overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important representation of the relevant mindsets of the citizens are, unfortunately, mostly neglected. After a review of major “traditional” urban models of structures behind urban scale, form, and dynamics, we turn to major recent modeling approaches triggered by recent advances in AI that enable multimodal generative models. Some of these models can create representations of geometries, networks and images, and reason flexibly at a human-compatible semantic level. They provide huge amounts of knowledge extracted from huge collections of text and image documents and cover the required rich representation spectrum including geographic knowledge by different knowledge sources, degrees of granularity and scales. We then discuss what these new opportunities mean for coping with the modeling challenges posed by cities, in particular with regard to the role and impact of citizens and their interactions within the city infrastructure. We propose to integrate these possibilities with existing approaches, such as agent-based models, which opens up new modeling spaces including rich citizen models which are able to also represent social interactions. Finally, we put forward some thoughts about a vision of a “social AI in a city ecosystem” that adds relevant citizen models to state-of-the-art structural and process models. This extended city representation will enable urban planners to establish citizen-oriented planning of city infrastructures, to make them into inviting environments that reconcile and foster human culture, city resilience and sustainability.

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An empirical investigation of the role of pre-training in lifelong learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10185-10234页

作者： Sanket Vaibhav Mehta Darshan Patil Sarath Chandar Emma Strubell School of Computer Science Carnegie Mellon University Pittsburgh PA Mila - Quebec AI Institute Université de Montréal Montreal QC Canada Mila - Quebec AI Institute Canada CIFAR AI Chair École Polytechnique de Montréal Montreal QC Canada

The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learning but also its potential to reduce energy waste by obviating excessive model re-training. A key challenge to this paradigm is the phenomenon of catastrophic forgetting. With the increasing popularity and success of pre-trained models in machine learning, we pose the question: What role does pre-training play in lifelong learning, specifically with respect to catastrophic forgetting? We investigate existing methods in the context of large, pre-trained models and evaluate their performance on a variety of text and image classification tasks, including a large-scale study using a novel data set of 15 diverse NLP tasks. Across all settings, we observe that generic pre-training implicitly alleviates the effects of catastrophic forgetting when learning multiple tasks sequentially compared to randomly initialized models. We then further investigate why pre-training alleviates forgetting in this setting. We study this phenomenon by analyzing the loss landscape, finding that pre-trained weights appear to ease forgetting by leading to wider minima. Based on this insight, we propose jointly optimizing for current task loss and loss basin sharpness to explicitly encourage wider basins during sequential fine-tuning. We show that this optimization approach outperforms several state-of-the-art task-sequential continual learning algorithms across multiple settings, occasionally even without retaining a memory that scales in size with the number of tasks.

关键词： lifelong learning continual learning pre-training flat minima sharpness-aware minimization

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Multi-Target Classification Using Deep Learning Models for Automotive Applications

Multi-Target Classification Using Deep Learning Models for A...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Soumya. A Linga Reddy Cenkeramaddi Chalavadi Vishnu Yash Vinod Lanjewar Krishna Mohan C Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India Department of Information and Communication Technology University of Agder Grimstad Norway Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur India

This paper presents a multi-perspective convolutional neural network (CNN) that extracts the class of objects supporting intelligent transportation systems. The proposed model is the visual geometry group (VGG) backbone network with custom feature extraction blocks, that use multilayer prediction heads. The model addresses both multi-class and multi-object classification tasks utilizing the automotive object detection dataset. The model is designed with multiple prediction heads to classify different objects in an image and enable object count prediction. A publicly available automotive object detection dataset with 19800 images and labels has been utilized. The dataset consists of five primary types of objects: Persons, Trucks, Motorbikes, Cars, and Cyclists. On the dataset, pre-trained models such as VGG, Resenet, EfficientNet, and DenseNet were tested and their classification performance was evaluated. The experimental results illustrate the superiority of the proposed VGG backbone deep learning CNN model in comparison to other pre-trained models.

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Directivity Improvement of MIMO Array Antenna using Novel Eight Element Array Configuration 3

Directivity Improvement of MIMO Array Antenna using Novel Ei...

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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Lafta, Alaa M. Zaboun, Abdul Razzaq T. Kosimova, Shakhlokhon Shather, Alaa. Sabah, Hawraa Ali Alchilibi, Haider National University of Science and Technology Dhi Qar Iraq Al-Esraa University College Forensic Sciences Department Baghdad Iraq Fergana Polytechnic Institute Department of Architecture Fergana Uzbekistan Al Kitab University Department of Computer Engineering Technology Altun Kopru Kirkuk00964 Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad10022 Iraq

ISBN: (纸本)9798350330724

In this study, the Directivity of the Microstrip Patch MIMO Antenna is a Novel Eight element array configuration in comparison with its performance of a Single element patch antenna. Directivity improvement and Return loss measured for different frequencies and values are compared with a Single element patch antenna. Novel Eight element MIMO array antenna is designed and compared with a Single patch element antenna. In this work, there are two groups (Eight element patch & Single element patch) each consisting of 20 samples with pre-test power or G-power of 80%. The mean Return loss in the Novel eight element MIMO array antenna is -18.39 dB and in the Single element antenna is -15.21 dB. These results showed that the Novel eight element array exhibited a better Directivity improvement (4.483 dB) than the single element antenna (2.261 dB) with the two-tailed significance of (p © 2023 IEEE.

关键词： Microstrip antennas

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Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

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Intelligent and Converged Networks 2021年第1期2卷 16-29页

作者： Yu Zhou Hatim Alhazmi Mohsen H.Alhazmi Alhussain Almarhabi Mofadal Alymani Mingju He Shengliang Peng Abdullah Samarkandi Zikang Sheng Huaxia Wang Yu-Dong Yao Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA the College of Information Science and Engineering Huaqiao UniversityXiamen 361021China the College of Engineering Architecture and TechnologyOklahoma State UniversityStillwaterOK 74078-1010USA

Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive *** tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol *** this paper,we investigate various identification and classification tasks related to fading channel parameters,signal distortions,Medium Access Control(MAC)protocols,radio signal types,and cellular ***,we utilize deep learning methods in those identification and classification *** evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.

关键词： cellular system deep learning signal classification spectrum awareness Convolutional Neural Network(CNN)

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Uncertainty quantification for charge transport in GNRs through particle Galerkin methods for the semiclassical Boltzmann equation

arXiv

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arXiv 2024年

作者： Medaglia, Andrea Nastasi, Giovanni Romano, Vittorio Zanella, Mattia Mathematical Institute University of Oxford United Kingdom Department of Engineering and Architecture University of Enna Kore Italy Department of Mathematics and Computer Science University of Catania Italy Department of Mathematics F. Casorati University of Pavia Italy

In this article, we investigate some issues related to the quantification of uncertainties associated with the electrical properties of graphene nanoribbons. The approach is suited to understand the effects of missing information linked to the difficulty of fixing some material parameters, such as the band gap, and the strength of the applied electric field. In particular, we focus on the extension of particle Galerkin methods for kinetic equations in the case of the semiclassical Boltzmann equation for charge transport in graphene nanoribbons with uncertainties. To this end, we develop an efficient particle scheme which allows us to parallelize the computation and then, after a suitable generalization of the scheme to the case of random inputs, we present a Galerkin reformulation of the particle dynamics, obtained by means of a generalized Polynomial Chaos approach, which allows the reconstruction of the kinetic distribution. As a consequence, the proposed particle-based scheme preserves the physical properties and the positivity of the distribution function also in the presence of a complex scattering in the transport equation of electrons. The impact of the uncertainty of the band gap and applied field on the electrical current is *** Codes 82D37, 82C70, 65C05, 82M31 Copyright © 2024, The Authors. All rights reserved.

关键词： Boltzmann equation

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Scalable Deep Learning for Categorization of Satellite Images 7

Scalable Deep Learning for Categorization of Satellite Image...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Swapna, B. Venkatessan, Radhika Taskeen, Fathima Indrapriya, K. Manjula, D. Muthukumar, D Surendiran Dr. M.G.R. Educational and Research Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Architecture The Department Tamil Nadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Architecture Tamil Nadu Chennai India Karpagam Faculty of Medical Sciences and Research Department of Community Medicine Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350341485

Satellite imagery serves a crucial role in diverse applications like disaster response, law enforcement, and environmental monitoring. These tasks often require manual identification of objects and structures within the images. However, due to the extensive geographic regions involved and a limited number of analysts available, there's a growing need for automation. Traditional methods of object detection and classification are proving insufficient and unreliable for addressing this challenge. This is where deep learning comes into play - a collection of machine learning techniques that holds the potential to automate these tasks effectively. In particular, convolutional neural networks (CNNs), a subset of deep learning, have shown remarkable promise in understanding images. This study focuses on applying CNNs to the complex task of recognizing various objects and facilities in high-resolution, multi-spectral satellite images. The research introduces a deep learning system that takes advantage of both satellite metadata and image features to accurately classify the dataset into different categories. The implementation of this system is carried out using Python, leveraging the Keras and TensorFlow libraries. Comparative analysis against existing systems reveals a significant enhancement in overall accuracy offered by this approach. © 2023 IEEE.

关键词： Python

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AEC Co-design workflow for cross-domain querying and reasoning using Semantic Web Technologies

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Automation in Construction 2025年 176卷

作者： Elshani, Diellza Lombardi, Alessio Hernandez, Daniel Staab, Steffen Fisher, Al Wortmann, Thomas Faculty of Architecture and Urban Planning University of Stuttgart Stuttgart Germany Buro Happold London United Kingdom Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany Electronics and Computer Science University of Southampton University Road Southampton United Kingdom

The architecture, Engineering, and Construction (AEC) industry faces data integration challenges due to fragmented silos and diverse data representations, hindering cross-domain queries and early detection of design constraints. Semantic Web Technologies (SWTs) address data integration challenges. This paper evaluates the impact of SWTs on co-design workflows by comparing them with alternative approaches to assess their effectiveness in supporting interdisciplinary collaboration and design constraint detection. Using Design science Research, a co-design methodology is developed that integrates SWTs with AEC tools for reasoning and federated querying. A component of this methodology is a bidirectional mapping strategy for translating object-oriented data models, demonstrated with the Building Habitat Object Model (BHoM), an AEC interoperability framework. Findings reveal that integrating SWTs enables reasoning and complex queries across federated datasets, improving co-design efficiency. These findings support AEC professionals in advancing co-design and data-driven decision-making, while also informing future research on integrating SWTs into AEC design workflows. © 2025

关键词： Mapping

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A New Approach Towards Identification of Individuals Belonging to Different Subpopulations Using Machine Learning Models and CLC Data

A New Approach Towards Identification of Individuals Belongi...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Małgorzata Charytanowicz Kajetan Perzanowski Maciej Januszczak Aleksandra Wołoszyn-Gałęza Maria Sobczuk Piotr Kulczycki Department of Computer Science Lublin University of Technology Lublin Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland Institute of Biological Sciences John Paul II Catholic University of Lublin Lublin Poland Museum and Institute of Zoology Polish Academy of Sciences Ustrzyki Dolne Poland Chair of Animal Genetics and Conservation Warsaw University of Life Sciences Warsaw Poland Faculty of Physics and Applied Computer Science AGH University of Krakow Krakow Poland

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Spatial distributions of wisents (European bison) Bison bonasus (Linnaeus 1758), were studied in the Bieszczady Mountains (south-eastern Poland) on the basis of telemetric data obtained from two subpopulations: the western – inhabiting the forest districts of Baligród, Komańcza, Cisna and Lesko, and the eastern – dwelling within the districts of Lutowiska, Stuposiany, and in Bieszczadzki National park. Data was collected between 2002-2021. In our study, we propose a novel approach for classifying wisent subpopulations that utilizes machine learning methods and CLC data. For this purpose, the performances of eight algorithms: Naive Bayes, Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, Extreme Gradient Boosting, k-Nearest Neighbors and Decision Tree were investigated. The algorithms were compared according to the following indicators: accuracy, Cohen’s-Kappa, precision, recall and F1 score. Their assessment was enhanced through the application of statistical inference (the Friedman test with post-hoc analysis) and SHAP values. The lowest results were achieved by Naive Bayes and Logistic Regression methods (accuracy of 73.69% and 74.43%, respectively), whilst significantly higher results were achieved by eXtreme Gradient Boosting, k-Nearest Neighbors and Decision Tree classifiers, with the classification accuracy exceeding 90% (91.81%, 92.8%, 93.47%, respectively). Based on the results, we conclude that CLC data represents a valuable source of information regarding the affinity of wisents towards different habitat conditions.

关键词： Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Inference algorithms Classification algorithms Bayes methods Telemetry Decision trees

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