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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Freda Elikem Dorbu Leila Hashemi-Beni Department of Computational Data Science and Engineering North Carolina A&T State University Department of Built Environment Geomatics Program North Carolina A&T State University

Acquisition of geospatial data by UAV has been acknowledged as an effective method of attaining reliable and quick high-resolution remote sensing data for analysis and decision-making in different applications such as agriculture as it produces timely results, and it is affordable. UAVs coupled with other technological applications such as robotics, computing and deep learning facilitate the execution of precision agriculture. The execution of individual tree identification enables vivid description of the crop peak height and canopy for accurate estimation of issues related to crop growth such as biomass reduction, lodging, and stunted growth. A geospatial intelligence algorithm was developed to determine the lodging state of the crops. First, the optical imagery (RGB) was automatically annotated using maximum likelihood classification algorithm for faster acquisition of ground truth and to verify with the manually annotated image data for deep learning training. The acquired images were split for training of fine-tuned three deep learning methods including U-net, U-net++ and attention-residual Unet. The results show that each model has the tendency to learn patterns in data and predict lodged crops. Results from the analysis were validated by visual assessment of the time-series aerial images and validation data acquired from the ground truth data using k-fold cross validation approach.

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Convolutional Neural Network-based image tamper detection with Error Level Analysis

Convolutional Neural Network-based image tamper detection wi...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Manjunatha S Swetha M D Rashmi S Ananda Kumar Subramanian Vinoth Kumar V Mallikarjuna Swamy S Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Computer Science and Engineering BNM Institute of Technology Bengaluru India Department of Computer Science and Engineering (Data Science) Dayananda Sagar College of Engineering Bengaluru India Dept of Computational Intelligence School of Computer Science Engineering VIT – University Vellore India School of Computer Science Engineering and Information Systems VIT – University Vellore India Dept of Electronics and Communication Engineering JSS Academy of Technical Education Bengaluru India

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution.

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CVIT: CONTINUOUS VISION TRANSFORMER FOR OPERATOR LEARNING

arXiv

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arXiv 2024年

作者： Wang, Sifan Seidman, Jacob H. Sankaran, Shyam Wang, Hanwen Pappas, George J. Perdikaris, Paris Institution for Foundation of Data Science Yale University United States Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania United States Department of Electrical and Systems Engineering University of Pennsylvania United States

Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer (CViT), a novel neural operator architecture that leverages advances in computer vision to address challenges in learning complex physical systems. CViT combines a vision transformer encoder, a novel grid-based coordinate embedding, and a query-wise cross-attention mechanism to effectively capture multi-scale dependencies. This design allows for flexible output representations and consistent evaluation at arbitrary resolutions. We demonstrate CViT’s effectiveness across a diverse range of partial differential equation (PDE) systems, including fluid dynamics, climate modeling, and reaction-diffusion processes. Our comprehensive experiments show that CViT achieves state-of-the-art performance on multiple benchmarks, often surpassing larger foundation models, even without extensive pretraining and roll-out fine-tuning. Taken together, CViT exhibits robust handling of discontinuous solutions, multi-scale features, and intricate spatio-temporal dynamics. Our contributions can be viewed as a significant step towards adapting advanced computer vision architectures for building more flexible and accurate machine learning models in the physical sciences. All data and code are publicly available at https://***/PredictiveIntelligenceLab/cvit. © 2024, CC BY-NC-SA.

关键词： Computer vision

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Generative VS non-Generative Models in engineering Shape Optimization

arXiv

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arXiv 2024年

作者： Usama, Muhammad Masood, Zahid Khan, Shahroz Kostas, Konstantinos Kaklis, Panagiotis Department of Naval Architecture Ocean and Marine Engineering University of Strathclyde Glasgow United Kingdom Department of Mechanical and Aerospace Engineering Nazarbayev University Astana Kazakhstan BAR Technologies Portsmouth United Kingdom Division: Numerical Analysis & Computational Science Group: Data Science Crete Heraklion Greece

In this work, we perform a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient design exploration and shape optimization. We apply these models in the case of airfoil/hydrofoil design and conduct the comparison on the resulting design spaces. A conventional Generative Adversarial Network (GAN) and a state-of-the-art generative model, the Performance-Augmented Diverse Generative Adversarial Network (PaDGAN), are juxtaposed with a linear non-generative model based on the coupling of the Karhunen-Loève Expansion and a physics-informed Shape Signature Vector (SSV-KLE). The comparison demonstrates that, with an appropriate shape encoding and a physics-augmented design space, non-generative models have the potential to cost-effectively generate high-performing valid designs with enhanced coverage of the design space. In this work, both approaches are applied to two large foil profile datasets comprising real-world and artificial designs generated through either a profile-generating parametric model or deep-learning approach. These datasets are further enriched with integral properties of their members' shapes as well as physics-informed parameters. Our results illustrate that the design spaces constructed by the non-generative model outperform the generative model in terms of design validity, generating robust latent spaces with none or significantly fewer invalid designs when compared to generative models. We additionally compare the performance and diversity of generated designs to provide further insights about the quality of the resulting spaces. We aspire that these findings will aid the engineering design community in making informed decisions when constructing designs spaces for shape optimization, as we have demonstrated that under certain conditions computationally inexpensive approaches can closely match or even outperform state-of-the art generative models. © 202

关键词： Shape optimization

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Large-scale group decision-making method based on knowledge differentiation and the grouping of alternatives into modifiable scenarios

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Procedia Computer science 2022年 214卷 927-934页

作者： José Ramón Trillo Enrique Herrera-Viedma María José Higueras-Ruiz Sergio Alonso Juan Antonio Morente-Molinera Francisco Javier Cabrerizo Department of Computer Science and Artificial Intelligence Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada 18071 Spain Department of Information and Communication University of Granada Granada 18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada 18071 Spain

Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other.

关键词： Large-Scale Group Decision-Making Consensus Clustering Modifiable Scenarios Knowledge Differentiation

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Fast Hardware-Aware Matrix-Free Algorithm for Higher-Order Finite-Element Discretized Matrix Multivector Products on Distributed Systems

SSRN

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SSRN 2023年

作者： Panigrahi, Gourab Kodali, Nikhil Panda, Debashis Motamarri, Phani Department of Computational and Data Sciences Indian Institute of Science CV Raman Road Karnataka Bengaluru560012 India Department of Chemical Engineering in Imperial College London United Kingdom

Recent hardware-aware matrix-free algorithms for higher-order finite-element (FE) discretized matrix-vector multiplications reduce floating point operations and data access costs compared to traditional sparse matrix approaches. This work proposes efficient matrix-free algorithms for evaluating FE discretized matrix-multivector products on both multi-node CPU and GPU architectures. We address a critical gap in existing matrix-free implementations, which are well suited only for the action of FE discretized matrices on a single vector. We employ batched evaluation strategies, with the batchsize tailored to underlying hardware architectures, leading to better data locality and enabling further parallelization. On CPUs, we utilize even-odd decomposition, SIMD vectorization, and overlapping computation and communication strategies. On GPUs, we employ strategies to overlap compute and data movement in conjunction with GPU shared memory, constant memory, and kernel fusion to reduce data accesses. Our implementation outperforms the baselines for Helmholtz operator action, achieving up to 1.4x improvement on one CPU node and up to 2.8x on one GPU node, while reaching up to 4.4x and 1.5x improvement on multiple nodes for CPUs (~ 3000 cores) and GPUs (~ 25 GPUs), respectively. We further benchmark the performance of the proposed implementation for solving a model eigenvalue problem for 1024 smallest eigenvalue-eigenvector pairs by employing the Chebyshev Filtered Subspace Iteration method, achieving up to 1.5x improvement on one CPU node and up to 2.2x on one GPU node while reaching up to 3.0x and 1.4x improvement on multinode CPUs (~ 3000 cores) and GPUs (~ 25 GPUs), respectively. © 2023, The Authors. All rights reserved.

关键词： Finite element method

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration

Exploring the Effectiveness of Deep Learning in Audio Compre...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： S. Pushparani K. Sashi Rekha V. M. Sivagami R. Usharani M. Jothi Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case.

关键词： Deep learning Training Filtering Bit rate Noise Computer architecture Feature extraction

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Usharani V. M. Sivagami K. Saravanan S. Pushparani K. Sashi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations.

关键词： Cloud computing Technological innovation Costs Biological system modeling Machine learning Predictive models Maintenance

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques

Predictive Analysis of Consumer Behavior Using Supervised Le...

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International Conference on computational and Characterization Techniques in engineering & sciences (CCTES)

作者： K. Sashi Rekha T. Amutha R. Usharani S. Pushparani V.M. Sivagami Department of Computer Science and Engineering SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.

关键词： Training Adaptation models Consumer behavior Supervised learning data preprocessing Reinforcement learning Feature extraction data models Predictive analytics Stress

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Deep Convolutional Neural Network Framework for Brain Tumor Classification using MRI Images 2

Deep Convolutional Neural Network Framework for Brain Tumor ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sakthi, U. Thangaraj, K. Tamizhselvi, A. Kirubakaran, M.K. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu TN Chennai603203 India Department of Information Technology Sona College of Technology Junction Main Road TN Salem636005 India Department of Information Technology St. Joseph's College of Engineering TN Chennai600119 India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology TN Chennai600119 India

ISBN: (纸本)9798350340235

Brain cancer is one of the high-risk diseases and increases the death rate in all countries, affecting both men and women. The early diagnosis and severity of brain cancer leads to better medical treatment and save people's lives. The machine learning procedure has been applied for early detection and treatment of brain cancer in the biomedical field by classifying them into low-risk and high-risk groups. In cancer research, the predictive and classification model has been developed using Deep Convolutional Neural Network (DCNN) algorithms for accurate decision making. The Magnetic Resonance Image (MRI) classification technique DCNN is advanced to detect and match feature points of training and test images. The DCNN classifier based on the outcome of feature points then classifies images. The key notion of this proposed research effort is to implement and execute the proposed DCNN algorithm on cancer patient datasets for risk level classification. The brain cancer affected patient details are collected from UCI machine learning data repository for experimental analysis. In this research study, the DCNN algorithm is proposed and it gives better accuracy and faster than the KNN, CNN and SVM. © 2023 IEEE.

关键词： Brain Cancer Convolutional Neural Convolutional Network Deep Learning Medical Resonance Image Classification

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