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Change Vector Change Indices Method for Unsupervised Remote Sensing Change Detection

Change Vector Change Indices Method for Unsupervised Remote ...

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Sujith Kumar, A. Sundaram, Venkatesan Meenakshi Prabhavathy, P. National Institute of Technology Department of Computer Science and Engineering Puducherry India School of Computer Science Engineeering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331540685

Unsupervised Remote sensing change detection is very important because it addresses the problem of scarcity in the data availability in training. Some of the researchers are successful in finding the change regions but are not able to classify what changes happened in those regions. Nearly every study faces challenges in generalizing the fluctuations occurring during both the growing and non-growing seasons. The current study utilizes a novel architecture consisting of two parts. The first part involves proposing change regions through the change vector calculation method and applying Otsu's Thresholding on the calculated change vectors, while the second part entails classifying these proposed change regions into vegetation change, building change, and non-change regions. non-change region classification process aims to eliminate false changes identified by the change vector analysis and Otsu's Thresholding. In the second stage, NDVI, NDWI, and NDMI indices are compared and the resulting change mask obtained is subjected for element-wise multiplication operation with the change mask obtained in the first stage. The study addressed the challenge of generalizing short-term changes, such as those occurring during the growing and non-growing seasons, by computing the median of pixels over a time range encompassing both types of changes. Additionally, the method was fine-tuned using NIT Puducherry data from two time periods. Subsequently, the entire Karaikal district was considered to examine the changes comprehensively. The present study outperformed different state of the art methods like CVI_OTSU, PCI_Kmeans, Continuous Change Detection and Classification (CCDC). The present study got better results with an accuracy of 86.07 and kappa coefficient of 0.7898. © 2024 IEEE.

关键词： Change detection

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A Reliable Approach for Automatic Grading of Diabetic Retinopathy from Fundus Images using Feature Extraction and Fusion 3

A Reliable Approach for Automatic Grading of Diabetic Retino...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Jasmine, R.Anita Ramla, M. Ramesh, M. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Applications Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science Chennai Kattankulathur India

ISBN: (纸本)9798350369083

Diabetic Retinopathy (DR) is widely recognized as the primary cause of visual impairment worldwide. Early intervention is crucial in preventing irreversible vision loss. Ophthalmologists conventionally utilize fundus cameras to capture retinal images and detect indications of diabetic retinopathy, thereby facilitating prompt diagnosis. The progress made in computational intelligence has significantly enhanced our ability to identify, predict, and categorize DR. The focus of this research is to present a proficient framework for the DR grading. The framework proposed employs Laplacian filters to reduce undesirable image distortions and enhance the overall quality of the images. The methodology involves the utilization of transfer learning through the pre-existing VGG19 Convolutional Neural Network architecture to pull out features from diverse pooling layers. The amalgamation of extracted features from multiple layers results in the formation of feature vectors. These vectors are utilized as inputs for training various machine learning algorithms, with the objective of identifying the most effective model for evaluating the severity of DR. Significantly, the research provides a rationale for its forecasts, guaranteeing the transparency of the algorithm and comprehensibility of the model, thereby validating its reliability. The model's exceptional precision renders it a promising candidate for broad implementation in clinical identification and prediction of DR, potentially augmenting global patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

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Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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Imp: Highly Capable Large Multimodal Models for Mobile Devices

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IEEE Transactions on Multimedia 2025年 27卷 2961-2974页

作者： Shao, Zhenwei Yu, Zhou Yu, Jun Ouyang, Xuecheng Zheng, Lihao Gai, Zhenbiao Wang, Mingyang Kuang, Zhenzhong Ding, Jiajun Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science Hangzhou310005 China Harbin Institute of Technology Shenzhen Department of Computer Science and Technology Shenzhen China Hangzhou Dianzi University HDU-ITMO Joint Institute Hangzhou310005 China

By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-heavy and computation-intensive, thus hindering their applicability in resource-constrained scenarios. To this end, several lightweight LMMs have been proposed successively to maximize the capabilities under constrained scale (e.g., 3B). Despite the encouraging results achieved by these methods, most of them only focus on one or two aspects of the design space, and the key design choices that influence model capability have not yet been thoroughly investigated. In this paper, we conduct a systematic study for lightweight LMMs from the aspects of model architecture, training strategy, and training data. Based on our findings, we obtain Imp - a family of highly capable LMMs at the 2B$\sim$4B scales. Notably, our Imp-3B model steadily outperforms all the existing lightweight LMMs of similar size, and even surpasses the state-of-the-art LMMs at the 13B scale. With low-bit quantization and resolution reduction techniques, our Imp model can be deployed on a Qualcomm Snapdragon 8Gen3 mobile chip with a high inference speed of about 13 tokens/s. © 2025 IEEE.

关键词： Visual languages

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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Corrections to "Reed Solomon Codes Against Adversarial Insertions and Deletions"

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IEEE Transactions on Information Theory 2025年第4期71卷 3237-3238页

作者： Con, Roni Shpilka, Amir Tamo, Itzhak Technion – Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Tel Aviv University Blavatnik School of Computer Science Tel Aviv6997801 Israel Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv6997801 Israel

The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that k q RS code that can decode from n - 2k + 1 insdel errors where (Formula presented). Here we prove the following. Theorem 1: For integers n and k q RS-code, where (Formula presented) is a prime power, that can decode from n - 2k + 1 adversarial insdel errors. Note that the exponent of n is 4k - 3 whereas in Theorem 9 it is 4k - 4. For constant dimensional codes, the field size is of order O(n4k-3) , and in particular, for k = 2 the field size is of order O(n5) . © 1963-2012 IEEE.

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Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification 27

Siamese Multiple Attention Temporal Convolution Networks for...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zheng, Zhipeng Jiang, Yuchen Zhang, Shiyao Wei, Xuetao Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798331505929

The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes. © 2024 IEEE.

关键词： Convolution

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Smart Business with Intelligent Business Cards (SBIBC)

Smart Business with Intelligent Business Cards (SBIBC)

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Kirubakaran, N. Preethi, S.T. Parijatham, R. Pramithi, R. Chennai Institute of Technology Department of Computer Science and Business Systems Chennai India S.A. Engineering College Department of Computer Science and Engineering Chennai India Madha Institute Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

The research emphasizes the creation of a powerful and efficient system for the automaticextraction of contact information from physical calling cards through computer vision and information extraction techniques. This is achieved by integrating Tesseract Optical Character Recognition (OCR) with the YOLO (You Only Look Once) object detection algorithm, which acts as a dynamic combination for text and relevant elements identification and digitization from calling cards. The developed system uses client-server architecture to enable communication and data processing seamlessly. The server handles computationally intensive tasks such as object detection, OCR processing, and data validation while the client side has an interactive user interface that enables easy uploading of images and retrieval of structured contact information. The optimizations include this: YOLO is customized for detecting particular card elements (name, phone number, email) and enhancing the capabilities of Tesseract OCR to improve effectiveness for text extraction from varied card formats or fonts in the system. It is designed to be scalable and efficient, processing large datasets without affecting accuracy. This approach not only eases the digitization of calling card details into databases but also goes a long way in contributing to the domains of computer vision, object detection, and mechanized information extraction. It extends the scope to broad applications in automated data entry, customer relationship management (CRM), and digital archival. It takes forward for AI-driven sustaining technologies the integration of these techniques into practical solutions by overcoming the challenges of data extraction from real-world images. © 2024 IEEE.

关键词： Optical character recognition

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Toward actionable testing of deep learning models

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science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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