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Research on Improvement of Landscape Expansion Index and Its Application based on GIS 3

Research on Improvement of Landscape Expansion Index and Its...

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3rd international conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Liu, Lu Xiao, Minzhi Wen, Youyue Kuang, Hongyan Yang, Ziyi Xiangnan University School of Chemistry and Environmental Science 889 Chenzhou Road Hunan Chenzhou423000 China South China Institute of Environmental Sciences Mee 18 Ruihe Road Guangdong Guangzhou510530 China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

Landscape expansion index (LEI) was an important research method to quantitatively describe and analyze the dynamic information in the process of landscape expansion. Aimed at solving the defects in the previous studies of landscape expansion index, such as completely relying on reference, a new method for calculating landscape expansion index was proposed, which was based on the theory of graph theory and spatial topological relation. According to the calculation results of the landscape expansion index, landscape expansion was divided into three types: infill type, 0.5 © 2022 IEEE.

关键词： pattern recognition

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Artificial intelligence Powered Early Detection of Heart Disease

Artificial Intelligence Powered Early Detection of Heart Dis...

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Smart Data intelligence (ICSMDI), international conference on

作者： T A Mohanaprakash Abirami P Navaneethakrİshan. M Savija J Ramya M Anbarasa Pandian A Department of CSE Panimalar Engineeering College Chennai Tamilnadu India Department of CSE St.Joseph College of Engineering Sriperumbudur Chennai Tamilnadu

In the healthcare industry, machine Learning (ML) plays a crucial role in disease prediction. A patient must go through a series of tests before a condition can be diagnosed. However, using machine learning techniques, the number of tests can be reduced. This simplified test has a significant impact on both time and performance. Early patient care has benefited from sound medical data analysis due to the growing amount of data generated by the medical and healthcare sectors. With the help of disease data, massive amounts of medical data can be mined for hidden pattern information. With a focus on heart diseases, this study evaluates and suggests a heart disease prediction based on the patient's symptoms using machine learning techniques such as SVM, MLR, and RF algorithms. The proposed method outperforms those currently in use in terms of accuracy, forecast speed, and consistency of outcomes. It is also appropriate to classify lung cancers using trained datasets for accurate identification.

关键词： Heart Support vector machines Radio frequency machine learning algorithms Medical services Prediction algorithms Classification algorithms

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Enhancing 3D Referential Grounding by Learning Coarse Spatial Relationships 27th

Enhancing 3D Referential Grounding by Learning Coarse Spati...

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27th international conference on pattern recognition, ICPR 2024

作者： Joshi, Soham Tripathi, Aditay Gopalakrishnan, Viswanath Chakraborty, Anirban International Institute of Information Technology Bangalore India Indian Institute of Science Bengaluru India

ISBN: (纸本)9783031781124

Large-scale pre-training is commonly used in 2D referential grounding tasks owing to the easy availability of a large number of image-text pairs with corresponding bounding box annotations. However, for 3D referential grounding, the unavailability of high-quality 3D scene-text pairs with annotations poses a significant challenge. To address this issue, we leverage the large corpus of 3D scenes with bounding box annotations of object instances and design an automated strategy to synthesize scene-text data for pre-training by utilising the coarse spatial relationships between the objects in the scene without any human supervision. The proposed strategy first clusters the 3D bounding boxes and then uses these clusters to create pairwise and triplet relations between the objects in the 3D scene. We achieved improved results consistently across various top-performing methods in 3D referential grounding, when the proposed pre-training strategy is deployed. In addition to pre-training with the samples containing coarse spatial relations, we also encode semantic relationships between the bounding boxes conditioned on the language utterance, using a compatibility measure between the box features and the language utterance. To evaluate the performance of our proposed techniques, we conduct experiments on large-scale publicly available datasets, namely ScanRefer and ReferIt3D (SR3D and NR3D). Our proposed techniques can be seamlessly integrated with any off-the-shelf 3D referential grounding method. Specifically, when integrated with BUTD-DETR, we observed notable improvements of 2.2% and 1% in performance on the SR3D and NR3D datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Enhancing Visual Question Answering in Vietnamese Using Large Language Models Combined with OCR Systems 3rd

Enhancing Visual Question Answering in Vietnamese Using Lar...

引用

3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Van Nha, Nguyen Huan, Phung The Tuan, Le Minh Son, Le Hoang Artificial Intelligence Research Center VNU Information Technology Institute Vietnam National University Hanoi Viet Nam Thai Nguyen University of Information and Communication Technology Thainguyen Viet Nam East Asia University of Technology Hanoi Viet Nam

ISBN: (纸本)9783031809422

This paper presents a novel approach to enhancing Visual Question Answering (VQA) systems in the Vietnamese language by integrating La rge Language Models (LLMs) with Optical Character recognition (OCR) technology. VQA systems are designed to understand and answer questions based on visual content, but their performance may not be good when dealing with textual information embedded in images. To address this challenge, we propose a hybrid framework that leverages the strengths of LLMs in natural language understanding and generation, alongside OCR systems for accurate text extraction from images. Our approach involves the preprocessing of images through OCR to extract textual data, which is then combined with visual features and processed by an LLM to generate more accurate and contextually relevant answers. We conducted extensive experiments on a Vietnamese VQA dataset to evaluate the effectiveness of our method. The results demonstrate significant improvements in the accuracy of the answer and contextual understanding, with a 2% increase in the BLEU score and a 1.11 increase in the CIDEr score compared to the state-of-the-art VQA models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Question answering

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HCMA'22: 3rd international Workshop on Human-centric Multimedia Analysis 22

HCMA'22: 3rd International Workshop on Human-centric Multime...

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30th ACM international conference on Multimedia (MM)

作者： Zhang, Dingwen Fang, Chaowei Liu, Wu Liu, Xinchen Song, Jingkuan Zhu, Hongyuan Huang, Wenbing Smith, John Northwestern Polytech Univ Xian Peoples R China Xidian Univ Xian Peoples R China JD AI Res Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China ASTAR Singapore Singapore Tsinghua Univ Beijing Peoples R China IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781450392037

The Third international Workshop on Human-Centric Multimedia Analysis concentrates on the tasks of human-centric analysis with multimedia and multimodal information. It involves multiple tasks such as face detection and recognition, human body pattern analysis, person re-identification, human action detection, etc. Today, multiple multimedia sensing technologies and large-scale computing infrastructures are emerging at a rapid velocity a wide variety of big multi-modality data for human-centric analysis, which provides rich knowledge to help tackle these challenges. Researchers have strived to push the limits of human-centric multimedia analysis in a wide variety of applications, such as intelligent surveillance, retailing, fashion design, and services. Therefore, this workshop aims to provide a platform to bridge the gap between the communities of human analysis and multimedia.

关键词： Human Analysis Multi-media and Multi-modal Analysis

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Semantic Segmentation of Point Cloud Based on Graph Neural Network 3

Semantic Segmentation of Point Cloud Based on Graph Neural N...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Duan, Shumin Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510667563

As a key technology in the field of computer vision, point cloud semantic segmentation has been widely used in intelligent life and has become a hot spot and a difficult area of research in 3D vision. Current research on the semantic segmentation of 3D point clouds is mainly focused on the field of deep learning, however, there are two problems with such models: insufficient local feature representation and long model training period. To this end, we propose a point cloud semantic segmentation model based on geometric segmentation and graph neural network to improve the performance of semantic segmentation and model training efficiency. In our method, Firstly geometric segmentation is used for sparse and feature representation of the original point cloud. A directed graph is then constructed to partition the point cloud into "superpoints" and "superedges" with specific feature descriptors. followed by a spatial transformer network (STN) as the backbone of embedded network to encode local features from these superpoints. Finally, the graph neural network is employed for the classification of architectural components such as walls, ceilings, floors, and beams. Experiments were conducted on the Stanford University Large Scale 3D Indoor Spatial Dataset (S3DIS) dataset. Compared with the SPG network that also takes the entire large scene as input, the proposed algorithm improves the overall accuracy (OA), mean accuracy (mAcc), and per-class intersection over union (mIoU) by 1.0%, 2.9%, and 3. 0%. © 2023 SPIE.

关键词： Semantic Segmentation

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Efficient Image Retrieval Technique with Local Edge Binary pattern Using Combined Color and Texture Features 1st

Efficient Image Retrieval Technique with Local Edge Binary P...

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1st international conference on Computational intelligence for Engineering and Management Applications, CIEMA 2022

作者： Sucharitha, G. Kalyani, B.J.D. Sekhar, Chandra G Srividya, Ch. Computer Science Engineering Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9789811984921

Image proceeding is foundation for pattern recognition and machine learning. The recent research focuses on image retrieval techniques with machine and deep learning methodologies. This paper recommends an other approach for extracting images based on combined characteristics of color and texture with edge binary pattern technique. The suggested descriptor first translates an RGB image into HSV color space. In this approach, the HSV color model makes use of the fundamental properties of color image as color, intensity, and brightness of an image. The hue (H) and saturation (S) components support to extract the color features, and the value component is more feasible to obtain the texture features;upon the value constituent of each image, local maximum edge binary patterns (LMEBPs) are used to find the relations among the pixels for every 3 × 3 matrix in order to extract texture features. Finally, all three histograms were used to extract the feature vector. The presented algorithm is experimented on two well-known color databases, Corel-10k and MIT-Visex. The comparative analysis of the proposed approach in terms of retrieval performance, with existing methods like CS-LBP, LEPSEG, LEPINV, has shown a substantial perfection in terms of precision and recall. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Waste Classification Using Deep Neural Networks 15th

Waste Classification Using Deep Neural Networks

引用

15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, Naween Dash, Yajnaseni Abraham, Ajith Nayak, Akanksha Mehta, Ansh Tandon, Kushagra School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810824

Waste management has been rapidly increasing worldwide lately due to the skyrocketing waste production and environmental and health-related issues. However, solving this issue using conventional approaches is not feasible. Various efforts are being made to make it more accessible through better technology, including automation. In this research, the Deep Neural Networks (DNN) techniques are utilized to classify waste with the help of image data. The proposed method is tested using multiple experiments for different waste classification tasks, including separating recyclables and organics from the waste. Different models, such as CNN, InceptionV3, ResNet50, and VGG16 are trained for the classification task. The presented work contributes to understanding the advancement of technology use in one of our generation’s most critical challenges – waste management. The obtained findings indicate an effective utilization of DNNs to acquire the necessary knowledge to enhance our future with more sustainable technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Waste treatment

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A Review on EMG-based pattern Identification Methods for Effective Controlling of Hand Prostheses

A Review on EMG-based Pattern Identification Methods for Eff...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： S. Ramkumar Dhanusha Rema T. Archana Devi K Elavarasi T Selvaganapathi S. Gokila Department of Computer Science Christ (Deemed to be University) Bangalore India Department of Food Technology Karpagam Academy of Higher Education Coimbatore India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of CSE Karpagam Institute of Technology Coimbatore India

The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper’s conclusion.

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Data Mining and AI for Early Diagnosis and Treatment Optimization in Autoimmune Encephalitis 3

Data Mining and AI for Early Diagnosis and Treatment Optimiz...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Natrayan, L. Ali, H. Mohammed Kaliappan, S. Kumar, Gotte Ranjith Saveetha School of Engineering Simats Department of Mechanical Engineering TN Chennai India Srm Institute of Science and Technology- Ramapuram Campus Department Mechanical Engineering TN Chennai India Lovely Professional University Division of Research and Development Punjab India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

To guarantee high-quality and trustworthy data, we first highlight the need of preprocessing. The dataset is prepared for analysis by using standard methods such as data cleansing, normalization, and feature engineering. The technique relies heavily on the feature selection process, which picks out the most informative aspects of a large collection of clinical, imaging, and laboratory data. ReliefF (Relief Feature Selection), Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA) are three of the most popular feature selection techniques utilized here. These techniques make it easier to find unique characteristics in data, which in turn makes machine learning models more effective. A logistic regression model is used to predict the likelihood of autoimmune encephalitis based on certain characteristics to make an early diagnosis. In addition, linear programming is used to incorporate treatment optimization as an optimization issue. This guarantees that the suggested strategy maximizes therapy effectiveness within the bounds of possible side effects, available resources, and individual patient preferences. By adhering to a code of ethics, we preserve patient privacy and meet all applicable regulations. The suggested technique provides a holistic answer for autoimmune encephalitis evaluation and management. It greatly enhances diagnostic precision, therapeutic efficacy, and computational efficiency by merging data mining, AI, and mathematical modeling. These developments, together with our dedication to ethical compliance, make our method a very attractive and effective option for the diagnosis and individualized treatment of autoimmune encephalitis. © 2023 IEEE.

关键词： Diagnosis

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