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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Chauhan, Rahul Sharma, Tushar Rawat, Ruchira Pillai, Rudresh Chythanya, Kanegonda Ravi Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350394474

This paper aims to present the implementation of two machine learning algorithms, Linear Regression and Lasso Regression for the task of predicting the price of the house located in the city of Bengaluru, India. The task of predicting house price accurately is quite difficult as it depends on a lot of factors, and needs of the buyer. For predicting these prices, we can try to create datasets which contains details of the factors that people look for while buying Houses such as size, location, bathroom etc. To analyses the data, we can usecertain algorithms such as linear regression, lasso regression etc. By using algorithms like this, we can reduce the margin of error of our model and try to make it more accurate and usable. Such models can be used by real estate agents,sellers as well as the people who want to buy house to get the best deal for them. In future,researcher can also integrate models like this with the real estate's websites like MagicBricks,99Acres to give better and more accurate recommendations to people. © 2024 IEEE.

关键词： Fuzzy logic

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Binary Classification for Video Forgery Detection Using REWIND Data Set 6th

Binary Classification for Video Forgery Detection Using REWI...

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Pandey, Raksha Kushwaha, Alok Kumar Singh Keerthi, Lavanuru Lakshmi Yogesh, Madiri Naveen, Bathakala Department of Computer Science and Engineering School of Studies Engineering and Technology Guru Ghasidas Viswavidyalaya A Central University Chhattisgarh Bilaspur India

ISBN: (纸本)9789819971367

Altering a video sequence such that objects within the frame are either added to or removed from the scene is one of the malicious video forgery techniques that are most often utilized. In the present article, we trained logistic regression model, which predict the copy-move forgeries in the videos. These model is trained on the REWIND video copy-move forgeries dataset. In this study, we trained and tested the data using Logistic regression model. Simulation results show that the Logistic Regression model achieves the 86% accuracy with REWIND dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Identification of variables in algorithms for natural language processing in the field of biological text mining 15

Identification of variables in algorithms for natural langua...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Arvind Kumar Bishnoi, Sunita Gowrishankar, J. ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India Vivekananda Global University Department of Chemistry Jaipur India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

Herbal language processing (HLP) algorithms are increasingly being used to enable computerized text mining of organic texts. NLP algorithms allow the automatic preprocessing, feature extraction, and interpretation of biological texts, which could help in the identity of biological sequences, systems, and activities. One of the key components of effective NLP algorithms is the identity of variables that are applicable to the project at hand, including the varieties of organic entities and activities cited within the textual content. The identity of variables is essential to make certain that the text is efficaciously interpreted and represented and to permit accurate mining consequences. A number of techniques are to be had to become aware of such variables, which include using supervised, semi-supervised, and unsupervised getting-to-know techniques. For instance, supervised gaining knowledge of strategies, along with device getting-to-know algorithms, may be used to become aware of training and homes from a categorized dataset. Alternatively, unsupervised getting-to-know techniques, together with subject matter modeling tactics, may be used to perceive relevant training and houses from an unlabeled dataset. In addition, feature extraction algorithms, inclusive of element-of-speech (POS) tagging, may be used to identify relevant terms and phrases from the textual content. © 2024 IEEE.

关键词： Biological Categorized Consequences Content Modeling Training

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Blockchain-Enabled Integration and Coordination for Decentralized E-Health Systems 3

Blockchain-Enabled Integration and Coordination for Decentra...

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

作者： Kumar, Vishesh Sachdeva, Shelly Pawar, Vijayant National Institute of Technology Delhi Delhi India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350369083

Electronic healthcare systems (EHS) have gained popularity in recent years due to technological growth and development in the healthcare sector. With advanced technologies in healthcare, there are several challenges in securing and maintaining patient data privacy. The challenges are the outcome of uncoordinated, repeated, and frequently duplicated information among several separate e-health service providers and private data that is kept insecure in centralized systems, causing the lack of data availability and secure data sharing. A promising technology called blockchain has emerged that can be used to protect data privacy and secure access while offering a decentralized management framework for massively distributed data. A unified solution for migrating conventional e-health systems from different independent ecosystems and integrating them with a single ecosystem based on blockchain is proposed. We focused on the challenges related to data availability and data storage using the Interplanetary File System (IPFS) for decentralized data storage and its coordination with blockchain for maintaining data integrity. The proposed solution focuses on the secure storage of patient data, controlled trackable sharing of sensitive data, and a solution for smooth migration from conventional healthcare to blockchain-based healthcare. © 2024 IEEE.

关键词： Electronic health record

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UCTransNeXt: Global and local information fusion for retinal vessel segmentation 24

UCTransNeXt: Global and local information fusion for retinal...

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2024 International Conference on Advanced Robotics, Automation engineering and Machine Learning, ARAEML 2024

作者： Wang, Yueru Li, Peiyuan Zhan, Qianyi Peng, Yue Jiang, Yizhang Xie, Zhenping Liu, Yuan School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Computer and Information Engineering Nanjing University of Technology Nanjing China

ISBN: (纸本)9798400717116

The precise segmentation of retinal blood vessels is essential for the early diagnosis of various ophthalmic diseases. Meanwhile, the U-shaped structure based on convolutional neural networks performs well in medical image segmentation. However, due to the local operation characteristics of convolution, the ability to extract global feature information is limited, making it challenging to improve segmentation accuracy on retinal vessels. This paper proposes a network model UCTransNeXt that integrates global and local information to address the above problems. First, a Transformer branch is added to the U-Net network encoder section in parallel with the original convolutional layers to model global contextual information. The BiFusion module effectively fuses this global information with the local feature information extracted by the convolutional neural network. Second, the traditional Transformer structure tends to focus on all positions globally. At the same time, the adopted Intra-Scale Transformer can better capture the local information within the same scale, resulting in higher segmentation accuracy. Also, in order to efficiently label and predict convolutional features, tokenized MLPs are used in the latent space to focus more on vessel local information. The experimental results show that the Dice coefficients of the network proposed in this article reached 90.60% and 83.84%, respectively, and the accuracy reached 98.62% and 97.25%, respectively. The proposed algorithm's overall performance surpasses existing algorithms, and the modules can effectively improve fine vessel segmentation. © 2024 ACM.

关键词： Ophthalmology

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Learning Efficient Deep Discriminative Spatial and Temporal Networks for Video Deblurring

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5708-5722页

作者： Jinshan Pan Long Sun Boming Xu Jiangxin Dong Jinhui Tang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. Experimental results show that the proposed method performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity.

关键词： Transformers Logic gates Optical flow Feature extraction Convolutional neural networks Convolution Computational efficiency Recurrent neural networks Computational modeling Complexity theory

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Resilient Observer-based Security Control for Cyber-Physical Systems under Multiple Attacks 13

Resilient Observer-based Security Control for Cyber-Physical...

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13th International Conference on Information science and technology, ICIST 2023

作者： Yang, Ximing Li, Tieshan Yang, Hanqing Gao, Xiaoyang Liu, Ran Long, Yue Chen, C.L. Philip School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350313925

The issue of security control for cyber-physical systems (CPSs) is now receiving a lot of attention. Among them, the network layer is more seriously affected by cyber-attacks than the physical layer. In this paper, we investigate the denial-of-service (DoS) attacks and false data injection (FDI) attack on data transmission. Firstly, the FDI attack that exists in the actuator is estimated by proposing the observer. Then, due to the presence of DoS attacks in the sensor channels, the estimation effect of the former observer is affected. To solve this problem, a new resilient observer is proposed by introducing the switching mechanism. The proposed resilient observer can effectively reduce the adverse effects of DoS attacks on data transmission. Next, a false data estimation-based security controller is proposed, which can ensure the stability of the closed-loop system. Finally, a simulation is given to verify the effectiveness of the proposed method. © 2023 IEEE.

关键词： Cyber Physical System

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FastDrag: Manipulate Anything in One Step 38

FastDrag: Manipulate Anything in One Step

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Xuanjia Guan, Jian Fan, Congyi Xu, Dongli Lin, Youtian Pan, Haiwei Feng, Pengming College of Computer Science and Technology Harbin Engineering University China School of Intelligence Science and Technology Nanjing University China State Key Laboratory of Space-Ground Integrated Information Technology China

Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks. However, prevailing methods typically adopt n-step iterations for latent semantic optimization to achieve drag-based image editing, which is time-consuming and limits practical applications. In this paper, we introduce a novel one-step drag-based image editing method, i.e., FastDrag, to accelerate the editing process. Central to our approach is a latent warpage function (LWF), which simulates the behavior of a stretched material to adjust the location of individual pixels within the latent space. This innovation achieves one-step latent semantic optimization and hence significantly promotes editing speeds. Meanwhile, null regions emerging after applying LWF are addressed by our proposed bilateral nearest neighbor interpolation (BNNI) strategy. This strategy interpolates these regions using similar features from neighboring areas, thus enhancing semantic integrity. Additionally, a consistency-preserving strategy is introduced to maintain the consistency between the edited and original images by adopting semantic information from the original image, saved as key and value pairs in self-attention module during diffusion inversion, to guide the diffusion sampling. Our FastDrag is validated on the DragBench dataset, demonstrating substantial improvements in processing time over existing methods, while achieving enhanced editing performance. Project page: https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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An Extensive Examination of Methods for Detecting and Classifying Malignant and Benign Skin Cancers Using Deep Learning Techniques 3

An Extensive Examination of Methods for Detecting and Classi...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pasumarthy, Rambabu Gondi, Lakshmeeswari Gitam School Of Technology Gitam Deemed To Be University Department Of Computer Science And Engineering Andhra Pradesh Vishakhapatnam India

ISBN: (纸本)9798350318609

Skin cancer is a pressing health concern responsible for numerous fatalities in today's world, often diagnosed in its advanced stages, making treatment planning and patient survival challenging. Medical image interpretation, such as MRI as well as CT scans, is crucial for skin cancer diagnosis, but the manual analysis is costly, time-consuming, and can be influenced by biases. DL (Deep learning), a cutting-edge technology, is gaining attention as it allows for the automatic evaluation and interpretation of medical images, reducing the need for human intervention. Deep learning approaches have demonstrated remarkable outcomes in various domains by automatically extracting intrinsic image features, excluding the need for manual feature extraction. This review delves into the application of DL approaches in the diagnosis & detection of cancer, with a particular focus on skin cancer. Detecting melanoma from samples of dermoscopic skin is a formidable task, but employing a machine vision tool, like a deep learning approach, can address some of these challenges. The study introduces an automated melanoma classifier on the basis of DCNN (Deep Convolutional Neural Network) to accurately differentiate malignant from benign melanoma. It also outlines the various deep learning models and steps involved in utilizing these models for skin cancer detection. Recent advancements in the skin cancer detection Deep Learning (DL) methods are explored and summarized, shedding light on the critical challenges that have been faced in accurately detecting the cancer of skin in its early stages. For this research, dermoscopic images from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC 2017, and ISIC 2020), containing diverse cancer samples, were obtained and evaluated based on metrics like precision, accuracy, specificity, recall, and F1-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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