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Implementation of machine learning techniques for disease diagnosis

Materials Today: Proceedings 2022年 51卷 2198-2201页

作者： Shachi Mall Ashutosh Srivastava Bireshwar Dass Mazumdar Manmohan Mishra Sunil L. Bangare A. Deepak Department of Computer Science & Engineering Institute of Technology & Management GIDA Gorakhpur Uttar Pradesh India Systems Engineering Department Of electrical engineering IIT- BHU Varanasi India Department of Computer Science &Engineering IERT Prayagraj 211002 India Department of Computer Application United Institute of Management Prayagraj 211010 India Department of Information Technology Sinhgad Academy of Engineering SavitribaiPhule Pune University Pune India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Recently, data mining and machine learning techniques have found widespread use in the field of healthcare. The objective of this study is to develop an automated method for diagnosing illnesses. A Fuzzy logic-based random forest approach and a thorough examination of the patient's medical records are used to diagnose the disease. Clinical diagnosis is performed with the aid of a doctor's expertise and understanding in traditional healthcare. It is more challenging to provide good healthcare in rural and remote areas because patients are more likely to travel a long distance to visit a specialist. Because the number of medical practitioners and facilities in these areas is limited, providing an expert diagnosis in a fair period of time is challenging. The problem can be solved by using expert systems for disease diagnosis that employ data mining techniques and fuzzy logic. Decision trees are often used in machine learning to predict outcomes. Fuzzy datasets are an excellent choice for describing medical facts and expert opinions. Fuzzy decision trees build simple decision trees using fuzzy input. In this proposed system, an expert system that diagnoses disease using a random forest algorithm and fuzzy decision trees is provided. The fuzzy decision trees increase the accuracy of the diagnostic system. On the UCI repository, the proposed method is assessed and found to be more efficient in sickness prediction than current strategies. Classification accuracy has risen as temporal complexity has decreased.

关键词： Data Mining Machine Learning Classification Decision Tree Prediction Heart Disease

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A Multi-Feature Fusion Method with Inception Architecture for Image Steganalysis

A Multi-Feature Fusion Method with Inception Architecture fo...

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International Conference on computer Supported Cooperative Work in Design

作者： Ming Jiang Shuo Zhang Feng Zhang Biao Guo Yun Li School of Computer Science and Information Security Guilin University Of Electronic Technology Guilin China Guangxi Metaverse Application Engineering Center Nanning China School of Information Engineering Guangxi Polytechnic of Construction Nanning China Guangxi Institute of Digital Technology Nanning China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image steganography and image steganalysis is one of the research hotspots in the field of information security, the abuse of steganography causes many security risks. As an attack method of image steganography, the main purpose of image steganalysis is to detect whether there is secret information in the image. Recent works have demonstrated the efficacy of CNN in the realm of image steganalysis. However, due to the limitation of convolution kernel size and lack of the ability to model long-range dependencies, CNN-based steganalysis methods face challenges with capturing the global features of steganographic images which are beneficial for image steganalysis. This paper proposes a multi-branch feature fusion network, called MFFNet, which effective extraction and fusion of different features of steganographic images to achieve better detection performance. Firstly, several residual attention blocks (RAB) are incorporated with spatial rich model filters (SRM) in the preprocessing phase to enhance the signal-to-noise ratio (SNR) of steganographic signals. Secondly, in the feature extraction phase, a three-branch parallel fusion module (TFM) is designed to fuse different branches to rich feature representations of steganographic images, where the global branch captures global features by a hardware-friendly (DFC) attention mechanism. Finally, the channel split mechanism is exploited to prevent feature redundancies and decrease the number of parameters. The experiments reflect that the proposed network outperforms several state-of-the-art image steganalysis methods.

关键词： Steganography Filters Fuses Federated learning Redundancy Information security Interference

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Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection

arXiv

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arXiv 2024年

作者： Wang, Hongsong Xu, Andi Ding, Pinle Gui, Jie School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China School of Cyber Science and Engineering Southeast University Nanjing210096 China Engineering Research Center of Blockchain Application Supervision And Management Southeast University Ministry of Education China Purple Mountain Laboratories Nanjing210000 China

Video Anomaly Detection (VAD) is essential for computer vision research. Existing VAD methods utilize either reconstruction-based or prediction-based frameworks. The former excels at detecting irregular patterns or structures, whereas the latter is capable of spotting abnormal deviations or trends. We address pose-based video anomaly detection and introduce a novel framework called Dual Conditioned Motion Diffusion (DCMD), which enjoys the advantages of both approaches. The DCMD integrates conditioned motion and conditioned embedding to comprehensively utilize the pose characteristics and latent semantics of observed movements, respectively. In the reverse diffusion process, a motion transformer is proposed to capture potential correlations from multi-layered characteristics within the spectrum space of human motion. To enhance the discriminability between normal and abnormal instances, we design a novel United Association Discrepancy (UAD) regularization that primarily relies on a Gaussian kernel-based time association and a self-attention-based global association. Finally, a mask completion strategy is introduced during the inference stage of the reverse diffusion process to enhance the utilization of conditioned motion for the prediction branch of anomaly detection. Extensive experiments on four datasets demonstrate that our method dramatically outperforms state-of-the-art methods and exhibits superior generalization performance. © 2024, CC BY-SA.

关键词： Motion capture

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ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution

arXiv

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arXiv 2023年

作者： Niu, Axi Trung, Pham Xuan Zhang, Kang Sun, Jinqiu Zhu, Yu Kweon, In So Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072 China School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of School of Astronautics Northwestern Polytechnical University Xi’an710072 China

Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinement process comes with the drawback of low inference speed, which strongly limits its applications. To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution). Specifically, our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our study also highlights the effectiveness of using a pre-trained SR model to provide the conditional image of the given low-resolution (LR) image to achieve superior high-resolution results. We demonstrate that our method surpasses previous attempts in qualitative and quantitative results through extensive experiments conducted on benchmark datasets such as Set5, Set14, Urban100, BSD100, and Manga109. Moreover, our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios. © 2023, CC BY.

关键词： Image enhancement

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A Comprehensive Review Analysis of Supervised Machine Learning Techniques

A Comprehensive Review Analysis of Supervised Machine Learni...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shivangi Ghildiyal Raghav Garg Saurabh Dhanik Argya Sarkar Gayatry Sharma Pushpendra Singh Kharayat Deptt. of Computer Application Invertis University Bareilly Uttar Pradesh India Deptt. of Computer Sci. & Engg Tula’s Institute Dehradun U.K. India School of Computing Graphic Era Hill University Bhimtal India Graduate School of Business Tula’s Institute Dehradun U.K. India Department of Computer Science MIET Kumaun Haldwani Uttarakhand India Department of Electrical Engineering Graphic Era Deemed to be university Dehradun India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This study presents an exhaustive review analysis of the supervised machine learning techniques. The different types of supervised machine learning methods which are discussed in this study are linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes, Gradient Boosting Machines (GBM) and Neural Networks. All these methods are showing its relevance and utility in the various domain of engineering and bio medical engineering. Using supervised machine learning methods, review evaluation involves assessing and deriving insight from evaluations, frequently in order to categorize them, identify themes, or forecast scores.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Neural networks Nearest neighbor methods Boosting Bayes methods Decision trees Random forests

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Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Xia, Yong The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The Australian Institute for Machine Learning The University of Adelaide AdelaideSA5000 Australia

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that inconsistent decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancy obtained from two decoders, by feeding the discrepancy as a feedback signal to the encoder. The core design of LeFeD is to enlarge the difference by training differentiated decoders, and then learn from the inconsistent information iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles such as uncertainty estimation and strong constraints, as well as setting a new state-of-the-art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/LeFeD © 2023, CC BY.

关键词： Decoding

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Research on Optimization Algorithm of Real Estate Enterprise Value Evaluation Based on BP Neural Network

Research on Optimization Algorithm of Real Estate Enterprise...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Niranjanamurthy M S. Surendran S. Saravanan Tulsidas Nakrani Mahaveerakannan R M. R. Arun Department of AI and ML BMS Institute of Technology and Management Avalahalli Bangalore Karnataka India Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Chennai Tamilnadu India Department of Computer Application Sankalchand Patel College of Engineering Sankalchand Patel University Visnagar Gujarat India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India

Because of its professional skills and expertise, it is able to persuade both the buyer and the selling and make the transaction easier. An optimal design scheme for real estate businesses' worth appraisals is proposed here, and it is based on BPNN (BP neural network). A worth appraisal system is developed by doing an analysis of several worth indicators pertaining to real estate companies, and then the system is optimized with the use of BPNN. In the final step, an analysis of the simulation test is performed. According to the findings of the simulation, the arithmetic has a certain accuracy, which is 8.34% higher than the accuracy of traditional arithmetic. On the premise of ensuring the efficient processing of data in batches and finding the rules therein, reduce the impact of human subjectivity, improve the accuracy and scientificity of real estate appraisal, and provide certain reference for the national and local governments to reasonably regulate house prices, so as to promote the stable operation and healthy development of the real estate market. In doing so, you will be able to promote the growth of the real estate market in a healthy manner.

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Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation

arXiv

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arXiv 2023年

作者： Chen, Ziyang Pan, Yongsheng Xia, Yong The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China The School of Biomedical and Engineering ShanghaiTech University Shanghai201210 China

Glaucoma is one of the leading causes of irreversible blindness. Segmentation of optic disc (OD) and optic cup (OC) on fundus images is a crucial step in glaucoma screening. Although many deep learning models have been constructed for this task, it remains challenging to train an OD/OC segmentation model that could be deployed successfully to different healthcare centers. The difficulties mainly comes from the domain shift issue, i.e., the fundus images collected at these centers usually vary greatly in the tone, contrast, and brightness. To address this issue, in this paper, we propose a novel unsupervised domain adaptation (UDA) method called Reconstruction-driven Dynamic Refinement Network (RDR-Net), where we employ a due-path segmentation backbone for simultaneous edge detection and region prediction and design three modules to alleviate the domain gap. The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way. It also uses a style-consistency constraint to force the network to retain more domain-invariant information. The low-level feature refinement (LFR) module employs input-specific dynamic convolutions to suppress the domain-variant information in the obtained low-level features. The prediction-map alignment (PMA) module elaborates the entropy-driven adversarial learning to encourage the network to generate source-like boundaries and regions. We evaluated our RDR-Net against state-of-the-art solutions on four public fundus image datasets. Our results indicate that RDR-Net is superior to competing models in both segmentation performance and generalization ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Learning from Multi-Perception Features for Real-Word Image Super-resolution

arXiv

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arXiv 2023年

作者： Niu, Axi Zhang, Kang Pham, Trung X. Wang, Pei Sun, Jinqiu Kweon, In So Zhang, Yanning The School of Computer Science Northwestern Polytechnical University Xi’an710072 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072 China The School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of The School of Astronautics Northwestern Polytechnical University Xi’an710072 China

Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-estimation-based methods may be inaccurate in estimating the degradation, making them less applicable to real-world LR images. On the other hand, blind-based methods are often limited by their fixed single perception information, which hinders their ability to handle diverse perceptual characteristics. To overcome this limitation, we propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information and a series of newly-designed Cross-Perception Blocks (CPB) to combine this information for effective super-resolution reconstruction. Additionally, we introduce a contrastive regularization term (CR) that improves the model’s learning capability by using newly generated HR and LR images as positive and negative samples for ground truth HR. Experimental results on challenging real-world SR datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in both qualitative and quantitative measures. © 2023, CC BY.

关键词： Feature extraction

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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Jitendra Kumar Katariya Dhananjay Yadav Gayathri C Vikas K. Kolekar Umesh M. Daivagna V. Pushparajesh Department of Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Bangalore Karnataka

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research.

关键词： Knowledge engineering Deep learning Recurrent neural networks Time series analysis Network architecture Internet Forecasting

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