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Prediction of Rheumatoid Arthritis disease using improved segmentation algorithm

Materials Today: Proceedings 2022年 62卷 4940-4945页

作者： Uma Nandhini, D. Naga Sai Pravallika, Janani Jabaseeli, Beulah Udhayakumar, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

Image processing and segmentation algorithms have improved to a great extent in diagnosing numerous complications by analyzing Electronic Medical Records (EMR) like X-Rays, CT scan and MRI Images. However the accuracy of predicting the outcome is still a major challenge, which mainly depends on the quality of algorithms used, the testing dataset, and the segmentation process. Therefore, it is essential to identify novel algorithms for image processing to improve the accuracy of prediction. One of the important medical complication that is considered for our research is Rheumatoid Arthritis (RA). It is a persistent immune disorder that causes reduced bone density, cartilage damage and inflammations in joints. Traditional methods, though cost effective like blood test, X- Ray analysis and cell diagnosis, they have their own error factors. On the other hand techniques like advanced thermal imaging are accurate but with extremely high cost. Therefore, the proposed idea is to comparatively assess the cost, accuracy and early detection of RA based on scan images of hands and fingers. A Novel Gaussian filtering and segmentation algorithms is proposed for removing unwanted disturbances and extracting the clear images of the joints, that segments only the affected part in the image to provide a better accuracy. Gamma correction luminosity is used for the sample images which is then processed for identifying the shadows of the image. Major implication of the proposed novel algorithms helps to detect the present stages of RA for affected persons and also predict the possibility of RA at early stages. © 2022

关键词： computerized tomography

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Recent Advances in In-Memory computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

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Nano-Micro Letters 2024年第7期16卷 1-30页

作者： Hangbo Zhou Sifan Li Kah-Wee Ang Yong-Wei Zhang Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Republic of Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Republic of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WaySingapore 138634Republic of Singapore

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing *** response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these *** devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological *** these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form *** review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential ***,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential *** primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

关键词： 2D materials Memristors Memtransistors Crossbar array In-memory computing

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Stage Wise Classification and Prediction of Non-Alcoholic Fatty Liver Disease using Hybrid Approach 2

Stage Wise Classification and Prediction of Non-Alcoholic Fa...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prabagaran, A. Balamurugan, N.M. Kalavathi, S. Padmanabhan, P. Angelin Blessy, J. Gupshup Technology India Pvt Ltd Bengaluru India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Information Technology Chennai India Noida Institute of Engineering & Technology Department of CSE Uttar Pradesh Greater Noida201306 India School of Computing Science and Engineering Galgotias Univeristy Dept of Computer Application India

ISBN: (纸本)9798350327533

The world's most serious chronic liver condition is Non-Alcoholic Fatty Liver Disease (NAFLD). The early stage prediction saved people's lives. Recent machine and deep learning, help to predict and classify diseases. The main goal of this work is to make an advanced prediction system that uses plasma lipid data and MRI images to predict the stage-wise classification of NAFLD images. The four main stages of liver disease are normal, steatosis, steatohepatitis, and cirrhosis. The machine and deep learning techniques to identify and classify cirrhosis disease in NAFLD. This research study proposes a machine and deep learning techniques with features that classify and predict the NAFLD. The random forest algorithm is used to predict the disease images into binary classifications, and a deep neural network is used for stage-wise classification. Initially, different features are used to classify the liver. This work uses 52 features, such as GLGCM and GLCM to predict and classify the fatty liver. The proposed method combined the initial prediction features using the random forest method and deep neural network to classify different stages of livers images. The experimental work is done with the help of MRI images and various iterations for evaluation. The experimental results were evaluated using three metrics, such as specificity, sensitivity, and accuracy. The predicted values of this methodology were 96.26%, 95.7%, and 97.6%. © 2024 IEEE.

关键词： Cirrhosis Hybrid Method Random Forest-Deep Neural Network Stage Wise Classification

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A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

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computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

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Ensuring Data Protection Using Machine Learning Integrating with Blockchain Technology 10th

Ensuring Data Protection Using Machine Learning Integrating ...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Diwan, Princy Khandelwal, Brijesh Dewangan, Bhupesh Kumar Department of Computer Science and Engineering Amity School of Engineering Amity University Raipur India Department of Computer Science and Engineering School of Engineering O. P. Jindal University Raigarh496109 India

ISBN: (纸本)9789811974540

In recent years, the emergence of blockchain technology (blockchain) has become a singular, most tumultuous, and trending technology. The decentralized info in blockchain underscores information security and confidentiality. Also, the agreement mechanism in it makes positive that information is secured and legit. Throughout this paper, we have got coated the analysis on combining blockchain and machine learning technologies and demonstrate that they are attending to collaborate with efficiency and effectiveness. Machine learning might even be a general language that comes with a spread of methods, machine learning, deep learning, and reinforcement learning. These ways square measure the core technology for big information analysis. As a distributed and append-only ledger system, a blockchain can be a natural tool for sharing and handling massive information from varied sources through the incorporation of good contracts. Additional expressly, blockchain will shield information security and promote information sharing. It also permits various countries to utilize distributed computing powers, for instance, IoT, for developing on-time prediction models with varied sources of knowledge. Blockchain systems can also generate large amounts of useful data from completely different sources, and the knowledge domain analysis on combining the two technologies is of nice potential and the combination of machine learning and blockchain will give extremely precise results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

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computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of Technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

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A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

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computers, Materials & Continua 2024年第12期81卷 3561-3582页

作者： Jianyong Wang Mingliang Gao Qilei Li Hyunbum Kim Gwanggil Jeon School of Electrical and Electronic Engineering Shandong University of TechnologyZibo255000China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK Department of Embedded Systems Engineering Incheon National UniversityIncheon22012Republic of Korea

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public *** counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and *** approaches vary in terms of supervision techniques,network architectures,and model ***,most crowd counting methods rely on fully supervised learning,which has proven to be ***,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often *** a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical *** paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision *** summarize and critically analyze the key methods in these two categories,highlighting their strengths and ***,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark *** believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

关键词： Crowd counting density estimation convolutional neural network(CNN) un/semi-supervised learning

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Utilizing Hybrid CNN Model and Machine Learning Techniques for the Identification of Pulmonary Fibrosis

Utilizing Hybrid CNN Model and Machine Learning Techniques f...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Saraswat, Anant Upasana Naik K, Bhaskar Sanskrithi School of Engineering Department of Computer Science and Engineering Andra Pradesh Puttaparthi India National Institute of Technology Patna Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350308167

Pulmonary fibrosis is a progressive lung disorder characterized by scar tissue formation, leading to a deterioration of the patient's respiratory function. The identification of such diseases heavily relies on medical imaging techniques, such as high-resolution computed tomography (HRCT) and radiographic images (X-rays). Unfortunately, the high cost and potential risks associated with radiation exposure make HRCT inaccessible to the majority of people. This paper proposes a hybrid model for classifying fibrotic and healthy lungs from chest X-ray images. Convolutional Neural Networks (CNNs) have emerged as the most widely used deep learning models for medical image classification. However, different CNN architectures employ distinct approaches to learning and interpreting images. So, the features of three well-known CNNs: Xception, InceptionResnetV2, and DenseNet121, are fused to form a comprehensive feature representation. Principal Component Analysis (PCA) is applied to alleviate overfitting and reduce dimensionality. Subsequently, a random forest classifier is employed on the principal components for accurate classification. By integrating these methods, we aim to enhance the classification of pulmonary fibrosis and promote early detection for improved patient outcomes. © 2023 IEEE.

关键词： CNN Machine Learning Pulmonary Fibrosis

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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Prediction Based Reversible Data Hiding for Gray-Scale Images 7

Prediction Based Reversible Data Hiding for Gray-Scale Image...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Uyyala, Ravi Bojjagani, Sriramulu Department of Computer Science And Engineering Hyderabad Gandipet India School of Engineering And Applied Sciences SRM-AP Department of Computer Science And Engineering Andhra Pradesh Amaravathi India

ISBN: (纸本)9798350371406

Predicting the pixel is crucial in prediction error expansion (PEE) based reversible data hiding (RDH). There are a number of methods for predicting pixels that can be found in research papers. The gradients are used to make a prediction about the current pixel. Gradients in the image can be used to make forecasts about the current pixel, and this has been the subject of extensive study. In this work, we suggest a three-by-three grid of surrounding pixels as a basis for prediction. Later, the histogram bin shifting technique was implemented to integrate more information with less distortion. In order to include more information, the suggested technique makes adaptive changes to the histogram based on local complexity. The experimental analysis demonstrates that When compared to the other, the proposed method is superior. © 2023 IEEE.

关键词： Pixels

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