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Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

computers, Materials & Continua 2022年第11期73卷 2275-2291页

作者： Prasanalakshmi Balaji B.Sri Revathi Praveetha Gobinathan Shermin Shamsudheen Thavavel Vaiyapuri Department of Computer Science King Khalid UniversitySaudi Arabia School of Electrical Engineering Vellore Institute of TechnologyIndia Department of Computer Science Jazan UniversitySaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical *** Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare *** recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image *** medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial *** newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death *** on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this *** context,primarily image quality enhancement procedures like noise removal and contrast normalization are *** the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are *** extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant *** selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar *** proposed model is evaluated in terms of accuracy,precision,recall,and f-

关键词： Deep belief neural network mayfly optimization gaussian filter contrast normalization grey level variance local binary pattern discrete wavelet transform

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Towards Non-Invasive Swallowing Assessment: An AI-Powered Interface for Swallowing Kinematic Analysis using High-Resolution Cervical Auscultation 46

Towards Non-Invasive Swallowing Assessment: An AI-Powered In...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Luo, Yuewen Anwar, Ayman Ren, Siyi Coyle, James L. Sejdic, Ervin University of Toronto Division of Engineering Science Faculty of Applied Science & Engineering TorontoON Canada University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing is a pivotal physiological function for human sustenance and hydration. Dysfunctions, termed dysphagia, necessitate prompt and precise diagnosis. Videofluoroscopic swallowing studies (VFSS) remain the gold standard for swallowing assessment but pose accessibility and radiation exposure concerns. High-resolution cervical auscultation (HRCA) presents a non-invasive alternative with comparable accuracy. Yet, existing studies have only assessed HRCA in tandem with VFSS, without applying it to clinical settings. We propose an AI-enhanced PC software deploying HRCA for real-time bedside swallowing screening. This software proficiently detects abnormalities and precisely measures key kinematic events, including upper esophageal sphincter opening and laryngeal vestibule closure duration. The system was evaluated through two studies, encompassing a VFSS comparative analysis and a usability study. Our findings underscored the software's superior operational efficiency and diagnostic accuracy, positioning our HRCA-based system as a refined and pragmatic alternative for dysphagia assessment. © 2024 IEEE.

关键词： User centered design

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Blind Polynomial Regression 48

Blind Polynomial Regression

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Natali, Alberto Leus, Geert Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9781728163277

Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of the polynomial. However, in many applications, the input may be partially known or not known at all, rendering conventional regression approaches not applicable. In this paper, we formally state the (potentially partial) blind regression problem, illustrate some of its theoretical properties, and propose an algorithmic approach to solve it. As a case-study, we apply our methods to a jitter-correction problem and corroborate its performance. © 2023 IEEE.

关键词： interpolation matrix factorization MUSIC polynomial regression Vandermonde

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An Extendable High Step-Up DC-DC Converter for Renewable Energy Applications

An Extendable High Step-Up DC-DC Converter for Renewable Ene...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Habibi, Saeed Rahimi, Ramin Ferdowsi, Mehdi Shamsi, Pourya University of Science and Technology Department of Electrical and Computer Engineering Missouri Rolla United States

ISBN: (纸本)9798350316445

In this paper, a high step-up DC-DC converter based on a switched-inductor-capacitor-diode (SLCD) cell is proposed. The proposed converter provides a high voltage gain, low voltage stress on the power switches and diodes, and a low input current ripple. Moreover, the proposed converter is extendable, meaning that the voltage gain could be further increased by using a higher number of proposed cells in the topology. The steady-state analysis and comparison of the proposed converter to the other existing converters are presented. A 200 W, 40 V to 380 V experimental setup is developed to verify the steady-state analysis of the proposed converter. © 2023 IEEE.

关键词： DC-DC High step-up converter switched-capacitor switched-capacitor-inductor switched-inductor

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Video Arbitrary Style Transfer via Style Attention and Contrastive Learning 5

Video Arbitrary Style Transfer via Style Attention and Contr...

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5th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2023

作者： Qian, Yangyang Qin, Wei Pei, Songwen Tan, Junhao School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350394160

With the development of Generative AI technologies, video style transfer has become a popular extra challenge of style transfer. Compared to traditional images style transfer tasks, video tasks bring new challenges in maintaining temporal consistency and desirable stylized results. To solve the problem, we proposed a two-stage network combining Style Attention and Contrast learning(SACNet). Specifically, in the first stage, we proposed an adaptive filter incorporating attention mechanism to deal with texture artifacts and edge artifacts. The filter adjusts the features both inner channels and inter channels. In the second stage, we proposed a Temporal-consistency Contrastive Loss Module(TCLM) to solve the problems of stability and flicker artifacts for stylized videos. The TCLM is defined by single-frame local patches with looser constraints. In addition, color loss function was introduced to adjust the stylized color saturation. In contrast to the current SOTA models for video style transfer, SACNet achieved an average LPIPS by 0.152 and a temporal-consistency loss by 0.034 on the MPI-Sintel dataset, it respectively achieved reductions by 5% and 20% on average. It was verified that SACNet could perform well in various style transfer tasks for images and videos. ©2023 IEEE.

关键词： Adaptive filters

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All-fiber real-time ultrafast ranging Lidar for motion management of patients during stereotactic radiotherapy 13

All-fiber real-time ultrafast ranging Lidar for motion manag...

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Biomedical Applications of Light Scattering XIII 2023

作者： Boroomandisorkhabi, Behzad Esmaeelpour, Mina Electrical and Computer Engineering Dept Missouri University of Science and Technology 65409 United States

ISBN: (纸本)9781510658912

We report a fiber-optic-based ultrafast time-stretch laser detection and ranging (Lidar) sensor with 10 MHz speed and 10 μm accuracy with 30 mm dynamic range for head motion detection under the thermoplastic mask during image-guided radiotherapy procedures. The sensor (1) is miniaturized and fits under the mask, (2) is small enough not to cause attenuation in radiation beams, (3) has a spatial resolution of a tenth of a millimeter, (4) is real-time, and (5) is immune to electromagnetic radiation. © 2023 SPIE.

关键词： Electromagnetic waves

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Dynamic Payback Period Estimation for Solar Power Plants Through ML Based Solar Power Generation Forecasts

Dynamic Payback Period Estimation for Solar Power Plants Thr...

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

作者： Singh, Tarun Raj Prajapati, Aryan Himmatlal Kumar, Nishant Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur India Indian Institute of Technology Jodhpur Department of Electrical Engineering Jodhpur India

ISBN: (纸本)9798331510022

Substantial capital is required to invest in solar power plants, which puts estimation of the payback period accurately at primary concern for stakeholders. In this paper, we proposed a novel method to estimate the payback period of a 10MW solar power plant through forecasting solar power generation by an optimized XGBoost Machine learning model. We began by forecasting the Global Horizontal Irradiance (GHI) and temperature with a temporal resolution of 1 hour for the upcoming year. Using these forecasted values, the expected power output of a single solar panel with given properties is calculated, which was further scaled for the entire plant to estimate the total power generated for entire year with a resolution of 1 hour. Monthly income from the plant is estimated by considering current electricity prices and the predicted power of each month. Plant investments accounted for various costs, including panels, land acquisition, maintenance, wiring, and equipment, while also considering compound interest on the initial investment (compounded monthly) with monthly loan repayments. By considering different scenarios, we determined the payback period by varying factors such as interest rates, land costs, panel efficiency, and maintenance expenses. Our findings offer offer valuable insights into the financial feasibility and potentiality of solar power plants and demonstrate how these factors influence the payback period of such plants. © 2024 IEEE.

关键词： Decentralized finance

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Improved Intelligent Ledger Construction for Realistic IoT Blockchain Networks

Improved Intelligent Ledger Construction for Realistic IoT B...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Rawlins, Charles Jagannathan, S. Missouri University of Science and Technology Electrical and Computer Engineering RollaMO United States

ISBN: (纸本)9798350322347

Scalability is essential for next-generation blockchain technology to integrate with large mobile networks like Internet of Things (IoT). The IOTA distributed ledger protocol has combined transaction generation and verification to address this, but at the expense of increased reliance on connectivity to resolve conflicts with a novel ledger data structure. Intelligent Ledger Construction (ILC), was proposed as an auditable lightweight reinforcement-learning scheme to address this constraint with proposal of local conflict resolution with machine-learning classification. This effort presents an improved reliability reward model to enhance training for ILC and further reduce adversarial gaming and resource usage. Testing this revision in an idealistic setting showed vast improvement in transaction throughput efficiency and delay while also reducing unapproved transactions by 60% in a realistic IoT-like network. Future work will improve auditability of the underlying intelligence for stronger security. © 2023 IEEE.

关键词： Blockchain

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Knowledge Enhancement and Feature Purification for Single-stage Joint Entity and Relation Extraction 29

Knowledge Enhancement and Feature Purification for Single-st...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhai, Guohao Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798350330717

Joint entity and relation extraction aim to achieve named entity recognition and relation extraction in unstructured text. We use the form of triples (subject, relation, object) to describe entity and relation. Joint entity and relation extraction play an important role in knowledge graph construction, question and answering, data analysis, and other natural language processing domains. Most of the existing works suffered from insufficient interaction between entity features and relation features due to the extraction order. Moreover, there is a prevalence of heterogeneous representations of entities and relations. They not only increase the amount of model design and the complexity of training, but also lead to the problem of exposure bias due to the extraction order. Therefore, in this paper, we propose KFRel, where we enhance the entity and relation representations by encoding text and relation. Then, the features are fused to enhance the entity and relation representations, and adopt a feature purification module, where the features are enhanced by a feature purification module that removes feature information irrelevant to the joint entity and relation extraction while retaining feature information of relevance. In addition, we adopt a single-stage joint entity and relation extraction module to address the issue of overlapping triples. This module aims to help improve the effectiveness of joint entity and relation extraction. Comprehensive experiments are conducted on two widely-used datasets, and the experimental results demonstrate that the proposed method is effective and outperforms the state-of-the-art baselines. © 2023 IEEE.

关键词： Feature Purification Joint Entity and Relation Extraction Knowledge Enhancement

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