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Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone

JOURNAL OF ULTRASOUND IN MEDICINE 2024年第9期43卷 1711-1722页

作者： Li, Qing Tran, Tho N. H. T. Guo, Jialin Li, Boyi Xu, Kailiang Le, Lawrence H. Ta, Dean Fudan Univ Dept Biomed Engn 2005 Songhu Rd Shanghai 200438 Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai First Matern & Infant Hosp Dept Neonatol Shanghai Peoples R China Fudan Univ Yiwu Res Inst Yiwu Zhejiang Peoples R China Univ Alberta Dept Radiol & Diagnost Imaging Edmonton AB Canada

Objectives-To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. Methods-This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the relieff algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. Results-The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (rho = 0.583, P value <.001), body length (rho = 0.583, P value <.001), and gestational age (rho = 0.557, P value <.001). Conclusion-These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

关键词： axial transmission bone quantitative ultrasound fuzzy C-means clustering relieff algorithm speed of sound

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Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals (May, 10.1007/s11571-022-09817-y, 2022)

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COGNITIVE NEURODYNAMICS 2023年第2期17卷 561-561页

作者： Balasubramanian, Kishore Ramya, K. Gayathri Devi, K. Dr Mahalingam Coll Engn & Technol Pollachi India PA Coll Engn & Technol Pollachi India Dr NGP Inst Technol Coimbatore Tamil Nadu India

Schizophrenia is a chronic mental disorder that impairs a person’s thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the relieff algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat algorithm over the similar techniques in predicting schizophrenia.

关键词： Schizophrenia EEG ANFIS Grey- Wolf optimization Bat optimization relieff algorithm

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Blast Furnace Hanging Diagnosis Model Based On relieff-Decision Tree

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ISIJ INTERNATIONAL 2024年第1期64卷 96-104页

作者： Li, Fumin Meng, Lingru Liu, Xiaojie Li, Xin Li, Hongyang Mi, Jianjun North China Univ Sci & Technol Sch Met & Energy Tangshan 063210 Hebei Peoples R China HBIS Grp Co Ltd Tangshan Branch Tangshan 063020 Hebei Peoples R China

Blast furnace hanging is an abnormal furnace condition with the highest frequency. The judgment of hanging mainly relies on manual analysis, but this approach has strong subjectivity and time lag. In order to solve the above problems, this paper proposes a relieff - Decision Tree based anomaly diagnosis model to identify the hanging. Firstly, 10 relevant parameters are extracted based on expert experience, and each characteristic parameter is weighted using relieff algorithm after preprocessing. Secondly, the calculated weights of the characteristic parameters are sorted from the largest to the smallest, and the seven most effective characteristic parameter sets are selected as the decision nodes of the decision tree, which is constructed according to the expert rules of hanging diagnosis to complete the training and testing of the classifier. Simulation results show that the accuracy of the relieff - Decision Tree model reaches 96.5%, and the recognition rate of the two anomalies of "burden stop" and "sudden rise of differential pressure" exceeds 80%, which is 83.1% and 87.5%, respectively, indicating that the performance of the model is good. Finally, the expert diagnosis system is built, which effectively improves the diagnosis efficiency of hanging.

关键词： blast furnace condition hanging diagnosis relieff algorithm decision tree classification.

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A paper quality and comment consistency detection model based on feature dimensionality reduction

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ALEXANDRIA ENGINEERING JOURNAL 2022年第12期61卷 10395-10405页

作者： Sheng, Xinlei Huo, Wenjie Zhang, Caijun Zhang, Xin Han, Yang North China Univ Sci & Technol Coll Sci Tangshan 063210 Peoples R China North China Univ Sci & Technol Dev Planning & Discipline Construct Div Tangshan 063210 Peoples R China

As a reflection of the scholar's mastery of basic theories and professional knowledge, the dissertation is an important yardstick for measuring the level of scientific research. At present, the problem of academic misconduct is becoming increasingly prominent, which is not only related to personal academic ethics, but also related to the overall development of the national academic and scientific research field. In the traditional method of evaluating the quality of papers, it is mainly based on the evaluation experts' comments and scores. However, there are cases that the evaluation experts' comments and scores are inconsistent in practice. To address this problem, we proposed a paper quality consistency detection model based on the nearest neighbor analysis dimensionality reduction algorithm. Compared with other traditional models, the experimental results show that the detection accuracy of XGBoost model after dimensionality reduction using nearest neighbor analysis algorithm reaches 85.81%. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Emotion analysis XGBoost Neighbourhood component analysis relieff algorithm

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Predictive model for construction labour productivity using hybrid feature selection and principal component analysis

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CANADIAN JOURNAL OF CIVIL ENGINEERING 2022年第8期49卷 1366-1378页

作者： Ebrahimi, Sara Kazerooni, Matin Sumati, Vuppuluri Fayek, Aminah Robinson Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 1H9 Canada

Construction labour productivity (CLP) is affected by numerous variables made up of subjective and objective factors. Thus, CLP modelling and prediction are a complex task, leading to high computational cost and the risk of overfitting of data. This paper proposes a predictive model for CLP by integrating hybrid feature selection (HFS), as a combination of filter and wrapper methods, with principal component analysis (PCA). This developed HFS-PCA method reduces the dimensionality and complexity of CLP data and obtains better prediction performance by identifying the most predictive factors. Identified factors are utilized as inputs for various classification methods to predict CLP. Finally, prediction errors of the classification methods with and without using the proposed HFS-PCA method are compared, and the most accurate classification method is selected to develop the CLP predictive model. Experimental results show that using HFS-PCA for CLP prediction leads to better performances compared with past studies.

关键词： construction labour productivity prediction hybrid feature selection principal component analysis genetic algo-rithm support vector machine relieff algorithm

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A Short-Term Prediction Model for Multi-energy Load of IES Based on Comprehensive Meteorological Index and Deep Learning Multi-network Fusion 4

A Short-Term Prediction Model for Multi-energy Load of IES B...

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4th International Conference on Clean and Green Energy Engineering (CGEE)

作者： He, Qingsong Liu, Qihao Luo, Yang Zhu, Ziyuan Miao, Junhao Pu, Tingxu Xihua Univ Sch Elect Engn & Elect Informat Chengdu Peoples R China

ISBN: (纸本)9798350339796

To improve community load situational awareness, cope with the stochastic and coupled nature of multiple energy load demands, and improve the accuracy of multi-energy load prediction, this paper proposes a short-term prediction model of multi-energy load for an integrated energy system based on comprehensive meteorological indices and deep learning multi-network fusion. Firstly, the basic construction of the integrated energy system is described. Secondly, a comprehensive meteorological index based on biometeorological indicators is introduced and the relieff feature selection algorithm is used to construct a correlation model between the meteorological index and loads. Then, a convolutional neural network and Bidirectional long short-term memory load prediction model that introduces the attention mechanism is developed by applying a deep learning multi-network fusion approach. Finally, the scientific and feasibility of the model is verified through the analysis of a case study. The results show that the model proposed in this paper can effectively improve the accuracy of integrated energy system load forecasting.

关键词： Comprehensive meteorological index CNN-BiLSTM-Attention IES load forecasting relieff algorithm

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Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images

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DISTRIBUTED AND PARALLEL DATABASES 2022年第4期40卷 627-655页

作者： Suresha, Halebeedu Subbaraya Parthasarathy, Srirangapatna Sampathkumaran Univ Mysore PET Res Ctr Dept ECE Mysore Karnataka India PES Coll Engn Dept ECE Mandya India

The automated magnetic resonance imaging (MRI) processing techniques are gaining more importance in Alzheimer disease (AD) recognition, because it effectively diagnosis the pathology of the brain. Currently, computer aided diagnosis based on image analysis is an emerging tool to support AD diagnosis. In this research study, a new system is developed for enhancing the performance of AD recognition. Initially, the brain images were acquired from three online datasets and one real-time dataset such as AD Neuroimaging Initiative (ADNI), Minimal Interval Resonance Imaging in AD (MIRIAD), and Open Access Series of Imaging Studies (OASIS) and National Institute of Mental Health and Neuro Sciences (NIMHANS). Then, adaptive histogram equalization (AHE) and grey wolf optimization based clustering algorithm (GWOCA) were applied for denoising and segmenting the brain tissues;grey matter (GM), cerebro-spinal fluid (CSF), and white matter (WM) from the acquired images. After segmentation, the feature extraction was performed by utilizing dual tree complex wavelet transform (DTCWT), local ternary pattern (LTP) and Tamura features to extract the feature vectors from the segmented brain tissues. Then, relieff methodology was used to select the active features from the extracted feature vectors. Finally, the selected active feature values were classified into three classes [AD, normal and mild cognitive impairment (MCI)] utilizing deep neural network (DNN) classifier. From the simulation result, it is clear that the proposed framework achieved good performance in disease classification and almost showed 2.2-6% enhancement in accuracy of all four datasets.

关键词： Alzheimer disease recognition and classification Deep neural network Grey wolf optimization based clustering algorithm Histogram equalization relieff algorithm

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Visual retrieval of digital media image features based on active noise control

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第6期22卷 2107-2120页

作者： Zhang, Ying Luo, Xiaobo Huaihua Polytech Coll Dept Informat & Art Design Huaihua 418000 Hunan Peoples R China

In order to improve the recall and precision of image retrieval, a visual retrieval method of digital media image features based on active noise is proposed. In this paper, Canny algorithm is used to detect the edge of the image to get the feature information of the edge of the image. The RGB color space model is used to decompose the color information of the image, and the color characteristic information of the image is obtained. Extracting image features to retain the useful information contained in the image as much as possible;In order to facilitate the visual retrieval of image features, reduce the retrieval complexity and further fuse image features, SPCA and relieff algorithms are used to reduce the dimensionality of image features, and the active noise control method is used to sharpen the image. After processing the results, a digital media image feature visual retrieval platform is established to realize the visual retrieval of digital media image features. Experimental results show that the proposed method has a high accuracy of over 80% and a high recall rate of 95.2%.

关键词： Active noise control image features visual retrieval canny algorithm wavelet PCA relieff algorithm

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AI-Powered Measurement of Ultrasonic Axial-Transmission Velocity for Pediatric Skeletal Development Evaluation

AI-Powered Measurement of Ultrasonic Axial-Transmission Velo...

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IEEE International Ultrasonics Symposium (IUS)

作者： Li, Qing Tran, Tho N. H. T. Guo, Jialin Xu, Kailiang Li, Boyi Le, Lawrence H. Ta, Dean Fudan Univ Ctr Biomed Engn Shanghai Peoples R China Fudan Univ Acad Engn & Technol Inst Biomed Engn & Technol Shanghai Peoples R China Shanghai First Matern & Infant Hosp Shanghai Peoples R China Univ Alberta Dept Radiol & Diagnost Imaging Edmonton AB Canada

ISBN: (数字)9781665466578

ISBN: (纸本)9781665466578

Bone health assessment is commonly used to evaluate skeletal development and maturation and to diagnose neonatal growth disorders. Implementing an automated ultrasonic system for pediatric bone evaluation, which is radiation-free and cost-effective, to complement conventional radiography is significant for clinical applications. This study mainly proposes a method, which combines the relieff algorithm with fuzzy C-means clustering, for fully automatic detection of wave arrival time and estimation of ultrasonic velocity from axial transmission waveforms measured from the bone of infants without the need for signal preprocessing. The results of in-vivo velocity measurement show good consistency with the ground truth modality, indicating the feasibility of the proposed method for further clinical studies and usages.

关键词： bone quantitative ultrasound axial transmission speed of sound fuzzy C-means clustering relieff algorithm

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Electrical energy prediction using a surface fitting model for an on-farm direct expansion bulk milk cooler (DXBMC) in South Africa

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2021年第3期19卷 778-794页

作者： Mhundwa, Russel Simon, Michael Univ Ft Hare Ft Hare Inst Technol Alice South Africa

Purpose This paper aims to show that a simplified surface fitting model can be efficient in determining the energy consumption during milk cooling by an on-farm direct expansion bulk milk cooler (DXBMC). The study reveals that milk volume and the temperature gradient between the room and the final milk temperature can effectively be used for predicting the energy consumption within 95% confidence bounds. Design/methodology/approach A data acquisition system comprised a Landis and Gyr E650 power meter, TMC6-HE temperature sensors, and HOBO UX120-006M 4-channel analog data logger was designed and built for monitoring of the DXBMC. The room temperature where the DXBMC is housed was measured using a TMC6-HE temperature sensor, connected to a Hobo UX120-006M four-channel analog data logger which was configured to log at one-minute intervals. The electrical energy consumed by the DXBMC was measured using a Landis and Gyr E650 meter while the volume of milk was extracted from on the farm records. Findings The results showed that the developed model can predict the electrical energy consumption of the DXBMC within an acceptable accuracy since 80% of the variation in the electrical energy consumption by the DXBMC was explained by the mathematical model. Also, milk volume and the temperature gradient between the room and final milk temperature in the BMC are primary and secondary contributors, respectively, to electrical energy consumption by the DXBMC. Based on the system that has been monitored the findings reveal that the DXBMC was operating within the expected efficiency level as evidenced by the optimized electrical energy consumption (EEC) closely mirroring the modelled EEC with a determination coefficient of 0.95. Research limitations/implications Only one system was monitored due to unavailability of funding to deploy several data acquisition systems across the country. The milk blending temperatures, effects of the insulation of the DXBMC, were not taken into account

关键词： Regression Direct expansion bulk milk cooler Milk cooling energy relieff algorithm Surface fitting model

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