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A machine learning-based hybrid recommender framework for smart medical systems

PEERJ COMPUTER SCIENCE 2024年 10卷 e1880页

作者： Wei, Jianhua Yan, Honglin Shao, Xiaoli Zhao, Lili Han, Lin Yan, Peng Wang, Shengyu Xian Med Univ Bidding Procurement Off Affiliated Hosp 1 Xian Peoples R China Xian Med Univ Dept Gastroenterol Affiliated Hosp 1 Xian Peoples R China Xian Med Univ Hosp Evaluat Affiliated Hosp 1 Xian Peoples R China Xian Med Univ Dept Sci Res Affiliated Hosp 1 Xian Peoples R China Xian Med Univ Affiliated Hosp 1 Pulm & Crit Care Med Xian Peoples R China

This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy. The second method implements the term frequency and inverse document frequency (TF-IDF) algorithm to construct a model based on the patient's symptom vector space, incorporating score weighting, modified cosine similarity, and K -means clustering. Then, the alternating least squares (ALS) matrix decomposition and user collaborative filtering algorithm are applied to calculate patients' predicted scores for doctors and recommend top -performing doctors. Experimental results show significant improvements in metrics called precision and recall rates compared to conventional methods, making the proposed approach a practical solution for department triage and doctor recommendation in medical appointment platforms.

关键词： Medical registration Medical evaluation Big data deep learning algorithms Doctor recommendations

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Challenges and limitations in computational prediction of protein misfolding in neurodegenerative diseases

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2024年 17卷 1323182页

作者： Krokidis, Marios G. Dimitrakopoulos, Georgios N. Vrahatis, Aristidis G. Exarchos, Themis P. Vlamos, Panagiotis Ionian Univ Dept Informat Bioinformat & Human Electrophysiol Lab Corfu Greece

The thermodynamic hypothesis of protein folding, especially the notion that the native state represents the most stable configuration, effectively constituting the global minimum of Gibbs free energy (G), possesses an inherent allure and intuitive appeal. [...]this perspective significantly streamlines the development of theories and models, as it obviates the necessity to unravel the mechanisms by which a protein attains its unique native conformation. Addressing the significant challenge posed by the enigmatic protein folding problem, meticulous organization characterizes the Critical Assessment of Protein Structure Prediction (CASP) competition. [...]it employs co-evolutionary analysis to map the co-variation of residues within protein sequences to physical interactions in protein structures. [...]it leverages deep neural networks to adeptly discern patterns in protein sequences and co-evolutionary couplings, subsequently translating them into contact maps.

关键词： protein misfolding structure prediction neurodegeneration fold recognition deep learning algorithms

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Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

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SENSORS 2024年第11期24卷 3529页

作者： Su, Zhihao Adam, Afzan Nasrudin, Mohammad Faidzul Prabuwono, Anton Satria Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia King Abdulaziz Univ Fac Comp & Informat Technol Rabigh Rabigh 21911 Saudi Arabia

Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a "box map" based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a "box map". In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research.

关键词： computer vision object detection deep learning algorithms proposal-free detector

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Development of a Portable Residual Chlorine Detection Device with a Combination of Microfluidic Chips and LS-BP Algorithm to Achieve Accurate Detection of Residual Chlorine in Water

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MICROMACHINES 2024年第8期15卷 1045-1045页

作者： Wang, Tongfei Niu, Jiping Pang, Haoran Meng, Xiaoyu Sun, Ruqian Xie, Jiaqing Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China

Chlorine is widely used for sterilization and disinfection of water, but the presence of excess residual chlorine in water poses a substantial threat to human health. At present, there is no portable device which can achieve accurate, rapid, low-cost, and convenient detection of residual chlorine in water. Therefore, it is necessary to develop a device that can perform accurate, rapid, low-cost, and convenient detection of residual chlorine in water. In this study, a portable residual chlorine detection device was developed. A microfluidic chip was studied to achieve efficient mixing of two-phase flow. This microfluidic chip was used for rapid mixing of reagents in the portable residual chlorine detection device, reducing the consumption of reagents, detection time, and device volume. A deep learning algorithm was proposed for predicting residual chlorine concentration in water, achieving precise detection. Firstly, the microfluidic chip structure for detecting mixed reagents was optimized, and the microfluidic chip was fabricated by a 3D-printing method. Secondly, a deep learning (LS-BP) algorithm was constructed and proposed for predicting residual chlorine concentration in water, which can realize dual-channel signal reading. Thirdly, the corresponding portable residual chlorine detection device was developed, and the detection device was compared with residual chlorine detection devices and methods in other studies. The comparison results indicate that the portable residual chlorine detection device has high detection accuracy, fast detection speed, low cost, and good convenience. The excellent performance of the portable residual chlorine detection device makes it suitable for detecting residual chlorine in drinking water, swimming pool water, aquaculture and other fields.

关键词： residual chlorine microfluidic chip LS-BP algorithm deep learning algorithms

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Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 91卷

作者： Huerta, Alvaro Martinez-Rodrigo, Arturo Bertomeu-Gonzalez, Vicente Ayo-Martin, Oscar Rieta, Jose J. Alcaraz, Rail Univ Castilla La Mancha Res Grp Elect Biomed & Telecommun Engn Cuenca Spain Miguel Hernandez Univ Clin Med Dept Elche Spain Univ Hosp Albacete Dept Neurol Albacete Spain Univ Politecn Valencia Elect Engn Dept BioMIT Org Valencia Spain Inst Tecnol Audiovisuales Campus Univ S-N Cuenca 16071 Spain

Current wearable electrocardiogram (ECG) recording systems have great potential to revolutionize early diagnosis of paroxysmal atrial fibrillation (AF). They are able to continuously acquire an ECG signal for long weeks and then increase the probability of detecting first brief, intermittent signs of the arrhythmia. However, the recorded signal is often broadly corrupted by noise and artifacts, and accurate assessment of its quality to avoid automated misdiagnosis and false alarms of AF is still an unsolved challenge. In this context, the present work is pioneer in exploring the usefulness of transforming the single-lead ECG signal into two common phase space (PS) representations, such as the Poincare plot and the first order difference graph, for evaluation of its quality. Several machine and deep learning models fed with features and images derived from these PS portraits reported a better performance than well-known previous methods, even when they were trained and validated on two separate databases. Indeed, in binary classification of high- and low-quality ECG excerpts, the generated PS-based algorithms reported a discriminant power greater than 85%, misclassifying less than 20% of high-quality AF episodes and non -normal rhythms as noisy excerpts. Moreover, because both PS reconstructions do not require any mathematical transformation, these algorithms also spent much less time in classifying each ECG excerpt in validation and testing stages than previous methods. As a consequence, ECG transformation to both PS portraits enables novel, simple, effective, and computational low-cost techniques, based both on machine and deep learning classifiers, for ECG quality assessment.

关键词： Signal quality assessment Paroxysmal atrial fibrillation Phase space portraits Machine learning classifiers deep learning algorithms

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Tree counting with high spatial-resolution satellite imagery based on deep neural networks

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ECOLOGICAL INDICATORS 2021年 125卷 107591-107591页

作者： Yao, Ling Liu, Tang Qin, Jun Lu, Ning Zhou, Chenghu Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzhou Peoples R China China Univ Geosci Beijing Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Forest inventory at single-tree level is of great importance to modern forest management. The inventory contains two critical parameters about trees, including their numbers and spatial locations. Traditional methods to catalogue single trees are laborious, while deep neural networks enable to discover the multi-scale features hidden in images and thus make it possible to count trees with remote sensing imagery. In this study, four different tree counting networks, which were constructed by remodeling four different classical deep convolutional neural networks, were evaluated to determine their abilities to grasp the relationship between remote sensing images and tree locations for automatic tree counting end-to-end. To this end, a tree counting dataset was constructed with remote sensing images of 0.8-m spatial resolution in distinct regions. This dataset consisted of 24 GF-II images and the corresponding manually annotated locations of trees based on these images. Thereafter, a large number of experiments were conducted to examine the performance of these networks in regards to tree counting. The results demonstrated that all networks could achieve the competitive performance (above 0.91) in terms of the determination coefficient (R-2) between the ground truth and the estimated values. The average accuracy of the Encoder-Decoder Network (one of the four networks) was greater than 91.58% and its R-2 was equal to 0.97, achieving the best performance. It has been found that the deep learning is an efficient and effective means for tree counting task.

关键词： Forestry deep learning algorithms Remote sensing Density estimation Tree counting

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A Review of Monitoring Methods for Tool Wear

International Core Journal of Engineering

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International Core Journal of Engineering 2023年第5期9卷 441-446页

作者： Lijie Yang Liqiang Wang Bo Shi

As a necessary tool in machining, tool wear is inevitable during milling. Therefore, developing reliable and convenient methods to predict the wear state of tools in industrial production and processing is of great significance in terms of improving productivity, reducing production costs, and maintaining the quality of workpiece. Traditional tool wear prediction methods can be categorized into direct prediction and indirect prediction based on the type of wear information obtained. With advancements in machinery manufacturing automation, rapid developments in sensor technology and control theory, and continuous updates and changes in tool wear state technologies, this paper reviews recent detection methods for tool wear. Specifically, it focuses on analyzing sensor selection for signal acquisition and intelligent diagnosis based on deep learning algorithms. Finally, the application status and shortcomings of tool wear detection are discussed in order to promote progress and development in tool wear detection technology.

关键词： Tool Wear Intelligent Detection Signal Acquisition deep learning algorithms

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A novel data-driven integrated detection method for network intrusion classification based on multi-feature imbalanced data

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第3期46卷 5893-5910页

作者： Wang, Chia-Hung Ye, Qing Cai, Jiongbiao Suo, Yifan Lin, Shengming Yuan, Jinchen Wu, Xiaojing Fujian Univ Technol Coll Comp Sci & Math Fuzhou Peoples R China Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Peoples R China Fujian Univ Technol Coll Elect Elect Engn & Phys Fuzhou Peoples R China

The multi-feature and imbalanced nature of network data has always been a challenge to be overcome in the field of network intrusion detection. The redundant features in data could reduce the overall quality of network data and the accuracy of detection models, because imbalance could lead to a decrease in the detection rate for minority classes. To improve the detection accuracy for imbalanced intrusion data, we develop a data-driven integrated detection method, which utilizes Recursive Feature Elimination (RFE) for feature selection, and screens out features that are conducive to model recognition for improving the overall quality of data analysis. In this work, we also apply the Adaptive Synthetic Sampling (ADASYN) method to generate the input data close to the original dataset, which aims to eliminate the data imbalance in the studied intrusion detection model. Besides, a novel VGG-ResNet classification algorithm is also proposed via integrating the convolutional block with the output feature map size of 128 from the Visual Geometry Group 16 (VGG16) of the deep learning algorithm and the residual block with output feature map size of 256 from the Residual Network 18 (ResNet18). Based on the numerical results conducted on the well-known NSL-KDD dataset and UNSW-NB15 dataset, it illustrates that our method can achieve the accuracy rates of 86.31% and 82.56% in those two test datasets, respectively. Moreover, it can be found that the present algorithm can achieve a better accuracy and performance in the experiments of comparing our method with several existing algorithms proposed in the recent three years.

关键词： Artificial Intelligence Classification algorithms deep learning algorithms Network Intrusion Detection Multi-class Pattern Classification

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Machine learning Methods Modeling Carbohydrate-Enriched Cyanobacteria Biomass Production in Wastewater Treatment Systems

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ENERGIES 2022年第7期15卷 2500页

作者： Rodriguez-Rangel, Hector Maria Arias, Dulce Alberto Morales-Rosales, Luis Gonzalez-Huitron, Victor Valenzuela Partida, Mario Garcia, Joan Tecnol Nacl Mexico Inst Tecnol Culiacan Juan de Dios Batiz 310 Pte Culiacan 80014 Sinaloa Mexico Univ Nacl Autonoma Mexico IER UNAM Inst Energias Renovables Priv Xochicalco S-N Temixco 62580 Morelos Mexico Univ Michoacana Fac Civil Engn Conacyt Morelia 58060 Michoacan Mexico Univ Politecn Cataluna GEMMA Environm Engn & Microbiol Res Grp Dept Civil & Environm Engn BarcelonaTech C Jordi Girona 1-3Bldg D1 E-08034 Barcelona Spain

One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. Bioprocess models are an essential tool for studying the simultaneous effect of complex factors on carbohydrate accumulation, optimizing the process, and reducing operational costs. In this sense, we use a dataset obtained from an empirical model that analyzed the accumulation of carbohydrates in a single process (simultaneous growth and accumulation) from real wastewater. In this experiment, there were no ideal conditions (limiting nutrient conditions), but rather these limitations are guaranteed by the operating conditions (hydraulic retention times/nutrient or carbon loads). Thus, the model integrates 18 variables that are affected and not only carbohydrates. The effect of these variables directly influences the accumulation of carbohydrates. Therefore, this paper analyzes artificial intelligence (AI) algorithms to develop a model to forecast biomass production in wastewater treatment systems. Carbohydrates were modeled using five artificial intelligence methods: (1) Artificial Neural Networks (ANNs), (2) Convolutional Neural Networks (CNN), (3) Long Short-Term Memory Network (LSTMs), (4) K-Nearest Neighbors (kNN), and (5) Random Forest (RF)). The AI methods allow learning how several components interact and if their combinations work faster than building the physical experiments over the same period of time. After comparing the five learning models, the CNN-1D model obtained the best results with an MSE (Mean Squared Error) = 0.0028. This result shows that the model adequately approximates the system's dynamics.

关键词： carbohydrate accumulation modeling deep learning algorithms microalgae resource recovery

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A bidirectional gated recurrent unit based novel stacking ensemble regressor for foretelling the global horizontal irradiance

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 44卷

作者： Gupta, Rahul Chandel, Aseem GL Bajaj Inst Technol & Management Dept Elect & Elect Engn Greater Noida 201310 Uttar Pradesh India Rajkiya Engn Coll Dept Elect Engn Mainpuri India

The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-BiGRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.

关键词： Stacking deep learning algorithms Gated recurrent unit Forecasting Ensemble model

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