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Assessing the impact of fractional vegetation cover on urban thermal environment: A case study of Hangzhou, China

SUSTAINABLE CITIES AND SOCIETY 2023年 96卷

作者： Zhang, Maomao Tan, Shukui Zhang, Cheng Han, Siyu Zou, Shangjun Chen, Enqing Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430079 Peoples R China Wuhan Donghu Univ Sch Foreign Language & Literature Wuhan 430212 Peoples R China

The large-scale urbanization has changed the surface characteristics of cities, seriously affected the urban heat balance, and worsened the urban thermal environment. The regulation of vegetation coverage on urban climate has received extensive attention. This study aims to assess and predict changes in the urban thermal environment of Hangzhou by monitoring the variation of the urban thermal field variance index (UTFVI) affected by fractional vegetation cover (FVC) changes. The results indicate that the area of high level FVC will likely decrease by 52.91%, while the area of lower level FVC will likely increase by 33.83% from 1990 to 2030. Furthermore, the area of the strongest level UTFVI shows a significant trend of expansion, increasing by 467.299 km2 from 1990 to 2030, while the area of other levels UTFVI shows different degrees of decrease. Specifically, the area of strong and stronger levels UTFVI decreased by 149.641 km2 and 271.464 km2, respectively. The spatial correlation between the FVC and UTFVI changes is significant and negative, with p-values less than 0.01 and negative co-efficients. The absolute value of the coefficient will decrease from 0.416 to 0.246 from 1990 to 2030, and the correlation between FVC and UTFVI changes shows a decreasing trend. The linear regression analysis demon-strates a significant negative correlation between the FVC and UTFVI, with coefficients ranging from-0.470 to-0.280 and p-values less than 0.001, further indicating that the FVC has a mitigating effect on the urban thermal environment. This study provides new insights into thermal environment governance and urban sustainable development.

关键词： Fractional vegetation cover Urban thermal environment Sustainable development machine learning algorithm Bivariate spatial autocorrelation analysis China

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Design and planning of urban ecological landscape using machine learning

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2022年第1期13卷 3-10页

作者： Zhang, Yajuan Zhang, Tongtong Xianyang Normal Univ Environm Design Xianyang City 712000 Shaanxi Peoples R China Xian Univ Architecture & Technol Mat Forming & Control Engn Xian 710000 Shaanxi Peoples R China

The purposes are to improve the air quality of the urban ecological environment and increase the green rate of the urban garden ecological landscape. machine learning (ML) algorithms are used to analyse and calculate the dust retention outcomes of different plants. Dust retention capabilities and spectral characteristics of several different plants are researched. Results demonstrate a significant correlation between plants and dust retention rate. Red sandalwood has 150 inversion bands, and the optimal inversion algorithm is Random Forest (RF). Zhu Jiao has 74 inversion bands, and the optimal inversion algorithm is the Support Vector machine (SVM). Ficus microcarpa has 80 inversion bands, and the optimal inversion algorithms are SVM and RF. ML algorithms provide better accuracy than correlation analysis, more suitable for calculating plants' dust retention capabilities. To sum up, ML algorithms can calculate the dust retention amounts of plants to better plan and design regional ecological landscapes.

关键词： dust retention effect spectral characteristics correlation analysis method machine learning algorithm

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machine learning for Early Osteosarcoma Detection: A Systematic Review

Machine Learning for Early Osteosarcoma Detection: A Systema...

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

作者： Kaur, Simran Badoni, Parveen Walia, Ranjan Singh, Gurwinder Chandigarh University Cse Mohali India Chandigarh University CSE-APEX & Ucrd Mohali India Chandigarh University CSE-APEX Mohali India

ISBN: (纸本)9798350308167

Cancer is a deadly disease, which is commonly seen in the persons. Osteosarcoma is also a form of cancer in bones. It is mostly found in legs and long bones. So it is very important in order to detect this disease correctly and then take proper diagnostics. By evaluating the diagnostic medical techniques such as X-ray scans, CT scans, the main aim is to accurately determine the diseased area in the bone tract. Due to noise and the lower number of slices per pixel in the scanned images, it is necessary to pre-process the images with a median filter in order to remove the noise. Here convolutional neural networks are also used in order to retrieve the images. The retrieved pictures are categorized and recorded using a CNN classifier in order to determine the stage of illness, which helps the clinician make treatment recommendations. © 2023 IEEE.

关键词： Bone Cancer Detection Cancer stage classification CNN algorithm machine learning algorithm Osteosarcoma

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Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study

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BIOMEDICAL ENGINEERING ONLINE 2023年第1期22卷 1-15页

作者： Peng, Wei Wang, Fei Sun, Shaoming Sun, Yining Chen, Jingcheng Wang, Mu Chinese Acad Sci Hefei Inst Phys Sci 350 Shushan Lake Rd Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Anhui Med Univ Hosp 2 Hefei 230601 Peoples R China CAS Hefei Inst Technol Innovat Hefei 230088 Peoples R China

PurposeThis study aimed to develop an interpretable machine learning model to predict the onset of myopia based on individual daily *** study was a prospective cohort study. At baseline, non-myopia children aged 6-13 years old were recruited, and individual data were collected through interviewing students and parents. One year after baseline, the incidence of myopia was evaluated based on visual acuity test and cycloplegic refraction measurement. Five algorithms, Random Forest, Support Vector machines, Gradient Boosting Decision Tree, CatBoost and Logistic Regression were utilized to develop different models and their performance was validated by area under curve (AUC). Shapley Additive exPlanations was applied to interpret the model output on the individual and global *** 2221 children, 260 (11.7%) developed myopia in 1 year. In univariable analysis, 26 features were associated with the myopia incidence. Catboost algorithm had the highest AUC of 0.951 in the model validation. The top 3 features for predicting myopia were parental myopia, grade and frequency of eye fatigue. A compact model using only 10 features was validated with an AUC of *** daily information contributed reliable predictors for childhood's myopia onset. The interpretable Catboost model presented the best prediction performance. Oversampling technology greatly improved model performance. This model could be a tool in myopia preventing and intervention that can help identify children who are at risk of myopia, and provide personalized prevention strategies based on contributions of risk factors to the individual prediction result.

关键词： Myopia Prediction Daily information machine learning algorithm Interpretability

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RERE-AS1 enhances the effect of CDK4/6 inhibitor Ribociclib and suppresses malignant phenotype in breast cancer via MEK/ERK pathway

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JOURNAL OF TRANSLATIONAL MEDICINE 2024年第1期22卷 1052页

作者： Huang, Zhidong Lou, Kaixin Qi, Mengyang Wang, Jinhui Li, Linwei Sun, Bo Wang, Chen Zhou, Xirui Chen, Debo Liu, Hong Tianjin Med Univ Canc Inst & Hosp Natl Clin Res Ctr Canc Tianjins Clin Res Ctr Canc Surg Dept Breast Canc 2Key Lab Canc Prevent & The Tianjin Peoples R China Fujian Med Univ Sch Clin Med Fuzhou Peoples R China Fujian Med Univ Quanzhou Hosp 1 Dept Breast Surg Quanzhou Peoples R China Tianjin Med Univ Sch Basic Med Tianjin Peoples R China

BackgroundCurrently, there is a lack of biomarkers to identify breast cancer (BC) patients who would benefit from CDK4/6 inhibitors. This study combined machine learning (ML) algorithms based on transcriptomic data with both in vivo and in vitro experiments to identify therapeutic efficacy-related biomarkers of the CDK4/6 inhibitor ribociclib from the perspective of long non-coding RNA (lncRNA).MethodsWe used the Genomics of Drug Sensitivity in Cancer database along with the "oncoPredict" algorithm to calculate the half maximal inhibitory concentration (IC50) values for ribociclib based on transcriptome data. ML algorithms were utilized to select key lncRNAs related to ribociclib and to establish a model which could be used for selection of potential beneficiaries of ribociclib. Cellular experiments were conducted to validate the ML analysis and explore the potential biological mechanisms by which RERE-AS1 influences ribociclib efficacy and malignant phenotype of BC cells. Correlation analysis with clinical pathological factors, RT-qPCR experiments on tissue specimens, and pan-cancer analysis were carried out to explore the expression pattern, and the prognostic and diagnostic potential of RERE-AS1 in *** have identified 11 key ribociclib-related lncRNAs and constructed an artificial neural network model (ANNM) based on lncRNA. Cellular experiments demonstrated that overexpression of RERE-AS1 promoted the anti-tumor activity of ribociclib in BC cells. Furthermore, RERE-AS1 is crucial in suppressing the malignant traits of BC cells through the reduction of MEK and ERK phosphorylation levels. Patients with smaller primary tumors and lower pathological stage exhibited higher levels of RERE-AS1 expression. Lastly, a pan-cancer analysis revealed that RERE-AS1 exhibits distinctly abnormal expression patterns, prognostic significance, and clinical diagnostic value in BC, compared to other *** ANNM established through ML algorithms can serve

关键词： Breast cancer CDK4/6 inhibitor Ribociclib LncRNA machine learning algorithm Malignant phenotype MEK/ERK pathway

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EEG Brain Signal Processing for Epilepsy Detection

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2023年第7期16卷 709-716页

作者： Jain, Shruti Paul, Sudip Sharma, Kshitij Jaypee Univ Informat Technol Dept ECE Solan 173234 Himachal Prades India North Eastern Hill Univ Dept Biomed Engn Shillong 793022 India

Background: Millions of neurons make up the human brain, and they play an important role in controlling the body's response to internal and external motor and sensory stimuli. These neurons can function as contact conduits between the human body and the brain. Analyzing brain signals or photographs will help one better understand cognitive function. These states are linked to a particular signal frequency that aids in the comprehension of how a complex brain system ***: Electroencephalography (EEG) is a useful method for locating brain waves associated with different countries on the scalp. Epilepsy is a condition where the brain or some part of it is overactive and sends too many signals. This results in seizures causing muscles to twitch or whole-body ***: In this paper, the author has designed a model to predict epilepsy using machine learning algorithms and deep learning models. For the machine learning algorithm, different features were extracted and a particle swarm optimization algorithm was used to select the best feature which was classified using wavelet ***16, Vgg19, and Inception V3 models are used for the detection of ***: The inception V3 model results in 97.87% accuracy which is better than all other techniques. 5.1% accuracy improvement has been observed using a machine learning algorithm. The model is compared using existing work and it has been observed that the proposed model results ***: The technique for modeling EEG signals and insight brain signals recorded during surgical procedures has been identified in detail. 0.7% and 0.13% accuracy improvement were achieved when the model is validated on Kaggle and CHB-MIT datasets respectively.

关键词： Electroencephalography epilepsy machine learning algorithm particle swarm optimization deep learning algorithm auto-regression

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A Configurable 10T SRAM-Based IMC Accelerator With Scaled-Voltage-Based Pulse Count Modulation for MAC and High-Throughput XAC

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IEEE TRANSACTIONS ON NANOTECHNOLOGY 2023年 22卷 222-227页

作者： Saragada, Prasanna Kumar Manna, Subhashish Singh, Amandeep Das, Bishnu Prasad Indian Inst Technol IIT Roorkee Dept Elect & Commun Engn Roorkee 247667 India

This work proposes a 10T SRAM-based in-memory computation (IMC) architecture that can be configured to perform linear multiply-and-accumulate (MAC) operations and high-throughput XNOR-and-accumulate (XAC) operations. The IMC-MAC operation is performed by using the proposed scaled-voltage-based pulse count modulation (PCM) technique, which improves the linearity and signal margin of the MAC operation. The IMC-XAC operation is performed by using the proposed single capacitor discharge (SCD) approach with various advantages such as no deterministic error in XAC output, low latency, and less variation compared to the traditional charge sharing (TCS)-based XAC operation. The post-layout simulations of the proposed IMC architecture in a 65-nm CMOS process shows that the IMC architecture achieves a signal margin of 44.3 mV using the proposed scaled-voltage-based PCM approach in IMC-MAC mode whereas 37% less variation using the proposed SCD approach in IMC-XAC mode. In IMC-MAC mode, we achieve a 54.6 GOPS, 273 TOPS/W, and 98.67%/88.72% classification accuracy on MNIST/CIFAR-10 dataset using the convolution neural network (CNN)/ResNet20 algorithm. In IMC-XAC mode, we achieve a 3276.8 GOPS, 1092.2 TOPS/W, and 97.12% classification accuracy on the MNIST dataset using the binary neural network (BNN) algorithm.

关键词： Random access memory Discharges (electric) Computer architecture Capacitors Voltage Phase change materials Transistors Hardware accelerator In-memory computation (IMC) machine learning algorithm multiply-and-accumulate (MAC) SRAM XNOR-and-accumulate (XAC)

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Identifying SH-IoT devices from network traffic characteristics using random forest classifier

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WIRELESS NETWORKS 2023年第1期30卷 405-419页

作者： Chowdhury, Rajarshi Roy Idris, Azam Che Abas, Pg Emeroylariffion Univ Brunei Darussalam Fac Integrated Technol Gadong Brunei

In the cyberspace, device identification has become one of the most important factors in improving security of a network, containing both Internet of Things (IoT) and non-IoT devices. Resource-constraint IoT devices are generally more vulnerable than non-IoT devices, to different kinds of security threats, including Mirai botnet and spoofing attacks. In this paper, a device fingerprinting (DFP) scheme has been proposed based on the analysis of network traffic characteristics. Four statistical features from two device-specific features have been selected using statistical assessment to generate DFP for classification task using a supervised machine learning Random Forest classifier. Experimental results have shown that the proposed DFP scheme is able to classify device type with 99.81% accuracy on the public UNSW dataset, whilst accuracies of 99.50% and 97.10% have been reported for the identification of individual IoT and non-IoT devices, respectively. The proposed DFP scheme has also demonstrated superior performance as compared to other DFP methods in the literature, despite using less number of features and packets for DFP. These signify that the proposed DFP scheme can be used as a network security reinforcement tool in a heterogeneous network environment.

关键词： Internet IoT Things (IoT) Network traffic traces Random forest Device classification machine learning algorithm

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Multitemporal relative landslide exposure and risk analysis for the sustainable development of rapidly growing cities

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LANDSLIDES 2023年第9期20卷 1781-1795页

作者： Di Napoli, Mariano Miele, Pietro Guerriero, Luigi Annibali Corona, Mariagiulia Calcaterra, Domenico Ramondini, Massimo Sellers, Chester Di Martire, Diego Univ Genoa Dept Earth Environm & Life Sci I-16132 Genoa Italy Federico II Univ Naples Dept Earth Environm & Resources Sci I-80126 Naples Italy Federico II Univ Naples Dept Civil Architectural & Environm Engn I-80125 Naples Italy Univ Azuay Inst Ecuatoriano Regimen Secc IERSE EC-01-01-981 Cuenca 010107 Ecuador

In recent decades, developing countries have experienced an increase in the impact of natural disasters due to ongoing climate change and the sustained expansion of urban areas. The intrinsic vulnerability of settlements, due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, has made such impacts particularly severe. Cuenca (Ecuador) is a significant example of a city that in recent decades has experienced considerable population growth (i.e. exposure) and an associated increase in loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, so an evaluation of multitemporal exposure to landslides and related risks is required. In this perspective, a potential urban planning tool is presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multitemporal analysis accounting for changes in exposure between 2010 and 2020 and an estimation of relative landside risk was carried out. Due to the absence of spatially distributed population data, energy supply contract data have been used as a proxy of the population. The results show that the current higher exposure and related relative risk are estimated for parishes (parroquias) located in the southern sector of the study area (i.e. Turi, Santa Ana, Tarqui, Nulti, Banos and Paccha). Moreover, the exposure multitemporal analysis indicates that most parishes located in the hilly areas bounding the city centre (i.e. Sayausi, San Joaquin, Tarqui, Sidcay, Banos, Ricaurte, Paccha and Chiquintad) are experiencing sustained population growth and will be potentially exposed to an increased risk with a consistently growing trend. The obtained relative risk map can be considered a valuable tool for guiding land planning, land management, occupation restriction and early warning strategy adoption in the area. The

关键词： Landslide susceptibility machine learning algorithm Relative risk assessment Cuenca Ecuador Latin America

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A Method Based on Improved Ant Colony algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake

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IEEE ACCESS 2023年 11卷 93180-93192页

作者： Wu, Chenhao Fu, Xueliang Li, Honghui Hu, Hua Li, Xue Zhang, Liqian Inner Mongolia Agr Univ Coll Comp & Informat Engn Hohhot 010018 Peoples R China Inner Mongolia Autonomous Reg Key Lab Big Data Res Hohhot 010018 Peoples R China

Chlorophyll-a (Chl-a) is an important parameter of water bodies, but due to the complexity of optics in water bodies, it is currently difficult to accurately predict Chl-a concentration in water bodies by traditional methods. In this paper, Sentinel-2 remote sensing images is used as the data source combined with measured data, and Ulansuhai Lake is taken as the study area. An adaptive ant colony exhaustive optimization (A-ACEO) algorithm is proposed for feature selection and combined with a novel intelligent algorithm of optimizing support vector regression (SVR) by genetic algorithm (GA) for prediction of Chl-a concentration. The ant colony optimization (ACO) algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GA-SVR model is built by optimizing SVR using GA with the selected feature bands as input, and comparing with the traditional SVR model. The simulation results show that under the same conditions, using A-ACEO algorithm to select feature bands as inputs can effectively reduce the model complexity, and improve the model prediction performance, which provides a valuable reference for monitoring Chl-a concentration in lakes.

关键词： Chlorophyll-a (Chl-a) genetic algorithm lake machine learning algorithm remote sensing support vector regression

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