Aplicación de la inteligencia artificial en la predicción de complicaciones en pacientes con Malaria by Eduardo Tuta-Quintero

“…Introduction: This study aims to develop a neural network (NN) that can serve as a useful tool for early diagnosis of complicated malaria.Materials and methods: In this study, a feedforward NN was developed, incorporating 10 clinical variables in the input nodes, hidden layer, and output node. The data were included in the input layer. …”

An Active Functionally Graded Piezocomposite Plate Subjected to a Stochastic Pressure by Marek PIETRZAKOWSKI

“…In the paper the analysis of random vibration of an actively damped laminated plate with functionally graded piezoelectric actuator layers is presented. …”
Get full text

Multi-scale inference of genetic trait architecture using biologically annotated neural networks. by Pinar Demetci, Wei Cheng, Gregory Darnell, Xiang Zhou, Sohini Ramachandran, Lorin Crawford

“…This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. …”
Get full text

A secure and efficient data deduplication framework for the internet of things via edge computing and blockchain by Zeng Wu, Hui Huang, Yuping Zhou, Chenhuang Wu

“…In this scheme, we propose a model based on parallel use of three-layer and two-layer architectures and introduce the RAndom REsponse (RARE) scheme to resist side-channel attacks. …”
Get full text

Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
Get full text

Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
Get full text

Selection of a Suitable Wall Pressure Spectrum Model for Estimating Flow-Induced Noise in Sonar Applications by V. Bhujanga Rao

“…Excitation of the sonar dome structure by random pressure fluctuations in turbulent boundary layer flow leads to acoustic radiation into the interior of the dome. …”
Get full text

Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…The author has implemented several machine learning models, including bagging, multi-layer perceptron, logistic regression, extreme gradient boosting, and random forest. …”
Get full text

Dynamic Response Analysis of Asphalt Pavement under Pavement-Unevenness Excitation by Heng Liu, Xiaoge Liu, Ankang Wei, Yingchun Cai

“…Poor bonding between interfaces can lead to increased stress and strain at the bottom of the surface layer.…”
Get full text

The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm by Zheng Zhang, Haobo Yang, Xusheng Bai, Shuo Zhang, Chaobin Xu

“…The mutation strategy combines a two-layer encoding approach: the first layer uses random mutations to enhance diversity, while the second layer performs goal-oriented mutations, with the selection probability of each layer dynamically adjusted during iterations. …”
Get full text

Enhancing Cloud Job Failure Prediction With a Novel Multilayer Voting-Based Framework by Ahmed Elkaradawy, Ayman Elshenawy, Hany Harb

“…Decision Trees, K-Nearest Neighbors, Extreme Gradient Boosting, Adaptive Boosting, and Artificial Neural Networks are the classifiers implemented in this layer. Once the failed jobs are detected, the second layer takes over to determine the failure type employing the Random Forest algorithm. …”
Get full text

PROBABILITY ANALYSIS ON LAST PLY FAILURE OF COMPOSITE LAMINATES BASED ON UNIVERSAL GENERATING FUNCTION METHOD by LIU ChengLong, ZHOU JinYu, QIU Rui, ZHUANG BaiLiang, HU Jian

“…This method is applicable to the reliability assessment of structural systems,in which there are many random variables（ including non-normal random variables）,the performance function is non-linear,and failure correlation between resistance sequences of laminates that contain common failure elements and failure elements within the resistance sequences due to sharing the same random load source is considered. …”
Get full text

An ELECTRA-Based Model for Power Safety Named Entity Recognition by Peng Liu, Zhenfu Sun, Biao Zhou

“…This model employs root mean square layer normalization (RMSNorm) and the switched gated linear unit (SwiGLU) activation function, which substitutes the conventional layer normalization (LayerNorm) and the Gaussian error linear units (GeLU). …”
Get full text

Effect of the difference enumeration attack on LowMC instances by Xinxin GE, Zhihu LI, Meiqin WANG, Kai HU

“…The LowMC is an algorithm with low multiplicative complexities.For the parameter with limited data complexities and low number of S-boxes, the difference enumeration attack was proposed, which could theoretically attack all rounds of the LowMC.Considering that the original attack is based on the random linear layer,the strength of LowMC algorithm against differential enumeration attacks under a specific linear layer deserves more study.The difference enumeration attack cannot reach theoretical rounds through the research on the so-called key initial round.In terms of some LowMC instances, the key initial round is smaller than the theoretical value, which leads to the failure of the difference enumeration attack.Since the number of rounds of the LowMC is completely based on existing attacks, the analysis is of great significance to the rounds design of the LowMC.…”
Get full text

CRF combined with ShapeBM shape priors for image labeling by Hao WANG, Lijun GUO, Yadong WANG, Rong ZHANG

“…Conditional random field (CRF) is a powerful model for image labeling,it is particularly well-suited to model local interactions among adjacent regions (e.g.superpixels).However,CRF doesn't consider the global constraint of objects.The overall shape of the object is used as a global constraint,the ShapeBM can be taken advantage of modeling the global shape of object,and then a new labeling model that combined the above two types of models was presented.The combination of CRF and ShapeBM was based on the superpixels,through the pooling technology was wed to establish the corresponding relationship between the CRF superpixel layer and the ShapeBM input layer.It enhanced the effectiveness of the combination of CRF and ShapeBM and improved the accuracy of the labeling.The experiments on the Penn-Fudan Pedestrians dataset and Caltech-UCSD Birds 200 dataset demonstrate that the model is more effective and efficient than others.…”
Get full text

Estimating motor symptom presence and severity in Parkinson’s disease from wrist accelerometer time series using ROCKET and InceptionTime by Cedric Donié, Neha Das, Satoshi Endo, Sandra Hirche

“…InceptionTime’s high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron on wrist motion data from PD patients. …”
Get full text

Hierarchical Embedded System Based on FPGA for Classification of Respiratory Diseases by Trong-Thanh Han, Kien Le Trung, Phuong Nguyen Anh, Anh Do Trung

“…The first layer contains recorded information, while the second and third layers contain features extracted from fixed-length sound segments classified via Random Forest. …”
Get full text

Enhanced Alzheimer’s Disease Prediction Through Integration of Protein-Protein Interaction Data and Meta-Learning by Hansa J. Thattil, M. N. Arunkumar, Francis Antony

“…We generated embeddings for Protein-Protein Interactions(PPI) using the node2vec algorithm, capturing interaction patterns in a low-dimensional space. HybridBoost, a two-layered model architecture, was then employed, with random forest and CatBoost classifiers in the first layer and CatBoost as the meta-learner in the second layer. …”
Get full text

THE EFFECT OF SURFACE ROUGHNESS ON THE SOIL LOSS by Khalid F. Hassan

“…The results showed that the large proportions of random roughness(large clods) and clear oriented roughness (soil ridges) in the tilled layer after soil discing have a greatest effect in reducing the amount of soil loss and potential erosivity of soil by wind action in comparison with soil chiseling .This is attributed to the fact that the soil clods easily crushed into fine aggregates after soil chiseling but remained unchanged in soil discing .The statistical analysis coming in agreement with the results of the lab and field investigation…”
Get full text

Structural features of the roughness profile of hardened steel parts after finishing turning by Ovsyannikov Victor, Nekrasov Roman, Putilova Ulyana, Gubenko Arseniy

“…The profile of the treated surface was analyzed using the theory of random processes and fractal geometry methods. As a result of the research, it was found that with a decrease in the arithmetic mean separation of the Ra profile, the proportion of the random component increases. …”
Get full text