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Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture classification

SENSORS 2024年第23期24卷 7705页

作者： Liu, Zhuofu Shu, Zihao Cascioli, Vincenzo Mccarthy, Peter W. Harbin Univ Sci & Technol Higher Educ Key Lab Measuring & Control Technol & Harbin 150080 Peoples R China Murdoch Univ Chiropract Clin Murdoch 6150 Australia Univ South Wales Fac Life Sci & Educ Pontypridd CF37 1DL Wales Durban Univ Technol Fac Hlth Sci ZA-1334 Durban South Africa

Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training;0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection.

关键词： sitting posture force-sensitive resistor triaxial accelerometers classification algorithm sensor verification accuracy computational efficiency

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High-resolution mass spectra processing for the identification of different pathological tissue types of brain tumors

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EUROPEAN JOURNAL OF MASS SPECTROMETRY 2017年第4期23卷 213-216页

作者： Zhvansky, E. S. Sorokin, A. A. Popov, I. A. Shurkhay, V. A. Potapov, A. A. Nikolaev, E. N. Moscow Inst Phys & Technol Dolgoprudnyi Moscow Region Russia Russian Acad Sci Inst Energy Problems Chem Phys Moscow Russia Minist Healthcare Russian Federat NN Burdenko Natl Sci & Pract Ctr Neurosurg Fed State Autonomous Inst Moscow Russia Skolkovo Inst Sci & Technol Skolkovo Moscow Region Russia

The purpose of the work is to demonstrate the possibilities of identifying the different types of pathological tissue identification directly through tissue mass spectrometry. Glioblastoma parts dissected during neurosurgical operation were investigated. Tumor fragments were investigated by the immunohistochemistry method and were identified as necrotic tissue with necrotized vessels, necrotic tissue with tumor stain, tumor with necrosis (tumor tissue as major), tumor, necrotized tumor (necrotic tissues as major), parts of tumor cells, boundary brain tissue, and brain tissue hyperplasia. The technique of classification of tumor tissues based on mass spectrometric profile data processing is suggested in this paper. Classifiers dividing the researched sample to the corresponding tissue type were created as a result of the processing. Classifiers of necrotic and tumor tissues are shown to yield a combined result when the tissue is heterogeneous and consists of both tumor cells and necrotic tissue.

关键词： Direct mass spectrometry ambient ionization brain tumor tissue identification classification algorithm neurosurgery

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Sensors-Based Ambient Assistant Living via E-Monitoring Technology

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Computers, Materials & Continua 2022年第12期73卷 4935-4952页

作者： Sadaf Hafeez Yazeed Yasin Ghadi Mohammed Alarfaj Tamara al Shloul Ahmad Jalal Shaharyar Kama Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted *** from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with *** systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human *** this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human *** features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization ***,the Masi entropy classifier is applied for action recognition and *** have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and *** experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC *** experimental results validated the proposed system.

关键词： classification algorithm human action recognition motion sensors machine learning Masi entropy

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Rapid phase stability calculations in fluid flow simulation using simple discriminating functions

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COMPUTERS & CHEMICAL ENGINEERING 2018年 108卷 112-127页

作者： Gaganis, Vassilis Tech Univ Crete Mineral Resources Engn Dept Khania 73100 Greece

This paper presents a new phase stability method that is applicable when repeated phase behavior calculations are needed as it is the case with multiphase fluid flow compositional simulation in upstream petroleum engineering. Two discriminating functions act as classifiers in such a way that a positive value of one of the two functions determines the stability state of the mixture. The two functions are generated off line, prior to the simulation, and their expressions are very simple so that they can be evaluated rapidly in a non-iterative way for every discretization block and at each timestep during the simulation. The CPU time required for phase stability calculations is dramatically reduced while still obtaining correct classification results corresponding to the global minimum of the system Gibbs energy function. The method can be applied to any chemical engineering problem where the class of several objects needs to be determined repeatedly and quickly. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multiphase fluid flow simulation Phase stability analysis Upstream petroleum engineering classification algorithm

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Using Pattern Position Distribution for Software Failure Detection

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第2期6卷 234-243页

作者： Li, Chunping Chen, Ziniu Du, Hao Wang, Hui Wilkie, George Augusto, Juan C. Liu, Jun Tsinghua Univ Sch Software Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Univ Ulster Fac Comp & Engn Jordanstown BT37 0QB North Ireland

We present a novel approach for using the pattern position distribution as features to detect software failure. In this approach, we divide an execution sequence into several sections and compute the pattern distribution in each section. The distribution of all patterns is then used as features to train a classifier. This approach outperforms conventional frequency based methods by more effectively identifying software failures occurring through misused software patterns. Comparative experiments show the effectiveness of our approach.

关键词： Sequential Patterns classification algorithm Software Failure Anomaly Detection

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Semi-overlap functions and novel fuzzy reasoning algorithms with applications

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INFORMATION SCIENCES 2022年 614卷 104-122页

作者： Zhang, Xiaohong Wang, Mei Bedregal, Benjamin Li, Mengyuan Liang, Rong Shaanxi Univ Sci & Technol Sch Math & Data Sci Xian 710021 Shaanxi Peoples R China Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Shaanxi Peoples R China Univ Fed Rio Grande do Norte Dept Informat & Matemat Aplicada Campus Univ S-N BR-59072970 Natal Brazil

In order to get a more general result related on fuzzy implications that induced by aggre-gation functions, we relax the definition of general overlap functions, more precisely, removing its right-continuous, and introduce a new kind of aggregation function, which called semi-overlap function. Subsequently, we explore some of their related algebraic properties and its corresponding residual implications. Moreover, serval scholars have pro-vided kinds of methods for fuzzy modus ponens (FMP, for short) problems, such as Zadeh's compositional rule of inference (CRI, for short), Wang's triple I method (TIM, for short) and quintuple implication principle (QIP, for short). Compared with CRI and TIM, QIP has some advantages in solving FMP problems. Based on the above theory foundation of semi -overlap functions and their residual implications, we further consider the QIP for FMP problems. Finally, we propose a new classification algorithm that based on semi-overlap functions and QIP, which called SO5I-FRC algorithm. Through the comparative tests, the average accuracy of SO5I-FRC algorithm is higher than FARC-HD algorithm. The experi-mental results indicate that semi-overlap functions and QIP have certain advantages and a wide range of applications in classification problems.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Overlap function Semi-overlap function Fuzzy reasoning Quintuple implication principle classification algorithm

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Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins

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JOURNAL OF MOLECULAR RECOGNITION 2006年第3期19卷 209-214页

作者： Söllner, J Intercell AG A-1030 Vienna Austria

Recently, new machine learning classifiers for the prediction of linear B-cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifiers as well as possibilities to improve the post test probability (PTP) to meet real world requirements such as high throughput epitope screening of whole proteomes. The major finding is that ROC convex hulls present an easy to use way to rank classifiers based on their prediction conservativity as well as to select candidates for ensemble classifiers when validating against the antigenicity profile of 10 HIV-1 proteins. We also show that linear models are at least equally efficient to model the available data when compared to multi-layer feed-forward neural networks. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： machine learning epitope classification algorithm ROC ensemble neural network

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Network embedding framework for driver gene discovery by combining functional and structural information

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BMC GENOMICS 2023年第1期24卷 1-13页

作者： Chu, Xin Guan, Boxin Dai, Lingyun Liu, Jin-xing Li, Feng Shang, Junliang Qufu Normal Univ Sch Comp Sci Rizhao 27826 Peoples R China

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

关键词： Driver gene Gene interaction network Network embedding Mutation data classification algorithm

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Design of a testing strategy using non-animal based test methods: Lessons learnt from the ACuteTox project

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TOXICOLOGY IN VITRO 2013年第4期27卷 1395-1401页

作者： Kopp-Schneider, Annette Prieto, Pilar Kinsner-Ovaskainen, Agnieszka Stanzel, Sven German Canc Res Ctr Dept Biostat D-69009 Heidelberg Germany European Commiss Joint Res Ctr Inst Hlth & Consumer Protect I-21027 Ispra VA Italy

In the framework of toxicology, a testing strategy can be viewed as a series of steps which are taken to come to a final prediction about a characteristic of a compound under study. The testing strategy is performed as a single-step procedure, usually called a test battery, using simultaneously all information collected on different endpoints, or as tiered approach in which a decision tree is followed. Design of a testing strategy involves statistical considerations, such as the development of a statistical prediction model. During the EU FP6 ACuteTox project, several prediction models were proposed on the basis of statistical classification algorithms which we illustrate here. The final choice of testing strategies was not based on statistical considerations alone. However, without thorough statistical evaluations a testing strategy cannot be identified. We present here a number of observations made from the statistical viewpoint which relate to the development of testing strategies. The points we make were derived from problems we had to deal with during the evaluation of this large research project. A central issue during the development of a prediction model is the danger of overfitting. Procedures are presented to deal with this challenge. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： classification algorithm Overfitting Prediction model Prediction error Testing strategy

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Protein secondary structure prediction: A survey of the state of the art

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2017年 76卷 379-402页

作者： Jiang, Qian Jin, Xin Lee, Shin-Jye Yao, Shaowen Yunnan Univ Sch Informat Kunming Yunnan Peoples R China Yunnan Univ Sch Software Kunming Yunnan Peoples R China Univ Cambridge Queens Coll Cambridge CB3 9ET England

Protein secondary structure prediction (PSSP) is a fundamental task in protein science and computational biology, and it can be used to understand protein 3-dimensional (3-D) structures, further, to learn their biological functions. In the past decade, a large number of methods have been proposed for PSSP. In order to learn the latest progress of PSSP, this paper provides a survey on the development of this field. It first introduces the background and related knowledge of PSSP, including basic concepts, data sets, input data features and prediction accuracy assessment. Then, it reviews the recent algorithmic developments of PSSP, which mainly focus on the latest decade. Finally, it summarizes the corresponding tendencies and challenges in this field. This survey concludes that although various PSSP methods have been proposed, there still exist several further improvements or potential research directions. We hope that the presented guidelines will help nonspecialists and specialists to learn the critical progress in PSSP in recent years. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Protein secondary structure prediction Machine learning Neural networks classification algorithm Feature extraction

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