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Analyzing the effects of congestion on planning time index–Grey models *** forest regression

International Journal of Transportation science and Technology 2023年第2期12卷 578-593页

作者： Shahriar Afandizadeh Zargari Navid Amoei Khorshidi Hamid Mirzahossein Hanif Heidari School of Civil Engineering Iran University of Science and Technology(IUST)TehranIran Department of Civil Engineering-Transportation Planning Faculty of Engineering and TechnologyImam Khomeini International University(IKIU)QazvinIran School of Mathematics and Computer Science Damghan UniversityDamghanIran

Travel time and its predictability level are important factors influencing travel behavior and the role of traffic congestion as an important factor in the unreliability of travel time is *** congestion is divided into two categories:Recurring and *** this paper,the effect of recurring congestion,defined as the ratio of the traffic speed over one hour to the free flow speed,will be investigated on the reliability of travel time in terms of the planning time index(PTI)on a 1.467-mile segment along the IS-64 freeway in Chesapeake,*** do so,two methods have been analyzed in this study:the grey models(GM)and the random forest regression(RFR).By using mean absolute percentage error(MAPE)as a criterion to judge,RFR could show a better and more satisfying performance in predicting PTI values when congestion *** the following,to make prediction results of RFR more understanding and easier to use,bagging and bootstrapping are used to improve the model results and more accurately predict the ***,the outputs were drawn using scatter plots for both peaks *** graphs has shown that for congestion values in the range of 1 to 0.9,PTI is reliable in both *** congestion starts to decrease from 0.9 and reaches 0.7 or 0.75,depending on peak type,PTI is moving in the unreliable area,but it isn’t ***,when the congestion value becomes smaller,the rate of change in PTI in both peaks increases.

关键词： Congestion Travel time Reliability Grey models Random forest

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Classification of Multi-view mammogram images using a parallel pre-trained models system 8

Classification of Multi-view mammogram images using a parall...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Kacher, Abdelhafidh Merati, Medjeded Mahmoudi, Saïd University of Tiaret LIM Research Laboratory Computer Science Department Algeria University of Mons Faculty of Engineering ILIA Department Belgium

ISBN: (纸本)9798350309249

Breast cancer is the main lead of women's cancer-related mortalities. Therefore, early detection is imperative for preventing breast cancer from developing to advanced stages. Moreover, the emergence of computer-aided diagnosis systems combined with Deep Learning techniques improve breast cancer diagnosis at its early stages. This study proposes a system for classifying mammogram images scanned from different views based on Deep learning techniques. In this paper, we propose combining four pre-trained models in one system employing the Transfer Learning technique with different input images of the same lesion. This proposed system provides a binary classification of the lesion into malignant or benign classes. The Mini-DDSM dataset images were used in this study;the images were pre-processed and augmented to achieve the best results. Several well-known pre-trained models for image classification (ResNet, Xception, DenseNet, MobileNet, and Inception) were experimented with for the proposed model construction. The study results showed that our proposed model achieved an accuracy of 94.18%, a precision of 94.26%, a recall of 94.16%, and an F1-Score of 94.21%, outperforming the pre-trained model on classifying mammogram images of the same dataset. © 2024 IEEE.

关键词： computer aided diagnosis

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Thermal ProPerTies of PolyProPylene fibrillaTed fibre reinforced lighTweighT foamed concreTe

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Archives of Metallurgy and Materials 2024年第4期69卷 1519-1527页

作者： Mydin, A.O. Al Bakri Abdullah, M.M. Majeed, S.S. Omar, R. Ahmad, R. Garus, S. UNIVERSITI SAINS MALAYSIA SCHOOL OF HOUSING BUILDING AND PLANNING PENANG11800 Malaysia PERLIS Malaysia NAWROZ UNIVERSITY COLLEGE OF ENGINEERING DEPARTMENT OF CIVIL ENGINEERING KURDISTAN REGION Iraq FACULTY OF TECHNOLOGY MANAGEMENT AND BUSINESS DEPARTMENT OF CONSTRUCTION MANAGEMENTPARIT RAJA BATU PAHAT JOHOR86400 Malaysia FACULTY OF MECHANICAL ENGINEERING & TECHNOLOGY UNIVERSITI MALAYSIA PERLIS PERLIS01000 Malaysia CZESTOCHOWA UNIVERSITY OF TECHNOLOGY FACULTY OF MECHANICAL ENGINEERING AND COMPUTER SCIENCE CZESTOCHOWA42-201 Poland

The global demand for energy has experienced a significant rise. The thermal performance of conventional concrete has been extensively studied, but there is still a significant amount of research needed to understand the effects of synthetic fibres on the thermal properties of lightweight foamed concrete (LFC). LFC popularity in the construction industry has increased rapidly due to the many benefits it offers over conventional concrete. Therefore, the goal of this study is to investigate the feasibility of incorporating polypropylene fibrillated fibre (PFF) into LFC to improve its thermal properties. The PFF was integrated into the LFC in diverse weight fractions, between 1% to 4%. Density of 1080 kg/m3 was made and evaluated. Spreadability, permeable porosity, conductivity, diffusivity and specific heat capacity were determined. The findings demonstrate that spreadability of fresh mix decreased when the weight fractions of PFF were increased from 0% (250 mm) to 4% (239 mm). The incorporation of 3% PFF to LFC greatly improves its conductivity and diffusivity. Specifically, the thermal conductivity decreases from 0.3307 W/mK (in the control) to 0.2643 W/mK, and the diffusivity decreases from 0.4426 m2/s (in the control) to 0.3843 m2/s. Compared to the control specimen, LFC’s porosity increased slightly from 43.6% to 44.9% when 3% PFF was added. The findings revealed that the PFF possesses significant underlying potential for utilisation in cementitious materials, potentially serving a pivotal function in either reducing the thermal-inducing property or improving the heat transfer of the concrete. The utilisation of LFC-PFF composites may also result in significant energy savings. © 2024. The Author(s).

关键词： Light weight concrete

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer Systems science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Whale Optimization Algorithm-Based Feature Selection Combined with the XGBoost Model for Breast Cancer Detection: A Comparative Study 9th

Whale Optimization Algorithm-Based Feature Selection Combine...

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9th International Conference on Smart City Applications, SCA 2024

作者： Benghazouani, Salsabila Nouh, Said Zakrani, Abdelali Department of Mathematics and Computer Science Faculty of Sciences Ben M’Sik Hassan II University Casablanca Morocco Department of Computer Science Engineering ENSAM Hassan II University Casablanca Morocco

ISBN: (纸本)9783031886522

Breast cancer has emerged as a significant cause of female mortality globally, underscoring the critical need for early detection and diagnosis. Machine Learning, as a cutting-edge technology, holds immense promise in addressing healthcare challenges, including breast cancer detection. Despite advancements in technology, accurately discerning malignant breast masses from benign ones remains a complex and challenging task in medical research. Feature selection techniques play a vital role in this endeavor, as they aim to identify the most pertinent and informative features from complex datasets, thus improving the accuracy and reliability of models for detecting breast cancer. Numerous recent studies have emphasized the efficacy of metaheuristic algorithms in optimizing various problems across different domains, with particular focus on the Whale Optimization Algorithm (WOA). This paper focuses on evaluating the effectiveness of different feature selection (FS) approaches, such as WOA-based Feature selection, Recursive Feature Elimination, and SelectfromModel, in conjunction with diverse machine learning approaches for detecting breast cancer. The study assesses methods, including Support Vector Machine, XGBoost, Decision Tree, and Logistic regression using performance evaluation measures including, recall, F1-score, accuracy, area under the curve (AUC), precision, and feature removal percentage. Notably, FS based WOA combined with XGBoost model demonstrated outstanding performance measures, with an accuracy rate of 99.4%, an F1-score of 99.4%, 100.0% precision, 98.8% recall, and a 99.4% AUC. These findings contribute valuable insights into enhancing breast cancer detection accuracy and facilitating early intervention strategies, thereby potentially improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Hand Sign Detection and Voice Conversion for the Hearing and Speech Impaired Using Convolutional Neural Networks 4th

Hand Sign Detection and Voice Conversion for the Hearing and...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Belgaum, Mohammad Riyaz Sowmya, Kurni Sireesha, Kuruva Priyanka, Tatagari Tony Vyshnavi, Uppara Uravakonda Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9789819994410

Sign Language Recognition (SLR) tries to convert sign language into written or spoken form to enable communication between a deaf-mute person and a normal person. The task of communicating with disabled and impaired persons is extremely challenging, owing to its complexity and variety of hand motions that have a major societal impact. In order to depict sign language motions, SLR approaches are increasingly being used. These classification models are based on hand-crafted characteristics. Building reliable features that can adapt to the diverse range of hand signs is nonetheless challenging. To address this issue, the authors proposed a special 3D convolutional neural network (CNN) to ease the communication between normal and mute communities. Our proposed model eliminates the requirement to create features by automatically extracting discriminative spatial–temporal features from the actual livestream without any prior information. To improve performance, the 3D CNN is fed with multiple channels of video feeds that include colour, depth, trajectory information, joint body locations, and depth clues. We illustrate the proposed model's superiority to the conventional methods based on hand-crafted characteristics by testing them against a real dataset collected using a depth camera. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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Optical Ciphering Scheme for Cancellable Speaker Identification System

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computer Systems science & engineering 2023年第4期45卷 563-578页

作者： Walid El-Shafai Marwa A.Elsayed Mohsen A.Rashwan Moawad I.Dessouky Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi EAbd El-Samie Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Communications Faculty of EngineeringCairo UniversityCairoEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Most current security and authentication systems are based on personal *** security problem is a major issue in the field of biometric *** is due to the use in databases of the original *** biometrics will forever be lost if these databases are *** privacy is the most important goal of cancelable *** order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification *** methodology to achieve non-invertibility is the employment of non-invertible *** work suggests an encryption process for cancellable speaker identification using a hybrid encryption *** system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption *** evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and *** encryption and cancelability effects are good and reveal good ***,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

关键词： Cancellable biometrics jigsaw transform FrFT DRPE speaker identification

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Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

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Frontiers in Heat and Mass Transfer 2024年第3期22卷 889-917页

作者： K.S.Rajendra Prasad Sathya Sai T.R.Seetharam Adithya Garimella Department of Mechanical and Industrial Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104India Sustainable Energy Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2600 AANetherlands Department of Mechanical Engineering PES UniversityBengaluru560085India

Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing *** are performed by varying the value ofΔT from5 to 30 K and P_(∞)/P_(cr)ratio from1.1 to *** of all the thermophysical properties of supercritical Nitrogen is *** wall temperatures are chosen in such a way that two values of Tw are less than T∗(T*is the temperature at which the fluid has a maximum value of Cp for the given pressure),one value equal to T∗and two values greater than T∗.Three different values of U∞are used to obtain Re∞range of 3.6×10_(4)to 4.74×10^(5)for forced convection without buoyancy effects and Gr_(∞)/Re^(2)_(∞)range of 0.011 to 3.107 for the case where buoyancy effects are *** different forms of correlations are proposed based on numerical predictions and are compared with actual numerical *** has been found that in all six forms of correlations,the maximum deviations are found to occur in those cases where the pseudocritical temperature TT∗lies between the wall temperature and bulk fluid temperature.

关键词： Supercritical nitrogen laminar flow numerical methods forced convection heat transfer isothermal horizontal surface

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LTL Model Checking for Verification of Electronic Medical Record (EMR) Design 8th

LTL Model Checking for Verification of Electronic Medical R...

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8th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2023

作者： Taryana, Acep Adzkiya, Dieky Mufid, Muhammad Syifa’ul Mukhlash, Imam Abate, Alessandro Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Engineering Faculty Universitas Jenderal Soedirman Purwokerto Indonesia Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9789819721351

This paper presents an in-depth investigation into the verification of a software design model, focusing on the context of Electronic Medical Record (EMR) systems. Verification plays a critical role in ensuring the correctness and reliability of such systems, making it a topic of paramount importance. In this study, we undertake the task of remodeling the EMR requirements using a finite transition system to facilitate rigorous analysis and verification. Additionally, we express the formal specification through Linear Temporal Logic (LTL), leveraging its expressive power and ability to capture temporal properties accurately. To assess the validity of the design model, we employ the widely recognized SPIN model checker, a tool known for its effectiveness in detecting errors and inconsistencies in software systems. Our analysis reveals several critical findings. Specifically, the model checker successfully identifies paths within the design model that fail to satisfy the specified LTL formula, shedding light on potential areas of concern. Moreover, the generation of a comprehensive counterexample provides valuable insights for further analysis and improvement of the software design. Overall, this research highlights the significance of verification techniques in software design and emphasizes the applicability of the finite transition system and LTL in this context. The findings underscore the importance of thorough verification and the potential benefits of employing tools like SPIN to enhance the reliability and robustness of EMR systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software design

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