Early and accurate detection of Heart Disease(HD)is critical for improving patient outcomes,as HD remains a leading cause of mortality worldwide.Timely and precise prediction can aid in preventive interventions,reduci...Early and accurate detection of Heart Disease(HD)is critical for improving patient outcomes,as HD remains a leading cause of mortality worldwide.Timely and precise prediction can aid in preventive interventions,reducing fatal risks associated with misdiagnosis.Machine learning(ML)models have gained significant attention in healthcare for their ability to assist professionals in diagnosing diseases with high accuracy.This study utilizes 918 instances from publicly available UCI and Kaggle datasets to develop and compare the performance of various ML models,including Adaptive Boosting(AB),Naïve Bayes(NB),Extreme Gradient Boosting(XGB),Bagging,and Logistic Regression(LR).Before model training,data preprocessing techniques such as handling missing values,outlier detection using Isolation Forest,and feature scaling were applied to improve model performance.The evaluation was conducted using performance metrics,including accuracy,precision,recall,and F1-score.Among the tested models,XGB demonstrated the highest predictive performance,achieving an accuracy of 94.34%and an F1-score of 95.19%,surpassing other models and previous studies in HD prediction.LR closely followed with an accuracy of 93.08%and an F1-score of 93.99%,indicating competitive performance.In contrast,NB exhibited the lowest performance,with an accuracy of 88.05%and an F1-score of 89.02%,highlighting its limitations in handling complex patterns within the dataset.Although ML models show superior performance as compared to previous studies,some limitations exist,including the use of publicly available datasets,which may not fully capture real-world clinical variations,and the lack of feature selection techniques,which could impact model interpretability and robustness.Despite these limitations,the findings highlight the potential of ML-based frameworks for accurate and efficient HD detection,demonstrating their value as decision-support tools in clinical settings.展开更多
This paper designs the thermal crystals composed of alloy materials with air holes and analyzes their properties of band structures,heat transmission,and flux spectra.Thermal crystals composed of Si-A(A=Ge,Sn,Pb)alloy...This paper designs the thermal crystals composed of alloy materials with air holes and analyzes their properties of band structures,heat transmission,and flux spectra.Thermal crystals composed of Si-A(A=Ge,Sn,Pb)alloys as background materials and air holes with square array are used to construct an elastic-constant periodic structure and their high-frequency phononic band is calculated by deploying finite element methods.Moreover,this paper investigates heat transmission through a finite array of thermally excited phonons and presents the thermal crystal with maximum heat transport.The results show that a wider bandgap could be achieved by increasing the air hole radius and decreasing the lattice constant.In the alloy materials,with increasing atomic radius and thus atomic mass(Ge,Sn,Pb),the frequency range(contributed to thermal conductivity)shifts towards lower frequency.Hence,the bandgap frequencies also shift toward low frequency,but this decreasing rate is not constant or in order,so former may have a faster or slower decreasing rate than the later.Thus,the frequency range for the contribution of heat transportation overlaps with the bandgap frequency range.The development of thermal crystals is promising for managing heat and controlling the propagation of the thermal wave.展开更多
In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The co...In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.展开更多
The aim of this paper is to propose a flexible and accurate methodology for the evaluation of the BIPV （building integrated photovoltaic） potential on industrial building roofs. The use of more realistic and case sp...The aim of this paper is to propose a flexible and accurate methodology for the evaluation of the BIPV （building integrated photovoltaic） potential on industrial building roofs. The use of more realistic and case specific data obtained by accurate technical on-site audits is proved to be of significant importance in the reliability of the proposed methodology results. Moreover, the most recent PV market information is used, considering however that this factor is rapidly changing during the last years, owed to the vast growth of the PV sector. To this end, emphasis is given on the country of Greece, where besides the fact that there is an increase of PV installations; no progress has been met in the use of BIPV systems in the industrial sector, opposite to the situation met in other EU countries. Acknowledging the above, the proposed methodology is currently applied so as to evaluate the BIPV potential of a large industrial zone close to the Greek capital, Athens. The results of this study can be used by both other researchers, for similar evaluations, and energy policy makers, to support the clean energy production concept on the basis of BIPV systems in industrial areas.展开更多
Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their...Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R235),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Early and accurate detection of Heart Disease(HD)is critical for improving patient outcomes,as HD remains a leading cause of mortality worldwide.Timely and precise prediction can aid in preventive interventions,reducing fatal risks associated with misdiagnosis.Machine learning(ML)models have gained significant attention in healthcare for their ability to assist professionals in diagnosing diseases with high accuracy.This study utilizes 918 instances from publicly available UCI and Kaggle datasets to develop and compare the performance of various ML models,including Adaptive Boosting(AB),Naïve Bayes(NB),Extreme Gradient Boosting(XGB),Bagging,and Logistic Regression(LR).Before model training,data preprocessing techniques such as handling missing values,outlier detection using Isolation Forest,and feature scaling were applied to improve model performance.The evaluation was conducted using performance metrics,including accuracy,precision,recall,and F1-score.Among the tested models,XGB demonstrated the highest predictive performance,achieving an accuracy of 94.34%and an F1-score of 95.19%,surpassing other models and previous studies in HD prediction.LR closely followed with an accuracy of 93.08%and an F1-score of 93.99%,indicating competitive performance.In contrast,NB exhibited the lowest performance,with an accuracy of 88.05%and an F1-score of 89.02%,highlighting its limitations in handling complex patterns within the dataset.Although ML models show superior performance as compared to previous studies,some limitations exist,including the use of publicly available datasets,which may not fully capture real-world clinical variations,and the lack of feature selection techniques,which could impact model interpretability and robustness.Despite these limitations,the findings highlight the potential of ML-based frameworks for accurate and efficient HD detection,demonstrating their value as decision-support tools in clinical settings.
基金National Natural Science Foundation of China(No.61975119)。
文摘This paper designs the thermal crystals composed of alloy materials with air holes and analyzes their properties of band structures,heat transmission,and flux spectra.Thermal crystals composed of Si-A(A=Ge,Sn,Pb)alloys as background materials and air holes with square array are used to construct an elastic-constant periodic structure and their high-frequency phononic band is calculated by deploying finite element methods.Moreover,this paper investigates heat transmission through a finite array of thermally excited phonons and presents the thermal crystal with maximum heat transport.The results show that a wider bandgap could be achieved by increasing the air hole radius and decreasing the lattice constant.In the alloy materials,with increasing atomic radius and thus atomic mass(Ge,Sn,Pb),the frequency range(contributed to thermal conductivity)shifts towards lower frequency.Hence,the bandgap frequencies also shift toward low frequency,but this decreasing rate is not constant or in order,so former may have a faster or slower decreasing rate than the later.Thus,the frequency range for the contribution of heat transportation overlaps with the bandgap frequency range.The development of thermal crystals is promising for managing heat and controlling the propagation of the thermal wave.
文摘In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.
文摘The aim of this paper is to propose a flexible and accurate methodology for the evaluation of the BIPV （building integrated photovoltaic） potential on industrial building roofs. The use of more realistic and case specific data obtained by accurate technical on-site audits is proved to be of significant importance in the reliability of the proposed methodology results. Moreover, the most recent PV market information is used, considering however that this factor is rapidly changing during the last years, owed to the vast growth of the PV sector. To this end, emphasis is given on the country of Greece, where besides the fact that there is an increase of PV installations; no progress has been met in the use of BIPV systems in the industrial sector, opposite to the situation met in other EU countries. Acknowledging the above, the proposed methodology is currently applied so as to evaluate the BIPV potential of a large industrial zone close to the Greek capital, Athens. The results of this study can be used by both other researchers, for similar evaluations, and energy policy makers, to support the clean energy production concept on the basis of BIPV systems in industrial areas.
文摘Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.
