As the economic value of cryptocurrencies continues to ascend, an increasing number of cybercriminals exploit malicious browser scripts to commandeer the system and network resources of victims for unauthorized crypto...
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Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy *** selection(Fs)is a critical prerequisite for effective ***,current Fs methods have two major ***,most approaches...
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Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy *** selection(Fs)is a critical prerequisite for effective ***,current Fs methods have two major ***,most approaches rely on single-source ranking information(ssRI)to evaluate features individually,which results in non-robust outcomes across different models and datasets due to the one-sided nature of ***,thermodynamic mechanism features are often overlooked,leading to incomplete initial feature libraries,making it challenging to select optimal features and achieve better diagnostic *** address these issues,a robust ensemble Fs method based on multi-source ranking information(MsRI)is *** employing an efficient strategy based on maximizing relevance while proper redundancy,the MsRI method fully leverages Mutual Information,Information Gain,Gain Ratio,Gini index,Chi-squared,and Relief-F from both qualitative and quantitative ***,comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature *** a methodological standpoint,a general framework for constructing the MsRI-based Fs method is *** proposed method is applied to chiller FD and tested across ten widely-used machine learning *** optimized features are selected from the original set of forty-two,achieving an average diagnostic accuracy of 98.40%and an average F-measure above 94.94%,demonstrating the effectiveness and generalizability of the MsRI *** to the ssRI approach,the MsRI method showssuperior robustness,with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53%to 6.12%.Moreover,the MsRI method reduced computation time by 98.62%compared to wrapper methods,without sacrificing accuracy.
Federated learning (FL) is a security paradigm using low-resource devices to contribute to the training without sharing local data;hence, it is very suitable for practical applications with strict privacy requirements...
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The study presents a prototype of the educational artistic virtual game CineGame Ukraine, designed to improve skills in film narration and storyboarding. The project aims to adapt a digital learning environment as a m...
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Electroencephalogram signals are used to depict emotional and stress disorders. To overcome issues of existing models, novel transfer learning-based bioinspired ensemble model for preemptive detection of stress and em...
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In recent years, brain-computer interfaces (BCIs) leveraging electroencephalography (EEG) signals for the control of external devices have garnered increasing attention. The information transfer rate of BCI has been s...
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In recent years, brain-computer interfaces (BCIs) leveraging electroencephalography (EEG) signals for the control of external devices have garnered increasing attention. The information transfer rate of BCI has been significantly improved by a lot of cutting-edge methods. The exploration of effective preprocessing in brain-computer interfaces, particularly in terms of identifying suitable preprocessing methods and determining the optimal sequence for their application, remains an area ripe for further investigation. To address this gap, thisstudy explores a range of preprocessing techniques, including but not limited to independent component analysis, surface Laplacian, bandpass filtering, and baseline correction, examining their potential contributions and synergies in the context of BCI applications. In this extensive research, a variety of preprocessing pipelines were rigorously tested across four EEG data sets, all of which were pertinent to motor imagery-based BCIs. These tests incorporated five EEG machine learning models, working in tandem with the preprocessing methods discussed earlier. The study's results highlighted that baseline correction and bandpass filtering consistently provided the most beneficial preprocessing effects. From the perspective of online deployment, after testing and time complexity analysis, thisstudy recommends baseline correction, bandpass filtering and surface Laplace as more suitable for online implementation. An interesting revelation of the study was the enhanced effectiveness of the surface Laplacian algorithm when used alongside algorithms that focus on spatial information. Using appropriate processing algorithms, we can even achieve results (92.91% and 88.11%) that exceed the sOTA feature extraction methods in some cases. such findings are instrumental in offering critical insights for the selection of effective preprocessing pipelines in EEG signal decoding. This, in turn, contributes to the advancement and refinement of b
sTEAM education is an educational approach of interdisciplinary teaching of science, technology, engineering, art, and mathematics. sTEAM education, however, is often viewed as only including art elements into sTEM te...
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sTEAM education is an educational approach of interdisciplinary teaching of science, technology, engineering, art, and mathematics. sTEAM education, however, is often viewed as only including art elements into sTEM teaching. Without true integration of the disciplines in sTEAM curricula, students rarely are exposed to the connection among disciplines, and self-identify assolely scientists, artists, or technophiles. sTEAM curricula also infrequently integrate design, which promotes creativity and innovation. Effective sTEAM curriculum and practices are needed to prepare students to face 21st century challenges and work demands. We designed a high school sTEAM educational module that integrated plant science, design, and emergent technologies through the creation of 3D models of plants and augmented and virtual reality (AVR) experiences and investigated its impact on students’ understanding of the intersection of art and design with science, learning and skills gains, and interests in sTEAM subjects and careers. The module used a project-basedlearning approach that relied on student teamwork and facilitation by educators. In this 3D plant modeling module, students: (1) investigated plants under research at a plant science research center, (2) designed and created 3D models of those plants, (3) learned about the application of 3D modeling in AVR platforms, and (4) disseminated project results. We used qualitative and quantitative research methods both before and after the implementation of the model to assess the impact of the 3D modeling module. student responses revealed that approximately half of the students had a good understanding of the intersection of art and design with science prior to the implementation of the module, while the other half gained this understanding after completing their projects. studentssaw art and design playing a role in science mainly by facilitating communication and further understanding and fostering new ideas. They also reported t
The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by ...
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Background and Objective: Thisstudy introduces multiscale feature learning to develop more robust and resilient activity recognition algorithms, aimed at accurately tracking and quantifying rehabilitation exercises w...
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Background and Objective: Thisstudy introduces multiscale feature learning to develop more robust and resilient activity recognition algorithms, aimed at accurately tracking and quantifying rehabilitation exercises while minimizing performance disparities acrosssubjects with varying motion-related characteristics. Methods: Advanced architectures designed to process multi-channel time series data using two parallel branches that extract features at different scales were developed and tested. Results: The results indicate that multiscale algorithms consistently outperform traditional approaches, demonstrating enhanced performance, particularly among patient subjects. specifically, the multiscale tCNN and multiscale CNN-LsTM achieved accuracies of 91% and 90%, respectively, while the multiscale ConvLsTM maintained strong performance at 89%. Notably, the multiscale Transformer emerged as the most effective model, achieving the best average accuracy of 93%. Conclusions: Thisresearch underscores the need to explore advanced methods for enhancing activity recognition systems in healthcare, where accurate exercise monitoring and evaluation are becoming essential for effective and personalized treatment in telemedicine services.
Background and Objective: In recent years, machine learning-based clinical decision support systems (CDss) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, th...
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Background and Objective: In recent years, machine learning-based clinical decision support systems (CDss) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models posessignificant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge. Methods: This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN- derived features with the inherent interpretability of radiomic features. Rad4XCNN diverges from conventional methodsbased on saliency maps, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps. Results: Using abreast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. some key results are: (i) CNN-derived features guarantee more robust accuracy when compared against ViTderived and radiomic features;(ii) conventional visualization map methods for explanation present several pitfalls;(iii) Rad4XCNN does not sacrifice model accuracy for their explainability;(iv) Rad4XCNN provides a global explanation enabling the physician to extract global insights and findings. Conclusions: Our method can mitigate some concerns related to the explainability-accuracy trade-off. Thisstudy highlighted the importance of proposing new methods for model explanation without affecting their accuracy.
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