The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine...
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The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI
Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholde...
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作者：
Warbhe, Mohan K.Bore, Joy JordanChaudari, Shiv Nath
Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India
The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accu...
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Fruit categorization presents a significant challenge due to the diverse range of fruit types and their similarities in color, shape, size, and structure. This challenge is addressed in this research by proposing a mu...
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Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in prac...
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Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software ***,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization ***,such modularization can be good from the quality metrics perspective but may not be acceptable to the *** produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization ***,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization *** assess the effectiveness of the proposed approach,we applied it over five software *** obtained remodularization solutions are evaluated with the software quality metrics and developers view of *** demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.
In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Pent...
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The void defect in intermetallic compounds(IMCs)layer at the joints caused by inhomogeneous atomic diffusion is one of the most important factors limiting the further development of Sn-based *** this work,the thermody...
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The void defect in intermetallic compounds(IMCs)layer at the joints caused by inhomogeneous atomic diffusion is one of the most important factors limiting the further development of Sn-based *** this work,the thermodynamic stability of IMCs(high-temperatureη-Cu_(6)Sn_(5)and o-Cu_(3)Sn phases)was improved by adding small amounts of indium(In),and the IMCs layers with moderate thickness,low defect concentrations and stable interface bonding were successfully *** formation order of compounds and the interfacial orientation relationships in IMCs layers,the atomic diffusion mechanism,and the growth tuning mechanism of In onη-Cu_(6)Sn_(5)and Cu_(3)Sn,after In adding,were discussed com-prehensively by combining calculations and *** is the first time that the classic heteroge-neous nucleation theory and CALPHAD data were used to obtain the critical nucleus radius ofη-Cu_(6)Sn_(5)and Cu_(3)Sn,and to explain in detail the main factors affecting the formation order and location of IMCs at joints during the welding process.A novel and systematic growth model about IMCs layers in the case of doping with alloying elements was *** growth tuning mechanism of In doping onη-Cu_(6)Sn_(5)and Cu_(3)Sn was further clarified based on the proposed model using first-principles *** growth model used in this study can provide insights into the development and design of multiele-ment Sn-based solders.
Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and gener...
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Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and generalization to real rain *** this paper,we propose a deep residual channel attention network(DeRCAN)for *** channel attention mechanism is able to capture the inherent properties of the feature space and thus facilitates more accurate estimations of structures and details for image *** addition,we further propose an unsupervised learning approach to better solve real rain images based on the proposed *** qualitative and quantitative evaluation results on both synthetic and real-world images demonstrate that the proposed DeRCAN performs favorably against state-of-the-art methods.
Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activitie...
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To investigate the enhancing effect of Mn on the performance of simultaneous catalytic oxidation of AsH_(3)and PH_(3)by CuO-Al_(2)O_(3)in a reducing atmosphere under micro-oxygen conditions,Cu-Mn modifiedγ-Al_(2)O_(3...
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To investigate the enhancing effect of Mn on the performance of simultaneous catalytic oxidation of AsH_(3)and PH_(3)by CuO-Al_(2)O_(3)in a reducing atmosphere under micro-oxygen conditions,Cu-Mn modifiedγ-Al_(2)O_(3)catalysts were *** characteristics of the catalysts showed that Mn reduced the crystallinity of the active CuO component,increased the number of oxygen vacancies and acidic sites on the catalyst surface,enhanced the mobility of surface oxygen,and the interaction between copper and manganese promoted the redox cycling ability of the catalysts and improved their oxidation performance,which increased the conversion frequency(TOF)by 2.54×10^(-2)to 3.07×10^(-2)sec^(-1).On the other hand,the introduction of Mn reduced the production of phosphate and As_(2)O_(3)on the catalyst surface by30.96%and 44.9%,which reduced the coverage and inerting of the active sites by phosphate and As_(2)O_(3),resulting in an 8 hr(6 hr)improvement in the stability of PH_(3)(AsH_(3))removal.
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