Identifying health conditions from facial images is crucial for the early detection of certain diseases and provides crucial information for timely intervention. This study introduces a novel ensemble convolutional ne...
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Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and...
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We tackle the task of generating opinion-based questions, focusing on providing users with samples of questions that express opinions relevant to their queries when utilizing search engines. The motivation is that Com...
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We study the approximation of function classification by machine learn-ing. We first determine all the NPN and NPNP classes of three-bit reversible and one to five-bits irreversible functions. Then we apply machine le...
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Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these paramete...
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Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.
The total graph of the space of m × n matrices over a field F is the graph with the set of vertices Mm×n(F) in which distinct matrices A and B are connected by an edge if and only if rank(A+B) n, whereas tha...
This paper presents a data-based approach to force control. For this purpose, representative data of the robot in operation is recorded to model it afterwards by methods of time series modeling. Specifically, a form o...
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Lasertrackers are used for tracking moving targets. Usually, interferometric distance measurement is used to determine the 3D coordinates of the target. In this paper, a tracker network is presented that uses triangul...
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This paper introduces a novel, extendable, no-code framework for integrating machine-learning algorithms into SQL using the Exasol database. The framework combines the strengths of the high-performance, parallel-proce...
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Load time series analysis is critical for resource management and optimization decisions,especially automated analysis *** research has insufficiently interpreted the overall characteristics of samples,leading to sign...
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Load time series analysis is critical for resource management and optimization decisions,especially automated analysis *** research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a *** paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample *** utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load *** the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong *** to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis *** results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable *** correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high ***,it exhibits significant advantages in general and low sample scenarios.
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