The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection m...
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The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was significant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classifi*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).
Providing accurate and timely traffic information such as arriving time of train plays a significant part in intelligent train status prediction. Maximum-speed train status forecast is a significant topic as far as ra...
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Latest measurements correlated to the cloud computing technology, found to be very unreliable. For smooth conduction of cloud technology, the report is getting more than 100 values i.e., being added to the cost of the...
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Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two p...
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Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23%compared with TCA+ algorithm on AEEEM datasets,and an average improvement of 5% on ReLink datasets.
Manihot esculenta, the scientific name for cassava, is an important staple crop that feeds millions of people in tropical countries and provides a substantial amount of carbohydrates. To minimize these losses and guar...
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This paper presents a cutting-edge framework for predicting psychological health risks in pregnant women, supported by robust analytics and a user-friendly application interface. Utilizing a dataset of 1504 postpartum...
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This paper presents a cutting-edge framework for predicting psychological health risks in pregnant women, supported by robust analytics and a user-friendly application interface. Utilizing a dataset of 1504 postpartum women, state-of-the-art machine learning algorithms, particularly Random Forest, achieved an impressive accuracy score of 0.7508. This underscores the framework's effectiveness in identifying psychological health risks with high precision. Beyond traditional accuracy metrics, the study adopts a comprehensive approach to performance evaluation, incorporating precision, recall, and F1 score to provide a nuanced understanding of classifier performance, essential for informed decision-making in healthcare settings. The primary goal is to establish a seamless computerized prediction pathway, enabling healthcare providers to proactively address mental well-being in pregnant women. The framework encompasses several key stages, including meticulous data collection, rigorous preprocessing, strategic feature selection, and algorithmic selection. Advanced data preprocessing techniques, such as outlier removal and null value elimination, were employed to enhance data quality and reliability. Feature selection focused on identifying pivotal attributes for precise prediction of psychological health risks, optimizing model efficacy. A distinguishing aspect of this research is its emphasis on user-centric application development. The bespoke Women's Mental Health Tracker, crafted using Python's Tkinter library, boasts a user-friendly interface with personalized recommendations, weekly progress tracking, access to a rich resource library, community support, reminders, and notifications. This empowers pregnant women to manage their mental well-being proactively with ease and confidence. Attribute analysis highlights critical psychological health indicators, including feelings of sadness, irritability, sleep disturbances, concentration issues, overeating, and anxiety. Wh
Sentence simplification is an essential task in natural language processing and aims to simplify complex sentences while retaining their primary *** date,the main research works on sentence simplification models have ...
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Sentence simplification is an essential task in natural language processing and aims to simplify complex sentences while retaining their primary *** date,the main research works on sentence simplification models have been based on sequence-to-sequence(Seq2Seq)***,these Seq2Seq models are incapable of analysing the hierarchical structure of sentences,which is of great significance for sentence *** problem can be addressed with an ON-MULTI-STAGE model constructed based on the improved MULTI-STAGE encoder *** this model,an ordered neurons network is introduced and can provide sentence-level structural infor-mation for the encoder and decoder.A weak attention connection method is then employed to make the decoder use the sentence-level structural *** results on two open data sets demonstrated that the constructed model outperforms the state-of-the-art baseline models in sentence simplification.
Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ...
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Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a need for a tactile sensor that can be deployed extensively across the robot's body.
Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily *** uses sensor devices to collect user behavior data,obtain human activity information and identify *** Logic Networ...
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Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily *** uses sensor devices to collect user behavior data,obtain human activity information and identify *** Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and *** can solve the problems of complexity and uncertainty,and has good knowledge expression ***,MLN structure learning is relatively weak and requires a lot of computing and storage ***,the MLN structure is derived from sensor data in the current *** that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be *** this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN *** a rulebase can reduce the time required for MLN structure *** apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning *** addition,we evaluate the parameters of the rulebase building scheme to check its stability.
Aiming at the current problems of low efficiency and poor accuracy of steel surface defect detection with few samples, as well as the challenges of large number of existing defect detection model parameters and low de...
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