Emotion recognition from speech is a significant research area in human–computer interaction and psychological assessments. This study proposes a novel three-stage process for emotion recognition from speech signals....
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In thicker polymer active layers charge collection efficiency suffers due to low carrier mobility and increased recombination losses. In thin absorber polymer solar cell to increase absorption, light-trapping techniqu...
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In thicker polymer active layers charge collection efficiency suffers due to low carrier mobility and increased recombination losses. In thin absorber polymer solar cell to increase absorption, light-trapping techniques and plasmonic structures are essential. This study investigates the effect of shell thickness on the photocurrent density of a poly(3-hexylthiophene): phenyl-C61- butyric acid methyl ester (P3HT:PCBM) polymer based solar cell incorporating core–shell nanoparticles with configurations of Au–Ag and Ag-Au core–shell nanoparticles. Through a series of simulation, the photocurrent density was calculated as a function of shell thickness. The results demonstrate that, for both nanoparticle configurations, the photocurrent density generally increases with shell thickness, reaching an optimal point before stabilizing or slightly decreasing. Additionally, the effects of dielectric shells made of SiO₂ and Al₂O₃ on its performance parameters were analyzed. The study also found that the photocurrent decreases with increasing shell thickness for both SiO₂ and Al₂O₃ shells, with a more pronounced decrease for SiO₂ due to its smaller refractive index and greater change in shorter wavelengths. The photocurrent density of 13.74 mA/cm2 is achieved for a cell with a thickness of 80 nm without nanoparticles. This value increases to 16.62 mA/cm2 for a cell incorporating Ag nanoparticles and reaches 19.3 mA/cm2 for a cell with Au–Ag core–shell nanoparticles at the optimal shell thickness. The power conversion efficiency of the polymer solar cell increases from 7.02% without nanoparticles to 8.67% with Ag, 8.45% with Au, and reaches the highest value of 10.26% with Au–Ag core–shell nanoparticles, highlighting the superior performance of the core–shell configuration. This superior performance is attributed to the enhanced plasmonic effects of the Au–Ag combination, which facilitates better light trapping and absorption. These findings underscore the importance of optimizing
Cervical cancer remains the top killer of women at a young age in the world, 85% of cases are detected in low-income countries. Preventive measures and therapeutic response are enhanced if potential hazards are identi...
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When a crowdsourcing approach is used to assist the classification of a set of items,the main objective is to classify this set of items by aggregating the worker-provided labels.A secondary objective is to assess the...
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When a crowdsourcing approach is used to assist the classification of a set of items,the main objective is to classify this set of items by aggregating the worker-provided labels.A secondary objective is to assess the workers’skill levels in this process.A classical model that achieves both objectives is the famous Dawid-Skene *** this paper,we consider a third objective in this context,namely,to learn a classifier that is capable of labelling future items without further assistance of crowd *** extending the DawidSkene model to include the item features into consideration,we develop a Classification-Oriented Dawid Skene(CODS)model,which achieves the three objectives *** effectiveness of CODS on this three dimensions of the problem space is demonstrated experimentally.
The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily fun...
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Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many ...
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In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while...
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Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient t...
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Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.
Since gastric cancer is growing fast, accurate and prompt diagnosis is essential, utilizing computer-aided diagnosis (CAD) systems is an efficient way to achieve this goal. Using methods related to computer vision ena...
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Since gastric cancer is growing fast, accurate and prompt diagnosis is essential, utilizing computer-aided diagnosis (CAD) systems is an efficient way to achieve this goal. Using methods related to computer vision enables more accurate predictions and faster diagnosis, leading to timely treatment. CAD systems can categorize photos effectively using deep learning techniques based on image analysis and classification. Accurate and timely classification of histopathology images is critical for enabling immediate treatment strategies, but remains challenging. We propose a hybrid deep learning and gradient-boosting approach that achieves high accuracy in classifying gastric histopathology images. This approach examines two classifiers for six networks known as pre-trained models to extract features. Extracted features will be fed to the classifiers separately. The inputs are gastric histopathological images. The GasHisSDB dataset provides these inputs containing histopathology gastric images in three 80px, 120px, and 160px cropping sizes. According to these achievements and experiments, we proposed the final method, which combines the EfficientNetV2B0 model to extract features from the images and then classify them using the CatBoost classifier. The results based on the accuracy score are 89.7%, 93.1%, and 93.9% in 80px, 120px, and 160px cropping sizes, respectively. Additional metrics including precision, recall, and F1-scores were above 0.9, demonstrating strong performance across various evaluation criteria. In another way, to approve and see the model efficiency, the GradCAM algorithm was implemented. Visualization via Grad-CAM illustrated discriminative regions identified by the model, confirming focused learning on histologically relevant features. The consistent accuracy and reliable detections across diverse evaluation metrics substantiate the robustness of the proposed deep learning and gradient-boosting approach for gastric cancer screening from histopathology
A recommender system is an approach performed by e-commerce for increasing smooth users’*** pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the ord...
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A recommender system is an approach performed by e-commerce for increasing smooth users’*** pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of *** work will present the implementation of sequence pattern mining for recommender systems within the domain of *** work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender *** feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target *** will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy *** work pre-sents a new hybrid recommender system based on optimized feature selection and systolic *** features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)*** systolic tree is used for pattern mining,and based on this,the recommendations are *** proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the *** was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.
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