Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classifi...
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Sentiment analysis depends on individuals’comments and opinions on *** from social media platforms like Twitter,Quora,or Facebook poses challenges due to informal language,including acronyms,misspellings,and ambiguou...
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Sentiment analysis depends on individuals’comments and opinions on *** from social media platforms like Twitter,Quora,or Facebook poses challenges due to informal language,including acronyms,misspellings,and ambiguous ***,hyperparameters in machine learning models significantly impact *** address these issues,we propose advanced feature engineering techniques in Natural Language Processing(NLP)and hyperparameter optimization to enhance prediction accuracy and generalization *** study employs Naïve Bayes,Logistic Regression(LR),Multi-layer Perceptron(MLP),and Support Vector Machine(SVM)to classify sentiments in tweets about Elon Musk’s potential acquisition of *** dataset,consisting of 100,000 tweets,is fetched using the Twitter representational state transfer application programming interface(REST API).We outline a sentiment analysis procedure to classify unstructured Twitter data,identify influential keywords,and categorize sentiments as Positive,Negative,or *** a hybrid Lexicon NLP approach,we extract contextually significant emotionally charged words and assign sentiment *** optimization via automated search methods ensures alignment with classifier performance *** achieved an impressive accuracy rate of 97%.Cross-validation minimizes random variations,providing a reliable assessment of the model’s generalization capabilities,and demonstrating the method’s accuracy in predicting sentiments with larger new unseen standard datasets,and varying sentiment.
In a limited space, such as pressurized rovers, insufficient personal space is a source of stress for astronauts. The aim of this study is to mitigate effects caused by personal space invasions. The proposed system fo...
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Traditional image denoising algorithms often struggle with real-world complexities such as spatially correlated noise, varying illumination conditions, sensor-specific noise patterns, motion blur, and structural disto...
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This paper proposes the utilization of a Parallel Active filter (PAF) controlled by two different methods, proportional-integral (PI) and hysteresis controllers, in combination with L-filter, in order to enhance the p...
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This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square wa...
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This article describes the invention of autonomous cannabis seeding equipment to reduce contamination and planting time. The automated cannabis seeder employs an NI myRIO control board to operate a y-axis stepper moto...
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ISBN:
(数字)9798331543273
ISBN:
(纸本)9798331543280
This article describes the invention of autonomous cannabis seeding equipment to reduce contamination and planting time. The automated cannabis seeder employs an NI myRIO control board to operate a y-axis stepper motor attached to a ball screw to move along the breadth of each row of 14 seed trays. Soil drilling and burial Drill all holes at the same depth. This promotes seed germination and lowers human-seed germ contamination. These 7 soil drill heads and 7 seeders use 3D printers to design and shape the workpiece. An 800-watt air pump smokes the cannabis seeds. Linear electric motors function in two rows in the z-axis. The buffer reverses the drowning process from row 14 to the first row, starting from the 3rd row and continuing through the final series of steps until 14 rows in the y-axis are complete. The hardware controls all program activities in LabVIEW. We tested the automated cannabis seeding machine 100 times on average, sowing cannabis seeds into 98 seed trays and dropping 93 seeds at 94.80% efficiency in 10.38 minutes. A 16 percent faster machine than human work is available.
The dehusking of wheat results in the significant annual production of wheat husk, an agricultural byproduct. This waste is often burned outdoors to generate energy, with the resulting ash (wheat husk ash, WHA) typica...
The dehusking of wheat results in the significant annual production of wheat husk, an agricultural byproduct. This waste is often burned outdoors to generate energy, with the resulting ash (wheat husk ash, WHA) typically disposed of in landfills, leading to environmental harm. To mitigate the negative environmental impact, this research explores the potential of utilizing wheat husk ash to enhance the mechanical properties of cement paste. The study involved the partial replacement of Portland cement in the mixture with varying proportions of wheat husk ash (0, 5, 10, 15, 20, 25 and 30%) to produce WHA blended cement with sample with 0% as the control, and examined the compressive strength of hardened cement paste (HCP) at different curing ages (3, 7, 14, and 28 days). Chemical water tests revealed that the water-to-cement ratio required for standard HCP increased linearly with the proportion of WHA in the mix. Additionally, it was found that the bulk density of HCP decreased as the percentage of WHA increased. Compressive strength tests of the HCP samples yielded promising results, particularly with 15% (30.04) and 20% (28.73) WHA replacement. The compressive strength improved by approximately 14% with 15% WHA replacement and by 10% with 20% WHA replacement. In conclusion, the incorporation of wheat husk ash (WHA) at optimal percentages in cement mixtures enhances the compressive strength of hardened cement paste, particularly after full curing. This study highlights not only the mechanical improvements achieved through WHA integration but also its potential to significantly reduce the carbon footprint of cement production, demonstrating a dual advantage of improved material performance and environmental sustainability.
A new phase distribution model of the scattered field from the sea surface is proposed, combining Geometrical-based Stochastic Channel Modeling (GSCM) and the Effective Roughness (ER) approaches. The presented model a...
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ISBN:
(数字)9788831299107
ISBN:
(纸本)9798350366327
A new phase distribution model of the scattered field from the sea surface is proposed, combining Geometrical-based Stochastic Channel Modeling (GSCM) and the Effective Roughness (ER) approaches. The presented model applies to the large-scale roughness of the sea surface. Assuming the sea surface elevation follows a Normal distribution, a phase relation model is derived, capturing the statistical correlation between distinct scattering points across the surface. The phase correlation function is derived from the sea surface's autocorrelation function (ACF), highlighting the influence of sea state parameters on the resulting phase distribution. The model demonstrates a strong dependence of the phase distribution on the characteristics of sea conditions and is applicable to any surface with a correlated Normal distributed roughness profile.
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