The first non-profiled side-channel attack (SCA) method using deep learning is Timon's Differential Deep Learning Analysis (DDLA). This method is effective in retrieving the secret key with the help of deep learni...
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Agriculture plays a major role in eradicating poverty, promoting prosperity, and nourishing a projected 10 billion people by 2050 globally. In a changing climate, achieving optimal agricultural yields requires a deepe...
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Agriculture plays a major role in eradicating poverty, promoting prosperity, and nourishing a projected 10 billion people by 2050 globally. In a changing climate, achieving optimal agricultural yields requires a deeper understanding of available natural resources and crops. This is especially important for places like the Navajo Nation, which faces significant challenges in food supply chain management due to various factors such as water demand, water quality, and insufficient information about land fertility and crops timings/seasons. Additionally, it is the largest Native American reservation in the U.S. It covers 27,425 square miles across Arizona, Utah, and New Mexico and has a population of 165,158 people, according to the 2020 census. Agriculture has been a key part of life in the Navajo Nation since the late 19th and early 20th centuries, playing a big role in the region’s development and stability. However, the lack of knowledge about decisions and actions during the crop growing season has resulted in lower crop productivity, as evidenced by the USDA statistical report for the Navajo Nation in 2012 and 2017. To support farmers by providing better decision-making and actionable insights, high-resolution, open-source Sentinel-2 satellite images are being used to develop advanced crop mapping techniques for identifying the spatial extent of various agricultural crops in the Navajo Nation. To address this, a collection of research papers was reviewed, leading to the development of a new methodology for analysing Sentinel-2 data from the 2017 and 2023 growing seasons within the Navajo Nation. The collected data was pre-processed by creating monthly median composites of surface reflectance to remove noise and enhance the results more accurately. After preprocessing, spectral indices were calculated from the spectral bands, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), GCVI (Green Chlorophyll Vegetation Index), and LSWI
The polarization of the light can be used as the core principle of fiber optic sensors. One of the physical quantities which can be detected or measured this way is the longitudinal tension of the fiber. A set of meas...
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Depression has become one of the most common mental illnesses in the *** better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a tr...
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Depression has become one of the most common mental illnesses in the *** better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep *** paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so ***,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and ***,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.
The software testing process accounted for nearly forty percent of the total software development cost, and one of the most important parts of software testing was test data generation. Performing this process manuall...
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ISBN:
(数字)9798331507565
ISBN:
(纸本)9798331507572
The software testing process accounted for nearly forty percent of the total software development cost, and one of the most important parts of software testing was test data generation. Performing this process manually led to slowdowns, increased costs, and errors. Therefore, we sought an automated method for generating this set to identify the maximum number of defects. In the process of generating test data, the problem could be transformed into an optimization problem, as their ability to intelligently search in the vast input space and reduce costs and time was superior. Thus, search algorithms could be used to generate test data. Genetic algorithms, particle swarm optimization, etc. were among the most widely used algorithms in this field, but previous algorithms also had drawbacks. In recent years, newer optimization algorithms with better performance were introduced. In this paper, the Coati Optimization Algorithm (COA) was used. This algorithm had lower complexity, appropriate exploration and exploitation phases, and lacked initial parameters compared to previous algorithms. However, the Coati Optimization Algorithm also included weaknesses. Under certain conditions, a solution might never move towards the best solution. Therefore, in this paper, an idea was proposed to improve the performance of the Coati Optimization Algorithm in corrective search ability. Optimization algorithms also needed guidance for search, and the stronger and faster this guidance, the better the quality of the obtained solutions. In this paper, a new fitness function was introduced, which had better performance compared to other fitness functions. Before applying the search algorithm to the data generation problem, first, the control flow graph of the program under test had to be drawn. Then, the execution paths of this program were extracted from the graph, so that the algorithm could generate test data by running on it. However, it should be noted that we would not be able to cove
Due to the direct contact between electrode and scalp, dry EEG electrodes are exposed to increased mechanical wear compared to conventional gel-based electrodes. However, state-of-the-art commercial cap systems common...
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The chance to preserve a life is provided by early identification of cervical cancer, which is the fourth most common malignancy among women globally. Early diagnosis can lower its frequency. However, due to a number ...
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In this paper, we present a novel dual broadband antenna tailored for vehicular applications. The antenna design incorporates circular and square patches, metal cylinders, and conductive vias. Our simulated results de...
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Predicting patient mortality risk in intensive care units (ICUs) is one of the tasks that has strategic significance in improving clinical decisions and health care outcomes. Disease mortality monitoring methods based...
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ISBN:
(数字)9798350368697
ISBN:
(纸本)9798350368703
Predicting patient mortality risk in intensive care units (ICUs) is one of the tasks that has strategic significance in improving clinical decisions and health care outcomes. Disease mortality monitoring methods based on machine learning models have shown efficacy; however, their susceptibility to adversarial attacks in the input data presents reliability and robustness challenges. The present work addresses these challenges by introducing an effective ensemble model enriched with adversarial training to increase the performance of mortality prediction models in the ICU context. The developed methodology combines a variety of ensemble methods, such as random forest, extreme gradient boosting, bagging, AdaBoost, extra trees, and the light gradient boosting machine. These approaches work by combining several algorithms and employing adversarial training strategies that put the stakeholder’s data in their correct order and bar data tampering at all points of the model development ecosystem. The set of experiments performed with the help of real ICU datasets proved that this approach provides better accuracy, robustness, and reliability of predictions than standard models do. The extra trees algorithm achieved the best accuracy among the tested models.
With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring o...
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