Cyberbullying is a severe issue that impacts teens and adults alike. Errors like hopelessness and suicide have resulted from it. The demand for material on social media platforms to be regulated is growing. In the wor...
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The news ticker is a common feature of many different news networks that display headlines and other *** ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory ***...
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The news ticker is a common feature of many different news networks that display headlines and other *** ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory *** this paper,we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news *** primary emphasis of this research is on ticker recognition methods and storage *** that end,the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification *** proposed learning architecture considers the grouping of homogeneousshaped *** incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual ***,experiments with a novel ArabicNews Ticker(Al-ENT)dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested *** proposed method attains 96.5%,outperforming the current state-of-the-art technique by 8.5%.The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.
Phase-resolved partial discharge (PRPD) measurement has been used for decades as a method of monitoring defects in electrically insulating materials. More recently, it has seen a renewed interest in the context of fla...
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Phase-resolved partial discharge (PRPD) measurement has been used for decades as a method of monitoring defects in electrically insulating materials. More recently, it has seen a renewed interest in the context of flash sintering, a novel ceramic densification process where the sample to be densified is subjected to an electric field in addition to the usual application of heat. In the context of flash sintering, the monitoring of partial discharge (PD) activity has shown that this activity increases when approaching the onset of the thermal runaway phenomenon leading to the quick densification of the material, and is influenced by environmental factors such as relative humidity or pressure. A new microcontroller-based PRPD measurement system architecture has recently been proposed as a means to explore this PD activity in further details. While PDRD measurement is traditionally carried out by comparing the measured partial discharge pattern to the waveform of the voltage applied to the device under test (DUT), we show in this work that expanding this bespoke measurement system to be able to simultaneously monitor the waveform of the current going through the DUT allows for the collection of data related to the electrical power transferred to the DUT during the process that displays peculiar features. In the present work, the DUT consists of a classical needle-plane setup. As pressure decreases down from atmospheric levels, the threshold voltage leading up to the apparition of discharges decreases following a trend similar to the classical Paschen curve. Additionally, the nature of the discharge activity transitions from low-amplitude, rapid-firing tightly packed trains of pulses to high-amplitude, longer-lasting and more spread out pulses. Simultaneous measurement of the discharges, applied voltage and current going through the DUT shows that this second type of discharge activity can be synchronous with an asymmetric, distorted current waveform having the same per
Artificial Intelligence (AI) has become an integral part of our lives, finding applications across various industries. Search algorithms play a crucial role in AI. This paper focuses on the comparison of different sea...
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Domain or statistical distribution shifts are a key staple of the wireless communication channel, because of the dynamics of the environment. Deep learning (DL) models for detecting multiple-input multiple-output (MIM...
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The crucial part of IoT-controlled devices is the collection of accurate data. However, manufacturers often use low-cost sensors to make everyday home devices affordable, which can compromise accuracy. Therefore, we i...
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In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure...
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In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.
The idea of the traditional histogram shifting technique is to hide a message within the cover-image pixel distribution. However, the embedding capacity is limited by the peak point occurrences. To solve this problem,...
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Recently, third-order cumulants (TOCs) as a non-conventional odd-order statistics, has been introduced to direction-of-arrival (DOA) estimation with the third-order nested array (TONA), which significantly increases t...
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Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input *** general,digital image denoising algorithms,executed on computers,present latency due to several iterations impleme...
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Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input *** general,digital image denoising algorithms,executed on computers,present latency due to several iterations implemented in,e.g.,graphics processing units(GPUs).While deep learning-enabled methods can operate non-iteratively,they also introduce latency and impose a significant computational burden,leading to increased power ***,we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images–implemented at the speed of light propagation within a thin diffractive visual processor that axially spans<250×λ,whereλis the wavelength of *** all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features,causing them to miss the output image Field-of-View(FoV)while retaining the object features of *** results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of~30–40%.We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz *** to their speed,power-efficiency,and minimal computational overhead,all-optical diffractive denoisers can be transformative for various image display and projection systems,including,e.g.,holographic displays.
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