The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compress...
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Precision payload delivery methods currently involve a winch to lower a payload attached to a cable or an actuated parachute with a closed-loop control system to adjust the trajectory of the payload. We propose a nove...
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Due to advancement in network technology, digital data is very popular medium for communication but eased to duplication and manipulation. As a result, diverse challenges related to information security and authentica...
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In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods f...
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With the rapid growth of IoT devices, ensuring robust network security has become a critical challenge. Traditional intrusion detection systems (IDSs) often face limitations in detecting sophisticated attacks within h...
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Due to advancements in artificial intelligence there are numerous deep fake images and video collections are available on the Internet and social media. The primary aim of the study is to analyse the deep fake images ...
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ISBN:
(数字)9798331518578
ISBN:
(纸本)9798331518585
Due to advancements in artificial intelligence there are numerous deep fake images and video collections are available on the Internet and social media. The primary aim of the study is to analyse the deep fake images and videos since the quality is constantly improving a novel method was developed for accurate detection of quality deep fakes. The suggested approach uses a customised convolutional neural network (CNN) technique that uses facial landmark detection to extract structured data from photos and video frames before feeding it into the methods of CNN. The customised CNN model is augment-based CNN for generation of fake images and fake data. Involves about 260 films from the data set in which 202 images are made up while others were real images. About 300 videos are used in which 250 videos are fake and 50 were real the proposed model achieved the accuracy of 95.58% and 0.97 AUC score that outperforms the existing models like MLP-CNN and CNN. Additionally, the method succeeds with greater accuracy then the conventional models like DST -Net, VGG 16 Efficient Net. This research study's primary goal is to create a new CNN learning method for identifying high-quality deep fake photos and videos.
Incoherent optical DNN accelerators (OAs) are booming thanks to unparalleled performance-per-watt and excellent scalability. To boost their innovative development, a recent work revolutionarily proposed automatic OA s...
ISBN:
(纸本)9798350323481
Incoherent optical DNN accelerators (OAs) are booming thanks to unparalleled performance-per-watt and excellent scalability. To boost their innovative development, a recent work revolutionarily proposed automatic OA search. However, the robustness and the acceleration performance of the generated OAs are below expectation, because the impacts of inter-tile data transfer and fabrication process & thermal variations (i.e., PTVs) on OAs were ignored. Both hinder OA design automation from being a reality. To resolve theses challenges, we develop FIONA, a novel framework for Fine-grained Incoherent Optical DNN Accelerator search towards both superior acceleration efficiency and inference robustness. Compared against 5 state-of-the-art incoherent OAs on 9 DNN benchmarks, extensive experiments and ablation studies validate the effectiveness of FIONA, achieving up to 198.01× acceleration efficiency improvement based on guaranteed robustness.
Today’s world highly depends on the web pages, applications, internet in their day-to-day life. To better understand and meet the demands of web-based applications, web usage mining employs data mining approaches to ...
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A patient affected by autism experiences significant impacts on their daily life, but early intervention can help mitigate its effects. In this research work, we introduced a more accurate and lightweight model to ide...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
A patient affected by autism experiences significant impacts on their daily life, but early intervention can help mitigate its effects. In this research work, we introduced a more accurate and lightweight model to identify Autism Spectrum Disorder (ASD) using the Toddlers dataset. Several data processing techniques were applied to the dataset before prediction: SMOTETomek, independent t-tests, and MinMaxScaler for balancing the dataset, statistical analysis, and data normalization, respectively. Finally, we applied eight machine learning (ML) classifiers Decision Tree (DT), Catboost, XGBoost, Voting, Stacking, Extra Trees, Gaussian Naive Bayes, and Bernoulli Naive Bayes for classification after selecting the one most significant feature based on Chi-squared scores. The experimental results show that the DT classifier outperformed the other classifiers and the current state-of-the-art models, achieving an accuracy of 100% in early ASD detection. This research work provides insightful information for healthcare professionals for automating ASD screening.
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