Modulo-(2q + 2q−1 ± 1) adders have recently been implemented using the regular parallel prefix (RPP) architecture, matching the speed of the widely used modulo-(2q ± 1) RPP adders. Consequently, we introduce...
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The emerging field of quantum materials involves an exciting new class of materials in which charge,spin,orbital,and lattice degrees of freedom are intertwined,exhibiting a plethora of exotic physical *** materials in...
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The emerging field of quantum materials involves an exciting new class of materials in which charge,spin,orbital,and lattice degrees of freedom are intertwined,exhibiting a plethora of exotic physical *** materials include,but are not limited to,superconductors,topological quantum matter,and systems with frustrated spins,which enable a wide range of potential applications in biomedicine,energy transport and conversion,quantum sensing,and quantum information processing。
Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyp...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.
The continuous advancement of communication systems necessitates the development of algorithms capable of identifying and correcting errors that may arise during data transmission and storage. This pursuit of reliabil...
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ISBN:
(数字)9798331522124
ISBN:
(纸本)9798331522131
The continuous advancement of communication systems necessitates the development of algorithms capable of identifying and correcting errors that may arise during data transmission and storage. This pursuit of reliability is particularly crucial in critical systems and sectors that are challenging to access, such as space exploration, passenger transportation, and financial services. In this context, the Error Correction Code (ECC) is a fundamental tool for providing a certain degree of reliability to these systems. This research proposes a novel technique to enhance the error correction capacity of ECCs by leveraging region overlapping. Specifically, we propose correcting data areas protected by more than one ECC, which allows for the inference of logical correlations between ECCs, thereby augmenting their error detection and correction capability. Our focus is bidimensional codeword structures, commonly known as 2D-ECCs, which entail a hierarchical arrangement of ECCs. We evaluated the ECC proposal, comparing its error correction and detection capabilities. Through this evaluation, we aim to demonstrate the technique's efficacy in bolstering the reliability and resilience of communication systems, particularly in critical domains where precision and accuracy are paramount.
Electrical stimulation is a powerful tool for targeted neurorehabilitation, and recent work in adaptive stimulation where stimulation can be adjusted in real-time has shown promise in improving stimulation outcomes an...
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In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery li...
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This study presents an AI-based model using ECG signals to predict left ventricular systolic dysfunction (LVSD) in pacemaker patients. A 1D convolutional neural network (CNN) combined with large language models proces...
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Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes...
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The advent of byte-addressable persistent memory (PM) has led to a resurgence of interest in adapting existing dynamic hashing schemes to PM. Compared with its two well-known peers (extendible hashing and linear hashi...
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ISBN:
(数字)9798350380408
ISBN:
(纸本)9798350380415
The advent of byte-addressable persistent memory (PM) has led to a resurgence of interest in adapting existing dynamic hashing schemes to PM. Compared with its two well-known peers (extendible hashing and linear hashing), spiral storage has received little attention due to its limitations. After an in-depth analysis, however, we discover that it has a good potential for PM. To show its strength, we develop a persistent spiral storage called PASS (Persistence-Aware Spiral Storage), which is facilitated by a group of new/existing techniques. Further, we conduct a comprehensive evaluation of PASS on a server equipped with Intel Optane DC Persistent Memory Modules (DCPMM). Experimental results demonstrate that compared with two state-of-the-art schemes it exhibits better performance.
Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization,...
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