Breast cancer (BC) is one of the most significant threats to women’s health worldwide, affecting one in eight women and causing over 42,250 deaths in 2024. Early detection plays a crucial role in improving patient ou...
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In this letter, we propose a joint distributed estimation and channel estimation algorithm for wireless sensor networks (WSNs). We assume a random gain channel model with a Beta prior, where the channel gain is an att...
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By enabling a highly accurate examination of the chest x-ray, deep learning, for example, is changing the methods of recognizing lung disorders. In order to classify lung diseases, such as bacterial pneumonia, viral p...
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Data quality assessment is one of the most fundamental operations executed during data integration. Data validity is a collection of validation rules applied to the dataset’s attributes. The validation rules provided...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
Soldering irons are a hand tool that is indispensable in the process of making small series of electronic devices. Soldering irons have evolved from very simple devices without temperature control to devices with comp...
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Traditionally, conical ridge horn antennas are used for feeding large reflectors, but they can cause grating lobes in arrays. This paper introduces a compact Vivaldi antenna for monopulse radar, featuring a planar fee...
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Predicting the outcomes of Formula 1 (F1) races presents a significant challenge due to the complex interplay of numerous factors, including driver skill, vehicle performance, team strategy, and unpredictable race-day...
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The rapid proliferation of Internet of Things (IoT) devices has led to a substantial increase in network packet traffic, raising significant privacy concerns. Although traffic encryption is employed to protect the pri...
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The rapid proliferation of Internet of Things (IoT) devices has led to a substantial increase in network packet traffic, raising significant privacy concerns. Although traffic encryption is employed to protect the privacy of IoT devices, attackers can still leverage Machine Learning (ML) and Deep Learning (DL) techniques to classify device types by analyzing packet characteristics, such as size and timing. The main challenges in the state of the art are the lack of effective methods for exposing privacy violations in encrypted IoT traffic, and the absence of robust defense mechanisms to mitigate privacy breaches caused by network traffic analysis. Considering these challenges, this study presents two key contributions: (i) a novel vector-based classification method that enhances device-type identification from encrypted IoT traffic using advanced ML and DL techniques, and (ii) a robust defense mechanism based on Differential Privacy (DP) and advanced padding techniques against traffic analysis attacks. Therefore, the study examines privacy risks associated with sequential IoT device data and evaluates the effectiveness of ML algorithms using two datasets. The results demonstrate that the proposed vector-based classification method significantly improves the attacker’s classification accuracy, even when privacy-preserving techniques, such as padding, are used to obscure device-type classification. For this purpose, the study evaluates eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for IoT traffic classification, achieving an accuracy rate of 99.61% with XGBoost, 96.74% with LSTM, and 96.94% with GRU. Additionally, the Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and GRU classification algorithms are also evaluated and compared with the XGBoost and LSTM classifiers for the proposed attack model. As a defense mechanism, DP is applied using the Fourier Perturbation Algorithm (FPA) to optimize padd
Surveillance drones equipped with video transmission capabilities play a crucial role in modern security systems, with the integration of OpenCV for object detection marking a significant advancement. This study evalu...
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