The rapid expansion of Internet of Things (IoT) networks has introduced new security challenges, necessitating efficient and reliable methods for intrusion detection. In this study, a detection framework based on hype...
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This study focuses on development of a secure and efficient image cryptosystem optimized for IoT devices. The proposed technique addresses the and resource constraints of IoT devices, it introduces a hybrid encryption...
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Currently, IoT rules many unmanned applications to improve supervision and productivity. The proposed work concentrates on the need for a cooling system for solar Photovoltaic (PV) panels to enhance its efficiency. An...
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This study investigates the correlation between stock prices and sentiment trends with the help of Pearson, Spearman, and Kendall Tau correlations. his was further analyzed using hypothesis testing method. The analysi...
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作者：
Salama, Wessam M.Aly, Moustafa H.Department of Computer Engineering
Faculty of Engineering Pharos University Canal El Mahmoudia Street Beside Green Plaza Complex 21648 Alexandria Egypt OSA Member
Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Marine Transport Alexandria1029 Egypt
Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent...
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Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to improve the channel estimation and lower the error rate. In order to mitigate inter-symbol interference and map the datasets, this paper introduces M-quadrature amplitude modulation (16-QAM) and orthogonal frequency division multiplexing (OFDM), which is based on quadrature phase shift keying (QPSK). Additionally, the existence or lack of cyclic prefixes forms the basis of our simulation. Additionally, the suggested models are investigated using pilot samples 2, 4, 8, and 64. Labeled OFDM signal samples, where the labels match the signal received after applying OFDM and passing through the medium, are used to train the proposed models. The DenseNet121 functions as a powerful feature extractor to extract intricate spatial information from received signal data. Sequential models like as RNN, LSTM, and GRU are used to model temporal dependencies in the retrieved features. RF is also utilized to exploit non-linear relationships and interactions between features to further increase prediction accuracy and reduce bit error rate (BER). By comparing the models using key metrics like accuracy, bit error rate (BER), and mean squared error (MSE), superior performance is attained based on the DenseNet121_RNN_GRU_RF model. Additionally, the DLMs are assessed against traditional methods like minimal mean square error (MMSE) and least squares (LS). Using the DenseNet121_RNN_GRU_RF model indicates a considerable gain over alternative architectures, with an improvement of 36.3% over DensNet121-RNN-LSTM-RF, according to a comparison of the suggested models without cyclic prefix for OFDM_QPSK. The improvement in percentages of roughly 63.3% over DensNet121-RNN-LSTM, 68.18% over De
Bladder cancer (BC) is characterized by the abnormal growth of cells in the bladder’s tissues. While urethral cytology can detect BC early, its sensitivity is low, necessitating more accurate diagnostic methods. This...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Bladder cancer (BC) is characterized by the abnormal growth of cells in the bladder’s tissues. While urethral cytology can detect BC early, its sensitivity is low, necessitating more accurate diagnostic methods. This research aimed to develop a machine learning-based BC classification model using clinical laboratory data. The study included 1,336 individuals with various types of cancer, including cystitis, BC, kidney cancer, uterine cancer, and prostate cancer. We employed a comprehensive data preprocessing pipeline followed by the application of eight diverse machine learning (ML) classifiers for the classification task. These classifiers included a Stacking Classifier, a Voting Classifier, a Decision Tree, a model tuned by Random Search, a Light Gradient Boosting Machine, a Multi-Layer Perceptron (MLP), a model tuned by GridSearchCV, and Gradient Boosting. The Stacking Classifier demonstrated superior performance in distinguishing BC from cystitis and other malignancies, achieving the highest accuracy (99.63%), precision (0.9963), recall (0.9963), F1 score (0.9963), specificity (0.9955), and AUC (1.0). This work highlights the potential of using clinical laboratory data with ML approaches to predict BC, offering a promising alternative to current diagnostic methods.
The images taken under varying lighting or adverse weather conditions exhibit different distributions in high-dimensional space, and make object detection networks perform poorly. In this paper, we propose a domain ad...
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Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more seriou...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more serious in multi-domain scenarios. In this paper, we propose to extract multi-layer features from the PrLM for efficient training since the training process is independent to its large backbone. Meanwhile, compared with the conventional feature extraction, we leverage prompts to induce PrLM for generating sentiment-aware features which lead to significant improvement on the sentiment analysis. In addition, most previous methods adopted a domain alignment paradigm for multi-domain learning, which becomes cumbersome when the number of domains is large. Therefore, we propose a novel prompt-augmented cross-domain contrastive learning for generalizable performance, which clusters samples with the same label under different prompts or domains. Our method is evaluated on two public multi-domain sentiment analysis benchmarks, which significantly outperforms recent state-of-the-art methods. Extensive ablation studies also verify the effectiveness of each proposed component.
The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and audit...
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ISBN:
(数字)9798331509675
ISBN:
(纸本)9798331509682
The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes.
Colorectal cancer (CRC) is a leading cause of cancer-related deaths in the United States. Reinforcement learning (RL) allows modelling the dynamic decision-making in CRC screening and early detection as a partially ob...
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