The goal of generalized zero-shot learning (GZSL) is to transfer knowledge from seen classes to unseen classes. However, a significant challenge is the single-category attributes are often inadequate to capture the in...
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The aim of this study was to build ten novel deep learning models using the GraphModo system and then evaluate their ability to detect lung abnormalities in medical *** research employs accuracy, precision, recall, F1...
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Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smar...
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In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare ...
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In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational *** proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic *** approach not only improves the *** are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning *** are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble *** implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline *** train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor *** used Monte Carlo simulations to extract these *** on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.
Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial *** the increasing...
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Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial *** the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may ***,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these *** overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)*** employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect *** integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user *** results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline *** approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.
In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these chal...
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Underwater robots rely on high-quality visual data for precise monitoring and manipulation, yet complex underwater environments often degrade image quality through color distortion, texture blurring, and detail loss. ...
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Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dy...
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Oral squamous cell carcinoma (OSCC) is among the most lethal types of cancers, especially realizing the significance of timely diagnosis. Insufficient training and awareness among primary healthcare practitioners migh...
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Oral squamous cell carcinoma (OSCC) is among the most lethal types of cancers, especially realizing the significance of timely diagnosis. Insufficient training and awareness among primary healthcare practitioners might result in more difficult treatments, longer hospital stays, and worse survival rates. This study aims to develop a lightweight model that diagnoses cancer in its early stages and incorporates deep transfer training methods with strategies based on convolution networks and domain-specific features of OSCC samples. This work proposes a new light sequential fusion model based on histopathological data for the diagnosis of OSCC with oral mucosa images. To boost detection performance, EfficientNet is incorporated with convolutional neural networks (CNNs) as well as with max-pooling layers. The application of max-pooling also brings down the spatial features and takes care of edges and texture which proves to be both curtailing the computational workload and enhancing the performance of the model. The legitimacy of the developed framework was assessed on 1224 histopathological images where images were categorized under normal and OSCC classes depending on malignancy grades. Moreover, to improve the model training, the framework applies fine preprocessing and augmentation of input parameters such as rotations, zooms, flips, contrasts, and crops. These augmentations help the model learn from different views and hence boost its detection capability across all the aspects of the data. Experimental findings shows that the proposed framework had an accuracy before enhancement of 91.0% and an accuracy after enhancement of 98.9%, which is even higher than such methods as the traditional single-model and the majority of modern methods. Additionally, our model is lightweight, requiring significantly less time than 0.05 segments on an average GPU. It is also conclude that the proposed model be run in clinical settings with modest computational capabilities. These resu
We study the reliability of the following simple mechanism for spreading information in a communication network in the presence of random message loss. Initially, some nodes have information that they want to distribu...
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