The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known...
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The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just
Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to d...
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Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to detect hate speech, there are significant research gaps. First, most studies used text data instead of other modalities such as videos or audio. Second, most studies explored traditional machine learning algorithms. However, due to the increase in complexities of computational tasks, there is need to employ complex techniques and methodologies. Third, majority of the research studies have either been evaluated using very few evaluation metrics or not statistically evaluated at all. Lastly, due to the opaque, black-box nature of the complex classifiers, there is need to use explainability techniques. This research aims to address these gaps by detecting hate speech in English and Kiswahili languages using videos manually collected from YouTube. The videos were converted to text and used to train various classifiers. The performance of these classifiers was evaluated using various evaluation and statistical measurements. The experimental results suggest that the random forest classifier achieved the highest results for both languages across all evaluation measurements compared to all classifiers used. The results for English language were: accuracy 98%, AUC 96%, precision 99%, recall 97%, F1 98%, specificity 98% and MCC 96% while the results for Kiswahili language were: accuracy 90%, AUC 94%, precision 93%, recall 92%, F1 94%, specificity 87% and MCC 75%. These results suggest that the random forest classifier is robust, effective and efficient in detecting hate speech in any language. This also implies that the classifier is reliable in detecting hate speech and other related problems in social media. However, to understand the classifiers’ decision-making process, we used the Local Interpretable Model-agnostic Explanations (LIME) technique to explain the
Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to...
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Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein...
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The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are ha...
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The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.
The Telecare Medical Information System (TMIS) faces challenges in securely exchanging sensitive health information between TMIS nodes. A Mutual Authenticated Key Agreement (MAKA) scheme is used to eliminate security ...
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Based on the retail inventory operation of Heilan Home,this study incorporates the price factor into inventory environment involving trapezoidal time-varying products.A joint pricing and ordering issue with deteriorat...
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Based on the retail inventory operation of Heilan Home,this study incorporates the price factor into inventory environment involving trapezoidal time-varying products.A joint pricing and ordering issue with deteriorating items under partial backlogged shortages is firstly explored in a fixed selling *** corresponding optimization model aiming at maximizing profit performance of inventory system is developed,the theoretical analysis of solving the model is further provided,and the modelling frame generalizes some inventory models in the existing ***,a solving algorithm for the model is designed to determine the optimal price,initial ordering quantity,shortage time point,and the maximum inventory ***,numerical examples are presented to illustrate the model,and the results show the robustness of the proposed model.
Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversari...
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Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det
Question classification (QC) is a process that involves classifying questions based on their type to enable systems to provide accurate responses by matching the question type with relevant information. To understand ...
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An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multipl...
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