Maternal health during pregnancy is influenced by various factors that significantly impact pregnancy outcomes. This paper aims to highlight these critical factors, promote awareness, and advocate proactive self-care ...
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Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is *** security approaches are being constantly developed to protect against evolving *** ensem...
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Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is *** security approaches are being constantly developed to protect against evolving *** ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior *** research work aimed to create a more diverse and effective ensemble *** this end,selected six classification models,Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbor(KNN),Decision Tree(DT),Support Vector Machine(SVM),and Random Forest(RF)from existing study to run as independent *** the individual models were trained,a Correlation-Based Diversity Matrix(CDM)was created by determining their *** models for the ensemble were chosen by the proposed Modified Minimization Approach for Model Subset Selection(Modified-MMS)from Lower triangular-CDM(L-CDM)as *** proposed algorithm performance was assessed using the Network Security Laboratory—Knowledge Discovery in Databases(NSL-KDD)dataset,and several performance metrics,including accuracy,precision,recall,and *** selecting a diverse set of models,the proposed system enhances the performance of an ensemble by reducing overfitting and increasing prediction *** proposed work achieved an impressive accuracy of 99.26%,using only two classification models in an ensemble,which surpasses the performance of a larger ensemble that employs six classification models.
Breast cancer in women is a significant public health concern worldwide, with many cases going undiagnosed until the advanced stages. Early detection is crucial for proper treatment and improved outcomes. There are so...
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Early detection of crop diseases is an art to prevent further loss in the yield. It not only prevents further spread but also increases the revenue for the farmers. Leaf is a major part of any plant and its good healt...
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ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and co...
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Delay Tolerant Networks (DTNs) have the ability to make communication possible without end-to-end connectivity using store-carry-forward technique. Efficient data dissemination in DTNs is very challenging problem due ...
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Early detection of crop diseases is an art to prevent further loss in the yield. It not only prevents further spread but also increases the revenue for the farmers. Leaf is a major part of any plant and its good healt...
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Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions i...
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Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas
The current research on domain text classification is primarily based on word granularity to facilitate the extraction and representation of text information. However, this approach is susceptible to the influence of ...
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In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is...
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