The Internet of Things (IoT) has a massive influence on machine-to-machine (M2M) and machine-to-human (M2H) technologies, which in turn have caused major changes in society. Country's ability to provide its citize...
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The topic of team composition, recomposition, and role selection in long-term educational programs that comprise of multiple smaller courses, such as coding bootcamps, has received limited attention in the literature,...
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The large number of dimensions, complexity and constraints of real world problems is the deriving force to assess the algorithms in diverse applications scenarios. Over a past decade, nature inspired algorithm are see...
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Air pollution has a negative impact on our ability to do daily tasks and on our standard of living. Ecosystems and human well-being are under danger. Recent years have seen a notable increase in heavy industry, leavin...
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In the healthcare industry, developing an efficient diagnostic system to classify liver cancer cells is a very perplexing and arduous task. Recently, several studies demonstrate that deep ensemble classifiers can achi...
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In the healthcare industry, developing an efficient diagnostic system to classify liver cancer cells is a very perplexing and arduous task. Recently, several studies demonstrate that deep ensemble classifiers can achieve better predictive accuracy than individual deep learning classifiers. The deep ensemble learners exploit more than one individual deep learner to achieve better classification results and improved generalization performance. When implementing an ensemble learning (multiple classifier) approach, the selection of the optimum learners from a crew is a critical issue and an effective learner assortment strategy is used to achieve better results. Several researchers have applied different approaches (e.g., rule-based algorithms, evolutionary computing, simulated annealing, etc.) to determine the optimal learners that can increase the performance of the diagnostic system. This work proposes a new classifier selection strategy to construct an ensemble called a contribution-based iterative base learner removal algorithm (CIBRA). The proposed algorithm finds out the best subset of individual learners by considering both prediction accuracy and diversity. The proposed CIBRA enables each base learner in a pool to have multiple chances to partake in an iteration of selection. CIBRA drops the classifiers only if they have no residual opportunities. This procedure is reiterated till no learner in the crew has any remaining possibility to partake in the selection round. In this study, we test various decision synthesis techniques to increase the performance of the ensemble classifier. To assess the performance of CIBRA, 8 standard cancer databases are exploited. Extensive simulation results divulge that two base classifiers are enough to classify liver cancer cells from hematoxylin and eosin (H&E) scans successfully. Based on the results obtained from this study, we construct an ensemble classifier using Dropout Extreme Learning Machine (DrpXLM) and Enhanced Convo
Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the ...
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This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, B...
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Autonomous Vehicles (AVs) are perceived as a revolutionary development in the worldwide transport sector and are believed to enhance safety significantly. An AV is equipped with a range of advanced technologies that a...
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This paper introduces a knowledge graph curation framework for the generation of linked open data in the domain of cyber law and ethics called the CKGLD. This framework utilizes both static metadata and dynamically ac...
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Rice production in India holds significant importance in terms of its contribution to the economy and food security. However, the expansion of this sector is currently facing challenges due to the increasing levels of...
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