NoSQL database has gained popularity in Big data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles ...
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ISBN:
(数字)9798331542313
ISBN:
(纸本)9798331542320
NoSQL database has gained popularity in Big data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of NoSQL database, and NoSQL database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of NoSQL database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model.
Recent advancements in machine learning (ML) have sparked widespread interest in integrating DevOps capabilities into software and services within the information technology sector. This objective has compelled organi...
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Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible data Hiding (PRDH) schem...
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In order to improve video delivery, an Enhanced Explicit Port Forwarding (EEPF) solution is proposed. data are divided into ordinary packets and video packets using a packet identification mechanism. Traditional mecha...
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In this experiment, high-temperature polyethylene terephthalate (PT) was mixed with epoxy resin (ER) that had been thinned with acetone. Sisal fibers were coated with the resulting product. Composites of Coated treate...
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Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locatin...
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ISBN:
(数字)9798350390025
ISBN:
(纸本)9798350390032
Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locating individuals who are depressed in a timely and accurate manner. This research investigates the effectiveness of Long Short-Term Mem- ory (LSTM) networks used in conjunction with DistilBERT, a small transformer-based model, in identifying indications of melancholy in posts made on social networking platforms. The text was cleaned up using a dataset that was obtained from Reddit, and then we used the mixed DistilBERT+LSTM model to evaluate how well it performed in comparison to the DistilBERT model by itself. The findings that we obtained indicate that both approaches produce results that are comparable but not flawless, with only slight variations in terms of accuracy, precision, recall, and F1 scores. The fact that this is the case demonstrates that there are issues with optimizing the model and displaying the data.
Diagnosing the contents of learning in brain activities is a long-standing research task in cognitive sciences. The current studies on cognitive diagnosis (CD) in education determine the status of knowledge concept (K...
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As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and *** the booming development of science and techno...
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As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and *** the booming development of science and technology,the number of papers has been growing *** like the fact that Internet of Things(IoT)allows the world to be connected in a flatter way,how will the network formed by massive academic papers look like?Most existing visualization methods can only handle up to hundreds of thousands of node size,which is much smaller than that of academic networks which are usually composed of millions or even more *** this paper,we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks(VSAN).Nodes can represent papers or authors while the edges means the relation(e.g.,citation,coauthorship)between *** order to comprehensively improve the visualization effect,three levels of optimization are taken into account in the whole design of VSAN in a progressive manner,i.e.,bearing scale,loading speed,and effect of layout *** main contributions are two folded:(1)We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts,thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities.(2)We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels,with the ability to quickly zoom between different *** addition,we propose a“jumping between nebula graphs”method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic *** our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in t
In recent years, the Internet of Things (IoT) has become a pivotal force in transforming various sectors, with agriculture being a prominent beneficiary. The integration of smart devices, sensors, and data-driven deci...
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Following the recent advances in technology, came advanced computational domains like, Internet of Things, Machine Learning, Artificial Intelligence, datascience, and many more. These fields really tend to help manki...
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ISBN:
(纸本)9781665493932
Following the recent advances in technology, came advanced computational domains like, Internet of Things, Machine Learning, Artificial Intelligence, datascience, and many more. These fields really tend to help mankind a lot. In this work, we would make use of Machine Learning aspects to perform prediction of diseases in plant. Specifically, spot the Early Blight Disease in the Potato Leaves. The potato plant, Solanum tuberosum, is a significant crop that is grown all over the world and generates large quantities of tubers that are a good source of nutrients. The potato has many medicinal benefits in addition to being a common staple diet. When the fluid out from tubers is consumed in moderation, it can treat gastric ulcers and relieve inflammation and acidity. Two harmful potato diseases, late blight and early blight, are pervasive. Everywhere potatoes are cultivated, both are present. The labels 'Early' and 'Late' allude to the relative timing of their field emergence, however both disorders might manifest simultaneously. In this work, we would focus on Early Blight. The fungus Alternaria solani, that can infect potatoes, tomatoes, several species of the potato genus, and some mustards, is the cause of early blight of potatoes. Young, actively growing plants are rarely impacted by this disease, commonly known as target spot. It first appears on elder leaves. Warm temperatures and heavy humidity foster Early Blight. This disease affects the tuber symptomized by dark, rounded to irregular dots being developed on the tuber. As the disease develops, the flesh of the potatoes commonly becomes water-soaked yellow to greenish yellow. For this work, we have collected a set of nearly 1000 samples of Early Blight affected Potato Leaves. Using that, we have modelled a Machine Learning Classification Paradigm that could potentially predict the occurrence of Early Blight Disease making using of classical classifier algorithms. Medical science have advanced to a great height.
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