Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the *** has increased the flexibility and transparency of the managed,centralized,and controlled *** the other ha...
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Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the *** has increased the flexibility and transparency of the managed,centralized,and controlled *** the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and *** issues have a significant detrimental impact on organizations,enterprises,and even ***,high performance,and real-time systems are necessary to achieve this *** a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last *** this paper,a novel HFS-LGBM IDS is proposed for ***,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature *** thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature *** optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of *** experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.
Polycystic Ovary Syndrome (PCOS) is a hormonal issue that occurs in women of adulthood. The World Health Organization (WHO) has identified PCOS as a prevalent endocrine illness that affects around 10% of women globall...
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ISBN:
(纸本)9798350372816
Polycystic Ovary Syndrome (PCOS) is a hormonal issue that occurs in women of adulthood. The World Health Organization (WHO) has identified PCOS as a prevalent endocrine illness that affects around 10% of women globally. It can result in various health complications, which includes infertility, metabolic complications such as insulin resistance, obesity, as well as cardiovascular problems, sleep apnea, endometrial cancer, and psychological disorders like anxiety and depression. Hence early diagnosis of PCOS is crucial. One of the diagnosis methods used for its detection is the Rotterdam criteria or Consensus. This diagnostic approach includes three criteria: Oligovulation or anovulation, presence of hyperandrogenism, and the identification of polycystic ovaries through ultrasound examination. Patients who meet two or more of these criteria can be diagnosed with PCOS. Cysts may indicate Polycystic Ovarian Disease (PCOD), a condition similar to PCOS. In PCOD, the ovaries release numerous immature or partially-mature eggs, which can develop into cysts over time. Among the numerous available techniques in the machine learning domain, only one criterion is typically assessed at a time, either clinical data or ultrasound, but not both simultaneously. The proposed system considers both and has two sections to aid in this process - one for detection through images and the other through clinical data. The dataset for the system includes 781 PCOS and 1143 Non-PCOS images, as well as clinical data from 541 patients, including 177 with PCOS and 43 features collected from open sources. Several models and techniques are used for the detection individually. A novel feature selection approach for CS-PCOS is employed, utilizing an optimized chi-squared mechanism. Additionally, overfitting is assessed using ten-fold cross-validation. Different pre-trained models are tried out for ultrasound images and the best is taken. Random forest is considered the best model for clinical data with
The use of machine learning models in intrusion detection systems (IDSs) takes more time to build the model with many features and degrade the performance. The present paper proposes an ensemble of filter feature sele...
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A sort of programming language characterized as a general-purpose language is applicable to a variety of applications. These languages typically have flexible and expressive syntax and semantics, allowing them to be u...
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Text classification is the most significant task in the data retrieval process through classifying text into various groups depending on the document’s content. The quick progression of electronic documents may produ...
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The proposed paper represents a new method for fish freshness detection based on deep learning and image processing. Our approach is an effective, accurate and Non-Invasive tool to classify fish quality using Convolut...
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As a coastal state, agricultural instability reduces output in Tamil Nadu. Agricultural traits and features provide data that may be used to get insights into Agri-facts. With the advent of the information technology ...
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Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stag...
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Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.
Cloud computing distributes task-parallel among the various *** with self-service supported and on-demand service have rapid *** these applications,cloud computing allocates the resources dynami-cally via the internet...
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Cloud computing distributes task-parallel among the various *** with self-service supported and on-demand service have rapid *** these applications,cloud computing allocates the resources dynami-cally via the internet according to user *** resource allocation is vital for fulfilling user *** contrast,improper resource allocations result to load imbalance,which leads to severe service *** cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and *** extensive needs and lack of optimal resource allo-cating scheme make cloud computing more *** paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the *** proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address *** proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response *** proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing *** obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other *** evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.
Our project, titled Enhancing Transportation Safety with YOLO-based CNN Autonomous Vehicles', pioneers a transformative approach to autonomous driving. Through the fusion of advanced machine learning techniques, s...
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