Search

Support Vector Machine (SVM) modelling approach focuses on dimensional reduction with a powerful generalization capability to solve problems such as non-linearity, and local extremes. However, the ability of SVM to learn and generalize depends on the collection of acceptable parameters that directly affect the output of the model. In addition, deploying relevant feature sets is also an issue in obtaining optimal classification. Thus, this work deploys eight swarm intelligence algorithms to improve the SVM classifier's accuracy by conducting two experiments: feature selection and tuning SVM parameters using the obtained feature set. The deployed swarm intelligence algorithms include Particle swarm optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), Salp Swarm Algorithm (SSA), Bat Algorithm (BA), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Multi Verse Optimization (MVO). The undertaken experiments were on six medical benchmark datasets that are of various dimensions. Evaluation was based on two metrics: classification accuracy and size of feature set. This work's significance is to present the comparison and determine which swarm algorithm is the best to be used in a large size medical dataset. The experimental results support existing literature that noted the swarm intelligence are useful feature selection as well as optimizing SVM parameter while maintaining acceptable accuracy. [ABSTRACT FROM AUTHOR]

According to a 2010 study, cervical cancer affects women all over the world and is the fourth most common disease among women after lung, colorectal, and breast cancer. Numerous studies are being conducted to improve pre-cancer screening methods. It is challenging for doctors to collect correct data due to inconsistent and unsystematic analysis techniques. Making a determination just from eye inspection is challenging. It is probably erroneous based on the sample of microscopic images studied. This is because to the blurring, noise, shadow, lighting, and artefact issues that can damage a microscopic image. This study has modified Bradley's binarization method in order to improvise nucleus detection on pap smear image. The modification method has performance that overcome other methods which is Bernsen, Bradley, Feng, Niblack, Nick, Otsu, Sauvola and Wolf. The analysis indicate modification method has value 75.03% on Fmeasure, 92.78% on accuracy, 13.61 on PSNR and 0.086 on NRM. This study helps the modification method inspired by Bradley shows an improvement in segmenting nucleus region. [ABSTRACT FROM AUTHOR]

Many traditional network encryption models are ill-suited for integration within Software Defined Networks (SDNs) due to their detachment from the control plane and their distinct programmable attributes. An SDN, functioning as a software component on a server, furnishes essential resources and intelligence for managing data plane programming within a network context. However, the adoption of SDNs brings forth challenges, such as concerns regarding scalability and reliability within centralized architectures. To counter these, the control plane's physical decentralization is contemplated, though this approach introduces its own set of complexities. This paper introduces a novel solution in the form of a double layer controller aimed at enhancing SDN security. The approach encompasses performance assessment metrics like packet loss, latency, bit rate, and network congestion. Notably, vulnerabilities arise from the potential remote manipulation of switch configurations to connect with an OpenFlow-capable controller. Acknowledging the limitations of existing methods, especially their inability to accommodate large-scale networks, the proposed technique leverages a double layer controller and employs multi-flow state dynamics to gather core network status information before conveying it to users. This approach not only enhances resilience and dependability compared to prior methodologies but also triggers alerts in response to unhealthy network metrics, thereby elevating accuracy and fortifying network security. [ABSTRACT FROM AUTHOR]

In this research, we evaluate the performance of total noise effect on two dimension (2- D) optical code-division multiple access (OCDMA) performance systems using 2-D Modified Double Weight MDW under various link parameters. The impact of the multi-access interference (MAI) and other noise effect on the system performance. The 2-D MDW is compared mathematically with other codes which use similar techniques. We analyzed and optimized the data rate and effective receive power. The performance and optimization of MDW code in OCDMA system are reported, the bit error rate (BER) can be significantly improved when the 2-D MDW code desired parameters are selected especially the cross correlation properties. It reduces the MAI in the system compensate BER and phase-induced intensity noise (PIIN) in incoherent OCDMA The analysis permits a thorough understanding of PIIN, shot and thermal noises impact on 2-D MDW OCDMA system performance. PIIN is the main noise factor in the OCDMA network. [ABSTRACT FROM AUTHOR]