Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K's optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.

/pdf/investigations-on-brain-tumor-classification-using-hybrid-31jcilhi.pdf

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

The synchronous reluctance motor (Sync-RM) is known for its higher reliability, faster dynamic response, higher efficiency, higher speed range, higher power density, and higher torque per ampere. Consequently, the scope of research into emerging Sync-RMs drive system has been extended. For different kinds of mechanical and electrical applications, motors are important components of any machine or tool. There are several types of motors available for various kinds of industrial applications, but the benefits of Sync-RM in medium- and low-power applications have eventually been very efficient compared to other motor types, regarding the factors such as high flux density per unit volume, high efficiency, requirements of low maintenance, and low electromagnetic obstruction. This research proposed a Sync-RM drive fed by a bridgeless LUO converter (BLLC) using the Glowworm Swarm Optimization (GSO) tuned proportional integral (PI) controller. High competence, higher power density, and a small structure of the motor drive are delivered by the PI-tuned converter. The output voltages are related to the Sync-RM motor of the proposed converter. Due to its minimum effort and modest development, the Sync-RM here is chosen for the drive. The proposed Sync-RM drive framework based on the LUO converter was evaluated by simulation using the Matlab/Simulink tool.

/pdf/a-bridgeless-luo-converter-with-glowworm-swarm-optimized-1u98pfti.pdf

A Bridgeless LUO Converter with Glowworm Swarm Optimized Tuned PI Controller for Electrical Applications

Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network

Hyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusion

Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

/pdf/a-proficient-adaptive-k-means-based-brain-tumor-segmentation-5eogq7yci2.pdf

A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO

Integrating the Internet into many applications has made securing users’ data and maintaining their privacy a significant concern. In recent years, blockchains (BC) have garnered much attention due to their distinctive properties, which include decentralization, immutability, anonymity, security, and auditability. BC technology was utilized in various nonfinancial applications, like the Internet of Things (IoT), wireless sensor networks (WSN), and cloud computing. The objective of this study is to conduct an analysis of previously published research and provide a summary of the efforts put into researching BC applications for network security. In this study, many networking technologies, including IoT, Industrial IoT, Cloud, WSN, VANET, and MANET, were used in conjunction with BC technology to investigate applications for network security. This study presents an analysis of network security, along with its limitations and contributions, with an overview of the BC evolution, BC architecture, its working principle, and its application, as well as the advantages and disadvantages associated with BC. In this study, recently published articles on BC-based solutions for network security and privacy preservation that were published between 2018 and 2022 are analyzed. The surveyed articles are categorized according to the network application, methodology, and contribution. In conclusion, an analysis of the implementation of BC technology across various networks and their issues and challenges are presented. 

https://www.internationaljournalssrg.org/../IJECE/2023/Volume10-Issue6/IJECE-V10I6P107.pdf

A Study on Blockchain Technologies for Security and Privacy Applications in a Network

Breast cancer (BC) is the world’s most common and rapidly spreading disease. BC can be controlled, reducing the death rate if detected early. As a result, some researchers have recently developed deep-learning-based efficient algorithms for predicting cancer cell growth using different modalities of medical imaging, for example, mammograms, tomosynthesis, MRI ultrasound, etc., for their efficiency and accuracy. Only a few review articles on BC diagnosis synthesize some existing studies. These investigations, unfortunately, are unable to identify new structures and modalities in the diagnosis of BC. The changing frameworks of DL for BC detection are the subject of this review. This assessment explores the strengths and limitations of earlier deep-learning (DL) based methods, investigates the datasets employed, and examines image preprocessing approaches based on different medical imaging modalities. The performance results with evaluation, obstacles, and further enhancement are also provided. 

/pdf/a-deep-learning-based-methodological-analysis-for-breast-17eu9dj9.pdf

A Deep Learning Based Methodological Analysis for Breast Cancer Classification

The main intention of the work is to achieve packet transmission steadiness for any abnormal condition and to detect the invalid path in Wireless Sensor Networks (WSN), using a novel improved communication steadiness routing (ICSR) over Cluster-Chain Mobile Agent Routing (CCM) is implemented. Materials and Methods: ICSR and CCM are implemented in this research work to increase path stability and minimize energy consumption in order to improve communication. Sample size is considered using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Results: To discover the incorrect path, a void path identification technique is used if a path is not kept active for an extended period of time, nodes in the route may miss data packets while communicating to each other. As a result, an energy-efficient stable way for routing is required to minimize energy consumption and increase path stability. Path stability, network lifetime, end to end delay packet delivery ratio, and energy consumption, network overhead are the metrics used to measure the performance of ICSR and CCM models at different study groups with p<0.05. ICSR provides a higher of 94.050% compared to CCM with 79.56% in minimizing energy consumption and to increase path stability. The significant charge was 0.000 (P<0.05) that shows the two groups were statistically noteworthy. Conclusion: The Novel Improved Communication Steadiness Routing routing model's performance is compared with Cluster-Chain Mobile agent routing (CCM) model, from the results it is clear that ICSR outperforms CCM model in all the parameters.

Analysis of a Wireless Sensor Network's Performance using Novel Improved Communication Steadiness Routing over Cluster-Chain Mobile Agent Routing

In the domain of remote sensing, the classification of hyperspectral image (HSI) has become a popular topic. In general, the complicated features of hyperspectral data cause the precise classification difficult for standard machine learning approaches. Deep learning-based HSI classification has lately received a lot of interest in the field of remote sensing and has shown promising results. As opposed to conventional hand-crafted feature-based classification approaches, deep learning can automatically learn complicated features of HSIs with a greater number of hierarchical layers. Because HSI's data structure is complicated, applying deep learning to it is difficult. The primary objective of this research is to propose a deep feature extraction model for HSI classification. Deep networks can extricate features of spatial and spectral from HSI data simultaneously, which is advantageous for increasing the performances of the proposed system. The squeeze and excitation (SE) network is combined with convolutional neural networks (SE-CNN) in this work to increase its performance in extracting features and classifying HSI. The squeeze and excitation block is designed to improve the representation quality of a CNN. Three benchmark datasets are utilized in the experiment to evaluate the proposed model: Pavia Centre, Pavia University, and Salinas. The proposed model's performance is validated by a performance comparison with current deep transfer learning approaches such as VGG-16, Inception-v3, and ResNet-50. In terms of accuracy on each class of datasets and overall accuracy, the proposed SE-CNN model outperforms the compared models. The proposed model achieved an overall accuracy of 96.05% for Pavia University, 98.94% for Pavia Centre dataset, and 96.33% for Salinas dataset.

/pdf/hyperspectral-image-classification-model-using-squeeze-and-1ezweh41.pdf

Hyperspectral Image Classification Model Using Squeeze and Excitation Network with Deep Learning

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