A bio-inspired swarm intelligence technique for social aware cognitive radio handovers

Cognitive communication model perform the investigation and surveillance of spectrum in cognitive radio networks abetment in advertent primary users (PUs) and in turn help in allocation of transmission space for secondary users (SUs). In effective performance of regulation of wireless channel handover strategy in cognitive computing systems, new computing models are desired in operating set of tasks to process business model, and interact naturally with humans or machine rather being programmed. Cognitive wireless network are trained via artificial intelligence (AI) and machine learning (ML) algorithms for dynamic processing of spectrum handovers. They assist human experts in making enhanced decisions by penetrating into the complexity of the handovers. This paper focuses on learning and reasoning features of cognitive radio (CR) by analyzing primary user (PU) and secondary user (SU) data communication using home location register (HLR) and visitor location register (VLR) database respectively. The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.

Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers

Modified Alexnet architecture for classification of diabetic retinopathy images

The explosive popularity of small-cell and Internet of Everything devices has tremendously increased traffic loads. This increase has revolutionised the current network into 5G technology, which demands increased capacity, high data rate and ultra-low latency. Two of the research focus areas for meeting these demands are exploring the spectrum resource and maximising the utilisation of its bands. However, the scarcity of the spectrum resource creates a serious challenge in achieving an efficient management scheme. This work aims to conduct an in-depth survey on recent spectrum sharing (SS) technologies towards 5G development and recent 5G-enabling technologies. SS techniques are classified, and SS surveys and related studies on SS techniques relevant to 5G networks are reviewed. The surveys and studies are categorised into one of the main SS techniques on the basis of network architecture, spectrum allocation behaviour and spectrum access method. Moreover, a detailed survey on cognitive radio (CR) technology in SS related to 5G implementation is performed. For a complete survey, discussions are conducted on the issues and challenges in the current implementation of SS and CR, and the means to support efficient 5G advancement are provided.

/pdf/5g-technology-towards-dynamic-spectrum-sharing-using-36lchze925.pdf

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

People give more importance concerning the overall quality of the modernized ecosystem. The pollution of air is one of the significant problems to be resolved as it restricted the ecological transformation of the modernized ecosystem. Therefore, it is fundamental to evaluate the implication of these ecological issues to enhance the urban ecosystem. This vital purpose of this research is to propose a canonical correlation analysis based hyper basis feedforward neural network classification (CCA-HBFNNC) model for evaluating sustainable urban environmental quality. The CCA-HBFNNC model initially acquires a large size of U.S. air pollution dataset as input. Then, a canonical correlative analysis based feature selection algorithm is applied in the CCA-HBFNNC model to select the key pollutant features, which bear fundamental implications to the modernize air pollution to maintain the level of urban sustainability. After the feature selection process, the CCA-HBFNNC model applies the HYPER BASIS FEEDFORWARD NEURAL NETWORK CLASSIFICATION (HBFNNC) algorithm in order to classify input air data based on chosen pollutants features. During the classification process, the HBFNNC algorithm used three critical layers namely hidden, output and input layers for efficiently categorizing each input data as higher or lower pollution level with higher accuracy. If the level of air pollution on the urban environment is higher, finally CCA-HBFNNC model significantly reduces the pollution level. In this way, the CCA-HBFNNC model attains improved urban sustainability levels when compared to sophisticated operation. An experimental evaluation of the CCA-HBFNNC model is determined in terms of CCA-HBFNNC model, time complexity and false-positive rate in consideration of the diversified number of air data retrieved from the big data sets. An investigational result shows that the proposed CCA-HBFNNC model can increases the sustainability level and minimizes the time complexity of urban development when contrasted with contemporary works.

https://link.springer.com/content/pdf/10.1007/s11063-020-10327-3.pdf

Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability

Cognitive radio systems necessitate the incorporation of cooperative spectrum sensing among cognitive users to increase the reliability of detection. We have found that cooperative spectrum sensing...

An Efficient Optimized Handover in Cognitive Radio Networks using Cooperative Spectrum Sensing

Salient region detection and segmentation from biological images is often a crucial step for image understanding. The initial contour selection during segmentation being a competent task and wrong differentiation between the foreground and background colours are compromised. In this paper, improved cell detection is introduced by using a region-based cell detection and segmentation method called Histogram Colour Contrast Seed Point Selection (HCC-SPS). In each pixel, the HCC model is able to group similar colour values, therefore addressing colour contrast in visual signal, resulting in accurate desired edge points. Second, considering the energy function, region-based seed point fine tunes the salient value and makes differentiation between salient and background points easier. Third, due to salient mapping function with pixel representation, the segmentation of biological images, done accurately. The results are compared with the existing system based on the parameters such as accuracy rate, segmentation time and mapping functions.

Region-based seed point cell segmentation and detection for biomedical image analysis

Cloud machine learning (CML) techniques offer contemporary machine learning services, with pre-trained models and a service to generate own personalized models This paper presents a completely unique emotional modeling methodology for incorporating human feeling into intelligent systems The projected approach includes a technique to elicit emotion factors from users, a replacement illustration of emotions and a framework for predicting and pursuit user’s emotional mechanical phenomenon over time The neural network based CML service has better training concert and enlarged exactness compare to other large scale deep learning systems Opinions are important to almost all human activities and cloud based sentiment analysis is concerned with the automatic extraction of sentiment related information from text With the rising popularity and availability of opinion rich resources such as personal blogs and online appraisal sites, new opportunities and issues arise as people now, actively use information technologies to explore and capture others opinions In the existing system, a segmentation ranking model is designed to score the usefulness of a segmentation candidate for sentiment classification A classification model is used for predicting the sentiment polarity of segmentation The joint framework is trained directly using the sentences annotated with only sentiment polarity, without the use of any syntactic or sentiment annotations in segmentation level However the existing system still has issue with classification accuracy results To improve the classification performance, in the proposed system, cloud integrate the support vector machine, naive bayes and neural network algorithms along with joint segmentation approaches has been proposed to classify the very positive, positive, neutral, negative and very negative features more effectively using important feature selection Also to handle the outliers we apply modified k-means clustering method on the given dataset It is used to cloud cluster the outliers and hence the label as well as unlabeled features is handled efficiently From the experimental result, we conclude that the proposed system yields better performance than the existing system

H. Anandakumar

Papers

A bio-inspired swarm intelligence technique for social aware cognitive radio handovers

Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers

An Efficient Optimized Handover in Cognitive Radio Networks using Cooperative Spectrum Sensing

Region-based seed point cell segmentation and detection for biomedical image analysis

Classification of sentence level sentiment analysis using cloud machine learning techniques