With increasing advancements in the field of telecommunication, the attainment of a higher data transfer rate is essentially a greater need to meet high-performance communication. The exploitation of the fuzzy system in the wireless telecommunication systems, especially in Fifth Generation Mobile Networks (or) 5G networks is a vital paradigm in telecommunication markets. A comprehensive survey is dealt in the paper, where it initially reviews the basic understanding of fuzzy systems over 5G telecommunication. The literature studies are collected from various repositories that include reference materials, Internet, and other books. The collection of articles is based on empirical or evidence-based from various peer-reviewed journals, conference proceedings, dissertations, and theses. Most of the existing soft computing models are streamlined to certain applications of 5G networking. Firstly, it is hence essential to provide the readers to find research gaps and new innovative models on wide varied applications of 5G. Secondly, it deals with the scenarios in which the fuzzy systems are developed under the 5G platform. Thirdly, it discusses the applicability of fuzzy logic systems on various 5G telecommunication applications. Finally, the paper derives the conclusions associated with various studies on the fuzzy systems that have been utilized for the improvement of 5G telecommunication systems.
<span>Medical image processing is an important aspect in diagnosis and treatment strategy. The tremendous volume of medical data has accelerated the need for automated analysis of this image, more so in the case Magnetic Resonance Imaging (MRI). An improved K-means algorithm and EM algorithm have been combined in the proposed approach to produce a hybrid strategy for better clustering and segmentation using Enhanced ICA. A classifier for based on Support Vector Machine (SVM) has been formulated and employed for the classification of brain tumors in Magnetic Resonance Images (MRI). The proposed SVM classifier used a kernel in the form of Gaussian radial basis function kernel (GRB kernel) to improve the classifier performance. The performance of the classifier has been validated through expert clinical opinion and calculation of performance measures. The results amply illustrate the suitability of the proposed classifier.</span>
Steganography is also known as data hiding, is a method of ensuring the security and confidentiality of digital data by hiding sensitive data in digital media. In video steganography, compression is the most demanding research area in block based video encoders. With the improvement of video coding equipment, the High Efficiency Video Coding (HEVC) delivers better coding efficiency. However, a large proportion of video frames showed system complexity and required a larger Coding Unit (CU) during encoding. Also, as the larger number of video frames were encoded, the time consumption was increased. To overcome such an issue, this research introduced the chaos with enhanced mapping technique to reduce computational complexity and fast encoding. In this research study, the position of each pixel of the secret video frame is calculated by the ELSB technique, where the existing LSB techniques have not considered this secret video frame's position which leads to high video distortion. In proposed chaos encryption with mapping method, the input video frames are encrypted by using logistic and henon mapping that simultaneously maintains quality and efficiency of the model. The HEVC technique compresses the input video frames and the proposed ELSB performs two functions namely replacement of LSB and matching of LSB which effectively embeds the secret video frames into cover video frames. Finally, the secret video frames are retrieved by HEVC technique with chaos decryption which provides security to the model. The results proved that proposed compression technique achieved PSNR of 35.81dB with average execution time of 40 seconds, but the existing H.264 compression technique achieved PSNR of 32.72dB with average execution time of 45 seconds.
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