International Journal on Future Revolution in Computer Science & Communication Engineering
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An Efficient Medical Image Processing Approach Based on a Cognitive Marine Predators Algorithm
Get PDFImage processing aims to enhance the image's quality such that it is simple for both people and robots to understand. Medical image processing and Biomedical signal processing have many conceptual similarities. Medical image processing involves evaluation, enhancement, and presentation. The focus of medical imaging is on obtaining photographs for both therapeutic and diagnostic reasons. In the existing Marine Predator Algorithm, different disadvantages are experienced when various automated optimization algorithms are used to the problem of ECG categorization. The proposed method follows the flow outlined here: data collection, image preprocessing using histogram equalization, segmentation using the Otsu threshold algorithm, feature extraction using the contour method, feature selection using the Neighborhood Component Analysis (NCA) algorithm, and Cognitive Marine Predator Algorithm (CMPA) as the proposed method. By using the Cognitive Marine Predators Algorithm (CMPA), base layers are fused to use the greatest feasible parameters, producing enhanced high-quality output images. Finally, the image processing performance is analyzed. The proposed approaches overcome the drawbacks of existing algorithms and increase the quality of medical images efficiently. 
Research-Based on Telecommunication in Mobile Service Provider's Performance using Enhanced Naive Bayes Classifier
Get PDFIn recent years, mobile service providers have rapidly expanded across all countries. Considering unpredictable development trends, mobile service providers are essential to knowledge-based service businesses. Performance may be improved by creating and disseminating new information through innovation activities based on the usage of business intelligence. This research examined the performance of mobile service providers across all countries utilizing an enhanced Naive Bayes classifier based on telecommunication. In comparison to quantitative variables, the naive Bayes performs quite well. In the beginning, data is collected and the normalization technique is used for data preprocessing. Feature extraction is carried out using “Term Frequency and Inverse Document Frequency (TF-IDF)”. “Decision Tree algorithm” is used for data analysis. Then the feature is selected using a two-stage Markov blanket algorithm. Enhanced Naïve Bayes Classifier is the proposed algorithm for telecommunication analysis and at last, the performance of the system is analyzed. This proposed algorithm is used to compare the mobile service provider's performances with existing algorithms. The proposed method measures the following metrics as Throughput, Packet loss, Packet duplication, and User quality of experience. The proposed algorithm is more effective and produces better results. 
6LoWPAN in Wireless Sensor Network with IoT in 5G Technology for Network Secure Routing and Energy Efficiency
Get PDFToday, interconnection and routing protocols must discover the best solution for secure data transformation with a variety of smart devices due to the growing influence of information technology, such as Internet of Things (IoT), in human life. In order to handle routing concerns with regard to new interconnection approaches like the 6LoWPAN protocol, it is required to offer an improved solution. This research propose novel technique in 6LoWPAN network secure routing and energy efficiency (EE) for WSN in IoT application based on 5G technology. Here the energy optimization has been carried out using clustered channel aware least square support vector machine (Cl_CHLSSVM). Then the secure routing has been carried out using fuzzy based Routing Protocol for low-power and Lossy Networks with kernel-particle swarm optimization (Fuz_RPL_KPSO). To serve needs of IoT applications, proposed method is cognizant of both node priorities as well as application priorities. Applications' sending rate allocation is modeled as a constrained optimization issue.Pxperimental analysis is carried out in terms of throughput of 96%, weighted fairness index of 77%, end-to-end delay of 59%, energy consumption of 86%, and buffer dropped packets of 51%
Design and Analysis of Reversible Data Hiding Using Hybrid Cryptographic and Steganographic approaches for Multiple Images
Get PDFData concealing is the process of including some helpful information on images. The majority of sensitive applications, such sending authentication data, benefit from data hiding. Reversible data hiding (RDH), also known as invertible or lossless data hiding in the field of signal processing, has been the subject of a lot of study. A piece of data that may be recovered from an image to disclose the original image is inserted into the image during the RDH process to generate a watermarked image. Lossless data hiding is being investigated as a strong and popular way to protect copyright in many sensitive applications, such as law enforcement, medical diagnostics, and remote sensing. Visible and invisible watermarking are the two types of watermarking algorithms. The watermark must be bold and clearly apparent in order to be visible. To be utilized for invisible watermarking, the watermark must be robust and visibly transparent. Reversible data hiding (RDH) creates a marked signal by encoding a piece of data into the host signal. Once the embedded data has been recovered, the original signal may be accurately retrieved. For photos shot in poor illumination, visual quality is more important than a high PSNR number. The DH method increases the contrast of the host picture while maintaining a high PSNR value. Histogram equalization may also be done concurrently by repeating the embedding process in order to relocate the top two bins in the input image's histogram for data embedding. It's critical to assess the images after data concealment to see how much the contrast has increased. Common picture quality assessments include peak signal to noise ratio (PSNR), relative structural similarity (RSS), relative mean brightness error (RMBE), relative entropy error (REE), relative contrast error (RCE), and global contrast factor (GCF). The main objective of this paper is to investigate the various quantitative metrics for evaluating contrast enhancement. The results show that the visual quality may be preserved by including a sufficient number of message bits in the input photographs
Numerical Simulation and Design of COVID-19 Forecasting Framework Using Efficient Data Analytics Methodologies
Get PDFThe COVID-19 pandemic hit globally in December 2019 when a certain virus strain from Wuhan, China started proliferating throughout the world. By the end of March 2020, lockdowns and curfews were imposed all over the world halting trade, commerce, education, and various other essential activities. It has been nearly a year since the WHO declared a pandemic but there is still a consistent rise of the cases even with the administration of various types of vaccines and preventive measure. One of the main struggles that the healthcare workers face is to find out the how the virus is spreading amongst a community. The knowledge of this can be used to stop the spread of virus. This is a very important step towards getting things back into momentum to restore activities globally. Many attempts have been made under epidemiology to study the spread of COVID and many mathematical models have emerged as a result that can help with this. A popular model that is used for estimating the effective reproduction number (Rt) has the shortcoming that it cannot simultaneously forecast the future number of cases. This work explores an extension of another model, the SIR-model, in which the model parameters are fitted to recorded data. This makes the model adaptive, opening up the possibilities for estimating the Rt daily and making predictions of future number of confirmed cases. The paper use this adaptive SIR-model (aSIR) to estimate the Rt and create forecasts of new cases in India. The paper purpose is to determine how precise aSIR-models are at estimating the Rt (when compared with FHM’s model). It will also analyze how accurate aSIR-models are at simultaneously forecasting the future spread of Covid-19 in India. The coronavirus spread can be mathematically modelled using factors such as the number of susceptible people, exposed people, infected people, asymptotic people and the number of recovered people. The Khan-Atangana system is an integer-order coronavirus model that uses the above-mentioned factors. Since the coronavirus model depends on the initial conditions, the Khan-Atangana model uses the Atangana-Baleanu operate as it has a non-variant and non-local kernel. Instead, we replace the equations with fractional-order derivatives using the Grünwald-Letnikov derivative. The fractional order derivatives need to be fed with initial conditions and are useful to determine the spread due to their non-local nature. This project proposes to solve these fractional-order derivatives using numerical methods and analyse the stability of this epidemiological model
Brain Tumor Image Processing Using Fine-Tuned Resnet-101 Classification Model
Get PDFMedical image processing relies heavily on the diagnosis of brain tumor images. It aids doctors in determining the correct diagnosis and management. One of the primary imaging methods for studying brain tissue is MR imaging. In recent years, deep learning techniques have shown significant potential in image processing. However, the modest quantity of medical images is a restriction of the classification of medical images. As a result of this restriction, fewer medical photos are available. Fine-tuned ResNet-101 (FR-101) is proposed to classify the brain tumor images to counteract this issue. Weiner filter is used to de-noise the acquired raw MR images, and the adaptive histogram equalization technique is used to improve contrast. A stacked autoencoder is utilized in the segmentation procedure to separate the tumor from healthy brain parts from the preprocessed data. The marker-based watershed technique is used to identify the tumor location and structure in the segmented data. The recommended approach is then used in the classification stage. To obtain the highest level of accuracy for our research, accuracy, precision, f1-score, recall, and mean absolute error are the measures of success are studied as well as a comparison of the suggested approach with a few other existing methods
Design and Modeling of Stock Market Forecasting Using Hybrid Optimization Techniques
Get PDFIn this paper, an artificial neural network-based stock market prediction model was developed. Today, a lot of individuals are making predictions about the direction of the bond, currency, equity, and stock markets. Forecasting fluctuations in stock market values is quite difficult for businesspeople and industries. Forecasting future value changes on the stock markets is exceedingly difficult since there are so many different economic, political, and psychological factors at play. Stock market forecasting is also a difficult endeavour since it depends on so many various known and unknown variables. There are several ways used to try to anticipate the share price, including technical analysis, fundamental analysis, time series analysis, and statistical analysis; however, none of these approaches has been shown to be a consistently reliable prediction tool.
We built three alternative Adaptive Neuro-Fuzzy Inference System (ANFIS) models to compare the outcomes. The average of the tuned models is used to create an ensemble model. Although comparable applications have been attempted in the literature, the data set is extremely difficult to work with because it only contains sharp peaks and falls with no seasonality. In this study, fuzzy c-means clustering, subtractive clustering, and grid partitioning are all used. The experiments we ran were designed to assess the effectiveness of various construction techniques used to our ANFIS models. When evaluating the outcomes, the metrics of R-squared and mean standard error are mostly taken into consideration. In the experiments, R-squared values of over.90 are attained
Numerical Simulation and Design of Machine Learning Based Real Time Fatigue Detection System
Get PDFThe proposed research is a step to implement real time image segmentation and drowsiness with help of machine learning methodologies. Image segmentation has been implemented in real time in which the segments of mouth and eyes have been segmented using image processing. Input can be provided by the help of real time image acquisition system such as webcam or internet of things based camera. From the video input, image frames has been extracted and processed to obtain real time features and using clustering algorithms segmentation has been achieved in real time. In the proposed work a Support Vector Machine (SVM) based machine learning method has been implemented emotion detection using facial expressions. The algorithm has been tested under variable luminance conditions and performed well with optimum accuracy as compared to contemporary research
Enhancement for Secured File Storage Using Modern Hybrid Cryptography
Get PDFIn a wide range of applications, from cloud storage to chat messaging, security is a major issue. In today's business world, there are several security dangers as well as a fiercely competitive environment. Thus we want a secure file storage solution to safeguard and convey their confidential data. Cryptography is a technique for encrypting or decrypting data to store information secretly and conceal its true meaning. The existing techniques include the fact that heavily encrypted, valid, and digitally signed material might be hard to obtain, even for an authorized user, at a time when access is essential for making decisions. This research suggests a modern hybrid cryptographic method to strengthen the security of file storage. The proposed algorithm follows the flow mentioned here: data collection, normalization technique is used for data preprocessing, and Advanced Encryption Standard (AES) is used for data encryption. Combining symmetric and asymmetric algorithms contributed to the growth of the modern hybrid cryptography algorithm. There are two types of encryption algorithms: Data Encryption Standard (DES), which is symmetric, and Rivest, Shamir, & Adleman (RSA), which is asymmetric. These two types of algorithms are then compared to see how well they perform in terms of encryption/decryptions time, key generation time, & file size. The proposed algorithm is very effective in enhancement for secured file storage using modern hybrid cryptography
Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization
Get PDFD2D communication technology enables the User Equipment (UE) in 5G networks to instantly connect with other UEs, with or without partial infrastructure involvement. In a Cloud Assisted energy harvesting system, it has improved user numbers and data transmission between mobile nodes. This research propose energy harvesting for mobile cloud computing in enhancing the QoS and latency of the network. The main aim of this research is to enhance energy optimization using discrete energy efficient offloading algorithm. The routing has been optimized using fuzzy logic cognitive Bellman-Ford routing algorithm. To identify the failing node and find an alternative node to deliver the seamless services, an unique weight-based approach has been presented. The method relies on two working node parameters: execution time and failure rate. Threshold values are specified for the parameters of the chosen master node. By contrasting the values with the threshold values, the alternative node is chosen. The experimental results shows comparative analysis in terms of throughput of 96%, QoS of 96%, latency of 28%, energy consumption of 51%, end-end delay of 41%, average power consumption of 41% and PDR of 85