Cloud‐based video streaming services:Trends,challenges,and opportunities

Cloud computing has drastically changed the delivery and consumption of live streaming content.The designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the technical and business issues surrounding cloudbased live streaming is provided,including the benefits of cloud computing,the various live streaming architectures,and the challenges that live streaming service providers face in delivering high‐quality,real‐time services.The different techniques used to improve the performance of video streaming,such as adaptive bit‐rate streaming,multicast distribution,and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined.Issues such as improving user experience and live streaming service performance using cutting‐edge technology,like artificial intelligence and machine learning are discussed.In addition,the legal and regulatory implications of cloud‐based live streaming,including issues with network neutrality,data privacy,and content moderation are addressed.The future of cloud computing for live streaming is examined in the section that follows,and it looks at the most likely new developments in terms of trends and technology.For technology vendors,live streaming service providers,and regulators,the findings have major policy‐relevant implications.Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector,as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

A Hybrid Approach for Network Intrusion Detection

Due to the widespread use of the internet and smart devices,various attacks like intrusion,zero-day,Malware,and security breaches are a constant threat to any organization’s network infrastructure.Thus,a Network Intrusion Detection System(NIDS)is required to detect attacks in network traffic.This paper proposes a new hybrid method for intrusion detection and attack categorization.The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization.In the first step,the dataset is preprocessed through the data transformation technique and min-max method.Secondly,the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model’s performance.Next,we use various Support Vector Machine(SVM)types to detect intrusion and the Adaptive Neuro-Fuzzy System(ANFIS)to categorize probe,U2R,R2U,and DDOS attacks.The validation of the proposed method is calculated through Fine Gaussian SVM(FGSVM),which is 99.3%for the binary class.Mean Square Error(MSE)is reported as 0.084964 for training data,0.0855203 for testing,and 0.084964 to validate multiclass categorization.

Motion-Based Activities Monitoring through Biometric Sensors Using Genetic Algorithm

Sensors and physical activity evaluation are quite limited for motionbased commercial devices.Sometimes the accelerometer of the smartwatch is utilized;walking is investigated.The combination can perform better in terms of sensors and that can be determined by sensors on both the smartwatch and phones,i.e.,accelerometer and gyroscope.For biometric efficiency,some of the diverse activities of daily routine have been evaluated,also with biometric authentication.The result shows that using the different computing techniques in phones and watch for biometric can provide a suitable output based on the mentioned activities.This indicates that the high feasibility and results of continuous biometrics analysis in terms of average daily routine activities.In this research,the set of rules with the real-valued attributes are evolved with the use of a genetic algorithm.With the help of real value genes,the real value attributes cab be encoded,and presentation of new methods which are represents not to cares in the rules.The rule sets which help in maximizing the number of accurate classifications of inputs and supervise classifications are viewed as an optimization problem.The use of Pitt approach to the ML(Machine Learning)and Genetic based system that includes a resolution mechanism among rules that are competing within the same rule sets is utilized.This enhances the efficiency of the overall system,as shown in the research.

Self-Management of Low Back Pain Using Neural Network

Low back pain(LBP)is a morbid condition that has afflicted several citizens in Europe.It has negatively impacted the European economy due to several man-days lost,with bed rest and forced inactivity being the usual LBP care and management steps.Direct models,which incorporate various regression analyses,have been executed for the investigation of this premise due to the simplicity of translation.However,such straight models fail to completely consider the impact of association brought about by a mix of nonlinear connections and autonomous factors.In this paper,we discuss a system that aids decision-making regarding the best-suited support system for LBP,allowing the individual to avail of reinforcement and improvement in its self-management.These activities are monitored with the help of a wearable sensor that helps in their detection and their classification as those that soothe or aggravate LBP and hence,should or should not be performed.This system helps the patients set their own boundaries and milestones with respect to suitable activities.This system also does windowing and feature extraction.The present study is an empirical and comparative analysis of the most suitable activities that patients suffering from low back pain can select.The evaluation shows that the system can distinguish between nine common daily activities effectively and helps self-monitor these activities for the efficient management of LBP.

Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.

Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network

Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2(SARS-Co V-2). The severity of the disease can be understood by the massive number of deaths and affected patients globally. If the diagnosis is fast-paced, the disease can be controlled in a better manner. Laboratory tests are available for diagnosis, but they are bounded by available testing kits and time. The use of radiological examinations that comprise Computed Tomography(CT) can be used for the diagnosis of the disease. Specifically, chest X-Ray images can be analysed to identify the presence of COVID-19 in a patient. In this paper, an automated method for the diagnosis of COVID-19 from the chest X-Ray images is proposed. The method presents an improved depthwise convolution neural network for analysing the chest X-Ray images. Wavelet decomposition is applied to integrate multiresolution analysis in the network. The frequency sub-bands obtained from the input images are fed in the network for identifying the disease.The network is designed to predict the class of the input image as normal, viral pneumonia, and COVID-19. The predicted output from the model is combined with Grad-CAM visualization for diagnosis. A comparative study with the existing methods is also performed. The metrics like accuracy, sensitivity, and F1-measure are calculated for performance evaluation. The performance of the proposed method is better than the existing methodologies and thus can be used for the effective diagnosis of the disease.