Prediction of Football Match Results Based on Edge Computing and Machine Learning Technology

With the rapid development of artificial intelligence, various machine learning algorithms have been widely used in the task of football match result prediction and have achieved certain results. However, traditional machine learning methods usually upload the results of previous competitions to the cloud server in a centralized manner, which brings problems such as network congestion, server computing pressure and computing delay. This paper proposes a football match result prediction method based on edge computing and machine learning technology. Specifically, we first extract some game data from the results of the previous games to construct the common features and characteristic features, respectively. Then, the feature extraction and classification task are deployed to multiple edge nodes.Finally, the results in all the edge nodes are uploaded to the cloud server and fused to make a decision. Experimental results have demonstrated the effectiveness of the proposed method.

Scope of Improvements in Aerospace Sector by Implementation of IoT

While considering the aerospace domain, the internet of things (IoT) provides the way for new development and this IoT technology allows many possibilities in the aerospace domain. This study aims to examine the theoretical aspect of IoT in the aerospace industry. And propose a system that enhances the flight journey experience from the flight booking of each customer. This will also improve the manufacturing end-to-end process in the aerospace industry with the help of IoT sensors. These can be achieved with help of the data collection (Previous sensor data), cloud computing, and machine learning. As per the proposing system, all IoT sensor data will be collected and saved the data in the cloud server. These data will be used for training the algorithm to achieve the optimum solution in the future.

Digital Twins by Physical Education Teaching Practice in Visual Sensing Training System

The paper expects to improve the efficiency and intelligence of somatosensory recognition technology in the application of physical education teaching practice. Firstly, the combination of induction recognition technology and the Internet is used. Secondly, through the Kinect sensor, bone data are acquired. Finally, the hidden Markov model (HMM) is used to simulate the experimental data. On the simulation results, a gait recognition algorithm is proposed. The gait recognition algorithm is used to identify the motion behaviour, and the results are displayed in the Web (World Wide Web) end built by the cloud server. Meantime, in view of the existing problems in the practice of physical education, combined with the establishment and operation of the Digital Twins (DTs) system, the camera source recognition architecture is carried out since the twin network and the two network branches share weights. This paper analyses these problems since the application of somatosensory recognition technology and puts forward the improvement methods. For the single problem of equipment in physical education, this paper puts forward the monitoring and identification function of the cloud server. It is to transmit data through Hypertext Transfer Protocol (HTTP) and locate and collect data through a monitoring terminal. For the lack of comprehensiveness and balance of sports plans, this paper proposes a scientific training plan and process customization based on Body Mass Index (BMI), analyses real-time data in the cloud, and makes scientific customization plans according to different students’ physical conditions. Moreover, 25 participants are invited to carry out the exercise detection and analysis experiment, and the joint monitoring of their daily movements is tested. This process has completed the design of a feasible and accurate platform for information collection and processing, which is convenient for managers and educators to comprehensively and scientifically master and manage the physical level and training of college students. The proposed method improves the recognition rate of the camera source to some extent and has important exploration significance in the field of action recognition.

Performance Analysis of Cloud-based Health Care Data Privacy System Using Hybrid Techniques

At present, due to the various hacking approaches, the protection for any data transmitted through any channel or mode is one of the important issues. Nowadays, providing data security is satisfactory, developments are extended for obtaining data among the transceivers. Security level depends on the size of a symmetric key which is employed for encryption and decryption using various cryptography systems management and in modern approaches like block and RF codes including AES use a larger size of key simultaneously and there exists security problems due to hacking approaches. To illustrate the protection level and hacking problems, a new ECC is presented as well as by employing scalar duplication, the synchronous key is generated and consists of point doubling and point addition. The created focuses are encrypted before transmission by using ECC-Elgamal-Holomorphic (ECCEH) and transferred through a distant channel and encipher data is failed at the receiver using ECCEH which includes the reverse process. The unique standards of cryptography context have been generated by MATLAB; the defined framework has endeavored to the extent that speed, delay as well as control, and many others are accepted in MATLAB 2017a. The user of the sender, the original information is transformed into integer value by employing Holomorphic and encodes it by utilizing the Elgamal ECC algorithm which employs point doubling and point addition. The encoded information is uploaded into the cloud for storage, here www.thingspeak.com is utilized for storage. When the user presents at the receiver request the cloud to access from it, initially the cloud server authenticates the access control strategies of the requester, and then access is provided by the cloud server. If the user authenticates the strategies, then encoded data can download and the original data is decoded by synchronous key employing ECC- Elgamal algorithm. Using original and decrypted data, various performance factors are calculated in terms of execution time, packet delivery ratio, throughput, latency and compare these results with conventional methods and found to be 12%, 31%, 24%, and 8% progress concerned with packet delivery ratio, latency, outturn and execution time.

Rank-based risk target data analysis using digital twin on oil pipeline network based on manifold learning

Oil and Gas Pipeline (OGP) projects face a wide scope of wellbeing and security Risk Factors (RFs) all around the world, especially in the oil and gas delivering nations having influencing climate and unsampled data. Lacking data about the reasons for pipeline risk predictor and unstructured data about the security of the OGP prevent endeavors of moderating such dangers. This paper, subsequently, means to foster a risk analyzing framework in view of a comprehensive methodology of recognizing, dissecting and positioning the related RFs, and assessing the conceivable pipeline characteristics. Hazard Mitigation Methods (HMMs), which are the initial steps of this approach. A new methodology has been created to direct disappointment investigation of pinhole erosion in pipelines utilizing the typical pipeline risk strategy and erosion climate reenactments during a full life pattern of the pipeline. Hence in the proposed work, manifold learning with rank based clustering algorithm is incorporated with the cloud server for improved data analysis. The probability risk rate is identified from the burst pressure by clustering the normal and leak category to improve the accuracy of the prediction system experimented on the lab-scale oil pipeline system. The numerical results like auto-correlation, periodogram, Laplace transformed P-P Plot are utilized to estimate the datasets restructured by the manifold learning approach. The obtained experimental results shows that the cloud server datasets are clustered with rank prioritization to make proactive decision in faster manner by distinguishing labelled and unlabeled pressure attributes.

Provably Secure Authentication Approach for Data Security in Cloud Using Hashing, Encryption, and Chebyshev-Based Authentication

Secure and efficient authentication mechanism becomes a major concern in cloud computing due to the data sharing among cloud server and user through internet. This paper proposed an efficient Hashing, Encryption and Chebyshev HEC-based authentication in order to provide security among data communication. With the formal and the informal security analysis, it has been demonstrated that the proposed HEC-based authentication approach provides data security more efficiently in cloud. The proposed approach amplifies the security issues and ensures the privacy and data security to the cloud user. Moreover, the proposed HEC-based authentication approach makes the system more robust and secured and has been verified with multiple scenarios. However, the proposed authentication approach requires less computational time and memory than the existing authentication techniques. The performance revealed by the proposed HEC-based authentication approach is measured in terms of computation time and memory as 26ms, and 1878bytes for 100Kb data size, respectively.

Smarter Data Availability Checks in the Cloud

Cloud computing offers clients flexible and cost-effective resources. Nevertheless, past incidents indicate that the cloud may misbehave by exposing or tampering with clients' data. Therefore, it is vital for clients to protect the confidentiality and integrity of their outsourced data. To address these issues, researchers proposed cryptographic protocols called “proof of storage” that let a client efficiently verify the integrity or availability of its data stored in a remote cloud server. However, in these schemes, the client either has to be online to perform the verification itself or has to delegate the verification to a fully trusted auditor. In this chapter, a new scheme is proposed that lets the client distribute its data replicas among multiple cloud servers to achieve high availability without the need for the client to be online for the verification and without a trusted auditor's involvement. The new scheme is mainly based on blockchain smart contracts. It illustrates how a combination of cloud computing and blockchain technology can resolve real-world problems.

An Improvement of Yield Production Rate for Crops by Predicting Disease Rate using Intelligent Decision System

Agriculture is the country's mainstay. Plant diseases reduce production and thus product prices. Clearly, prices of edible and non-edible goods rose dramatically after the outbreak. We can save plants and correct pricing inconsistencies using automated disease detection. Using light detection and range (LIDAR) to identify plant diseases lets farmers handle dense volumes with minimal human intervention. To address the limitations of passive systems like climate, light variations, viewing angle, and canopy architecture, LIDAR sensors are used. The DSRC was used to receive an alert signal from the cloud server and convey it to farmers in real-time via cluster heads. For each concept, we evaluate its strengths and weaknesses, as well as the potential for future research. This research work aims to improve the way deep neural networks identify plant diseases. Google Net, Inceptionv3, Res Net 50, and Improved Vgg19 are evaluated before Biased CNN. Finally, our proposed Biased CNN (B-CNN) methodology boosted farmers' production by 93% per area.

Deep Reinforcement Learning based pedestrian and independent vehicle safety fortification using intelligent perception

The Light Detection and Ranging (LiDAR) sensor is utilized to track each sensed obstructions at their respective locations with their relative distance, speed, and direction; such sensitive information forwards to the cloud server to predict the vehicle-hit, traffic congestion and road damages. Learn the behaviour of the state to produce an appropriate reward as the recommendation to avoid tragedy. Deep Reinforcement Learning and Q-network predict the complexity and uncertainty of the environment to generate optimal reward to states. Consequently, it activates automatic emergency braking and safe parking assistance to the vehicles. In addition, the proposed work provides safer transport for pedestrians and independent vehicles. Compared to the newer methods, the proposed system experimental results achieved 92.15% higher prediction rate accuracy. Finally, the proposed system saves many humans, animal lives from the vehicle hit, suggests drivers for rerouting to avoid unpredictable traffic, saves fuel consumption, and avoids carbon emission.

A Study on Autonomous Driving Simulation Using Deep Learning Process Model

Along with artificial intelligence technologies, deep learning technology, which has recently received a great deal of attention, has been studied on the basis of developed artificial neural networks. This thesis deals with the detection, recognition, judgment, and control that are included in the basic technologies of the autonomous driving subsystems to achieve fully autonomous driving. And this work solves many problems in this area. The use of the CARLA simulation in this project is the development of a deep learning intelligent autonomous driving system in the road environment. Autonomous driving recognizes the situation by processing the data collected through images from multiple sensors or lidars and cameras in real-time. In the cloud server process using real data, explore various deep learning models for traffic flow prediction, return the model trained onboard, perform the prediction and solve the problem of fully autonomous driving, including a module of control, which is a CARLA simulation.