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Aim: The primary aim of this paper is to improve the exactness of the classification of coronary illness predictions using the help of machine learning algorithms. We will be comparing the novel Random Forest with the Support Vector Machine to find out which of these can give us the best accuracy. Material and Methods: The study used 184 samples with Novel Random Forest and Support vector machine is executed with varying training and testing splits for anticipating the accuracy for coronary illness prediction with the 80% of G-power value and the Coronary disease data were gathered from several web sources and included current research findings, a threshold of 0.05%, a confidence range of 95%, a mean, and a standard deviation. The accuracy rate of the classifiers is used to evaluate their performance using the Coronary disease dataset. The difference between the two groups is statistically negligible. (p = 0.225; p > 0.05). Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest is 90.16% and support vector machine is 81.97%. Conclusion: Expectation of heart infection using the Novel Random Forest (RF) algorithm appears to be significantly better than the Support Vector Machine (SVM) with improved accuracy.

Aim: The cardiac is a vital organ in the human body. Life is entirely dependent on the functional workings of the heart. Cardiovascular disease refers to diseases of the cardiac and the blood vessels that supply it. The primary goal of this article is to perform on the exactness of the arrangement of coronary disease expectation utilizing the assistance of AI calculations. We will be comparing the novel Random forest with Naive Bayes to find out which of these can give us the best accuracy. Material and Methods: The study used 1402 samples with Novel Random Forest and Naive Bayes is executed with varying training and testing splits for foreseeing the exactness for coronary illness forecast with the 80% of G-power value and cardiovascular disease information were collected from various website domains using the most recent research and a threshold of 0.05%, a 95% uncertainty range, a mean, and a confidence interval. The accuracy rate of the classifiers is used to evaluate their performance using the heart disease dataset. A statistically insignificant difference exists among the two groups p=0.199; p>0.05. Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest 90.16% and Naive Bayes 85.25% is obtained. Conclusion: Prediction of Heart disease using With enhanced accuracy, the novel Random Forest(RF) the technique appears to be far better to the Naive Bayes(NB).

Aim: Coronary illness is one of the main sources of mortality in this current world. A basic test in the field of clinical information assessment is the expectation of cardiovascular infection. The fundamental target of this paper is to work on the exactness of the Data mining algorithms being used to improve heart disease prediction. We will be comparing the novel Random forest with K Nearest Neighbor to find out which of these can give us the best accuracy. Material and Methods: The study used 98 samples with Novel Random Forest and K Nearest Neighbor is executed with varying training and testing splits for predicting the accuracy for heart disease prediction with the 80% of G-power value and data about cardiovascular disease was gathered from numerous web sources, including latest research findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the accuracy rate of the classifiers is examined using the heart disease dataset.. There was a statistical insignificant difference among Innovative Random Forest and K Nearest Neighbor p = 0.484 (p>0.05). Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest 90.16% and K Nearest Neighbor 67.21% is obtained. Conclusion: Prediction of Heart disease using the innovative Random Forest (RF) technique appears to be a significant improvement over the K Nearest Neighbor (KNN) algorithm in terms of accuracy.

Aim: Heart attacks are usually caused due to blockages, partially or completely, of the heart’s veins or arteries that constrict the flow of blood from or to the heart. The primary objective of this review aims to be seen as the most appropriate algorithm to give us the ideal prediction. We will be comparing the novel Random forest with Logistic regression to find out which of these can give us the best accuracy. Material and Methods: The study used 143 samples with novel Random Forest and Logistic Regression is executed with varying training and testing splits for foreseeing the accuracy of coronary disease prediction with the 80% of G-power value and heart disease data were gathered from multiple web sources, including latest The study’s findings and criterion were 0.05%, with a 95% probability value, average, and confidence interval. The performance accuracy rate of the classifiers is used to evaluate the coronary disease dataset. There was a statistically significant value test between the novel Random Forest and Logistic Regression is 0.046 (p

Artificial intelligence (AI) is a machine intelligence technology with vast potential for a smart industrial revolution. This study aims to create new electric-powered transportation such as bicycles, motorbikes, and other vehicles. The combination of high-speed, resiliency, and low-latency connection, as well as AI and Internet of Things (IoT) technologies, will enable the transformation to fully autonomous vehicles. This electric motorcycle tracks its speed, location, and other data using IoT logic. A smart charging system has also been developed to keep them ready to use effectively. The proposed research also reduces the damage to the environment efficiently.

The extensive use of sensor technology in every sphere of life, along with the continuous digitization of society, makes it realistic to anticipate that the planet will soon be patched with small-sized devices all over the place in the not-too-distant future. These devices give monitoring and surveillance capabilities, as well as access to a vast digital universe of information, among other things. Finding data and information, as well as processing enquiries, is made much easier thanks to the seamless transmission of information over wireless media, which is based on the “anywhere, anytime, everywhere” paradigm that allows information to be sent anywhere, at any time. Sensing networks came into existence as a consequence of the downsizing of wireless devices that are capable of receiving information from a source, transferring it, and processing it. Sensor networks, although they share many of the features, uses, and limits of ad hoc networks, have their own set of capabilities that are unique to them. While performing their responsibilities, sensor networks must contend with a variety of security issues, including unsecured wireless channels, physical compromise, and reprogramming. Because of the small size and ubiquitous availability of wireless sensor networks, compromise attacks are the most severe kind of attack in this environment (WSNs). With the proliferation of wireless sensor networks (WSNs), it is becoming more difficult to rely only on machine learning techniques. We sought to tackle the security challenge by developing a key management system as well as a secure routing mechanism. We are building scalable key management approaches that are resistant to node compromise and node replication attacks, which we will demonstrate in our proposed study, by using deployment-driven localization of security information and leveraging distributed key management. Using a security-aware selection of the next hop on the route to the destination, we were able to design safe routing algorithms that were effective.

Mobile ad hoc networks (MANET) have been seen as a related advancement to Group Key Management (GKM) applications. Remembering the true objective to guarantee amass applications and disallow uncertified clients from getting to the correspondence data that cannot be anchored by a remote MANET, including IP multicast, the singular gathered data content must remain encoded by a typical shared gathering key. Key administration is required to anchor the assurance of gathering the key and to safeguard those gathering data. GKM framework is associated with the remote system condition partners with three issues: execution, security, and system versatility. This article focuses on the Unmanned Aerial Vehicle (UAV)-mobile backbone node (MBN) remote system performance. The UAV-MBN Network condition is a military system that includes a proposal to group an important administrative structure. A half-and-half gathering key administration technique, which works on each target of UAV-MBN, is included in an arrangement to start two basic remote gathering key administration difficulties: (1) operational performance and (2) multiple-enrollment development. By working with minimal small-scale key administration, this strategy can diminish the execution cost associated with the key administration along with the increment operational execution of remote GKM. Scaled-down key organization is carried out in the context of these movement units. The key administration approach also restricted the operational procedure and decreased the operation’s cost in terms of key generation, figuring, and associated correspondence. Overall, the HGKM strategy that has been introduced enhances the operational process and functions effectively in reasonable remote areas.

Virtualization plays an indispensable role in improving the efficacy and agility of cloud computing. This process involves in assigning resources to the cloud application users based on the requirements, where most of the resources are being virtual in nature. These resources are utilized by the users for implementing tasks for a certain time period. Virtualization assists in effective usage of hardware resources. Based on the application, the users may require definite amount of resources to be utilized in a definite time period. Thus, a deadline that is a start time and end time for every resource needs to be considered. Deadline specifically relates to time limit for the implementation of tasks in the workflow. In this paper, the resource allocation is optimized based on deadline as the optimization parameter using modified fish swarm algorithm (FSA).

The improvements in tuning the website and improving the visitors’ retention are done by deploying the efficient weblog mining and navigational pattern prediction model. This crucial application initially performs data clearing and initialization procedures until the hidden knowledge is extracted as output. To obtain good results, the quality of the input data has to be promisingly good, and hence, more focus should be given to pre-processing and data cleaning operations. Other than this, the major challenge faced is the poor scalability during navigational pattern prediction. In this paper, the scalability of weblog mining is improved by using suitable pre-processing and data cleaning operations. This method uses a tree-based clustering algorithm to mine the relevant items from the datasets and to predict the navigational behavior of the users. The algorithm focus will be mainly on density-based learning to cluster and predict future requests. The proposed method is evaluated over BUS log data, where the data is of greater significance since it contains the log data of all the students in the university. The conducted experiments prove the effectiveness and applicability of weblog mining by using the proposed algorithm.