The Internet of Things, sometimes known as IoT, is a relatively new kind of Internet connectivity... more The Internet of Things, sometimes known as IoT, is a relatively new kind of Internet connectivity that connects physical objects to the Internet in a way that was not possible in the past. The Internet of Things is another name for this concept (IoT). The Internet of Things has a larger attack surface as a result of its hyperconnectivity and heterogeneity, both of which are characteristics of the IoT. In addition, since the Internet of Things devices are deployed in managed and uncontrolled contexts, it is conceivable for malicious actors to build new attacks that target these devices. As a result, the Internet of Things (IoT) requires self-protection security systems that are able to autonomously interpret attacks in IoT traffic and efficiently handle the attack scenario by triggering appropriate reactions at a pace that is faster than what is currently available. In order to fulfill this requirement, fog computing must be utilised. This type of computing has the capability of inte...
Because of the vast number of applications and the ambiguity in application methods, handwritten ... more Because of the vast number of applications and the ambiguity in application methods, handwritten character recognition has garnered widespread recognition and increased prominence in the community of pattern recognition researchers ever since it was first developed. This is due to the fact that application methods can be quite ambiguous. Computer in the cloud, on the other hand, allows for suitable network access on demand to a shared pool of customizable computing resources and digital devices. According to those knowledgeable in the subject, the standard filtering techniques are not enough when it comes to the process of denoising images. In many different approaches to machine learning, information is lost not just during the filtering process itself but also at other points during the process. When a convolutional neural network is going through its pooling operation, the internal data representation either becomes misaligned or entirely vanishes (CNN). The reconstruction of low...
This study describes a modified approach for the detection of cardiac abnormalities and QRS compl... more This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.
Three-phase induction motors are becoming increasingly popular for electric cars and industrial u... more Three-phase induction motors are becoming increasingly popular for electric cars and industrial uses because of their improved efficiency and simplicity of production, among other things. Many enterprises and industries use induction motors in several rotating applications. However, it is a difficult talent to master when it comes to controlling the speed of an induction motor for various purposes. This study examines the performance of a three-phase induction motor using approaches such as field-oriented control and direct torque control. This work utilized the fractional order Darwinian particle swarm optimization (FODPSO) method in fuzzy methodology to optimize a motor’s performance. Field-oriented control (FOC) and Direct torque control (DTC) methods are regulated by FODPSO, which is compared to standard FOC and DTC methods. MATLAB-Simulink was used to compare the outcomes of each system’s simulation model to determine which one performed the best. The support vector machine-dir...
One of the salient factors to look at during wireless network planning is developing a modified p... more One of the salient factors to look at during wireless network planning is developing a modified path loss prediction models to suit a new environment other than the one it was originally designed for. This helps to give accurate predictive outcomes. This paper seeks to demonstrate the effects of applying correction factors on radio propagation model used in planning for 4G-WiMAX network through a comparative analysis between estimated and field data collected on received power for a 4G-WiMAX site. Four existing models were considered for this research; COST 231 Hata, Extended COST 231 Hata, SUI (Stanford University Interim) and Ericsson models. In order to optimize and validate the effectiveness of the proposed models, the mean square error (MSE) and correlation coefficient were calculated for each model between the predicted and the measured received power for the selected area before and after applying an appropriate correction factor. Based on this, the Extended COST-231 Hata prediction model proved to correlate well with the measured values since it showed least Mean Square Error (MSE) but with highest correlation coefficient. Through comparative analysis of the corrected models, the Extended COST-231 Hata model could be applied for effective planning of the radio systems in Ghana and the subregion at large.
The new IoT apps will not be able to inspire people to utilize them and may ultimately lose all t... more The new IoT apps will not be able to inspire people to utilize them and may ultimately lose all their potential if an interoperable and trustworthy ecosystem is not provided. IoT has its extra security difficulties such as information storage, administration, privacy concerns, and authentication. The presently deployed IoT apps have encountered various security and privacy assaults globally. Due to being less secure and low powered, the IoT devices present a simple entryway to the adversaries to obtain access to the corporate networks, leading to giving easy control over all of the data of the users. The objective of this doctorate proposed work will be to solve the security associated difficulties in multiple IoT domains like the e-commerce, vehicular ad hoc networks (VANET), mobile ad hoc networks (MANET), and Internet of Drones (IoD). The proposed study focuses on the development of a distributed framework for IoT based on blockchain. The framework includes the usage of Ethereum-...
is study describes a modi ed approach for the detection of cardiac abnormalities and QRS complexe... more is study describes a modi ed approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classi ers. e suggested technique overtakes prevailing approaches in terms of both sensitivity and speci city, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classi ers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. e technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classi ers. e results imply that the SVM classi er can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classi er also categorises ECG beats using DWT characteristics collected from ECG signals.
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Papers by Awal Halifa