Browsing by Author "Odun-Ayo, Isaac"
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Item Automatic Re-Formulation of user’s Irrational Behavior in Speech Recognition using Acoustic Nudging Model(Journal of Computer Science, 2020-12-12) Ajayi, Lydia Kehinde; Azeta, Ambrose; Odun-Ayo, Isaac: In automatic speech recognition for development of automatic speech recognition applications, there has been numerous claims on the presence of speech recognition errors known as classified into lexical and acoustic errors. These errors distort speech signals thereby depreciating the accuracy and performance rate of speech recognition applications. Even though lexical speech recognition error problem has been partially combated, acoustic speech recognition error referred to as user’s acoustic irrational behavior is being ignored causing high error rate with low accuracy which is the bone of contention and an impediment factor in the wide adoption of speech recognition technology. Users do not always behave in a rational manner especially when dealing with a particular speech recognition application. The persistent presence of these user’s acoustic irrational behavior in speech have intensified the essential need to automatically detect and correct such errors, as current researches only focus on detecting user’s acoustic irrational behavior but not correcting/reformulating/re-sizing this error. Hence, this paper provides an acoustic nudging model that will perform automatic correction/reformulation of user’s acoustic irrational behavior in speech to achieve higher performance and accuracy using different acoustic parameters which are based in Pitch, Time gaps between words, Timbre descend and ascend time and Loudness. This study was able to discover a foundation for reducing error rate and achieve higher performance, as well as improve accuracy in speech recognition applications through detection and re-formulation of user’s acoustic irrational behavior in speech signal automatically, thereby making the model applicable to any speech recognition applications. The outcome of this study would be useful in enhancing accuracy and performance in the context of automatic speech recognitionItem Cloud middleware and services-a systematic mapping review(2020) Odun-Ayo, Isaac; Adebiyi, MarionCloud computing currently plays a crucial role in the delivery of vital information technology services. A unique aspect of cloud computing is the cloud middleware and other related entities that support applications and networks. A specific field of research may be considered, particularly as regards cloud middleware and services at all levels, and thus needs analysis and paper surveys to elucidate possible study limitations. The purpose of this paper is to perform a systematic mapping for studies that capture cloud computing middleware, stacks, tools and services. The methodology adopted for this study is a systematic mapping review. The results showed that more papers on the contribution facet were published with tool, model, method and process having 18.10%, 13.79%, 6.03% and 8.62% respectively. In addition, in terms of tool, evaluation and solution research had the largest number of articles with 14.17% and 26.77% respectively. A striking feature of the systemic map is the high number of articles in solution research with respect to all aspects of the features applied in the studies. This study showed clearly that there are gaps in cloud computing middleware and delivery services that would interest researchers and industry professionals desirous of research in this area.Item An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning(2021) Odun-Ayo, Isaac; Toro-Abasi, WilliamsCross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting crosssite scripting attacks