Browsing by Author "Adetiba, Emmanuel"
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Item Evolution of artificial intelligence languages – a systematic literature review(arXiv, 2020) Adetiba, Emmanuel; John, Temitope M.; Akinrinmad, Adekunle A.The field of Artificial Intelligence (AI) has undoubtedly received significant attention in recent years. AI is being adopted to provide solutions to problems in fields such as medicine, engineering, education, government and several other domains. In order to analyze the state of the art of research in the field of AI, we present a systematic literature review focusing on the Evolution of AI programming languages. We followed the systematic literature review method by searching relevant databases like SCOPUS, IEEE Xplore and Google Scholar. EndNote reference manager was used to catalog the relevant extracted papers. Our search returned a total of 6565 documents, whereof 69 studies were retained. Of the 69 retained studies, 15 documents discussed LISP programming language, another 34 discussed PROLOG programming language, the remaining 20 documents were spread between Logic and Object Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA programming languages. This review provides information on the year of implementation, development team, capabilities, limitations and applications of each of the AI programming languages discussed. The information in this review could guide practitioners and researchers in AI to make the right choice of languages to implement their novel AI methodsItem Experimentations with OpenStack System Logs and Support Vector Machine for an Anomaly Detection Model in a Private Cloud Infrastructure(2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020, 2020) Akanle, Matthew; Adetiba, Emmanuel; Victor AkandeAnomaly detection is a crucial aspect of cloud computing that is becoming increasingly challenging. This is because a huge amount of system logs is usually generated in both private and public cloud infrastructure, thereby complicating manual inspection by System Administrators. In order to address this challenge, an experimental investigation was carried out in this study towards the development of an anomaly detection model for OpenStack private cloud infrastructure. Firstly, OpenStack system logs were curated from the Loghub corpus as experimental dataset for the study. The logs were parsed using Iterative Partitioning Log Mining (IPLoM) algorithm to produce structured event log templates. Discriminative numerical features were extracted from the event log templates using Term Frequency Inverse Document Frequency (TF-IDF) algorithm. Thereafter, Support Vector Machine (SVM) classifier with varying kernels was trained to evolve an acceptable classifier experimentally. The SVM classifier with linear and RBF kernels outperformed other kernels with acceptable accuracy, precision, recall and Fmeasure.Item LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset(Journal of Computer Science, 2021-01-07) Adetiba, Emmanuel; Ajayi, Oluwaseun T.; Kala, Jules R.Plants are very important living organisms on earth because humans and animals depend on them for nutrition, oxygen, medicine and balance in the ecosystem. Therefore, plant species recognition is critical to the improvement of agricultural productivity, mitigation of climate change and the discovery of new medicinal plants. However, species recognition has remained a difficult task even for trained botanists, because using the traditional approaches, an expert on a specie may be unfamiliar with others. Thus, researchers and practitioners are increasingly interested in the automation of species recognition problem. Recently, deep learning algorithms such as Convolutional Neural Network (CNN) have provided huge breakthroughs in various computer vision tasks compared to their shallow predecessors. Deep learning automates features extraction by learning salient representations of the data and subsequently classifies the features using a supervised learning approach. Inspired by this capability, we leveraged on five pre-trained CNN models and Leafsnap image dataset of 185 plant species to experimentally evolve an accurate species recognition model in this study. Among the pre-trained models, MobileNetV2 with ADAM optimizer gave the highest testing accuracy of 92.33%. This result provides a basis for developing a mobile app for automated species recognition on the field. This will augment existing efforts to alleviate the difficulties of manual species recognition by botanists, farmers, biologists, nature tourists as well as conservationistsItem Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey(2022) Nzanzu, Patrick Vingi; Adetiba, Emmanuel; Noma-Osaghae, EtinosaCloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, expItem A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem(Applied Computational Intelligence and So Computing, 2021) Molo, Mbasa Joaquim; Badejo, Joke A.; Adetiba, EmmanuelCloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. %is technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of %ings (IoT), several challenges have emerged with these new technologies. %erefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.