Abiodun Esther Omolara

Bio: Abiodun Esther Omolara is an academic researcher from Universiti Sains Malaysia. The author has contributed to research in topics: Computer science & Encryption. The author has an hindex of 4, co-authored 7 publications receiving 577 citations. Previous affiliations of Abiodun Esther Omolara include Ahmadu Bello University & University of Abuja.

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Abstract: The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

A Deception Model Robust to Eavesdropping Over Communication for Social Network Systems

Abstract: Communication security deals with attributes such as confidentiality, integrity, and availability. The current strategies used to achieve covertness of communication employs encryption. Encryption techniques minimize eavesdropping on the conversation between the conversing parties by transforming the message into an unreadable form. However, it does not prevent or discourage eavesdroppers from stealing and attempting to decrypt the encrypted messages using a brute-force attack or by randomly guessing the key. The probability of the eavesdropper acquiring the key and recovering the message is high as he/she can distinguish a correct key from incorrect keys based on the output of the decryption. This is because a message has some structure-texts, pictures, and videos. Thus, an attempt at decrypting with a wrong key yields random gibberish that does not comply with the expected structure. Furthermore, the consistent increase in computational power implies that stolen encrypted data may gradually debilitate to a brute-force attack. Thus, causing the eavesdropper to learn the content of the message. To this end, the objective of this research is to reinforce the current encryption measures with a decoy-based deception model where the eavesdropper is discouraged from stealing encrypted message by confounding his resources and time. Our proposed model leverages its foundation from decoys, deception, and artificial intelligence. An instant messaging application was developed and integrated with the proposed model as a proof of concept. Further details regarding the design, analysis, and implementation of the proposed model are substantiated. The result shows that the proposed model reinforces state-of-the-art encryption schemes and will serve as an effective component for discouraging eavesdropping and curtailing brute-force attack on encrypted messages.

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Abstract: Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Abstract: The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Abstract: Microgrids have recently emerged as a building block for smart grids combining distributed renewable energy sources (RESs), energy storage devices, and load management methodologies. The intermittent nature of RESs brings several challenges to the smart microgrids, such as reliability, power quality, and balance between supply and demand. Thus, forecasting power generation from RESs, such as wind turbines and solar panels, is becoming essential for the efficient and perpetual operations of the power grid and it also helps in attaining optimal utilization of RESs. Energy demand forecasting is also an integral part of smart microgrids that helps in planning the power generation and energy trading with commercial grid. Machine learning (ML) and deep learning (DL) based models are promising solutions for predicting consumers’ demands and energy generations from RESs. In this context, this manuscript provides a comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting. It also discusses the datasets used to train and test the different DL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work. Even though there are a few related surveys regarding energy management in smart grid applications, they are focused on a specific production application such as either solar or wind. Moreover, none of the surveys review the forecasting schemes for production and load side simultaneously. Finally, previous surveys do not consider the datasets used for forecasting despite their significance in DL-based forecasting approaches. Hence, our survey work is intrinsically different due to its data-centered view, along with presenting DL-based applications for load and energy generation forecasting in both residential and commercial sectors. The comparison of different DL approaches discussed in this manuscript reveals that the efficiency of such forecasting methods is highly dependent on the amount of the historical data and thus a large number of data storage devices and high processing power devices are required to deal with big data. Finally, this study raises several open research problems and opportunities in the area of renewable energy forecasting for smart microgrids.