A grid-computing paradigm delivers the processing power of massively parallel computation to all subscribed users. Current trends, research, and developments in grid computing show that the available grid resources exist as non-storable compute commodities and are distributed geographically – the grid problem. To solve the grid problem, several initiatives have developed frameworks for grid economy and have proposed several algorithms towards an optimized resources scheduling in a grid environment. However, since the grid resources availability depends on the time of usage and are transient, such generic approaches lack the ability to capture the realistic valuation of the resources and fail to guarantee the certainty in their availability measured as Quality of Service (QoS). Uncertainties in grid resource availability do not guarantee a user expected QoS without over committing (e.g., storing the non-storable resources) resources to the users. To guarantee QoS (satisfy a users’ computing needs), we propose a price-based and quality-aware model that captures the realistic value of the grid compute commodities. We use the financial option theory from a real option perspective to value grid resources by treating them as real assets. We discuss a set of results on pricing grid compute cycles. Our results are based on the compute cycle usage obtained from the WestGrid node at the University of Manitoba. We extend and generalize our study to any grid in general but with specific reference to the WestGrid. Keywords: Financial Options, Price Stochasticity, Compute Cycles, Real Options, Quality of Service. Allenotor, D. & Oyemade, D. A. (2022): A Price-Based Grid Resources Pricing Approach for Non-Storable Real AssetsJournal of Advances in Mathematical & Computational Science. Vol. 10, No. 2. Pp 1-18 DOI: dx.doi.org/10.22624/AIMS/MATHS/V10N2P1. Available online at www.isteams.net/mathematics-computationaljournal.

One of the requirements for mining cryptocurrency (Crypto) is that the secured ledger of the blockchain must be updated. However, updating the secured ledger requires that the miner develop and solve complex mathematical equations in higher orders hexadecimal 64-digit solution called a hash. In addition to this challenge, the mining processes of cryptocurrency are both resource and cost-intensive. The resources required include, but not limited to mining software, hardware, power (energy usage), CPU or compute cycles, and NP-hard problem. Apart from these numerous challenges that are associated with mining cryptocurrency, the amount of speed that is required to mine a single block is core. Cryptocurrency mining speed requirement is significantly important because only miners that can have the fastest mining device are most likely to get the reward (profit) from competing for a block. In this paper, we designed and implemented a model to speed up mining process which is capable of giving miners an advantage to arrive at a block earlier. The novelty of our architecture is that our design is based on high performance computing paradigm where we achieve processor speed up by parallelizing the number of processors p. We experimented by varying p = 4, 8, 16. Our experimental results where we used the MC6800 simulated on Easy68k emulator demonstrate feasibility of our proposed model and prove that speed was an essential key to cryptocurrency mining. Keywords: Cryptocurrency, Blockchain, Architecture, Mining, Speedup, Bitcoin. CISDI Journal Reference Format Allenotor, D. & Oyemade, D. A. (2021): An Optimized Parallel Hybrid Architecture for Cryptocurrency Mining. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 12 No 1, Pp 95-104 DOI - https://doi.org/ 10.22624/AIMS/CISDI/V12N1P10. Available online at www.isteams.net/cisdijournal

Racial conflicts have become even more prevalent than before. As a result, social media companies are continuously being slammed for their inadequate response to the problem caused by racial discrimination. For example, the year 2020 witnessed a worldwide movement calling for racial equality and justice. The movement began after an African American male was suffocated and murdered by an NYPD police officer. Since then, there has been significant research efforts focusing on social media and the role it has played in amplifying racism. Similarly, the government of the United Kingdom have threatened to make social media companies legally accountable for the racist content on their platform after the witnessed increase of racist abuse on footballers in 2021. English football clubs have also threatened a boycott of social media in a bid to eradicate online hate. To solve this problem, we will track down past events and social media trends which are likely to have triggered racist reactions and retrieve annotated comments from public social media sites like Facebook, Instagram, Twitter, YouTube and TikTok. We will create an unbiased dataset of racist comments across social media platforms. We will be building a classification model using machine learning to detect racist comments on social media platforms. We propose a machine learning model for the automatic detection of racist comment across social media platforms. The results we obtained from our research shows that the support vector machine-trained model performs the best with an accuracy of 88.19%. The models proposed in this research outperformed most of the pre-existing models for the same task. Keywords: Race, Racism, Cyberbully, Hate Speech, Support Vector Machine, Confusion Matrix Journal Reference Format: Allenotor, D. & Oyemade, D. A. (2021): A Classification Model Based on Machine Learning for Detecting Racist Comments on Social Media Platforms Journal of Behavioural Informatics, Digital Humanities and Development Research. Vol. 7.No. 1, Pp 121-136. ICT University USA Endowed Research Series Publication in collaboration with SMART-Africa. Available online at https://www.isteams.net.behavioraljournal. Article DOI No - dx.doi.org/10.22624/AIMS/BHI/V7N1P9