Browsing by Author "Ahene, Emmanuel"
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- ItemA blockchain-based certificateless public key signature scheme for vehicle-to-infrastructure communication in VANETs(Journal of Systems Architecture, 2019-10) Ali, Ikram; Gervais, Mwitende; Ahene, Emmanuel; Li, Fagen; 0000-0002-2893-4249; 0000-0002-0810-1055Vehicular Ad Hoc Networks (VANETs) have been developing based on the state-of-art in wireless network communication technologies to improve traffic on roads. However, there are some threats to security and privacy due to the open wireless environment in VANETs and the high speed of vehicles. The uthentication of messages related to traffic which are exchanged with the vehicles and the Road-Side Unit (RSU) is considered one of the most VANETs necessary security requirements. In this context, several schemes have been designed to secure the traffic-related messages in VANETs. However, these schemes suffer from high computational costs in signatures’ verification. To minimize the computational cost of signature generation and verification, we propose an efficient Certificateless Public Key Signature (CL-PKS) scheme using bilinear pairing to provide conditional privacy-preserving authentication for Vehicle-To-Infrastructure (V2I) communication in VANETs. The CL-PKS scheme supports batch signature verification and aggregate signature verification functions to speed up verification process. In addition to this, we include blockchain to our CL-PKS scheme to implement revocation transparency of pseudo-identities efficiently before verifying the signatures. Furthermore, this scheme provides security proof and protection against different types of attacks. The proposed scheme incurs lower computational cost as compared to that incurred by existing schemes.
- ItemA Multi-objective Optimization Approach to Workflow Scheduling in Clouds Considering Fault Recovery(Korean Society for Internet Information, 2016-03) Xu, Heyang; Yang, Bo; Qi, Weiwei; Ahene, Emmanuel; 0000-0002-0810-1055Workflow scheduling is one of the challenging problems in cloud computing, especially when service reliability is considered. To improve cloud service reliability, fault tolerance techniques such as fault recovery can be employed. Practically, fault recovery has impact on the performance of workflow scheduling. Such impact deserves detailed research. Only few research works on workflow scheduling consider fault recovery and its impact. In this paper, we investigate the problem of workflow scheduling in clouds, considering the probability that cloud resources may fail during execution. We formulate this problem as a multi-objective optimization model. The first optimization objective is to minimize the overall completion time and the second one is to minimize the overall execution cost. Based on the proposed optimization model, we develop a heuristic-based algorithm called Min-min based time and cost tradeoff (MTCT). We perform extensive simulations with four different real world scientific workflows to verify the validity of the proposed model and evaluate the performance of our algorithm. The results show that, as expected, fault recovery has significant impact on the two performance criteria, and the proposed MTCT algorithm is useful for real life workflow scheduling when both of the two optimization objectives are considered
- ItemCold Boot Attack on Encrypted Containers for Forensic Investigations(KSII Transactions On Internet And Information Systems,, 2022-09) Twum, Frimpong; Lagoh, Emmanuel Mawuli; Missah, Yaw Marfo; Najim, Ussiph; Ahene, Emmanuel; 0000-0002-1869-7542; 0000-0002-2926-681X; 0000-0002-6973-7495; 0000-0002-0810-1055
- ItemEvaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection(IEEE Access, 2022-02) Teye, Martha T.; Missah, Yaw Marfo; Ahene, Emmanuel; Twum, Frimpong; 0000-0002-2370-4700; 0000-0002-2926-681X; 0000-0002-0810-1055; 0000-0002-1869-7542Valuable decisions and highly prioritized analysis now depend on applications such as facial biometrics, social media photo tagging, and human robots interactions. However, the ability to successfully deploy such applications is based on their efficiencies on tested use cases taking into consideration possible edge cases. Over the years, lots of generalized solutions have been implemented to mimic human emotions including sarcasm. However, factors such as geographical location or cultural difference have not been explored fully amidst its relevance in resolving ethical issues and improving conversational AI (Artificial Intelligence). In this paper, we seek to address the potential challenges in the usage of conversational AI within Black African society. We develop an emotion prediction model with accuracies ranging between 85% and 96%. Our model combines both speech and image data to detect the seven basic emotions with a focus on also identifying sarcasm. It uses 3-layers of the Convolutional Neural Network in addition to a new Audio-Frame Mean Expression (AFME) algorithm and focuses on model pre-processing and post processing stages. In the end, our proposed solution contributes to maintaining the credibility of an emotion recognition system in conversational AIs.
- ItemRainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana(IEEE Access, 2022-12) Appiah-Badu, Nana Kofi Ahoi; Missah, Yaw Marfo; Amekudzi, Leonard K.; Ussiph, Najim; Frimpong, Twum; Ahene, Emmanuel; 0000-0002-3029-4498; 0000-0002-2926-681X; 0000-0002-2186-3425; 0000-0002-6973-7495; 0000-0002-1869-7542; 0000-0002-0810-1055Accurate rainfall prediction has become very complicated in recent times due to climate change and variability. The efficiency of classification algorithms in rainfall prediction has flourished. The study contributes to using various classification algorithms for rainfall prediction in the different ecological zones of Ghana. The classification algorithms include Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB) and K-Nearest Neighbour (KNN). The dataset, consisting of various climatic attributes, was sourced from the Ghana Meteorological Agency spanning 1980 – 2019. The performance of the classification algorithms was examined based on precision, recall, f1-score, accuracy and execution time with various training and testing data ratios. On all three training and testing ratios: 70:30, 80:20 and 90:10, RF, XGB and MLP performed well, whereas KNN performed least across all zones. In terms of the execution time of the models, Decision Tree is consistently portrayed as the fastest, whereas MLP used the most run time.