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                <text>Laurie Butgereit, Chumani Charlie, Bester Chimbo, Willie Chinyamurindi, Liezel Cilliers, Paul Danquah, Saurav Deoraj, Saheed Faremi, Yinusa Faremi, Stephen Fashoto, Emmanuel Freeman, Vusumzi Funda, Fatima Gamieldien, Dennis Gookyi, Marilou Gravides, Raymond Gyaang, Andre Henney, Sanele Hlabisa, Abdulati Jahbel</text>
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                <text>Author Index Page 1 Author Index Abrahams, Shamir 175 Abugosseisa, Muna 26 Agbehadji, Israel Edem 114, 121 Agbehadzi, Richard Koblah 121 Agyemang, Justice 181 Ahmad, Tohari 52, 64 Akinnuwesi, Boluwaji 89 Akinyemi, Dr Lateef Adesola 187 Allie, Mogammad 193 Ampaguey, Jocelyn 215 Aroba, Oluwasegun Julius 38 Arthur, Ewura 1, 181 Asiedu, Derek 181 Avulekpor, Ebenezer 108 Batani, John 7, 12, 89 Bayaga, Anass 20 Blose, Max 187 Buhat-Mendoza, Dorothy 215 Butgereit, Laurie 26 Charlie, Chumani 32 Chimbo, Bester 102 Chinyamurindi, Willie 96 Cilliers, Liezel 32, 96 Danquah, Paul 1 Deoraj, Saurav 38 Faremi, Saheed 89 Faremi, Yinusa 89 Fashoto, Stephen 89 Freeman, Emmanuel 108, 114, 121 Funda, Vusumzi 44 Gamieldien, Fatima 193 Gookyi, Dennis 1, 181 Gravides, Marilou 215 Gyaang, Raymond 1 Henney, Andre 140, 193 Hlabisa, Sanele 199 Jahbel, Abdulati 52 237 2024 …</text>
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                <text>Re-engaging with Europe and the Commonwealth in a Post-Brexit Era</text>
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                <text>n this chapter, the discussion revolves around Africa’s engagement with the new Europe in a post-Brexit era – which, like AfCFTA, also became effective on 1 January 2021. The chapter discusses, on a broader level, Europe–Africa relations in the creative industries. Specific examples are drawn from the Commonwealth Agenda, and the France/German/Dutch relations at both the public and private spheres.</text>
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                <text>This study examines the impact of HIV/AIDS scourge on economic growth of some selected Sub-Sahara African countries. The three sub-Sahara African regions (Southern, West and Eastern) are categorized into lowest and highest HIV prevalence rate, using the global report of the UNAIDS 2012 data. In each region, the lowest and the highest HIV prevalence rate countries are selected respectively. Thus, we consider a panel of three countries in each category over a period from 1995–2012. We used these data to estimate the cross-country level regressions of these two categories, using panel data models. Thus, our results are in twofold: For lowest HIV prevalence rate category, the pooled OLS model was the “best” model. This pooled OLS model indicated that, for one unit increase in HIV prevalence rate, the gross domestic product per capita of countries is expected to decrease by US $23.46, holding all other variables constant. Again for highest HIV prevalence rate category, the fixed effect model was the “best” model. The fixed effect model revealed that, one unit increase in HIV prevalence rate will cause the gross domestic product per capita of countries to decrease by US $9.98, holding all other variables constant. Generally, for a unit increase in HIV prevalence rate, the impact of HIV on the economy is two-thirds larger in lowest HIV prevalence rate countries than that of the highest HIV prevalence rate countries.</text>
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                <text>Emmanuel Kyei, Peter Appiahene, Mighty Abra Ayidzoe, Obed Appiah, Justice Asare, Emmanuel Freeman, William Brown-Acquaye</text>
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                <text>In recent years, Facial Emotional Recognition (FER) has garnered significant attention for its pivotal role across various applications, including human-computer interaction, healthcare, and sentiment analysis. This study is motivated by the need to enhance the accuracy and efficiency of FER systems, with a particular focus on leveraging Convolutional Neural Networks (CNNs) featuring dense architectures. Charles Darwin's groundbreaking work on facial expressions as indicators of human emotional states inspires this research, which aims to elevate FER systems for applications spanning therapy, human-machine interactions, and diverse domains like healthcare, education, and entertainment. While previous studies have recognized CNNs' ability to improve FER accuracy through intricate feature extraction, the evolving nature of the field calls for exploring novel CNN architectures and techniques to further enhance precision and efficiency. In this study, we develop and implement a deep learning model capable of classifying images into seven discrete emotion categories, representing universal human emotions. This objective is achieved by implementing a five-block CNN-based learning algorithm consisting of 44 layers designed to progressively capture complex facial features and expressive patterns. The CNN model performed well on FER and FERG datasets, with accuracy rates of 0.97 and 0.98, showcasing proficiency in facial expression classification. Comparative analysis highlighted its competitive accuracy, emphasizing the importance of feature extraction and architecture design. This research advances facial emotion recognition …</text>
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                <text>PRINCE MODEY, Gaddafi Adbdul-Salaam, Alimatu Sadia Shirazu, Mohammad Hossein Anisi, Emmanuel Freeman</text>
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                <text>Residue Number System (RNS) is a number system providing borrow-free subtraction, carry-free addition, parallel operations, and one-step multiplication. These capabilities make RNS applications in Digital Signal Processing and Wireless Networks a good choice. Although RNS has proven to solve the problem of carry propagation in binary number systems, it has the problem of reverse conversion, which limits its implementation in digital systems likewise sensor nodes. In this paper, we proposed an RNS reverse converter based on the moduli set {[[EQUATION]]} and the residue number [[EQUATION]]. The proposed scheme re-grouped the moduli set into two sub-moduli sets with their corresponding residue representations. The proposed method in this study requires [[EQUATION]] bit hardware resources for its implementation and also imposed [[EQUATION]] delay in its performance. The proposed RNS reverse converter needs lesser hardware resources for its implementation compared to other state of the art, making it suitable for hardware implementation in sensor networks. The proposed scheme demonstrates reliable and secure data transmission in Wireless Sensor Networks (WSNs) by using the RRNS QC encoder/decoder scheme.</text>
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                <text>Automatic pancreas segmentation of CT scans enables physicians to identify and monitor the abnormalities in the pancreas. This facilitates intraoperative assistance, surgical planning, prognosis, and diagnosis. Nonetheless, the size and location of the pancreas in the CT image input data present a significant challenge for automatic segmentation, and the intricacy of the background region confounds deep segmentation networks. To solve this difficulty, we propose the Encoder-Corollary Atrous Spatial Pyramid Pooling-Decoder Network (EcD-Net) for locating and segmenting the pancreas. This two-tiered method begins with a coarse segmentation stage for locating the pancreas within the overall CT image. Using the detected image from the first tier, a fine segmentation network based on U-Net is applied to segment the target organ (pancreas). A novel Saturated Multiview Dense Module (SMD-Module) is presented to enhance information gradient flow for the stability of the training process and easier convergence at the fine stage. A novel Corollary Atrous Spatial Pyramid Pooling Module (CASPP-Module) is proposed to simultaneously capture low-level details and high-level global context information to enhance the pancreas segmentation accuracy, extract global and local spatial data, and detect the pancreas at varied scales. On the publicly available National Institute of Health (NIH) pancreas dataset, our proposed EcD-Net surpasses previous state-of-the-art methods with a mean Dice Similarity Coefficient (DSC) of 88.84%, mean Precision (PRE), Recall (REC), and Intersection over Union (IoU) of 91.85%, 88.21%, and 89.94% respectively …</text>
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                <text>Extend your on-premises Windows Server deployments to the cloud with Azure. In Mastering Windows Server 2022 with Azure Cloud Services: IaaS, PaaS, and SaaS, 5-time Microsoft MVP winner William Panek delivers a comprehensive and practical blueprint for planning, implementing, and managing environments that include Azure IaaS-hosted Windows Server-based workloads. You'll learn to use the expansive, hybrid capabilities of Azure, how to migrate virtual and physical server workloads to Azure IaaS, PaaS, and SaaS, and how to manage and secure Azure virtual machines running Windows Server 2022. This book also offers: Foundational explanations of core Azure capabilities, including Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS); explorations of the tools you'll need to implement Azure solutions, including Windows Admin Center and PowerShell Examples of implementing identity in Hybrid scenarios, including Azure AD DS on Azure IaaS and managed AD DS Perfect for IT professionals who manage on-premises Windows Server environments, seek to use Azure to manage server workloads, and want to secure virtual machines running on Windows Server 2022, Mastering Windows Server 2022 with Azure Cloud Services: IaaS, PaaS, and SaaS is also a must-read resource for anyone involved in administering or operating Microsoft Azure IaaS workloads.</text>
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