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3,714 results on '"Abdallah, Mohamed"'

1. Learn to Slice, Slice to Learn: Unveiling Online Optimization and Reinforcement Learning for Slicing AI Services

Author

Abo-eleneen, Amr, Helmy, Menna, Abdellatif, Alaa Awad, Erbad, Aiman, Mohamed, Amr, and Abdallah, Mohamed

Subjects
Computer Science - Networking and Internet Architecture
Abstract

In the face of increasing demand for zero-touch networks to automate network management and operations, two pivotal concepts have emerged: "Learn to Slice" (L2S) and "Slice to Learn" (S2L). L2S involves leveraging Artificial intelligence (AI) techniques to optimize network slicing for general services, while S2L centers on tailoring network slices to meet the specific needs of various AI services. The complexity of optimizing and automating S2L surpasses that of L2S due to intricate AI services' requirements, such as handling uncontrollable parameters, learning in adversarial conditions, and achieving long-term performance goals. This paper aims to automate and optimize S2L by integrating the two concepts of L2S and S2L by using an intelligent slicing agent to solve S2L. Indeed, we choose two candidate slicing agents, namely the Exploration and Exploitation (EXP3) and Deep Q-Network (DQN) from the Online Convex Optimization (OCO) and Deep Reinforcement Learning (DRL) frameworks, and compare them. Our evaluation involves a series of carefully designed experiments that offer valuable insights into the strengths and limitations of EXP3 and DQN in slicing for AI services, thereby contributing to the advancement of zero-touch network capabilities., Comment: 9 pages, 2 figures and 2 tables magazine paper

Published
2024

2. Exploring Retrieval Augmented Generation in Arabic

Author

El-Beltagy, Samhaa R. and Abdallah, Mohamed A.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Recently, Retrieval Augmented Generation (RAG) has emerged as a powerful technique in natural language processing, combining the strengths of retrieval-based and generation-based models to enhance text generation tasks. However, the application of RAG in Arabic, a language with unique characteristics and resource constraints, remains underexplored. This paper presents a comprehensive case study on the implementation and evaluation of RAG for Arabic text. The work focuses on exploring various semantic embedding models in the retrieval stage and several LLMs in the generation stage, in order to investigate what works and what doesn't in the context of Arabic. The work also touches upon the issue of variations between document dialect and query dialect in the retrieval stage. Results show that existing semantic embedding models and LLMs can be effectively employed to build Arabic RAG pipelines.

Published
2024

3. Optimized Federated Multitask Learning in Mobile Edge Networks: A Hybrid Client Selection and Model Aggregation Approach

Author

Hamood, Moqbel, Albaseer, Abdullatif, Abdallah, Mohamed, Al-Fuqaha, Ala, and Mohamed, Amr

Subjects
Computer Science - Networking and Internet Architecture
Abstract

We propose clustered federated multitask learning to address statistical challenges in non-independent and identically distributed data across clients. Our approach tackles complexities in hierarchical wireless networks by clustering clients based on data distribution similarities and assigning specialized models to each cluster. These complexities include slower convergence and mismatched model allocation due to hierarchical model aggregation and client selection. The proposed framework features a two-phase client selection and a two-level model aggregation scheme. It ensures fairness and effective participation using greedy and round-robin methods. Our approach significantly enhances convergence speed, reduces training time, and decreases energy consumption by up to 60%, ensuring clients receive models tailored to their specific data needs., Comment: 17 pages, 11 figures, Journal

Published
2024

4. Charging Ahead: A Hierarchical Adversarial Framework for Counteracting Advanced Cyber Threats in EV Charging Stations

Author

Al-Mehdhar, Mohammed, Albaseer, Abdullatif, Abdallah, Mohamed, and Al-Fuqaha, Ala

Subjects
Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture
Abstract

The increasing popularity of electric vehicles (EVs) necessitates robust defenses against sophisticated cyber threats. A significant challenge arises when EVs intentionally provide false information to gain higher charging priority, potentially causing grid instability. While various approaches have been proposed in existing literature to address this issue, they often overlook the possibility of attackers using advanced techniques like deep reinforcement learning (DRL) or other complex deep learning methods to achieve such attacks. In response to this, this paper introduces a hierarchical adversarial framework using DRL (HADRL), which effectively detects stealthy cyberattacks on EV charging stations, especially those leading to denial of charging. Our approach includes a dual approach, where the first scheme leverages DRL to develop advanced and stealthy attack methods that can bypass basic intrusion detection systems (IDS). Second, we implement a DRL-based scheme within the IDS at EV charging stations, aiming to detect and counter these sophisticated attacks. This scheme is trained with datasets created from the first scheme, resulting in a robust and efficient IDS. We evaluated the effectiveness of our framework against the recent literature approaches, and the results show that our IDS can accurately detect deceptive EVs with a low false alarm rate, even when confronted with attacks not represented in the training dataset., Comment: Accepted for IEEE VTC Spring 2024

Published
2024

5. FedPot: A Quality-Aware Collaborative and Incentivized Honeypot-Based Detector for Smart Grid Networks

Author

Albaseer, Abdullatif, Abdi, Nima, Abdallah, Mohamed, Qaraqe, Marwa, and Alkuwari, Saif

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Honeypot technologies provide an effective defense strategy for the Industrial Internet of Things (IIoT), particularly in enhancing the Advanced Metering Infrastructure's (AMI) security by bolstering the network intrusion detection system. For this security paradigm to be fully realized, it necessitates the active participation of small-scale power suppliers (SPSs) in implementing honeypots and engaging in collaborative data sharing with traditional power retailers (TPRs). To motivate this interaction, TPRs incentivize data sharing with tangible rewards. However, without access to an SPS's confidential data, it is daunting for TPRs to validate shared data, thereby risking SPSs' privacy and increasing sharing costs due to voluminous honeypot logs. These challenges can be resolved by utilizing Federated Learning (FL), a distributed machine learning (ML) technique that allows for model training without data relocation. However, the conventional FL algorithm lacks the requisite functionality for both the security defense model and the rewards system of the AMI network. This work presents two solutions: first, an enhanced and cost-efficient FedAvg algorithm incorporating a novel data quality measure, and second, FedPot, the development of an effective security model with a fair incentives mechanism under an FL architecture. Accordingly, SPSs are limited to sharing the ML model they learn after efficiently measuring their local data quality, whereas TPRs can verify the participants' uploaded models and fairly compensate each participant for their contributions through rewards. Simulation results, drawn from realistic mircorgrid network log datasets, demonstrate that the proposed solutions outperform state-of-the-art techniques by enhancing the security model and guaranteeing fair reward distributions., Comment: Accepted for IEEE TNSM

Published
2024

7. Energy-Aware Service Offloading for Semantic Communications in Wireless Networks

Author

Saadat, Hassan, Albaseer, Abdullatif, Abdallah, Mohamed, Mohamed, Amr, and Erbad, Aiman

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Today, wireless networks are becoming responsible for serving intelligent applications, such as extended reality and metaverse, holographic telepresence, autonomous transportation, and collaborative robots. Although current fifth-generation (5G) networks can provide high data rates in terms of Gigabytes/second, they cannot cope with the high demands of the aforementioned applications, especially in terms of the size of the high-quality live videos and images that need to be communicated in real-time. Therefore, with the help of artificial intelligence (AI)-based future sixth-generation (6G) networks, the semantic communication concept can provide the services demanded by these applications. Unlike Shannon's classical information theory, semantic communication urges the use of the semantics (meaningful contents) of the data in designing more efficient data communication schemes. Hence, in this paper, we model semantic communication as an energy minimization framework in heterogeneous wireless networks with respect to delay and quality-of-service constraints. Then, we propose a sub-optimal solution to the NP-hard combinatorial mixed-integer nonlinear programming problem (MINLP) by utilizing efficient techniques such as discrete optimization variables' relaxation. In addition, AI-based autoencoder and classifier are trained and deployed to perform semantic extraction, reconstruction, and classification services. Finally, we compare our proposed sub-optimal solution with different state-of-the-art methods, and the obtained results demonstrate its superiority., Comment: Accepted for IEEE ICC 2024

Published
2024

8. Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach

Author

Hamood, Moqbel, Albaseer, Abdullatif, Abdallah, Mohamed, and Al-Fuqaha, Ala

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning
Abstract

Clustered Federated Multitask Learning (CFL) has gained considerable attention as an effective strategy for overcoming statistical challenges, particularly when dealing with non independent and identically distributed (non IID) data across multiple users. However, much of the existing research on CFL operates under the unrealistic premise that devices have access to accurate ground truth labels. This assumption becomes especially problematic in hierarchical wireless networks (HWNs), where edge networks contain a large amount of unlabeled data, resulting in slower convergence rates and increased processing times, particularly when dealing with two layers of model aggregation. To address these issues, we introduce a novel framework, Clustered Federated Semi-Supervised Learning (CFSL), designed for more realistic HWN scenarios. Our approach leverages a best-performing specialized model algorithm, wherein each device is assigned a specialized model that is highly adept at generating accurate pseudo-labels for unlabeled data, even when the data stems from diverse environments. We validate the efficacy of CFSL through extensive experiments, comparing it with existing methods highlighted in recent literature. Our numerical results demonstrate that CFSL significantly improves upon key metrics such as testing accuracy, labeling accuracy, and labeling latency under varying proportions of labeled and unlabeled data while also accommodating the non-IID nature of the data and the unique characteristics of wireless edge networks., Comment: Accepted for IEEE Wireless Communications and Networking Conference (WCNC) 2024

Published
2024

9. The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

Author

Abdi, Nima, Albaseer, Abdullatif, and Abdallah, Mohamed

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

As smart grids (SG) increasingly rely on advanced technologies like sensors and communication systems for efficient energy generation, distribution, and consumption, they become enticing targets for sophisticated cyberattacks. These evolving threats demand robust security measures to maintain the stability and resilience of modern energy systems. While extensive research has been conducted, a comprehensive exploration of proactive cyber defense strategies utilizing Deep Learning (DL) in {SG} remains scarce in the literature. This survey bridges this gap, studying the latest DL techniques for proactive cyber defense. The survey begins with an overview of related works and our distinct contributions, followed by an examination of SG infrastructure. Next, we classify various cyber defense techniques into reactive and proactive categories. A significant focus is placed on DL-enabled proactive defenses, where we provide a comprehensive taxonomy of DL approaches, highlighting their roles and relevance in the proactive security of SG. Subsequently, we analyze the most significant DL-based methods currently in use. Further, we explore Moving Target Defense, a proactive defense strategy, and its interactions with DL methodologies. We then provide an overview of benchmark datasets used in this domain to substantiate the discourse.{ This is followed by a critical discussion on their practical implications and broader impact on cybersecurity in Smart Grids.} The survey finally lists the challenges associated with deploying DL-based security systems within SG, followed by an outlook on future developments in this key field., Comment: To appear in the IEEE internet of Things journal

Published
2024

13. Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

Author

Al-Maliki, Shawqi, Qayyum, Adnan, Ali, Hassan, Abdallah, Mohamed, Qadir, Junaid, Hoang, Dinh Thai, Niyato, Dusit, and Al-Fuqaha, Ala

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Deep Neural Networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples -- input samples that have been perturbed to force DNN-based models to make errors. As a result, Adversarial Machine Learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in anti-social AI applications. The emergence of new AI technologies that leverage Large Language Models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing anti-social applications at scale. AdvML for Social Good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent pro-social applications. Regulators, practitioners, and researchers should collaborate to encourage the development of pro-social applications and hinder the development of anti-social ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community.

Published
2023

14. Resistance patterns, virulence determinants, and biofilm genes of multidrug-resistant Pseudomonas aeruginosa isolated from fish and fish handlers

Author

Rasha M. M. Abou Elez, Eman Mohamed Fayek Zahra, Rasha M. A. Gharieb, Mohamed Elsayed Mohamed Mohamed, Mohamed Samir, Alaaeldin Mohamed Saad, and Abdallah Mohamed Amin Merwad

Subjects
Antibiotic resistance, Biofilm formation, Biofilm genes, P. aeruginosa, Virulence genes, Medicine, Science
Abstract

Abstract Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic bacterium that is widely distributed in aquatic environments and causes major economic losses in fish and public health hazards.This study aimed to identify the occurrence of P. aeruginosa in samples collected from fish and fish handlers, and to investigate the antimicrobial susceptibility, virulence determinants, and biofilm genes of P. aeruginosa isolates. A total of 276 samples were cross-sectionally collected from Nile tilapia (53), Golden grey mullet (52), Mediterranean horse mackerel (50), Striped red mullet (71), and fish handlers (50) at five different retail fish markets in Damietta Governorate, Egypt. Pseudomonas species (spp.) were biochemically identified in 57.9% of the total examined samples. Peudomonas aeruginosa were the most prevalent species isolated from the fish and human samples via PCR technique. Peudomonas aeruginosa isolates exhibited full resistance (100%) to tobramycin (TOB), gentamicin (CN), and colistin (CL), with a high level of susceptibility (88.5%) to imipenem (IPM) using the disk diffusion method. Most P. aeruginosa isolates (84.6%) exhibited drug resistance, with 61.5% were multidrug resistance (MDR) and 23.1% were extensive drug resistance (XDR). Most isolates had at least four virulence-associated genes (lasB, toxA, exoU, and oprL) and three biofilm genes (psIA, peIA, and lasR) by using uniplex PCR. The lasI, and rhlR Quorum Sensing (QS) genes were identified in 84.6% and 61.5% in the examined P. aeruginosa isolates, respectively. The highest mortality rate in Nile tilapia experimentally infected with P. aeruginosa isolate encoding most of virulent genes. Multivariate analyses revealed high heterogeneity among the examined isolates. This study revealed the emergence of virulent and drug resistant P. aeruginosa isolates in fish, poses high risks to consumers and food. Thus, strict hygienic measures should be considered when catching, handling, and storing fish, in addition to the routine application of antimicrobial susceptibility testing.

Published
2024
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25. Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning

Author

Albaseer, Abdullatif, Abdallah, Mohamed, Al-Fuqaha, Ala, Mohammed, Abegaz, Erbad, Aiman, and Dobre, Octavia A.

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices {is} selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed while equipping every user with a customized model tailored to its data distribution., Comment: To appear in IEEE Transactions on Network and Service Management, Special issue on Federated Learning for the Management of Networked Systems

Published
2023

26. Comparative Study between the Neuroprotective Effect of Curcumin and Vitamin C against Neurotoxicity Induced By Acetamiprid on the Cerebellum of Male Adult Albino Rats: A Histological Study

Author

Amira Shahat Mohamed, Maha Abdelbaki Ahmed, and Dorreia AbdAllah Mohamed

Subjects
acetamiprid neurotoxicity curcumin, vitamin c cerebellum, Medicine
Abstract

Background: Acetamiprid (ACE) is a neonicotinoid insecticide, known in the trade market as: Mospilan, Assilan and Chipco. It is used to get rid of sucking insects. Its intake might cause toxic effects on the cerebellum. Objectives: The study aimed to determine the pathological effects of acetamiprid on the cerebellar cortex of male adult albino rats and the role of both Curcumin and Vitamin C in altering those pathological effects. Materials and methods: Forty male adult albino rats were used in this study. The rats were divided into four groups (n=10 each): group I (control) and received corn oil, group II (treated with acetamiprid orally), group III (treated with acetamiprid + Curcumin orally), group IV (treated with acetamiprid + Vitamin C orally). Cerebellar tissue samples were used for histopathological examination using light and transmission electron microscopy. Morphometric and statistical studies were done to measure Purkinje cell (PC) number and the thickness of the granule layer. Results: There was a significant structural damage of cerebellar cortical tissue in group II. Group III showed restoring of the pathological structure of cerebellar cortex compared to group II. Group IV showed less improvement compared to group III. Conclusion: Curcumin and vitamin C could have a protective effect against neurotoxicity induced by acetamiprid via their antioxidant property.

Published
2024
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30. Robot Arm Trajectory Synthesis Based on Metaheuristic Optimization Techniques

Author

Ben Abdallah, Mohamed Amine, Khemili, Imed, Aifaoui, Nizar, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Chouchane, Mnaouar, editor, Abdennadher, Moez, editor, Aifaoui, Nizar, editor, Bouaziz, Slim, editor, Affi, Zouhaier, editor, Romdhane, Lotfi, editor, and Benamara, Abdelmajid, editor

Published
2024
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31. Hybrid Metaheuristic Optimization Algorithm for the Synthesis of a Flexible Four Bar Mechanism

Author

Khemili, Imed, Ben Ali, Skander, Ben Abdallah, Mohamed Amine, Aifaoui, Nizar, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Chouchane, Mnaouar, editor, Abdennadher, Moez, editor, Aifaoui, Nizar, editor, Bouaziz, Slim, editor, Affi, Zouhaier, editor, Romdhane, Lotfi, editor, and Benamara, Abdelmajid, editor

Published
2024
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32. Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation

Author

Al-Maliki, Shawqi, Bouanani, Faissal El, Abdallah, Mohamed, Qadir, Junaid, and Al-Fuqaha, Ala

Subjects
Computer Science - Machine Learning
Abstract

Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data. Augmenting smart city applications with online machine learning models can handle this issue at test time, albeit with high cost and unreliable performance. To overcome this limitation, we propose to endow test-time adaptation with a systematic active fine-tuning (SAF) layer that is characterized by three key aspects: a continuity aspect that adapts to ever-present data distribution shifts; intelligence aspect that recognizes the importance of fine-tuning as a distribution-shift-aware process that occurs at the appropriate time to address the recently detected data distribution shifts; and cost-effectiveness aspect that involves budgeted human-machine collaboration to make relabeling cost-effective and practical for diverse smart city applications. Our empirical results show that our proposed approach outperforms the traditional test-time adaptation by a factor of two.

Published
2022

33. Guanidine dicycloamine-based analogs: green chemistry synthesis, biological investigation, and molecular docking studies as promising antibacterial and antiglycation leads

Author

El-Remaily, Mahmoud Abd El Aleem. A. A., Aboelez, Moustafa O., Ezelarab, Hend A. A., Selim, Heba Mohammed Refat M., Taha, Enas A., Mohamed, Shaaban K., Soliman, Ahmed M., Abdallah, Mohamed S., Fawy, Mariam A., Hassany, Mohamed A., Ahmed, Nessar, Alsaggaf, Azhaar T., El Hamd, Mohamed A., and Kamel, Moumen S.

Published
2024
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36. Unraveling the phylogenetics of genetically closely related species, Haemaphysalis japonica and Haemaphysalis megaspinosa, using entire tick mitogenomes and microbiomes

Author

Mohamed Abdallah Mohamed Moustafa, Wessam M. A. Mohamed, Elisha Chatanga, Doaa Naguib, Keita Matsuno, Alexander W. Gofton, Stephen C. Barker, Nariaki Nonaka, and Ryo Nakao

Subjects
Haemaphysalis, Mitogenome, Microbiome, Coxiella, Endosymbiont, Tick, Medicine, Science
Abstract

Abstract Ticks have a profound impact on public health. Haemaphysalis is one of the most widespread genera in Asia, including Japan. The taxonomy and genetic differentiation of Haemaphysalis spp. is challenging. For instance, previous studies struggled to distinguish Haemaphysalis japonica and Haemaphysalis megaspinosa due to the dearth of nucleotide sequence polymorphisms in widely used barcoding genes. The classification of H. japonica japonica and its related sub-species Haemaphysalis japonica douglasi or Haemaphysalis jezoensis is also confused due to their high morphological similarity and a lack of molecular data that support the current classification. We used mitogenomes and microbiomes of H. japonica and H. megaspinosa to gain deeper insights into the phylogenetic relationships and genetic divergence between two species. Phylogenetic analyses of concatenated nucleotide sequences of protein-coding genes and ribosomal DNA genes distinguished H. japonica and H. megaspinosa as monophyletic clades, with further subdivision within the H. japonica clade. The 16S rRNA and NAD5 genes were valuable markers for distinguishing H. japonica and H. megaspinosa. Population genetic structure analyses indicated that genetic variation within populations accounted for a large proportion of the total variation compared to variation between populations. Microbiome analyses revealed differences in alpha and beta diversity between H. japonica and H. megaspinosa: H. japonica had the higher diversity. Coxiella sp., a likely endosymbiont, was found in both Haemaphysalis species. The abundance profiles of likely endosymbionts, pathogens, and commensals differed between H. japonica and H. megaspinosa: H. megaspinosa was more diverse.

Published
2024
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37. Effect of adding short foot exercise to hip and knee focused exercises in treatment of patients with patellofemoral pain syndrome: a randomized controlled trial

Author

Abdallah Mohamed kamel, Karim Ghuiba, Dina S Abd Allah, Nadia Abdelazem Fayaz, and Nasr Awad Abdelkader

Subjects
Anterior knee pain, Patellofemoral pain syndrome, Short foot exercise, Orthopedic surgery, RD701-811, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Background Patellofemoral pain syndrome is considered a common cause of anterior knee pain that could disturb function and limit daily activities. The purpose of the study was to investigate the effect of adding short foot exercise on pain, function, balance, and hip abductors, and quadriceps muscles strength in the treatment of patients with patellofemoral pain syndrome. Methods Twenty-eight male and female patients with patellofemoral pain syndrome with age ranged from 18 to 35 years old participated in this study. They were equally and randomly assigned into two groups; the study group which received short foot exercise in addition to hip and knee exercises (n = 14) and thecontrol group which received hip and knee exercises only (n = 14). Participants received their interventions during 6 consecutive weeks (12 sessions). Pain intensity, function, abductors quadriceps muscle strength, and balance were assessed using the Visual Analog Scale, anterior knee pain scale (AKPS), hand-held dynamometer, and the Biodex Balance System respectively. All measurements were taken before and after 6 weeks of intervention in both groups. Multivariate analysis of variance was performed to compare the within and between groups effects for measured variables. Results The within-group comparison showed significant improvement in pain severity, function, balance, and hip abductors, and quadriceps muscles strength in both groups post-treatment compared with pre-treatment. Between groups analysis, however, showed no significant statistical difference between both groups in all variables, except in pain, function, and mediolateral stability which showed better improvement compared to the control group. Conclusions Adding short foot exercise to hip and knee exercises improved pain, function, and mediolateral stability in patients with patellofemoral pain syndrome. Trial registration clinicaltrials.gov. NO: NCT05383781. Date 19/ 5/2022.

Published
2024
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38. Outcomes after surgery for children in Africa (ASOS-Paeds): a 14-day prospective observational cohort study

Author

Torborg, Alexandra, Meyer, Heidi, El Fiky, Mahmoud, Fawzy, Maher, Elhadi, Muhammed, Ademuyiwa, Adesoji O, Osinaike, Babatunde Babasola, Hewitt-Smith, Adam, Nabukenya, Mary T, Bisegerwa, Ronald, Bouaoud, Souad, Abdoun, Meriem, El Adib, Ahmed Rhassane, Kifle Belachew, Fitsum, Gebre, Meseret, Taye, Desalegn Bekele, Kechiche, Nahla, Fadalla, Tarig, Abdallah, Bareeq, Chaibou, Maman Sani, Nyarko, Mame Yaa Adobea, Ki, Kélan Bertille, Shalongo, Sarah, Mulwafu, Wakisa, Thomson, Emma, Traore, Mamadou Mour, Ndonga, Andrew, Bittaye, Mustapha, Samateh, Ahmadou Lamin, Munlemvo, Dolly M., Kalongo, Jean Jacques, Coulibaly, Yacaria, Coulibaly, Youssouf, Ravelojaona, Vaonandianina, ANDRIAMANARIVO, Lalatiana, RAHERISON, Arsitide Romain, RANDRIAMIZAO, Harifetra Mamy Richard, RAMKALAWAN, Kushal, Omar, Mohamed Abdinor, Ndikontar, Raymond, Joseph, Donamou, Dahir, Shukri, Mohamed, Mubarak, Ali Daoud, Hassan, Ndarukwa, Pisirai, OTIOBANDA, Gilbert Fabrice, Banguti, Paulin, Neil, Kara, Derbew, Miliard, Fanny, Marvin, Smalle, Isaac, Taylor, Elliott H, Duvenage, Hanel, Hardy, Anneli, Kluyts, Hyla, Pearse, Rupert, Biccard, Bruce, AARON, Olurotimi Idowu, Abd Elazeem Mohammed, Hossam Aldein Samir, Abdalkarim, Batool, Abdalla, Abubaker, Abdallah, Mohamed Abubaker Ahmed, Abdeewi, Saedah, Abdel Ghafar, Taqwa, Abdelaleem, Ali, Abdelaleem, Ibrahim Abdelmonaem, Abdelgader, Khansaa, Abdelgadir, Waffa, Abdelhafez, Mohammed, Abdelhalim, Ahmed, Abdelkabir, Mohammed, Abdelkader Osman, Mohamed, Abdelkarim, Maha, Abdelkarim, Mohamed, Abdelmohsen, Sarah Magdy, Abdelnassir, Mazin, Abdelrahman, Ahmed Saber Mohamed, Abdelwahed, Aya Elsayed, Abdelzaher, Mohamed, Abderrahim, Baba Ahmed, Abdoulaye, Touré, Abdulai, Samira, Abdulghaffar, Yunus A., Abdullah, Fatimaalzahraa, Abdullahi, Lawal Barau, Abdullahi, Muzammil, Abdulrazik, Sarah, Abdulsalam, Khalifa Ibrahim, Abdulwahed, Eman, Abdus-Salam, Rukiyat, ABE, TOLUSHE, Abera Mulugeta, Gersam, Aboelghait, Aml Ali, Abol Oyoun, Nariman, Aboubekr, Boumediene, Abraham, Meera, Abu, Mohammed, Abuagila, Ahlam ali, Abubakar, Maimuna, Abugilah, Mohammed, Abuzeid, Issa A, Achouri, Djelloul, Acquah, Serwah Akua, Adam, Nusiba Bushra Ahmed, ADAMU, AUWAL, Adamu, Kabir Musa, ADAMU, MUHAMMAD, ADAMU, Sani, Adane, Samuel Gashu, Adeaga, Mojolaoluwa, Adebayo, Sikiru, Adedire, Adejare, Adegoke, Paul Aderemi, Adeniyi, Adebayo Augustine, Adeoye, Ibukunoluwa, Aderibigbe, Gbenga, ADEROUNMU, Azeezat, ADEYEMI, WILLIAMS, ADEYEMO, Adekunle, Adigun, Tinuola, Adika, Enoch Delad, ADISA, Adewale O, Adjei, Esther, Adjepong-Tandoh, Ernest Kwame, Ads, Alaa Mohamed, ADUMAH, Dr Collins Chijioke, ADUMAH, Lilian ogechi, Adzamli, Innocent, Afari, Jonas, Afedo, Wisdom, Affan, Abubaker, AFOLAYAN, Ayodeji Olawale, Agaba, Stuart, Agbeno, Evans, Agbonrofo, Peter, Aghadi, Ifeanyi, AGU, EDITH, Agyen, Thomas, Agyen-Mensah, Kwasi, Ahensan, Daasebre, Ahmad, Misbahu Haruna, AHMED, Awrayit, Ahmed, Linda, Ahmed, Nidal Youseef Altaher Aboh, Ahmed, Rubaa, Ahmed Jroush, Mohamed, ahmed maghur, Hasan, AHOGNI, G.N. Geofroid, Ait Yahia, Smain, Aji, Narjiss, Aji, Sani Ali, Akerele, William, Akhideno, Irene, Akinmokun, Israel, AKINNIYI, Akin Taofeek, Akinniyi, Ayodeji, AKINYEMI, Samuel, Akitoye, Olumide Adeleke, AKPAETTE, Iniofon Clement, Akuma, Terungwa Jacob, Akuokor, Daniel, Akwei, Clement Nii-Akwei, Al Bashir, Rayan Badran Hamed, Al Gharyani, Mohamed Fathi, al Islam ben Jouira, Rayet, Aladelusi, Timothy, Alakaloko, Felix, Alameen, Hind, Alameen Moheyaldeen, Mohammed, Alaogaly, Mona, Alarabi, Rehab, Alawami, Milud, Alazabi, Basma Masaud, Alazabi, Mona, Albakosh, Bashir Abobaker, ALBDULRRAZIQ, HUSAYN MOHAMMED ElFEETOURI, Aldieb, Asmma, Aldressi, Wafa, Alegbeleye, Godwin E., Alfa, Yakubu, Alhadad, Qamrah, Alhaddad, Arwa R, Alhaddad, Hayfa Faraj, Alhadi, Aliya, Alhamali, Aya, Alharam, Abtisam, Alhlafi, Majduldeen, Alhouwasi, Basmah, Alhudhairy, Sara, Ali, Abdallah Motasim Ahmed, Ali, Ahmed Jama, Ali, Almuaz, Ali, Asma, Ali, Enas, Ali, Mutwakil, Ali, Salem, Ali, Yunusa Y., Ali Ahmed, Abdelbaset, Aliozor, Sampson, Aljamal, Sarah, Alkaseek, Akram, Alkhalifa, Elmustafa, Alkoni, Samah, Allie, Abbaas, Almelyan, Khawla, Almugaddami, Ayman, Almujreesi, Asmaa, Alqady, Eithar, Alragheai, Alaa Ahmed, Alshareea, Entisar, Alshareef, Abulnasir, Alsori, Mohamed, Altomy, Seham Ahmed, Al-Touny, Aiman, Al-Touny, Shimaa A., Alum Aguma, Rachel, Alwaer, Nuha Miftah, Al-zletni, Hadeel, Alzwai, Marwa, Amaambo, Nelago, Amah, Christopher C, Amary, Marwa, Amengle, Ludovic Albert, Amesho, Shiwana Lineekela Omwene, Ametepe, Mawuli, Amkhatirah, Emad, Amnaina, Mohamed Gamal, Amoah, Benjamin, Amoah, Joseph Kofi, Amo-Aidoo, Nana Adjoa Simitsewa, Amoako-Boateng, Mabel, Ampong, Jude, Anane-Fenin, Betty, Anarfi, Samuel, Andriamanarivo, Mamy Lalatiana, Aniakwo, Luke, Aniteye, Ernest, Ankrah, Levi Nii-Ayi, Anno, Audrey, Anyanwu, Lofty-John Chukwuemeka, Anyigba, Edem, Appeadu-Mensah, William, Appiah-Thompson, Peter, Apraku-Peprah, Ewuarabena Lydia, Aremu, Shuaib Kayode, Arinaitwe, Moses, Armah, Ralph, Arthur, Augustine, Arthur, Douglas, Asah-Opoku, Kwaku, Asante, Mabel, Asante-Asamani, Alvin, Asare, Angbo, Asasira, Lausa, Ashfersh, Mohamed, ASHINDOITIANG, John A., Ashong, Joycelyn, Ashraf Salah, Mohamed, Asiedu, Charles, Asiedu, Isaac, Asiyanbi, Kolawole, Asla, Amir, Asman, Wilfred, Asoegwu, Emeka J., Assalhi, Mohamed, Assim, Claudia, Asudo, Felicia Dele, Atai, Alice Gertrude, Ateeqa, Sara Bin, Atim, Terkaa, Atindama, Solomon, ATIQUI, IJLAL, Atrih, Zoubir, Attah, Raphael Avidime, Awad, Ahmed K, Awedew, Atalel Fentahun, Aween, Husayn, Awere-Kyere, Lawrence, Awindaogo, Joseph, Awori Achani, Margaret, Ayad, Kusay, Azab, Ahmed, Azas, Alberta, Aziza, Bochra, Azize, Diallo Abdoul, AZOUI, Abdelkrim, Azouz, Jomana, Baba, Suleiman, Babalola, Olakunle Fatai, Babiker, Mona, Baddoo, Daniel, Badi, Aml, BADMUS, Sarat Abolore, Badr, Helmy, Bah, Abdoulie, Bah, Fatoumata Yakhie, Bah, Kajali, Bah, Marma Tumaneh, Bahroun, Sumayyah, Baidoo, Ebikela, Baidoo, Kenneth, Baidoo, Richard, Bakare, Adewumi, Bakeer, Hiba Baliad, Baky Fahmy, Mohamed A, Balogun, James, Bamigboye, Babatunde, Bankah, Patrick, Banson, Mabel, Barhouma, Yehia Emad, Barongo, Mugisha, BASHIR RABIU, MOHAMMED, Bassem, Adham, Bedair, Mohamed Adel Ali, Beeharry, Hemanshu Rambojan, Beeharry, Shanjugsing, Bekele, Sintayehu, Belie, Orimisan, Belkhair, Abdulmunem, Ben Ahmed, Yosra, Ben Ashur, Abir, Ben Hamida, Bahaeddin, Benade, Christia, BENMANSEUR, Sawsen, Bensebti, Amina Amel, BERDAI, Mohamed Adnane, Beyuo, Vera, Biala, Marwa, Bilson-Amoah, Estella, Bin wali, Salema Subhi, Binnawara, Muhannud, Birlie Chekol, Wubie, Birqeeq, Ghada, Biyase, Thuli, Blankson, Paa Kwesi, Boakye, Benedict, Boakye-Acheampong, Kwame, Boakye-Yiadom, Kwaku, Boateng, Joseph, Bobaker, Salem, Bode, Christopher, Bogoslovskiy, Alexander, Bolarinwa, Eniola Sefiu, Boretti, Lorenzo, Botchway, Maame Tekyiwa, Botha, Christo, BOUDA, B. David, BOURENANE, Haithem, BOUZBID, Sabiha, Boye, Jeffrey, Branny, Mthelebofu, Brown, George Darko, Brown, Warren, Bua, Emmanuel, BWALA, KEFAS JOHN, Camara, Bakary, Camara, M'mah Lamine, Carol, Tisana, Ceesay, Winston, Chafee, Karim, Chaklie Agegnehu, Bewuketu, Chamir, Congo, Chaziya, Peter YC, Chellan, Chantal, Cheniki, Narimane, Chennouf, Sarra, Chepkoech, Eglah, Chilango, Creamy, Chinda, John Yola, Chokwe, Thomas M., Choutri, Hichem, Christian, Nana Ama, Chukwu, Isaac, Chummun, Girish, Cilliers, Celeste, Cloete, Estie, Collison, Carol, Cronje, Larissa, Daary, Dennis, DAD, Bouzid, Daddy, Hadjara, Dahilo, Enoch Auta, Dairam, Jenitha, Dalaf, Manar Salim, Damson, Pempho, Daneji, Sulaiman Muhammad, Daniel, Adekunle, Daoud, Asmaa, Daoud, Hassan, Darat, Tarik Darat, Darko, Kwadwo Opoku, Darko, Kwame, Davidson, Kerryn, Davies, Abigail, Dawang, Yusuf Davou, Dayal, Kishan, Dayie, Makafui, de Goede, Adele, Deelawar, Bibi Waardanaaz, Derwish, Khawla, Desalu, Ibironke, Dessalegn Beza, Andinet, Dhege, Celestino, Dhilraj, Deepika, Diallo, Thierno Sadou, Diaw, Mbaye, Diaw Diop, Amadou, DIENE, Mansour, Dieng, Mactar, Dippenaar, Tinus, Djagbletey, Robert, Djedid, Nihel Klouche, Djouonang, Kamga Telly, Dominique, Shep, Drammeh, Basiru, Drissi, Hajer, du Bruyn, Aritha, Dube, Thandeka, Dufe, Rebecca, Dung, Dido, Earl, Ettiene, Ebrahem, Osama Khalifa Ali, Ebrahim, Zahier, Edena, Morrison E., Effa Ngono, Rosa, Egbuchulem, Kelvin, Egdeer, Amin, Eguma, Stella A., Ehimhantie, Martins, EJIOFOR, Ogochukwu Chidi, Ejuma, Lucy O., Ekenze, Sebastian, Ekhmaj, Reyad Almokhtar, Ekor, Oluwayemisi, EKPA, Sifonobong, Ekpemo, Chidi Samuel, Ekudo, Joseph, Ekwunife, Okechukwu Hyginus, El Koraichi, Alae, El Magrahi, Hamida, El Mejrab, Mohsen, El Sadek, Rania, El YOUBI, Haitam, Eladani, Oman, Elamesh, Sara Abdel Hamid, Elamien, Mohanad, Elamin Elnour, Moheyaldien Ahmed, Elbadawy, Merihan A, Elbaseet, Hesham, Elderwy, Ahmad A., Elebute, Olumide, Elgamal, Mostafa, Elgenidy, Anas, Elghareeb, Ahmed, Elgherwi, Laila, Elhadad, Rasha, Elhadi, Ahmed, Elhassan, Mohamed, Elkhouly, Abdallah Mohamed, Ellebedy, Mohamed, ELMAJRI, MOHAMED FUAD, Elmandouh, Omar, Elmandouh, Reem, Elmorsi, Rami, ELOMBILA, Marie, Elsadek, Menan, Elsalhawy, Shady, Elsayem, Karam, Elshafiey, Mahmoud, El-Sharkawi, Mohammad, Elshazly, Mohamed, Eltaub, Darine, Eltayeb, Almoutaz A, Eltayeb, Mohammed Eltayeb Zainelabdean, Eltegani Abdalla, Abeer, ElWakeel, Mai, Embu, Henry, Emoru, Arthur, Enicker, Basil, Enti, Donald, Entsua-Mensah, Kow, Eseile, Samuel Ideyonbe, Essuman, Vera Adobea, Et-taghy, Hiba, Etwire, Victor, Eyaman, Kuba Daniel, Ezbeida, Mabroukah, EZEKIEL, ANTHONY SABO, Ezidiegwu, Stanley Ugochukwu, Ezomike, Uchechukwu Obiora, FABOYA, Omolara, Fadlalmola, Hammad.A., FAGBAYIMU, Oluwatobiloba Micheal, Faida, Hamza, FALL, Khady, Farahat, Sherif, Faraj, Ali, Faraj, Noora, Farghaly, Amal, Farhat, Karima Omar Ahmed, Farinyaro, Aliyu Umar, Fathi Bani, Ghada, Fattah, Ahmed, Fening, Nana, Fentahun Emrie, Assefa, Fidieley, Melody, Fikadu Keneni, Dame, Fischer, Monique, Flint, Margot, Fodo, Naledi, Fofana, Naby, Fokeerah, Nitish, Folami, Emmanuel, Folokwe, Siyasanga, Fonternel, Doors, Fosi Kamga, Gacelle, Fotso, Luc Kamga, Fourtounas, Maria, Frankish, Leanne, Gabier, Ilhaam, Gacii, Vernon M., Gaffoor, M Sheik, Gagara, Moussa, GALADIMA, Hajara Aminu, Gamubaka, Richard, Ganey, Mike, Ganiyu, Oseni Oyediran, Gasa, Nompumelelo, Gatheru, Antony P., Gawu, Victoria Sena, Gaya, Subha Shita Devi, GAYE, Ibrahima, Gebremichael Ganta, Ashagre, Gelaw, Kassahun Girma, Geldenhuys, Lieze, Getachew Tegegn, Ayenachew, Ghemmied, Malak, Ghmagh, Reem, GILES, AHEREZA, Ginsburg, Ricky Ginsburg, Girma, Kassahun, Gjam, Fatima, Glover-Addy, Hope, Gobin, Veekash, Gomeh, Patricia, Gomez, Dimingo, Gorelyk, Alexandro, Gossaye, Abay, Govender, Veneshree, Grant, Jenny, Grayson, Britney L., Grobbelaar, Mariette, Gueye, Khadim Rassoul, GUIRO, Habibou, Gumede, Simphiwe, Gurure, Desire, Gusibat, Anwar, Gyeke-Boafo, Nana Kwame, HACHEMI, Sihem, Haddis, Kullehe, Haidar, Arwa, Haif, Assia, Hameed-Ikram, Sarwat, Hamid, Haytham, Hamukwaya, Dilona, Hanson, Nana Andoh, Hanzi, Joseph, Hardcastle, Timothy, Harissou, Adamou, Hasan, Ameerah, Hasan, Hayat Ben, Hasan, Najat Ben, Hashi, Abdullahi Said, Hashish, Amel A, Hassaan, Ibrahim, Hassan, Sadiq, Hassan, Sakariye Abdullaahi, Hassan, Tasneem, Hassan, Zeinab, Hassane, Maman Lawal, Hassanein, Mohamed, Hawu, Yoli, Haywood, David, Heelan, Halima, Hendricks, Natalie, Hillah, Ayayi, Hlela, Qinisile, HMAMOUCHI, Badreddine, Hoko, Zanele, Honny, Dorothy, Honore, Samba, Houidi, Senda, Human, Thys, Hussain, Eiman, Hussain Kona, Moataz Hashim, Hussein, Yara, Ibekwe, Titus Sunday, Ibiyemi, Akeem, IBIYEYE, TAIBAT, Ibrahim, Ibrahim Ali, Ibrahim, Lawal Ibrahim, Ibrahim, Soaleh, Ibrahim Abubakar, Amina, Ibrahim Alain, Traore, Idipo, Frieda, Idoko, Godwin, Idowu, Olusola, Idris, Mohammad El-Amin, Igaga, Elizabeth Namugaya, Iindongo, Etuuva, IITULA, Petrus, IKOTUN, Oluwafunmilayo, ILLE, Gloria, Imposo, Desire Hubert, Invernizzi, Jonathan, Irungu, Eric, Isbayqah, Areej Mohammed, Isbayqah, Eenas Mohammed, Ismael, Guibla, Ismail, Ali M, Itambi, Asoh Maxwell, Jabang, John Nute, Jaga, Rudhir, Jaganath, Ushir, Jaiteh, Lamin, Jallow, Cherno S, James, Olutayo, Javed, Sayed, Jithoo, Sandhya, Jlidi, Said, Joel, Lessan, Johnson, Marianne, JONES, TAIWO, Jooma, Zainub, Joomye, Shehzaad, Joosab, Mehboob, JOUINI, Riadh, Jubail, Mohamed J, Juggoo, Chaya, Jumbi, Timothy Mwai, Kaabar, Nejib, Kabirou, Mourtala, Kabiru, Abdulkadir Muhammad, Kabre, B. Yvette, Kache, Stephen, Kacimi, Salah Eddine Oussama, KADAS, ABUBAKAR SAIDU, KAHANSIM, Barminas, Kalipa, Mandisa, Kalongo, Jean Jacques Kabuley, Kalu, Nmesomachi Enyidiya, Kamate, Benoi, Kamwangen, Gracia Mitonga, Kandjimi, Matti, Kanjana-Zondo, Nokuzola, Kankpeyeng, Lawrence, Kapalamula, Tiyamike, Karadji, Souleymane, Kargbo, Mohamed Alieu, Karghul, Mohamed, Kaskar, Razeena, Kasker, Razeena, Kasobya, Faith, Kassem, Ossama, Kateregga, George, Kayima, Peter, Kedwany, Ahmed M., Ken-Amoah, Sebastian, Kenneth, Tomanya Kakura, KERISSE, Amina Nour El-Houd, KERKENI, Yosra, Khairi, Rania, Khaled, Mohamed, Khalifa, Eissa, Khalifa, Marwa Suliman, Khalil, Mohamed Kamal, Khattab, Mohamed S I, Khodary, Ahmed Refaat, Khumalo, Bridget Florence, Khumalo, Phindile, Kigayi, Jean Pierre, Kimutai, Timothy Kiprotich, KINDO, Bassirou, KIRFI, ABDULLAHI MUSA, Koggoh, Patience, Koko, Alshaima A, Kopieniak, Marcin, Kotagiri, Chandra, Kotey, Emily, Kouicem, Aya Tinhinane, Kpangkpari, Richard, Kudoh, Vincent, Kufonya, Norman, Kuhn, Warren, Kutor, Jasper, Kwakye, Akosua, Kynes, J. Matthew, Lambrechts, Lelanie, Lamiri, Rachida, LANRE, OLOKO NASIRUDEEN, Larvie, Prince, Lateef, Azeez Kehinde, LATRECHE, Samir, Lawal, Taiwo, Leballo, Gontse, Lebereki, Simon, Lee, Dorinda, Leeb, Gregory, Leonard, Tristan, LEYONO-MAWANDZA, Peggy Dalliah Gallou, Likongo, Ted Botawaosenge, Limalia, Ziyaad, LIMAN, HARUNA USMAN, Loae, Nada, Lompoli, Beinvenue Nkoy Ena, Lusungu, Dodo, M.Mokhtar, Fatma AL-Zahraa, Madany, Mohie El-Din Mostafa, Maddy, Reginald Jeff, Madombwe, Gladmore, Mafabi, Solomon, Magashi, Mahmoud Kawu, Maharaj, Sanvir, Mahfouz, Shaimaa Mahmoud, Mahlare, Korowe Rose Voncil, Mahmoud, Fathia, Maikassoua, Mamane, Maison, Patrick, Maiwald, Dela, Makhoba, Philisiwe, Makinita, Sewela Grace, Makou epse Tolefac, Myriam, Malau, Thomas Kefas, Mamathuntsha, Tshilidzi Godfrey, Mamo, Tihitena Negussie, Mamuda, Atiku, Mandundzo, Paidamoyo, Mangray, Hansie, Mani, Salma, Manneh, Ebrima K, Mansour, Noureldin Mohamed, Manyere, DV, Mapurisa, Amarylis, Mare, Pieter, Martin, Mogammad Ebrahim, Mashaal, Abdelhafeez, Mashaya, Sonela, Masilela, Patience Busisiwe, Mathebula, Ruth, Mathinya, Tlhapane, Matlala, Tumelo Kwena, Matlou, Mabitsela, Matos-Puig, Roel, Matoug, Salmin, Maudarbocus, Mohammad Jeelani, Mavesere, Haziel Pindukai, Mavila, Jackson, Mayet, Shafeeqa, Maygag, Mohamed, Mbatha, Nonhlanhla, Mbatudde, Rita, Mbiya Kapinga, Anne, Mbuyamba, Jojo, Mbuyi, Ali T, Mdlalose, Nkosinathi, Prowling, Megan, Mejeni, Nathalie, Mekonnen Ejigu, Yayehyirad, Merghani, Safa, Metogo, Junette Epse Njoki, Mhiri, Riadh, Mhone, Lyness, Michael, Afieharo, Miko, Abdullahi Mustapha, Milad, Ahmed, Mishra, Ravi, Mjadu, Londiwe, Mkhontwana, Nokonwaba, Mlambo, Nompilo, Mncwango, Zama, Mngoma, Gcina, Mnguni, Mzamo, Modekwe, Victor Ifeanyichukwu, Mogane, Palesa, Moghazy, Rama, Mogotsi, Kena, Mohalal, Mohamed Salah, Mohamed, Amin Awad Alamin, Mohamed, Maria, Mohamed, Molhema Eltaib Elamin, Mohamed, Suleyman Abdullahi, Mohamedkheir, Mohamed Abdelmoneim, Mohammad, Ahmad Lofty, Mohammad, Alhassan Datti, Mohammad, Aminu Mohammad, Mohammed, Abdulrahman, Mohammed, Muhanned, Mohammed, Rabiu Isah, Mohammed, Rehab, Mohammed, Taha Salah Abdelmaksaod, Mohammedosman, Doaa, Mohsen, Siham Moftah, Molla Getahun, Amsalu, Moloisi, Makwati, Monib, Fatma A, Moodley, Kirushin, Moopanar, Manogran, Morgan, Fatma, Moris, Baluku, Morna, Martin, Moses, Vaughn, Mostafa, Mahmoud Mohamed, Motiang, Mammie, Motseoile, Toni, Motshabi, Palesa, MOUSSAOUI, Nassima, Mpoto, Dany Bolimo, MPOY EMY MONKESSA, Christ Mayick, MRARA, BUSISIWE, Mshelbwala, Philip Mari, Msherghi, Ahmed, Msibi, Trevor, Mubunda, Raphael Kapend, Muhammad, Abubakar Bala, Muhammad, Saminu, Muhanguzi, Joshua, Muhindo, Ruth, Mukenga, Martin Mamba, Mukuna, Patrick Miteo, Mulewa, Deogracias, Munanzvi, Kudzayi Sarah, Mungur, Luckshmanraj, Munubi, Aziz, Munyalo, Francisca Syovata, Muriithi, Julius M., Musa, Abdullahi Aliyu, Musa, Kareem, Musa, Mosaab Abdelhafiz Ebrahim, Musana, Fred, Musewu, Tongo Douglas, Musiitwa, Albino Kiboonwa, Mwangi, Caroline M., Mwepu, Idesbald Mwebe, Mwepu, Michel Ilunga, Mwika, Peter Mwirichia, Mwiti, Timothy M., Myeni, Physician, Mzoneli, Nosisi, Naana, Reyam, Nabukenya, Gladys, Nabunya, Susan, Naidoo, Alishka, Naidoo, Verushka, Naidu, Priyanka, Nakyanzi, Caroline, Nambi, Esther, Nampawu, Mary Juliet, Nampiina, Gorret, Namutebi, Hasifah, Nana, Benedict, Nanda, Joëlle Sandra Youssa, Nanimambi, Juliana, Nantongo, Betty, Napolitano, Luisa, Naser, Alg, Nassar, Ahmad Sammy, Nassar, Muhammad Sammy, Nasser, Nadine, Nawezo, Jacob Gerald, NDIAYE, Alain, NDIAYE, Cheikh Ahmed Tidiane, Ndiaye, Françoise, Ndibarekera, Sarah Harriet, Ndjoko, Sylvie Mishondo, Ndlovu, Msizi, Nduwayezu, Richard, Negash, Samuel, Nehema, Sarah, Neizer, Margaret, NEJMI, Sifeddine, Nezam-Parast, Masoud, Ng How Tseung, Kenny, Ngcelwane, Thandokazi, Ngene, Ikenna, Nghidinwa, Hilka, Ngissah, Reuben, Ngock, George Farrar Fola Ngock, Ngouane, Diane, Ngumi, Zipporah, Nibret, Yonas, NIENGO OUTSOUTA, Gilles, Njie, Masirending, NJOKANMA, Rapheal Azuka, Nkhata, Lister, Nkhuna, Nyajane Thomas, Nkosi, Nobuhle, Nkosi, Sebenzile, Nkwembe, Christophe Mualuka, Nnaji, Chimaobi, Nneji-Akazie, Tochukwu, Nongqo, Nezisa, Nortey, Michael, Noutakdie Tochie, Joel, Nsaful, Josephine, Nsimire, Berthe Barhayiga, Nte, Stanley K., Ntshingila, Cebile, Ntsie, Nthabiseng Precious, Ntsoane, David, Ntumy, Michael Yao, Nuer-Allornuvor, Gloria, Nuhu, Samuel, Nutsuklo, Prudence, Nwachukwu, Callistus Uchenna, NWAFULUME, NNAEMEKA, Nwangwu, Emmanuel, Nwankwo, Elochukwu P, Nyame, Clement Agyekum, Nyamekye, Evelyn, Nyankah, Eunice, Nyoka-Mokgalong, Cecilia, Oase, Divine, Obande, Joseph Orinya, Obbeng, Ambe, Obeng-Adjei, Grace-Imelda, Obianyo, Ijeoma, Obianyo, Nene E, Obiechina, Sylvester, OBRI, Abraham I., ODI, TEMITOPE, Odingo, Jonathan, Oelofsen, Siobhan, Ofori, Emmanuel, Ofori-Adjei, Dziffa, Ogaji, Idoko Monday, Ogundoyin, Olakayode Olaolu, OGUNLEYE, OLABISI, OGUNS, Abayomi, Ogunsua, Oluseyi, Ohemeng-Mensah, Elvis, Ojediran, Olubukola, Ojediran, Oluwabukade, Ojewuyi, Abiodun, Ojewuyi, Olufemi, Ojo, Adedoyin, OJO, Olugbenga Olalekan, Ojo, Omotayo, Okedare, Amos, Okenwa, Samuel C., Oko, Adariku Godwin, Okojie, Nkechi, Okonkwo, Leonard Nduka, Okoth, Peter, Okunlola, Abiodun Idowu, Okunlola, Cecilia Kehinde, Okurut, Mathew, Oladimeji, Motunrayo, Oladiran, Ajibola, Olagunju, Ganiyat R., Olajide, Abdul-Rahman Lukman, Olajide, Adewale. Timothy, Olang, Patrick R., Olayinka, Oluwakemi, Olori, Samson, Olulana, Dare, Olulana, Dare Isaac, Olusanya, Bolutife, Omar, Duaa Eisa, Onakpoya, Uvie, ONeil, Matthew, Onen, Hudson, ONYEKA, Chinonso, Oosthuizen, Alexis, Opandoh, Isabella, Opiyo, Sophy, Oppong, Jonathan, Orewole, Tesleem Olayinka, Orji, Mathias, Osagie, Olabisi, Osagie, Osasumwen T., Osaheni, Osayomwanbo, Osama Sleem, Adham, Osawa, Francis Omondi, Osei, Fred, Osei-Nketiah, Samuel, Osei-Poku, Dorcas, Osman, Alaa, Osman Ahmed, Mohammed, Osman Suliman, Sarah Omer, Otchere, Kofi, Othman, Amani Alsayd Abdulsalam, Othman, Eman, Othman, May, Otim, Paul, Otim, Tonny, Otman, Rema Hassan, Otoki, Violet, OUDJHIH, Messa, OUEDRAOGO, Issaka, OUEDRAOGO, P. Justin, Ousmane Hamady, Issa, Ouyahia, Amel, OWOJUYIGBE, Afolabi, Owoo, Christian, Owoo, Precious, Owusu Boamah, Matthew, Oyedele, Abisola, Oyedepo, Olanrewaju, Oyegbola, Christianah, Panday, Juniata, Parker, Ewomazino U. Evi, Parker, Ilyas, Parker, Robert K., Pembe, Julie Ndjondo, Percivale, Beatrice, Pereko, Janet, Pérez, Mariela, Perumal, Neville, Pillay, Leresse, Pretorius, Robyn, Prinsloo, Roz, Pryce, Charles, Puryag, Ashveen, QUADRI, Oladeji Raheem, Quansah, Kofi, Quarcoopome, Cornelia, Quarshie, Amanda, Quartson, Elizabeth, Quashie-Sam, James, Rabiu, Ayuba, Rabiu, Taofeek, Rahma, Manal, Rahman, Ganiyu Adebisi, Rais, Mounira, Rajah, Chantal, Rakotondrainibe, Aurélia, Ramakrishnan, Rema, Ramatou, Sabo, Ramdawon, Brinda, Ramdhani, Kirthi, Ramkaun, Yeswant, RAPHAEL, OSELE, Raslan, Hani Mohammed Ahmed, Redelinghuys, Cara, Riffi, Omar, Rikhotso, Hundzukani, Roberts, Charles AP, Robertson, Caroline, Roland, Nchufor, Roos, John, S. Abdalgadir, Esra, Saad, Alshaimaa, Saad, Mahmoud M., Saad El-Tanekhy, Aalaa, Saadi, Cyrine, Saadu, Tasiu, Saber, Mohamed, Sabir Yassin, Fatima Mohammed, Sabo, Vinishe Yakubu, Sabra, Tarek Abdelazem, Saeid, Dawoud Amhimmid, Safar, Amna, Sagboze, Sandra, Sahnoun, Lassaad, Salahu, Babangida Mohammed, Salami, Kelvin, Salawu, Adedayo Idris, Saleh, Hawazen, Saleh, Ismail Ali, Saleh, Khetam Mohamed, Salele, Aliyu Mohammed, Salem, Fatima, Salem, Osama, Salih, Mohammed Ali Ismael Alamin, Salisu, Ibrahim, Sall, Mouhamedoun, SAMB, Cheikh Fall, Sangak, Isam A, Sanoussi, Nanzir Moctar, Sanya, Douglas, Sanyang, Anaumana B, Sarpong, Pokua, SARR, Joseph Niame, Schnaubelt, Romy, Searyoh, Kafui, SECK, Ndèye Fatou, Secka, Abdoulie Sering, Seif, Mohamed, Seilbea, Yvonne, Semret Hailu, Berhe, Sepenu, Perez, Sewlall, Janice, Seyi-Olajide, Justina, Shai, Shiluva, Shalaby, Abdullah Mohamad Omar, SHAPHAT, IBRAHIM, Shava, Garai, Sheidu Owuda, Abdullahi, Sheshe, Abdurrahman Abba, Shetiwy, Mosab, Shezi, Nomusa, Shihab, Maryam Husam, Shitakumuna, Helena, Shitaye, Nebiyu, Shitta, Andrew H., Sholadoye, Tunde Talib, Shouasha, Princely, Shu'aibu, Naziru Garba, Shuiap, Nouran musbah, Sibeko, Bongekile, Sikhakhane, Sebe, Sikwete, Guigui, Sime Gizaw, Habtamu, Simelane, Nhlanhlenhle, Simon, Edwina, Singh, Usha, SIRAJALDIN, Abdulla, Siriboe, Esme, Siyothula, Thozama, Siyotula, Thozama, Smart-Yeboah, Awo, SMITH, Saidat, Solala, Sivuyisiwe, Soliman, Eman A., Solo, Corinne Eulalie, Sombéwendin Charles, Ilboudo, Sonaike, Monisola, Songden, David Zumnan, Sottie, Daniel, Soualili, Zineddine, Soula, Enas, Souleymane, Sidibe, SOWANDE, Oludayo Adedapo, Spytko, Alex, Srir, Daria Omar M, Ssebuguzi, Lawrence, Stegmann, George Frederik, Strauss, Lindsey, Struwig, Estee, Succi, Marcello, Suleiman, Abdul-Rasheed, Suliman, Mazin, Swartz, Mikhail, Taha, Taha M., Takai, Idris Usman, Takou, Bougoue Horline, Takrouney, Mohammed Hamada, Takure, Augustine, TALABI, Ademola Olusegun, Tall, Mamadou, Taute, Carla, Tawfik, Mohamed, Taylor, Jenna, Tembe, Dias Salomao, Temesgen, Fissha, Tesfaye, Emnet, Theko, Dineo, Thiart, Mari, Thompson, Ruary, Thuer, Linda, Tientcheu Fabrice, Tim, Tilahun, Zekaryas Belete, Tilahun Woldetsadik, Tsion, Timo, Manuella, Timotews, Namene, Tjiyokola, David, Tolani, Musliu Adetola, TOUABTI, Souhem, Traoré, Diakaridia, Tsegha, Livingfaith Jighjigh, Tseli, Mbeki, Tumuhimbise, Christine, Tumukunde, Janat, Tunkara, Salihu F S, Turshan, Laila, Turton, Edwin, Uchendu, Chukwudi Chukwuemeka, UDIE, Gabriel U., UDOSEN, Joseph E., Ugalahi, Mary, Ugwu, Euphemia M., UGWU, Ikechukwu Ethelbert, Ugwu, Jideofor Okechukwu, Ugwunne, Chuka A, Ukpabio, Ukpabio E.I., Umar, Aminu Muhammed, UMEH, Chizoba Linda, Ungen, Rowena, Usang, Usang, Usenbo, T, Usman, Mustapha Ibrahim, UWAYESU, Roda, Van Aswegen, Benjamin, van der Byl, Ashley, van der Linde, Pieter, van der Walt, Stephan, van Schalkwyk, Hendrik Petrus, van Tonder, Charme, van Vuuren, Suleen, van Wyk, Janri, van Zyl, Sudene, Wabule, Agnes, Wacays, Abdirahman, Waheed Mowafy, Ghada, Waisiko, Bethleen, Walawah, David, Walithandia, Eziron, Wamwaki, John, Wataaka, Nicholas, Wessels, Nelia, Williams, Emmanuel, WILLIAMS, Omolara, Woldegiorgis, Abel, Wolfaardt, George, Wondossen, Mekete, Woodun, Ritish, Workineh, Saleamlak Tigabie, Wubetu, Solomon, Yahia, Mohamed, Yakubu, Hamisu, Yakubu, Saidu Yusuf, Yalewu, Dawit Zerihun, YAMEOGO, T. Azer-Clovis, Yeboah, Francis, YENYI AHUKA LONGOMBE, Thérèse, Younes, Eman, Young, Chad, Younis, Nageia, Younus, Tarig Yousuf Ibrahim, YUSUF, STEPHEN, Zaki, Fatima, Zbida, Ibrahim, Zenda, Thubelihle, ZERIZER, Yassine, Zingoni, Kudzai, Zitouni, Hayett, ZONGO, P. Valentin, Zubi, Abdalrahem, Zulu, Nonhlanhla, and Zulu, Nqobile

Published
2024
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39. Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data

Author

Tabatabai, Shadha, Mohammed, Ihab, Qolomany, Basheer, Albasser, Abdullatif, Ahmad, Kashif, Abdallah, Mohamed, and Al-Fuqaha, Ala

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. However, FL faces many challenges, including the difficulty to obtain high accuracy, high communication cost between clients and the server, and security attacks related to adversarial ML. To tackle these three challenges, we propose an FL algorithm inspired by evolutionary techniques. The proposed algorithm groups clients randomly in many clusters, each with a model selected randomly to explore the performance of different models. The clusters are then trained in a repetitive process where the worst performing cluster is removed in each iteration until one cluster remains. In each iteration, some clients are expelled from clusters either due to using poisoned data or low performance. The surviving clients are exploited in the next iteration. The remaining cluster with surviving clients is then used for training the best FL model (i.e., remaining FL model). Communication cost is reduced since fewer clients are used in the final training of the FL model. To evaluate the performance of the proposed algorithm, we conduct a number of experiments using FEMNIST dataset and compare the result against the random FL algorithm. The experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of accuracy, communication cost, and security., Comment: Accepted at 2022 IWCMC

Published
2022

47. Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

Author

Alwarafy, Abdulmalik, Ciftler, Bekir Sait, Abdallah, Mohamed, Hamdi, Mounir, and Al-Dhahir, Naofal

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This paper considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation heterogeneous wireless networks (HetNets). We consider a future HetNet comprised of multi-RATs and serving multi-connectivity edge devices (EDs), and we formulate the problem as mixed-integer non-linear programming (MINP) problem. Due to the high complexity and combinatorial nature of this problem and the difficulty to solve it using conventional methods, we propose a hierarchical multi-agent deep reinforcement learning (DRL)-based framework, called DeepRAT, to solve it efficiently and learn system dynamics. In particular, the DeepRAT framework decomposes the problem into two main stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q Network (DQN) algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using simulations, we demonstrate how the various DRL agents efficiently interact to learn system dynamics and derive the global optimal policy. Furthermore, our simulation results show that the proposed DeepRAT algorithm outperforms existing state-of-the-art heuristic approaches in terms of network utility. Finally, we quantitatively show the ability of the DeepRAT model to quickly and dynamically adapt to abrupt changes in network dynamics, such as EDs mobility.

Published
2022
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49. Prevalence of malaria infection and factors associated among HIV-infected adult patients attending HIV care and treatment clinic at Kitete regional referral hospital in Tabora region, Tanzania: a cross-sectional study

Author

Hamad Nnimbo, Doreen Kamori, Nsiande Lema, and Abdallah Mohamed

Subjects
HIV, Malaria, Prevalence, Tanzania, Science
Abstract

Abstract Background HIV and malaria are serious public health concerns, particularly in Tanzania. HIV-infected individuals are more likely to get malaria and its complications. However, data on the interaction of the two diseases in Tanzania are limited. This cross-sectional study aimed to determine the prevalence of malaria infection and associated factors among HIV-infected adults attending HIV care and treatment clinic at Kitete regional referral hospital in Tabora region, Tanzania. Methodology The cross-sectional study was carried out between March and May 2022 at Kitete regional referral hospital in Tanzania. A total of 246 HIV-infected adults were selected by systematic random sampling. Malaria was diagnosed using both malaria rapid diagnostic test (mRDT) and malaria microscopy. Social demographic data were collected using a structured questionnaire, while clinical history and laboratory parameters were extracted from patients’ files. Data were analyzed using STATA version 15.1, and a p value

Published
2023
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