Franklin Open最新文献

{"title":"Mathematical modeling of malaria epidemic dynamics with enlightenment and therapy intervention using the Laplace-Adomian decomposition method and Caputo fractional order","authors":"Akeem Olarewaju Yunus,&nbsp;Morufu Oyedunsi Olayiwola","doi":"10.1016/j.fraope.2024.100147","DOIUrl":"10.1016/j.fraope.2024.100147","url":null,"abstract":"<div><p>This paper examines malaria, a prevalent mosquito-borne disease in Africa that causes fever, chills, and headaches. Diagnosis involves blood tests, and treatment primarily relies on antimalarial drugs. Mathematical modeling is crucial for disease prevention and eradication strategies. The study uses deterministic models to analyze global malaria transmission patterns, focusing on enlightened therapy's effectiveness as a control measure.</p><p>Four compartmental models depict susceptible, latent, infected, and recovered populations, exploring various disease spread scenarios while ensuring model stability and reliability. Epidemiological principles identify disease-free and endemic equilibria, calculating the basic reproduction number. Stability analysis utilizes Lyapunov functions, supported by Laplace transformation and MAPLE18 simulations for solution derivation.</p><p>Furthermore, the study investigates the impact of fractional-order derivatives on transmission dynamics and control strategies, analyzing the effects of increasing fractional derivative orders using graphical representations. This research provides insights valuable for public health initiatives and malaria eradication efforts, emphasizing the role of Caputo fractional derivatives in refining malaria control strategies and elucidating the findings for a broader readership appeal.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277318632400077X/pdfft?md5=8e07dc6ab44684bcecb7c781be62bd4e&pid=1-s2.0-S277318632400077X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells","authors":"Bhukya Laxman ,&nbsp;Ramesh Gugulothu ,&nbsp;Surender Reddy Salkuti","doi":"10.1016/j.fraope.2024.100161","DOIUrl":"10.1016/j.fraope.2024.100161","url":null,"abstract":"<div><div>Fuel cells are one of the most promising renewable energy sources, offering advantages like reliability, eco-friendliness, and low pollutant emissions, which have spurred rapid advancements in power generation technologies. However, fuel cells face significant challenges, including high initial costs, limited fuel availability, and the difficulty of maintaining operation at the maximum power point, which hinders their use in stand-alone applications. In this paper, a Modified Particle Swarm Optimization (MPSO) method is proposed for maximum power point tracking (MPPT) to optimize the power output of Proton Exchange Membrane Fuel Cells (PEMFCs). The proposed method dynamically adjusts to key operational parameters such as cell temperature, hydrogen partial pressure, and membrane water content, areas that have not been comprehensively addressed in previous research. In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. Under rapid temperature fluctuations in the fuel cell, the proposed MPSO MPPT method achieved a maximum power of 1223.5 W with an average of 5.66 iterations. In comparison, the meta-heuristic particle swarm optimization (PSO) method and the conventional perturb and observe (P&amp;O) method achieved maximum power outputs of 1218.5 W and 1213.65 W, respectively, with PSO requiring 12.33 iterations. Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. The MPSO method is implemented in the Simulink/MATLAB environment and is compared with the Perturb &amp; Observe (P&amp;O) and Conventional PSO (CPSO) methods. The results demonstrate that the proposed MPSO approach outperforms these traditional techniques in terms of tracking speed, efficiency, and stability under varying conditions. This successful implementation lays a strong foundation for future integration into real-world PEMFC systems.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100161"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Bayesian inference for under-five mortality data","authors":"M.K. Mwanga ,&nbsp;S.S. Mirau ,&nbsp;J.M. Tchuenche ,&nbsp;I.S. Mbalawata","doi":"10.1016/j.fraope.2024.100163","DOIUrl":"10.1016/j.fraope.2024.100163","url":null,"abstract":"<div><div>Under-five mortality remains a significant global health challenge, with millions of children dying before their fifth birthday each year. This study explores the application of fuzzy Bayesian inference for under-five mortality data using Tanzania as a case study. Fuzzy Bayesian inference has emerged as a promising technique that combines the flexibility of fuzzy set theory with the probabilistic framework of Bayesian inference. The study employs fuzzy sets and membership functions to represent the linguistic terms and their degrees of membership, along with the Poisson distribution to model the mortality rate. The results demonstrate the potential of fuzzy Bayesian inference for analyzing under-five mortality rates. This approach provides a more nuanced understanding of the complex mortality patterns.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet","authors":"Bader Alsharif ,&nbsp;Easa Alalwany ,&nbsp;Mohammad Ilyas","doi":"10.1016/j.fraope.2024.100165","DOIUrl":"10.1016/j.fraope.2024.100165","url":null,"abstract":"<div><div>Sign language serves as a sophisticated means of communication vital to individuals who are deaf or hard of hearing, relying on hand movements, facial expressions, and body language to convey nuanced meaning. American Sign Language (ASL) exemplifies this linguistic complexity with its distinct grammar and syntax. The advancement of real-time ASL gesture recognition has explored diverse methodologies, including motion sensors and computer vision techniques. This study specifically addresses the recognition of ASL alphabet gestures using computer vision through Mediapipe for hand movement tracking and YOLOv8 for training the deep learning model. The model achieved notable performance metrics: precision of 98%, recall rate of 98%, F1 score of 99%, mean Average Precision (mAP) of 98%, and mAP50-95 of 93%, underscoring its exceptional accuracy and sturdy capabilities.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Squeeze & Excitation joint with Combined Channel and Spatial Attention for Pathology Image Super-Resolution","authors":"Mansoor Hayat","doi":"10.1016/j.fraope.2024.100170","DOIUrl":"10.1016/j.fraope.2024.100170","url":null,"abstract":"<div><div>Super-resolution (SR) techniques are pivotal in enhancing low-resolution images and crucial in medical diagnosis, where detail and clarity are paramount. Traditional pixel-loss-based SR methods, while adept at producing high-resolution (HR) images, often result in artifice content. This loss of information compromises both the visual experience and the accuracy of subsequent diagnoses. Addressing this, we have developed an innovative SR approach integrating a joint Squeeze and Excitation (SE) mechanism with a Combined Channel and Spatial Attention (CCSA) mechanism. The SE mechanism effectively recalibrates channel-wise feature responses, enhancing the representational capacity of the network. Meanwhile, the CCSA mechanism focuses on extracting spatial and channel-wise features, ensuring that critical high-frequency details are preserved. The dual approach significantly refines the quality of the images, maintaining essential details necessary for accurate medical diagnosis. To validate our proposed approach, we used a benchmark dataset, bcSR, tailored to challenge SR models to focus on broader and more critical regions. Comparative analysis proves that our model excels in performance over existing state-of-the-art methods. In conclusion, our proposed SR Network, with its innovative SE and CCSA mechanisms, offers a potent tool for pathology image SR. It elevates the quality of super-resolved images, which will significantly aid in the accuracy and efficiency of medical diagnoses, providing a valuable asset to medical professionals.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of deep learning techniques for rice disease recognition: Current trends and future directions","authors":"Hassan Muhammad Yusuf,&nbsp;Sahabi Ali Yusuf,&nbsp;Amina Hassan Abubakar,&nbsp;Mohammed Abdullahi,&nbsp;Ibrahim Hayatu Hassan","doi":"10.1016/j.fraope.2024.100154","DOIUrl":"10.1016/j.fraope.2024.100154","url":null,"abstract":"<div><p>This systematic review paper provides a comprehensive analysis of the recent advances in deep learning techniques for rice disease recognition. Rice is one of the most important crops in the world, providing food for more than half of the global population. However, rice diseases pose a major threat to rice production and can cause significant yield losses. In recent years, deep learning techniques have shown great potential in automating the process of rice disease recognition, which can help in early disease detection and management. This paper reviews the current trends in deep learning techniques for rice disease recognition, including various pre-processing and augmentation techniques, as well as popular deep learning models such as convolutional neural networks (CNNs) and their variants. The paper also provides an in-depth analysis of the different datasets used in the studies, along with their limitations and challenges. Furthermore, the paper discusses the future directions for research in this field, such as the need for larger and more diverse datasets, the development of novel deep learning architectures, and the integration of other data sources such as weather data and satellite imagery. The paper concludes by summarizing the key findings of the systematic review and highlighting the potential impact of deep learning techniques in rice disease recognition. In addition, the review provides a useful resource for researchers and practitioners in the field of agricultural technology and can help in the development of more accurate and efficient automated systems for rice disease detection and management.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000847/pdfft?md5=5e94b88fa5fc882b8022488a1fb61fed&pid=1-s2.0-S2773186324000847-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters","authors":"Hauwau Abdulrahman Aliyu ,&nbsp;Ibrahim Olawale Muritala ,&nbsp;Habeeb Bello-Salau ,&nbsp;Salisu Mohammed ,&nbsp;Adeiza James Onumanyi ,&nbsp;Ore-Ofe Ajayi","doi":"10.1016/j.fraope.2024.100153","DOIUrl":"10.1016/j.fraope.2024.100153","url":null,"abstract":"<div><p>Diabetes mellitus poses a global health concern, prompting the development of machine learning algorithms designed to construct a model for the accurate classification of patients, enabling precise diagnoses and early-stage treatment. However, the efficacy of classifying diabetes patients through machine learning relies on datasets, often plagued by imbalance, leading to biased classification and inaccurate diagnoses. Previous research attempts, employing techniques like random sampling (under-sampling and oversampling) and the Synthetic Minority Oversampling Technique (SMOTE), have struggled to achieve optimally balanced datasets. Additionally, setting the best parameters for machine learning classifiers remains a challenging task. To address these issues, this research focuses on devising a methodological metaheuristic optimization, a machine learning algorithm tailored for diabetes data balancing, and classifier hyperparameter tuning. Leveraging Particle Swarm Optimization (PSO) algorithm for diabetes data balancing and a genetic algorithm to select the optimal architecture for various machine learning classifiers. The study compares the performance of the proposed metaheuristic data balancer and classifier architecture parameter tuner using classification metrics (F1 score, Average Precision–Recall (APR), AUC, and accuracy). The PSO balanced dataset emerges as the most effective in classifying diabetes, with an Average Percentage Improvement (API) in classification performance metrics: 20.78% accuracy, 16.79% area under the curve for receiver operating characteristics, and a significant 32.78% enhancement in APR. Moreover, the XGBOOST classifier trained with a genetic algorithm demonstrates minimal computational training time for the Centre for Disease Control and Prevention (CDC) diabetes dataset compared to the artificial neural network and random forest classifier. Notably, the imbalanced CDC diabetes dataset exhibits the least APR compared to random under-sampling and the PSO data balancing technique.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000835/pdfft?md5=57a6344698bfd841a4a5715b104a987b&pid=1-s2.0-S2773186324000835-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the convergence of Fourier representations and Schwartz distributions","authors":"Pushpendra Singh ,&nbsp;Amit Singhal ,&nbsp;Binish Fatimah ,&nbsp;Anubha Gupta ,&nbsp;Shiv Dutt Joshi","doi":"10.1016/j.fraope.2024.100155","DOIUrl":"10.1016/j.fraope.2024.100155","url":null,"abstract":"<div><p>While Fourier theory remains a cornerstone for analyzing and interpreting the spectral content of diverse signals, its limitations often surface in practical applications. Notably, popular signals like sinusoids, Dirac deltas, signums, and unit steps lack convergent Fourier representations within the conventional framework. This discrepancy necessitates distribution theory to build and interpret suitable representations for such signals. However, existing literature in signal processing and communication engineering often glosses over these intricacies, leaving researchers grappling with obscure concepts regarding the very existence of Fourier representations for non-conforming signals. This work bridges this critical gap by offering a comprehensive exploration of the conditions guaranteeing the existence of Fourier representations. We introduce a novel linear space – the Gauss–Schwartz (GS) function space – and its corresponding class of tempered superexponential (TSE) distributions. We demonstrate that the Fourier transform (FT) acts as an isomorphism on the GS space of test functions and, by duality, on TSE distributions. Crucially, the GS space proves to be minimal in the sense that its dual, encompassing TSE distributions, represents the largest possible linear space over which the FT can be defined through duality. This theoretical advancement signifies a twofold contribution. Firstly, it clarifies the existence and interpretation of Fourier representations for prevalent signals that evade conventional analysis. Secondly, the introduction of the GS-TSE framework expands the reach of the FT to encompass a broader spectrum of functions, enabling novel applications in diverse fields. Ultimately, this work paves the way for a more robust and accessible understanding of Fourier analysis, empowering researchers and practitioners to leverage its full potential in exploring and processing a wider range of signals.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000859/pdfft?md5=2a02550ddf4dafd90b5648dc61d9e810&pid=1-s2.0-S2773186324000859-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new observer-based controller for continuous-time nonlinear systems with applications on electrical energy systems","authors":"Mohammad Fahim Hassan ,&nbsp;Elham Aljuwaiser","doi":"10.1016/j.fraope.2024.100145","DOIUrl":"10.1016/j.fraope.2024.100145","url":null,"abstract":"<div><p>In this paper a new observer-based controller is developed for continuous-time input constrained nonlinear systems. Firstly, a new developed continuous time Regularized Least Square (CRLS) estimator is presented. The estimated states are then used to generate the desired constrained state feedback control strategies to regulate the performance of a perturbed system to the desired steady state. Unlike the well-known continuous-time nonlinear state estimators, such as the high gain observer and others, the developed observer does not show large overshoots at the start of the estimation process, the estimation errors reach zero steady state very shortly, has no pre-specified limitations on the class of nonlinear systems to be estimated, …etc. As a result, when used as an element in observer-based controlled systems, then the desired behaviors of the state trajectories of the controlled system are reached very shortly. The asymptotic stability of the proposed constrained observer-based controlled nonlinear system is rigorously analyzed. Simulation results are presented to justify the applicability and the efficiency of the proposed approach. Firstly, to show the superiority of the developed CRLS estimator, the states of illustrative practical problems are firstly estimated using the proposed approach. The achieved results are then compared with those achieved from applying the most famous and widely used observers in the literature. Then, two nonlinear practical control problems are used to demonstrate the effectiveness of the developed observer- based control approach.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000756/pdfft?md5=fe8126a1dc3eb80b95782a06c4b07703&pid=1-s2.0-S2773186324000756-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications","authors":"Ibrahim Hayatu Hassan ,&nbsp;Mohammed Abdullahi ,&nbsp;Jeremiah Isuwa ,&nbsp;Sahabi Ali Yusuf ,&nbsp;Ibrahim Tetengi Aliyu","doi":"10.1016/j.fraope.2024.100141","DOIUrl":"10.1016/j.fraope.2024.100141","url":null,"abstract":"<div><p>Metaheuristic algorithms are commonly used in solving complex and NP-hard optimization problems in various fields. These algorithms have become popular because of their ability to explore and exploit solutions in various problem domains. Honey Badger Algorithm (HBA) is a population-based metaheuristic optimization algorithm inspired by the dynamic hunting strategy of honey badgers, utilizing honey and digging-seeking techniques. Since its introduction in 2020, HBA has garnered widespread attention and has been applied across various domains. This review aims to comprehensively survey the improvement and application of HBA in solving various optimization problems. Additionally, the survey conducts a meta-analysis of the HBA's improvements, hybridization and application since its introduction. According to the result of the survey, 52 studies presented improved HBA using chaotic maps, levy flight mechanism, adaptive mechanisms, transfer functions, multi-objective mechanism and opposition based learning techniques, 20 studies presented a hybrid HBA with other metaheuristics algorithms and 101 studies uses the original HBA for solving various optimization problems. According to the survey, the wide acceptance of the HBA within the research community stems from its straightforwardness, ease of use, efficient computational time, accelerated convergence speed, high efficacy, and capability to address different kind of optimization issues, distinguishing it from the well-known optimization approches presented.</p></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"8 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773186324000719/pdfft?md5=d41ffa4109b4d70e83a83596181c1237&pid=1-s2.0-S2773186324000719-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}