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Evaluation of water quality and accurate prediction of water pollution indicators are key components in water resource management and water pollution control. The use of biological early warning systems (BEWS), in which living organisms are used as biosensors, allows for a comprehensive assessment of the aquatic environment state and a timely response in the event of an emergency. In this paper, we examine three machine learning algorithms (Theta, Croston and Prophet) to forecast bivalves’ activity data obtained from the BEWS developed by the authors. An algorithm for anomalies detection in bivalves’ activity data was developed. Our results showed that for one of the anomalies, Prophet was the best method, and for the other two, the anomaly detection time did not differ between the methods. A comparison of methods in terms of computational speed showed the advantage of the Croston method. This anomaly detection algorithm can be effectively incorporated into the software of biological early warning systems, facilitating rapid responses to changes in the aquatic environment.

Because scripture represents the direct word of God, it is the most sacred source for authority in the scriptural monotheisms. Scripture is conveyed to humanity through extraordinary individuals known as prophets. But if the purported prophet is a false prophet, then the conveyed message is false, resulting in the collapse of the structure upon which religious authority rests. This problem was recognized in the Hebrew Bible, and accusations of flawed prophecy and deceitful prophets figure prominently in relations between the scriptural monotheisms. Jews do not accept the authenticity of Jesus’ role as messiah (and prophet) in the NT, and Jews and Christians do not accept the authenticity of Muhammad’s role as prophet in the Qur’an. But the rejection is unidirectional. As a rule, established religions cannot accept new prophets, while new religions accept the prophets that lived before, though with certain qualifications. Each of the three classic scriptural monotheisms also declares an “end” to prophecy after the canonization of its scripture. Yet despite these deadlines, each acknowledges that God could nevertheless send another prophet, leaving open the wild card for new prophecy, new revelation, and even new religion; within each of the classic scriptural monotheisms, individuals arose after the canonization of scripture who were deemed by many to be prophets or something “like” prophets. This essay presents a preliminary phenomenology of prophethood, around which much of the religious polemics between the scriptural monotheisms are constructed.

Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall efficiency of power resource utilization. However, existing models cannot accurately capture the nonlinear features of electricity data, leading to a decline in the forecasting performance. To relieve this issue, this paper designs an innovative load forecasting method, named Prophet–CEEMDAN–ARBiLSTM, which consists of Prophet, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the residual Bidirectional Long Short-Term Memory (BiLSTM) network. Specifically, this paper firstly employs the Prophet method to learn cyclic and trend features from input data, aiming to discern the influence of these features on the short-term electricity load. Then, the paper adopts CEEMDAN to decompose the residual series and yield components with distinct modalities. In the end, this paper designs the advanced residual BiLSTM (ARBiLSTM) block as the input of the above extracted features to obtain the forecasting results. By conducting multiple experiments on the New England public dataset, it demonstrates that the Prophet–CEEMDAN–ARBiLSTM method can achieve better performance compared with the existing Prophet-based ones.

Since May 2022, over 64,000 Monkeypox cases have been confirmed globally up until September 2022. The United States leads the world in cases, with over 25,000 cases nationally. This recent escalation of the Monkeypox outbreak has become a severe and urgent worldwide public health concern. We aimed to develop an efficient forecasting tool that allows health experts to implement effective prevention policies for Monkeypox and shed light on the case development of diseases that share similar characteristics to Monkeypox. This research utilized five machine learning models, namely, ARIMA, LSTM, Prophet, NeuralProphet, and a stacking model, on the Monkeypox datasets from the CDC official website to forecast the next 7-day trend of Monkeypox cases in the United States. The result showed that NeuralProphet achieved the most optimal performance with a RMSE of 49.27 and R2 of 0.76. Further, the final trained NeuralProphet was employed to forecast seven days of out-of-sample cases. On the basis of cases, our model demonstrated 95% accuracy.

The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms to analyze data in real time, providing valuable insights to decision makers. Dairy farming in diverse climates is challenging and requires well-designed structures to regulate internal environmental parameters. This study explores the application of the Facebook-developed Prophet algorithm to predict indoor temperatures in a dairy farm over a 72 h horizon. Exogenous variables sourced from the Open-Meteo platform improve the accuracy of the model. The paper details case study construction, data acquisition, preprocessing, and model training, highlighting the importance of seasonality in environmental variables. Model validation using key metrics shows consistent accuracy across different dates, as the mean absolute percentage error on daily base ranges from 1.71% to 2.62%. The results indicate excellent model performance, especially considering the operational context. The study concludes that black box models, such as the Prophet algorithm, are effective for predicting indoor temperatures in livestock buildings and provide valuable insights for environmental control and optimization in livestock production. Future research should explore gray box models that integrate physical building characteristics to improve predictive performance and HVAC system control.

Solar and wind are now the fastest-growing power generation resources, being ecologically benign and economical. Solar and wind forecasts are significantly noteworthy for their accurate evaluation of renewable power generation and, eventually, their ability to provide profit to the power generation industry, power grid system and local customers. The present study has proposed a Prophet-model-based method to predict solar and wind resources in the Doomadgee area of Far North Queensland (FNQ), Australia. A SARIMA modelling approach is also implemented and compared with Prophet. The Prophet model produces comparatively less errors than SARIMA such as a root mean squared error (RMSE) of 0.284 and a mean absolute error (MAE) of 0.394 for solar, as well as a MAE of 0.427 and a RMSE of 0.527 for wind. So, it can be concluded that the Prophet model is efficient in terms of its better prediction and better fitting in comparison to SARIMA. In addition, the present study depicts how the selected region can meet energy demands using their local renewable resources, something that can potentially replace the present dirty and costly diesel power generation of the region.

The automobile is an important part of transportation systems. Accurate prediction of sales prospects of different power vehicles can provide an important reference for national scientific decision making, flexible operation of enterprises and rational purchases of consumers. Considering that China has achieved the goal of 20% sales of new energy vehicles ahead of schedule in 2025, in order to accurately judge the competition pattern of new and old kinetic energy vehicles in the future, the automobile market is divided into three types according to power types: traditional fuel vehicles, new energy vehicles and plug-in hybrid vehicles. Based on the monthly sales data of automobiles from March 2016 to March 2023, the prediction effects of multiple models are compared from the perspective of univariate prediction. Secondly, based on the perspective of multivariate prediction, combined with the data of economic, social and technical factors, a multivariate prediction model with high prediction accuracy is selected. On this basis, the sales volume of various power vehicles from April 2023 to December 2025 is predicted. Univariate prediction results show that in 2025, the penetration rates of three types of vehicles will reach 43.8%, 44.4% and 11.8%, respectively, and multivariate prediction results show that the penetration rates will reach 51.0%, 37.9% and 11.1%, respectively.

This article aims to provide an a priori argument—termed the Flourishment Argument, for the veracity of the Christian conception of the Abrahamic religion that centres on God’s action of sending a divine and atoning prophet into the world. This specific informal argument will be presented through the formulation of a set of a priori reasons for why God would seek to interact with the world—developed in light of the work of Richard Swinburne, John Finnis, Linda Zagzebski and Alexander Pruss—which, in combination, will provide individuals with grounds for believing in the veracity of these important Christian teachings.

Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we examine different techniques to forecast sale prices applied by an Italian food wholesaler, as a step towards the automation of pricing tasks usually taken care by human workforce. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool by Facebook based on a generalized additive model, and to deep learning models exploiting Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good benchmark for the problem under study. Our results indicate that ARIMA models and LSTM neural networks perform similarly for the forecasting task under consideration, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is quick and easy to use, but considerably less accurate.

The models for forecasting time series with seasonal variability can be used to build automatic real-time control systems. For example, predicting the water flowing in a wastewater treatment plant can be used to calculate the optimal electricity consumption. The article describes a performance analysis of various machine learning methods (SARIMA, Holt-Winters Exponential Smoothing, ETS, Facebook Prophet, XGBoost, and Long Short-Term Memory) and data-preprocessing algorithms implemented in Python. The general methodology of model building and the requirements of the input data sets are described. All models use actual data from sensors of the monitoring system. The novelty of this work is in an approach that allows using limited history data sets to obtain predictions with reasonable accuracy. The implemented algorithms made it possible to achieve an R-Squared accuracy of more than 0.95. The forecasting calculation time is minimized, which can be used to run the algorithm in real-time control and embedded systems.

Islamic feminism, as a discourse within feminism, aims to re-read the Qur’an from a modern egalitarian perspective, which is outside the traditional and patriarchal interpretation of Islam. Islamic feminists reclaim an ethical vision of the Qur’an by presenting a reinterpretation, especially regarding verses that deprive women from having equal rights in the family, as well as in society. However, while Islamic feminism presents a gender equal interpretation of the Qur’an and raises new discourses and debates on gender relations in an Islamic context, a critical insight of Islamic feminism can provide a new gender and religious consciousness that, in turn, develops further perspectives on gender equality in a religious context. This paper aims to provide a critique of Islamic feminism from a social psychological perspective of gender using the theory of Abdulkarim Soroush. His theory considers revelation as the prophet’s word resulting from his religious experience. Soroush defines revelation as an inspiration; in this way, revelation or Qur’an is not directly God’s word, but Muhammad’s word resulting from a divine experience. Accordingly, this paper deals with a social psychological perspective of the lived experience of the prophet as a man in a certain epoch of history, in which the lived experiences of women were not represented, and the revelation or the Qur’an is based on a male lived experience. It begins with an overview of Islamic feminists as well as the more general current of Islamic reformists and their efforts to view the revelation as the word of the prophet in order to avoid attributing the non-scientific content of the Qur’an to the direct word of God. This is followed by a critique of Islamic feminism based on Abdulkarim Soroush’s theory of the recognition of the revelation as the word of the prophet, as well as gender theories from the field of social psychology.

Even though Industrial Kitchens (IKs) are among the highest energy intensity spaces, very little work has been done to forecast their consumption. This work explores the possibility of increasing the accuracy of the consumption forecast in an IK by forecasting disaggregated appliance consumption and comparing these results with the forecast of the total consumption of these appliances (Virtual Aggregate—VA). To do so, three different methods are used: the statistical method (Prophet), classic Machine Learning (ML) method such as random forest (RF), and deep learning (DL) method, namely long short-term memory (LSTM). This work uses individual appliance electricity consumption data collected from a Portuguese restaurant over a period of four consecutive weeks. The obtained results suggest that Prophet and RF are the more viable options. The former achieved the best performance in aggregated data, whereas the latter showed better forecasting results for most of the individual loads. Regarding the performance of the VA against the sum of individual appliance forecasts, all models perform better in the former. However, the very small difference across the results shows that this is a viable alternative to forecast aggregated consumption when only individual appliance consumption data are available.

Vessel management calls for real-time traffic flow prediction, which is difficult under complex circumstances (incidents, weather, etc.). In this paper, a multimodal learning method named Prophet-and-GRU (P&G) considering weather conditions is proposed. This model can learn both features of the long-term and interdependence of multiple inputs. There are three parts of our model: first, the Decomposing Layer uses an improved Seasonal and Trend Decomposition Using Loess (STL) based on Prophet to decompose flow data; second, the Processing Layer uses a Sequence2Sequence (S2S) module based on Gated Recurrent Units (GRU) and attention mechanism with a special mask to extract nonlinear correlation features; third, the Joint Predicting Layer produces the final prediction result. The experimental results show that the proposed model predicts traffic with an accuracy of over 90%, which outperforms advanced models. In addition, this model can trace real-time traffic flow when there is a sudden drop.

The prognostics and health management disciplines provide an efficient solution to improve a system’s durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem’s remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.

The insistence that the prophets of the Hebrew Bible were “forthtellers, not foretellers” is ubiquitous in academic, liberal Christian, and even secular circles. It categorically denies that the prophets of ancient Israel predicted the future and characterizes them instead as voices of social critique. This article explains the origins of the phrase, its philosophical and religious underpinnings in Protestant, Enlightenment, Romantic, “scientific” and “modern” thought and traces its rhetorical usage in religious debate.

Many models for change point detection from time series remote sensing images have been developed to date. For forest ecosystems, fire disturbance detection models have always been an important topic. However, due to a lack of benchmark datasets, it is difficult to determine which model is appropriate. Therefore, we collected and generated a benchmark dataset specifically for forest fire disturbance detection, named CUG-FFireMCD1. The CUG-FFireMCD1 contains a total of 132 pieces of MODIS MOD13A2 time series, and each time series contains at least one fire disturbance. The occurrence time for a forest fire disturbance was determined using the National Cryosphere DesertDataCenter(NCDC) website, and the precise latitude and longitude coordinates were determined using the FireCCI51 dataset. In addition, we selected four commonly used time series change detection models and validate the advantages and limitations of the four models through dataset analysis. Finally, we use the detection results of the models and their applicable scenarios to label the additional change points. The four models we used are breaks for additive season and trend (BFAST), Prophet, continuous change detection and classification (CCDC), and Landsat-based detection of trends in disturbance and recovery (LandTrendR). The experiments show that the BFAST outperformed the other three models in forest fire disturbance detection from MOD13A2 time series, with the successful-detection-proportion rate of 96.2% with the benchmark dataset. The detection effect of the Prophet model is not as good as that of BFAST, but it also performs well, with the successful-detection-proportion rate of 87.9%. The detection results of CCDC and LandTrendR are similar, and the detection success rate is lower than that of BFAST and Prophet, but their detection results can be used as data support for labeling work. However, to apply them perfectly to MOD13A2 time series change detection, it is best to do some model adaptation. In summary, the CUG-FFireMCD1 data were verified using different types of time series change detection models, and the change points we marked are credible. The CUG-FFireMCD1 will surely provide a reliable benchmark for model optimization and the accuracy verification of remote sensing time series change detection.

Accurate vessel traffic flow prediction is significant for maritime traffic guidance and control. According to the characteristics of vessel traffic flow data, a new hybrid model, named DWT–Prophet, is proposed based on the discrete wavelet decomposition and Prophet framework for the prediction of vessel traffic flow. First, vessel traffic flow was decomposed into a low-frequency component and several high-frequency components by wavelet decomposition. Second, Prophet was trained to predict the components, respectively. Finally, the prediction results of the components were reconstructed to complete the prediction. The experimental results demonstrate that the hybrid DWT–Prophet outperformed the single Prophet, long short-term memory, random forest, and support vector regression (SVR). Moreover, the practicability of the new forecasting method was improved effectively.

Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and marketing data. However, the use of demand forecasting has little been studied in the container shipping industry. Improved visibility into the demand for container shipments has been a long-held objective of industry stakeholders. This paper addresses the shortcomings of both short-term and long-term shipment demand forecasting for the Australian container shipping industry. In this study, we compare three forecasting models, namely, the seasonal auto-regressive integrated moving average (SARIMA), Holt–Winters’ seasonal method and Facebook’s Prophet, to find the best fitting model for short-term and long-term import demand forecasting in the Australian shipping industry. Demand data from three years, i.e., 2016–2018, is used for the Asia–Oceania trade lane. The mean absolute percentage error (MAPE), root mean squared error (RMSE) and 2-fold walk-forward cross-validation are used for the model evaluation. The experiment results observed from the selected metrics suggest that Prophet outperforms the other models in its comparison for container shipment demand forecasting.

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle’s destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA–GSO approach. The proposed FA–GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.

Surface particulate matter with an aerodynamic diameter of 2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

The Delphi method is one of the basic tools for forecasting values in various types of issues. It uses the knowledge of experts, which is properly aggregated (e.g., in the form of descriptive statistics measures) and returns to the previous group of experts again, thus starting the next round of forecasting. The multi-stage prediction under the Delphi method allows for better stabilization of the results, which is extremely important in the process of forecasting. Experts in the forecasting process often have access to time series forecasting software but do not necessarily use it. Therefore, it seems advisable to add to the aggregate the value obtained using forecasting software. The advantage of this approach is in saving the time and costs of obtaining a forecast. That should be understood as a smaller burden on data analysts and the value of their work. According to the above mentioned key factors, the main contribution of the article is the use of a virtual expert in the form of a computer-enhanced mathematical tool, i.e., a programming library for a forecasting time series. The chosen software tool is the Prophet library—a Facebook tool that can be used in Python or R programming languages.

The ḥadīth, “whoever harms a dhimmī I shall be his foe on the Day of Judgment’, can be found as an end clause to covenants which the Prophet Muḥammad issued to Christian, Jewish, and Magian communities. As it is highly unlikely for different non-Muslim communities to have forged this Prophetic statement at the end of their respective documents, this paper argues that this utterance is authentic and can be confidently traced back to the Prophet. This paper examines the occurrence of this statement as a ḥadīth in the Islamic literature and notes how it was dismissed by scholars of tradition who only accepted one of its variants. The paper then compares the rights granted to non-Muslims in the covenants to those conveyed in a number of ḥadīths and notes the discrepancies between early Islam’s official documents and the legal injunctions found in Muslim tradition. It argues that the ḥadīths on the rights of non-Muslims oftentimes reflect legal maxims of scholars living in the ‘Abbasīd era and that these were back-projected to the Prophet and his Companions using fictitious isnāds. Finally, this paper concludes by recommending the incorporation of the Prophet’s official decrees, which includes the covenants, within the fabric of Islamic law.

Delay tolerant network (DTN) protocol was proposed for a network where connectivity is not available. In DTN, a message is delivered to a destination node via store-carry-forward approach while using opportunistic contacts. Probabilistic routing protocol for intermittently connected networks (PRoPHET) is one of the widely studied DTN protocols. In PRoPHET, a message is forwarded to a contact node, if the contact node has a higher delivery predictability to the destination node of the message. In this paper, we propose an improved opportunistic routing protocol, where context information of average distance travelled and average time elapsed from the reception of a message to the delivery of the message to the destination node is used. In the proposed protocol, the average distance and average time are updated whenever a message is delivered to a destination node. Then, both average distance and average time as well as delivery predictability of PRoPHET protocol are used to decide a message forwarding. The performance of the proposed protocol is analyzed and compared with that of PRoPHET and reachable probability centrality (RPC) protocol, which is one of the latest protocols using the contact history information of a mobile node. Simulation results show that the proposed protocol has better performance than both PRoPHET and RPC, from the aspect of delivery ratio, overhead ratio, and delivery latency for varying buffer size, message generation interval, and the number of nodes.

Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters.

Is authoritarianism intrinsic to Islam? Is Islam incompatible with democracy? These questions are frequently debated in the context of the study of the relationship between the Western and Islamic civilization. The debate has gained momentum since the last decade of the twentieth century, especially after the collapse of the former Soviet Union and the subsequent transition of socialist states in Eastern Europe and other authoritarian states in Asia and Latin America to democracy. The publication of The Clash of Civilizations by American scholar Samuel Huntington, in which he presented a controversial argument about a cultural divide and clash between the Islamic world and the West, pushed the debate even further. Apart from Muslim intellectuals, Western academics have spent a significant amount of time on these questions, with a multitude of articles and volumes examining the compatibility of Islam and democracy. In this paper, we will examine Islam’s relationship with democracy from normative and philosophical viewpoints, examining how the established values and principles of Islam as reflected in the Qur’anic and prophetic traditions correspond to Western democratic norms and practices. In order to obtain a profound understanding of this subject, we have delved into, through content analysis, the thoughts of several early modernist Islamic scholars who have had tremendous impact on contemporary Islamic revivalist movements throughout the world, and interviewed a number of contemporary Islamic thinkers in Bangladesh.

The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease's spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures.