Many assumptions believe that classical Islamic theology is no longer relevant in the contemporary era, either because its discussion tends to be theocentric and far from the progress of humanism or because it is considered no longer relevant to answer contemporary issues such as secularism, pluralism, globalization, or neo-atheism. Departing from these problems, efforts have emerged for new readings of classical theology by paying more attention to the context and needs of the times. Among these efforts is the renewal of (...) theology by highlighting its methodological aspects, as was done by a contemporary Moroccan theologian and philosopher, Ṭahā ‘Abdurraḥmān. This study attempts to describe and analyze the style of renewal of theology from the perspective of Ṭahā ‘Abdurraḥmān. This research is a character study using qualitative methods, in which the existing data is analyzed critically. This article shows that Ṭahā ‘Abdurraḥmān’s style of renewal of ‘ilm al-kalām focuses on efforts to reform the methodology of classical kalam by reforming the method of dialogue science (munaẓarah) through contemporary linguistic and logical tools, rearranging analogy patterns (qiyas), and building a logic of kalam based on the principle’s logic and morality as well as the amalgamation of theoretical aspect and practical aspect. The significance of Ṭahā ‘Abdurraḥmān’s renewal in contemporary Islamic theology lies in the theorizing of concepts in classical theology with a contemporary approach as well as the construction of the methodology of contemporary Islamic theology to answer ever-evolving theological issues. (shrink)

Critical realist thought has theorised convincingly that epistemic relativism is constellationally embedded in ontological realism which in turn necessitates judgemental rationality. In social scie...

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are (...) created by merging multiple smaller classes of crimes. Exploratory data analysis is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well. (shrink)

Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of (...) ECG. Single Shoot Detection MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder. (shrink)

This paper suggests philosophical foundations for mixed methods research based on the philosophy of critical realism. In particular, it suggests that the critical realist idea of the primacy of ontology helps bridge the apparent paradigmatic gap between qualitative and quantitative research. It illustrates this foundational idea by showing why and how a multi-disciplinary team used a mixed methods approach to understand the significance of religion in prison through a multi-site study of religious conversion to Islam in prison and how this (...) gives a better account than a single method approach. The mixed method research design used in the project sets out a new way of mapping and understanding religious conversion and differences within a faith group that draws on the emancipatory potential of critical realist thought. (shrink)

Building on the insights of the late Roy Bhaskar and the late Roger Matthews, as well as some recent developments in ultra-realist criminology, this article introduces and delineates some core inte...

ABSTRACT Critical realist thought has theorised convincingly that epistemic relativism is constellationally embedded in ontological realism which in turn necessitates judgemental rationality. In social science, judgemental rationality involves acting upon plausible decisions about competing points of view. However, the tools for doing this are, as yet, under-articulated. This paper addresses this absence by articulating triangulation and depth-reflexivity as two tools for doing judgemental rationality in empirical research. It draws on the experiences of a diverse team working on an international comparative (...) research project on conversion to Islam in prisons. It demonstrates how epistemic and relational gaps between researchers and research subjects can be bridged by mobilising the ‘laminated’ properties and personal attributes of a diverse research team that factors in attributes that are absent as well as those present. The biographical experiences of the team are analyzed in a variety of intersecting dimensions: faith, ethnicity/ethno-culture, gender, class and professionality. (shrink)

The purpose of this study is to examine the moderating effect of supervisor support on the relationship between the need to be unique and the innovative behavior. People not only strive to belong to a group but also want to be unique from others and feel exceptional. Individuals’ innovative behavior is one of the things that makes them feel different from other people. Because developing a new idea, supporting this idea, putting this idea into practice, and the positive achievements of (...) this idea distinguish people who exhibit innovative behavior from others. It depends on the behavior of supervisors whether people who break away from typical practices and procedures to feel unique and special continue to act in innovative ways. In this context, it is vitally essential for supervisors to support people who display innovative behavior. The research was conducted on employees working in the education sector in Mersin. We employed the confirmatory factor analysis to examine the fitness of the model and moderation was tested. As hypothesized, the need to be unique had a significant and positive effect on innovative behavior. This finding is consistent with existing literature and thus advance knowledge on need to be unique and innovative behavior, particular in education sector. Nonetheless, it has been determined that supervisor support doesn’t have a moderator role on the relationship between the need to be unique and innovative behavior. Although there are some researches in the literature on consumer experiences about the need to be unique and innovative behavior, but literature on education section is sparse and still long way to go to evaluate its’ reflections on the workplace. (shrink)

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a (...) dataset of images by applying a convolutional neural network. The extraction of leaf gas enables identification of the actual level of chlorophyll and nitrogen which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color-based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf. (shrink)

The rapid increase in COVID-19 cases has become the symbol of fear, anxiety, and panic among people around the globe. Mass media has played an active role in community education by addressing the health information of this pandemic. People interact by sharing their ideas and feelings through social media platforms. There is a considerable need to implement different measures and better perceive COVID-19 pertinent facts and information by demystifying public sentiments. In this study, the Quarantine Life dataset of thousand tweets (...) is based on #Quarantine, #Quarantine Days, #Quarantine Life, #My Pandemic Plan, and #Quarantine and Chill from January to September 2020 has been collected from Twitter. The extracted data have been scrubbed through preprocessing techniques. The sentiments and topics extracted from tweets have been analyzed through the TEXT BLOB, VADER, and AFFIN approach. Results show that people were distressed and fearful due to the COVID-19 pandemic. However, most people enjoyed by playing games, watching movies, and reading books during the lockdown period. According to the present meta-analysis, physical activity interventions are beneficial for patients with dementia in terms of cognition. The proposed framework illustrates the insight impact of COVID-19 on human physiological and mainly focuses on the evaluation of sentiment dynamics at the topical level. (shrink)

The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D-CNN for extracting features. A global max-pooling layer is used to reduce dimensions. (...) A dense layer for classification and a dropout layer are also used to reduce overfitting and improve generalization error in the neural network. A fully connected layer is the last layer to predict between two classes. Two movie review datasets are used and widely accepted by the research community. The first dataset contains 25,000 samples, half positive and half negative, whereas the second dataset contains 50,000 specimens of movie reviews. Our neural network model performs sentiment classification among positive and negative movie reviews called binary classification. The model achieves 92% accuracy on both datasets, which is more efficient than traditional machine learning models. (shrink)