2821 research outputs found
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An analysis of customers’ perceptions and attitudes towards Islamic Banking services using components of customer relationship management (CRM) in the UK and Bahrain
Full text linkOver the past decades, the growth of Islamic banking (IB) has significantly been increased globally in retail and full-fledged banking. Islamic Economy (IE) is a driving force of Islamic finance and an alternative banking of conventional banking. The aim of IB is to bring interest (Riba) free, avoid uncertainty (Gharar) and risk-free return in banking and finance to bring social justice in the economy. However, at a time of such tremendous success, IB faces considerable criticism due to various issues such as quality products, services, management, and marketing. It may suggest that banking institutions must prove that their offerings are complying with the Shariah principles and are in line with customer’s expectations, which are also the objectives of Customer Relationship Management (CRM). To achieve the objectives, this study employed primary sources that help to raw information and first hand evidence while a mixed-method research was conducted. A valid total of 351 customers participated in the survey while 12 interviews were conducted among IB professionals in the UK and Bahrain.
Among the key findings of questionnaire survey, perception and satisfaction towards Islamic bank of both the UK and Bahrain were comparatively positive than negative and minor in differences based on the statistical significance. Chi-square tests and regression analysis shows majority Muslim customers have influence in adopting IB services in Bahrain while lack of awareness is major factors among increasing British consumers to undertake IB services. Finding also suggests that there are high co-relations on satisfaction and recommendation in attracting customers towards IB services. On contrary, this study finds no co-relations and negative impact in adopting IB services due to familiarity of IB terms, gender, age or marital status among survey participants.
However, interview among professionals revealed that the concerns are remained in innovative products, quality services, staff knowledge, role of Shariah scholars and authentic marketing. On the other hand, it is also found that lack of awareness among customers seriously hinders IB industry to grow its full potential. This confirms the greater needs of CRM implementation in IB industry. Another key finding is management and investment in IB, which is instrumental for survival of Islamic bank in the financial industry. Because bank professionals have serious doubt about the UK investor’s intension due to lack of funding which has reduced services and operations significantly. While in Bahrain, too many banks exist damaging the reputation of IB resulting merger or closing up the operations. This research suggested acquiring customer’s feedback seriously using operational components of CRM to reconnect customers with innovative products to regain customers’ trust and thus growth of Islamic banking. This research has also compared, contrasted, and synthesized the findings and made recommendation in line with CRM to Islamic financial institutions to undertake appropriate strategies and to implement them to bridge a bond between customers and IB’s products
A framework for leveraging the incorporation of AI, BIM, and IoT to achieve smart sustainable cities
No full textThis study investigates the significance of leveraging the incorporation of Artificial Intelligence (AI),
Building Information Modeling (BIM), and the Internet of Things (IoT) to Achieve smart sustainable
cities. Understanding their applications for Architecture, Engineering, and Construction (AEC) projects.
The study encompasses three key dimensions: Design Optimization and Performance Simulation,
Material and Life Cycle Sustainability, and Operational Efficiency and Environmental Impact. By
leveraging BIM and AI, the research explores the integration of renewable energy, sustainable material
selection, and smart building controls. BIM and AI experts were given a structured questionnaire, which
was then analysed using SPSS. Descriptive and correlation analyses reveal significant positive
correlations between energy efficiency and design visualization, construction sustainability
visualization, as well as adaptability and education through visualization. The proposed framework
deepens the capabilities of the combination of different technologies towards Smart Sustainable Cities.
This work not only contributes theoretical insights to the field but also provides practical implications
for industry professionals striving to enhance sustainable practices in AEC projects. Further studies to
encourage a combination of other recent technologies to improve smart sustainable cities' performance
Harnessing the capabilities of machine learning for enhanced cybersecurity and protection against digital threats
Full text linkAs breaches get smarter and more frequent, machine learning and cybersecurity are effective defenses. The beginning emphasizes the necessity for better cyberattack protections in today's IT environment. Modern dangers are so complex that trying new methods is crucial. Machine learning, known for its self-learning, is a formidable partner in this effort. It discusses the study's objectives and the importance of comparing the new and old methods. We discuss the dataset settings, assessment metrics, ablation experiments, and experimental setup used to evaluate the recommended technique in the methods section. Trustworthy and repeatable outcomes come from well-designed experiments. A variety of cyber threat scenarios are carefully assembled to provide educators with a thorough practice environment. The recommended approach has an accuracy score of 0.85, whereas standard methods average 0.72. The recommended approach has 0.78 memory, compared to 0.65 for existing methods. The proposed approach outperforms the best method, which has an F1 score of 0.68. The strategy works since the ROC AUC value is 0.92, substantially higher than 0.78 for standard methods. This evidence often reveals that the proposed technology performs better than others, reducing phony warnings and preventing internet threats
EL-RFHC: Optimized ensemble learners using RFHC for intrusion attacks classification
Full text linkThe extensive growth of mobile technology leads to magnifying the usage of digital gadgets around the world. This requires a fast-interconnecting communication medium to transfer the data between the devices. Meanwhile, the intruders attempt to make huge traffic in the network that leads to loss of data. To identify the intrusion attacks, ensemble Machine Learning (ML) classifiers are applied using the various feature variables importance. However, most of the transmitting data contains high dimensions with numerous variables leads to more execution time to classify the attacks. This study initiated the novel approach fusion of the Random Forest classifier and High Correlation (RFHC) feature selection approach to diminish the quantity of the variables. Also, the count of intrusion attacks class is lower than the normal class leads to generating an imbalanced dataset. Hence, Synthetic Minority Over-Sampling Technique (SMOTE) is suggested to create a balanced dataset for multi-class classification, and Un-upsampled data for binary-class classification respectively. The pre-processed dataset fed into the ensemble machine learners, and attention mechanism-based LSTM to classify as various intrusion attacks and normal data. This research work focused on reducing the CICIDS2017 dataset’s variable dimensions from 71 to 34 using RFHC. The performance results showed that RF classifier performed better with accuracy of 99.4 %, precision 99.4 %, average recall 99.2 % and average F1-score 99.6 % in binary-class classification, and Extreme Gradient Boosting (XGBoost) achieved better accuracy of 99.7 %, precision 98.7 %, average recall 99.5 % and average F1-score 99.2 % in multi-class classification
Comparative analysis of machine and deep learning techniques for credit card fraud prediction in the financial sector
No full textCredit card fraud is one of the most common
forms of fraud in the financial industry. Machine learning
and deep learning models have been widely used to predict
this fraud, but there are still several challenges to predicting
credit card fraud transactions. This study aims to conduct a
comprehensive and comparative analysis of modern machine
learning, deep learning, hybrid, and transformer models for
predicting credit card fraud, with a focus on determining a
best-fit model that can effectively and efficiently reduce the
financial losses associated with fraud for individuals,
businesses, and the entire economy amongst the state of-the art modern techniques. SMOTE and ADASYN oversampling
techniques are applied to mitigate class imbalance before the
model is trained and their performance evaluated using
accuracy, precision, recall, and F1 score metrics. Ensemble
methods random forest and XGBoost emerge as top
performers, benefiting from operating on diverse decision
trees. However, deep learning models like BiLSTM show
comparable results, automatically extracting valuable
features; this led to the hybridization of the three models
Insights and challenges of working with perfectionism in sport
No full textPerfectionism is complex and ambiguous. However, there is little known about the experiences of sport psychology practitioners when working with perfectionistic athletes. This article presents a commentary on my personal insights when working with perfectionism and the challenges that I have faced. Here, I also draw on the literature from my work and from others to help illustrate these challenges. Recommendations for sport psychology practitioners in conducting these specific challenges are then presented. The article ends by outlining a personal reflection of working with perfectionistic athletes, followed by recommendations for good practice
Non-Alcoholic fatty liver disease prediction with feature optimized XGBoost model
No full textNon-alcoholic fatty liver disease (NAFLD) is an
expanding health threat, posing significant risks for long-term complications. Early detection and intervention are
crucial, but traditional diagnostic methods can be
expensive and invasive. This study investigates the
utilization of machine learning models for predicting liver
diseases from various out-sourced datasets. .We employed
Decision Trees, Random Forests, and Support Vector
Machines (SVMs) to predict NAFLD based on various
clinical and demographic features. Model performance
was evaluated by calculating accuracy, precision, deviation
and accuracy-score. All these models achieved promising
accuracy levels, ranging from 80% to 90%, showcasing
their potential for NAFLD prediction. Among them, XGBoost demonstrated the highest performance, with an
accuracy of 90% and more. This study demonstrates the
effectiveness of machine learning models in predicting
NAFLD with high accuracy using readily available data.
Further research with larger sized and more varied
datasets will vindicate these models for real-world
application in clinical settings
Blockchain-assisted Internet of Things (IoT) framework for malware detection using metaheuristic algorithm and deep learning algorithm
No full textThe Internet of things (IoT) is an all-encompassing web of devices whose evolution is reaching
unimaginable levels of convenience and data-driven insights. IoT networks are becoming more transformative, and
this is happening amidst concerns on their security, especially when routing attacks are involved and pose threats to
data integrity as well as the entire system security. To overcome these concerns, we propose a comprehensive and
multi-layered security model for IoT routing that is designed specifically for this task. The main elements of our
proposed solution comprise a solid data validation, encryption, and intrusion detection system – which are the
cornerstone of IoT networks’ reliability and security. However, IoT systems are resource-limited in essence, which
means that the approaches different from the traditional ones should be sought. RSA encryption with SHA3-512
integrity verification algorithm for the purposes of secure data transmission is the method we apply. A Thermal
Exchange Optimization (TEO) algorithm is used to deal with the generation of optimal cryptographic keys, hence
outperforming the traditional key generation method. Intrusion detection, as a fundamental element of IoT security,
is tackled through deployment of a Convolutional Neural Network (CNN) solution. An intrusion detection system
that is additionally enhanced using a Q-learning-based Whale Optimization Algorithm is being used to make the
system more precise and efficient in adapting to the dynamic IoT environment. A complete set of simulations was
performed with Python to ensure the efficiency of our proposed solution. This evaluation report shows that our
approach is more resistant to the routing attacks, and, moreover, it surpasses the existing model in both security and
functionality. This study is not just focused on the issue of routing security in IoT but also presents a highly reliable
and innovative solution, thereby, contributing to the ongoing discussions on the security of the network infrastructure
of Io
Empowering community actors: training taxi and private hire vehicle drivers as allies in combating organized crime
No full textThis chapter explores the integral role of taxi and Private Hire Vehicle (PHV) services in combating organized crime, with a specific focus on child sexual exploitation as highlighted by cases like Operation Stovewood. The research scrutinizes the socio-economic impacts of organized crime on individuals and communities, emphasizing the need for a collaborative approach to address these issues. It particularly examines the involvement of the taxi and PHV industry in these crimes and advocates for effective public-private collaboration in tackling organized crime. Organized crime, including activities like human trafficking and extortion, undermines societal stability, fostering violence and eroding public trust in institutions. The chapter presents a disturbing dimension of this crime: the exploitation of certain taxi/PHV drivers in child sexual exploitation scandals, as seen in investigations across cities like Rotherham. This revelation has led to a critical inquiry into taxi and PHV licensing, aiming to address regulatory gaps and ensure the safeguarding of vulnerable individuals. The research adopts a comprehensive methodology, analyzing Operation Stovewood as a case study and reviewing academic and government reports on the effectiveness of policies and legal frameworks governing taxi and PHV licensing. It also explores the ethical and legal responsibilities of regulating taxi and PHV services, balancing safety measures with privacy rights. Key to this discourse is the necessity of public-private collaboration in combating organized crime. The strategic positioning of taxi and PHV drivers as local surveillance agents offers a unique opportunity in this fight. The chapter proposes comprehensive training, legal structures, and ethical guidelines to empower drivers in this role, supplemented by technological advancements like CCTV in taxis and PHVs
The mediating role of flourishing on religious faith and psychological distress: a pilot survey
Full text linkFlourishing is a growing topic in positive psychology, and the positive influences of flourishing have been well documented. Although recent literature has shown that religion has an impact on one’s physical and psychological well-being in positive ways, the relationship between religiosity and flourishing has surprisingly not been studied. The present study aimed to explore the relationship of religious faith with flourishing and psychological distress. An online survey has successfully recruited 267 participants from UK and Taiwan. The survey used standardised inventories including the PERMA Profiler, the Santa Clara Strength of Religious Faith Questionnaire, and the Clinical Outcome in Routine Evaluation to measure flourishing, religious faith and psychological distress respectively. Results show that participants with strong religious faith do have higher levels of flourishing. Yet Path Analysis shows that participants who have stronger religious faith is indirectly related to lower psychological distress through the mediating effect of flourishing. Suggestions for future research and implications of the findings are discussed