Enhancing Business Sustainability through an Intelligent Framework for Unveiling Financial Frauds

Rhada; Saad

doi:https://doi.org/10.54216/JSDGT.040105

Fusion: Practice and Applications | 2024

Performance Evaluation and Real-world Challenges of IoT-Based Smart Fuel Filling Systems with Embedded Intelligence

Integrating the Internet of Things (IoT) with smart fueling systems has the potential to revolutionize the fuel industry, leading to better resource management and increased operational efficiency. With the increasing integration of machine learning techniques, these systems are capable of self-learning, adaptation, and predictive decision making. However, the effectiveness of these advanced systems in real-life situations remains an area of intense interest and research. in operational efficiency and reduces resource waste by 10% compared to conventional systems. Additionally, system bottlenecks were identified mainly in data trans- mission  (delayed by up to 20% in high  traffic cases) and hardware malfunctions due  to environmental factors. End user feedback  indicates a satisfaction level of 85%, with an emphasis on system responsiveness and fuel prediction recommendations. Challenges mainly come from software issues, unwanted environmental interference and  ’some initial resistance from users accustomed to conventional systems. However, with data in hand, the benefits of integrating intelligence into IoT-based fueling systems offer a sustainable and efficient future for the fuel industry. Recommendations are made to improve data transmission channels, develop  robust hardware for extreme conditions, and conduct targeted user education campaigns.

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Muneer Sadeq ALqazan mail -

Mohamed Ben Ammar mail -

Monji Kherallah mail -

Fahmi Kammoun mail

link https://doi.org/10.54216/FPA.140204

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

An ICT-based Framework for Innovative Integration between BIM and Lean Practices Obtaining Smart Sustainable Cities

Smart sustainable cities rely on the latest technologies and apply recent knowledge like Information and Communication Technologies (ICT), BIM, and lean construction to expand people's eminence of life, smooth urban maneuvers and facilities more competent, and develop their competitiveness while confirming that they achieve the economic, social, environmental, and cultural demands of current and forthcoming generations. This paper explores the synergies between Building Information Modelling (BIM) visualisation and Lean construction practices to enhance Architecture, Engineering, and Construction (AEC) industry performance. A structured questionnaire was distributed among BIM and lean experts and analysed by SPSS. The study uses descriptive and correlation analyses to assess ten key lean practices, revealing high industry adoption and favorable mean scores. Notably, BIM-enhanced clash detection and coordination lead with a score of 4.4 out of 5. Correlation analysis establishes significant positive associations between BIM visualisation and practices such as just-in-time production, value stream mapping, lean pull systems, work sequencing, standardised work, and continuous improvement. The findings accentuate the pivotal role of BIM in optimising lean practices, offering valuable insights for practitioners seeking to elevate AEC industry performance through strategic integration. Future studies endeavors are recommended to investigate several alternative avenues to enhance the integration between BIM and Lean practices in the AEC industry. Furthermore, the forthcoming researchers are advised to validate the proposed framework.

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Fawaz Saleh mail -

Ashraf Elhendawi mail -

Abdul Salam Darwish mail -

Peter Farrell mail

link https://doi.org/10.54216/FPA.140205

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Leveraging Advanced Machine Learning Methods to Enhance Multilevel Fusion Score Level Computations

This research introduces a novel technique for determining numerous fusion score levels that works with many datasets and purposes. Each of the four system pieces works together. These are Feature Engineering, Ensemble Learning, deep neural networks (DNNs), and Transfer Learning. In feature engineering, raw data is totally transformed. This stage stresses the importance of PCA and MI for predictive power. AdaBoost is added during ensemble learning. It repeatedly teaches weak learners and adjusts weights depending on errors to create a strong ensemble model. Weighted input processing, ReLU activation, and dropout layers smoothly integrate DNNs. These reveal minor data patterns and correlations. In transfer learning (fine-tuning), a trained model is modified for the feature-engineered dataset. In comparative testing, the recommended technique had greater accuracy, precision, recall, F1 score, AUC-ROC, and training duration. Efficiency measures reduce reasoning time, memory, parameter count, model size, and energy utilization. Visualizations demonstrate resource consumption, method scores, and reasoning time distribution in research. This mathematical framework improves multilayer fusion score level computations, performs well, and is versatile in many scenarios, making it a good choice for large and diverse datasets.

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Rajesh Tiwari mail -

Satyanand Singh mail -

G. Shanmugaraj mail -

Suresh Kumar Mandala mail -

Ch. L. N. Deepika mail -

Bhanu Pratap Soni mail -

Jiuliasi V. Uluiburotu mail

link https://doi.org/10.54216/FPA.140206

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Energy Efficient Cluster Head Selection Using Hybrid RL-PSO Approach

Wireless Sensor Networks (WSNs) are crucial in several applications, highlighting the need of effective clustering and fault detection systems.  This paper introduces a novel approach that uses Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to optimize cluster head selection and enhance fault detection capabilities within WSNs. The proposed hybrid algorithm operates in two phases, combining the explorative capabilities of RL with the optimization process of PSO to select cluster heads based on residual energy and connectivity considerations. By continuously monitoring the network's residual energy state and the number of active nodes, the proposed method ensures prolonged network lifetime and improved overall performance. Our experimental results demonstrate the superior performance of the hybrid RL-PSO approach compared to traditional clustering algorithms, showcasing significant improvements in optimizer accuracy, residual energy preservation, and fault detection efficiency.

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Arpita Choudhary mail -

N. C. Barwar mail -

Vikas Chouhan mail

link https://doi.org/10.54216/JISIoT.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Improving Support vector machine for Imbalanced big data classification

A significant proportion of one type of pattern and a relatively small quantity of another type of pattern can be found in many unbalanced real data sets. In addition, finding significant observations and excluding influential observations is effectively accomplished through diagnostic analysis. Support vector machines (SVM), a common classification technique, perform poorly on imbalanced datasets and when influential observations exist. In this research, the pigeon optimization algorithm as a metaheuristic algorithm is employed to address the influence observation issues in SVM. Experiments are done on three real sets of data. Our approach provides higher classification accuracy compared to other widely used algorithms. This approach could be used for further biological, chemical, and medical datasets.

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Alaa Abdulazeez Qanbar mail -

Zakariya Yahya Algamal mail

link https://doi.org/10.54216/JISIoT.110202

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Unraveling the Complexity: A DEMATEL Analysis of the Negative Impact of Artificial Intelligence (AI) Adoption among Students in Higher Education

This research employs DEMATEL analysis as a methodological approach to thoroughly examine the adverse consequences of implementing Artificial Intelligence (AI) among students enrolled at Universiti Teknologi MARA (UiTM) Negeri Sembilan, Malaysia. The analysis encompasses three distinct professional cohorts: student representatives, academic staff, and upper management. Through a systematic analysis of causal relationships between multiple factors, this study aims to identify and prioritize the fundamental elements contributing to the negative consequences associated with integrating artificial intelligence. The prominence of privacy and security concerns as a causal factor highlights the importance of implementing strong data protection measures and adhering to ethical practices related to AI. Furthermore, various factors connected with personal disconnection, restricted adaptability, dependance on technology, and insufficient emotional intelligence influence the adverse outcomes of artificial intelligence implementation among students. The results underscore the necessity of implementing focused interventions and strategies to tackle these difficulties and guarantee a harmonious and advantageous integration of artificial intelligence in students' educational journeys. Higher education institutions can effectively harness the advantages of AI while ensuring their students' welfare and educational achievements by recognizing and proactively addressing any potential limitations.

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Zahari Md Rodzi mail -

Wan Normila Mohamad mail -

Zhang Lu mail -

Faisal Al-Sharqi mail -

Rawan A. shlaka mail -

Ashraf Al-Quran mail -

Ali M. Alorsan Bany Awad mail

link https://doi.org/10.54216/JISIoT.110203

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Adaptive feature selection based on machine learning algorithms for Lung tumors diagnosis and the COVID-19 index

Early detection of Lung tumors, which is lethal and equally affects men and women, is challenging. In order to decrease mortality rates and raise survival rates, early detection and classification of Lung tumors is essential. However, at the start of 2020, the entire planet would be afflicted with a coronavirus that causes a fatal sickness (COVID-19). CT imaging is a good tool to detect illness among the various COVID-19 screening techniques available. On the other hand, alternative methods of disease detection take a lot of time. Deep learning, a type of machine learning, opens up a wealth of opportunities for investigating and assessing tumor features using CT scans, allowing for improved disease prediction, diagnosis, and classification. Using CNN, DNN, and VGG-16 models, the suggested approach in this research gives unambiguous and accurate categorization.

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Bashar Talib Al-Nuaimi mail -

Ruaa Azzah Suhail mail -

Sanaa adnan abbas mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JISIoT.110204

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Advanced Intrusion Detection in Vehicular Networks: Empowering Security through Hybrid Off-loading Techniques and Enhanced Radial Bias Neural Network

Over the last several decades, the implementation of ITS has shown to be the most efficient and successful strategy for expanding the variety of current transportation networks. Vehicle-based offloading of data going to be essential for forthcoming networking innovations like D2D and 5G due to the substantial contribution it makes to efficiently using network capability while wasting minimal power. Information transmissions that would normally need a cellular network's infrastructure may instead be made using alternative networking mechanisms including Bluetooth, WiFi, and opportunistic communications. Data offloading has the ability to significantly increase the efficiency with which network resources are used. The offloading of data from vehicles has a considerable impact on the strain on cellular networks. It helps the network achieve higher throughput by facilitating the simultaneous reception of data by a large number of users. First, we must establish that the problem of Vehicular data offloading is an NP-hard target set selection (TSS) issue before we can even begin to characterize it. Using a combination of Hybrid PSO and GWO, TSS selects a small group of nodes to do the redundant data exchange (Particle Swarm Optimization with Gray Wolf Optimization). Collaboration between individuals and ISPs to identify effective aim sets may provide useful insights. If malicious users are present in the target group, they may slow down network activity by spoofing or by reducing the network's offloading capacity. It is possible that the whole network's performance would suffer as a direct result of these malicious users. In this study, we suggest a hybrid approach to communication for specifying the intended audience. We take use of the characteristics of opinion dynamics amongst users to get around the issue of overlapping community detection. Trust-based metrics inferred from users' activities are used to ensure the safety of the target set. In order to call 911, the suggested work additionally incorporates a method of sorting and classifying the offload limitations through Radial Bias Neural Network (RBNN). The following may be determined with the use of the proposed work's performance indicators: precision, entropy, and delay.

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Prashant Kumar Shukla mail -

Ratish Agarwal mail

link https://doi.org/10.54216/JISIoT.110205

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2024

Enhancing Business Sustainability through Stock Analysis: A Machine Learning Approach

This study explores the integration of machine learning methodologies in stock analysis to enhance the understanding of the relationship between sustainable business practices and financial performance. Against the backdrop of a shifting investment landscape that emphasizes responsible and informed decision-making, our research addresses the need for innovative approaches in evaluating stocks within a sustainability framework. Leveraging a combination of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and linear regression, we systematically analyze a dataset comprising sustainability metrics and stock performance. The DBSCAN clustering identifies distinct groups of stocks based on sustainability profiles, offering novel insights into market segmentation. Concurrently, linear regression models quantitatively reveal the impact of sustainability metrics on stock outcomes. The results affirm the significance of sustainability considerations in investment decisions, presenting a compelling case for the adoption of machine learning techniques in responsible investing strategies.

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Noura Metawa mail -

Saad Metawa mail

link https://doi.org/10.54216/JSDGT.040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2024

Enhancing Business Sustainability through an Intelligent Framework for Unveiling Financial Frauds

The aim of this research is to examine the convergence of intelligent frameworks and financial fraud detection as a strategic approach for strengthening business sustainability in the banking industry. A rigorous preprocessing regimen, which includes data cleansing, normalization, and SMOTE algorithm application for class rebalancing, sets the stage for a refined dataset. Our proposed framework employs Logistic Regression, Decision Trees, and Gradient Boosting models to conduct a multifaceted analysis that accommodates both linear and non-linear relationships within the data. The results are presented through visual representations such as distribution plots and RoC curves that confirm the effectiveness of the framework in detecting potentially fraudulent activities. The comparative analysis offers detailed insights into how versatile the framework is. This study contributes to the broader discourse on intelligent systems in financial fraud detection with practical implications for businesses seeking to enhance their sustainability through advanced risk management strategies.

groups

Rhada Boujlil mail -

Saad Alsunbul mail

link https://doi.org/10.54216/JSDGT.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new