Enhanced Credit Card Fraud Detection Using Deep Learning Techniques

Ola Imran; Ali Yakoob

doi:https://doi.org/10.54216/JCIM.140223

International Journal of Neutrosophic Science | 2024

New algebraic structures approach towards complex interval valued Q-neutrosophic subbisemiring of bisemiring

The notion of complex interval-valued q-neutrosophic subbisemiring (CIVqNSBS) is developed and examined. Additionally, we examine the homomorphic features and significant attributes of CIVqNSBS. We suggest the CIVqNSBS level sets for bisemirings. Consider a complex neutrosophic subset of bisemiring Δ, denoted as &alefsym; if and only if every non-empty level set Z(∂,♭) is a subbisemiring, where ∂, ♭ ∈ D[0, 1], then Z= )Z,Z, Z) is a CIVqNSBS of Δ. Let &alefsym; be the strongest complex neutrosophic relation of bisemiring Δ, and let Ψ be a CIVqNSBS of bisemiring Δ, if and only if Ψ is a CIVqNSBS of Δ × Δ, then &alefsym; is a CIVqNSBS of bisemiring Δ. We show that homomorphic images of all CIVqNSBSs are CIVqNSBSs, and homomorphic pre-images of all CIVqNSBSs are CIVqNSBSs. There are examples given to illustrate our results.  

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Sharifah Sakinah Syed ahmad mail -

Nasreen Kausar mail -

Murugan Palanikumar mail

link https://doi.org/10.54216/IJNS.240434

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Comprehensive Decision-Making with Spherical Fermatean Neutrosophic Sets in Structural Engineering

This study introduces the Spherical Fermatean Neutrosophic Sets (SFNSs), representing a significant advancement in the realm of Neutrosophic Sets (NSs) and Fermatean neutrosophic sets (FNSs). In decision making scenarios involving diverse perspectives, a mere average of decision values may fail to capture the entire spectrum of viewpoints. To address this limitation, the SFNS is proposed as a comprehensive solution. It features a spherical representation that encompasses membership, non-membership and indeterminacy functions at its core, complemented by a defined radius. This spherical construct facilitates the encapsulation of all decision makers’ opinions within its bounds, providing a holistic perspective. Leveraging its geometric structure, the SFNS excels in resolving ambiguity and risk with greater accuracy and effectiveness compared to conventional FNSs. This innovative approach aims to better accommodate the complexities of decision making involving diverse perspectives. Selecting the best material for a structural engineering project is given as numerical example at the end.

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P. Roopadevi1, M. Karpagadevi mail -

M. Karpagadevi mail -

S. Krishnaprakash mail -

Said Broumi mail -

S. Gomathi mail

link https://doi.org/10.54216/IJNS.240433

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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Journal of Intelligent Systems and Internet of Things | 2024

Bridging the Gap between Technology and Medicine through the Revolutionary Impact of the Healthcare Internet of Things on Remote Patient Monitoring

Healthcare Internet of Things (IoT) initiatives that aim to integrate technology and medicine are shaking the sector to its foundations. The revolutionary potential of the proposed strategy is shown here as we investigate the far-reaching consequences of the Healthcare IoT on remote patient monitoring. The beginning sets the stage by underlining the significance of bridging the gap between technology and medicine. Our multi-pronged approach comprises Internet of Things (IoT) remote monitoring, cloud-based analysis, artificial intelligence (AI) integrated diagnostics, real-time alerts, and predictive analytics. Our study's results demonstrate that the proposed approach is superior to the status quo. The area of remote patient monitoring has profited considerably from the employment of traditional approaches, such as the fusion of data from wearable sensors, analysis in the cloud, diagnostics that utilize artificial intelligence, real-time monitoring, predictive modeling, and smart alarm systems. The suggested strategy, however, performs very well across all of the most important measures of assessment. Comparatively, the accuracy rate of the conventional wearable sensor fusion approach was only 76%, whereas our suggested method reached 89%. Our strategy was also more accurate than the standard approach (88% vs. 73%). When compared to the recall rate of 68% produced by conventional methods, our suggested strategy significantly outperformed the competition. It's a great option for hospitals and clinics since it improves diagnostic precision and speed without breaking the bank.

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Kiran Sree Pokkuluri mail -

Vibha Tiwari mail -

Jyoti Uikey mail -

Prerna Mehta mail -

Chopparapu Srinivasa Rao mail -

Annamaraju Thanuja mail

link https://doi.org/10.54216/JISIoT.130217

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Optimizing Sensor Localization and Cluster Performance in Wireless Sensor Networks through Internet of Thing (IoT) and Boosted Weight Centroid Algorithm

Localization is an extremely important component of applications that make use of wireless sensor networks. It has a substantial impact on academics as well as real-time sensor deployment applications in the aim of lowering the amount of energy that is used while simultaneously locating unknown nodes. The process of obtaining the coordinates along an axis that represent the locations of the sensor nodes is referred to as localization. The accuracy of locating the positions of the nodes varies depending on the environmental conditions, the type of nodes, the type of application, and the type of localization methods used. A standard localization method known as distance vector hop (DV-hop) localization will be able to determine the positions of unknown nodes with typical accuracy with the assistance of beacon nodes based on Internet of things. The DV-hop and improved weighted centroid localization algorithms, in addition to the suggested boosted weight centroid-based localization approach, are both addressed in this article. The suggested boosted weight centroid localization technique is utilized to find nodes in the remote area of the WSN while conserving energy. This is accomplished with the assistance of measurements involving both the nodes and the centroid. The modified weight metric is utilized in the process of carrying out the task of localisation of an unknown node. The performance of BWCLA is evaluated based on a number of different metrics, including accuracy in localization, average localization error, total packets utilized, and energy usage.

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Krishna Kumar .N mail -

Surya Kiran Chebrolu mail -

R. Manikandan mail -

Aby K Thomas mail -

Peruri Venkata Anusha mail -

Hari Prasad Bhupathi mail

link https://doi.org/10.54216/JISIoT.130218

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Security Implications of IoT-Enabled Mobile Net Facial Recognition System

Face recognition technology is gaining popularity for security, access management, and user identification. A novel facial recognition method employing cutting-edge deep learning algorithms and attention processes reduces false positives in this study. This technique was designed to approach facial recognition differently. We demonstrate statistically substantial recognition gains over current approaches through extensive research and experimentation. The recommended solution uses an attention device and a complex feature extraction module. The pieces work together to highlight distinctive characteristics and facial identifiers. To optimize performance and generalization across datasets, data addition and hyper parameter adjustment are used to fine-tune the model. We do this for maximum benefit. Studies on the issue may help us understand the multiple reasons that make ablation so successful. We also discuss facial recognition technology's moral difficulties, including fairness and user privacy. We also emphasize cautious distribution. Our findings expand facial recognition technology knowledge and pave the way for future studies. This study demonstrates that better Mobile Net models and Internet of Things technologies increase the accuracy of mobile facial recognition. The project overcomes the challenge of providing powerful AI tools in resource-constrained situations by utilizing IoT infrastructures and effective, lightweight Mobile Net architectures. Extensive testing demonstrates that the technique increases identification rates and outperforms existing models, showing its suitability for real-time operations. The Internet of Things enables data mobility and cross-device model usage. This illustrates that the IoT ecosystem can enable effective and scalable security solutions.

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Sumit Thakur mail -

Nikhat Raza khan mail

link https://doi.org/10.54216/JISIoT.130219

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

The Healthcare IoTs as a Paradigm Shift in Healthcare Management, Patient Treatment, and Healthcare Data Processing

When it comes to hospital administration, patient care, and medical data analysis, the Healthcare Internet of Things (HIoT) is nothing short of a paradigm revolution. We dive into this new paradigm to examine its far-reaching effects and revolutionary possibilities in the healthcare system. The context is established by introducing HIoT as a game-changing development in healthcare. Using the IoT to network several devices, this model paves the way for real-time patient monitoring, streamlined inventory management, and integrated telemedicine. The healthcare industry as we know it will be transformed by HIoT as it strives to improve resource allocation, simplify operations, and provide proactive patient care. Our investigation includes a thorough appraisal of how HIoT will affect many facets of medical treatment. We use many research approaches and quality indicators for this evaluation. We may evaluate the viability and scalability of HIoT solutions by testing them in experimental settings that mimic real-world healthcare settings. To provide a precise depiction of the healthcare system, dataset environments use well maintained medical data sources. The performance and efficacy of HIoT technologies may be evaluated using measurable criteria such as sensitivity (0.94), specificity (0.89), F1-Score (0.91), ROC-AUC (0.95), and cost savings ($150,000). To determine the relative importance of each part of the HIoT ecosystem, researchers undertake "ablation studies. Our findings provide a clear picture of the disruptive potential of HIoT. Better patient outcomes may be ensured via early interventions thanks to the improved accuracy (0.92), efficiency (9.2), and satisfaction (9.2) provided by the suggested HIoT technique for patient monitoring. When healthcare and telemedicine are combined, the success rate of remote consultations increases to 95%, response times decrease to 15 minutes, and more people have access to medical treatment.

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Amit Kumar Chandanan mail -

Prabha Rani Sikdar mail -

C. Raja mail -

Saiyed Faiayaz Waris mail -

Manoj Kumar .T mail -

Kiran Bhopate mail

link https://doi.org/10.54216/JISIoT.130220

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Harnessing Artificial Intelligence for Enhanced Efficiency in Academic Writing and Research

In the recent past, there has been a surge in the use of artificial intelligence (AI) in the development of smart technologies for the purpose of improving efficiency in writing academic papers and conducting researches. However, the potential of using AI in the improvement of scholarly processes has not been optimally realized due to low awareness and visibility of the tool among the users. In this respect, this paper aims to describe the following tools of AI which can be applied in the research process including literature search and manuscript preparation. To assess the AI technology, the current literature in form of case studies was reviewed and this included the automated literature search engines, citation management software, natural language processing tools and data analysis tools. It also reveals that AI approaches can also help in decreasing the amount of time spent in article and data search, citation, citation management, and even in the generation of quality publications. This essay also examines the ethical issues of using artificial intelligence in research and any bias that may be present. In conclusion, it is necessary to underline that AI can be useful in improving the results of learning processes. But it is crucial that the researchers are trained well and are put in a position to doubt the outcome produced by the AI. Thus, the purpose of the paper is to discuss how AI is being used in academia at the moment and what could be done to expand its use in the future.

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Alaa A. Qaffas mail

link https://doi.org/10.54216/FPA.160209

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Speaker Identification in Crowd Speech Audio using Convolutional Neural Networks

Crowd speaker identification is the most advanced technology in the field of audio identification and personal user experience which researchers have extensively focused on, but still, science hasn’t been able to achieve high results in crowed identification. This work aims to design and implement a novel crowd speech identification method that can identify speakers in a multi speaker environment, (two, three, four and five speakers). This work will be implemented through two phases. The training phase is the Convolutional Neural Network (CNN) training and testing phase. Through this phase, the training will be implemented on data generated via the Combinatorial Cartesian Product approach. This approach uses two primary processes, the Computation of the Cartesian product process and combinatorial selection process. The second phase is the prediction phase. The aim of this phase is to check the CNN trained in the first phase, through testing it on new crowed audios than the data that the CNN was trained on in the first phase, these new crowded audios exist in the Ghadeer-Speech-Crowd-Corpus (GSCC) dataset, which is a new database designed through this work. Compared to the state-of-the-art speaker identification in multi speaker environment approaches, the results are impressive, with a recognition rate of 99.5% for audio with three speakers, 98.5% for music with four speakers, and 96.4% for audio with five speakers.

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Ghadeer Qasim Ali mail -

Husam Ali Abdulmohsin mail

link https://doi.org/10.54216/FPA.160208

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

A Hybrid Deep Learning Model for Securing Smart City Networks Against Flooding Attack

Due to the increasing digitization of city processes, there has been a significant shift in how cities are governed and how people make their living. However, several types of attacks could target smart cities, and Flooding Attacks (FA) are the most dangerous type. It is also a major issue for many people and programs using the Internet nowadays. Security in smart cities refers to preventative measures necessary to shield the city and its residents from direct or indirect harm by attackers who try to crash the system and deny legitimate users the use of the services. Smart city security, in contrast to standard security mechanisms, necessitates new and creative approaches to protecting the systems and applications while considering characteristics like resource limitations, distributed architecture nature, and geographic distribution. Smart cities are vulnerable to several particular issues, including faulty communication, insufficient data, and privilege protection. Therefore, a hybrid CRNN model that consists of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) algorithms is employed for the detection of Flood Attacks based on the classification of traffic data. Subsequently, the performance of the CRNN is tested and evaluated using the CIC-Bell-DNS-EXF-2021 dataset. The obtained accuracy results of the proposed CRNN model achieved in FA detection is 99.2%.

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Bashar Ahmed Khalaf mail -

Siti Hajar Othman mail -

Shukor Abd Razak mail -

Alexandros Konios mail

link https://doi.org/10.54216/JCIM.140222

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Enhanced Credit Card Fraud Detection Using Deep Learning Techniques

Credit card fraud is a huge challenge in the financial sector, causing huge losses every year. The problem is exacerbated by increased marketing and sophisticated fraudulent activities. This study addresses the important issue of accurate real-time detection of fraudulent transactions to minimize financial losses and enhance transactional security. The main objective of this study is to develop a comprehensive fraud detection algorithm using deep learning techniques, specially designed to address the complexity and volume of modern credit card transactions. Key contributions of this research include the presentation of a new deep learning algorithm optimized for credit card fraud detection, the integration of feature engineering techniques to improve the performance of the model, and a potential scalable solution analysis in real-time Significant improvement in proven rates. The results show that the proposed deep learning-based model achieves higher accuracy and lower false positive rate, giving financial institutions a significant advantage in protecting against fraudulent activities about the character. This study highlights the power of deep learning in reforming fraud detection systems, and lays the foundation for future developments in this important area.

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Ola Imran Obaid mail -

Ali Yakoob Al-Sultan mail

link https://doi.org/10.54216/JCIM.140223

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new