Using a Digital Storytelling-Based Electronic Program to develop primary stage pupils’ EFL reading comprehension skills

Amr Mohamed El; Ahmed Zakaria

doi:https://doi.org/10.54216/FPA.160113

International Journal of Neutrosophic Science | 2024

Optimal Single-Valued Neutrosophic Sine Trigonometric Aggregation Operators for Accurate Financial Fraud Detection Model

Financial fraud may be regarded as any fraud targeting financial organisations including crypto exchanges, banks, fintech, and lending organizations, or any criminal activity associated with the payment process. Financial fraud detection cites protocol set prepared to circumvent the destruction produced by fraudulent activities happening in financial service suppliers. Ecological financial fraud detection (FD) includes the usage of ethical and sustainable performs within fraud actions recognition from the financial area. In recent times, DL and ML techniques have been used in CCF recognition owing to their ability to construct a robust mechanism to discover fraud businesses. Therefore, this study develops an Optimal Single Valued Neutrosophic Sine Trigonometric Aggregation Operator (O-SVNSTAO) for Accurate Financial Fraud Detection Model. The genetic-inspired particle swarm optimization (GIPSO) feature selection model efficiently discerns the relevant attribute from sophisticated financial databases, improving the model's discriminative power while alleviating dimensionality problems. Consequently, the SVNSTAO classifier leverages the features selected to discern complicated features inherent in fraudulent actions, which facilitates accurate diagnosis. Moreover, the COA parameter tuning mechanism enhances the SVNSTAO model's parameter, which ensures adaptability and optimum performance to varied fraud settings. Empirical analysis of real-time financial datasets demonstrates the superiority of O-SVNSTAO technique over classical methods, underlining its effectiveness in discovering financial fraud with exceptional efficiency and reliability

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Fadoua Kouki mail

link https://doi.org/10.54216/IJNS.230434

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System for Predicting Financial Crisis

Financial technology (Fintech) is paramount in driving advanced technologies, economies, society, and several other sectors. Smart Fintech is the new-generation Fintech, primarily stimulated and endowed by compuational technology. Smart Fintech syndicates DSAI and renovates economies and finance for dynamic, smart, customized, automated services and systems, economies and financial companies, and the industry. The strength and development of the country’s economies are assessed by the correct forecasting. Financial crisis prediction (FCP) has the substantial consequence on the economies. Previous studies mainly emphasise statistical, DL, and ML methodologies for predicting the financial well-being of the business. Therefore, this article develops a new Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System (AOA-ANFIS) technique for Predicting Financial Crisis. The presented AOA-ANFIS technique aims to predict the presence of financial crises or not. The model incorporates three major elements: Arithmetic Optimization Algorithm (AOA) for feature selection, Adaptive Neuro-Fuzzy Inference System (ANFIS) as the classification algorithm, and Bat Optimization Algorithm (BOA) for parameter tuning. The AOA feature selection model effectively detects the important attributes from a large proportion of financial indicators, augmenting the model's prediction capability while decreasing computational difficulty. Subsequently, the ANFIS classifier exploits the features selected for capturing the intricate non-linear relations intrinsic in financial data, permitting accurate crisis calculation. Additionally, the BOA parameter tuning model augments the ANFIS model's parameters, ensuring robustness and optimum performance. Experimental outcomes on varied financial databases validate the higher efficiency of the AOA-ANFIS technique over underlying processes, demonstrating its effectiveness in forecasting financial crises with great reliability and precision.

groups

Fadoua Kouki mail

link https://doi.org/10.54216/IJNS.230435

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Neutrosophic Fuzzy Interval Sets and its Extension through MCDM and Applications in E-Management

we are introducing the model-type operators over Interval-Valued Fuzzy Neutrosophic Sets with time moments [IVFNS] and learn a few of their properties with numerical examples to demonstrate the defined operations and operators. Also introduce various distance measures over the extension of interval neutrosophic sets as well as apply the introduced measures in ecological management in this direct to decide the type of corrosion disturbing some towns for valuable management to be taken, using this normalized distance measures. The extensions of neutrosophic connection values and non-connection values be not used for all time probable positive to our fulfillment, but this concept IVTNFS part has more significant responsibility at this point since the time progress with IVN-fuzzy sets provide the accurate solution in factual situations similarly, conclusion making, career deciding and so on. This is the main reason for taking in the extensions of neutrosophic sets.

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A. Manshath mail -

K. Rajesh mail -

M. Logeshwari mail -

R. Saranya mail

link https://doi.org/10.54216/IJNS.240225

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

ODESMAN: Optimizing Decision-Making in Complex Environments: Integrating Neutrosophic and Fuzzy Logic for Advanced System Modeling

Within the domain of complex systems, inherent uncertainties, and ambiguities that traditional models frequently find difficult to handle pose a constant challenge to decision-making. To dramatically improve decision-making frameworks, this study presents a novel methodology called "ODESMAN," which synergistically integrates fuzzy logic with neutrosophic sets. Neutrosophic sets, on the other hand, allow one to express the degrees of truth, untruth, and indeterminacy as shifts rather than fixed points. Therefore, their use is more elegant than the existing methods offered. The implementation of fuzzy logic into such sets may provide a high level of effectiveness in managing uncertainty, which can be predicted and quantified. For example, the model allows accounting for uncertainty in the system inputs and processes up to 20%, the variability of truth values 10-50%, and the overall uncertainty 15-30%. The application of the model in practice, specifically in the emergency response, and the supply chain system permitted achieving a 40% increase in flexibility capacity and a 25% improvement in decision-making approaches compared to the traditional frameworks. Therefore, the practical strength and broad utility of the model can be proved, which validates its efficiency and allows broad implementation of this complex theoretical framework into the existing systems.

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Shaik Khaja Mohiddin mail -

Abdul Ahad mail -

N. Murugavalli mail -

V. Kavitha mail -

S. Venkata Suryanarayana mail -

M. Sundar Raj mail

link https://doi.org/10.54216/IJNS.240226

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Personal Data Protection Model in IOMT-Blockchain on Secured Bit-Count Transmutation Data Encryption Approach

The Internet of Medical Things (IoMT) has paved the way for innovative approaches to collecting and managing medical data. With the large and sensitive medical data being processed hence, the need for a strong identity and privacy become necessary. The present paper suggests a comprehensive method of PriMedGuard which aims at protection of the personal medical information. The first stage will be data collection from devices and sensors, then data cleaning to transform the data into the required format. There is also a safety system in the system that registers and authenticates authorized entities as well as ETDO (Enhanced Tasmanian Devil Optimization algorithm) is used for generating asymmetric cryptographic keys. The data is encrypted using the Secure Bit-Count Transmutation (SBCT) Data Encryption Algorithm and then put in the locations provided by the InterPlanetary File System (IPFS), a decentralized and distributed storage system. A safe smart contract on the blockchain is created so that the data retrieval is secure and MedSecEnsemble Detection is proposed as an intrusion detection technique in the IoMT network. By using this method, data will stay available while at the same time integrity, confidentiality and protection against vulnerabilities are ensured. Hence, the Internet of Medical Things ecosystem will be secured from unauthorized access and possible security threats…

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Sultan Almotairi mail -

Santosh Reddy Addula mail -

Olayan Alharbi mail -

Zaid Alzaid mail -

Yasser M. Hausawi mail -

Jaber Almutairi mail

link https://doi.org/10.54216/FPA.160111

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Efficient Intrusion Detection using OptCNN-LSTM Model based on hybrid Correlation-based Feature Selection in IoMT

Intrusion detection in the IoMT (Internet of Medical Things) represents the process of keeping track of and discovering unauthorized or malicious actions in medical devices and networks. Some of its benefits include early detection of potential threats, prevention of data breaches, and protection of patient privacy. Aside from these benefits, some difficulties are evident, like alarm fatigue due to false positives, the complexity in the standardizing detection across different devices, and resource limits that hinder qualitative implementations, thus leaving some vulnerabilities in the healthcare infrastructure. This paper proposes a new Efficient Intrusion Detection model based on the Correlation-Based Feature Selection and the OptCNN-LSTM model to address these problems. The proposed methodology comprises five key phases: (i) Data Acquisition (ii) Pre-processing (iii) Feature Extraction (iv) Feature Selection (v) OptCNN-LSTM Model-based intrusion detection. The raw data is first gathered and then preprocessed using z-score normalization and data cleaning. Then, the best features are extracted using central tendency, the degree of dispersion, and correlation. A mixed IHHO-PSO feature with the Correlation-based Feature Selection (CFS) framework is employed to choose the best features amongst the collected features. At last, the OptCNN-LSTM model is performed to detect the intrusion in the IoMT based on features-selected data. The CNN is tuned using the Levy Flight Optimization (LF) which can be further combined with the LSTM to get the expected results. The code is written in Python and the model is then run to determine its performance which is measured in terms of accuracy, precision, f-measure, and a Receiver Operating Characteristic Curve (ROC). Compared to the current models, the proposed model has the highest accuracies 97.6% and 96.5% for learning rates 70 and 80, respectively…

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Sultan Almotairi mail -

Deepak Dasaratha Rao mail -

Olayan Alharbi mail -

Zaid Alzaid mail -

Yasser M. Hausawi mail -

Jaber Almutairi mail

link https://doi.org/10.54216/FPA.160112

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2024

Computational genetic epidemiology: Leveraging HPC for large-scale AI models based on Cyber Security

To better understand disease susceptibility and prevention, computational genetic epidemiology is leading research. This paper introduces "GenomeMinds," a breakthrough method for scaling large-scale AI models for disease risk prediction. HPC was used to develop the method. GenomeMinds is compared to six standard methods to demonstrate its benefits. GenomeMinds' incredible potential is shown by real-world performance assessments. These measures evaluate data processing speed, forecast accuracy, scalability, computer efficiency, privacy, and ethics. GenomeMinds benefits are shown via scatter plots, which visually compare data. According to the data, GenomeMinds may revolutionize computational genetic epidemiology by doing well across all criteria. GenomeMinds has faster data processing, better prediction accuracy, stronger scalability, higher computational efficiency, enhanced privacy and security, and a comprehensive ethical awareness.

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Vadali Pitchi Raju mail -

Tushar Kumar Pandey mail -

Rajeev Shrivastava mail -

Rajesh Tiwari mail -

S. Anjali Devi mail -

Neerugatti Varipallay vishwanath mail

link https://doi.org/10.54216/JCIM.130214

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Innovations in Cyber Security Algorithms for Databases Enhancing Data Retrieval and Management

The term "Innovations in Cyber Security Algorithms for Databases Enhancing Data Retrieval and Management" refers to a book that studies novel techniques for tackling problems related to digital data. The integration of three complicated methods—DQO, DSS, and RAI—is the major focus of attention in this piece of writing. DQO makes use of machine learning to optimize query processing on the fly to meet fluctuating workloads. This is done to accommodate such workloads. To address issues pertaining to the scale of distributed systems, distributed storage systems (DSS) convey data in an effective manner by utilizing consistent hashing. The RAI algorithm adjusts the index architecture in response to the query patterns to achieve real-time flexibility. In this way, the process of looking for information that is frequently asked about is sped up. The methodology that has been suggested is superior to six different ways that are often used in terms of its adaptability, scalability, and real-time capabilities. This article will give a thorough model for improving data management in computer systems. The objective of this essay is to present the model.

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Shyam S. Gupta mail -

Pankaj Kumar mail -

Rajeev Shrivastava mail -

Satyabrata Jena mail -

Tushar Kumar Pandey mail -

Ankita Nigam mail

link https://doi.org/10.54216/JCIM.130215

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Cybersecurity Approaches for Securing Digital Marketing Data

The Energy Internet was enabled by quick energy sector developments due to greater digital technologies and increased environmental concerns. Energy demand management is crucial in this changing environment, as rigid models give way to more flexible ones. This research examines "Demand Dynamics in the Energy Internet" and suggests consumer and prosumer response plans. This concept regarding energy consumption and management is novel. Our work revolves around several essential aims. First, it examines the Energy Internet's role in the energy transition. It emphasizes energy savings, carbon reduction, and energy system reliability. We emphasize the need to transition away from centralized energy generation to one that is more flexible and involves active consumers and prosumers. This research examines how digital technology, particularly the Internet of Things, enables adaptable demand-side tactics. Real-time data analytics and smart meters help consumers and prosumers utilize energy efficiently. A transition like this is difficult. Data protection, hacking, and behaviour must be addressed. Our study demonstrates that these issues can be addressed immediately. Since one-size-fits-all is not adequate in this changing environment, we emphasize the need for customization to satisfy the individual demands of multiple parties, including conventional customers and prosumers. It also discusses energy Internet-targeted response strategies and their possibilities. We can reduce energy usage and make energy more sustainable, efficient, and consumer-focused by switching from passive consumption to active involvement and control.

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Mohammad Arif mail -

Anjali Goswami mail -

CH. M. H. Saibaba mail -

K. Sharada mail -

Tushar Kumar Pandey mail -

Ankita Nigam mail

link https://doi.org/10.54216/JCIM.130216

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Using a Digital Storytelling-Based Electronic Program to develop primary stage pupils’ EFL reading comprehension skills

The present study aimed to identify the effect of using a digital storytelling-based electronic program to develop reading comprehension skills of Pupils with Learning Difficulties. The study adopted a quasi-experimental pre-post design with two groups. Each group consisted of 30 pupils. The experimental group was taught through using the digital storytelling-based electronic program, whereas the control one was taught through the traditional method. The researchers prepared a reading comprehension skills test as an instrument to collect data. The results showed that the pupils of the experimental group achieved better results than those of the control one. The results revealed the effectiveness of the Digital Storytelling-Based Electronic Program in developing primary-fifth pupils’ reading comprehension skills.

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Amr Mohamed El Koshiry mail -

Ahmed Zakaria Hegazy mail

link https://doi.org/10.54216/FPA.160113

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new