PrivaNet-FL: Enhancing Privacy and Minimizing Energy Overhead for Federated Learning System on Edge Devices

D.; M.

doi:https://doi.org/10.54216/JCIM.160203

Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Cloud Computing for Digital Education: Implications for Student Achievement

The research evaluates the effects, which cloud computing and digital educational methods have on scholarly performance. The research used descriptive statistics combined with t-tests alongside ANOVA and regression analysis for interpreting the data findings. The collected data shows students use cloud computing moderately and employ digital education extensively although their educational outcomes stay average. Cloud computing usage exhibited similar levels of acceptance between male and female students however, students from arts streams programs demonstrated increased interest. Cloud computing usage along with digital education experienced superior adoption rates among students residing in rural areas than students settled in urban areas. Research data showed a major statistical linkage between digital education and the levels of academic performance. The educational institution types together with parental work status shaped student interaction with digital educational resources. The study's findings highlight the significant roles played by cloud computing and online learning in raising students' academic performance. The research implies that mixing technology with current education practices will boost educational results while demonstrating why digital competence stands vital in present-day education systems. Academic achievement rates improved in direct proportion to the amount of digital education use by students alongside the fact that private institution students demonstrated higher application of cloud computing platforms and female students demonstrated superior academic outcomes when compared to male students. Numerous students adopt both cloud computing systems and digital education methods because such technology usage is prevalent at accuracy 91.4% of the total students. Out of all the analyses done in the research, the overall F1-score is 92.5, and the fault tolerance of 93.8%.

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Nasser El-Kanj mail -

Chadi El Nar mail -

Marina Abdurashidova mail

link https://doi.org/10.54216/JISIoT.160223

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

An Intelligent Financial Risk Management System Using Pythagorean Neutrosophic Fuzzy Graphs with Growth Optimization Algorithm

One of the most effective devices to model uncertainty in decision-making difficulties is the neutrosophic set (NS) and its extensions, namely interval NS (INS), interval complex NS (ICNS), and complex NS (CNS). An effective device to demonstrate ambiguities and uncertainty in decision-making is the NS, which is the more conventional standard set, intuitionistic fuzzy set (IFS), and fuzzy set (FS) by including 3 scores of falsehood, indeterminacy, and truth of established statements. Financial risk management is a massive field with different and developing modules, as demonstrated by either its historic growth or present classic example. It is a procedure to address the uncertainty originating from financial markets. It consists of calculating the financial threats dealing with organization and emerging management tactics by internal policies and priorities. A risk-management method is an experience control and accounting system. In this manuscript, we develop an Intelligent Risk Management Approach for Financial Crisis Using Pythagorean Neutrosophic Fuzzy Graphs and Metaheuristic Optimization Algorithms (IRMFC-PNFGMOA). The main intention of IRMFC-PNFGMOA technique is to analyse and develop effective methodologies for measuring and managing financial risk in dynamic market conditions. Initially, the data pre-processing stage applies Z-score normalization to clean, transform, and structure raw data to improve the quality. Besides, the Aquila optimization algorithm (AOA) has been deployed for the selection of feature processes to identify and retain the most relevant features from input data. For the classification process, the proposed IRMFC-PNFGMOA model designs pythagorean neutrosophic fuzzy graphs (PNFG) technique. To further optimize model performance, the growth optimizer (GO) algorithm is utilized for hyperparameter tuning to ensure that the best hyperparameters are selected for enhanced accuracy. To exhibit the enhanced performance of the presented IRMFC-PNFGMOA model, a comprehensive experimental analysis is made. The comparative results reported the improvised characteristics of the IRMFC-PNFGMOA model.

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N. Metawa mail -

Olim Astanakulov mail -

Umarova Navbakhor Shokirovna mail

link https://doi.org/10.54216/JISIoT.160224

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2025

Analysis of Investment Attractiveness of Countries: A Comprehensive Assessment Using Econometric Models

This article analyzes the investment attractiveness of various countries by developing ranking systems and econometric models. These models, based on key economic indicators, evaluate countries' investment potential and provide forecasted values for the Global Innovation Index (GII). Using a weighted scoring method, we rank countries according to their investment attractiveness. The study further constructs an econometric model to explore the relationship between investment factors and innovation development, highlighting key areas for policy improvement.

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Eshonkulova Sayyorabonu mail

link https://doi.org/10.54216/JSDGT.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2025

The Role of E-Commerce Development in Shaping the Global Market Conjuncture

This article explores the transformative role of e-commerce in reshaping the global market landscape. Through an in-depth examination of digital trade, supply chain realignment, consumer behavior, and global economic integration, the study assesses how the development of e-commerce has transcended traditional market boundaries and redefined competition, pricing, and logistics. It evaluates the influence of technological infrastructure, regulatory frameworks, and international cooperation in driving the growth of e-commerce and highlights key challenges, including data security, digital inequality, and market volatility. The article concludes with a critical outlook on the structural shifts e-commerce introduces to the global market conjuncture and its implications for the future of trade and economic policy.

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Hamroyeva Umida mail

link https://doi.org/10.54216/JSDGT.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

AI-based model for Enhancing Credit Risk and Delinquency Management in Banks

Credit risk assessment along with delinquency management in banking receives substantial improvements from the introduction of Artificial Intelligence (AI) and behavioural insights. This research creates an extensive behavioural credit-scoring model through its discovery of crucial psychological characteristics including integrity and self-efficacy and locus of control and materialism that greatly affect credit default and wilful delinquency. A thorough evaluation of the predictive model occurs through logistic regression and confirmatory factor analysis (CFA) based analysis on 376 respondent data. Self-efficacy together with internal locus of control and materialism demonstrate significant power as predictors for credit risk and the willingness of individuals to default voluntarily is directly influenced by integrity and self-esteem. The ability of Artificial intelligence approaches to forecasting depends on behavioural constructs to optimize precision accuracy, reduce credit risk estimation errors, and provide opportunities for early prevention. The model delivers 92.1% accurate Default Risk classifications together with 91.0% precise predictions for Liquidity Risk while maintaining a Default Risk AUC-ROC measure of 0.96, which signifies its advanced predictive capabilities. The research demonstrates that artificial intelligence alongside behavioural credit scoring systems can enhance financial lending decisions while stabilizing credit markets.

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Noura Metawa mail -

Sally Afchal mail -

Nasser El-Kanj mail

link https://doi.org/10.54216/JCIM.160120

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Application of Neutrosophic Pentagonal Controlled Metric Space via Orthogonality in Traffic Flow Network Using Integral Equation

In this paper, we researched and confirmed some of the axioms of NOPCMS (Neutrosophic orthogonal pentagonal controlled metric space). We used NOPCMS to translate the Banach contraction principle in the formerly defined spaces. Several cases were numerically evaluated, and certain findings were supported, in or- der to review what we found. Furthermore, by demonstrating their existence with a unique and comprehensive solution, we deliver proof of usage and implementation.

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M. Rathivel mail -

M. Jeyaraman mail -

Rahul Shukla mail

link https://doi.org/10.54216/IJNS.260308

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2025

MADM-Strategy using Grey Relational Analysis under Rough Single-Valued Pentapartitioned Neutrosophic Set Environment

This paper aims to introduce various operations in the context of the Rough Single-Valued Pentapartitioned Neutrosophic Set (RSVPNS) environment. Then, based on Grey Relational Analysis (GRA), we propose a Multi-Attribute Decision-Making (MADM) technique. Additionally, we present a practical numerical example to validate the proposed MADM technique in the context of selecting a tourist place for government initiatives aimed at enhancing its attractiveness to tourists.

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Suman Das mail -

Rakhal Das mail -

Prasanna Poojary mail -

Surapati Pramanik mail -

Vadiraja Bhatta G. R. mail

link https://doi.org/10.54216/IJNS.260309

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Performance Comparison of Wavelet Transforms based Medical Image Compression

Medical image analysis plays a vital role in diagnosis of diseases and the need of the day is to arrive at a simple and efficient compression technique. This paper proposes a comparative analysis of three different wavelet based medical image compression techniques. First algorithm is based on Bi-orthogonal wavelet with Parallel coding  (BiWT-PC) , second is based on Haar wavelet with block coding  (HWT-BC) and third algorithm is based on stationary wavelet transform with Parallel coding (SWT-PC). In this work, 3D medical image is converted into 2D slices and preprocessed using lifting scheme. Wavelet transform is applied to this preprocessed image, which divides the image into multilevel sub-bands. Then, the suitable encoding method is applied to get the compressed image. At the receiver side, the original image is recovered back by applying inverse wavelet transform and proper decoding over the compressed image. Experimentations are carried out over MRI and CT images with four quantitative metrics such as PSNR, CR, DcT and EcT. From the experimental analysis, it is observed that SWT-PC method is quite efficient since it has high PSNR and low CR.

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V. Anusuya mail -

Stency V. S. mail -

G. Srividhya mail -

M. K. Mohammed Faizel mail -

G. Arul Kumaran mail -

R. Santhosh mail -

P. Sherubha mail

link https://doi.org/10.54216/JCIM.160201

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhanced Intrusion Detection Using AI-Driven Data Balancing and VQ-VAE-Based Feature Extraction

Network security faces significant challenges due to the increasing sophistication of cyber threats and the inherent class imbalance in intrusion detection datasets. To address this issue, a hybrid Boundary Equilibrium Generative Adversarial Network (BEGFAN) and Vector Quantization Variational Autoencoder (VQVAE) framework, termed BVQVAE, is proposed for Network Intrusion Detection Systems (NIDS). The framework involves preprocessing, feature extraction, and class balancing to enhance classification accuracy. Missing values are imputed, categorical features are label-encoded, and numerical attributes are normalized to ensure a structured dataset. BEGAN generates synthetic samples to mitigate class imbalance, while VQVAE extracts essential features using an encoder with quantization and a decoder for network traffic reconstruction. The model is evaluated on NSL-KDD and UNSW-NB15 datasets, achieving 82.56% accuracy, with precision, recall, G-mean, and F1-score of 86.53%, 87.65%, 86.21%, and 87.08%, respectively.

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Shivanthana S. mail -

Manicka Raja M. mail -

Lalitha Krishnasamy mail -

Karthik R. mail -

R. Venkatesan mail

link https://doi.org/10.54216/JCIM.160202

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

PrivaNet-FL: Enhancing Privacy and Minimizing Energy Overhead for Federated Learning System on Edge Devices

In recent years, federated learning (FL) has emerged as a decentralized approach to model training, enhancing data privacy by retaining data on local edge devices. While existing privacy-preserving FL frameworks, like Secure Aggregation and Homomorphic Encryption, protect data through encrypted aggregation, they often face challenges with high communication overhead, significant computational demands, and increased energy consumption. Differential privacy approaches, though customizable via privacy budgets, may also degrade model accuracy due to added noise. Addressing these limitations, we propose PrivaNet-FL (Privacy-Optimized Network for Federated Learning), an advanced FL model that optimizes privacy techniques with minimal energy costs in edge environments. PrivaNet-FL incorporates adaptive privacy and efficiency management across edge devices, such as IoT sensors and smartphones, where data processing and real-time privacy adjustments conserve energy while maintaining data security. The framework consists of three main workflows: (1) Adaptive Privacy-Scaling-modulating privacy based on device constraints, ensuring optimal energy usage through dynamic adjustments of noise in differential privacy or encryption complexity; (2) Lightweight Encryption and Secure Aggregation-employing low-complexity encryption and secure aggregation techniques, such as random masking and distributed averaging, to minimize energy without compromising data privacy; and (3) Energy-Aware Communication-Efficient FL-leveraging model compression, energy-aware scheduling, and differential privacy with controlled noise to reduce communication and energy overhead. Results demonstrate that PrivaNet-FL achieves superior model accuracy with reduced energy and communication costs compared to traditional FL methods, making it ideal for privacy-sensitive and resource-limited edge applications.

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D. Gowthami mail -

M. Vigenesh mail

link https://doi.org/10.54216/JCIM.160203

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new