Stochastic Diffusion Process Models for Driving Innovation in Market-Driven Product Development

Saurabh

doi:https://doi.org/10.54216/AJBOR.110203

Fusion: Practice and Applications | 2025

Revolutionizing E-Commerce Security: Unveiling an Innovative Deep Learning-Based Strategy for Detecting Financial Fraud

An inventive deep learning-based method for identifying financial fraud, revolutionizing e-commerce security in the process. The research offers a state-of-the-art setup that makes use of deep learning computations in the dynamic world of online exchanges, where the possibility of fraudulent activity is a danger. Since frauds are known to be erratic and lack consistency, it might be challenging to spot them. Con artists exploit the latest developments in technology. They manage to evade security measures, which results in millions of dollars being lost. One method of tracking fraudulent exchanges is to use information-mining techniques to investigate and detect unusual behaviours. Interactions. In contrast to deep learning techniques as auto encoders, convolutional neural networks (CNN), restricted Boltzmann machines (RBM), and deep belief networks (DBN), this paper aims to benchmark several machine-learning techniques, such as k-nearest neighbour (KNN), irregular forest, and support vector machines (SVM). The three-evaluation metrics that are really employed are the Area Under the ROC Curve (AUC), the Matthews Correlation Coefficient (MCC), and the Cost of Failure.

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Aditi Sharma mail -

S. Phani Praveen mail -

Vipin Tiwari mail -

Pradeep Kumar Arya mail -

Deepak Parvathaneni Naga Srinivasu mail -

Mukta Patel mail

link https://doi.org/10.54216/FPA.170227

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Neutrosophic Methods and Linguistic Tools for Interpreting Human Perceptions in Complex Decision-Making

This study addresses a particular issue in relation to the disambiguation of human views, which remains critical in the current era that is the quest for suitable instruments that can cognize, simulate, and interpret the multilayered nature of the standpoint. Today, in contexts where a decision must be made that requires a synthesis of different and often-opposed points of view, such methods are very limited. This methodological gap focuses on the question where ways and means are lacking, which combine analytical accuracy and the flexibility of approaches for dealing with huge amounts of complex and unstructured information. To mitigate this problem, the study seeks for the application of neutrosophic methods and languages as a new approach for understanding human perceptions, which present a great deal of uncertainty. From the combined angles of neutrosophic logic and special linguistic devices, images from different practical situations are scrutinized. The results indicate that this method not only enhances the accuracy with which human subjectivity is simulated but also renders stronger analytical models for application in the area of organizational strategy, public policy formulation and even marketing research. In conclusion, this research extends new and significant methodological boundaries to the social and applied sciences and provides a useful approach to the problem of interpretation and decision-making in a multidimensional and time-changing society.

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María Lorena Merızalde Avıles mail -

Emver Nivela Ortega mail -

Kleber Eduardo Carrion Leon mail -

Wiem Abdelbaki mail

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

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Fusion: Practice and Applications | 2024

A Hybrid Neutrosophic Hierarchical Method with SWOT Analysis to Face Complexity and Uncertainty

This paper addresses the question that is global as decision making in the scenario of ambiguity. Given the conflicting or less dependable information, it also becomes necessary to look for approaches that assist us. Conventional strategic planning approaches work relatively well with straightforward and precise information. These become inadequate with situations that are ambiguous. To address this challenge, we adopt the Neutrosophic Hierarchy Method that integrates with SWOT analysis in addressing the challenge. As such, we learn to evaluate or assess the four components of SWOT: Strengths, opportunities, weaknesses and threats in wider terms. However, we do appreciate that often what we assess is not black and white but in shades of color. The conclusion is that for complex decision-making, this approach seems more appropriate and offers better results than others offer. The key aim of this article is to put forth a novel perspective on how decisions should be made in the face of uncertainty. Most of all, we expect to be helpful to both policymakers and strategists in the sense of providing a tool, which can be useful when it comes to the practical inconsistencies that are quite frequently in excess of reasonable solutions.

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Milena Avarez Tapia mail -

Carlos G. Rosero Martínez mail -

Josue R. Lımaıco Mına mail -

Saidkarimova Matlyuba Ishanovna mail

link https://doi.org/10.54216/FPA.160213

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Dynamic Feature Weighting for Efficient Multi-Script Identification Using YafNet: A Deep CNN Approach

Script identification is crucial for document analysis and optical character recognition (OCR). This study proposes YafNet, a novel convolutional neural network (CNN) architecture, developed from scratch, to tackle the challenges of script identification in both handwritten and printed word images. YafNet dynamically weights features, enabling it to learn and combine multimodal features for accurate script identification. To evaluate its efficacy, we use the imbalanced ICDAR 2021 Script Identification in the Wild (SIW 2021) competition dataset. Experimental results demonstrate that YafNet outperforms conventional approaches, particularly when trained on mixed handwritten and printed data. It achieves high classification accuracy, balanced accuracy, and ROC AUC scores, indicating its robustness and generalizability. The incorporation of data augmentation and external data further enhances performance, underscoring the model's potential for real-world applications.

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Yahia Menassel mail -

Rashiq Rafiq Marie mail -

Faycel Abbas mail -

Abdeljalil Gattal mail -

Mohammed Al-Sarem mail

link https://doi.org/10.54216/JISIoT.140220

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Deep Learning-Based Steganalysis for Detection and Classification of Possible Hidden Content in Images

Steganalysis can be defined as the science that addresses the process of identifying and detecting hidden information or data within various types of digital media. Recently, Deep Learning (DL) approaches have been employed to build steganalysis systems. However, the problem with steganalysis systems adopting a DL approach is their low accuracy and their need for effective datasets to be used for the training. In this paper, we introduce a DL-based Steganalysis system for the detection and classification of hidden content in images. Our system, called Steg-Analysis Convolutional Neural Network (SA-CNN), relies on a Convolutional Neural Network (CNN) and uses High Pass Filter (HPF) and extra-embedded data. We also propose a preprocessing-based data hiding method to increase the accuracy of SA-CNN in detecting hidden content. Therefore, this ensures the imperceptibility of images used for training SA-CNN. In addition, we use another CNN, called Malicious-Benign Classification CNN (MBC-CNN), that we have developed to classify the extracted hidden content into Malicious or Benign classes. Compared with existing systems, SA-CNN shows a better performance in terms of accuracy, under increased hiding rates ranging from 0.1 to 1.0 bpp, reaching 90%.

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Mostafa A. Ahmad mail -

Eftkhar Al-Qhtani mail -

Ahmed H. Samak mail -

Amr Ibrahim mail -

Mourad Elloumi mail -

Ali Ahmed mail

link https://doi.org/10.54216/FPA.170228

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Feature Selection based on Improved Differential Evolution (DE) Algorithm for E-mail Classification

Spam e-mail has become a pervasive nuisance in today's digital world, posing significant challenges to efficient communication and information dissemination. Dealing with huge amounts of data with irrelevant and redundant features, which leads to high dimension. Nowadays, with the growth of using the internet, finding the secure E-mail classification system for cloud computing is a very important topic. Additionally, determining the best algorithm for choosing a subset of features has a big impact on how well automatic email classification works, making it one of the major issues. Among these is the Differential Evolution (DE) algorithm, which is computationally costly because of the slow convergence rate and evolutionary process. To address these issues, this study offers an intelligent scheme called Opposition Differential Evolution (ODE), which combines the Opposition Based Learning (OBL) and DE algorithms for effective automated feature subset selection. Its effectiveness is assessed using the support vector machine (SVM) to present a strong performance when evaluating the e-mail spam classification rate. Moreover, the OBL is used to accelerate and increase the convergence rate of traditional DE. To determine which features, contribute most to the reliability of the email spam classification, the subset features based on ODE that was used as feature subset selection are used.To assess the effectiveness of the proposed scheme, extensive experiments are conducted on spambase” and “spamassassin” benchmark email datasets, comprising a diverse collection of spam and non-spam emails. The results demonstrate that the Opposition Differential Evolution (ODE) algorithm yields superior performance compared to traditional machine learning and evolutionary techniques, displaying its robustness and efficiency in identifying spam emails accurately. The ODE algorithm effectively handles high-dimensional feature spaces, enhancing the model's discriminatory power while maintaining computational efficiency. Compared to the suggested ODE-SVM technique, which yields a result of 96.79 percent, the full-feature accuracy result was 93.55 percent. Additionally, empirical results demonstrate that our scheme may efficiently increase the number of features needed to improve the accuracy of the email spam classification.

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Nadir Omer mail

link https://doi.org/10.54216/FPA.170229

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Robust Multimodal Fusion of Transfer Learning Framework for Leukemia Cancer Detection and Classification using Biomedical Images

Leukemia is a form of blood cancer that targets white blood cells (WBC) and stands as a major cause of mortality worldwide. During the center of human bones, leukaemia is presented and provides blood cell generation with leukocytes and WBC, and if some cell comes to be blasted, then it grows a fatal illness. For that reason, the analysis of leukaemia in its initial stages aids significantly in the treatment accompanied by saving the life. At present, leukemia analysis is done by visual assessment of biomedical images of blood cells, which is time-consuming, tedious, and wants to train specialists. Consequently, the lack of an early, automatic, and effectual leukemia recognition model is a major problem in hospitals. A few automated techniques like deep learning (DL) and Machine learning (ML) methodologies for leukemia cancer identification are presented that offer remarkable and effectual results. This study develops a Robust Multimodal Fusion of Transfer Learning Framework for Leukemia Cancer Detection and Classification (RMFTLF-LCDC) algorithm. The RMFTLF-LCDC system mostly suggests identifying and classifying the existence of leukemia cancer on biomedical imaging. At first, the RMFTLF-LCDC model applies image preprocessing using a kernel correlation filter (KCF) to eliminate the noise. For the feature extraction process, the multimodal fusion of CapsNet models, including RES-CapsNet, VGG-CapsNet, and GN-CapsNet are implemented to improve the representation of features by providing more accurate initial information to subsequent capsule layers. In addition, the recurrent spiking neural network with the spiking convolutional block attention module (RSNN-CBAM) technique is performed for the leukemia cancer detection process. At last, the improved Harris hawk optimization (IHHO) approach-based hyperparameter choice can be executed to improve the classification outcomes of the RSNN-CBAM system. The efficiency of the RMFTLF-LCDC method has been validated by comprehensive studies using the benchmark image dataset. The numerical result shows that the RMFTLF-LCDC method has better performance and scalability across other recent techniques.

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Arwa Darwish Alzughaibi mail

link https://doi.org/10.54216/FPA.170230

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

Soft Computing with Neutrosophic and fractional order frameworks: A state-of-the-Art review

This study reviews a comprehensive mathematical framework known as neutrosophic soft sets, which combines neutrosophic theory with the soft set theory. Also, we review neutrosophic fractional order functions. For decision making, this framework effectively conveys ambiguity and uncertainty. The developments in soft set theory and neutrosophic set theory are thoroughly examined in this article. We review the advancements of both theories in general. We examine the qualities, applications, and theoretical underpinnings of both theories. We study the combination of neutrosophic soft set theory and logic. The study talks about important new developments and techniques that make neutrosophic soft suites better at solving difficult real-world problems that aren’t always clear. To promote the advancement of the discipline, we also provide a comprehensive overview of the theories derived from literature methodologies, and propose potential avenues for future research. This review serves as an important resource for researchers and practitioners wishing to utilize neutrophil suites in their work. It provides a deeper understanding of the potential effects and applications. This review also addresses a discussion on fractional order neutrosophic sets (FONS). The fractional order component offers an additional degree of freedom, enhancing the adaptability of neutrosophic sets for many applications.

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Kottakkaran Sooppy Nisar mail -

Muhammad Farman mail -

Harish Garg mail -

Mahmoud Abdel-Aty mail

link https://doi.org/10.54216/IJNS.250345

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

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American Journal of Business and Operations Research | 2024

Development of Automated Statistical and Optimized Models with Soft Computing Techniques for Business finance Operations

As part of the scope of the Artificial Neural Network – Particle Swarm Optimization (ANN-PSO) notion, the computational capability of ANNs is integrated with the optimization potential of PSO. This method proves to be very effective in solving complex non-linear forecasting problems where traditional approaches would not be effective. The data interactions that exist are the ones that are modelled and captured by the ANN component. However, the PSO method is charged with the duty of minimizing the biases and weights used in the ANN to ensure that the model attains the global minimum without being trapped in tiny local minimum. The application of this framework can be extended to cash forecast used in business like the one above in which a days of cash requirement forecast is created based on experience and factors like holidays, pay check effects and working days. However, the given contribution of the PSO element in learning process is linked with continuous transformation of variables under the basic guidelines of swarming intelligence, it makes the learning session of ANN more efficient. Therefore, the degree of accuracy of forecasts that are given by such configurations is improved, especially in the conditions that are in a state of steady evolution. The ANN-PSO model mirrors similar attributes, including its ability to process data in parallel and furthermore, its high compatibility with large-scale data as well as it robustness when working with both non-linear and linear data set. Incorporating the PSO into a model enhances the range of possible solutions and given the peculiarity of the gradient-based approach, it reduces mistakes more effectively than the conventional techniques. They suggested that by applying ANN with PSO the framework act as an efficient tool for prediction and for solving various issues in several fields. In this case, the ANN-PSO strategy suggested here works out to an impressive overall accuracy of over 98% compared to the previous systems.

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Doug Young Song mail

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

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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American Journal of Business and Operations Research | 2024

Stochastic Diffusion Process Models for Driving Innovation in Market-Driven Product Development

In order to analyses the diffusion of new technological products in rapidly changing market environments, this paper presents two new stochastic diffusion models: SDM1 and SDM2. The two models also utilize stochastic market size function in capturing rather random growth of potential users, inherent in most real-world markets. SDM1 apply the exponential distribution to model the market growth rate to consider the cases characterized by the high increase, while SDM2 adapt the Erlang distribution to reflect the S-shape to consider the long-term adoptions. The presented models rely on stochastic differential equations with recourse to calculus, and they adopt stochastic geometric Brownian motion and logistic growth function for adoption rates. This makes it possible to capture effects of learning as well as the non-regularity of adoption over time. The empirical results of benchmark models by using Apple iPhones and Samsung Galaxy smartphones sales data show the better performance of SDM1 and SDM2. The performance of the methodologies is measured using parameters, the goodness-of-fit tests and the forecast accuracy that all show that the proposed methods are very efficient. These models have a rich theoretical background, which comprises the foundation for explaining adoption patterns, which in turn will facilitate the behaviour of managers and policymakers towards understanding consumers, controlling inventory, and designing significant marketing strategies for technology products in a stochastic world. Both SDM1 and SDM2, the suggested algorithms, outperform the state-of-the-art techniques in terms of accuracy. SDM1 outperforms the other models with an accuracy of 95.32 percent. SDM2's greater accuracy in forecasting is shown by its outperformance of all techniques, which stands at 97.3%.

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Saurabh Singh mail

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

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new