Financial Data Analysis for Financial Management Based on Cloud Computing Using Deep Reinforcement Learning Model

Parviz; Mansur; Nilufar; Alexey; Andrey; Olga

doi:https://doi.org/10.54216/FPA.180204

Journal of Intelligent Systems and Internet of Things | 2025

Explainable AI-Driven Gait Analysis Using Wearable Internet of Things (Wiot) and Human Activity Recognition

Due to the rapid expansion of the Internet of Things (IoT), supportive systems for healthcare have made significant advancements in both diagnosis and treatment processes. To provide optimal support in clinical settings and daily activities, these systems must accurately detect human movements. Real-time gait analysis plays a crucial role in developing advanced supportive systems. While machine learning and deep learning algorithms have significantly improved gait detection accuracy, many existing models primarily focus on enhancing detection accuracy, often neglecting computational overhead, which can affect real-time applicability. This paper proposes a novel hybrid combination of Sparse Gate Recurrent Units (SGRUs) and Devil Feared Feed Forward Networks (DFFFN) to effectively recognize human activities based on gait data. These data are gathered through Wearable Internet of Things (WIoT) devices. The SGRU and DFFFN networks extract spatio-temporal features for classification, enabling accurate gait recognition. Moreover, Explainable Artificial Intelligence (EAI) assesses the interoperability, scalability, and reliability of the proposed hybrid deep learning framework. Extensive experiments were conducted on real-time datasets and benchmark datasets, including WHU-Gait and OU-ISIR, to validate the algorithm’s efficacy against existing hybrid methods. SHAP models were also employed to evaluate feature importance and predict the degree of interoperability and robustness. The experimental results show that the method, combining Sparse GRUs and Tasmanian Devil Optimization (TDO)-inspired classifiers, achieves superior accuracy and computational efficiency compared to existing models. Tested on real-time and benchmark datasets, the model demonstrates significant potential for real-time healthcare applications, with an AUC of 0.988 on real-time data. These findings suggest that the approach offers practical benefits for improving gait recognition in clinical settings.

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Ponugoti Kalpana mail -

Sarangam Kodati mail -

L. Smitha mail -

Dhasaratham mail -

Nara Sreekanth mail -

Aseel Smerat mail -

Muhannad Akram Ahmad mail

link https://doi.org/10.54216/JISIoT.150205

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Ensemble of Machine Learning Model with Tuna Swarm Optimization-Driven Feature Selection for Cybersecurity Threat Detection and Classification Approach

The initial identification of cybersecurity events like attacks is challenging provided the continuously growing threat environment. Despite state-of-the-art surveillance, advanced attackers can apply for more than 100 days in a system before being detected. Guaranteeing cyber security is a composite task that depends on area of interest and needs cognitive capabilities to control possible threats from larger quantities of network data. The most important task of a cyber-security analyst is to safeguard a network from damage. Numerous technological developments in network and information security have enabled progressive monitoring and threat detection for the predictors, but the responsibilities they carried out could not be automated completely. Hence, in recent times’ Artificial intelligence (AI), mainly deep learning (DL) and machine learning (ML) algorithms, has been utilized to expand a beneficial data-driven intrusion detection system (IDS). Many standard ML classification methods provide intelligent facilities in the area of cyber-security, mainly for intrusion detection. This study develops a Tuna Swarm Optimization-Driven Feature Selection with Ensemble of Machine Learning Models for Cybersecurity Threat Detection and Classification (TSOFSEML-CTDC) technique. The proposed TSOFSEML-CTDC model concentrates on detecting and classifying intrusions on the network. Initially, the TSOFSEML-CTDC algorithm performs data preprocessing using min-max normalization to convert an input data into a beneficial format. Then, the feature selection process has been carried out using tuna swarm optimization (TSO) algorithm. For the classification of intrusion detection, ensemble of ML techniques was employed such as support vector regression (SVR) approach, least-square support vector machines (LSSVM) method, and modified extreme learning machine (MELM) technique.  At last, the hyperactive parameter optimization process is executed by using the coati optimization algorithm (COA). The experimental evaluation of the TSOFSEML-CTDC model occurs using a benchmark dataset. The stimulated results emphasized the enhanced performance of the TSOFSEML-CTDC method compared to existing approaches.

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K. Anitha mail -

K. Rajiv Gandhi mail

link https://doi.org/10.54216/JISIoT.150206

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Journal of Artificial Intelligence and Metaheuristics | 2025

Lung Cancer Prediction from Smoking Cause by Machine Learning Classification Models

The incidence of lung cancer varies in males and females, which occurs due to the abnormal and uncontrolled growth of cells in the lungs. It has a greater predilection in males as compared to females. Smoking is the most important risk factor for lung cancer. It causes serious breathing issues and also affects other organs. It increases the mortality rate both in young adults as well as in the older age group. Therefore, there is improvement in medical technologies to facilitate specialized diagnosis and treatment, but the mortality has not been controlled to a satisfactory extent. It is important to take preventive measures and precautions at the initial stages. Machine learning brings various advancements to the medical sector due to which various diseases can be detected at an early stage. In this paper, we presented different machine learning classifier techniques used for the classification of the present lung cancer data in the UCI machine learning repository as benign and malignant. The dataset is divided into cancerous and non-cancerous by converting the input data into binary form and using the classifier technique in theWeka tool. This specifically includes classifiers used: Logistic Regression, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Trees, and Na¨ıve Bayes. In addition, we study the effect of data preprocessing methods on our prediction accuracy, such as data normalization and feature selection. The study seeks to help develop various reliable resources for lung cancer identification, which are critical for diagnosing and treating patients in a timely manner and improving their outcomes.

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Nada M. Sallam mail -

P. K. Dutta mail

link https://doi.org/10.54216/JAIM.090105

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2025

Comparative Analysis of Machine Learning Models for Daytime Power Generation Prediction

This paper proposes to evaluate how different machine learning techniques can be used to predict daytime power generation based on the ”Daily Power Generation Data” dataset. As a result of six models, which contain Random Forest Regressor, Decision Tree Regressor, Nearest Neighbors, Linear Regression, MLP Regressor, and SVR, a clear understanding has been accomplished by assessing the performance using multiple metrics. First of all, the Random Forest Regressor turned out to be the best in terms of the Mean Squared Error (MSE) of 3.57×10−6, which was the lowest among the three ML models. The introduction of the paper highlights the role of precise planning of the power market and the consecutive sections describing the topic mathematically. The table below, with a total list of performance issues, explains why the Random Forest Regressor is the superior full-proof model using the lowest MSE, highest explained variance, and great resistance to outlying samples. The paper thus gave various useful approval criteria that, to a great extent, we can choose the best model out of them because the Random Forest Regressor was in a position to get the highest performance metrics.

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Marwa M. Eid mail -

Anis Ben Ghorbal mail

link https://doi.org/10.54216/JAIM.090106

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Development of Neutrosophic Pareto Distribution for Survival Analysis

We provide a neutrosophic approach to the Pareto model, which is widely used to model survival data. In this paper, the neutrosophic Pareto model (NPM) is constructed under the framework of neutrosophic statistics, that can manage uncertain nature of data, commonly occur in many real word problems. This formulation generalizes the&ensp;classical model and is a useful method for dealing with fuzzy or uncertain data typically encountered in many applications in survival data. Using neutrosophic statistical framework, few key mathematic qualities of the proposed model such as its moments, survival function, and hazard rate are presented in the study. These properties are motivated and rigorously established to ensure theoretical soundness of the proposed model. Moreover, the maximum likelihood estimation (MLE) is used to estimate the neutrosophic parameters of the distribution. This approach is essential for deriving accurate parameter estimates from the data available, especially in cases where uncertainty or imprecision is present within the data as it is usually the case for any real-world situation. Based on the simulation experiment, we display the adequate performance of the suggested model. The simulations allow us to evaluate the performance of the routine as well as the stability of the model parameters across different settings. At the end, the real data analysis is conducted to show the applicability of proposed approach. The proposed model processes such a dataset filled with a range of uncertain values and presents its possibilities to be applied for information extraction from real world data sets that are abundant in uncertainty. Our results open a new avenue for neutrosophic statistical model approaches to the analysis&ensp;of survival data in subsequent studies.

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Ahmedia Musa M. Ibrahim mail

link https://doi.org/10.54216/IJNS.250426

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

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International Journal of BIM and Engineering Science | 2025

Sustainable Practices in the Design, Modeling, and Evaluation of Medical Centers: A Case Study of Basilia City

This study concludes that integrating sustainable practices into the design and modeling of medical centers significantly contributes to enhancing resource efficiency and reducing the environmental impact of healthcare facilities. The sustainability of these facilities can be further improved using advanced technologies such as Building Information Modeling (BIM), which simultaneously enhances the well-being of patients and staff. The study also highlights the importance of adopting globally recognized sustainability assessment systems and adapting them to suit the local context to ensure effective sustainability in future medical centers.

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Yasmmen Mashtah mail -

Alaa J. kadi mail -

Batoul Hasanin mail

link https://doi.org/10.54216/IJBES.100209

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

AlertFusion-OptiNet: An Advanced SIEM Alert Management System for IoT Environments using CMRO and AlertQ-Net

SIEM, which stands for Security Information and Event Management, is a collection of services and solutions that give businesses the capacity to gather, examine, and handle security-related data in real time from all areas of their IT infrastructure. This study presents AlertFusion-OptiNet, a sophisticated SIEM alert management architecture intended for effective alert handling and intrusion detection. The proposed CMRO algorithm (a hybrid of Coot Bird Optimization and Mug Ring Algorithm) is used to select the best features after the system integrates data from multiple sources (raw logs, network traffic, and security alerts), applies preprocessing to eliminate redundancy and inconsistencies, and extracts features using techniques like LDA, GloVe, statistical analysis, and DWT. PCA is then used to reduce dimensionality. The shortcomings of current intrusion detection systems include delayed alert replies, poor feature selection, and ineffective management of heterogeneous datasets. Two-channel CNNs, LSTM, and Bi-RNNs are used in AlertFusion-OptiNet's hybrid detection model to improve accuracy and real-time detection, while AlertQ-Net uses reinforcement learning to handle and monitor alerts continuously. The proposed AlertFusion-OptiNet accomplished 99.43% and outruns SOTA models.

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Abdullah Alenizi mail

link https://doi.org/10.54216/FPA.180201

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Boosting Financial Risk Prediction Model Using Attention Mechanism Based Recurrent Neural Networks with Red‐Tailed Hawks Algorithm

The systemic prediction of financial risk issues has become a main attention in the area of finance. Financial risk is the main likelihood that stockholders will lose currency after they finance a business that has debt if the business flow of cash demonstrates insufficient to see its economic requirements. The incorporation of deep learning (DL) methods into financial risk forecast and investigation has altered conventional techniques. While traditional quantitative systems often trust basic metrics such as the highest reduction, the arrival of DL requires a more nuanced assessment, highlighting the model's generalization capability, particularly in market crises like stock market crashes. DL techniques are efficient in removing intricate patterns from massive data collections and become an effective model for forecasting financial trends. In this paper, we offer Boosting Financial Risk Prediction Model Using Attention Mechanism with Red‐Tailed Hawk (BFRPM-AMRTH) Algorithm. The presented BFRPM-AMRTH model aims to address the challenges of identifying and mitigating potential financial threats in a dynamic environment. Initially, the BFRPM-AMRTH technique applies the linear scaling normalization (LSN) data normalization technique to standardize the input features and ensure consistency across the dataset. In addition, the long short-term memory auto encoder with attention mechanism (LSTMA-AE) technique can be employed for classifying financial risks. Eventually, the red‐tailed hawk (RTH) algorithm adjusts the hyperparameter values of the LSTMA-AE algorithm optimally and outcomes in greater classification performance. To ensure the improved performance of BFRPM-AMRTH system, a huge range of simulation studies has been achieved and the obtained outcomes establish the advancement of the BFRPM-AMRTH system over the existing techniques

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Ilyos Abdullayev mail -

Hafis Hajiyev mail -

Mahfuza Sattarova mail -

Elena Klochko mail

link https://doi.org/10.54216/FPA.180202

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Enhancing Visibility on Social Media with Categorization Machine Learning Analysis

A considerable number of individuals concentrate their engagement on social media sites, notably Instagram, YouTube, and Facebook, where they may adeptly exploit their popularity. A considerable volume of research studies has been conducted across diverse social networks to examine user profiles and their relationship with popularity. The primary emphasis of research concerning social media has centered on theme analysis, encompassing domains such as health, creativity, and awareness. This study utilizes K-means clustering to classify social media articles and determine the characteristics that contribute to their popularity. Gathered data from publications by international influencers during an eight-month duration. Producing roughly 161 posts daily and around 1092 posts monthly seeks to improve metrics including views, likes, and dislikes on social media. This strategy is designed to facilitate the growth of the platform's popularity, thereby maximizing visibility and outreach. The analysis focused on three factors: virility, appeal, and publicity rates. Classified the posts into five fundamental groups: casual, quality, number, support, and leader. The study yielded important findings on optimal publication timing, ideal video length, follower metrics, biography and caption lengths, and hashtag utilization. Researchers found some interesting things that will help people who use social media and brand owners make better marketing plans.

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Haitham S. Hasan mail

link https://doi.org/10.54216/FPA.180203

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Financial Data Analysis for Financial Management Based on Cloud Computing Using Deep Reinforcement Learning Model

Maintainable ﬁnancial fraud detection includes the usage of viable and decent performs in the recognition of fraudulent actions in ﬁnancial region. A credit card is susceptible to cyber threats, which leads to a fraud of credit card. The fraudster does dishonest action by attaining illegal access to credit card information and this action affects an economic loss for the user as well as company. At present, deep learning (DL) and machine learning (ML), systems were deployed in financial fraud detection owing to their features’ ability of making a great device to find out fraudulent dealings. This paper presents a Financial Data Analysis for Financial Management Based on Cloud Computing Using Deep Reinforcement Learning Model (FDAFM-CCDRLM). The main intention of FDAFM-CCDRLM model is to improve analysis of financial data in the economic management. Initially, the min-max normalization is employed in the data normalization stage to convert a data of input into a suitable format. Besides, the proposed FDAFM-CCDRLM model designs a black‐winged kite algorithm (BKA) for the subset of feature selection process. For the classification process, the double deep Q‐network (DDQN) algorithm has been executed. At last, the artificial bee colony (ABC) algorithm-based hyperparameter range method is done for improving the classification outcomes of the DDQN model. The experimental evaluation of the FDAFM-CCDRLM system can be tested on a benchmark database. The extensive outcomes highlight the significant solution of the FDAFM-CCDRLM approach to the financial data analysis classification process

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Parviz Gurbanov mail -

Mansur Matkarimov mail -

Nilufar Sapayeva mail -

Alexey Nedelkin mail -

Andrey Kulik mail -

Olga Zanina mail

link https://doi.org/10.54216/FPA.180204

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new