International Journal of Advances in Soft Computing and its Applications

Generating Natural Language from AAC Cards for Children with Autism using a Sequence-to-Sequence System

Lely Hiryanto — 2026-03-11

Communication deficits are a common challenge experienced by children with Autism Spectrum Disorder (ASD). Existing Augmentative and Alternative Communication (AAC) applications, such as VICARA, are limited to unidirectional communication—only transmitting raw card sequences to another application (e.g., "I, Eat, Noodle"). This limitation renders the conversation flow non-interactive and prone to miscommunication. This study aims to develop a new version of VICARA, named Talk of the Heart AAC, a bidirectional Android-based application capable of bridging the communication gap between autistic children and their caregivers. The application is designed to translate the constructed card sequences into complete, natural, and semantically logical sentences in Bahasa Indonesia through the implementation of a Sequence-to-Sequence model with a Gated Recurrent Unit architecture augmented by an attention mechanism. To train the model, a synthetic dataset comprising 7,000 data pairs was generated using the Google Gemini API and validated by a speech-language pathology expert. Model evaluation results demonstrated a good performance in sentence translation, yielding a BLEU score of 42.95%, ROUGE-1 94.94%, ROUGE-2 87.12%, and ROUGE-L 93.70%. From the application perspective, non-functional testing produced an average System Usability Scale score of 91.67, categorized as "excellent."

FinTech and AI technologies for Enhancing Evidence Verification and Dispute Resolution in Financial Litigation

Mohammad Sulieman Jaradat — 2026-03-10

The digitization of financial services represents the redesigning of evidence creation, storage, and challenging in court. Nonetheless, current verification approaches produce delays for fast-moving digital and multifaceted transactions. The paper implements and tests the performance of combining Artificial Intelligence (AI) and Financial Technology (FinTech) to develop the consistency of evidence verification and accelerate dispute resolution through the context of financial litigation. A techno-legal method is presented and is structured of three pillars, which comprise blockchain system for immutable provenance and accurate transactional timestamping including smart contracts; machine-learning analytics aimed at sensing anomalies and fraud patterns through varied payment and ledgers systems; and RegTech modules, which automate AML/KYC procedures, and which attempt to create auditable compliance records. The three pillars enhance three assurance layers, which comprise analytical inference, provenance integrity, and compliance attestation that is combined to a judicial interpretation layer by supporting understandable outputs deigned for evidences reassessment. Based on a sequence of particular scenarios that involve suspected structuring or layering, disputed transfers, and contractual breaches, the study investigates the way the method produces evidence objects, which are interpretable, traceable, and verifiable according to principles that are admissible and due-process. Furthermore, problems of implementation such as bias control, data governance, model risk, and privacy are studied in this paper. As a result, the method is generally applicable, but is particularly based on justice system digitization efforts, including those recently being applied in Saudi Arabia.

A Robust Smart Grid-Aware Cloud Computing Framework for Sustainable Energy Management

Udit Mamodiya — 2026-03-09

The growing use of renewable energy as a part of a smart grid infrastructure has raised new challenges relating to the coordination of the computational workload scheduling and the availability of intermittent energy in a distributed cloud infrastructure. The traditional cloud scheduling systems do not work with knowledge of the current pattern of renewable generation and thus the people have to rely more on the non-renewable grid energy and the intensity of carbon emissions is also high. In order to overcome this drawback, the current study is a proposal of a Smart Grid-Aware Cloud Computing Framework, with an embedded Grid-Aware Adaptive Scheduling Algorithm to dynamically schedule the execution of the computational workload of renewable-sustainable cloud nodes. The suggested framework incorporates the knowledge of renewable availability, estimation of sustainability threshold, and migration control with carbon awareness into the scheduling of tasks. Experimental evaluation conducted under heterogeneous workload demand and renewable generation conditions demonstrates improved Renewable Utilization Ratio of 0.79 compared to 0.66 achieved by reinforcement learning–based adaptive scheduling methods. The proposed framework further reduces normalized computational energy consumption to 0.81 and lowers carbon emission index to 0.52, while maintaining acceptable scheduling latency of 1.07 under renewable-aware workload migration. These findings suggest that the introduction of renewable conscious scheduling tools in cloud infrastructures can make the execution performance in smart grid settings to be greatly more sustainable.

AI-Driven Fake News Detection Using Multimodal Features and Hybrid Deep Learning Models

Tarek Ghazi Kanan — 2026-03-09

With the growing amount of news published online every day, distinguishing reliable information from fabricated content has become increasingly challenging. This research introduces an AI-based system designed to detect and classify fake news using a combination of different types of features. Instead of relying only on the written text, the system brings together linguistic cues, article-level metadata, and indicators related to how the news is shared or structured. To achieve this, we develop a hybrid deep learning model that integrates transformer-based language models with additional neural components capable of capturing contextual and relational patterns. The study follows a complete pipeline—from collecting the dataset to preprocessing, feature extraction, model building, and evaluation. The results show that the multimodal and hybrid approach provides more accurate and consistent detection compared to single-feature models. This work aims to support more reliable news verification tools and reduce the impact of misinformation in the digital environment.

Secure and Adaptive IoT Network Architecture for Smart Hospitals with Load Balancing in Diabetic Care Systems

Ala Mughaid — 2026-03-09

The digital healthcare sector, often referred to as the smart hospital domain, has emerged as one of the most transformative and crucial areas in modern technological advancement. Its significant impact is primarily due to the ability to efficiently gather and manage patient data. This study proposes a network design specifically tailored for healthcare environments, with a focus on diabetes-related applications. Using the Cisco Packet Tracer simulation tool, this research assists healthcare providers specializing in diabetes care in making informed decisions regarding their network infrastructure. The primary objective of this work is to demonstrate a practical implementation of network systems that are suitable for diabetes care, emphasizing the need for high-performance connectivity. Special attention is given to optimizing bandwidth usage and addressing load balancing challenges within simulated environments. The network’s effectiveness is assessed using various metrics, including traffic load simulations to evaluate scalability and reliability, as well as security assessments, such as penetration testing. These evaluations aim to enhance operational efficiency and improve the quality of care provided to patients.

Bitcoin Price Forecasting Leveraging X Data and Sentiment Indicators Via an LSTM-Enhanced Deep Learning Architecture

Yunus Ozen — 2026-03-05

Role of sentiment analysis in forecasting cryptocurrency market trends. The research employs a Long Short-Term Memory (LSTM) deep learning model integrated with sentimentdata to enhance predictive accuracy. Tweets related to Bitcoin were collected during 2022–2023 and analyzed using the VADER tool, which classified them into positive, neutral, and negative categories. Sentiment scores were combined with historical Bitcoin prices to investigate their correlation before developing predictive LSTM models. The findings reveal that social media sentiment significantly influences Bitcoin price fluctuations, particularly during periods of high volatility. Incorporating sentiment in-formation improved the model’s performance, as indicated by lower error metrics (MAE, RMSE, and MSE) compared to price-only models. This demonstrates that market emotions expressed on social media can serve as valuable predictive indicators in cryptocurrency forecasting. The study concludes that integrating sentiment data enhances the accuracy of Bitcoin price prediction and highlights the importance of emotional factors in market dynamics. Future research should extend this approach to other cryptocurrencies, multilingualsentiment contexts, and real-time analysis to further advance forecasting capabilitiesin digital financial markets.

Predicting Apartment Prices in Jordan Using Ensemble Machine Learning Algorithms to Support Investment and Urban Planning Decisions

Nisrean Thalji — 2026-03-05

The housing market is of great significance to the development and advancement of cities, but customary forms of property valuation are frequently biased, time-consuming, and not always effective. This paper focuses on the city of Irbid in Jordan, aiming to collect all the information on apartments and houses, predict the prices of properties, and clarify the key factors influencing the prices. Following the comprehensive cleaning process of the data and exploratory analysis, three ensemble machine learning models were trained and optimized to achieve accurate price predictions. The performance of all three models demonstrated excellent and consistent predictions, highlighting the efficiency of ensemble methods in predicting property prices. SHAP analysis indicated that the size of the house, the number of bedrooms, the number of lounges as well as the location are the most significant factors influencing the prices in Irbid. This reflects the functioning of the local market.

A Spatial Approach to Correlated High-Dimensional Stunting Data in Indonesia Using a Modified Generalized Lasso

Septian Rahardiantoro — 2026-03-04

Stunting remains a significant public health issue in Indonesia. Although the national prevalence declined by 6.1% in 2024, several provinces continue to exhibit alarmingly high rates. This study aims to explore the spatial patterns of stunting across Indonesia, evaluate the performance of the generalized lasso model in identifying potential regional coefficient groupings based on various neighborhood structures, and determine the most influential factors contributing to stunting. The data, sourced from Statistics Indonesia and the Ministry of Home Affairs in 2024, cover 38 provinces and include ten predictor variables. The analysis employs a modified elastic net approach within the generalized lasso framework by incorporating a custom penalty matrix into the L₂ regularization term to mitigate multicollinearity among predictors. The proposed models were evaluated against the Spatial Autoregressive (SAR) model, standard elastic net, and standard generalized lasso using specified neighborhood adjacency methods and tuning parameters. Optimal tuning parameters were selected using the Approximate Leave-One-Covariate-Out Cross-Validation (ALOCV) method. The best-performing model was identified as the K-Nearest Neighbors (KNN) model with k=3 and the custom penalty matrix, based on an optimal balance of degrees of freedom, lower AIC and RMSE, and optimum sensitivity criteria. The results reveal that the most influential factors associated with stunting prevalence in 2024 include the poverty rate—particularly in southern Kalimantan, several provinces in Sumatra, and East Nusa Tenggara—and child health insurance coverage, which spans provinces across Indonesia.

“SK@D” “SK@D” Smart Kit at Door - Internet of Things (IoT) Based Smart System Enabling Notifications

Renuka Agrawal — 2026-03-04

The research proposes an innovative device that provides a cost-effective solution for receiving alerts and remote monitoring for the contents of a bag hanging outside doors in metros. It leverages the power of the Internet of Things (IoT) to keep owner informed about the status of the bag, whether the expected delivery item is picked up or not. Smart bags are often used by travelers or other persons for charging their gadgets and for different activities. Serving the purpose, they are designed for, their usage for daily household activities is limited. Proposed invention is a smart bag hanging outside an apartment. There is a need to develop an application-specific smart bag with instant notifications for the delivery of small perishable items such as dairy products or flowers

Due to lack of alerts, items remain inside the bag, throughout the day, rendering them unusable. To address a real-world problem related to security, convenience and the changing landscape of e-commerce, the present work is an ideal solution. It facilitates the power of IoT and electronic systems to offer an innovative solution with broad interdisciplinary relevance.

The key contributions of the methodology are to Design and develop a compact, efficient Smart Bag that notifies the owner when items are dropped. Also, to prevents spoilage of dairy and consumables by informed decision-making. Besides the proposed bag captures images of the person dropping items and sends alert notification to intended owners for making suitable arrangements for pickup of deliverables.

The key contributions of the methodology are to Design and develop a compact, efficient Smart Bag that notifies the owner when items are dropped. Also to prevents spoilage of dairy and consumables by informed decision-making. Besides the proposed bag captures images of the person dropping items and sends alert notification.

Enhanced Intrusion Detection Systems Dataset Synthesis Using Conditional Generative Adversarial Networks with the Adaptive Whale Optimization Algorithm

Ateka Hani — 2026-03-04

Modern networks, especially in the IoT era, require intrusion detection systems (IDSs) to ensure security and integrity. The growing diversity and data-driven nature of cyberattacks necessitate the generation of high-quality synthetic attack data for training robust detection models. In this domain, Generative Adversarial Networks (GANs) have proven to be a versatile and powerful tool. However, GAN training is unstable, as it often fails to converge and produces suboptimal outputs. To address these challenges, this study proposes a Conditional GAN (CGAN) enhanced with the Adaptive Whale Optimization Algorithm (AWOA). Relax-Alpha is a feedback mechanism that adjusts the generator’s updates based on trends in discriminator accuracy. It aims to provide dynamic training control, stabilizing training, and increasing the realism and diversity of synthetic samples. Several adaptation strategies were evaluated, including discrete and continuous alpha adjustments. The model was tested on the CIC IoT 2023 dataset through 5-fold cross-validation. The experiments show strong classification and generation quality; CGAN-AWOA achieved 98.59% accuracy.

A Deep Learning-Based Integrative Framework for Cancer Subtype Classification Leveraging Multi-Omics Data: A Software Engineering Approach

Hamzeh Aljawawdeh — 2026-03-04

The cancer subtypes classification is a significant aspect of oncology; it helps to predict the disease and effective treatment. High-throughput analysis techniques have been enhanced to enable researchers to generate large-scale multi-omics assemblies. Despite these enhancements, integrating heterogeneous molecular layers while maintaining interpretability is still challenging. This research attempt aims to present a deep learning framework for cancer subtype classification. The framework uses data from the Cancer Genome Atlas project, including genomic, epigenomic, and proteomic data. To develop the framework, a multimodal neural network architecture was used, where each omics pattern is processed via a dedicated branch before features are integrated. Furthermore, an advanced attention technique has been utilised to improve interpretability. The proposed framework reached an overall classification accuracy of 92.3%. For testing, reserved experimental data for breast and lung adenocarcinoma subtypes were set aside, and methods that perform better than common baseline approaches were used. In follow-up tests, the model stayed stable even when the data were noisy, which suggests that the same framework could be applied to other cancer subtypes, including colorectal cancer. These results suggest that multi-layer, interpretable deep learning models can support accurate cancer classification and more precise oncology that is grounded in biological evidence.

A Hybrid Deep Neural Network and Morphological Knowledge to Enhance Arabic Lemmatization

Samir BELAYACHI — 2026-02-16

Lemmatization is performed in many natural language processing applications during the preprocessing stage, enabling more efficient text analysis and the extraction of relevant information. The Arabic language is associated with the following challenges of lemmatization: the morphological richness of the language, the high usage of concatenations and the omission of the diacritical marks. To overcome those issues, we will introduce a new lemmatizer to improve the functionality of a deep learning framework. The latter will be a BLSTM network, with an additional filtering layer based on morphological features. To alleviate the effect of out-of-vocabulary words, a statistical layer built on Hidden Markov Models was introduced. Tests done on a reference corpus showed that accuracy was enhanced by a margin of over nine percentage points on the use of the two layers (filtering and statistical). In addition, the results were compared with state-of-the-art lemmatizers on an independent corpus which proved that the proposed approach was superior.

Predicting Bitcoin Prices Using Deep Learning

Manaf Ahmed — 2026-02-11

Predicting cryptocurrency price is challenging owing to high volatility, less historical data, and the impact of external parameters like news, public sentiment, and regulatory announcements. This challenge is tackled in this research by employing models of deep learning like Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU)—to predict Bitcoin's OHLC prices daily. Based on historical time-series data of Coin Codex, the research uses an autoencoder-based feature extraction method with five-day sliding window method for sequence generation. Hyperband optimization is used to tune hyperparameter of each model. The result shows that BiLSTM performs better than all the other models with minimum Mean Squared Error (MSE = 0.001183), Mean Absolute Error (MAE = 0.026090), and maximum R² score (0.980596) after optimization. The results emphasize the significance of deep learning in capturing nonlinear dynamics in time series of financial applications and bear testimony to the effectiveness of hyperparameter tuning in enhancing model accuracy. The study enhances the development of prediction tools for digital asset markets and enables more informed investment decisions.

A Deep Learning Approach for Predicting Hospital Length of Stay for People with Diabetes using Electronic Health Records (EHRs)

Rafat Hammad — 2026-02-10

Diabetes, which substantially affects the people lives as well as the healthcare systems worldwide, has become the primary area of concern. The purpose of this study is to predict the length of hospital stay among diabetic patients using machine learning (ML), deep learning (DL), along with electronic health record (EHR) data. This work evaluated several models: XGBoost, Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network with LSTM (RNN-LSTM), and an ensemble model combining GAT and CNN in terms of performance metrics used, such as MAE, MSE, and R². Among all the models tested, MLP using the top 20 features performed the best, recording an MAE of 0.37 and an R² of 0.62. The model emphasized certain critical aspects concerning feature selection and dimensionality reduction, which enhanced the algorithm's accuracy, especially in cases with numerous redundant or verbose variables. More generally, these findings point to the potential that predictive modeling and artificial intelligence have in optimizing the allocation of hospital resources, reducing the costs of health care, and improving patient outcomes, thereby paving the way forward in the development of some clinical decision support tools to manage inpatient diabetic care.

Towards Personalized Lipid Management: Predicting Statins Therapy Eligibility through Machine Learning Models

Amal Alzu'bi — 2026-02-10

Cardiovascular diseases are among the leading causes of death worldwide and a major contributor to the deterioration of quality of life. Therefore, it is highly beneficial to follow the clinical guidelines and recommendations for preventing and treating cardiovascular diseases at their early stages. Cholesterol-lowering drugs such as Statins are considered first-line medications for the prevention of atherosclerotic cardiovascular diseases (ASCVD). However, it is not easy to determine patients’ eligibility for statin therapy. In this work, we built efficient and accurate prediction models based on several machine learning algorithms for predicting patients' eligibility for Statins using several cardiovascular disease risk factors. The results indicated that the gradient boosting classifier achieved 95.6% accuracy and 99.0% area under the curve in predicting patients' eligibility for statin therapy. Other simpler but more explainable algorithms such as decision tree and logistic regression also demonstrated good performance.

AI-Powered Security-Aware Reconfiguration in Cyber-Physical Systems for Smart Healthcare and Energy Domains

Theyazn H.H Aldhyani — 2026-02-08

Cyber-physical systems (CPS) in smart energy and smart healthcare must continue to be safe, fast, and reliable, even in the face of evolving cyber threats and fluctuating network/edge conditions. An AI-based, security-aware framework of CPS reconfiguration is presented in this paper. It is comprised of (i) privacy-preserving federated anomaly detection at distributed edge nodes, (ii) a reinforcement-learning decision module that selects risk-aware reconfiguration actions under the objectives of latency, energy, and safety, and (iii) auditability, backed by blockchain, to provide trustworthy governance and accountability in the aftermath of the event. The detection module learns behavioral baselines at the local level and shares only protected updates to support data minimization and privacy compliance while still achieving high accuracy in heterogeneous deployments. The RL controller dynamically modifies actions to reduce service disruption, quicken recovery, and avert unsafe control transitions. A lightweight operational ledger captures reconfiguration activities and trust updates to provide auditable governance in multi-stakeholder CPS settings. In energy and healthcare CPS scenarios, there is strong operational performance. The system achieves 97.8% detection accuracy and an F1-score of 97.4% with an end-to-end latency of 41ms, coupled with a reconfiguration time of 65ms and a mean time to recovery of 5.6 seconds. The framework provides 99.4% uptime, consumes approximately 10 W at the edge, and with low error rates (2.1% false positive and 1.0% false negative), achieves 19.5 Mbps secure throughput. These results demonstrate that self-reconfigurable CPS can maintain mission-critical operational continuity while enhancing privacy, scalability, and governance in large-scale deployments.

Emotion-Aware Adaptive User Interfaces for Enhanced User Experience Using Multi-Modal Deep Learning

Ahmed Alshehri — 2026-02-05

Emotion-conscious computing is decisive in the further development of human-centered digital interaction, but the direct role in improving the User Experience (UX) has not been studied in detail. This paper introduces an Emotion-Aware Adaptive User Interface (EAAUI) system, which uses multi-modal deep learning to enhance usability, engagement and cognitive efficiency by adapting to emotions in real-time. The suggested solution combines facial expressions, speech prosody, and physiological cues with the help of the hybrid deep learning structure that consists of Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory networks (BiLSTMs), and Transformer-based attention systems to provide strong emotion detection and fusion. Emotions sensed dynamically are the motivators of adaptive interface changes, such as simplifying layout, modulating colors, and providing personal feedback. The framework is tested on benchmark datasets (AffectNet, RAVDESS and DEAP) and controlled user study with 30 people. The experimental findings show that the emotion recognition performance is good with an accuracy of 89.6% and an F1-score of 0.88. According to the UX view, the adaptive interface made task completion (21% less) and error reduction (18% less) significantly faster, user engagement (26% more) higher, and the System Usability Scale (SUS) score was 82.5. The results validate the claim that the operational effect of emotion-aware adaptive interfaces is beneficial to user experience, as they provide viable implications to UX-based applications in the educational, healthcare, and intelligent interactive systems.

A Privacy-Preserving Blockchain and Machine Learning Framework for Secure Next-Generation Genomic Data Analysis

Sami Morsi — 2026-02-05

Next-generation genomic data analysis faces ongoing challenges in collaboration, privacy, and scalability. With data protection and no access control deficiency, centralized systems lack sufficient protection for sensitive genomic data. The first-of-its-kind analysis of genomics combined with integrated machine learning and homomorphic encryption with the new privacy-preserving computational framework will be revolutionary. The use of smart contracts, which is unlike other frameworks, for access control, tokenized encrypted, and suspended federated model training during the suspension of other research nodes will be unprecedented. Simulation of genomic datasets (0 Major issues were identified in file sync performance and data protection and security) Vis-a-vis the other frameworks, the Model Proposed outperformed traditional, federated and HE based frameworks with significant. Of the models proposed, this one is the most impressive with a score of 94% precision, 92% recall, 0.93 computed F1 score, and 0.96 Area Under Curve (AUC). The model with the best performance. With 5K genomic datasets in a pilot simulation, collaboration improved by 25% with no breaches in the datasets. The promise of this design to ethically and securely address privacy-preserving genomic data analysis and the subsequent use of Artificial Intelligence in biomedical systems will be groundbreaking.

Comparative Study of LSTM, GRU, and BRNN Performance: large-Scale Data Analytics (EMG Signal Classification)

Karim Mohammed Aljebory — 2026-02-05

Electromyography (EMG) signals play a pivotal role in biomedical applications, such as prosthetic control and human-computer interaction, where advanced classification methods for accurate muscle activity translation are essential. This study evaluates the performance of three neural network architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BRNN) for EMG signal classification. The EMG signals were preprocessed using Digital Wavelet Transform (DWT) with Daubechies 2 wavelet to extract time-frequency features. Experiments were conducted on a large-scale training dataset comprising 672 subject recordings across six hand gestures, enabling a robust, data-driven comparison. The best classification accuracy of LSTM, GRU, and Bidirectional RNN was achieved, corresponding to cD7, with values of 93.04±1.52, 92.72±1.26, and 91.59±0.97, respectively. However, these models exhibited varying degrees of sensitivity to additive noise, particularly at deeper DWT levels. The findings highlight the trade-offs between accuracy and noise tolerance, providing insights for optimizing EMG-based gesture recognition systems in real-world applications. The final analysis confirms that LSTM outperforms the other models for real-time EMG classification, while GRU and BRNN offer a favourable balance between accuracy and computational efficiency. Looking ahead, the effective handling of large-scale, high-dimensional EMG data yields significant improvements in performance, particularly for prosthetic and real-time control applications.

Super learner ensemble-based internal quality assessment of watermelon via integration of tapping acoustics and rind texture analysis

Ketsarin Chawgien — 2026-01-22

Watermelon (Citrullus lanatus) is a widely cultivated fruit recognized for its high sugar content. Accurate detection of maturity and soluble solid content (SSC) is essential to ensure optimal harvest timing, sweetness, and market value, as well as to manage resource usage efficiently. This study introduces a low-cost, portable, and non-destructive approach for maturity classification and SSC estimation in Kinnaree watermelon by integrating tapping acoustics and rind texture analysis with ensemble learning algorithms. Tapping-induced acoustic signals were analyzed to extract key resonant features, while rind texture was quantified using image processing techniques. Selected features from both data sources, combined with watermelon mass, were utilized for three-class maturity classification and SSC regression modeling. Machine learning (ML) algorithms were used to map complex and nonlinear relationships between features and watermelon quality attributes. Results demonstrated that acoustic features and fruit mass were critical for maturity classification. Visual features were essential for SSC estimation. Super learner ensemble demonstrates superior predictive accuracy compared to other models, both in classifying ripeness and predicting the SSC of watermelons. Comparative studies with earlier methods confirmed the effectiveness and competitiveness of the proposed technology for non-destructive evaluation of watermelon quality.

Enhanced Data Embedding Using Adaptive Neural Networks with Modified Whale Optimization Algorithm

Nameer El Emam — 2026-01-20

This research paper introduces a new steganography algorithm for embedding large amounts of secret messages within a color image. Five security levels are incorporated to ensure reliable protection. To embed data in a randomized way, the algorithm uses a segmentation technique called New Adaptive Image Segmentation (NAIS). By analyzing the properties of each byte, this method determines the appropriate size of secret data to replace each byte and color in the image. Additionally, the algorithm features a machine-learning component inspired by an Adaptive Neural Network (ANN) combined with a modified version of the Whale Optimization Algorithm (MWOA). The findings show that strong imperceptibility is achieved with the stego-image, even with a large payload, reaching four bits per byte (4-bpb) at specific bytes. When the machine learning model ANN_MWOA is used, the highest PSNR reaches 79.58dB for the Baboon color image with a payload of (16384) bits, while PSNR decreases by 1% when applying ANN_WOA. Moreover, the proposed method outperforms previous approaches by an average of 2%. Additional metrics (MSE, SNR, Euclidean Norm, and others) are used to confirm that the proposed algorithm efficiently embeds hidden data.

Modeling Anomalous Diffusion with the Fractional Brusselator

Maysoon Qousini — 2026-02-12

This paper studies the fractional reaction-diffusion Brusselator model, which incorporates fractional-time derivatives to describe memory effects and anomalous diffusion in pattern formation. A fully discrete numerical scheme is developed using an L1 approximation for the fractional derivative and a finite difference method for spatial discretization. Theoretical analysis proves the uniqueness, asymptotic stability, and convergence of the scheme. Numerical simulations demonstrate the emergence of stationary Turing patterns under appropriate conditions, validating the model’s ability to capture complex spatiotemporal dynamics. The work provides a reliable computational framework for exploring fractional reaction-diffusion systems in two dimensions.

Legal Investigation of Financial Fraud Using Artificial Intelligence

Belal Zaqaibeh — 2026-02-12

Financial fraud remains a serious global challenge, requiring innovative investigative techniques that seamlessly integrate Artificial Intelligence (AI) with legal systems. Conventional fraud investigation methods, which largely rely on AI-driven tools combined with manual legal analysis, often suffer from limited efficiency, scalability, and adaptability when addressing evolving financial fraud patterns. In this study, a hybrid AI-driven framework is proposed to effectively bridge financial fraud investigation and legal analysis. The proposed model integrates AI-based analytical systems with legal reasoning mechanisms to enhance the accuracy and reliability of fraud investigations. The framework has been validated using real-world financial fraud cases and legal investigation records. The results demonstrate that integrating AI-driven systems with fraud investigation processes significantly improves detection accuracy and accelerates investigation timelines. Moreover, the proposed approach enhances the efficiency of financial fraud case analysis by leveraging AI for legal interpretation and decision support. This study also addresses key challenges associated with the application of explainable artificial intelligence (XAI) in judicial contexts. By narrowing the gap between AI-based fraud detection and legal enforcement, the proposed framework contributes to making fraud investigations more transparent, accountable, and legally defensible. The findings confirm that the approach is effective in strengthening forensic financial investigations, reducing investigative effort, and ensuring compliance with continuously evolving financial regulations. Furthermore, sustainability is emphasized as a critical consideration in modern fraud investigation practices. The adoption of artificial intelligence within sustainable legal frameworks may support long-term regulatory compliance and responsible financial governance.