International Journal of Advances in Soft Computing and its 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.

Modeling Anomalous Diffusion with the Fractional Brusselator

Maysoon Qousini — 2026-01-18

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.

Research Article 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.

Legal Investigation of Financial Fraud Using Artificial Intelligence

Belal Zaqaibeh — 2026-01-13

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.