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Current Issue [Vol. 12, No. 07] [July 2026]
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| Paper Title | :: | Deep Feature-Based Writer Constraint Modeling for Offline Signature Verification |
| Author Name | :: | Dr. Annapurna H |
| Country | :: | India |
| Page Number | :: | 01-10 |
Offline handwritten signature verification remains a challenging biometric authentication task due to significant intra-writer variations and the presence of skilled forgeries. This paper proposes a Deep Feature-Based Statistical Writer Constraint Model (WSCM) for offline signature verification by integrating deep feature extraction using a pretrained ResNet18 network with writer-specific statistical modeling. Deep feature representations are extracted from genuine reference signatures and used to construct feature-wise statistical constraints that characterize the expected variation of each writer. During verification, a query signature is evaluated against the learned statistical constraints, and a normalized Constraint Score is computed to determine its authenticity. The proposed approach eliminates the need for conventional classifier-based decision making while providing an interpretable verification process through feature-level constraint analysis. Experiments conducted on the CEDAR offline signature dataset under three training–testing configurations demonstrate that the proposed framework achieves a maximum verification accuracy of 84.70% with competitive False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER). The results demonstrate that writer-specific statistical constraint modeling provides an effective, computationally efficient, and explainable solution for offline signature verification.
Keywords: Deep feature extraction, Offline signature verification, ResNet18, Statistical constraint model, Writer-specific modeling.
Keywords: Deep feature extraction, Offline signature verification, ResNet18, Statistical constraint model, Writer-specific modeling.
[1]. R. Plamondon and G. Lorette, Automatic signature verification and writer identification—The state of the art, Pattern Recognition, 22(2), 1989, 107–131.
[2]. D. Impedovo and G. Pirlo, Automatic signature verification: The state of the art, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 38(5), 2008, 609–635.
[3]. L. G. Hafemann, R. Sabourin, and L. S. Oliveira, Offline handwritten signature verification—Literature review, ACM Computing Surveys, 50(2), 2017, Article 17.
[4]. K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, 770–778.
[5]. M. M. Hameed, R. Ahmad, M. L. M. Kiah, and G. Murtaza, Machine learning-based offline signature verification systems: A systematic review, Signal Processing: Image Communication, 93, 2021, 116139.
[2]. D. Impedovo and G. Pirlo, Automatic signature verification: The state of the art, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 38(5), 2008, 609–635.
[3]. L. G. Hafemann, R. Sabourin, and L. S. Oliveira, Offline handwritten signature verification—Literature review, ACM Computing Surveys, 50(2), 2017, Article 17.
[4]. K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, 770–778.
[5]. M. M. Hameed, R. Ahmad, M. L. M. Kiah, and G. Murtaza, Machine learning-based offline signature verification systems: A systematic review, Signal Processing: Image Communication, 93, 2021, 116139.
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| Paper Title | :: | Ozone Concentrations in Mexico City using Machine Learning with Multiple Time Series |
| Author Name | :: | M. Sc. Zenteno Jiménez José Roberto |
| Country | :: | Mexico |
| Page Number | :: | 11-34 |
Tropospheric ozone (O3) pollution in Mexico City represents a critical and persistent environmental challenge, characterized by nonlinear dynamics and a strong dependence on meteorological factors and chemical precursors. This study proposes a comprehensive methodological framework to analyze and predict O3 concentrations, overcoming the limitations of traditional univariate models. The methodology is structured in three phases. First, a Vector Autoregressive (VAR) model is applied along with Impulse-Response Functions (IRFs) to examine the stochastic interdependence and the system's response to shocks in the variables. Second, Machine Learning techniques, specifically Randomization, are implemented. Forest and Gradient Boosting ( GBoosting ) integrates key exogenous variables such as nitrogen dioxide (NO2) and daily temperature to capture complex nonlinear patterns. Finally, long-term sequential memory capacity is evaluated using a Deep Learning architecture based on Gated Recurrent Units (GRUs). Experimental results indicate that [mention your main finding here, e.g., the hybrid model or GBoosting reduced the mean squared error by X compared to the baseline models]. The analysis confirms that the inclusion of NO2 and temperature as exogenous predictors is crucial for model accuracy. This study demonstrates that combining econometrics and neural networks offers a robust tool for environmental monitoring and public health decision-making.
Keywords:Ozone (O3), Time Series, VAR, Random Forest , Gradient Boosting , GRU, Mexico City.
Keywords:Ozone (O3), Time Series, VAR, Random Forest , Gradient Boosting , GRU, Mexico City.
[1]. Vector Autoregression (VAR) and Impulse-Response Functions (IRF)
Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48(1), 1–48.
Hamilton, JD (1994). Time Series Analysis. Princeton University Press.
Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer.
[2]. Ensemble Models: Random Forest (RF) and Gradient Boosting (GBoosting)
Breiman , L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232.
[3]. Deep Learning: Gated Reurrent Units (GRU)
Cho, K., Van Merriënboer, B., Gulcehre , C., Bahdanau , D., Bougares , F., Schwenk , H., & Bengio , Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Tracnslation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Hochreiter , S., & Schmidhuber , J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
[4]. Machine Learning
Souza, A., Jimenez, JRZ, Oliveira Junior, JF., et al., 2025. Statistical Modeling of PM2.5 Concentrations: Prediction of Extreme Events and Evaluation of Advanced Methods for Air Quality Management. Journal of Atmospheric Science Research. 8(3): 67–92. DOI: https://doi.org/10.30564/jasr.v8i3.10878
Zenteno Jimenez Jose Roberto International Journal of Latest Research in Engineering and Technology (IJLRET) ISSN: 2454-5031 www.ijlret.com || Volume 11 - Issue 04 || April 2025 || PP. 09-25 Analysis of the Behavior of the Ozone Time Series in México City Using Machine Learning Trend 2010 - 2024
[5]. Hybrid Application References
Bakar, MAA, Ariff , NM, Nadzir , MSM, Ong, LW, & Suris , FNA (2022). Prediction of multivariate air quality time series data using long short-term memory network. Malaysian Journal of Fundamental and Applied Sciences, 18, 52–59.
HuiyongWu, Tongtong Yang, Hongkun Li & Ziwei Zhou , Air quality prediction model based on mRMR –RF feature selection and ISSA–LSTM, 2023 Aug 7;13:12825
Yu NieNga , Han Ying Limb, Ying ChyiChamc , Mohd Aftar Abu Bakara, Noratiqah Mohd Ariffa, Comparison Between LSTM, GRU and VARIMA in Forecasting of Air Quality Time Series Data, Malaysian Journal of Fundamental and Applied Sciences, Vol. 20 (2024) 1248-1260
Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48(1), 1–48.
Hamilton, JD (1994). Time Series Analysis. Princeton University Press.
Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer.
[2]. Ensemble Models: Random Forest (RF) and Gradient Boosting (GBoosting)
Breiman , L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232.
[3]. Deep Learning: Gated Reurrent Units (GRU)
Cho, K., Van Merriënboer, B., Gulcehre , C., Bahdanau , D., Bougares , F., Schwenk , H., & Bengio , Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Tracnslation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Hochreiter , S., & Schmidhuber , J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
[4]. Machine Learning
Souza, A., Jimenez, JRZ, Oliveira Junior, JF., et al., 2025. Statistical Modeling of PM2.5 Concentrations: Prediction of Extreme Events and Evaluation of Advanced Methods for Air Quality Management. Journal of Atmospheric Science Research. 8(3): 67–92. DOI: https://doi.org/10.30564/jasr.v8i3.10878
Zenteno Jimenez Jose Roberto International Journal of Latest Research in Engineering and Technology (IJLRET) ISSN: 2454-5031 www.ijlret.com || Volume 11 - Issue 04 || April 2025 || PP. 09-25 Analysis of the Behavior of the Ozone Time Series in México City Using Machine Learning Trend 2010 - 2024
[5]. Hybrid Application References
Bakar, MAA, Ariff , NM, Nadzir , MSM, Ong, LW, & Suris , FNA (2022). Prediction of multivariate air quality time series data using long short-term memory network. Malaysian Journal of Fundamental and Applied Sciences, 18, 52–59.
HuiyongWu, Tongtong Yang, Hongkun Li & Ziwei Zhou , Air quality prediction model based on mRMR –RF feature selection and ISSA–LSTM, 2023 Aug 7;13:12825
Yu NieNga , Han Ying Limb, Ying ChyiChamc , Mohd Aftar Abu Bakara, Noratiqah Mohd Ariffa, Comparison Between LSTM, GRU and VARIMA in Forecasting of Air Quality Time Series Data, Malaysian Journal of Fundamental and Applied Sciences, Vol. 20 (2024) 1248-1260
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| Paper Title | :: | The Linear Transformation Matrix associated with the ZJ Transformation – Applications |
| Author Name | :: | M. Sc. Zenteno Jiménez José Roberto |
| Country | :: | Mexico |
| Page Number | :: | 35-43 |
This article presents an alternative methodology based on Functional Analysis in Finite-Dimensional Spaces, which completely dispenses with classical analytical integration. By constructing a local linear space and using the Commutation Theorem for Linear Operators, we demonstrate that it is possible to encapsulate the dynamics of the transformation kernel 𝑒−𝑧/𝛽 𝑡 and the objective function within an Integrated Vector Basis.
Keywords:ZJ Transform, Linear Transformation, Matrix, Matrix Associated with the Linear Transformation, Integral Transform, Linear Algebra, Vector Spaces.
Keywords:ZJ Transform, Linear Transformation, Matrix, Matrix Associated with the Linear Transformation, Integral Transform, Linear Algebra, Vector Spaces.
[1]. Axler, S. (2015).Linear Algebra Done Right (3rd ed.). Springer. (Chapter 5: "Eigenvalues, Eigenvectors, and Diagonalizable Operators").
[2]. Friedberg, SH, Insel, AJ, & Spence, LE (2003). Linear Algebra (4th ed.). Prentice Hall. (Chapter 7, "Canonical Forms", The Existence of Jordan's Base).
[3]. Hoffman, K., & Kunze, R. (1971). Linear Algebra (2nd ed.). Prentice-Hall. (Chapter 3: "Linear Transformations ", Isomorphism between linear operators and matrices, Chapter 7, "The Rational and Jordan Forms ", the decomposition of operators).
[4]. Strang, G. (2006). Linear Algebra and Its Applications (4th ed.). Thomson Brooks/Cole. (Section 8.3 "The Jordan Form" relationship between equations differentials and the Jordan blocks).
[5]. Trefethen, LN (2000). Spectral Methods in MATLAB. SIAM. (Differential operations become differentiation matrices in function spaces).
[2]. Friedberg, SH, Insel, AJ, & Spence, LE (2003). Linear Algebra (4th ed.). Prentice Hall. (Chapter 7, "Canonical Forms", The Existence of Jordan's Base).
[3]. Hoffman, K., & Kunze, R. (1971). Linear Algebra (2nd ed.). Prentice-Hall. (Chapter 3: "Linear Transformations ", Isomorphism between linear operators and matrices, Chapter 7, "The Rational and Jordan Forms ", the decomposition of operators).
[4]. Strang, G. (2006). Linear Algebra and Its Applications (4th ed.). Thomson Brooks/Cole. (Section 8.3 "The Jordan Form" relationship between equations differentials and the Jordan blocks).
[5]. Trefethen, LN (2000). Spectral Methods in MATLAB. SIAM. (Differential operations become differentiation matrices in function spaces).
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| Paper Title | :: | From Digital Transformation to AI Adoption: A Three-Year Reflection on Organizational Adaptation and Sustainability in Chiang Rai Province, Thailand |
| Author Name | :: | Kasidit Chaiphawang || Sermsiri Nindum || Siripan Jeenaboonrueng || Niwest Jeenaboonrueng || Peeraya Cheunwong || Darunphop Udnan |
| Country | :: | Thailand |
| Page Number | :: | 44-52 |
Digital Transformation and Artificial Intelligence (AI) adoption have become essential drivers of organizational capability development and adaptation to technological change. This study examines the three-year evolutionary pathway of organizations in Chiang Rai Province, transitioning from Digital Transformation to AI Adoption, with a focus on technological capability, organizational adaptation, operational performance, and sustainability.
A comparative longitudinal research design was employed using secondary empirical data from two studies conducted during different transformation stages: digital transformation and organizational efficiency, and AI utilization for supply chain management under the BCG Economy Model. Comparative analysis and conceptual synthesis were applied to identify organizational transformation patterns.
The findings indicate that organizations initially developed digital capabilities through technology adoption, data management, process improvement, and human capability enhancement, leading to improved operational efficiency. Subsequently, organizations advanced toward AI adoption by applying AI for data analytics, prediction, decision support, and supply chain management, enhancing organizational agility and sustainable value creation.
This study proposes an integrated transformation framework: Digital Transformation Capability → AI Adoption Capability → Organizational Adaptation → Sustainable Organizational Performance. The findings provide theoretical and practical implications for organizations and policymakers in developing systematic digital and AI-driven transformation strategies.
Keywords:Digital Transformation; Artificial Intelligence Adoption; Organizational Adaptation; Supply Chain Management; Sustainability; Chiang Rai Province.
A comparative longitudinal research design was employed using secondary empirical data from two studies conducted during different transformation stages: digital transformation and organizational efficiency, and AI utilization for supply chain management under the BCG Economy Model. Comparative analysis and conceptual synthesis were applied to identify organizational transformation patterns.
The findings indicate that organizations initially developed digital capabilities through technology adoption, data management, process improvement, and human capability enhancement, leading to improved operational efficiency. Subsequently, organizations advanced toward AI adoption by applying AI for data analytics, prediction, decision support, and supply chain management, enhancing organizational agility and sustainable value creation.
This study proposes an integrated transformation framework: Digital Transformation Capability → AI Adoption Capability → Organizational Adaptation → Sustainable Organizational Performance. The findings provide theoretical and practical implications for organizations and policymakers in developing systematic digital and AI-driven transformation strategies.
Keywords:Digital Transformation; Artificial Intelligence Adoption; Organizational Adaptation; Supply Chain Management; Sustainability; Chiang Rai Province.
[1] G. Vial, Understanding digital transformation: A review and a research agenda, The Journal of Strategic Information Systems, 28(2), 2019, 118-144.
[2] P.C. Verhoef, T. Broekhuizen, Y. Bart, A. Bhattacharya, J.Q. Dong, N. Fabian, and M. Haenlein, Digital transformation: A multidisciplinary reflection and research agenda, Journal of Business Research, 122, 2021, 889-901.
[3] K. Chaiphawang, The Relationship and Explained Variation of Digital transformation toward the Efficiency of Organization in Chiang Rai Province, Business Administration and Management Journal Review, 16(2), 2024, 204-233.
[4] K. Chaiphawang, S. Nindum, S. Jeenaboonrueng, N. Jeenaboonrueng, and B. Benchakorn, Artificial intelligence-enabled supply chain competency as a driver of bio-circular-green (BCG) sustainability performance: Empirical evidence from Thailand. International Journal of Latest Research in Engineering and Management (IJLREM), 10(1), 2026, 19-21.
[5] D.J. Teece, Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance, Strategic Management Journal, 28(13), 2007, 1319-1350.
[2] P.C. Verhoef, T. Broekhuizen, Y. Bart, A. Bhattacharya, J.Q. Dong, N. Fabian, and M. Haenlein, Digital transformation: A multidisciplinary reflection and research agenda, Journal of Business Research, 122, 2021, 889-901.
[3] K. Chaiphawang, The Relationship and Explained Variation of Digital transformation toward the Efficiency of Organization in Chiang Rai Province, Business Administration and Management Journal Review, 16(2), 2024, 204-233.
[4] K. Chaiphawang, S. Nindum, S. Jeenaboonrueng, N. Jeenaboonrueng, and B. Benchakorn, Artificial intelligence-enabled supply chain competency as a driver of bio-circular-green (BCG) sustainability performance: Empirical evidence from Thailand. International Journal of Latest Research in Engineering and Management (IJLREM), 10(1), 2026, 19-21.
[5] D.J. Teece, Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance, Strategic Management Journal, 28(13), 2007, 1319-1350.