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رویکردی نوین برای شناسایی ترافیک رمزنگاری‌شده با استفاده از شبکه کلموگورف-آرنولد KANFlow: A Novel Approach to Encrypted Traffic Identification Using Kolmogorov-Arnold Network 1 1 یکی از مشکلات اصلی در شبکه‌های مدرن، طبقه‌بندی ترافیک رمزنگاری شده‌ای است که از جریان داده‌های پردازش‌نشده به‌دلیل مشکل در ساختار و الگوهای پنهان به‌وجود می‌آید. در این مقاله، چهارچوبی جدید به‌نام seqKAN معرفی شده‌است که با ترکیب شبکه‌های LSTM و یا CNN برای استخراج وابستگی‌های زمانی و معماری KAN برای مدل‌سازی روابط غیرخطی، عملکرد دقیقی در تحلیل جریان‌های شبکه ارائه می‌دهد. برای افزایش دقت و قابلیت اطمینان، این مدل با روش‌های RKHS و ODE ترکیب شده‌است و تأثیر مستقل و ترکیبی هر کدام، از طریق یک مطالعه حذفی[1] بررسی شده‌است. نتایج روی مجموعه‌داده‌های واقعی رمزنگاری‌شده نشان می‌دهد افزودن لایه RKHS نقش مؤثری در افزایش دقت و مقاومت مدل دارد؛ همچنین، با استفاده از تحلیل‌های کمی SHAP و LIME و قابلیت بصری‌سازی ذاتی  KAN، نحوه شناسایی ویژگی‌های موثر و یادگیری خودکار روابط معنادار در مدل بررسی شده‌است. معماری پیشنهادی تعادلی مناسب میان دقت، کارایی و تفسیرپذیری برقرار کرده و راهکاری مؤثر برای تحلیل هوشمند ترافیک شبکه ارائه می‌دهد. [1] Ablation Study 2 With the growing usage of encryption protocols like VPN, and Tor in digital communication, identification and classification of encrypted traffic has been one of the core issues in network security and traffic management. It is a major contributor to quality of service (QoS) assurance, resource allocation, user identification, and anomaly detection. But the sophistication of encrypted traffic structure and the vagueness of behavioral patterns have drastically decreased the effectiveness of conventional approaches like deep packet inspection (DPI). In spite of the progress, typical deep learning models also encounter great difficulties in dealing with encrypted data; they typically need a huge amount of labeled data and lack the capacity to analyze unbalanced data. To tackle these difficulties, this study proposes a novel hybrid architecture named seqKAN with enhanced interpretability and high accuracy. seqKAN integrates the temporal modeling capability of sequential networks like LSTM with the distinctive characteristics of Kolmogorov-Arnold networks (KAN), such as examining nonlinear relationships and intrinsic mathematical transparency. The framework also enjoys high flexibility and generalizability with the use of modules like Reproducible Hilbert Space Mapping (RKHS) and Neural Ordinary Differential Equations (ODE). Experiments are performed on benchmark datasets comprising Tor and VPN traffic (ISCXTor2016 and ISCXVPN2016). In this context, by meticulously filtering out the streams and addressing unbalanced data via class weighting, the model's stable performance is guaranteed. Ablation Study demonstrate that the inclusion of the RKHS layer significantly contributes to the improvement of the model's accuracy and robustness, particularly in encrypted settings. Among the models compared, the seqKAN approach delivered the best performance in F1 score and demonstrated clear superiority in the classification of encrypted traffic. In addition, the interpretability of the model was quantitatively and qualitatively demonstrated with standard feature importance analysis techniques (SHAP and LIME) and KAN's inherent visual analysis. seqKAN successfully automatically extracted key features and patterns in the flow packets and clearly explained each decision; this transparency evidently illustrates the model's superiority over typical methods. Finally, this research shows that the seqKAN architecture provides a comprehensive, efficient, and reliable solution for intelligent traffic analysis in complex network environments by creating a smart balance between accuracy, computational efficiency, and interpretability. The findings of this research highlight the high potential of KAN-based hybrid models as the basis for the next generation of transparent and reliable network security tools 18 3 2025/03/15 1403/12/25 2026/02/8 1404/11/19 علی رهنما ali rahnema دانشجوی دکترای فناوری اطلاعات، دانشگاه قم، قم، ایران ایران ali.rahnema70@gmail.com زهرا آخوداد zahra akhoodad هیأت علمی پژوهشگاه توسعه فناوری‌های پیشرفته، تهران، ایران ایران akhodad@rcdat.ir Encrypted Traffic Classification Kolmogorov-Arnold Network (KAN) Deep Learning Model Interpretability Ablation Study شناسایی ترافیک شبکه کلموگورف-آرنولد یادگیری عمیق 1. W. Wang, M. Zhu, J. Wang, X. Zeng, and Z. Yang, "End-To-end encrypted traffic classification with one-dimensional convolution neural networks," in 2017 IEEE International Conference on Intelligence and Security Informatics: Security and Big Data, ISI 2017, Aug. 2017, pp. 43-48. doi: 10.1109/ISI.2017.8004872. [DOI:10.1109/ISI.2017.8004872]##2. S. Zander, T. Nguyen, and G. Armitage, "Automated traffic classification and application identification using machine learning," Proc. - Conf. Local Comput. Networks, LCN, vol. 2005, pp. 250-257, 2005, doi: 10.1109/LCN.2005.35. [DOI:10.1109/LCN.2005.35]##3. B. Yamansavascilar, M. A. Guvensan, A. G. Yavuz, and M. E. Karsligil, "Application identification via network traffic classification," 2017 Int. Conf. Comput. Netw. Commun. ICNC 2017, pp. 843-848, 2017, doi: 10.1109/ICCNC.2017.7876241. [DOI:10.1109/ICCNC.2017.7876241]##4. N. V. Verde, G. Ateniese, E. Gabrielli, L. V. Mancini, and A. Spognardi, "No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone," Feb. 2014, [Online]. Available: http://arxiv.org/abs/1402.1940 [DOI:10.1109/ICDCS.2014.30]##5. M. Conti, L. V. Mancini, R. Spolaor, and N. V. Verde, "Analyzing Android Encrypted Network Traffic to Identify User Actions," IEEE Trans. Inf. Forensics Secur., vol. 11, no. 1, pp. 114-125, Jan. 2016, doi: 10.1109/TIFS.2015.2478741. [DOI:10.1109/TIFS.2015.2478741]##6. M. Lotfollahi, R. S. H. Zade, M. J. Siavoshani, and M. Saberian, "Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning," Sep. 2017, [Online]. Available: http://arxiv.org/abs/1709.02656##7. R. Dubin, A. Dvir, O. Pele, and O. Hadar, "I Know What You Saw Last Minute - Encrypted HTTP Adaptive Video Streaming Title Classification," Feb. 2016, doi: 10.1109/TIFS.2017.2730819. [DOI:10.1109/TIFS.2017.2730819]##8. R. Schuster, V. Shmatikov, and E. Tromer, "Beauty and the burst: Remote identification of encrypted video streams," Proc. 26th USENIX Secur. Symp., pp. 1357-1374, 2017.##9. T. Shapira and Y. Shavitt, "FlowPic: A Generic Representation for Encrypted Traffic Classification and Applications Identification," IEEE Trans. Netw. Serv. Manag., vol. 18, no. 2, pp. 1218-1232, Jun. 2021, doi: 10.1109/TNSM.2021.3071441. [DOI:10.1109/TNSM.2021.3071441]##10. Z. Cao, G. Xiong, Y. Zhao, Z. Li, and L. Guo, "A survey on encrypted traffic classification," Commun. Comput. Inf. Sci., vol. 490, pp. 73-81, 2014, doi: 10.1007/978-3-662-45670-5_8. [DOI:10.1007/978-3-662-45670-5_8]##11. S. Roy, T. Shapira, and Y. Shavitt, "Fast and lean encrypted Internet traffic classification," Comput. Commun., vol. 186, pp. 166-173, Mar. 2022, doi: 10.1016/j.comcom.2022.02.003. [DOI:10.1016/j.comcom.2022.02.003]##12. Z. Chen, K. He, J. Li, and Y. Geng, "Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks," Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017, vol. 2018-Janua, pp. 1271-1276, 2017, doi: 10.1109/BigData.2017.8258054. [DOI:10.1109/BigData.2017.8258054]##13. X. Lin, G. Xiong, G. Gou, Z. Li, J. Shi, and J. Yu, "ET-BERT : A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification," vol. 1, pp. 633-642, doi: 10.1145/3485447.3512217. [DOI:10.1145/3485447.3512217]##14. Z. Liu, "TransECA-Net : A Transformer-Based Model for Encrypted Traffic Classification," 2025. [DOI:10.3390/app15062977]##15. Koukoulis, I. Syrigos, and T. Korakis, "Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems".##16. T. T. T. Nguyen and G. Armitage, "A Semi-Supervised Learning Framework for Encrypted Traffic Classification Based on Supervised Contrastive Learning and Masked Sequence Prediction Tasks," vol. 10, no. 4, pp. 56-76, 2025, doi: 10.1109/ICAACE65325.2025.11020246. [DOI:10.1109/ICAACE65325.2025.11020246]##17. E. Horowicz, T. Shapira, and Y. Shavitt, "Self-Supervised Traffic Classification : Flow Embedding and Few-Shot Solutions," IEEE Trans. Netw. Serv. Manag., vol. PP, no. September, p. 1, 2024, doi: 10.1109/TNSM.2024.3366848. [DOI:10.1109/TNSM.2024.3366848]##18. T. Shapira and Y. Shavitt, "FlowPic: Encrypted Internet Traffic Classification is as Easy as Image Recognition," in INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019, Apr. 2019, pp. 680-687. doi: 10.1109/INFCOMW.2019.8845315. [DOI:10.1109/INFCOMW.2019.8845315]##19. S. Yu and Y. Won, "A survey of methods for encrypted network traffic fingerprinting," Math. Biosci. Eng., vol. 20, no. 2, pp. 2183-2202, 2023, doi: 10.3934/mbe.2023101. [DOI:10.3934/mbe.2023101]##20. L. D. Manocchio, S. Layeghy, W. W. Lo, G. K. Kulatilleke, M. Sarhan, and M. Portmann, "FlowTransformer : A transformer framework for flow-based network intrusion detection systems," Expert Syst. Appl., vol. 241, no. July 2023, p. 122564, 2024, doi: 10.1016/j.eswa.2023.122564. [DOI:10.1016/j.eswa.2023.122564]##21. C. Sun, B. Chen, Y. Bu, S. Zhang, and D. Zhang, "Lightweight Traffic Classification Model Based on Deep Learning," vol. 2022, no. 2, 2022, doi: 10.1155/2022/3539919. [DOI:10.1155/2022/3539919]##22. G. Draper-Gil, A. H. Lashkari, M. S. I. Mamun, and A. A. Ghorbani, "Characterization of encrypted and VPN traffic using time-related features," in ICISSP 2016 - Proceedings of the 2nd International Conference on Information Systems Security and Privacy, 2016, pp. 407-414. doi: 10.5220/0005740704070414. [DOI:10.5220/0005740704070414]##23. H. Lashkari, G. D. Gil, M. S. I. Mamun, and A. A. Ghorbani, "Characterization of tor traffic using time based features," in ICISSP 2017 - Proceedings of the 3rd International Conference on Information Systems Security and Privacy, 2017, vol. 2017-January, pp. 253-262. doi: 10.5220/0006105602530262. [DOI:10.5220/0006105602530262]##24. Rahimi and B. Recht, "Random Features for Large-Scale Kernel Machines," no. 1, pp. 1-8.##1. W. Wang, M. Zhu, J. Wang, X. Zeng, and Z. Yang, "End-To-end encrypted traffic classification with one-dimensional convolution neural networks," in 2017 IEEE International Conference on Intelligence and Security Informatics: Security and Big Data, ISI 2017, Aug. 2017, pp. 43-48. doi: 10.1109/ISI.2017.8004872. [DOI:10.1109/ISI.2017.8004872]##2. S. Zander, T. Nguyen, and G. Armitage, "Automated traffic classification and application identification using machine learning," Proc. - Conf. Local Comput. Networks, LCN, vol. 2005, pp. 250-257, 2005, doi: 10.1109/LCN.2005.35. [DOI:10.1109/LCN.2005.35]##3. B. Yamansavascilar, M. A. Guvensan, A. G. Yavuz, and M. E. Karsligil, "Application identification via network traffic classification," 2017 Int. Conf. Comput. Netw. Commun. ICNC 2017, pp. 843-848, 2017, doi: 10.1109/ICCNC.2017.7876241. [DOI:10.1109/ICCNC.2017.7876241]##4. N. V. Verde, G. Ateniese, E. Gabrielli, L. V. Mancini, and A. Spognardi, "No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone," Feb. 2014, [Online]. Available: http://arxiv.org/abs/1402.1940 [DOI:10.1109/ICDCS.2014.30]##5. M. Conti, L. V. Mancini, R. Spolaor, and N. V. Verde, "Analyzing Android Encrypted Network Traffic to Identify User Actions," IEEE Trans. Inf. Forensics Secur., vol. 11, no. 1, pp. 114-125, Jan. 2016, doi: 10.1109/TIFS.2015.2478741. [DOI:10.1109/TIFS.2015.2478741]##6. M. Lotfollahi, R. S. H. Zade, M. J. Siavoshani, and M. Saberian, "Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning," Sep. 2017, [Online]. Available: http://arxiv.org/abs/1709.02656##7. R. Dubin, A. Dvir, O. Pele, and O. Hadar, "I Know What You Saw Last Minute - Encrypted HTTP Adaptive Video Streaming Title Classification," Feb. 2016, doi: 10.1109/TIFS.2017.2730819. [DOI:10.1109/TIFS.2017.2730819]##8. R. Schuster, V. Shmatikov, and E. Tromer, "Beauty and the burst: Remote identification of encrypted video streams," Proc. 26th USENIX Secur. Symp., pp. 1357-1374, 2017.##9. T. Shapira and Y. Shavitt, "FlowPic: A Generic Representation for Encrypted Traffic Classification and Applications Identification," IEEE Trans. Netw. Serv. Manag., vol. 18, no. 2, pp. 1218-1232, Jun. 2021, doi: 10.1109/TNSM.2021.3071441. [DOI:10.1109/TNSM.2021.3071441]##10. Z. Cao, G. Xiong, Y. Zhao, Z. Li, and L. Guo, "A survey on encrypted traffic classification," Commun. Comput. Inf. Sci., vol. 490, pp. 73-81, 2014, doi: 10.1007/978-3-662-45670-5_8. [DOI:10.1007/978-3-662-45670-5_8]##11. S. Roy, T. Shapira, and Y. Shavitt, "Fast and lean encrypted Internet traffic classification," Comput. Commun., vol. 186, pp. 166-173, Mar. 2022, doi: 10.1016/j.comcom.2022.02.003. [DOI:10.1016/j.comcom.2022.02.003]##12. Z. Chen, K. He, J. Li, and Y. Geng, "Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks," Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017, vol. 2018-Janua, pp. 1271-1276, 2017, doi: 10.1109/BigData.2017.8258054. [DOI:10.1109/BigData.2017.8258054]##13. X. Lin, G. Xiong, G. Gou, Z. Li, J. Shi, and J. Yu, "ET-BERT : A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification," vol. 1, pp. 633-642, doi: 10.1145/3485447.3512217. [DOI:10.1145/3485447.3512217]##14. Z. Liu, "TransECA-Net : A Transformer-Based Model for Encrypted Traffic Classification," 2025. [DOI:10.3390/app15062977]##15. Koukoulis, I. Syrigos, and T. Korakis, "Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems".##16. T. T. T. Nguyen and G. Armitage, "A Semi-Supervised Learning Framework for Encrypted Traffic Classification Based on Supervised Contrastive Learning and Masked Sequence Prediction Tasks," vol. 10, no. 4, pp. 56-76, 2025, doi: 10.1109/ICAACE65325.2025.11020246. [DOI:10.1109/ICAACE65325.2025.11020246]##17. E. Horowicz, T. Shapira, and Y. Shavitt, "Self-Supervised Traffic Classification : Flow Embedding and Few-Shot Solutions," IEEE Trans. Netw. Serv. Manag., vol. PP, no. September, p. 1, 2024, doi: 10.1109/TNSM.2024.3366848. [DOI:10.1109/TNSM.2024.3366848]##18. T. Shapira and Y. Shavitt, "FlowPic: Encrypted Internet Traffic Classification is as Easy as Image Recognition," in INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019, Apr. 2019, pp. 680-687. doi: 10.1109/INFCOMW.2019.8845315. [DOI:10.1109/INFCOMW.2019.8845315]##19. S. Yu and Y. Won, "A survey of methods for encrypted network traffic fingerprinting," Math. Biosci. Eng., vol. 20, no. 2, pp. 2183-2202, 2023, doi: 10.3934/mbe.2023101. [DOI:10.3934/mbe.2023101]##20. L. D. Manocchio, S. Layeghy, W. W. Lo, G. K. Kulatilleke, M. Sarhan, and M. Portmann, "FlowTransformer : A transformer framework for flow-based network intrusion detection systems," Expert Syst. Appl., vol. 241, no. July 2023, p. 122564, 2024, doi: 10.1016/j.eswa.2023.122564. [DOI:10.1016/j.eswa.2023.122564]##21. C. Sun, B. Chen, Y. Bu, S. Zhang, and D. Zhang, "Lightweight Traffic Classification Model Based on Deep Learning," vol. 2022, no. 2, 2022, doi: 10.1155/2022/3539919. [DOI:10.1155/2022/3539919]##22. G. Draper-Gil, A. H. Lashkari, M. S. I. Mamun, and A. A. Ghorbani, "Characterization of encrypted and VPN traffic using time-related features," in ICISSP 2016 - Proceedings of the 2nd International Conference on Information Systems Security and Privacy, 2016, pp. 407-414. doi: 10.5220/0005740704070414. [DOI:10.5220/0005740704070414]##23. H. Lashkari, G. D. Gil, M. S. I. Mamun, and A. A. Ghorbani, "Characterization of tor traffic using time based features," in ICISSP 2017 - Proceedings of the 3rd International Conference on Information Systems Security and Privacy, 2017, vol. 2017-January, pp. 253-262. doi: 10.5220/0006105602530262. [DOI:10.5220/0006105602530262]##24. Rahimi and B. Recht, "Random Features for Large-Scale Kernel Machines," no. 1, pp. 1-8. ##

تشخیص بیماری ADHD به‌وسیله پردازش غیرخطی پاسخ برانگیخته شنوایی ساقه مغز (ABR) ADHD recognition by processing nonlinear features of ABR based on a new innovative method for extracting Geometry features from phase space trajectory 1 1 تشخیص ADHD در سال‌های نخست تولد، برای طراحی برنامه درمانی بهتر برای بیماران ضروری است؛ بر اساس آخرین پژوهش‌ها، تشخیص این اختلال از روی ABR[1] روشی مؤثر است. باتوجه به اینکه در اصل ABR غیرخطی است، پردازش غیرخطی می‌تواند برای طبقه‌بندی گروه‌های نرمال و [2]ADHD از یکدیگر مؤثرتر باشد. در این پژوهش، سیگنال ABR دو گروه از کودکان شامل 37 کودک عادی و 31 کودک دارای اختلال ADHD ثبت شده‌است. در ابتدا سیگنال زمانی دوبعدی ABR با روش جای‌گذاری[3] به سیگنال سه‌بعدی تبدیل شد تا از روی آن فضای فاز سیگنال ترسیم شود. در فضای فاز به بررسی صحت این فرض که ممکن است سیگنال‌های نرمال و ADHD در زمان‌های متفاوت در نوع حضور و حرکت بین محوطه‌های مختلف فضای فاز تفاوت داشته باشند پرداخته شد؛ به این منظور ابتدا فضای فاز به المان‌های حجم[4] مساوی بسته به طول هر سیگنال تقسیم‌بندی شد و سپس با تعریف و استخراج ویژگی‌های هندسی جدید در فضای فاز، روش جدیدی برای طبقه‌بندی دو گروه ارائه شده‌است. این رویکرد شامل بررسی اشغال فضای وکسل‌ها به‌وسیله نقاط ترژکتوری داده‌ها در فضای فاز است؛ درنهایت کارایی این روش و ویژگی‌ها ارزیابی شد و بهترین نتیجه در ویژگی مینیمم محلی استخراج‌شده با روش جدید و با استفاده از طبقه‌بندی‌کننده‌های KNN و SVM مشاهده شد. بهترین دقت حدود 98.53 است که افزایش قابل‌توجهی در دقت را در مقایسه با رویکردهای پردازش خطی نشان می‌دهد.   [1] Auditory Brainstem Response [2] Attention Deficit Hyperactivity Disorder [3] Embedding [4] Voxels 2 ADHD recognition in first years after birth is essential to consider a better treatment plan for patients and consequently helps children who suffer from this disorder to promote their communication abilities. According to the latest researches, ADHD recognition through ABR is a useful method for this purpose instead of surveys or other oral methods. Considering the basis of ABR which is nonlinear, so nonlinear processing methods can be more effective for classification normal and ADHD groups from each other than other linear classifications. In this paper ABR signal of two groups of children including 37 normal and 31 with ADHD disorder have been recorded in a rehabilitation center. To that end, in this study firstly by using embedding method, the two-dimensional ABR signal converted to three-dimensional signals to be ready for drawing and calculating the phase space arguments. In the phase space the accuracy of this hypothesis was checked that if ADHD and normal signals in different time would show different behavior in occupying and movement through the voxels in the phase space, for this purpose at first the phase space data should be produced, but it is important to have access to the raw data of phase space because if the methods that only print the phase space were been used the access of raw data would be impossible, Actually using these method for drawing phase space is in conflict with this study’s objectives, because it is not important to just draw the phase space of normal or ADHD signals but it is necessary to divide the phase space of individual signals to equal voxels and then determine that any sample of trajectory points is located in which of these voxels over the time. So, it is crucial to access the raw data of each axis of phase space of any signal to use them for diving that volume to voxels and then trace the attendance of trajectory in these voxels and make a new mapped signal that introduce the voxel number of each point of trajectory against time.  Therefore the source data of any axis of any signal has been produced by embedding method that it is discussed before, it means that we replace any x(t) by {x(t), x(t+lag), x(t+2 lag)} and they are three axis of new three dimensional space, so we can use these axis for diving this space to different voxels. The phase space should be divided to equal voxels depending on the length of each signal, then a novel method of classification has been developed by extracting new geometry features in the phase space. This approach includes dividing three-dimensional phase space to equal voxels and checking space voxel’s occupation by trajectory points in these voxels. As trajectory points has the same time sequence as time series of original signals so trajectories can be used in mentioned new method to check time occupation of trajectories in phase space. Then by changing each sample value by its voxel number of that sample, a mapping method was developed, this new mapped signal is the basis of later analysis and feature extractions in this study. Briefly, this mapped signal has been formed by taking some steps including producing the phase space signal from ABR, dividing the phase space to voxels and predicating voxel numbers to each sample. Then to find some distinctions between ADHD and normal group mapped signal, four groups of features have been developed in this study including temporal features, extremum features, histogram features, spatial occupational, Lyapunov features. Temporal features are some common features including min, max, mean, median, variance, skewness, kurtosis. But further in accuracy analysis of project it has been cleared that time or sample limited min and max (called extremum features) are more effective and can make a better distinction result. Histogram features focus is on the most repeated voxel numbers which means which voxels has been most occupied by trajectory during its journey through the phased space. Spatial occupational features deal with the span or extension of total trajectory path by introducing some features like the biggest or smallest voxel number which has been occupied by at least one point of trajectory, as it is can be interpreted from these two latter features, the subtraction of the biggest and smallest occupied voxel number can represent a vision of extension of trajectory. Another feature in this category is the total voxels quantity which are occupied by at least one of the points of the trajectory. The Lyapunov related feature also calculate the total number of non-zero voxels in a specific duration of time than can represent the chaotic grade of the signal, beside the difference of two adjacent time duration Lyapunov feature and show the gradient of chaotic level of the signal that shows the Lyapunov gradient. Finally, efficiency of this method and features have been evaluated and best result observed in local minimum feature extracted by the new method and using KNN and SVM classifiers, the best accuracy is about 98.53 that shows a significant increase in accuracy in comparison with linear processing approaches. In the other words, this distinction gained from the minimum feature shows that there are some places among the lower voxel numbers in trajectories which Normal group of signals desire to occupy more than ADHD group. According to anatomical data which is extractable from ABR signal in different points of time is reachable in the final mapped data also, because the time sequence of first samples is saved in the final data. It means that we can check the distinction areas in phase space and adopt them with time or their anatomical generating source. From the physiologic point of view, these distinction areas in the phase space are correspond to the activity of the primary auditory neurons in the cochlear nerve and lower levels of the brainstem. 38 19 2025/03/152024/08/3 1403/5/13 2026/02/82025/07/21 1404/4/30 علی اکبر علی دوست قادیکلایی Ali Akbar Alidoust Ghadikolayi دانشجوی دکتری مهندسی پزشکی دانشکده فناوری‌های نوین، واحد تهران جنوب، دانشگاه آزاد، تهران، ایران ایران eng.alidoost@gmail.com علی مطیع نصرآبادی Ali Motie Nasrabadi استاد گروه مهندسی پزشکی، دانشکده فنی و مهندسی دانشگاه شاهد، تهران، ایران ایران nasrabadi@shahed.ac.ir سعید ملایری Saeed Malayeri مدرس پاره وقت، دانشگاه علوم پزشکی و خدمات بهداشتی درمانی ایران، دانشکده علوم توانبخشی، تهران، ایران ایران malayeris@gmail.com Attention-Deficit Hyperactivity Disorder Auditory Brainstem Response Phase Space Trajectory اختلال بیش‌فعالی و نقصان توجه پاسخ ساقه مغز به تحریک شنوایی فضای فاز ترژکتوری 1. E. Lahat, E. Avital, J. Ban, M. Berkovitch, A. Arlazoroff, and M. Aladjem, "BAEP studies in children with attention deficit disorder," Developmental Medicine & Child Neurology, vol. 37, (2). 119-123, 1995. [DOI:10.1111/j.1469-8749.1995.tb11980.x]##2. A. Puente, A. Ysunza, M. Pamplona, A. Silva-Rojas, and C. Lara, "Short latency and long latency auditory evoked responses in children with attention deficit disorder," International Journal of Pediatric Otorhinolaryngology, vol. 62, no. 1, pp. 45-51, 2002. [DOI:10.1016/S0165-5876(01)00596-1]##3. J. B. Firszt, R. D. Chambers, N. Kraus, and R. M. Reeder, "Neurophysiology of cochlear implant users I: Effects of stimulus current level and electrode site on the electrical ABR, MLR, and N1-P2 response," Ear and Hearing, vol. 23, no. 6, pp. 502-515, 2002. [DOI:10.1097/00003446-200212000-00002]##4. N. Russo, T. Nicol, G. Musacchia, and N. Kraus, "Brainstem responses to speech syllables," Clinical Neurophysiology, vol. 115, no. 9, pp. 2021-2030, 2004. [DOI:10.1016/j.clinph.2004.04.003]##5. B. Wible, T. Nicol, and N. Kraus, "Atypical brainstem representation of onset and formant structure of speech sounds in children with language-based learning problems," Biological Psychology, vol. 67, no. 3, pp. 299-317, 2004. [DOI:10.1016/j.biopsycho.2004.02.002]##6. N. Russo, T. Nicol, G. Musacchia, and N. Kraus, "Brainstem responses to speech syllables," Clinical Neurophysiology, vol. 115, no. 9, pp. 2021-2030, 2004. [DOI:10.1016/j.clinph.2004.04.003]##7. N. Russo, T. Nicol, B. Trommer, S. Zecker, and N. Kraus, "Brainstem transcription of speech is disrupted in children with autism spectrum disorders," Developmental Science, vol. 12, no. 4, pp. 557-567, 2009. [DOI:10.1111/j.1467-7687.2008.00790.x]##8. N. Vaney, Y. Anjana, and F. Khaliq, "No auditory conduction abnormality in children with attention deficit hyperactivity disorder," Functional Neurology, vol. 26, no. 3, p. 159, 2011.##۹. اسماعیل‌پور، ز. (۱۳۹۱)، «بکارگیری روش‌های غیرخطی در تشخیص و طبقه‌بندی کودکان مبتلا به ADHD با استفاده از پاسخ شنوایی ساقه مغز و طبقه‌بندی کننده Wavelet-SVM»، پایان‌نامه کارشناسی ارشد، دانشگاه شاهد، تهران، ایران.##9. Z. Esmailpour, "Application of nonlinear methods in diagnosis and classification of children with ADHD using auditory brainstem response and Wavelet-SVM classifier," M.S. thesis, Shahed University, Tehran, Iran, 2012.##10. R. Sharma and R. B. Pachori, "Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions," Expert Systems with Applications, vol. 42, no. 3, pp. 1106-1117, 2015. [DOI:10.1016/j.eswa.2014.08.030]##11. R. Y. Karimui and S. Azadi, "Cardiac arrhythmia classification using the phase space sorted by Poincare sections," Biocybernetics and Biomedical Engineering, vol. 37, no. 4, pp. 690-700, 2017. [DOI:10.1016/j.bbe.2017.08.005]##12. R. Y. Karimui, S. Azadi, and P. Keshavarzi, "The ADHD effect on the high-dimensional phase space trajectories of EEG signals," Chaos, Solitons & Fractals, vol. 121, pp. 39-49, 2019. [DOI:10.1016/j.chaos.2019.02.004]##13. M. Z. Soroush, K. Maghooli, S. K. Setarehdan, and A. M. Nasrabadi, "Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory," Medical Hypotheses, vol. 127, pp. 34-45, 2019. [DOI:10.1016/j.mehy.2019.03.025]##14. A. M. Nasrabadi, "Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory," Medical Hypotheses, vol. 127, pp. 34-45, 2019. [DOI:10.1016/j.mehy.2019.03.025]##15. H. Ansarinasab, A. Mohammadi, and F. Rahmani, "Nonlinear analysis of brain functional networks in children with ADHD using correlation probability of recurrences (CPR)," Journal of Neuroscience Methods, vol. 398, pp. 109852-109863, 2023.##16. K. Curtin, M. Wang, and R. Stern, "Application of recurrence quantification analysis (RQA) to explore dynamic patterns of default mode network in children with ADHD using fMRI," Biological Psychiatry, vol. 92, no. 4, 257-268, 2022.##17. J. Sun, Y. Zhao, and K. Chen, "Investigation of speech auditory brainstem response (speech-ABR) characteristics in preschool children with ADHD," Journal of Speech, Language, and Hearing Research, vol. 67, 2, 452-461, 2024. [DOI:10.1044/2024_JSLHR-23-00454]##۱۸. مهرنام، ع.، احمدی، م.، ربیعی، ع. (۱۳۹۲)، «تحلیل سیگنال‌های مغزی با استفاده از نمودارهای بازگشتی و کاربرد آن در شناسایی پاسخ‌های تک‌آزمونERP»، نشریه پردازش علائم و داده‌ها، ۱۰(۴)، صص ۱-۱۰.##18. A. Mehrnam, M. Ahmadi, and A. Rabiei, "Analysis of brain signals using recurrence plots and its application in single-trial ERP detection," Journal of Signal and Data Processing, vol. 10, no. 4, pp. 1-10, 2013.##۱۹. جعفری، ز. (۱۳۸۹)، درسنامه پاسخ‌های برانگیخته شنوایی، تهران: انتشارات دانژه.##19. Z. Jafari, Auditory Evoked Responses, Tehran, Iran: Danjeh Publications, 2010.##1. E. Lahat, E. Avital, J. Ban, M. Berkovitch, A. Arlazoroff, and M. Aladjem, "BAEP studies in children with attention deficit disorder," Developmental Medicine & Child Neurology, vol. 37, (2). 119-123, 1995. [DOI:10.1111/j.1469-8749.1995.tb11980.x]##2. A. Puente, A. Ysunza, M. Pamplona, A. Silva-Rojas, and C. Lara, "Short latency and long latency auditory evoked responses in children with attention deficit disorder," International Journal of Pediatric Otorhinolaryngology, vol. 62, no. 1, pp. 45-51, 2002. [DOI:10.1016/S0165-5876(01)00596-1]##3. J. B. Firszt, R. D. Chambers, N. Kraus, and R. M. Reeder, "Neurophysiology of cochlear implant users I: Effects of stimulus current level and electrode site on the electrical ABR, MLR, and N1-P2 response," Ear and Hearing, vol. 23, no. 6, pp. 502-515, 2002. [DOI:10.1097/00003446-200212000-00002]##4. N. Russo, T. Nicol, G. Musacchia, and N. Kraus, "Brainstem responses to speech syllables," Clinical Neurophysiology, vol. 115, no. 9, pp. 2021-2030, 2004. [DOI:10.1016/j.clinph.2004.04.003]##5. B. Wible, T. Nicol, and N. Kraus, "Atypical brainstem representation of onset and formant structure of speech sounds in children with language-based learning problems," Biological Psychology, vol. 67, no. 3, pp. 299-317, 2004. [DOI:10.1016/j.biopsycho.2004.02.002]##6. N. Russo, T. Nicol, G. Musacchia, and N. Kraus, "Brainstem responses to speech syllables," Clinical Neurophysiology, vol. 115, no. 9, pp. 2021-2030, 2004. [DOI:10.1016/j.clinph.2004.04.003]##7. N. Russo, T. Nicol, B. Trommer, S. Zecker, and N. Kraus, "Brainstem transcription of speech is disrupted in children with autism spectrum disorders," Developmental Science, vol. 12, no. 4, pp. 557-567, 2009. [DOI:10.1111/j.1467-7687.2008.00790.x]##8. N. Vaney, Y. Anjana, and F. Khaliq, "No auditory conduction abnormality in children with attention deficit hyperactivity disorder," Functional Neurology, vol. 26, no. 3, p. 159, 2011.##۹. اسماعیل‌پور، ز. (۱۳۹۱)، «بکارگیری روش‌های غیرخطی در تشخیص و طبقه‌بندی کودکان مبتلا به ADHD با استفاده از پاسخ شنوایی ساقه مغز و طبقه‌بندی کننده Wavelet-SVM»، پایان‌نامه کارشناسی ارشد، دانشگاه شاهد، تهران، ایران.##9. Z. Esmailpour, "Application of nonlinear methods in diagnosis and classification of children with ADHD using auditory brainstem response and Wavelet-SVM classifier," M.S. thesis, Shahed University, Tehran, Iran, 2012.##10. R. Sharma and R. B. Pachori, "Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions," Expert Systems with Applications, vol. 42, no. 3, pp. 1106-1117, 2015. [DOI:10.1016/j.eswa.2014.08.030]##11. R. Y. Karimui and S. Azadi, "Cardiac arrhythmia classification using the phase space sorted by Poincare sections," Biocybernetics and Biomedical Engineering, vol. 37, no. 4, pp. 690-700, 2017. [DOI:10.1016/j.bbe.2017.08.005]##12. R. Y. Karimui, S. Azadi, and P. Keshavarzi, "The ADHD effect on the high-dimensional phase space trajectories of EEG signals," Chaos, Solitons & Fractals, vol. 121, pp. 39-49, 2019. [DOI:10.1016/j.chaos.2019.02.004]##13. M. Z. Soroush, K. Maghooli, S. K. Setarehdan, and A. M. Nasrabadi, "Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory," Medical Hypotheses, vol. 127, pp. 34-45, 2019. [DOI:10.1016/j.mehy.2019.03.025]##14. A. M. Nasrabadi, "Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory," Medical Hypotheses, vol. 127, pp. 34-45, 2019. [DOI:10.1016/j.mehy.2019.03.025]##15. H. Ansarinasab, A. Mohammadi, and F. Rahmani, "Nonlinear analysis of brain functional networks in children with ADHD using correlation probability of recurrences (CPR)," Journal of Neuroscience Methods, vol. 398, pp. 109852-109863, 2023.##16. K. Curtin, M. Wang, and R. Stern, "Application of recurrence quantification analysis (RQA) to explore dynamic patterns of default mode network in children with ADHD using fMRI," Biological Psychiatry, vol. 92, no. 4, 257-268, 2022.##17. J. Sun, Y. Zhao, and K. Chen, "Investigation of speech auditory brainstem response (speech-ABR) characteristics in preschool children with ADHD," Journal of Speech, Language, and Hearing Research, vol. 67, 2, 452-461, 2024. [DOI:10.1044/2024_JSLHR-23-00454]##۱۸. مهرنام، ع.، احمدی، م.، ربیعی، ع. (۱۳۹۲)، «تحلیل سیگنال‌های مغزی با استفاده از نمودارهای بازگشتی و کاربرد آن در شناسایی پاسخ‌های تک‌آزمونERP»، نشریه پردازش علائم و داده‌ها، ۱۰(۴)، صص ۱-۱۰.##18. A. Mehrnam, M. Ahmadi, and A. Rabiei, "Analysis of brain signals using recurrence plots and its application in single-trial ERP detection," Journal of Signal and Data Processing, vol. 10, no. 4, pp. 1-10, 2013.##۱۹. جعفری، ز. (۱۳۸۹)، درسنامه پاسخ‌های برانگیخته شنوایی، تهران: انتشارات دانژه.##19. Z. Jafari, Auditory Evoked Responses, Tehran, Iran: Danjeh Publications, 2010. ##

بخش‌بندی معنایی بی‌ناظر تصاویر RGB-D با استفاده از ترکیب روش برش گراف و میدان تصادفی شرطی Unsupervised Semantic Segmentation of RGB-D Images Using Combination of Conditional Random Field with Graph Cuts 1 1 هدف بخش­بندی معنایی، اختصاص برچسب متناسب به مجموعه‌ای از پیکسل‌های یک شی در یک تصویر با توجه به مشخصات ظاهری و معنایی آن است. این مسئله یکی از چالش برانگیزترین کارها در علم پردازش تصویر و بینایی ماشین است و در سال‌های اخیر بسیار مورد توجه جامعه بینایی ماشین قرار گرفته است. در این مقاله، روشی برای بخش­بندی معنایی تصاویر RGB-D به‌صورت لایه‌به‌لایه ارائه شده‌است. الگوریتم پیشنهادی، ویژگی‌های ظاهری و اطلاعات عمق را در یک مدل میدان تصادفی شرطی (CRF) بدون نظارت یک‌پارچه می‌کند و از یک روش برش گراف کمک می‌گیرد تا یک صحنه را به لایه‌های منسجم و معنادار تقسیم کند. روش پیشنهادی از برش‌های گراف برای بهینه‌سازی فرایند برچسب‌گذاری استفاده می‌کند. در این مقاله برای ارزیابی عملکرد روش پیشنهادی از نظر کمی و کیفی از دو مجموعه‌داده مختلف استفاده شده‌است که هریک ویژگی‌های منحصربه‌فردی دارند؛ همچنین برای مقایسه روش پیشنهادی نتایج به‌دست‌آمده با هشت روش بخش‌بندی معنایی نظارت‌شده و بدون ناظر دیگر مقایسه شده‌اند. نتایج آنالیزها نشان می‌دهد که CRF بدون نظارت می‌تواند به‌اندازه روش‌های نظارت‌شده دقیق باشد و در بسیاری از موارد حتی می‌تواند بهتر از سایر روش‌های بخش‌بندی عمل کند. 2 Semantic segmentation seeks to give a set of pixels depicting an object in an image suitable labels depending on their appearance and semantic characteristics. Though it is still one of the most difficult issues in image processing and computer vision, this work has attracted a lot of interest recently. The availability of RGBD sensors has introduced new possibilities for segmentation by incorporating depth information alongside color. However, effectively combining these modalities presents challenges due to misalignments and depth inaccuracies. This paper proposes CRFCut, a novel unsupervised segmentation method that utilizes a Conditional Random Field (CRF) model optimized with graph cuts to segment RGBD images into coherent regions. The method recursively divides regions into foreground and background layers, employing superpixel-based appearance segmentation for the RGB component and integrating depth cues to refine results. This approach enables robust segmentation, even in the presence of noisy or incomplete depth information. The CRFCut algorithm begins by separating the depth image into foreground and background regions using a median depth threshold. This initial step requires no preprocessing and provides the basis for further segmentation. Simultaneously, the RGB image is segmented into superpixels using an appearance-based approach, such as the mean-shift algorithm. These superpixels and the depth regions are combined within a CRF model, where labels are assigned by minimizing the energy function using the graph-cut α-expansion algorithm. The algorithm is applied recursively to subdivided regions, allowing finer segmentation in a parallelizable manner. The proposed method was evaluated on two datasets: the NYUv2 dataset and the MIT dataset. The NYUv2 dataset, which includes 1449 RGBD images with annotated object classes, demonstrated the superior performance of CRFCut compared to five state-of-the-art segmentation techniques in Table 1. In the MIT dataset, which provides human-labeled sequences of indoor and outdoor scenes, CRFCut achieved comparable or better results, even with depth maps generated from 2D images using existing estimation methods (Table 2). The RandIndex metric was used to evaluate segmentation accuracy, and qualitative results, as shown in Figures 3 and 4, highlight CRFCut’s  robustness, particularly with noisy or imprecise depth data. In summary, CRFCut introduces an unsupervised CRF-based approach that integrates RGB and depth information for accurate scene segmentation. By leveraging graph-cut optimization and a recursive structure, the method achieves high-quality segmentation results with minimal preprocessing. Despite some limitations, such as challenges in distinguishing adjacent objects with similar features, CRFCut offers a promising framework for real-time segmentation of RGBD images. Future work will address these limitations by incorporating supervised techniques and improving depth data quality for enhanced performance.   52 39 2025/03/152024/08/32024/11/27 1403/9/7 2026/02/82025/07/212025/07/21 1404/4/30 سیدسعید میرکمالی Seyedsaeid Mirkamali استادیار گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه پیام‌نور، تهران، ایران ایران s.mirkamali@pnu.ac.ir Semantic Segmentation RGB-D Image Combination of Conditional Random Field Graph Cuts بخش‌بندی لایه‌به‌لایه تصویر RGB-D میدان تصادفی شرطی CRF برش گراف 1. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 5, pp. 603-619, 2002. [DOI:10.1109/34.1000236]##2. Z. Wu, Z. Zhou, G. Allibert, C. Stolz, C. Demonceaux, and C. Ma, "Transformer fusion for indoor rgb-d semantic segmentation," Computer Vision and Image Understanding, vol. 249, p. 104174, 2024. [DOI:10.1016/j.cviu.2024.104174]##3. C. Liu, W. T. Freeman, E. 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ارائه یک راهکار مبتنی بر یادگیری عمیق برای تشخیص بدافزارهای اندرویدی A Deep Learning Based Method for Android Malware Detection 1 1 اندروید، سیستم‌ عاملی متن‌باز با سهم بازار گسترده، یکی از اهداف اصلی توسعه‌دهندگان بدافزار است. افزایش تنوع دستگاه‌های مبتنی‌بر اندروید و پیچیدگی روزافزون بدافزارها، شناسایی آن‌ها را به چالشی جدی تبدیل کرده‌است. در این پژوهش، روشی مبتنی‌بر یادگیری عمیق برای تشخیص بدافزارها ارائه شده‌است که از تحلیل ایستا بهره می‌برد؛ این روش با تبدیل بایت‌کدهای خام برنامه‌ها به سیگنال‌های صوتی، استخراج اطلاعات مهم از این سیگنال‌ها و آموزش یک مدل با کمک یادگیری انتقالی، به صحت 99.3درصد و دقت 99.8درصد رسید. با تکیه‌بر نگاشت بایت‌کدهای خام به حوزه صوت، علاوه‌بر ایجاد دیدگاه جعبه‌سیاه‌گونه، پیچیدگی محاسباتی کاهش و دقت شناسایی افزایش پیدا کرد. یکی از نوآوری‌های این روش که آن را برای استفاده در کاربردهای صنعتی و بازارهای اندرویدی مناسب می‌سازد، قابلیت آن در تشخیص بدافزارهای مبهم‌شده و عملکرد موفق آن در این زمینه است؛ موضوعی که یکی از چالش‌های اساسی در حوزه تشخیص بدافزارهای اندرویدی به‌شمار می‌رود. 2 The Android operating system, an open-source platform supported by Google, has become a cornerstone of modern technology due to its widespread adoption in diverse devices, including smartphones, smart TVs, and wearables. This extensive reach has established Android as a dominant force in the global market but simultaneously made it a primary target for malware developers. The growing sophistication and frequency of mobile malware attacks pose significant challenges for users and Android app distribution platforms. These attacks exploit the open nature of the Android ecosystem and increasingly employ advanced techniques such as obfuscation, rendering traditional detection methods less effective. In response to these challenges, this study introduces an innovative approach to malware detection leveraging image and audio processing in combination with deep learning techniques. Our proposed methodology addresses the limitations of existing methods by providing a scalable, high-accuracy solution suitable for industrial deployment. The research is based on static analysis. During the static analysis, executable file bytes are transformed into audio signals, and features extracted from these signals are used to train a deep learning model. This model achieved an impressive accuracy of 99.3%, with a precision of 99.8% and a recall of 99.1%. The novelty of our approach lies in its ability to detect obfuscated malware, a critical and challenging aspect of modern malware detection. By mapping executable files to the audio domain in static analysis, our method effectively reduces computational complexity while enhancing detection accuracy. The proposed framework was validated on a diverse and comprehensive dataset, showcasing its capability to distinguish between benign and malicious applications with high reliability. Furthermore, the method's design ensures practical applicability in real-world scenarios, particularly in app distribution platforms where rapid and accurate malware detection is crucial. This research contributes a novel, efficient, and scalable malware detection solution that addresses the challenges posed by obfuscation and computational demands. The proposed framework not only advances the state-of-the-art in Android malware detection but also lays the groundwork for future research exploring hybrid analysis techniques and real-time detection capabilities.   70 53 2025/03/152024/08/32024/11/272025/02/1 1403/11/13 2026/02/82025/07/212025/07/212026/02/3 1404/11/14 علی علیائی طرقبه Ali Olyaei Torqabeh دانشجوی دکترای مهندسی کامپیوتر – نرم‌افزار، گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران ایران ali.olyaei@mail.um.ac.ir عباس رسول زادگان Abbas Rasoolzadegan دانشیار گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران ایران rasoolzadegan@um.ac.ir Android Malware Detection Deep Learning Static Analysis Image Processing Audio Signal Processing بدافزارهای اندروید یادگیری عمیق تحلیل ایستا پردازش صوت پردازش تصویر. 1. J. 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برقراری اعتماد در شبکه‌های بین‌خودرویی مبتنی بر زنجیره قالبی با بهره‌گیری از استنتاج فازی و ساختار Chord Presenting a model for establishing trust in inter-vehicle networks based on blockchain using fuzzy inference and Chord structure 1 1 با پیشرفت فناوری در حوزه اینترنت اشیا، شبکه بین‌خودرویی[1] تحولی در دنیای فناوری ایجاد کرده‌است. در این شبکه‌ها، گره‌ها به‌عنوان مسیریاب و میزبان فعالیت کرده و اطلاعات مسیر را بین وسایل نقلیه و RSUها به اشتراک می‌گذارند. چالش اصلی این شبکه، اعتماد به پیام‌های مبادله‌شده ‌است که نیاز به صحت‌سنجی گره‌ها پیش از ارتباط دارد. در طرح پیشنهادی، هر وسیله نقلیه به‌محض مشاهده یک رخ‌داد، پیامی را به‌صورت همه‌پخشی ارسال می‌کند و RSU برای اعتبارسنجی، میزان اعتماد گره فرستنده را بررسی می‌کند؛ در این راستا، برای جلوگیری از تکرار رخ‌دادها، پیام‌های تکراری منتشر نمی‌شوند؛ به‌منظور افزایش کارایی، ساختار Chord به‌جای الگوریتم‌های اجماع زمان‌بر به‌کار گرفته شده‌است. این سامانه شامل مراحل امتیازدهی به پیام‌ها، محاسبه اعتماد و تشکیل گروه‌های ارزیاب به‌وسیله RSUها است. نتایج نشان می‌دهد که قابلیت اطمینان پیام‌ها در این روش شش درصد نسبت به SBTMS و یازده درصد نسبت به POW بهبود یافته‌است. این پژوهش با ترکیب VANET، فناوری زنجیره قالبی و منطق فازی، مدلی کارآمد برای افزایش قابلیت اعتماد در شبکه‌های بین‌خودرویی ارائه می‌دهد.   [1] VANET 2 With the swift advancement of the Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs) have become a crucial component in enabling smart transportation systems by supporting real-time communication between vehicles and roadside units (RSUs). In these networks, vehicles function as mobile nodes that generate and transmit data across the system. A major challenge in VANETs is ensuring the integrity and trustworthiness of shared messages, as any malicious or inaccurate information could severely impact safety and system performance. This research introduces a trust management framework that integrates VANET with blockchain technology and fuzzy logic to improve the reliability of vehicle-to-vehicle communication. When an event is detected, a vehicle instantly broadcasts a corresponding message. RSUs then evaluate the sender’s trust level and verify the message before validation. To minimize communication overhead and avoid duplication, repeated messages are filtered prior to distribution. Unlike conventional trust models that depend on computationally heavy consensus mechanisms such as Proof of Work (PoW), the proposed system adopts a Chord-based distributed architecture. This approach significantly lowers processing times and boosts scalability. The framework utilizes a multi-phase trust evaluation process involving message scoring, dynamic trust calculation, and formation of evaluator groups by RSUs. Simulations reveal notable gains in message credibility: a 6% increase compared to the Score-Based Trust Management System (SBTMS) and an 11% improvement over PoW-based approaches. These results underline the effectiveness of the proposed model in achieving a balance between security, scalability, and low latency in VANET environments. By merging VANET architecture with decentralized trust mechanisms and soft computing techniques, this study presents an innovative and pragmatic solution to one of the key challenges in vehicular communications—facilitating secure, efficient, and trustworthy message exchange in highly dynamic, distributed networks. 100 71 2025/03/152024/08/32024/11/272025/02/12025/03/11 1403/12/21 2026/02/82025/07/212025/07/212026/02/32026/02/8 1404/11/19 نیلوفر خسروی راد niloufar khosravirad دانشجوی دکتری، مهندسی کامپیوتر، دانشگاه آزاد اسلامی، قم، ایران ایران niloufar.khosravirad73@gmail.com رضا احسن reza ahsan استادیار دانشکده مهندسی کامپیوتر، واحد قم، دانشگاه آزاد اسلامی، قم، ایران ایران ahsan.ac.ir@gmial.co احمد شریف ahmad sharif استادیار دانشکده مهندسی کامپیوتر، واحد قم، دانشگاه آزاد اسلامی، قم، ایران ایران asharif@iau.ac.ir علی کریمی ali karimi دانشجوی دکتری، مهندسی کامپیوتر، آزاد اسلامی، قم، ایران ایران a.alikarimi91@gmail.com Blockchain Chord structure fuzzy inference Inter-vehicle network reliability شبکه بینخودرویی زنجیره قالبی ساختار Chord استنتاج فازی قابلیت اعتماد 1. 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پیش‌بینی درجات تومور مغزی گلیوما با استفاده از یادگیری ماشین گروهی Predicting glioma brain tumor grades using ensemble machine learning 1 1 گلیوماها شایع‌ترین تومورهای اولیه دستگاه عصبی‌اند که انواع مختلفی از تومورها با درجات مختلف بدخیمی را شامل می‌شوند. تشخیص و درجه‌بندی دقیق این تومورها به‌دلیل ماهیت تهاجمی و پیش‌رونده برخی از انواع آن، به یک چالش اساسی در حوزه پزشکی تبدیل شده‌است. این پژوهش بر روی یک مجموعه‌داده عمومی شامل ۸۳۹ نمونه از پایگاه‌ داده TCGA (اطلس ژنوم سرطان) انجام شده‌است؛ داده‌ها شامل بیست ویژگی ژنتیکی (وضعیت جهش در ژن‌هایی همانند IDH1، TP53 و ATRX) و سه ویژگی بالینی (نژاد، جنسیت و سن) هستند که هدف، دسته‌بندی تومورها به دو گروه گلیوما درجه پایین و گلیوبلاستوما است؛ تاکنون چندین مدل یادگیری ماشین برای حل مسئله تشخیص و درجه‌بندی گلیوما ارائه شده‌است که به نتایج قابل ‌قبولی دسته یافته‌اند؛ بااین‌حال، تلاش در این زمینه جهت توسعه مدلی با بیشترین کارایی در دسته‌بندی و درجه‌بندی تومورها نیاز است؛ زیرا کارایی مدل‌های موجود با حالت ایدئال فاصله زیادی دارد. نوآوری این پژوهش، ارائه یک چهارچوب یادگیری ماشین گروهی دولایه (EML) است که برای نخستین‌بار از ترکیب چهار مدل یادگیر پایه شامل یادگیری ماشین بردار پشتیبان (SVM)، تقویت دسته‌بندی (CatBoost)، درخت­های به‌شدت تصادفی (ERT) و جنگل تصادفی (RF) جهت تنوع‌بخشی و افزایش دقت دسته‌بندی و همچنین یک متامدل شامل رگرسیون لجستیک (LR) استفاده می‌کند؛ همچنین، از یک رویکرد ترکیبی خلاقانه متشکل از الگوریتم‌های Boruta و SHAP برای انتخاب زیرمجموعه بهینه ویژگی‌ها بهره گرفته شده‌است. این رویکرد با هدف کاهش واریانس، جلوگیری از بیش‌برازش و افزایش دقت پیش‌بینی طراحی شده‌است. نتایج آزمایش‌ها روی مجموعه‌داده آزمون حاکی از آن است که مدل پیشنهادی ELM با کسب مقدار صحت 89.29 درصد روی مجموعه‌داده آزمون بهترین رتبه را در بین سایر مدل‌های همتا کسب کرده‌است؛ همچنین الگوریتم‌های RF و LR به‌ترتیب با کسب مقدار صحت 87.27 درصد و 86.88 درصد روی داده‌های آزمون در جایگاه دوم و سوم قرار گرفتند. 2 Gliomas, or in other words, aggressive and progressive brain tumors, lead to great complexity in the diagnosis and treatment of patients. While recent machine learning models provided encouraging results in glioma diagnosis and grading, the topic is open, and more efforts are needed. Existing models, despite encouraging results, often fall short of the ideal diagnostic state, highlighting the need for further research to develop robust and high-performing predictive models . This study introduces an optimized ensemble machine learning (EML) model designed to maximize classification (grading) performance and mitigate the pervasive issue of overfitting in glioma grading. Our approach employs a two-layer architecture that synergistically combines diverse weak and base learners. In the first layer, a diverse set of learners, including support vector machine (SVM), categorical boosting (CatBoost), extremely randomized trees (ERT), and random forest (RF), is integrated. This initial ensemble aims to capture a broad spectrum of grading patterns and enhance the overall accuracy by leveraging the complementary strengths of each base model. The outputs from this first layer, representing diversified classification probabilities, are then fed into a second-layer logistic regression (LR) model. This layer refines the predictions, performing the ultimate classification while explicitly addressing and eliminating the overfitting problem, thereby promoting better generalization to unseen data. To rigorously evaluate the performance of the proposed ELM model, a comprehensive comparison was conducted against its constituent base learners and counterpart machine learning models. All models were assessed using a standard, publicly available glioma dataset. To prevent overfitting, examine the robustness of models, and evaluate models fairly, a 5-fold cross-validation strategy is used in experiments. The effectiveness of models was measured using four performance metrics, including accuracy, recall, precision, and F1-score. The experimental results demonstrate the superior performance of the proposed EML model. Across all evaluated metrics, our model consistently outperformed the individual base learners and other benchmarked algorithms, securing the top rank in terms of accuracy. Specifically, the LR model operating on the first-layer ensemble predictions proved highly effective in both enhancing accuracy and preventing overfitting. Following our proposed model, the standalone LR and RF models demonstrated commendable performance, ranking second and third, respectively . The findings of this study underscore the significant potential of an optimized EML model for advancing the field of glioma tumor grading. The proposed model generated promising results and mitigated overfitting through integrating diverse base learners and using an LR model as a meta-model. The results reveal that the proposed model is a reliable and robust tool that can aid Clinical specialists in effectively diagnosing and classifying gliomas, ultimately paving the way for improved patient satisfaction. 122 101 2025/03/152024/08/32024/11/272025/02/12025/03/112025/08/12 1404/5/21 2026/02/82025/07/212025/07/212026/02/32026/02/82025/10/6 1404/7/14 حجت امامی Hojjat Emami دانشیار دانشکده فنی و مهندسی، گروه مهندسی کامپیوتر، دانشگاه بناب، بناب، ایران ایران emami@ubonab.ac.ir بابک آذرنوید Babak Azarnavid استادیار دانشکده علوم پایه، گروه ریاضی و علوم کامپیوتر، دانشگاه بناب، بناب، ایران ایران babakazarnavid@ubonab.ac.ir محسن عبدالحسین‌زاده Mohsen Abdolhosseinzadeh استادیار دانشکده علوم پایه، گروه ریاضی و علوم کامپیوتر، دانشگاه بناب، بناب، ایران ایران mohsen.ab@ubonab.ac.ir Brain tumor tumor detection glioma data mining group learning parameter optimization تومور مغزی گلیوما تشخیص گلیوما داده‌کاوی یادگیری گروهی بهینه‌سازی پارامتر 1. D. N. 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