Multi-Modal Deep Learning for Non-Invasive Elder Safety Monitoring and Hybrid Architectures Using Gas Sensor Arrays and Binary Positional IoT Data

المؤلفون

  • Hanan Ramadhan Electrical Engineering department , Higher Institute of Science and Technology, Ajdabiya , Libya المؤلف

DOI:

https://doi.org/10.65405/wnk81w20

الكلمات المفتاحية:

مراقبة سلامة كبار السن، التعلم العميق متعدد الوسائط، الشبكات العصبية طويلة المدى (LSTM)، الشبكات العصبية التلافيفية (CNN) ، البنى الهجينة، مصفوفات مستشعرات الغاز، إنترنت الأشياء، الاستشعار غير الجراحي، كشف الشذوذ في النشاط، في مكان الإقامة

الملخص

تقارن هذه الدراسة بين بنى LSTM وCNN والبنية الهجينة CNN-LSTM لرصد سلامة كبار السن مع الحفاظ على الخصوصية، وذلك باستخدام بيانات استشعار متعددة الوسائط من مجموعة بيانات UCI Single Elder Home Monitoring (444,631 حالة من قراءات الحركة بالأشعة تحت الحمراء وقياسات الغازات البيئية). وباستخدام تصحيح بيئي قائم على تحليل المكونات الرئيسية (PCA) للحد من انحراف المستشعرات، واستخراج ميزات خاصة بالبنية، ميّزت النماذج بين الأنشطة الطبيعية والشذوذات التي تُهدد السلامة. حققت البنية الهجينة CNN-LSTM أداءً فائقًا (مقياس F1: 0.942 ± 0.018) مقارنةً بـ LSTM المستقلة (0.911) وCNN (0.887)، مستفيدةً من قدراتها المزدوجة في نمذجة التبعية الزمنية وتحديد المواقع المكانية. يُسهم هذا البحث في توفير إطار عمل مُثبت وقابل للتطوير لتحديد مخاطر سلامة كبار السن باستخدام مستشعرات غير بصرية، مما يُزيل ثغرات الخصوصية في المراقبة القائمة على الكاميرات، ويُمكّن من رصد المنازل بشكل موثوق ومستمر لتطبيقات رعاية كبار السن في منازلهم. تكمن الجدة في دمج مصفوفات المستشعرات الكيميائية مع تتبع المواقع ضمن تصميم هجين للتعلم العميق، حيث يعزز تصحيح الانحراف المدفوع بتحليل المكونات الرئيسية الموثوقية، ويؤسس دمج استخلاص الميزات المكانية مع النمذجة الزمنية أساسًا يولي الخصوصية أولوية قصوى لتقنيات المساعدة من الجيل التالي. يُعزز هذا النهج حلولًا أخلاقية تدعم الحياة المستقلة دون المساس بالكرامة، ويتصدى بشكل مباشر لتحديات الشيخوخة السكانية العالمية من خلال ابتكار تقني قوي في أنظمة المعيشة المساعدة المُحيطة لرعاية كبار السن.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

المراجع

Marín, J. D. Llano-Viles, Z. Haddi, A. Perera-Lluna, and J. Fonollosa. "(2023).Home monitoring for older singles: A gas sensor array system." Sensors and Actuators B: Chemical, vol. 393, p. 134036, 2023. https://doi.org/10.1016/j.snb.2023.134036

Jetlawei, S. et al. "(2025)Temporal Intelligence and Algorithmic Equity: A Multi-Phase Framework for Predictive Student Success in Higher Education." Comprehensive Science Journal, vol. 9, no. 36, pp. 1574-1595, 2025. https://doi.org/10.65405/f0xx5p02P

Mishra, B. S. R. Shanmugam, K. C. Yeo, and S. Thennadil. "(2025). SDN-enabled IoT security frameworks—A review of existing challenges." Technologies, vol. 13, no. 3, p. 121, 2025. https://doi.org/10.3390/technologies13030121

Otokwala, A. U. Petrovski, and H. Kalutarage. "(2024). Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of Things." International Journal of Information Security, vol. 23, no. 4, pp. 2559-2581, 2024. https://doi.org/10.1007/s10207-024-00855-7

Fares, M. I. A. Abd Elaziz, A. O. Aseeri, H. S. Zied, and A. G. Abdellatif. (2025)."TFKAN: Transformer based on Kolmogorov–Arnold networks for intrusion detection in IoT environment." Egyptian Informatics Journal, vol. 30, p. 100666, 2025. https://doi.org/10.1016/j.eij.2025.100666

Benmalek M. and A. Seddiki. (2025)."Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection." Data Science and Management, 2025. https://doi.org/10.1016/j.dsm.2025.02.005

Ben Dalla, L. Ö. Karal, M. El-Sseid, and A. Alsharif. "(2026). An IoT-enabled, THD-based fault detection and predictive maintenance framework for solar PV systems in harsh climates: Integrating DFT and machine learning for enhanced performance and resilience." Wadi Alshatti University Journal of Pure and Applied Sciences, vol. 4, no. 1, 2026. https://doi.org/10.63318/waujpasv4i1

Islam, M. M. W. M. Abdullah, and B. N. Saha. (2025). "Privacy-preserving hierarchical fog federated learning (PP-HFFL) for IoT intrusion detection." Sensors, vol. 25, no. 23, p. 7296, 2025. https://doi.org/10.3390/s25237296

Teixeira, L. R. Almeida, P. Rodrigues, M. Antunes, D. Gomes, and R. L. Aguiar. (2025)."Beyond performance comparing the costs of applying deep and shallow learning." Computer Communications, p. 108312, 2025. https://doi.org/10.1016/j.comcom.2025.108312

Zhou, S. et al. "(2026).Advances in machine learning-enabled self-powered flexible sensing materials and their applications." Advanced Materials Technologies, p. e01512, 2026. https://doi.org/10.1002/admt.202501512

Ebenezer, A. L. B. Sasithradevi, and C. Baskar. (2025) "Advances in bioinspired sensing-electronic nose and eye technologies for food quality assessment: A review." IEEE Sensors Journal, 2025. https://doi.org/10.1109/JSEN.2025.3590474

Lamaakal, I. et al. (2025)"A comprehensive survey on tiny machine learning for human behavior analysis." IEEE Internet of Things Journal, 2025. https://doi.org/10.1109/JIOT.2025.3565688

‏ Shehadeh, A. et al. (2021) "Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression." Automation in Construction, vol. 129, p. 103827, 2021. https://doi.org/10.1016/j.autcon.2021.103827

Aljohani. A. (2023)"Predictive analytics and machine learning for real-time supply chain risk mitigation and agility." Sustainability, vol. 15, no. 20, p. 15088, 2023. https://doi.org/10.3390/su152015088

Al Mamlook, L. J. R. E. Wells, and R. Sawyer. (2023)."Machine-learning models for predicting surgical site infections using patient pre-operative risk and surgical procedure factors." American Journal of Infection Control, vol. 51, no. 5, pp. 544-550, 2023. https://doi.org/10.1016/j.ajic.2022.08.013

Alotaibi. B. (2025)"A review of resilient IoT systems: Trends, challenges, and future directions." Preprints.org, 2025. https://www.preprints.org/manuscript/202512.1717

Dalla L. O. F. B. and T. M. A. Ahmad. (2020)"The sustainable efficiency of modeling a correspondence undergraduate transaction framework by using generic modeling environment (GME)." International Journal of Engineering and Modern Technology, vol. 6, no. 1, 2020. https://www.iiardpub.org

Ben Dalla L. O. F., T. D. Medeni, I. T. Medeni, and M. Ulubay. (2025)."Enhancing healthcare efficiency at Almasara Hospital: Distributed data analysis and patient risk management." Economy: Strategy and Practice, vol. 19, no. 4, pp. 54-72, 2025. https://doi.org/10.51176/1997-9967-2024-4-54-72

Dalla L. O. B., Ö. Karal, and A. Degirmenciyi. (2025)."Leveraging LSTM for adaptive intrusion detection in IoT networks: A case study on the RT-IoT2022 dataset implemented on CPU computer device machine." 2025. https://doi.org/10.6543/X:4102659

Dalla L. O. F. B. "IT security cloud computing." 2020 Innovations in Intelligent IT Security Cloud Computing Conference (IISCCC), pp. 1-7, 2020. https://doi.org/10.16377/ITSCC 50717.2020.9259880

Ben Dalla L., T. M. Medeni, S. Z. Zbeida, and İ. M. Medeni. (2024)."Unveiling the evolutionary journey based on tracing the historical relationship between artificial neural networks and deep learning." The International Journal of Engineering & Information Technology (IJEIT), vol. 12, no. 1, pp. 104-110, 2024. https://doi.org/10.36602/ijeit.v12i1.484

Dalla, L. O. B., T. D. Medeni, and İ. T. Medeni. (2024)."Evaluating the impact of artificial intelligence-driven prompts on the efficacy of academic writing in scientific research." Afro-Asian Journal of Scientific Research (AAJSR), pp. 48-60, 2024. https://doi.org/10.7654/X.26.733

Degirmenci ,A. and O. Karal. (2022). "iMCOD: Incremental multi-class outlier detection model in data streams." Knowledge-Based Systems, vol. 258, p. 109950, 2022. https://doi.org/10.1016/j.knosys.2022.109950

Karim A. M., et al. (2020).A novel framework using deep auto-encoders based linear model for data classification." Sensors, vol. 20, no. 21, p. 6378, 2020. https://doi.org/10.3390/s20216378

Dalla. L. O. F. B. (2020)."The sustainable efficiency of modeling a correspondence undergraduate transaction framework by using generic modeling environment (GME)." International Journal of Engineering and Modern Technology, vol. 6, no. 1, 2020. https://www.iiardpub.org

Dalla. L. O. F. B. (2020).Lean software development practices and principles in terms of observations and evolution methods to increase work environment productivity." International Journal of Engineering and Modern Technology, vol. 6, no. 1, pp. 23-45, 2020. https://doi.org/10.6754/s20206543

Karal Ö.. ".(2020).Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation." 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1-5, 2020. https://doi.org/10.1109/ASYU50717.2020.9259880

Dalla. L. O. F. B. ".(2020).The influence of hospital management framework by the usage of electronic healthcare record to avoid risk management (Department of Communicable Diseases at Misurata Teaching Hospital: Case study)." EHRM, vol. 20, no. 4, pp. 22-52, 2020. https://doi.org/20.51176/1954-9923-2020-4-22-52

Ben Dalla. L. O. F. (2021)."Literature review (LR) on the powerful of research methodology processes life cycle." 2021 Powerful of Research Methodology Processes Life Cycle Conference (TPRMPLCC), pp. 1-10, 2021. https://doi.org/10.16543/TPRMPLCC50717.2020.92876580

Ogundokun, R. O. P. A. Owolawi, and E. Van Wyk. "(2025).LiteRT-IDSNet: A lightweight hybrid deep learning framework for real-time intrusion detection in industrial IoT using the RT-IoT 2022 dataset." 2025 60th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), pp. 1-4, 2025. https://doi.org/10.1109/ICEST66328.2025.11098207

Dalla L. O. F. B., A. M. A. El-Sseid, T. M. Alarbi, and M. A. M. E. S. Ahmad. ".(2020).A domain specific modeling language framework (DSL) for representative medical prescription by using generic modeling environment (GME)." International Journal of Engineering and Modern Technology, vol. 6, no. 2, 2020. https://www.iiardpub.org.

Ben Dalla L. O. F.(2021). "Literature review (LR) on the dominant of research methodology." 2020 LRDRMC Conference, pp. 1-14, 2021. https://doi.org/10.6754/LRDRMC56412.2020.45987623

Dalla L. O. B., Ö. Karal, and A. Degirmenciyi. (2025)."Leveraging LSTM for adaptive intrusion detection in IoT networks: A case study on the RT-IoT2022 dataset implemented on CPU computer device machine." 5th International Conference on Engineering, Natural and Social Sciences, 2025. https://www.icensos.com/

Yalman Y., et al. "(2022).Prediction of voltage sag relative location with data-driven algorithms in distribution grid." Energies, vol. 15, no. 18, p. 6641, 2022. https://doi.org/10.3390/en15186641

Arık D. T., Ö. Karal, and A. B. Şahin. "(2020).A comparative study of artificial neural networks and naïve Bayes techniques for the classification of radar targets." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 4, pp. 1779-1788, 2020. https://doi.org/10.17798/bitlisfen.676973

Uysal Z., et al. ".(2016).A heart rate monitoring application using wireless sensor network system based on Bluetooth with MATLAB GUI." International Journal of Engineering Science and Computing, vol. 6, p. 2862, 2016. http://ijesc.org/

Dulkadir S. E. Z. G. İ. N., et al. "(2020).The effect of radiation on the forward and reverse bias current–voltage (I–V) characteristics of Au/(Bi4Ti3O12/SiO2)/n-Si (MFIS) structures." Journal of Materials Science: Materials in Electronics, vol. 31, no. 15, pp. 12514-12521, 2020. https://doi.org/10.1007/s1

Muttaqi M., A. Degirmenci, and O. Karal. ".(2022).US accent recognition using machine learning methods." 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1-6, 2022. https://doi.org/10.1109/ASYU56188.2022.9925265

Karal, Ö. and L. O. F. B. Dalla. "(2025).Lung nodule characterization in CT scans using hybrid 3D attention U-Net segmentation and transfer learning-based classification approach." Comprehensive Journal of Science, vol. 10, no. 37, 2025. https://www.sicst.ly

Çakır M., A. Degirmenci, and O. Karal. ".(2022).Exploring the behavioural factors of cervical cancer using ANOVA and machine learning techniques." International Conference on Science, Engineering Management and Information Technology, pp. 249-260, 2022. https://doi.org/10.1007/978-3-031-40395-8_18

Sinecen M., et al. ".(2009).Diagnosis of prostat cancer using artificial neural networks." 2009 14th National Biomedical Engineering Meeting, pp. 1-3, 2009. https://doi.org/10.1109/BIYOMUT.2009.5130296

Yumus, M. M. Apaydin, A. Degirmenci, and O. Karal, “.(2020).Missing data imputation using machine learning based methods to improve HCC survival prediction,” 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings, Oct. 2020, doi: 10.1109/SIU49456.2020.9302222.

Dalla ,L. O. F. B. and T. M. A. Ahmad. ".(2023).Heart disease prediction via using machine learning techniques with distributed system and Weka visualization." Journal of Southwest Jiaotong University, vol. 58, no. 4, pp. 322-333, 2023. https://doi.org/10.35741/issn.0258-2724.58.4.26

Ben Dalla, L. Ö. Karal, M. El-Sseid, and A. Alsharif. ".(2026).An IoT-enabled, THD-based fault detection and predictive maintenance framework for solar PV systems in harsh climates: Integrating DFT and machine learning for enhanced performance and resilience." Wadi Alshatti University Journal of Pure and Applied Sciences, vol. 4, no. 1, pp. 41-55, 2026. https://doi.org/10.63318/waujpasv4i1_05

Osman, M. et al. ".(2026).A new approach for low-latency, high-accuracy anomaly detection at the edge: Benchmarking quantized autoencoders, LSTMs, and lightweight transformers on RT-IoT2022 time-series traffic." Wadi Alshatti University Journal of Pure and Applied Sciences, vol. 4, no. 1, pp. 110-121, 2026. https://doi.org/10.63318/waujpasv4i1_12

Elghaffi, F., O, Mohammed, Dalla, L., Ahmed, A., Agila, A., & EL-Sseid, M. (2026). Hybrid matrix-ensemble framework for chronic kidney disease diagnosis." Wadi Alshatti University Journal of Pure and Applied Sciences, vol. 4, no. 1, pp. 264-276, 2026. https://doi.org/10.63318/waujpasv4i1_28

Dalla L. O. B., B., Karal, Ö., Degirmenci, A., EL-Sseid, M. A. M., Essgaer, M., & Alsharif, A. (2025). Edge intelligence for real-time image recognition: A lightweight neural scheduler via using execution-time signatures on heterogeneous edge devices." Scientific Journal for Publishing in Health Research and Technology, pp. 74-85, 2025.

Ahmed, F., A.-Z. Othman, and A. Ukasha. "(2025).Multi-class classification of skin cancer images using a deep learning-based convolutional neural network (CNN)." Wadi Alshatti University Journal of Pure and Applied Sciences, vol. 3, no. 2, pp. 230-243, 2025. https://doi.org/10.63318/waujpasv3i2_29

Rai, R. K. and D. K. Singh. (2026). 1Department of Computer Science & Engineering MNNIT.‏ https://doi.org/10.36227/techrxiv.177092129.99567847/

Cao, Y. et al. ".(2026).Low-cost mechanical sonar mapping with artifact removal in confined spaces." IEEE Transactions on Instrumentation and Measurement, 2026. https://doi.org/10.1109/TIM.2026.3652719

Lonchakov, A. M. Sinitca, and D. I. Kaplun. ".(2026).Computer vision-based medical imaging techniques: Past, present, and future." IEEE Access, 2026. https://doi.org/10.1109/ACCESS.2026.3654393

التنزيلات

منشور

2026-03-01

كيفية الاقتباس

Multi-Modal Deep Learning for Non-Invasive Elder Safety Monitoring and Hybrid Architectures Using Gas Sensor Arrays and Binary Positional IoT Data. (2026). مجلة العلوم الشاملة, 10(39), 3274-3295. https://doi.org/10.65405/wnk81w20