A Lightweight Deep Learning Framework using Resource-Efficient Batch Normalization for Sarcasm Detection
DOI:
https://doi.org/10.32628/CSEIT251143Keywords:
Lightweight Convolutional Neural Networks (CNN), Lightweight Diabetic Retinopathy Detection, Lightweight Mineral Classification, Batch Normalisation (BN), Edge devices, Energy EfficiencyAbstract
Communication is not always direct; it often involves nuanced elements like humor, irony, and sarcasm. This study introduces a novel two-level approach for sarcasm detection, leveraging Convolutional Neural Networks (CNNs). Convolutional neural networks (CNNs) are crucial for many deep learning applications, yet their deployment on IoT devices is challenged by resource constraints and the need for low latency, particularly in on-device training. Traditional methods of deploying large CNN models on these devices often lead to suboptimal performance and increased energy consumption. To address this, our paper proposes an energy- efficient CNN design by optimising batch normalisation operations. Batch normalisation is vital for deep learning, aiding in faster convergence and stabilising gradient flow, but there has been limited research on creating energy-efficient and lightweight CNNs with optimised batch normalisation. This study proposes a 3R (reduce, reuse, recycle) optimisation technique for batch normalization. This technique introduces an energy-efficient CNN architecture. We investigate the use of batch normalization optimization to streamline memory usage and computational complexity, aiming to uphold or improve model performance on CPU- based systems. Additionally, we evaluate its effectiveness across diverse datasets, focusing on energy efficiency and adaptability in different settings. Furthermore, we analyze how batch normalization influences the performance and effectiveness of activation functions and pooling layers in neural network designs. Our results highlight batch normalization’s ability to enhance computational efficiency, particularly on devices with limited resources.
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L. Alzubaidi, J. Zhang, A. J. Humaidi, et al., “Review of deep learn- ing: Concepts, cnn architectures, challenges, applications, future direc- tions,” Journal of big Data, vol. 8, pp. 1–74, 2021. DOI: https://doi.org/10.1186/s40537-021-00444-8
Z. Zhang and A. Z. Kouzani, “Implementation of dnns on iot devices,” Neural Computing and Applications, vol. 32, no. 5, pp. 1327–1356, 2020. DOI: https://doi.org/10.1007/s00521-019-04550-w
J. M. Jose and S. Benedict, “Deepasd framework: A deep learning- assisted automatic sarcasm detection in facial emotions,” in 2023 8th International Conference on Communication and Electronics Systems (ICCES), IEEE, 2023, pp. 998–1004. DOI: https://doi.org/10.1109/ICCES57224.2023.10192647
H.-I. Liu, M. Galindo, H. Xie, et al., “Lightweight deep learning for resource-constrained environments: A survey,” ACM Computing Sur- veys, 2024. DOI: https://doi.org/10.1145/3657282
K. Y. Chan, B. Abu-Salih, R. Qaddoura, et al., “Deep neural networks in the cloud: Review, applications, challenges and research directions,” Neurocomputing, p. 126 327, 2023. DOI: https://doi.org/10.1016/j.neucom.2023.126327
M. Christopher, J. M. Jose, T. Thomas, and S. Benedict, “Catboost and genetic algorithm implementations for university recommendation systems,” in 2022 International Conference on Inventive Computation Technologies (ICICT), IEEE, Jul. 2022, pp. 436–443. doI: 10.1109/ ICICT54344.2022.9850819. DOI: https://doi.org/10.1109/ICICT54344.2022.9850798
J. M. Jose and J. Jeeva, “Energy-reduced bio-inspired 1d-cnn for audio emotion recognition,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11, no. 3, Jun. 2023. doI: 10.32628/CSEIT25113386. [Online]. Avail- able: https://doi.org/10.32628/CSEIT25113386. DOI: https://doi.org/10.32628/CSEIT25113386
H. Wu, X. Li, and Y. Deng, “Deep learning-driven wireless communi- cation for edge-cloud computing: Opportunities and challenges,” Jour- nal of Cloud Computing, vol. 9, no. 1, p. 21, 2020. DOI: https://doi.org/10.1186/s13677-020-00168-9
L. Ye, Z. Wang, Y. Liu, et al., “The challenges and emerging tech- nologies for low-power artificial intelligence iot systems,” IEEE Trans- actions on Circuits and Systems I: Regular Papers, vol. 68, no. 12, pp. 4821–4834, 2021. DOI: https://doi.org/10.1109/TCSI.2021.3095622
C. Garbin, X. Zhu, and O. Marques, “Dropout vs. batch normalization: An empirical study of their impact to deep learning,” Multimedia tools and applications, vol. 79, no. 19, pp. 12 777–12 815, 2020. DOI: https://doi.org/10.1007/s11042-019-08453-9
M. Awais, M. T. B. Iqbal, and S.-H. Bae, “Revisiting internal covariate shift for batch normalization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 11, pp. 5082–5092, 2020. DOI: https://doi.org/10.1109/TNNLS.2020.3026784
R. Banner, I. Hubara, E. Hoffer, and D. Soudry, “Scalable methods for 8-bit training of neural networks,” Advances in neural information processing systems, vol. 31, 2018.
M. M. Kalayeh and M. Shah, “Training faster by separating modes of variation in batch-normalized models,” IEEE transactions on pattern analysis and machine intelligence, vol. 42, no. 6, pp. 1483–1500, 2019. DOI: https://doi.org/10.1109/TPAMI.2019.2895781
H. Daneshmand, J. Kohler, F. Bach, T. Hofmann, and A. Lucchi, “Batch normalization provably avoids ranks collapse for randomly ini- tialised deep networks,” Advances in Neural Information Processing Systems, vol. 33, pp. 18 387–18 398, 2020.
S. Santurkar, D. Tsipras, A. Ilyas, and A. Madry, “How does batch nor- malization help optimization?” Advances in neural information pro- cessing systems, vol. 31, 2018.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep net- work training by reducing internal covariate shift,” in International conference on machine learning, pmlr, 2015, pp. 448–456.
L. Huang, J. Qin, Y. Zhou, F. Zhu, L. Liu, and L. Shao, “Normaliza- tion techniques in training dnns: Methodology, analysis and applica- tion,” IEEE transactions on pattern analysis and machine intelligence, vol. 45, no. 8, pp. 10 173–10 196, 2023. DOI: https://doi.org/10.1109/TPAMI.2023.3250241
L. Huang and A. D. Galinsky, “Was that sarcastic or supportive? why receiving sarcasm improves perspective-taking,” Current Opinion in Psychology, p. 101 709, 2023. DOI: https://doi.org/10.1016/j.copsyc.2023.101709
A. Lieberman and J. Schroeder, “Two social lives: How differences be- tween online and offline interaction influence social outcomes,” Cur- rent opinion in psychology, vol. 31, pp. 16–21, 2020. DOI: https://doi.org/10.1016/j.copsyc.2019.06.022
S. Gates, “Recent dickens studies—2015,” Dickens Studies Annual: Essays on Victorian Fiction, vol. 48, no. 1, pp. 285–394, 2017. DOI: https://doi.org/10.5325/dickstudannu.48.2017.0285
Y. Yeshurun, M. Nguyen, and U. Hasson, “The default mode network: Where the idiosyncratic self meets the shared social world,” Nature reviews neuroscience, vol. 22, no. 3, pp. 181–192, 2021. DOI: https://doi.org/10.1038/s41583-020-00420-w
J. K. McNulty, “When positive processes hurt relationships,” Current directions in psychological science, vol. 19, no. 3, pp. 167–171, 2010. DOI: https://doi.org/10.1177/0963721410370298
D. Keltner, L. Capps, A. M. Kring, R. C. Young, and E. A. Heerey, “Just teasing: A conceptual analysis and empirical review.,” Psycho- logical bulletin, vol. 127, no. 2, p. 229, 2001. DOI: https://doi.org/10.1037//0033-2909.127.2.229
J. M. Jose and J. Jose, “An efficient sarcasm detection in audio using parameter-reduced depthwise cnn,” International Journal of Innova- tive Research in Technology (IJIRT), vol. 12, no. 1, Jun. 2025, Inde- pendent Researcher, IssN: 2349-6002.
F. Quesque and Y. Rossetti, “What do theory-of-mind tasks actually measure? theory and practice,” Perspectives on Psychological Science, vol. 15, no. 2, pp. 384–396, 2020. DOI: https://doi.org/10.1177/1745691619896607
J. Cui, H. L. Colston, and G. Jiang, “Is that a genuine smile? emoji- based sarcasm interpretation across the lifespan,” Metaphor and Sym- bol, vol. 39, no. 3, pp. 195–216, 2024. DOI: https://doi.org/10.1080/10926488.2024.2314595
J. M. Jose, “Optimizing neural network energy efficiency through low- rank factorisation and pde-driven dense layers,” International Journal of Research Publication and Reviews, vol. 2, no. 2, pp. 5483–5487, 2025, ISSN: 2022. [Online]. Available: https://www.ijrpr.com.
S. Benedict, S. V. Reddy, M. Bhagyalakshmi, J. M. Jose, and R. Prodan, “Performance improvement strategies of edge-enabled social impact applications,” in 2023 International Conference on Inventive Computation Technologies (ICICT), IEEE, 2023, pp. 1696–1703. doI: 10 . 1109 / ICICT57992 . 2023 . 10160004. [Online]. Available: https://doi.org/10.1109/ICICT57992.2023.10160004. DOI: https://doi.org/10.1109/ICICT57646.2023.10134420
S. K. Kumar and J. M. Jose, “A survey on synthesizing images with generative adversial networks,” International Journal of Research Publication and Reviews, vol. 2, no. 2, p. 5, 2021. [Online]. Available: https://www.ijrpr.com.
E. O. Lange, J. M. Jose, S. Benedict, and M. Gerndt, “Automated en- ergy modeling framework for microcontroller-based edge computing nodes,” in International Conference on Advanced Network Technolo- gies and Intelligent Computing, Springer Nature Switzerland, 2022, pp. 422–437. doI: 10.1007/978- 3- 031- 27082- 5_32. [Online]. Available: https:// doi. org/ 10 . 1007 / 978 - 3 - 031 - 27082 - 5_32.
D. Vinoth and P. Prabhavathy, “An intelligent machine learning-based sarcasm detection and classification model on social networks,” The Journal of Supercomputing, vol. 78, no. 8, pp. 10 575–10 594, 2022. DOI: https://doi.org/10.1007/s11227-022-04312-x
A. Jindal, J. M. Jose, S. Benedict, and M. Gerndt, “Lora-powered energy-efficient object detection mechanism in edge computing nodes,” in 2022 Sixth International Conference on I-SMAC (IoT in So- cial, Mobile, Analytics and Cloud) (I-SMAC), IEEE, 2022, pp. 237–244. doI: 10.1109/I-SMAC55078.2022.9984414. [Online]. Available: https://doi.org/10.1109/I-SMAC55078.2022.9984414. DOI: https://doi.org/10.1109/I-SMAC55078.2022.9987393
S. Castro, D. Hazarika, V. Pe´rez-Rosas, R. Zimmermann, R. Mihalcea, and S. Poria, “Towards multimodal sarcasm detection (an obviously perfect paper),” arXiv preprint arXiv:1906.01815, 2019. DOI: https://doi.org/10.18653/v1/P19-1455
C. I. Eke, A. A. Norman, L. Shuib, and H. F. Nweke, “Sarcasm iden- tification in textual data: Systematic review, research challenges and open directions,” Artificial Intelligence Review, vol. 53, pp. 4215– 4258, 2020. DOI: https://doi.org/10.1007/s10462-019-09791-8
A. Al Maruf, F. Khanam, M. M. Haque, Z. M. Jiyad, F. Mridha, and Z. Aung, “Challenges and opportunities of text-based emotion detection: A survey,” IEEE Access, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3356357
A.-C. Ba˘roiu and S, . Tra˘us, an-Matu, “Automatic sarcasm detection: Systematic literature review,” Information, vol. 13, no. 8, p. 399, 2022. DOI: https://doi.org/10.3390/info13080399
A. Jamali, S. K. Roy, and P. Ghamisi, “Wetmapformer: A unified deep cnn and vision transformer for complex wetland mapping,” Inter- national Journal of Applied Earth Observation and Geoinformation, vol. 120, p. 103 333, 2023. DOI: https://doi.org/10.1016/j.jag.2023.103333
H. Fang, D. Liang, and W. Xiang, “Single-stage extensive semantic fusion for multi-modal sarcasm detection,” Array, vol. 22, p. 100 344, 2024. DOI: https://doi.org/10.1016/j.array.2024.100344
S. Sharma, S. Ramaneswaran, M. S. Akhtar, and T. Chakraborty, “Emotion-aware multimodal fusion for meme emotion detection,” IEEE Transactions on Affective Computing, 2024. DOI: https://doi.org/10.1109/TAFFC.2024.3378698
J. Wang, Y. Yang, Y. Jiang, M. Ma, Z. Xie, and T. Li, “Cross-modal incongruity aligning and collaborating for multi-modal sarcasm detec- tion,” Information Fusion, vol. 103, p. 102 132, 2024. DOI: https://doi.org/10.1016/j.inffus.2023.102132
D. Jain, A. Kumar, and G. Garg, “Sarcasm detection in mash-up lan- guage using soft-attention based bi-directional lstm and feature-rich cnn,” Applied Soft Computing, vol. 91, p. 106 198, 2020. DOI: https://doi.org/10.1016/j.asoc.2020.106198
B. N. Hiremath and M. M. Patil, “Sarcasm detection using cognitive features of visual data by learning model,” Expert Systems with Appli- cations, vol. 184, p. 115 476, 2021. DOI: https://doi.org/10.1016/j.eswa.2021.115476
S. Das and A. K. Kolya, “Parallel deep learning-driven sarcasm de- tection from pop culture text and english humor literature,” in Pro- ceedings of Research and Applications in Artificial Intelligence: RAAI 2020, Springer, 2021, pp. 63–73. DOI: https://doi.org/10.1007/978-981-16-1543-6_6
M. Bedi, S. Kumar, M. S. Akhtar, and T. Chakraborty, “Multi-modal sarcasm detection and humor classification in code-mixed conversa- tions,” IEEE Transactions on Affective Computing, vol. 14, no. 2,pp. 1363–1375, 2021. DOI: https://doi.org/10.1109/TAFFC.2021.3083522
F. Yao, X. Sun, H. Yu, W. Zhang, W. Liang, and K. Fu, “Mimicking the brain’s cognition of sarcasm from multidisciplines for twitter sar- casm detection,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 1, pp. 228–242, 2021. DOI: https://doi.org/10.1109/TNNLS.2021.3093416
J. Li, H. Pan, Z. Lin, P. Fu, and W. Wang, “Sarcasm detection with commonsense knowledge,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 3192–3201, 2021. DOI: https://doi.org/10.1109/TASLP.2021.3120601
D. S. Chauhan, G. V. Singh, A. Arora, A. Ekbal, and P. Bhattacharyya, “An emoji-aware multitask framework for multimodal sarcasm detec- tion,” Knowledge-Based Systems, vol. 257, p. 109 924, 2022. DOI: https://doi.org/10.1016/j.knosys.2022.109924
P. Goel, R. Jain, A. Nayyar, S. Singhal, and M. Srivastava, “Sar- casm detection using deep learning and ensemble learning,” Multime- dia Tools and Applications, vol. 81, no. 30, pp. 43 229–43 252, 2022. DOI: https://doi.org/10.1007/s11042-022-12930-z
N. Ding, S.-w. Tian, and L. Yu, “A multimodal fusion method for sar- casm detection based on late fusion,” Multimedia Tools and Applica- tions, vol. 81, no. 6, pp. 8597–8616, 2022. DOI: https://doi.org/10.1007/s11042-022-12122-9
B. Li, Z. Qian, P. Li, and Q. Zhu, “Multi-modal fusion network for rumor detection with texts and images,” in International Conference on Multimedia Modeling, Springer, 2022, pp. 15–27. DOI: https://doi.org/10.1007/978-3-030-98358-1_2
T.-h. Cheung and K.-m. Lam, “Crossmodal bipolar attention for mul- timodal classification on social media,” Neurocomputing, vol. 514, pp. 1–12, 2022. DOI: https://doi.org/10.1016/j.neucom.2022.09.140
T. Yue, R. Mao, H. Wang, Z. Hu, and E. Cambria, “Knowlenet: Knowl- edge fusion network for multimodal sarcasm detection,” Information Fusion, vol. 100, p. 101 921, 2023. DOI: https://doi.org/10.1016/j.inffus.2023.101921
Y. Zhang, J. Wang, Y. Liu, et al., “A multitask learning model for mul- timodal sarcasm, sentiment and emotion recognition in conversations,” Information Fusion, vol. 93, pp. 282–301, 2023. DOI: https://doi.org/10.1016/j.inffus.2023.01.005
A. Kumar, S. R. Sangwan, A. K. Singh, and G. Wadhwa, “Hybrid deep learning model for sarcasm detection in indian indigenous language using word-emoji embeddings,” ACM Transactions on Asian and Low- Resource Language Information Processing, vol. 22, no. 5, pp. 1–20, 2023. DOI: https://doi.org/10.1145/3519299
Y. Zhang, Y. Yu, D. Zhao, et al., “Learning multi-task commonness and uniqueness for multi-modal sarcasm detection and sentiment analysis in conversation,” IEEE Transactions on Artificial Intelligence, 2023. DOI: https://doi.org/10.1109/TAI.2023.3298328
C. Zhu, M. Chen, S. Zhang, et al., “Skeafn: Sentiment knowledge en- hanced attention fusion network for multimodal sentiment analysis,” Information Fusion, vol. 100, p. 101 958, 2023. DOI: https://doi.org/10.1016/j.inffus.2023.101958
J. Liu, S. Tian, L. Yu, X. Shi, and F. Wang, “Image-text fusion trans- former network for sarcasm detection,” Multimedia Tools and Applica- tions, pp. 1–15, 2023.
A. Alzu’bi, L. Bani Younis, A. Abuarqoub, and M. Hammoudeh, “Multimodal deep learning with discriminant descriptors for offen- sive memes detection,” ACM Journal of Data and Information Quality, vol. 15, no. 3, pp. 1–16, 2023. DOI: https://doi.org/10.1145/3597308
Y. Zhang, D. Ma, P. Tiwari, et al., “Stance-level sarcasm detection with bert and stance-centered graph attention networks,” ACM Transactions on Internet Technology, vol. 23, no. 2, pp. 1–21, 2023. DOI: https://doi.org/10.1145/3533430
Y. Liu, Y. Zhang, and D. Song, “A quantum probability driven frame- work for joint multi-modal sarcasm, sentiment and emotion analysis,” IEEE Transactions on Affective Computing, 2023. DOI: https://doi.org/10.1109/TAFFC.2023.3279145
R. M. Albalawi, A. T. Jamal, A. O. Khadidos, and A. M. Al- hothali, “Multimodal arabic rumors detection,” IEEE Access, vol. 11, pp. 9716–9730, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3240373
A. Mohan, A. M. Nair, B. Jayakumar, and S. Muraleedharan, “Sarcasm detection using bidirectional encoder representations from transform- ers and graph convolutional networks,” Procedia Computer Science, vol. 218, pp. 93–102, 2023. DOI: https://doi.org/10.1016/j.procs.2022.12.405
O. Vitman, Y. Kostiuk, G. Sidorov, and A. Gelbukh, “Sarcasm detec- tion framework using context, emotion and sentiment features,” Expert Systems with Applications, vol. 234, p. 121 068, 2023. DOI: https://doi.org/10.1016/j.eswa.2023.121068
X. Guo, A. Kot, and A. W.-K. Kong, “Pace-adaptive and noise-resistant contrastive learning for multimodal feature fusion,” IEEE Transactions on Multimedia, 2023. DOI: https://doi.org/10.1109/TMM.2023.3252270
S. Chatterjee, S. Bhattacharjee, K. Ghosh, A. K. Das, and S. Banerjee, “Class-biased sarcasm detection using bilstm variational autoencoder- based synthetic oversampling,” Soft Computing, vol. 27, no. 9, pp. 5603–5620, 2023. DOI: https://doi.org/10.1007/s00500-023-07956-w
L. Wu, Y. Long, C. Gao, Z. Wang, and Y. Zhang, “Mfir: Multimodal fu- sion and inconsistency reasoning for explainable fake news detection,” Information Fusion, vol. 100, p. 101 944, 2023. DOI: https://doi.org/10.1016/j.inffus.2023.101944
V. Sukhavasi and V. Dondeti, “Effective automated transformer model based sarcasm detection using multilingual data,” Multimedia Tools and Applications, pp. 1–32, 2023. DOI: https://doi.org/10.1007/s11042-023-17302-9
Y. Zhang, Y. Yu, M. Wang, M. Huang, and M. S. Hossain, “Self- adaptive representation learning model for multi-modal sentiment and sarcasm joint analysis,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 20, no. 5, pp. 1–17, 2024. DOI: https://doi.org/10.1145/3635311
H. Liu, R. Wei, G. Tu, J. Lin, C. Liu, and D. Jiang, “Sarcasm driven by sentiment: A sentiment-aware hierarchical fusion network for mul- timodal sarcasm detection,” Information Fusion, vol. 108, p. 102 353, 2024. DOI: https://doi.org/10.1016/j.inffus.2024.102353
Y. Li, Y. Li, S. Zhang, et al., “An attention-based, context-aware mul- timodal fusion method for sarcasm detection using inter-modality in- consistency,” Knowledge-Based Systems, vol. 287, p. 111 457, 2024. DOI: https://doi.org/10.1016/j.knosys.2024.111457
H. Dai, H. Wang, X. Zhang, and H. Sun, “Memory-efficient batch normalization by one-pass computation for on-device training,” IEEE Transactions on Circuits and Systems II: Express Briefs, 2024. DOI: https://doi.org/10.1109/TCSII.2024.3354738
H. Zhang, Y. Shu, Q. Deng, H. Sun, W. Zhao, and Y. Ha, “Wdvr-ram: A 0.25–1.2 v, 2.6–76 pops/w charge-domain in-memory-computing bina- rized cnn accelerator for dynamic aiot workloads,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 10, pp. 3964– 3977, 2023. doI: 10.1109/TCSI.2023.3294296. DOI: https://doi.org/10.1109/TCSI.2023.3294296
B. Li, H. Wang, F. Luo, X. Zhang, H. Sun, and N. Zheng, “Acbn: Ap- proximate calculated batch normalization for efficient dnn on-device training processor,” IEEE Transactions on Very Large Scale Integra- tion (VLSI) Systems, vol. 31, no. 6, pp. 738–748, 2023. doI: 10.1109/ TVLSI.2023.3262787. DOI: https://doi.org/10.1109/TVLSI.2023.3262787
R. Khurana and S. Hodges, “Beyond the prototype: Understanding the challenge of scaling hardware device production,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1–11. DOI: https://doi.org/10.1145/3313831.3376761
K. F. Goser, “Implementation of artificial neural networks into hard- ware: Concepts and limitations,” Mathematics and computers in simu- lation, vol. 41, no. 1-2, pp. 161–171, 1996. DOI: https://doi.org/10.1016/0378-4754(95)00068-2
T. V. Lakshmi and C. V. Krishna Reddy, “Classification of skin lesions by incorporating drop-block and batch normalization layers in repre- sentative cnn models,” Arabian Journal for Science and Engineering, vol. 49, no. 3, pp. 3671–3684, 2024. DOI: https://doi.org/10.1007/s13369-023-08131-x
H.-I. Lin, R. Mandal, and F. S. Wibowo, “Bn-lstm-based energy con- sumption modeling approach for an industrial robot manipulator,” Robotics and Computer-Integrated Manufacturing, vol. 85, p. 102 629, 2024. DOI: https://doi.org/10.1016/j.rcim.2023.102629
L. Zhao, Y. Teng, and L. Wang, “Logit normalization for long-tail ob- ject detection,” International Journal of Computer Vision, pp. 1–21, 2024.
S. Tang, B. C. Khoo, Y. Zhu, K. M. Lim, and S. Yuan, “A light deep adaptive framework toward fault diagnosis of a hydraulic piston pump,” Applied Acoustics, vol. 217, p. 109 807, 2024. DOI: https://doi.org/10.1016/j.apacoust.2023.109807
C. Yang, Z. Lin, Z. Lan, R. Chen, L. Wei, and Y. Liu, “Evolution- ary channel pruning for real-time object detection,” Knowledge-Based Systems, vol. 287, p. 111 432, 2024. DOI: https://doi.org/10.1016/j.knosys.2024.111432
J. M. Jose, “Edge intelligence: Architecture, scope and applications,” International Journal of Research Publication and Reviews, vol. 2, no. 2, p. 5, 2022. [Online]. Available: https://www.ijrpr.com.
B. Liang, L. Gui, Y. He, E. Cambria, and R. Xu, “Fusion and discrimi- nation: A multimodal graph contrastive learning framework for multi- modal sarcasm detection,” IEEE Transactions on Affective Computing, 2024. DOI: https://doi.org/10.1109/TAFFC.2024.3380375
P. Tiwari, L. Zhang, Z. Qu, and G. Muhammad, “Quantum fuzzy neural network for multimodal sentiment and sarcasm detection,” Information Fusion, vol. 103, p. 102 085, 2024. DOI: https://doi.org/10.1016/j.inffus.2023.102085
H. Fang, D. Liang, and W. Xiang, “Multi-modal sarcasm detection based on multi-channel enhanced fusion model,” Neurocomputing, p. 127 440, 2024. DOI: https://doi.org/10.1016/j.neucom.2024.127440
S. Kusal, S. Patil, J. Choudrie, K. Kotecha, D. Vora, and I. Pappas, “A systematic review of applications of natural language processing and future challenges with special emphasis in text-based emotion detec- tion,” Artificial Intelligence Review, vol. 56, no. 12, pp. 15 129–15 215, 2023. DOI: https://doi.org/10.1007/s10462-023-10509-0
L. K. Chan, N. G. Patil, J. Y. Chen, J. C. Lam, C. S. Lau, and M. S. Ip, “Advantages of video trigger in problem-based learning,” Medical teacher, vol. 32, no. 9, pp. 760–765, 2010. DOI: https://doi.org/10.3109/01421591003686260
H. Cao, D. G. Cooper, M. K. Keutmann, R. C. Gur, A. Nenkova, and R. Verma, “Crema-d: Crowd-sourced emotional multimodal ac- tors dataset,” IEEE transactions on affective computing, vol. 5, no. 4, pp. 377–390, 2014. DOI: https://doi.org/10.1109/TAFFC.2014.2336244
H. S. Cheang and M. D. Pell, “The sound of sarcasm,” Speech commu- nication, vol. 50, no. 5, pp. 366–381, 2008. DOI: https://doi.org/10.1016/j.specom.2007.11.003
S. Abbas, S. Ojo, A. Al Hejaili, et al., “Artificial intelligence frame- work for heart disease classification from audio signals,” Scientific Re- ports, vol. 14, no. 1, p. 3123, 2024. DOI: https://doi.org/10.1038/s41598-024-53778-7
S. A. Qureshi, L. Hussain, M. Rafique, et al., “Eml-psp: A novel ensemble machine learning-based physical security paradigm using cross-domain ultra-fused feature extraction with hybrid data augmen- tation scheme,” Expert Systems with Applications, vol. 243, p. 122 863, 2024. DOI: https://doi.org/10.1016/j.eswa.2023.122863
A. Sujeesha, J. Mala, and R. Rajan, “Automatic music mood classifi- cation using multi-modal attention framework,” Engineering Applica- tions of Artificial Intelligence, vol. 128, p. 107 355, 2024. DOI: https://doi.org/10.1016/j.engappai.2023.107355
S. Attardo, J. Eisterhold, J. Hay, and I. Poggi, “Multimodal markers of irony and sarcasm,” 2003. DOI: https://doi.org/10.1515/humr.2003.012
S. Tabacaru and M. Lemmens, “Raised eyebrows as gestural triggers in humour: The case of sarcasm and hyper-understanding,” The European Journal of Humour Research, vol. 2, no. 2, pp. 11–31, 2014. DOI: https://doi.org/10.7592/EJHR2014.2.2.tabacaru
S. Tabacaru, “Faces of sarcasm,” The diversity of irony, vol. 65, p. 256, 2020. DOI: https://doi.org/10.1515/9783110652246-012
J. Mounts, “A history of sarcasm: Effects of balanced use of sarcasm in a relationship,” 2012.
J. Kim, “How korean efl learners understand sarcasm in l2 english,” Journal of Pragmatics, vol. 60, pp. 193–206, 2014. DOI: https://doi.org/10.1016/j.pragma.2013.08.016
A. Ray, S. Mishra, A. Nunna, and P. Bhattacharyya, “A multi- modal corpus for emotion recognition in sarcasm,” arXiv preprint arXiv:2206.02119, 2022.
F. Schmid, K. Koutini, and G. Widmer, “Dynamic convolutional neural networks as efficient pre-trained audio models,” IEEE/ACM Transac- tions on Audio, Speech, and Language Processing, 2024. DOI: https://doi.org/10.1109/TASLP.2024.3376984
J. Zhao, X. Mao, and L. Chen, “Speech emotion recognition using deep 1d & 2d cnn lstm networks,” Biomedical signal processing and control, vol. 47, pp. 312–323, 2019. DOI: https://doi.org/10.1016/j.bspc.2018.08.035
K. Chandra, A. Xie, J. Ragan-Kelley, and E. Meijer, “Gradient descent: The ultimate optimizer,” Advances in Neural Information Processing Systems, vol. 35, pp. 8214–8225, 2022.
S. Bock, J. Goppold, and M. Weiß, “An improvement of the conver- gence proof of the adam-optimizer,” arXiv preprint arXiv:1804.10587, 2018.
J. Duda, “Sgd momentum optimizer with step estimation by online parabola model,” arXiv preprint arXiv:1907.07063, 2019.
Kaggle, Minerals Identification Dataset, https:// www . kaggle. com/datasets/asiedubrempong/minerals-identification- dataset, 2024.
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