Comparative analysis for augmented decision-making applications using deep learning models


  • P. Durga School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh 522237, India
  • S. Karthikeyan School of Computer Science and Engineering, KPR Institute of Engineering and Technology, Arasur, Tamil Nadu 641407, India



autonomous vehicles, decision making, deep learning, disease prediction, risk prediction, sentiment analysis


Now a days decision-making plays a significant role in various applications and several research. For applications such as diseases, intelligent routing systems, and online shopping carts such as e-commerce sites, recommended systems are developed based on sentiment analysis (SA) and take accurate decision-making based on the predictions and analyze the accurate decisions based on the result analysis. When it comes to practical uses, deep learning (DL) has by far been the most popular. DL becomes an indispensable domain for several tasks in science and engineering. It is very difficult to take decisions based on traditional tests in various research areas such as disease prediction, textual sentiment analysis, and risk prediction of autonomous vehicles due to the lack of accuracy and long time for results. To address this, various approaches are proposed to adopt. Decision-making is based on multi-criticism, which is more useful to solve critical issues in making accurate decisions than previous approaches. In this paper, an improved and augmented decision-making deep learning algorithm is discussed and shows the comparison among the various DL algorithms. The performance is calculated according to the parameters.


Alojail, M., & Bhatia, S. (2020). A novel technique for behavioral analytics using ensemble learning algorithms in E-commerce. IEEE Access, 8, 150072-150080.

Arabneydi, J., & Aghdam, A. G. (2020). Deep teams: Decentralized decision making with finite and infinite number of agents. IEEE Transactions on Automatic Control, 65(10), 4230-4245.

Aradi, S. (2020). Survey of deep reinforcement learning for motion planning of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(2), 740-759.

Arockia Panimalar, S., Krishnakumar, A. (2023). A review of churn prediction models using different machine learning and deep learning approaches in cloud environments. Journal of Current Science and Technology, 13(1),136-161. DOI:10.14456/jcst.2023.12136

Banerjee, T., Batta, D., Jain, A., Karthikeyan, S., Mehndiratta, H., & Hari Kishan, K. (2021, January 2–3). Deep belief convolutional neural network with artificial image creation by gans based diagnosis of pneumonia in radiological samples of the pectoralis major [Conference presentation]. Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE 2021. Springer Singapore.

Banerjee, T., Jain, A., Sethuraman, S. C., Satapathy, S. C., Karthikeyan, S., & Jubilson, A. (2022). Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite. Multimedia Tools and Applications, 81(10), 13237-13251.

Chakraborty, K., Bhattacharyya, S., & Bag, R. (2020). A survey of sentiment analysis from social media data. IEEE Transactions on Computational Social Systems, 7(2), 450-464.

Chen, T., Zhu, J., Zeng, Z., & Jia, X. (2021). Compressor fault diagnosis knowledge: A benchmark dataset for knowledge extraction from maintenance log sheets based on sequence labeling. IEEE Access, 9, 59394-59405.

Chenyang, L., & Chan, S. C. (2020). A joint detection and recognition approach to lung cancer diagnosis from CT images with label uncertainty. IEEE Access, 8, 228905-228921.

Ding, H., Cen, Q., Si, X., Pan, Z., & Chen, X. (2022). Automatic glottis segmentation for laryngeal endoscopic images based on U-Net. Biomedical Signal Processing and Control, 71(A), Article 103116.

Elhadad, M. K., Li, K. F., & Gebali, F. (2020). Detecting misleading information on COVID-19. IEEE Access, 8, 165201-165215.

Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhang, Y. (2020). A decision-making strategy for vehicle autonomous braking in emergency via deep reinforcement learning. IEEE transactions on vehicular technology, 69(6), 5876-5888.

Gao, J., Tian, L., Wang, J., Chen, Y., Song, B., & Hu, X. (2020). Similar disease prediction with heterogeneous disease information networks. IEEE Transactions on NanoBioscience, 19(3), 571-578.

Ge, R., Zhang, R., & Wang, P. (2020). Prediction of chronic diseases with multi-label neural network. IEEE Access, 8, 138210-138216.

Geweid, G. G., & Abdallah, M. A. (2019). A new automatic identification method of heart failure using improved support vector machine based on duality optimization technique. IEEE Access, 7, 149595-149611.

Haq, A. U., Li, J. P., Memon, M. H., Malik, A., Ahmad, T., Ali, A., ... & Shahid, M. (2019). Feature selection based on L1-norm support vector machine and effective recognition system for Parkinson’s disease using voice recordings. IEEE access, 7, 37718-37734.

Iqbal, S., Siddiqui, G. F., Rehman, A., Hussain, L., Saba, T., Tariq, U., & Abbasi, A. A. (2021). Prostate cancer detection using deep learning and traditional techniques. IEEE Access, 9, 27085-27100.

Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. N. (2020). A systematic review on the use of AI and ML for fighting the COVID-19 pandemic. IEEE Transactions on Artificial Intelligence, 1(3), 258-270.

Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., ... & Mohyuddin, W. (2020). Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access, 8, 109581-109595.

Jeong, Y., & Yi, K. (2020). Bidirectional long shot-term memory-based interactive motion prediction of cut-in vehicles in urban environments. IEEE Access, 8, 106183-106197.

Ju, R., Hu, C., & Li, Q. (2017). Early diagnosis of Alzheimer's disease based on resting-state brain networks and deep learning. IEEE/ACM transactions on computational biology and bioinformatics, 16(1), 244-257.

Kraising, T., Wongthai, W., Phoka, T., Niruntasukrat, A. and Ruttanapahat, N. (2022). A deep learning model for air leak detection from a pipe fitting using an accelerometer. Asia-Pacific Journal of Science and Technology, 28(2). Article APST-28-02-11.

Li, J. P., Haq, A. U., Din, S. U., Khan, J., Khan, A., & Saboor, A. (2020a). Heart disease identification method using machine learning classification in e-healthcare. IEEE Access, 8, 107562-107582.

Li, L. F., Wang, X., Hu, W. J., Xiong, N. N., Du, Y. X., & Li, B. S. (2020b). Deep learning in skin disease image recognition: A review. IEEE Access, 8, 208264-208280.

Li, L., Zhang, Q., Wang, X., Zhang, J., Wang, T., Gao, T. L., ... & Wang, F. Y. (2020c). Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on computational social systems, 7(2), 556-562.

Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019). Deep learning for hyperspectral image classification: An overview. IEEE Transactions on Geoscience and Remote Sensing, 57(9), 6690-6709.

Li, Z., Zheng, T., Wang, Y., Cao, Z., Guo, Z., & Fu, H. (2020). A novel method for imbalanced fault diagnosis of rotating machinery based on generative adversarial networks. IEEE Transactions on Instrumentation and Measurement, 70, 1-17.

Liao, J., Liu, T., Tang, X., Mu, X., Huang, B., & Cao, D. (2020). Decision-making strategy on highway for autonomous vehicles using deep reinforcement learning. IEEE Access, 8, 177804-177814.

Ling, C., Zhang, T., & Chen, Y. (2019). Customer purchase intent prediction under online multi-channel promotion: A feature-combined deep learning framework. IEEE Access, 7, 112963-112976.

Liu, R., Shi, Y., Ji, C., & Jia, M. (2019a). A survey of sentiment analysis based on transfer learning. IEEE Access, 7, 85401-85412.

Liu, Y., Wang, X., Li, L., Cheng, S., & Chen, Z. (2019b). A novel lane change decision-making model of autonomous vehicle based on support vector machine. IEEE access, 7, 26543-26550.

Lu, Y., Xu, X., Zhang, X., Qian, L., & Zhou, X. (2020). Hierarchical reinforcement learning for autonomous decision making and motion planning of intelligent vehicles. IEEE Access, 8, 209776-209789.

Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 7, 81542-81554.

Muhammad, K., Ullah, A., Lloret, J., Del Ser, J., & de Albuquerque, V. H. C. (2020). Deep learning for safe autonomous driving: Current challenges and future directions. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4316-4336.

Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., & Shoaib, M. (2020). A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access, 8, 55135-55144.

Oh, Y., Park, S., & Ye, J. C. (2020). Deep learning COVID-19 features on CXR using limited training data sets. IEEE transactions on medical imaging, 39(8), 2688-2700.

Onan, A. (2018a). An ensemble scheme based on language function analysis and feature engineering for text genre classification. Journal of Information Science, 44(1), 28-47.

Onan, A. (2018b). Biomedical text categorization based on ensemble pruning and optimized topic modelling. Computational and Mathematical Methods in Medicine, 2018.

Onan, A. (2019a). Consensus clustering-based undersampling approach to imbalanced learning. Scientific Programming, 2019. 1-14.

Onan, A. (2019b). Topic-enriched word embeddings for sarcasm identification [Conference presentation]. In Software Engineering Methods in Intelligent Algorithms: Proceedings of 8th Computer Science On-line Conference 2019, Vol. 1 8. Springer International Publishing.

Onan, A. (2021). Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurrency and Computation: Practice and Experience, 33(23), Article e5909.

Onan, A. (2022). Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. Journal of King Saud University-Computer and Information Sciences, 34(5), 2098-2117.

Onan, A., & Korukoğlu, S. (2017). A feature selection model based on genetic rank aggregation for text sentiment classification. Journal of Information Science, 43(1), 25-38.

Onan, A., & Toçoğlu, M. A. (2021). A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification. IEEE Access, 9, 7701-7722.

Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232-247.

Onan, A., Korukoğlu, S., & Bulut, H. (2017). A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification. Information Processing & Management, 53(4), 814-833.

Otter, D. W., Medina, J. R., & Kalita, J. K. (2020). A survey of the usages of deep learning for natural language processing. IEEE transactions on neural networks and learning systems, 32(2), 604-624.

Qin, J., Chen, L., Liu, Y., Liu, C., Feng, C., & Chen, B. (2019). A machine learning methodology for diagnosing chronic kidney disease. IEEE Access, 8, 20991-21002.

Ramathulasi, T., & Rajasekharababu, M. (2022). Augmented latent Dirichlet allocation model via word embedded clusters for mashup service clustering. Concurrency and Computation: Practice and Experience, 34(15), Article e6896.

Sanson, J. B., Tomé, P. M., Castanheira, D., Gameiro, A., & Monteiro, P. P. (2020). High-resolution delay-Doppler estimation using received communication signals for OFDM radar-communication system. IEEE Transactions on Vehicular Technology, 69(11), 13112-13123.

Shakeel, P. M., Tobely, T. E. E., Al-Feel, H., Manogaran, G., & Baskar, S. (2019). Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access, 7, 5577-5588.

Shi, X., Wong, Y. D., Chai, C., & Li, M. Z. F. (2020). An automated machine learning (AutoML) method of risk prediction for decision-making of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(11), 7145-7154.

Wan, F., Guo, G., Zhang, C., Guo, Q., & Liu, J. (2019). Outlier detection for monitoring data using stacked autoencoder. IEEE Access, 7, 173827-173837.

Wang, C., Han, D., Liu, Q., & Luo, S. (2018). A deep learning approach for credit scoring of peer-to-peer lending using attention mechanism LSTM. IEEE Access, 7, 2161-2168.

Wang, T., Lu, K., Chow, K. P., & Zhu, Q. (2020). COVID-19 sensing: negative sentiment analysis on social media in China via BERT model. IEEE Access, 8, 138162-138169.

Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE access, 8, 23522-23530.

Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., & Wei, M. (2019). A review on deep learning applications in prognostics and health management. IEEE Access, 7, 162415-162438.




How to Cite

P. Durga, & S. Karthikeyan. (2023). Comparative analysis for augmented decision-making applications using deep learning models. Journal of Current Science and Technology, 13(3), 791–803.



Review Article