2024

[J41] D. Das, D. R. Nayak, S. V. Bhandary, U. R. Acharya, “CDAM-Net: Channel Shuffle Dual Attention Based Multi-Scale CNN for Efficient Glaucoma Detection Using Fundus Images”, Engineering Applications of Artificial Intelligence, Elsevier, 2024. (In press)

[J40] Babita, D. R. Nayak, "RDTNet: A residual deformable attention based transformer network for breast cancer classification", Expert Systems with Applications, Elsevier, vol. 249, 2024. 

[J39] P. Sharma, D. R. Nayak, B. Balabantaray, M. Tanveer, R. Nayak, "A Survey on Cancer Detection via Convolutional Neural Networks: Current Challenges and Future Directions", Neural Networks, Elsevier, vol. 169, 2024.

​

2023   

[J38] D. Das, D. R. Nayak, R. B. Pachori, "CA-Net: A Novel Cascaded Attention-based Network for Multi-stage Glaucoma Classification using Fundus Images", IEEE Transactions on Instrumentation & Measurement, 2023. (Accepted) 

[J37] T. K. Dutta, D. R. Nayak, Y. Zhang, "ARM-Net: Attention-guided Residual Multiscale CNN for Multiclass Brain Tumor Classification using MR Images", Biomedical Signal Processing and Control, Elsevier, 2023. (Accepted)

[J36] H. K. Gajera, D. R. Nayak, M. A. Zaveri, "M2CE: Multi-CNN ensemble approach for improved multiclass classification of skin lesion", Expert Systems, 2023. (Accepted) 

[C15]. D. Das, D. R. Nayak, "GS-Net: Global Self-Attention Guided CNN for Multi-Stage Glaucoma Classification", in 30th IEEE International Conference on Image Processing (ICIP 2023), 2023, Kuala Lumpur, Malaysia. (Accepted) (Core Rank B)

[J35] Y Zhang, J. M. Gorriz, D. R. Nayak, "Optimization Algorithms and Machine Learning Techniques in Medical Image Analysis", Mathematical Biosciences and Engineering, vol. 20, 2023. 

[J34] R. Nayak, D. R. Nayak, U. Sinha, V. Arora, R. B. Pachori, "An Efficient Deep Learning Method for Detection of COVID-19 Infection using Chest X-ray Images", Diagnostics, vol. 13, 2023.

​

2022

​

[C14] S. Majhi, D. R. Nayak, R. Dash, P. K. Sa, "Multi-level 3DCNN with Min-Max Ranking Loss for Weakly-supervised Video Anomaly Detection", in 29th International Conference on Neural Information Processing (ICONIP 2022), 2022. (Core Rank A)

[C13] H. K. Gajera, M. A. Zaveri, D. R. Nayak, "Towards Exploring Deep Features for Efficient Melanoma Diagnosis in Dermoscopic Images", in 20th OITS International Conference on Information Technology 2022 (OCIT 2022), Bhubaneswar, India

[J33] H. K. Gajera, D. R. Nayak, M. A. Zaveri, “A Comprehensive Analysis of Dermoscopy Images for Melanoma Detection via Deep CNN Features”, Biomedical Signal Processing and Control, Elsevier, vol. 79, September 2022.  (IF: 5.076)

[J32] A. Joshi, D. R. Nayak, "MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis from CT Images", IEEE Journal of Biomedical and Health Informatics (Formerly known as IEEE Transactions on Information Technology in Biomedicine), IEEE, vol. 26, no. 11, pp. 5355 - 5363, 2022. (IF: 7.021)

[J31] A. Joshi, D. R. Nayak, D. Das, Y. Zhang, "LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scan", ACM Transactions on Management Information Systems, ACM, vol. 14, no. 1, pp. 1-17, 2022. (IF: 2.5)

[C12]. T. K. Dutta, D. R. Nayak, "CDANet: Channel Split Dual Attention based CNN for Brain Tumor Classification in MR Images", in 29th IEEE International Conference on Image Processing (ICIP 2022), 2022, Bordeaux, France. (Accepted) (Core Rank B)

[C11]. A. Joshi, D. R. Nayak, "GDenseMNet: Global Dense Multiscale Feature Learning Network for Efficient COVID-19 Detection in CT Images", in 2022 International Joint Conference on Neural Networks (IJCNN), 2022, Padua, Italy. (Accepted) (Core Rank A)

[C10]. H. K. Gajera, D. R. Nayak, M. A. Zaveri, "Fusion of Local and Global Feature Representation with Sparse Autoencoder for Improved Melanoma Classification", in 44th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022, Glasgow, Scotland, UK. (Accepted) 

[J30]. H. K. Gajera, M. A. Zaveri, D. R. Nayak, "Patch based Local Deep Feature Extraction for Automated Skin Cancer Classification", International Journal of Imaging Systems and Technology, Wiley, 2022. (IF: 2.000) [In press]

​

2021

​

[J29]. S. Lu, D. R. Nayak, S. Wang, Y. Zhang, "A Cerebral Microbleed Diagnosis Method via FeatureNet and Ensembled Randomized Neural Networks", Applied Soft Computing, Elsevier, vol. 109, 2021. (IF: 8.263) (In press)

[J28]. D. R. Nayak, D. Das, B. Majhi, S. V. Bhandary, U. R. Acharya, “ECNet: An Evolutionary Convolutional Network for Automated Glaucoma Detection using Fundus Images”, Biomedical Signal Processing and Control, Elsevier, vol. 67, 2021.  (IF: 5.076)

[J27]. P. P. Sarangi, D. R. Nayak, M. Panda, B. Majhi, "A Feature-Level Fusion based Improved Multimodal Biometric Recognition System using Ear and Profile Face", Journal of Ambient Intelligence and Humanized Computing, Springer, 2021 (In press) (IF: 3.662).

[J26]. S. Wang, D. R. Nayak, D. S. Guttery, X. Zhang, Y. Zhang, "COVID-19 Classification by CCSHNet with Deep Fusion using Transfer Learning and Discriminant Correlation Analysis", Information Fusion, Elsevier, vol. 68, pp. 131-148, 2021 (IF: 17.564).

[C9]. S. Majhi, D. R. Nayak, "Feature Modulating Two-stream Deep Convolutional Neural Network for Glaucoma Detection in Fundus Images" in 6th IAPR International Conference on Computer Vision & Image Processing (CVIP2021), 2021, IIT Ropar, India. (Accepted) 

[C8]. H. K. Gajera, M. A. Zaveri, D. R. Nayak, "Improving the Performance of Melanoma Detection in Dermoscopy Images using Deep CNN Features" in 19th International Conference on Artificial Intelligence in Medicine (AIME 2021), 2021, Porto, Portugal. (Core Ranking B)

​

2020

​

[J25]. S. R. Nayak, D. R. Nayak, U. Sinha, V. Arora, R. B. Pachori, "Application of Deep Learning Techniques for Detection of COVID-19 Cases using Chest X-ray Images: A Comprehensive Study," Biomedical Signal Processing and Control, Elsevier, vol. 64, 2020. (IF: 5.076)

[J24]. N. B. Kar D. R. Nayak, K. Babu, Y. Zhang, "A Hybrid Feature Descriptor with Jaya Optimized Least Squares SVM for Facial Expression Recognition", IET Image Processing, IEEE, 2020 (In press) 

[J23]. Y. Zhang, D. R. Nayak, X. Zhang, S. Wang, "Diagnosis of Secondary Pulmonary Tuberculosis by an Eight-layer Improved Convolutional Neural Network with Data Augmentation and Stochastic Pooling", Journal of Ambient Intelligence and Humanized Computing, Springer, 2020. (IF: 3.662)

[J22]. D. Das, D. R. Nayak, R. Dash, B. Majhi, “MJCN: Multi-Objective Jaya Convolutional Network for Handwritten Optical Character Recognition”, Multimedia Tools and Applications, Springer, vol. 79, pp. 33023-33042, 2020. (IF: 2.577)

[J21]. D. R. Nayak, R. Dash, B. Majhi, “Automated Diagnosis of Multi-class Brain Abnormalities using MRI Images: A Deep Convolutional Neural Network based Method", Pattern Recognition Letters, Elsevier, vol. 138, pp. 385-391, 2020. (IF: 4.757)

[J20]. C. Tang, D. R. Nayak, S. Wang, "Least‐square Support Vector Machine and Wavelet Selection for Hearing Loss Identification", Computer Modeling in Engineering & Sciences, vol. 125, pp. 299-313, 2020. (IF: 2.067)

[J19]. D. Das, D. R. Nayak, R. Dash, B. Majhi, "H-WordNet: A Holistic Convolutional Neural Network Approach for Handwritten Word Recognition", IET Image Processing, IEEE, vol. 14, pp. 1794-1805, 2020 

[J18]. D. R. Nayak, R. Dash, B. Majhi, R. B. Pachori, Y. Zhang, “A Deep Stacked Random Vector Functional Link Network Autoencoder for Diagnosis of Brain Abnormalities and Breast Cancer", Biomedical Signal Processing & Control, Elsevier, vol. 58, 2020. (IF: 5.076)

[J17]. D. R. Nayak, R. Dash, X. Chang, B. Majhi, S. Bakshi, “Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features," IEEE Transactions on Sustainable Computing, IEEE, vol. 5, pp. 416-427, 2020. (IF: 4.908) 

​

2019

​

[J16]. D. R. Nayak, R. Dash, B. Majhi, Y. Zhang, “A Hybrid Regularized Extreme Learning Machine for Automated Detection of Pathological Brain," Biocybernetics and Biomedical Engineering, Elsevier, vol. 39, pp. 880 - 892, 2019. (IF: 5.687) 

[J15]. D. R. Nayak, R. Dash, B. Majhi, U. R. Acharya, “Application of Fast Curvelet Tsallis Entropy and Kernel Random Vector Functional Link Network for Automated Detection of Multiclass Brain Abnormalities," Computerized Medical Imaging and Graphics, Elsevier, vol. 77, 2019. (IF: 7.422) [link]

[J14]. D. R. Nayak, Y. Zhang, D. Das, S. Panda, "MJaya-ELM: A Jaya Algorithm with Mutation and Extreme Learning Machine based Approach for Sensorineural Hearing Loss Detection," Applied Soft Computing, Elsevier, vol. 83, 2019. (IF: 8.263)  [link]

[J13]. D. Das, D. R. Nayak, R. Dash, B. Majhi, “An Empirical Evaluation of Extreme Learning Machine: Application to Handwritten Character Recognition," Multimedia Tools and Applications, Springer, vol. 78, no. 14, pp.  19495–19523, 2019. (IF: 2.577)  [link]

[J12]. D. R. Nayak, D. Das, R. Dash, S. Majhi, B. Majhi, “Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images," Multimedia Tools and Applications, Springer, vol. 79, pp. 15381–15396, 2019. (IF: 2.577) [link]

​

2018

​

[J11]. D. R. Nayak, R. Dash, B. Majhi, “Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection," Neurocomputing, Elsevier, vol. 282, pp. 232-247, 2018. (IF: 5.779) 

[J10]. D. R. Nayak, R. Dash, B. Majhi, S. Wang, “Combining extreme learning machine with modified sine cosine algorithm for pathological brain detection," Computers & Electrical Engineering, Elsevier, vol. 68, pp. 366-380, 2018. (IF: 4.152)

[J9]. D. R. Nayak, R. Dash, B. Majhi, “An improved pathological brain detection system based on two-dimensional PCA and evolutionary extreme learning machine," Journal of Medical Systems, Springer, vol. 42, no. 1, pp. 1-15, 2018. (IF: 4.920)

[J8]. D. R. Nayak, R. Dash, B. Majhi, “Development of pathological brain detection system using Jaya optimized improved extreme learning machine and orthogonal ripplet-II transform," Multimedia Tools and Applications, Springer, vol. 77, pp. 22705-22733, 2018. (IF: 2.577)

[J7]. D. R. Nayak, R. Dash, B. Majhi, “Pathological brain detection using curvelet features and least squares SVM," Multimedia Tools and Applications, Springer, vol. 77, issue 3, pp. 3833-3856, 2018. (IF: 2.577)

[C7]. D. R. Nayak, R. Dash, Z. Lu, S. Lu, B. Majhi, "SCA-RELM: A New Regularized Extreme Learning Machine Based on Sine Cosine Algorithm for Automated Detection of Pathological Brain”, in 27th International Symposium on Robot and Human Interactive Communication (RO-MAN 2018), IEEE, 2018, pp. 764-769, Nanjing, China. [link]

​

2017

​

[J6]. D. R. Nayak, R. Dash, B. Majhi, V. Prasad, “Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach," Expert Systems with Applications, Elsevier, vol. 88, pp. 152-164, 2017. [link]

[J5]. D. R. Nayak, R. Dash, B. Majhi, “Stationary wavelet transform and AdaBoost with SVM based pathological brain detection in MRI scanning," CNS & Neurological Disorders Drug Targets, Bentham Science, vol. 16, no. 2, pp. 137-149, 2017. [link]

[J4]. Y. Zhang, D. R. Nayak, M. Yang, Y. Shao, B. Liu, S. Wang, “Detection of unilateral hearing loss by stationary wavelet entropy," CNS & Neurological Disorders Drug Targets, Bentham Science, vol. 16, no. 2, pp. 122-128, 2017. [link] 

[J3]. S H Wang, XX Zhou, JQ Yang, DR Nayak, C. Reyes, ZC Dong, R. Watson, C. Cattani, J. Young, W. Gray, LX Han, Y. Yao, LT Fang, P. Phillips, SD Du, YD Zhang, “ANTIDOTE: A Wechat Public Platform Provide Urgent Treatment for Patients with Tetrodotoxin Intoxication," Basic & Clinical Pharmacology & Toxicology, Wiley, vol. 121, 2017. [link]

[C6]. D. R. Nayak, R. Dash, B. Majhi, “Pathological Brain Detection using Extreme Learning Machine Trained with Improved Whale Optimization Algorithm”, in 9th International Conference on Advances in Pattern Recognition (ICAPR 2017), IEEE, 2017, ISI Bangalore. [link]

[C5]. D. R. Nayak, R. Dash, B. Majhi, “An Improved Extreme Learning Machine for Pathological Brain Detection”, in 2017 IEEE Region 10 Conference (TENCON), IEEE, 2017, pp. 13-18, Penang, Malaysia. [link]

[C4]. S. Ranjan, D. R. Nayak, K. S. Kumar, R. Dash, B. Majhi, “Hyperspectral image classification: A k-means clustering based approach”, in 4th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 2017, pp. 1-7, Coimbatore, India. [link]

​

2016

​

[J2]. D. R. Nayak, R. Dash, B. Majhi, “Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests," Neurocomputing, Elsevier, vol. 177, pp. 188–197, 2016. [link]

[J1]. D. R. Nayak, R. Dash, B. Majhi, J. Mohammed, “Non-linear cellular automata based edge detector for optical character images," Simulation: Transactions of the Society for Modeling and Simulation International, vol. 92, issue 9, pp. 849-859, 2016. [link] 

[C3]. D. R. Nayak, R. Dash, B. Majhi, “Salt and Pepper Noise Reduction Schemes Using Cellular Automata”, in Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics (ICACNI), Springer, 2016, pp. 427-435. [link]

​

2015

[C2]. D. R. Nayak, R. Dash, B. Majhi, “Classification of Brain MR Images Using Discrete Wavelet Transform and Random Forests”, in Proceedings of 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), IEEE, 2015, IIT Patna. [link]

[C1]. D. R. Nayak, R. Dash, B. Majhi, “Least Squares SVM Approach for Abnormal Brain Detection in MRI Using Multiresolution Analysis”, in International Conference on Computing, Communication and Security (ICCCS), IEEE, 2015, pp. 1-6, Mauritius. [link]

​