Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer’s disorder detection using EEG signals

Janghel RR, Rathore YK. Deep convolution neural network based system for early diagnosis of Alzheimer’s disease. Irbm. 2021;42(4):258–67.

Article  Google Scholar 

Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG dynamics for early diagnosis of Alzheimer’s disease using permutation entropy neuromarker. Comput Methods Programs Biomed. 2021;206: 106116.

Article  PubMed  Google Scholar 

Sidulova M, Nehme N, Park CH. Towards Explainable Image Analysis for Alzheimer’s Disease and Mild Cognitive Impairment Diagnosis. In 2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) (pp. 1–6). IEEE, (2021).

Tavakoli N, Karimi Z, AsadiJouzani S, Azizi N, Rezakhani S, Tobeiha A. Machine Learning-Based Brain Diseases Diagnosing in Electroencephalogram Signals, Alzheimer’s, and Parkinson’s. In Prognostic Models in Healthcare: AI and Statistical Approaches (pp. 161–191). Singapore: Springer Nature Singapore, (2022).

Sharma R, Goel T, Tanveer M, Lin CT, Murugan R. Deep learning based diagnosis and prognosis of Alzheimer’s disease: A comprehensive review. IEEE Transactions on Cognitive and Developmental Systems, (2023).

Dixit S, Gaikwad A, Vyas V, Shindikar M, Kamble K. United Neurological study of disorders: Alzheimer’s disease, Parkinson's disease detection, Anxiety detection, and Stress detection using various Machine learning Algorithms. In 2022 International Conference on Signal and Information Processing (IConSIP) (pp. 1–6). IEEE, (2022).

Arjaria SK, Rathore AS, Bisen D, Bhattacharyya S. Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease. Annals of Data Science, pp.1–29, (2022).

Shajin FH, Salini P, Rajesh P, Nagoji Rao VK. Efficient framework for brain tumour classification using hierarchical deep learning neural network classifier. Comput Methods Biomech Biomed Eng: Imaging Vis. 2023;11(3):750–7.

Google Scholar 

Fouladi S, Safaei AA, Arshad NI, Ebadi MJ, Ahmadian A. The use of artificial neural networks to diagnose Alzheimer’s disease from brain images. Multimed Tools Appl. 2022;81(26):37681–721.

Article  Google Scholar 

Deepa N, Chokkalingam SP. Optimization of VGG16 utilizing the arithmetic optimization algorithm for early detection of Alzheimer’s disease. Biomed Signal Process Control. 2022;74: 103455.

Article  Google Scholar 

Ghoraani B, Boettcher LN, Hssayeni MD, Rosenfeld A, Tolea MI, Galvin JE. Detection of mild cognitive impairment and Alzheimer’s disease using dual-task gait assessments and machine learning. Biomed Signal Process Control. 2021;64: 102249.

Article  PubMed  Google Scholar 

Tu Y, Lin S, Qiao J, Zhuang Y, Zhang P. Alzheimer’s disease diagnosis via multimodal feature fusion. Comput Biol Med. 2022;148: 105901.

Article  PubMed  Google Scholar 

Dao Q, El-Yacoubi MA, Rigaud AS. Detection of alzheimer disease on online handwriting using 1D convolutional neural network. IEEE Access. 2022;11:2148–55.

Article  Google Scholar 

English M, Kumar C, Ditterline BL, Drazin D, Dietz N. Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer’s Disease, and Schizophrenia. Machine Learning in Clinical Neuroscience: Foundations and Applications, pp.349–361, (2022).

Seifallahi M, Mehraban AH, Galvin JE, Ghoraani B. Alzheimer’s disease detection using comprehensive analysis of Timed Up and Go test via Kinect V. 2 camera and machine learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, pp.1589–1600, (2022).

Sudharsan M, Thailambal G. Alzheimer’s disease prediction using machine learning techniques and principal component analysis (PCA). Materials Today: Proceedings (2021).

Liu J, Li M, Luo Y, Yang S, Li W, Bi Y. Alzheimer’s disease detection using depthwise separable convolutional neural networks. Comput Methods Programs Biomed. 2021;203: 106032.

Article  PubMed  Google Scholar 

NoorulJulaiha A, Priyatharshini R. A Study on Automatic Detection of Alzheimer’s Disease Using Multimodalities. In Rising Threats in Expert Applications and Solutions: Proceedings of FICR-TEAS 2022 (pp. 631–642). Singapore: Springer Nature Singapore (2022).

Alorf A, Khan MUG. Multi-label classification of Alzheimer’s disease stages from resting-state fMRI-based correlation connectivity data and deep learning. Comput Biol Med. 2022;151: 106240.

Article  PubMed  Google Scholar 

Kaplan E, Dogan S, Tuncer T, Baygin M, Altunisik E. Feed-forward LPQNet based automatic alzheimer’s disease detection model. Comput Biol Med. 2021;137: 104828.

Article  PubMed  Google Scholar 

Zhou Y, Lu Y, Pei Z. Intelligent diagnosis of Alzheimer’s disease based on internet of things monitoring system and deep learning classification method. Microprocess Microsyst. 2021;83: 104007.

Article  Google Scholar 

Helaly HA, Badawy M, Haikal AY. Deep learning approach for early detection of Alzheimer’s disease. Cogn Comput. 2022;14(5):1711–27.

Article  Google Scholar 

Alvi AM, Siuly S, Wang H, Wang K, Whittaker F. A deep learning based framework for diagnosis of mild cognitive impairment. Knowl-Based Syst. 2022;248: 108815.

Article  Google Scholar 

Petti U, Baker S, Korhonen A. A systematic literature review of automatic Alzheimer’s disease detection from speech and language. J Am Med Inform Assoc. 2020;27(11):1784–97.

Article  PubMed  PubMed Central  Google Scholar 

Alvi AM, Siuly S, De Cola MC, Wang H. Dram-net: A deep residual alzheimer's diseases and mild cognitive impairment detection network using eeg data. In International Conference on Health Information Science (pp. 42–53). Cham: Springer Nature Switzerland, (2022).

Alvi AM, Siuly S, Wang H. A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. IEEE Trans Emerg Topics Comput Intell. 2022;7(2):375–88.

Article  Google Scholar 

Comput (2023) https://doi.org/10.1007/s12652-023-04683-w

Ebrahimi A, Luo S. Disease neuroimaging initiative, FTAS: convolutional neural networks for Alzheimer’s disease detection on MRI images. J Med Imaging. 2021;8(2):024503–024503.

Article  Google Scholar 

Alvi AM, Siuly S, Wang H. Developing a deep learning based approach for anomalies detection from EEG data. In International Conference on Web Information Systems Engineering (pp. 591–602). Cham: Springer International Publishing. (2021).

Al-Shoukry S, Rassem TH, Makbol NM. Alzheimer’s diseases detection by using deep learning algorithms: a mini-review. IEEE Access. 2020;8:77131–41.

Article  Google Scholar 

Dogan S, Baygin M, Tasci B, Loh HW, Barua PD, Tuncer T, Acharya UR. Primate brain pattern-based automated Alzheimer’s disease detection model using EEG signals. Cogn Neurodyn. 2023;17(3):647–59.

Article  PubMed  Google Scholar 

Alvi AM, Siuly S, Wang H. Challenges in electroencephalography data processing using machine learning approaches. In Australasian Database Conference (pp. 177–184). Cham: Springer International Publishing (2022).

https://ieee-dataport.org/documents/eeg-signal-dataset

Saab S Jr, Fu Y, Ray A, Hauser M. A dynamically stabilized recurrent neural network. Neural Process Lett. 2022;54(2):1195–209.

Article  Google Scholar 

El-Dabah MA, Hassan MH, Kamel S, Zawbaa HM. Robust parameters tuning of different power system stabilizers using a quantum artificial gorilla troops optimizer. IEEE Access. 2022;10:82560–79.

Article  Google Scholar 

Karthick R, Senthilselvi A, Meenalochini P, Senthil Pandi S. An optimal partitioning and floor planning for VLSI circuit design based on a hybrid bio-inspired whale optimization and adaptive bird swarm optimization (WO-ABSO) algorithm. J Circuits, Syst Comput. 2023;32(08):2350273.

Article  Google Scholar 

Karthick R, Dawood MS, Meenalochini P. Analysis of vital signs using remote photoplethysmography (RPPG). J Ambient Intell Humaniz Comput. 2023;14(12):16729–39.

Article  Google Scholar 

Meenalochini P, Karthick R, Sakthivel E. An Efficient Control Strategy for an Extended Switched Coupled Inductor Quasi-Z-Source Inverter for 3Φ Grid Connected System. J Circuits, SystComput (2023).

Jasper Gnana Chandran J, Karthick R, Rajagopal R, Meenalochini P. Dual-channel capsule generative adversarial network optimized with golden eagle optimization for pediatric bone age assessment from hand X-ray image. Int J Pattern Recognit Artif Intell. 2023;37(02):2354001.

Article  Google Scholar 

Zhang M, Liu J, Wang Y, Piao Y, Yao S, Ji W, Li J, Lu H, Luo Z. Dynamic context-sensitive filtering network for video salient object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1553–1563),(2021).

Zhang K, Ma C, Xu Y, Chen P, Du J. Feature extraction method based on adaptive and concise empirical wavelet transform and its applications in bearing fault diagnosis. Measurement. 2021;172: 108976.

Article  Google Scholar 

Loddo A, Buttau S, Di Ruberto C. Deep learning based pipelines for Alzheimer’s disease diagnosis: a comparative study and a novel deep-ensemble method. Comput Biol Med. 2022;141: 105032.

Article  PubMed  Google Scholar 

Rad EM, Azarnoosh M, Ghoshuni M, Khalilzadeh MM. Diagnosis of mild Alzheimer’s disease by EEG and ERP signals using linear and nonlinear classifiers. Biomed Signal Process Control. 2021;70: 103049.

Article  Google Scholar 

Rodrigues PM, Bispo BC, Garrett C, Alves D, Teixeira JP, Freitas D. Lacsogram: a new EEG tool to diagnose Alzheimer’s disease. IEEE J Biomed Health Inform. 2021;25(9):3384–95.

Article  PubMed  Google Scholar 

Safi MS, Safi SMM. Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters. Biomed Signal Process Control. 2021;65: 102338.

Article  Google Scholar 

AlSharabi K, Salamah YB, Abdurraqeeb AM, Aljalal M, Alturki FA. EEG signal processing for Alzheimer’s disorders using discrete wavelet transform and machine learning approaches. IEEE Access. 2022;10:89781–97.

Article  Google Scholar 

Alvi AM, Siuly S, Wang H. Neurological abnormality detection from electroencephalography data: a review. Artif Intell Rev. 2022;55(3):2275–312.

Article  Google Scholar 

Kibriya H, Masood M, Nawaz M, Nazir T. Multiclass classification of brain tumors using a novel CNN architecture. Multimed Tools Appl. 2022;81(21):29847–63.

Article  Google Scholar 

Puri DV, Nalbalwar SL, Nandgaonkar AB, Gawande JP, Wagh A. Automatic detection of Alzheimer’s disease from EEG signals using low-complexity orthogonal wavelet filter banks. Biomed Signal Process Control. 2023;81: 104439.

Article  Google Scholar 

Rajagopal RK, Karthick R, Meenalochini P, Kalaichelvi T. Deep convolutional spiking neural network optimized with arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomed Signal Process Control. 2023;79: 104197.

Article  Google Scholar 

Karthick R, Senthilselvi A, Meenalochini P, Senthil Pandi S. Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA. Circuits, Syst Signal Process. 2022;41(9):5254–82.

Article  Google Scholar 

Karthick R, Sundararajan M. SPIDER-based out-of-order execution scheme for Ht-MPSOC. Int J Adv Intell Paradig. 2021;19(1):28–41.

Google Scholar 

Karthick R, Meenalochini P. Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA). Journal of the National Science Foundation of Sri Lanka. (2020);48(4).

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