An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Fifteenth International Conference on Machine Vision (ICMV 2022)

Dang

Miyazaki

et al. 2023

It is a well-known fact that the quality of a seed highly impacts the germination of a rice seed. The age of the seed is one of the primary key points in assessing the seed quality. Therefore, this study aims to develop an AI-based machine-learning model to classify age-wise rice seeds. This study employs the SURF-BOF-based Cascaded-ANFIS algorithm for the implementation of the classifier. The proposed model performances were compared to the VGG16. Moreover, this research contributes a novel Japanese rice seed dataset to the scientific community. Furthermore, a 10-Fold cross-validation is performed to evaluate the robustness of the novel approach. The K-fold cross-validation's mean accuracy confirmed the proposed algorithm's higher robustness in the age-wise classification of rice seeds. Nevertheless, the results were evaluated using the confusion matrix and metrics such as precision, recall, and F1-Score. The Accuracy of Akitakomachi, Koshihikari, Yandao-8, and rice variety classification is 99%, 99%, 92%, and 97%. Analysis of the results determines the ability to classify rice by age and the general robustness of the algorithm.

Section: Feature Extractionmentioning

confidence: 99%

An efficient approach for age-wise rice seeds classification using SURF-BOF with modified cascaded-ANFIS algorithm

Fifteenth International Conference on Machine Vision (ICMV 2022)

Dang

Miyazaki

et al. 2023

“…If not, the algorithm advances to the next iteration. This article for implementation provides a thorough introduction to the Cascaded-ANFIS algorithm with pseudo-code [31], [32], [69].…”

Section: Projecting Algorithm -Cascaded-anfismentioning

confidence: 99%

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Chathuranika

et al. 2023

IEEE Access

Self Cite

The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFISbased model outperforms the other algorithms. The correlation coefficient of each algorithm's predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

“…ANFIS has been extensively used to create novel and hybrid algorithms because of its high performance, and dependability [17,38,39]. Additionally, these algorithms have demonstrated promising results in various applications [40][41][42].…”

Section: Adaptive Network Based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka

Dang

et al. 2023

PLoS ONE

Self Cite

Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions.