Parasitic egg recognition using convolution and attention network.

Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.

Streamflow classification by employing various machine learning models for peninsular Malaysia.

Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3 days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2 days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting.

RGB-D based multi-modal deep learning for spacecraft and debris recognition.

Recognition of space objects including spacecraft and debris is one of the main components in the space situational awareness (SSA) system. Various tasks such as satellite formation, on-orbit servicing, and active debris removal require object recognition to be done perfectly. The recognition task in actual space imagery is highly complex because the sensing conditions are largely diverse. The conditions include various backgrounds affected by noise, several orbital scenarios, high contrast, low signal-to-noise ratio, and various object sizes. To address the problem of space recognition, this paper proposes a multi-modal learning solution using various deep learning models. To extract features from RGB images that have spacecraft and debris, various convolutional neural network (CNN) based models such as ResNet, EfficientNet, and DenseNet were explored. Furthermore, RGB based vision transformer was demonstrated. Additionally, End-to-End CNN was used for classification of depth images. The final decision of the proposed solution combines the two decisions from RGB based and Depth-based models. The experiments were carried out using a novel dataset called SPARK which was generated under a realistic space simulation environment. The dataset includes various images with eleven categories, and it is divided into 150 k of RGB images and 150 k of depth images. The proposed combination of RGB based vision transformer and Depth-based End-to-End CNN showed higher performance and better results in terms of accuracy (85%), precision (86%), recall (85%), and F1 score (84%). Therefore, the proposed multi-modal deep learning is a good feasible solution to be utilized in real tasks of SSA system.