Deep Learning Modeling of Groundwater Pollution Sources

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

This paper examines the enhancement of security measures for the Internet of Things (IoT) systems through the application of Machine Learning (ML) techniques. As the number of IoT devices continues to rise, ensuring their security has become increasingly critical, given that conventional methods frequently struggle to identify advanced threats. This study explores the implementation of several ML algorithms, including Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to identify anomalies and intrusions within IoT networks. By conducting a comprehensive review of existing research and experiments, it highlights the effectiveness of ML in enhancing IoT security, with high detection rates for various threats, including botnet attacks, Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) incidents, and intrusion attempts. DoS/DDoS attacks and many types of botnets are the most devastating attacks that have been spreading for a long time, and they are still branching out in new ways against IoT networks. They can damage IoT services and prevent these services from being used by legitimate users. Therefore, securing IoT networks becomes a significant concern. The proposed model is used to increasingly monitor network traffic for any deviations from standard patterns IoT networks. This paper also stresses the necessity of utilising suitable datasets and feature selection techniques to enhance the efficacy of ML models. To train our model, we have utilized a dataset called the IoT23 dataset, which is one of the most recent datasets that has many IoT scenarios and anomalous activities. Further-more, we utilised two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA), and then we compared the results of these algorithms when training our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by CFS However, for training and testing times metrics, DT performance was superior across both feature selection methods.

Predictive modeling and data analysis are severely hampered by the enormous dimensionality and complexity of medical datasets. Enhancing model performance, interpretability, and computing efficiency all depend on careful feature selection. In order to extract the most pertinent characteristics from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, this paper offers a novel hybrid feature selection method that combines the Harvest Algorithm, Convolutional Neural Networks (CNN), and Correlation-Based Feature Selection (CFS). De-identified medical records, including diagnostic codes, vital signs, prescriptions, and other clinical observations, are all included in the MIMIC-III dataset. Our suggested approach makes use of CNN for deep feature extraction, the Harvest Algorithm for the first feature subset creation, and CFS for the final feature selection based on correlation measures. Test findings show that when compared to conventional feature selection techniques Random Forest with Information Gain Method (RF-IG) and SVM with Recursive Feature Elimination (SVM-RFE), our hybrid strategy greatly increases the accuracy and efficiency of prediction models. The chosen characteristics demonstrate the potential of our approach in clinical decision support and medical data analysis by offering significant insights into important variables influencing patient outcomes.

The objective of this study was to observe the potential of machine vision (MV) approach for the classification of eight citrus varieties. The leaf images of eight citrus varieties that were grapefruit, Moussami, Malta, Lemon, Kinow, Local lemon, Fuetrells, and Malta Shakri. These were acquired by a digital camera in an open environment without any complex laboratory setup. The acquired digital images dataset was transformed into the multi-feature dataset that was the combination of binary, histogram, texture, spectral, rotational, scalability and translational (RST) invariant features. For each citrus leaf image, total 57 multi-features were acquired on every non-overlapping region of interest (ROI), i.e. (32x32), (64x64), (128x128), and (256x256). Furthermore, the optimized 15 features using the supervised correlation-based feature selection (CFS) technique were acquired. The optimized multi-features dataset to different MV classifiers namely Multilayer Perceptron (MLP), Random Forest (RF), J48 and Naïve Bayes using10-fold cross-validation method were plugged-in. The results produced by MLP presented an average overall accuracy of 98.14% on ROIs (256x256) outperforming the other classifiers. The classification accuracy values by MLP on the eight citrus leaf varieties, namely; Grapefruit, Moussami, Malta, Lemon, Kinow, Local lemon, Fuetrells, and Malta Shakri were observed 98%, 98.75%, 99.25%, 97.5%, 97%, 95.87%, 95.5%, and 99.37% respectively.

A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems

Most of the oil wells in China use sucker rod pump. Traditionally, manual patrol is used to find the working conditions of pumping wells. In the trend of oilfield automation and intelligence, this traditional working mode can not adapt to the increasingly complex working conditions of the oilfield. Therefore, artificial intelligence technology has been widely applied to the automatic diagnosis of pumping unit well conditions. This paper mainly studies the trend prediction of working conditions, and proposes a trend prediction model based on long and short time(LSTM) memory neural network and convolutional neural network. Based on the real oilfield data, this paper trained the trend prediction model. The average accuracy of the model in the test set reached 86%, which can meet the actual needs of the production site, provide scientific decision-making basis for dynamic optimization of pumping well measures, reduce the maintenance workload and improve the operation efficiency of the oilfield.

The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring the flooded and flood-prone areas in the An Giang province in the Mekong Delta, especially its rice fields. Time series floodable area maps were generated from five images per month taken during the wet season (6–7 months) over two years (2019 and 2020). The methodology was based on automatic image classification through the application of Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) and random forests (RFs). Based on the segmentation technique, a three-level classification algorithm was developed to generate maps of the development of floods and floodable areas during the wet season. A modification of the backscatter intensity was noted for both polarizations, in accordance with the evolution of the phenology of the rice fields. The results show that the CNN-based methods can produce more reliable maps (99%) compared to the MLP and RF (97%). Indeed, in the classification process, feature extraction based on segmentation and CNNs has demonstrated an effective improvement in prediction performance of land use land cover (LULC) classes, deriving complex decision boundaries between flooded and non-flooded areas. The results show that between 53% and 58% of rice paddies areas and 9% and 14% of built-up areas are affected by the flooding in 2019 and 2020 respectively. Our methodology and results could support the development of the flood monitoring database and hazard management in the Mekong Delta.

Hyperspectral remote sensing data contains a large number of spectral bands along with a large a number of features. So for classification purposes, it becomes imperative to reduce the number of spectral bands as well as to reduce the number of features to achieve higher performance in terms of accuracy and computational complexity. Principal component analysis (PCA) and minimum noise fraction (MNF) were used for dimension reduction of spectral bands, on the other hand correlation based feature selection (CFS) technique was employed for selection of features. Parametric classifier like maximum likelihood classifier (MLC), advanced non parametric classifiers like support vector machine (SVM), Multilayer Perceptron (MLP) and ensemble random forest (RF) classifier were used for investigation in two test datasets of different land cover characteristics of Hyperion sensor. It was observed that all of the classifiers have behaved differently with the dimension reduced dataset produced either in spectral bands or feature level. Most of the classifiers have achieved higher performance with dimension reduced dataset produced by MNF as compared to PCA. On the other hand, dataset with selected features could not give noticeably better classification performance than the datasets with entire features. However, computational complexity of the classifiers has been reduced greatly while they were classified with selected features.

Financial markets, such as the stock market, bond market and foreign exchange market, are important channels for fund transfer. As a graphical analysis tool, candlestick charts use graphs to display the open, high, low, and close prices in a specific period. In the past, there have been attempts to identify the characteristics of candlesticks based on Gramian Angular Field (GAF) images, but they are not perfect. In this study, we implemented Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), AdaBoost, Random Forest (RF) and XGBoost models, we found that the use of deep learning models is not the best choice for the recognition of candlestick features based on GAF images. Comparing these models, MLP and CNN are better than AdaBoost and RF, but worse than XGBoost. Our results show that for the candlestick pattern classification problem based on GAF images, it is unnecessary to use complex CNNs and traditional machine learning models can also achieve satisfactory results with much less computation resources.

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Hyperspectral Image (HSI) classification is one of the fundamental tasks in the field of remote sensing data analysis. CNN (Convolutional Neural Network) has been proven to be an effective deep learning model, which can extract high-level features directly from the raw data and thereby utilize rich information contained in HSI data. However, labor cost to label enough HIS data for training model is usually expensive, so that it is a strong demand of utilizing limited training data to get a satisfied classification accuracy. In this paper, we put forward a deep cube CNN model – DCNR, which is composed of a cube neighbor HSI pixels strategy, a deep CNN and a random forest classifier. In DCNR model, cubic samples, containing spectral-spatial information, are generated by putting each target pixel and its neighbors together. Then features with high representative ability, extracted by applying a specially designed cube CNN model on each cubic sample, are fed into the random forest classifier for the classification of the target pixel. Results show that DCNR model can achieve classification accuracy of 96.78%, 96.08% and 94.85% on KSC, IP and SA datasets respectively with 20% samples as training set, and 85.03%, 83.45 and 62.17% on KSC, IP and SA datasets respectively with only 1% samples as training set, significantly outperforming random forest and cube CNN models.

The proliferation of Distributed Denial-of-Service (DDoS) attacks poses critical threats to network infrastructure, while conventional intrusion detection systems struggle to adapt to evolving attack patterns. Although ensemble learning methods achieve high accuracy on benchmark datasets, their opaque decision-making processes hinder deployment in Security Operations Centers (SOCs). To address this interpretability-performance gap, we propose an explainable machine learning framework integrating comparative benchmarking with quantitative interpretability analysis using the CIC-DDoS2019 dataset. Six supervised algorithms Decision Tree, Random Forest, XGBoost, LightGBM, Multilayer Perceptron, and Naïve Bayes were evaluated under standardized preprocessing protocols including random undersampling (50:50 class ratio), correlation-based feature selection (r > 0.9 threshold), and three-tier validation combining hold-out testing, train-validation splits, and 5-fold stratified cross-validation. LightGBM achieved optimal performance with 99.96% accuracy and F1-score of 0.9996, outperforming simple baselines by 0.35 percentage points while demonstrating superior computational efficiency. Beyond conventional performance metrics, we introduce the Feature Stability Score (FSS), a novel quantitative measure of SHAP-based feature importance consistency across validation folds. Spearman correlation analysis reveals a strong positive relationship between FSS and model robustness measured by cross-validation variance (ρ = 0.857, p = 0.014), establishing that stable feature attributions predict superior generalization. SHAP analysis identifies Flow Duration, Bwd Packet Length Mean, Fwd Packet Length Max, and Flow IAT Mean as dominant attack indicators. This integrated framework demonstrates that combining explainable AI with ensemble learning enables accurate, robust, and interpretable DDoS detection suitable for operational cybersecurity deployments.

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

This work presents quantitative prediction of severity of the disease caused by Phytophthora infestans in potato crops using machine learning algorithms such as multilayer perceptron, deep learning convolutional neural networks, support vector regression, and random forests. The machine learning algorithms are trained using datasets extracted from multispectral data captured at the canopy level with an unmanned aerial vehicle, carrying an inexpensive digital camera. The results indicate that deep learning convolutional neural networks, random forests and multilayer perceptron using band differences can predict the level of Phytophthora infestans affectation on potato crops with acceptable accuracy.

Analytic elements method and particle swarm optimization based simulation–optimization model for groundwater management