Web Based Information System for Wind Turbine Power Generation

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation. The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others. The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance. Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.

Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning (DL) algorithms have been used to solve complex problems, such as multiple exposures and their interactions. This study developed predictive models for cause-specific mortality using ML and DL algorithms with the daily or hourly measured meteorological and air pollution data. The ML algorithm improved the performance compared to the conventional methods, even though the optimal algorithm depended on the adverse health outcomes. The best algorithms were extreme gradient boosting, ridge, and elastic net, respectively, for non-accidental, cardiovascular, and respiratory mortality with daily measurement; they were superior to the generalized additive model reducing a mean absolute error by 4.7%, 4.9%, and 16.8%, respectively. With hourly measurements, the ML model tended to outperform the conventional models, even though hourly data, instead of daily data, did not enhance the performance in some models. The proposed model allows a better understanding and development of robust predictive models for health outcomes using multiple environmental exposures.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-21768-9.

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

Objectives: Language detection is the process of identifying a language associated with a text. The proposed system aims to detect the Dravidian language that is associated with the given text using different machine learning and deep learning algorithms. The paper presents an empirical analysis of the results obtained using the different models. It also aims to evaluate the performance of a language agnostic model for the purpose of language detection. Method: An empirical analysis of Dravidian language identification in social media text using machine learning and deep learning approaches with k-fold cross validation has been implemented. The identification of Dravidian languages, including Tamil, Malayalam, Tamil Code Mix, and Malayalam Code Mix, is performed using both machine learning (ML) and deep learning algorithms. The machine learning algorithms used for language detection are Naive Bayes (NB), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). The supervised Deep Learning (DL) models used include BERT, mBERT and language agnostic models. Findings: The language agnostic model outperform all other models considering the task of language detection in Dravidian languages. The results of both the ML and DL models are analyzed empirically with performance measures like accuracy, precision, recall, and f1-score. The accuracy associated with different machine learning algorithms varies from 85% to 89%. It is evident from the experimental result that the deep learning model outperformed with an accuracy of 98%. Novelty: The proposed system emphasizes on the use of the language agnostic model to implement the process of detecting Dravidian languages associated with the given text which provides a promising result of 98% accuracy which is higher than the existing methodologies. Keywords: Language, Machine learning, Deep learning, Transformer model, Encoder, Decoder

Aim: Machine learning (ML) and deep learning (DL) predictive models have been employed widely in clinical settings. Their potential support and aid to the clinician of providing an objective measure that can be shared among different centers enables the possibility of building more robust multicentric studies. This study aimed to propose a user-friendly and low-cost tool for COVID-19 mortality prediction using both an ML and a DL approach. Method: We enrolled 2348 patients from several hospitals in the Province of Reggio Emilia. Overall, 19 clinical features were provided by the Radiology Units of Azienda USL-IRCCS of Reggio Emilia, and 5892 radiomic features were extracted from each COVID-19 patient’s high-resolution computed tomography. We built and trained two classifiers to predict COVID-19 mortality: a machine learning algorithm, or support vector machine (SVM), and a deep learning model, or feedforward neural network (FNN). In order to evaluate the impact of the different feature sets on the final performance of the classifiers, we repeated the training session three times, first using only clinical features, then employing only radiomic features, and finally combining both information. Results: We obtained similar performances for both the machine learning and deep learning algorithms, with the best area under the receiver operating characteristic (ROC) curve, or AUC, obtained exploiting both clinical and radiomic information: 0.803 for the machine learning model and 0.864 for the deep learning model. Conclusions: Our work, performed on large and heterogeneous datasets (i.e., data from different CT scanners), confirms the results obtained in the recent literature. Such algorithms have the potential to be included in a clinical practice framework since they can not only be applied to COVID-19 mortality prediction but also to other classification problems such as diabetic prediction, asthma prediction, and cancer metastases prediction. Our study proves that the lesion’s inhomogeneity depicted by radiomic features combined with clinical information is relevant for COVID-19 mortality prediction.

The process of building new dataset and the existence of such a data followed the urgent need for the existence of datasets that are specialized in educational lectures, so this will need an accurate classification algorithm to classify it, the benefit of classify such dataset is to minimize the workload of classifying each file manually and individually. In the present paper, authors perform experimentations for conducting an empirical deep learning study, especially, convolutional neural network, for three new datasets of educational lectures which are (PDF, Word and PowerPoint datasets), The three new datasets using real data educational resources lectures collected from various document projects of different universities and institutions. The architecture has been applied to the task of the text classification in the domain of the document with documents data-sets have been obtained from a variety of projects on actual document cases. The aim of the present study is to initially test the performance of each dataset (PDF, Word, and PowerPoint dataset) through using four machine learning classification algorithms which are (Bayes Net, Random Forest, Random Committee, and OneR). Second goal is experimenting the efficiency of the approach of the deep learning in the tasks of classification and after that, comparing the efficiencies with the efficiencies of traditional machine learning classification methods. Mainly two classification techniques used to maximize the benefits of the classification process, the first one is to use the deep learning algorithm which shows an accuracy of classifying file between (95 and 96%) for three new dataset files and standard machine learning algorithms (OneR, Random forest, Bayes net, and Random Committee ) these algorithm shows accuracy 91% for PDF Dataset using random forest and random committee algorithms, for Word dataset the accuracy is 46% using random committee, and for the last dataset PowerPoint the accuracy is 77% using random forest, Therefore, we will choose Deep learning algorithm because it gives higher results and accuracy than machine learning algorithms.

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

Deep learning and its application in geochemical mapping

Nanobodies (Nbs) achieve high solubility and stability due to four conserved residues referred to as the Nb tetrad. While several studies have highlighted the importance of the Nbs tetrad to their stability, a detailed molecular picture of their role has not been provided. In this work, we have used the Rosetta package to engineer synthetic Nbs lacking the Nb tetrad and used the Rosetta Energy Function to assess the structural features of the native and designed Nbs concerning the presence of the Nb tetrad. To develop a classification model, we have benchmarked three different machine learning (ML) and deep learning (DL) algorithms and concluded that more complex models led to better binary classification for our dataset. Our results show that these two classes of Nbs differ significantly in features related to solvation energy and native-like structural properties. Notably, the loss of stability due to the tetrad’s absence is chiefly driven by the entropic contribution.

The dynamic modulus (|E*|) of hot-mix asphalt (HMA) is a crucial mechanistic characteristic essential in defining the strain response of asphalt concrete (AC) mixtures under varying loading rates and temperatures. This paper aims to conduct a comprehensive investigation of classical machine learning (ML) and deep learning (DL) algorithms as applied to the prediction of |E*| and compare their performance with renowned |E*| regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D, and Hirsch). Eight state-of-the-art ML and DL algorithms are attempted with diverse structures, including multiple linear regression (MLR), decision trees (DT), support vector regression (SVR), ensemble trees (ET), Gaussian process regression (GPR), artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN). A comprehensive database was assembled, incorporating 50 AC mixtures, of which 25 were from the Kingdom of Saudi Arabia and 25 were from the state of Idaho, USA. This database encompasses an extensive dataset of 3,720 |E*| measurements, associated with thirteen input features representing the proposed AC mixtures’ aggregate gradations, binder characteristics, and volumetric properties. This pioneering study surpasses existing research by examining various algorithms to predict |E*| on the same dataset, applying them with different structures and individual optimization to achieve optimal performance. The developed models are evaluated based on multi-stage assessment criteria, including the accuracy and complexity performance measures and rationality based on a sensitivity analysis. The multi-stage comparative analysis results reveal that the bagging ETs, GPR with exponential kernel, and DT record the highest prediction accuracy; however, only the bagging ETs yield the highest accuracy, lowest training and testing complexity, and rational trends throughout the sensitivity analysis. The research outcome has the potential to provide pavement engineers with advanced tools for predicting |E*| and, therefore, optimizing pavement designs and rehabilitations. Doi: 10.28991/CEJ-2025-011-01-06 Full Text: PDF

Deep learning has gained immense popularity in scientific computing, and its algorithms are widely used in complex problem-solving industries. Every deep learning algorithm use different types of neural networks to perform indented tasks. Deep learning (DL) algorithms have emerged from different machine learning and soft computing methodologies. Since then, a number of deep learning (DL) algorithms have been recently introduced in the scientific community and applied in various application fields. Today, the use of DLs has become indispensable due to their intelligence, effective learning, accuracy and reliability in model creation. However, a comprehensive list of DL algorithms has not yet been presented in the scientific literature. This article lists the most popular DL algorithms and their application areas. Deep learning uses ANN artificial neural networks to perform convoluted calculations on huge amounts of data. It is a type of machine learning based on the structure and function of the human brain. Deep learning algorithms train machines by learning from examples. Industries such as healthcare, e-commerce, entertainment and advertising often use deep learning.

Internet of Drones (IoDs) are the emerging form of futuristic IOT devices where unmanned aerial vehicles have a complete network connection capability using internet. Nowadays, IoDs have been widely used in different application areas such as military missions, civilian purposes, disaster management, traffic surveillance and many more. These IoDs use different approaches to accomplish targets by following some paths using its sensors’ range. With the proliferation of IODs in different operations, improvement is required in the functionality related to autonomy and remote sensing capability for the purpose of capturing robust real time obstacle detection and avoidance system. But IoDs still suffer from many problems such as the drainage of energy, building a communication link, automating the task of detection, management of traffic, maintaining quality of services, etc. To overcome these problems, machine learning (ML) and deep learning (DL) algorithms have proved to provide promising alternative for enhancing the functionality of IoDs in the real-world realms. This chapter reviews the most recent development of IoDs based on ML and DL approaches. It provides the detailed explanation about different techniques of ML and DL used for improving the model of IoDs with the aspect of navigation, battery scheduling, object tracking and collision avoidance and security. The impacts of ML and DL algorithms on the operations performed by IoDs are also analyzed. It includes various challenges and gaps present in the current scenario, to allow future researchers to develop solutions for designing new generation Internet of Drones.