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

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.

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.

Wind energy has the possibility for bringing out energy in a very constant and sustainable manner being a notable and eligible source. Although, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and therefore, the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which will also aid in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day to day wind speed info using machine learning and deep learning algorithms. A way is put forward by us to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term these algorithms may be used for wind generation rate with historical relation to wind speed info. Index Terms: Wind turbine,machine learning algorithm

Deep leaning in food safety and authenticity detection: An integrative review and future prospects

Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this paper, we propose a method, which combines feature extraction layers of a convolutional neural network with traditional machine learning algorithms, such as, support vector machine, gradient boosting machines, and random forest. All of the proposed hybrid models and the above mentioned machine learning algorithms are trained on three different datasets: MNIST, Fashion-MNIST, and CIFAR-10. Results show that the proposed hybrid models are more successful than traditional models while they are being trained from raw pixel values. In this study, we empower traditional machine learning algorithms for classification using feature extraction ability of deep neural network architectures and we are inspired by transfer learning methodology to this.

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.

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

This paper presents a high-spatial-resolution dataset with remote sensing images of the Brazilian Cerrado for land use and land cover classification. The Biome Cerrado Dataset (CerraData) is a large database created from 150 scenes of the CBERS-4A satellite. Images were created by merging the near-infrared, green, and blue bands. Moreover, pan-sharpening was performed between all the scenes and their respective panchromatic bands, resulting in a final spatial resolution of two meters. A total of 2.5 million tiles of 256x256 pixels were derived from these scenes. From this total, 50 thousand tiles were labeled. We also conducted a few-shot learning experiment considering a training set with only 100 samples, 11 deep neural networks (DNNs), and two traditional machine learning (ML) algorithms, i.e., support vector machine (SVM) and random forest (RF). Results show that the DNN DenseNet-161 was the best model but its performance can be improved if it is used only as a feature extractor, leaving the classification task for the traditional ML algorithms. However, by decreasing the size of the training set, smarter approaches are needed. The labeled subset of CerraData as well as the source code we developed to support this study are available on-line: https://github.com/ai41uc/CerraData-code-data.

Recent advancements in computer technology have precipitated a shift towards virtual environments, accelerated by the COVID-19 pandemic. Cybercriminals have capitalized on this trend, transitioning their activities to exploit vulnerabilities in cyberspace. Malicious software (malware) has emerged as a preferred tool for launching cyber-attacks, continually evolving with sophisticated obfuscation and packing techniques to evade detection. Traditional machine learning (ML) algorithms, once effective in identifying malware, are now struggling to keep pace with these advancements. In response, deep learning (DL) algorithms offer a promising solution, leveraging their ability to discern intricate patterns and correlations within data. This study proposes a novel hybrid deep-learning-based architecture, integrating two pre-trained network models to enhance classification accuracy. Through extensive evaluation on datasets including Malimg, Microsoft BIG 2015, and Malevis, the proposed method demonstrates significant improvements in accuracy, outperforming existing ML-based malware detection methods in the literature. Specifically, the proposed method achieves an impressive accuracy of 97.78% on the Malimg dataset, underscoring its effectiveness in combating sophisticated malware variants. Keywords — Malware, malware classification, malware detection, malware variants, deep neural networks, transfer learning, deep learning.

The research examines the accuracy of current solution models for the Arabic text sentiment classification, including traditional machine learning and deep learning algorithms. The main aim is to detect the opinion and emotion expressed in Telecom companies’ customers tweets. Three supervised machine learning algorithms, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), and one deep learning algorithm, Convolutional Neural Network (CNN) were applied to classify the sentiment of 1098 unique Arabic textual tweets. The research results show that deep learning CNN using Word Embedding achieved higher performance in terms of accuracy with F1 score = 0.81. Furthermore, in the aspect classification task, the results reveal that applying Part of Speech (POS) features with deep learning CNN algorithm was efficient and reached 75 % accuracy using a dataset consisting of 1277 tweets. Additionally, in this study, we added an additional task of extracting the geographical location information from the tweet content. The location detection model achieved the following precision values: 0.6 and 0.89 for both Point of Interest (POI) and city (CIT).

A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law

An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

Deep learning algorithms can be used to design systems that report data on patients and deliver warnings to medical applications or electronic health information if there are changes in the patient's health. These systems could be created using deep Learning. This helps verify that patients get the proper effective care at the proper time for each specific patient. A healthcare decision support system was presented using the Internet of Things and deep learning methods. In the proposed system, we examined the capability of integrating deep learning technology into automatic diagnosis and IoT capabilities for faster message exchange over the Internet. We have selected the suitable Neural Network structure (number of best-hidden layers and activation function classes) to construct the e-health system. In addition, the e-health system relied on data from doctors to understand the Neural Network. In the validation method, the total evaluation of the proposed healthcare system for diagnostics provides dependability under various patient conditions. Based on evaluation and simulation findings, a dual hidden layer of feed-forward NN and its neurons store the tanh function more effectively than other NN. To overcome challenges, this study will integrate artificial intelligence with IoT. This study aims to determine the NN's optimal layer counts and activation function variations.

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.

Integration of machine learning algorithms in fashion design classification brought transformational change by allowing the automated analysis, categorization, and prediction of fashion items based on different attributes of the item. This paper reviews the state-of-the-art in fashion design classification through machine learning techniques. Review of literature, methodology, and challenges in this area indicate that an array of algorithms and methods, stretching from traditional machine learning algorithms to convolutional neural networks and further to transfer learning approaches, is being tried and tested. In this paper, I will discuss performance comparison among several machine learning algorithms, pinning their strengths, limitations, and possible applications in the fashion industry. We further elaborate on crucial challenges, such as touching on the issue of data variability, interpretability, and others on ethical consideration issues, all pointing to the need for fairness and sustainability with respect to the representation of reality in algorithmic decision-making. This paper aims to inform researchers, practitioners, and stakeholders of the opportunities and challenges brought about by the use of machine learning in a fast-paced world like fashion, hence demystifying current directions in landscape classification of fashion design.