Extensive Feature Engineering Research Articles

Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction

Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature selection. This study investigates the impact of various feature selection techniques on the performance of ML algorithms for landslide displacement prediction. The Shuping and Baishuihe landslides in China’s Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent ML algorithms. Both static ML models, including backpropagation neural network (BPNN), support vector machine (SVM), and dynamic models, such as long short-term memory (LSTM), and gated recurrent unit (GRU), are included. Each ML model is evaluated under three feature engineering techniques: raw multivariate time series, and feature selection under maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). The results demonstrate that appropriate feature selection methods could significantly improve the performance of static ML models. In contrast, dynamic models effectively leverage inherent capabilities in capturing temporal dynamics within raw multivariate time series, seeing marginal gains with extensive feature engineering compared to no feature selection strategy. The optimal feature selection approach varies based on the ML model and specific landslide, highlighting the importance of case-specific assessments. The findings in this study offer guidance on integrating feature selection techniques with different machine learning models to maximize the robustness and generalizability of data-driven landslide displacement prediction frameworks.

An Advanced Diagnostic Approach for Broken Rotor Bar Detection and Classification in DTC Controlled Induction Motors by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT Methodology

This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC’s closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions.

Identifying grain size in ASTM A36 steel using ultrasonic backscattered signals and machine learning

Ultrasonic nondestructive techniques can be useful tools in the microstructural classification of metallic alloys. Among the most common techniques for characterizing materials, backscattered signal analysis stands out because it does not require a back surface echo. This study classifies five ASTM A36 steel samples with varying grain sizes using ultrasonic waves. For each sample, ultrasound signals were obtained using two ultrasonic array probes with center frequencies of 5 and 10 MHz. An extensive feature engineering process was conducted using the tfresh package in Python, which extracts a wide range of features from time series data, transforming raw ultrasound signals into a structured format. Seven machine learning models were evaluated based on accuracy, precision, recall, and F1 score, with performances ranging from 70 to 100%. The XGBoost model exhibited the best performance with 10 MHz probe signals, achieving 100% accuracy. An additional validation test was conducted to evaluate the models’ generalization capability, considering inter-specimen variabilities. Despite a slight reduction in prediction metrics, the XGBoost model maintained good performance, with accuracy between 89% and 100% across all frequencies. Such findings demonstrate that backscattered grain noise can effectively identify grain sizes provided that an adequate machine learning model is used.

Automated Building Information Modeling Compliance Check through a Large Language Model Combined with Deep Learning and Ontology

Ensuring compliance with complex industry standards and regulations during the design and implementation phases of construction projects is a significant challenge in the building information modeling (BIM) domain. Traditional manual compliance checking methods are inefficient and error-prone, failing to meet modern engineering demands. Natural language processing (NLP) and deep learning methods have improved efficiency and accuracy in rule interpretation and compliance checking. However, these methods still require extensive manual feature engineering, large, annotated datasets, and significant computational resources. Large language models (LLMs) provide robust language understanding with minimal labeled data due to their pre-training and few-shot learning capabilities. However, their application in the AEC field is still limited by the need for fine-tuning for specific tasks, handling complex texts with nested clauses and conditional statements. This study introduces an innovative automated compliance checking framework that integrates LLM, deep learning models, and ontology knowledge models. The use of LLM is motivated by its few-shot learning capability, which significantly reduces the need for large, annotated datasets required by previous methods. Deep learning is employed to preliminarily classify regulatory texts, which further enhances the accuracy of structured information extraction by the LLM compared to directly feeding raw data into the LLM. This novel combination of deep learning and LLM significantly enhances the efficiency and accuracy of compliance checks by automating the processing of regulatory texts and reducing manual intervention. This approach is crucial for architects, engineers, project managers, and regulators, providing a scalable and adaptable solution for automated compliance in the construction industry with broad application prospects.

Game provenance graph-based representation learning vs metrics-based machine learning: An empirical comparison on predictive game analytics tasks

Predicting events and behavior in games is a crucial task of game analytics, both during gameplay, such as predicting wins, and after, like forecasting player churn. Game metrics have been widely adopted due to their human-readable and task-oriented nature. However, these metrics vary between games and require expert knowledge. Game analytics has evolved with techniques that primarily handle non-tabular and sequential data, like graphs and time series. However, existing methods still depend on traditional game metrics from extensive feature engineering. In parallel, graph representation learning has been applied to game provenance graphs — structures that log game events as nodes and link them through causal relationships. This approach may allow analysts to bypass traditional game metrics and learn relevant downstream features during training. This paper compares this metric-less graph-based representation learning with traditional metric-based machine learning in two predictive game analytics tasks: win prediction and hit prediction. Our results show that the same graph-based representation learning architecture achieves competitive results in win prediction and outperforms metric-based approaches in hit prediction, suggesting its suitability for predictive game analytics tasks. This method could significantly benefit as it does not require game-specific metric expertise, and its learned features are versatile across multiple tasks.

Real Estate Price Prediction using ANN

This report explores the use of machine learning to predict real estate prices, addressing challenges such as market dynamics and data complexity. Through extensive data analysis and feature engineering, the study implements decision trees, random forests, and neural networks to capture the intricate factors affecting property values. The performance of these models is rigorously evaluated, providing stakeholders with reliable forecasts and insights into key price determinants. This research enhances decision-making in the real estate sector by improving the accuracy and transparency of price predictions. A Graphical User Interface is also developed to enter required information and display the predicted price. Keywords—Machine Learning, Price Prediction, Artificial Neural Network

Robust Intelligent Malware Detection Using Deep Learning

Abstract: In the modern era of technology, malicious software, or malware, holds a serious security hazard as computer users, businesses, and governments see an uptick in malware attacks. In attempts to identify unknown malware, current malware detection solutions use dynamic as well as static examination of malware signatures and behavior patterns, which takes time and is unsuccessful. Modern malware employs evasive strategies such as metamorphosis and polymorphism to rapidly alter its actions and produce a multitude of variants. Machine learning algorithms (MLAs) are being used more and more to do an efficient malware analysis because new malware is primarily versions of current malware. Extensive feature engineering, feature learning, and feature representation are needed for this. It is likely to fully eliminate the feature engineering stage by utilizing sophisticated MLAs like deep learning. Even though there have been a few fresh investigations in the field, the algorithms' performance is skewed by the training set. It is a prerequisite to reduce bias and figure out these techniques holistically in order to develop new, improved techniques for successful zero-day malware detection. This paper fills a vacuum in the literature by comparing and contrasting deep learning architectures with standard MLAs for malware detection, classification, and categorization using public and private datasets. The public and private dataset’s train and test splits, which were gathered during distinctly different periods, are not connected to one another in the experimental study. Furthermore, we provide a new method of image processing with ideal parameters for deep learning architectures and MLAs. In response to a thorough scientific assessment of these methodologies, deep learning architectures perform more efficiently than traditional MLAs. All in all, our work suggests a scalable and multimodal deep learning system for real-time malware detection through visual means. An improved technique for successful zero-day malware detection is the visualization and deep learning architectures for static, dynamic, and image processing based blended methods in a big data environment.

Improved deep learning intrusion detection model based on GAN data enhancement

With the development of information technology, the number and methods of cyber attacks continue to increase, making network security issues increasingly important. Intrusion detection has become a vital means of dealing with cyber threats. Current intrusion detection methods predominantly rely on machine learning. However, machine learning suffers from limitations in detection capability and the requirement for extensive feature engineering. Additionally, current intrusion detection datasets face the challenge of data imbalance. To address these challenges, this paper proposes a novel solution leveraging Generative Adversarial Networks (GANs) to balance the dataset and introduces an attention mechanism into the generator to efficiently extract key feature information, the mechanism can effectively sort the key information of the data and quickly capture important features. Subsequently, a combination of 1D Convolutional Neural Networks (1DCNN) and Bidirectional Gated Recurrent Units (BiGRU) is employed to construct a classification model capable of extracting both spatial and temporal features. Furthermore, Particle Swarm Optimization (PSO) is utilized to optimize the input weights and hidden biases of the model, so as to further improve the accuracy and robustness of the model. Finally, the model is trained and implemented for network intrusion detection. To demonstrate the applicability of the model, experiments were conducted using the NSL-KDD dataset and the UNSW-NB15 dataset. The final results showed that the proposed model outperformed other models, achieving accuracies of 99.15% and 97.33% on the respective datasets. This indicates that the model improves the efficiency of network intrusion detection and better ensures the effectiveness of network security.

Visual coating inspection framework via self-labeling and multi-stage deep learning strategies

AbstractAn instantaneous and precise coating inspection method is imperative to mitigate the risk of flaws, defects, and discrepancies on coated surfaces. While many studies have demonstrated the effectiveness of automated visual inspection (AVI) approaches enhanced by computer vision and deep learning, critical challenges exist for practical applications in the manufacturing domain. Computer vision has proven to be inflexible, demanding sophisticated algorithms for diverse feature extraction. In deep learning, supervised approaches are constrained by the need for annotated datasets, whereas unsupervised methods often result in lower performance. Addressing these challenges, this paper proposes a novel deep learning-based automated visual inspection (AVI) framework designed to minimize the necessity for extensive feature engineering, programming, and manual data annotation in classifying fuel injection nozzles and discerning their coating interfaces from scratch. This proposed framework comprises six integral components: It begins by distinguishing between coated and uncoated nozzles through gray level co-occurrence matrix (GLCM)-based texture analysis and autoencoder (AE)-based classification. This is followed by cropping surface images from uncoated nozzles, and then building an AE model to estimate the coating interface locations on coated nozzles. The next step involves generating autonomously annotated datasets derived from these estimated coating interface locations. Subsequently, a convolutional neural network (CNN)-based detection model is trained to accurately localize the coating interface locations. The final component focuses on enhancing model performance and trustworthiness. This framework demonstrated over 95% accuracy in pinpointing the coating interfaces within the error range of ± 6 pixels and processed at a rate of 7.18 images per second. Additionally, explainable artificial intelligence (XAI) techniques such as t-distributed stochastic neighbor embedding (t-SNE) and the integrated gradient substantiated the reliability of the models.

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

ABSTRACT The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.

ResDeepSurv: A Survival Model for Deep Neural Networks Based on Residual Blocks and Self-attention Mechanism.

Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling. To address these limitations, we propose a novel residual-based self-attention deep neural network for survival modeling, called ResDeepSurv, which combines the benefits of neural networks and the Cox proportional hazards regression model. The model proposed in our study simulates the distribution of survival time and the correlation between covariates and outcomes, but does not impose strict assumptions on the basic distribution of survival data. This approach effectively accounts for both linear and nonlinear risk functions in survival data analysis. The performance of our model in analyzing survival data with various risk functions is on par with or even superior to that of other existing survival analysis methods. Furthermore, we validate the superior performance of our model in comparison to currently existing methods by evaluating multiple publicly available clinical datasets. Through this study, we prove the effectiveness of our proposed model in survival analysis, providing a promising alternative to traditional approaches. The application of deep learning techniques and the ability to capture complex relationships between covariates and survival outcomes without relying on extensive feature engineering make our model a valuable tool for personalized medicine and decision-making in clinical practice.

Federated Multitask Learning for Complaint Identification Using Graph Attention Network

Prior study on automatically identifying complaints on social media has relied on extensive feature engineering in centralized settings, with no consideration for the decentralized, non-identically independently distributed (Non-IID), and privacy-conscious aspect of complaints, which can hinder data collection, distribution, and learning. In this work, we propose a Graph Attention Network (GAT) based multi-task framework that intends to learn two closely related tasks, complaint detection (primary task) and sentiment classification (auxiliary task), simultaneously in federated-learning scenarios. We propose the Federated Combination (FedComb) algorithm, a two-sided adaptive optimization technique that simultaneously optimizes global and local models. The proposed methodology outperforms several baselines for the intended task of recognizing complaints in decentralized settings, according to quantitative and qualitative studies on two benchmark datasets. The resources are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/appy1608/IEEE-TAI_FedCI_GAT</uri> .

LiteTransNet: An interpretable approach for landslide displacement prediction using transformer model with attention mechanism

Accurate landslide displacement prediction is crucial for effective early warning systems to mitigate hazards. The importance of historical information varies with time during prediction due to the underlying landslide deformation mechanism. Despite advances in dynamic machine learning models like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), they struggle to fully capture and interpret the varying importance of historical information during prediction, resulting in decreased accuracy and limited understanding of landslide physical mechanisms. Additionally, these approaches rely on manual feature selection, which is independent of the learning process and therefore is prone to estimation errors. To address these challenges, we propose LiteTransNet, a Lightweighted Transformer Network tailored for landslide displacement prediction. Built on the revolutionary Transformer model for sequential data, LiteTransNet leverages localized self-attention to selectively focus on relevant timestamps and provides interpretable results through its attention heatmap. Furthermore, LiteTransNet performs end-to-end time series modeling without extensive feature engineering. We validate LiteTransNet on two landslides in China's Three Gorges Reservoir Area and demonstrate its improved accuracy over recurrent baselines utilizing feature selection methods. Notably, the attention heatmap generated by LiteTransNet provides interpretable insights into the model's temporal dependency learning. It uncovers the varying importance of historical information at different timestamps, showcasing how LiteTransNet's attention aligns with specific periods of the external environment that cause significant disruptions in the landslide system. Our findings reveal that these disruptions continuously impact displacement prediction until their effects fade or are replaced by subsequent intense disturbances, which traditional recurrent methods are unable to capture. Further experiment shows that LiteTransNet enhances efficiency through its streamlined architecture and its parallelization capability. Overall, LiteTransNet innovates landslide prediction by providing accuracy, interpretability, and efficiency, enhancing understanding of deformation mechanisms to facilitate effective early warning systems.

Broad learning: A GPU-free image-based malware classification

In today’s cybersecurity landscape, software security companies encounter a significant challenge in detecting new and unknown malware. Despite the introduction of various machine learning and deep learning tools designed to identify malicious software based on static and dynamic features, achieving the desired level of accuracy remains elusive. This challenge is exacerbated by factors such as encryption, packing, limited distribution, and uneven allocation of malware samples across different families. Moreover, deep learning techniques demand substantial time, computational resources (specifically GPUs), and expertise from data scientists for practical malware analysis. In response to these challenges, we propose a novel GPU-free approach called Image-based Malware Classification using Broad Learning (IMCBL) to address these issues. Our method integrates visualization, feature decomposition, and broad learning architecture to enhance malware detection and classification. We convert raw malware binaries into images, reducing the necessity for extensive feature engineering. These images transform using truncated Singular Value Decomposition (SVD) to reduce the feature vector size, expediting the training process while mitigating model overfitting. The transformed feature vector is then input into our proposed Broad Learning (BL) system, which facilitates malware detection and classification. The BL architecture, structured as a flat network mapping original inputs to feature nodes and expanding the structure in enhancement nodes, ensures efficient and effective classification without the need for retraining. This dynamic and incremental learning capability sets IMCBL apart, making it superior to existing deep learning architectures. To validate our approach, we conducted extensive experiments using five benchmark malware datasets, including the Microsoft Windows malware challenge dataset, the Malimg Windows malware dataset, the IoT-Android mobile malware dataset, the Big Windows malware dataset, and an obfuscated Windows malware dataset. The results demonstrate IMCBL’s remarkable success in classifying most malware samples, even under obfuscation attacks, performing comparably or outperforming current methods using similar benchmarks. Specifically, IMCBL achieved 95.58% accuracy for the Microsoft Windows malware dataset, 97.64% accuracy for the Malimg Windows malware dataset, 96.51% accuracy for IoT Android malware datasets, and 96.19% accuracy for the extensive Windows malware dataset. Additionally, IMCBL demonstrated 93.04% accuracy for an obfuscated Windows malware dataset, which contains both packed and unpacked malware samples. Notably, IMCBL exhibits an exponential advantage in computation overhead, including training and prediction time, when compared to traditional machine learning and advanced state-of-the-art deep learning architectures such as VGG16, ResNet50, and InceptionV3.

Durian Species Classification Using Deep Learning Method

Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy.

VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models

This study introduces VF-Pred, a novel framework developed for the purpose of detecting virulence factors (VFs) through the analysis of genomic data. VFs are crucial for pathogens to successfully infect host tissue and evade the immune system, leading to the onset of infectious diseases. Identifying VFs accurately is of utmost importance in the quest for developing potent drugs and vaccines to counter these diseases. To accomplish this, VF-Pred combines various feature engineering techniques to generate inputs for distinct machine learning classification models. The collective predictions of these models are then consolidated by a final downstream model using an innovative ensembling approach. One notable aspect of VF-Pred is the inclusion of a novel Seq-Alignment feature, which significantly enhances the accuracy of the employed machine learning algorithms. The framework was meticulously trained on 982 features obtained from extensive feature engineering, utilizing a comprehensive ensemble of 25 models. The new downstream ensembling technique adopted by VF-Pred surpasses existing stacking strategies and other ensembling methods, delivering superior performance in VF detection. There have been similar studies done earlier, VF-Pred stands out in comparison showing higher accuracy (83.5 %), higher sensitivity (87 %) towards identification of VFs. Accessible through a user-friendly web page, VF-Pred can be accessed by providing the identifier and protein sequence, enabling the prediction of high or low likelihoods of VFs. Overall, VF-Pred showcases a highly promising methodology for the identification of VFs, potentially paving the way for the development of more effective strategies in the battle against infectious diseases.

Privacy‐preserving peak time forecasting with Learning to Rank XGBoost and extensive feature engineering

AbstractIn modern power systems, predicting the time when peak loads will occur is crucial for improving efficiency and minimising the possibility of network sections becoming overloaded. However, most works in the load forecasting field are not focusing on a dedicated peak time forecast and are not dealing with load data privacy. At the same time, developing methods for forecasting peak electricity usage that protect customers' data privacy is essential since it could encourage customers to share their energy usage data, leading to more data points for the effective management and planning of power grids. Hence, the authors employ a dedicated Learning to Rank XGBoost algorithm to forecast peak times with only ranks of loads instead of absolute load magnitudes as input data, thereby offering potential privacy‐preserving properties. We show that the presented Learning to Rank XGBoost model yields comparable results to a benchmark XGBoost load forecasting model. Additionally, we describe our extensive feature engineering process and a state‐of‐the‐art Bayesian hyperparameter optimisation for selecting model parameters, which leads to a significant improvement of forecasting accuracy. Our method was used in the context of the final round of the international BigDEAL load forecasting challenge 2022, where we consistently achieved high‐ranking results in the peak time track and an overall fourth rank in the peak load forecasting track with our general XGBoost model.

A computational approach towards food-wine recommendations

Food-wine pairing is an essential study in the culinary world and requires extensive research of the underlying food and wine pairing principles. To understand the principles of pairing, we must understand the characteristics of food, and wine, the interaction between them, and certain classic food-wine pairing norms that are religiously adhered to. To take this understanding to a broader class of masses, there is a need for a recommender system that considers all the trivial and non-trivial details of food-wine pairing and the preferences of individual users to provide the perfect pairing. To apply these pairing principles in the context of recommender systems, we require abstract features such as flavor, aroma, major ingredients, type of meat, and many others. To achieve this, our recommender system relies on text mining techniques and sentiment analysis at its core to extract the relevant information from the text in the dataset. Such extensive feature engineering has not been undertaken before in the context of generating food and wine recommendations. We have created a recommender system that embodies the age-old essence of wine paring, considering not only the food wine characteristics but also the user’s preference of pairing (congruent or contrast).

A CNN Approach for Emotion Recognition via EEG

Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature extraction and machine learning techniques, which often struggle to capture intricate emotional nuances and may require extensive handcrafted feature engineering. To address these limitations, we propose a novel approach utilizing convolutional neural networks (CNNs) for EEG emotion recognition. Unlike traditional methods, our CNN-based approach learns discriminative cues directly from raw EEG signals, bypassing the need for intricate feature engineering. This approach not only simplifies the preprocessing pipeline but also allows for the extraction of more informative features. We achieve state-of-the-art performance on benchmark emotion datasets, namely DEAP and SEED datasets, showcasing the superiority of our approach in capturing subtle emotional cues. In particular, accuracies of 96.32% and 92.54% were achieved on SEED and DEAP datasets, respectively. Further, our pipeline is robust against noise and artefact interference, enhancing its applicability in real-world scenarios.

Autoencoder Feature Residuals for Network Intrusion Detection: One-Class Pretraining for Improved Performance

The proliferation of novel attacks and growing amounts of data has caused practitioners in the field of network intrusion detection to constantly work towards keeping up with this evolving adversarial landscape. Researchers have been seeking to harness deep learning techniques in efforts to detect zero-day attacks and allow network intrusion detection systems to more efficiently alert network operators. The technique outlined in this work uses a one-class training process to shape autoencoder feature residuals for the effective detection of network attacks. Compared to an original set of input features, we show that autoencoder feature residuals are a suitable replacement, and often perform at least as well as the original feature set. This quality allows autoencoder feature residuals to prevent the need for extensive feature engineering without reducing classification performance. Additionally, it is found that without generating new data compared to an original feature set, using autoencoder feature residuals often improves classifier performance. Practical side effects from using autoencoder feature residuals emerge by analyzing the potential data compression benefits they provide.