Traditional Machine Learning Methods Research Articles

Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360

Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment—a critical facet of establishing trustworthy AI systems. Tackling data bias through techniques like reweighting samples proves effective for promoting fairness. This paper undertakes a systematic exploration of reweighting samples for conventional Machine-Learning (ML) models, utilizing five models for binary classification on datasets such as Adult Income and COMPAS, incorporating various protected attributes. In particular, AI Fairness 360 (AIF360) from IBM, a versatile open-source library aimed at identifying and mitigating bias in machine-learning models throughout the entire AI application lifecycle, is employed as the foundation for conducting this systematic exploration. The evaluation of prediction outcomes employs five fairness metrics from AIF360, elucidating the nuanced and model-specific efficacy of reweighting samples in fostering fairness within traditional ML frameworks. Experimental results illustrate that reweighting samples effectively reduces bias in traditional ML methods for classification tasks. For instance, after reweighting samples, the balanced accuracy of Decision Tree (DT) improves to 100%, and its bias, as measured by fairness metrics such as Average Odds Difference (AOD), Equal Opportunity Difference (EOD), and Theil Index (TI), is mitigated to 0. However, reweighting samples does not effectively enhance the fairness performance of K Nearest Neighbor (KNN). This sheds light on the intricate dynamics of bias, underscoring the complexity involved in achieving fairness across different models and scenarios.

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

Abstract: Sentiment analysis is a crucial component of natural language processing that seeks to determine the emotional sentiment expressed in a given text. This study investigates sentiment analysis in the Arabic language through a comprehensive approach that integrates traditional machine learning methods with sophisticated deep learning models. We examine the efficacy of conventional algorithms such as Support Vector Machines (SVM) and Naive Bayes, as well as sophisticated neural network architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and the Arabic variant of Bidirectional Encoder Representations from Transformers (BERT). The primary novelty of our approach is in the ensemble method, which combines many approaches to enhance the precision of sentiment categorization in Arabic text. To address the particular challenges presented by Arabic sentiment analysis, such as the intricate structure of the language and the diverse regional variations, we utilize a tailored preprocessing pipeline to effectively handle the nuances of Arabic text. Our comprehensive analysis of various datasets demonstrates that the ensemble technique outperforms individual model benchmarks and offers novel insights into the interplay between different machine learning paradigms in Arabic NLP. The results emphasize the ability of hybrid approaches to improve Arabic sentiment analysis, providing a solid basis for future research and practical applications in understanding the sentiments of Arabic consumers. This study is a significant addition to the expanding domain of Arabic Natural Language Processing (NLP). This resource offers a comprehensive and advanced methodology for utilizing machine learning and deep learning methods to comprehend and analyse the intricate aspects of sentiment in the Arabic language

Bidirectional machine learning-assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization.

In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Swarm intelligence organized hybrid-kernel extreme learning machine (SIO-HKELM) was proposed to approximate the forward and inverse input-output correlation with a high accuracy using the DNAPL transport numerical simulation model. An adaptive inverse-HKELM was established for preliminary estimation of the source characteristics and contaminant transport parameters to correct prior information and generate high-quality initial starting points of parallel searching. A local accurate forward-HKELM surrogate of the numerical model was embedded in the searching system for avoiding repetitive CPU-demanding likelihood evaluations. A sensitivity-based Metropolis criterion (MC), incorporating the dynamic particle swarm optimization (SD-PSO) algorithm, was developed for improving the search ergodicity and realizing precise inversion of all the unknown variables with drastic variations in sensitivity to the likelihood function. Results showed that the generalization capability and robustness of SIO-HKELM were superior to those of the traditional machine learning methods, including KELM and support vector regression (SVR), and it sufficiently approximated the forward and inverse input-output mapping of the numerical model with testing determination coefficients of 0.9944 and 0.6440, respectively. With high-quality prior information and initial starting points generated by the adaptive inverse-HKELM feed approach, the uncertainty in the inversion outputs was reduced, and the searching process rapidly converged to reasonable posterior distributions in around 60 iterations. Compared with the widely used multichain Markov chain Monte Carlo (MCMC) approach, the parallel searching lines generated by SD-PSO-MC adequately covered the searching space, and the "equifinality" effect was more effectively restrained by reducing the relative errors of all the point estimations to less than 8%. Therefore, the real source information reflected by the statistical characteristics of the SD-PSO-MC inversion outputs was more precise than that obtained using the multichain MCMC approach.

PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.

Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts

Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian languages, which are both low-resource languages, with the latter being an extremely low-resource language. In the study, we used both traditional baseline machine learning methods, as well as a wide suite of deep learning methods especially focusing on hybrid networks and transformer-based multilingual models. For the data, we collected over 5000 both Bangla and Chittagonian text samples from social media. Krippendorff’s alpha and Cohen’s kappa were used to measure the reliability of the dataset annotations. Traditional machine learning methods used in this research achieved accuracies ranging from 0.63 to 0.711, with SVM emerging as the top performer. Furthermore, employing ensemble models such as Bagging with 0.70 accuracy, Boosting with 0.69 accuracy, and Voting with 0.72 accuracy yielded promising results. In contrast, deep learning models, notably CNN, achieved accuracies ranging from 0.69 to 0.811, thus outperforming traditional ML approaches, with CNN exhibiting the highest accuracy. We also proposed a series of hybrid network-based models, including BiLSTM+GRU with an accuracy of 0.799, CNN+LSTM with 0.801 accuracy, CNN+BiLSTM with 0.78 accuracy, and CNN+GRU with 0.804 accuracy. Notably, the most complex model, (CNN+LSTM)+BiLSTM, attained an accuracy of 0.82, thus showcasing the efficacy of hybrid architectures. Furthermore, we explored transformer-based models, such as XLM-Roberta with 0.841 accuracy, Bangla BERT with 0.822 accuracy, Multilingual BERT with 0.821 accuracy, BERT with 0.82 accuracy, and Bangla ELECTRA with 0.785 accuracy, which showed significantly enhanced accuracy levels. Our analysis demonstrates that deep learning methods can be highly effective in addressing the pervasive issue of cyberbullying in several different linguistic contexts. We show that transformer models can efficiently circumvent the language dependence problem that plagues conventional transfer learning methods. Our findings suggest that hybrid approaches and transformer-based embeddings can effectively tackle the problem of cyberbullying across online platforms.

Building recognition and classification using deep learning in civil engineering projects

The recognition and regulation of buildings are essential aspects of urban management to prevent illegal constructions and maintain public safety and resources. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Therefore, the study of automatic, accurate building identification method is very necessary. Based on this, Introducing advanced algorithms like Faster R-CNN and DRNet signifies a significant step towards automating accurate building identification. The utilization of Faster R-CNN as a basic training model combined with DRNet demonstrates promising results in accurately recognizing buildings. The experimental analysis highlights the potential of the proposed method, achieving an impressive 82.1% mean Average Precision (mAP) for landmark buildings. Accurate prediction of building coordinates further strengthens the effectiveness of the proposed approach. Comparative analysis showcases the superiority of the proposed model in recognizing buildings not only in normal images but also in complex environmental settings. The successful implementation of advanced algorithms in building recognition contributes to more efficient urban management and development. Continued research in automatic building identification methods is crucial for addressing challenges in urban planning and management, ensuring sustainable city development.

Deep learning based crop-type mapping using SAR and optical data fusion

Accurate crop maps are essential in various applications related to food security management activities. Remote Sensing is the primary data source for land-use and land-cover monitoring applications. However, high spectral, spatial, and temporal variations of crop types during their phonological stages make crop mapping challenging. According to the complementary behaviors of optical and radar data, a deep learning-based feature-level fusion strategy is proposed for accurate crop-type mapping. The proposed method uses Sparse Auto-Encoders (SAE) as its base classifier and can involve spatial information in two data-preparation and post-processing steps. Involving spatial information in the classification process can significantly improve crop mapping. The post-processing step uses the guided filter as an edge-aware filtering process that uses field boundary information that implicitly exists in data. Accuracy analysis was performed using two pairs of RapidEye and UAVSAR data from two agricultural areas in Canada. The suitability and higher performance of the proposed method were demonstrated compared to traditional machine learning and common decision-level fusion methods. Accuracy metrics were improved by at least 3% and 1% in two datasets. Class F-scores were also significantly enhanced in oat and wheat classes (up to 20%).

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance.

Static analysis of two-side supported 2-ply laminated glass panes through physics-informed neural networks

This paper utilizes an emerging machine learning technique, namely physics-informed neural networks (PINNs), to analyze the static mechanical behavior of two-side supported 2-ply laminated glass panes. The PINN model utilizes prior physical information in the form of partial differential equations during model training, eliminating the need for tremendous amounts of data required by traditional data-driven machine learning methods. The governing equations for the structural behavior of two-side supported 2-ply laminated glass panes are derived, based on which a PINN framework encompassing the neural network architecture and the loss functions is constructed. The training stability is evidently improved after the linearly inducing process, with the relative error maintained within a reasonable range. Extensive validation examples and case studies are provided to demonstrate the accuracy and effectiveness of the proposed method. Composite action analysis results reveal that below 70 % in the design is a conservative estimate for thinner laminated glass. Furthermore, the PINN method achieves exceptional computational efficiency, reducing computational times from six hours to just a few minutes for the uncertainty evaluation of laminated glass, indicating its potential in engineering practice.

Identification of cloud-to-ground lightning and intra-cloud lightning based on their radiated electric field signatures using different types of neural networks and machine learning classifiers

The scope of this paper is to test the efficiency of three different neural networks in the area of cloud-to-ground/intra-cloud lightning classification using a not very large data set of electric field data. Effective neural network learning verified for a small lightning event dataset but with whole 2-seconds particular record length is an important feature of fast and efficient procedures implemented into lightning location systems among which discrimination between cloud-to-ground and intra-cloud lightning is one of the most important task. Measurement data obtained at the Lightning Observatory in Rzeszow were used for the analysis. About 100 extremely low/medium frequency bandwidth electric field of cloud-to ground lightning registrations and the same number of intra-cloud lightning events were taken into account. During the study 81% of those registrations were dedicated for learning of neural networks while the remaining 19% were put into the testing set. All data were preselected manually with consideration of the electric field variation and information from the lightning location system database. Lightning events were selected uniformly with the well-pronounced presence of their basic components and reported distance to the registration station which ensured a wide variation of expected lightning electric field signatures. The multilayer perceptron neural network, the radial basis function neural network and the convolutional neural network were tested and compared in different configurations while distinguishing lightning. The results were compared with other conventional classification approaches, such as traditional machine learning methods as well as one a little more contemporary architecture, the long short-term memory neural network. The multilayer perceptron neural network achieved the best detection accuracy overall. Presented research is an extension of lightning identification studies available so far mainly based on the convolutional neural network by the results achieved using the multilayer perceptron neural network, the radial basis function neural network, the long short-term memory neural network and several machine learning-based classification methods.

AN IN-DEPTH ANALYSIS OF ARTIFICIAL INTELLIGENCE APPROACHES FOR RAINFALL PREDICTION

Natural disasters and floods brought on by heavy rainfall pose serious threats to human health and lives every year on a global scale. The intricacy of meteorological data makes it difficult to provide accurate rainfall predictions, despite their critical importance in nations like India where agriculture is the primary occupation. Rainfall forecasting has recently benefited from Artificial Intelligence (AI) developments such as Deep Learning (DL) and Machine Learning (ML) techniques. This article provides a comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year. The findings show that DL approaches are more effective than traditional ML methods and shallow neural network models. This research is important as it has significant impacts on agriculture, disaster preparedness, and water resource management. Finally, it outlines future research directions for further advancements in rainfall prediction through AI methodologies.

AP-GAN-DNN based creep fracture life prediction for 7050 aluminum alloy

7050 aluminum alloy has high strength and excellent fracture toughness. However, damage can be caused to the 7050 aluminum alloy by creep at elevated temperatures. The accuracy of conventional creep life prediction methods may fall short of meeting the desired standards. And the application of machine learning (ML) methods is limited due to the scarcity and dispersion of creep sample data. Generative Adversarial Networks (GAN) are often used for data enhancement, but suffer from “pattern collapse” under small sample conditions. To solve the above problems, this paper proposes a creep fracture life prediction method, which combines the affinity propagation (AP) clustering algorithm, GAN, and deep neural networks (DNN) for accurate creep fracture life prediction. The AP clustering algorithm for adaptive clustering was utilized to better reflect the creep sample distribution. Independent GAN models were trained for each cluster to better capture distribution characteristics of the data and generate synthetic data that was highly similar to the real data. The DNN models were trained using the synthetic data and then predicted using real creep fracture life data. The presented method was compared with traditional physical and machine learning methods, and the method combining K-Means, GAN, and DNN. The experimental results show that the method proposed in this paper has better prediction accuracy in a small sample creep fracture life dataset.

Zero-Ran Sniff: A zero-day ransomware early detection method based on zero-shot learning

Ransomware attacks, which blackmail victims into paying a ransom by locking their devices or encrypting their files, have become one of the major threats to network security. Conventional anti-ransomware tools often fail to detect zero-day ransomware attacks due to the inability to obtain zero-day ransomware signatures in advance to train detection models. In addition, zero-day ransomware attacks often use sophisticated encryption techniques to launch attacks on new vulnerabilities, and these encryption attacks cause irreversible damage to victims' digital files even if they choose to pay a ransom. Hence, it is imperative and urgent to detect unknown ransomware attacks at the earliest possible stage, ideally before the encryption phase. To this end, this paper proposes Zero-Ran Sniff (ZRS), an early zero-day ransomware detection method based on zero-shot learning, which can detect zero-day ransomware attacks in the early stage. ZRS leverages the portable executable header (PE header) feature from executable files to identify ransomware. It comprises two stages: an auto-encoding network-based core attribute learning (AE-CAL) stage and a self-attentive mechanism-based convolutional neural network inference Stage (SA-CNN-IS). During the AE-CAL stage, the core features of known and unknown classes of ransomware are extracted using self-encoding networks, and the SA-CNN-IS phase identifies ransomware. To the best of our knowledge, we are the first to explore the use of zero-shot learning for zero-day ransomware early detection. Experimental results demonstrate that the proposed ZRS outperforms traditional machine learning methods. Compared to previous zero-day detection work, ZRS achieves a recall of 98.47% and an accuracy of 96.31%

Quantum machine learning for drowsiness detection with EEG signals

Human reliability is an increasingly important area in various fields for accident prevention. Monitoring human biological parameters, such as metabolic agents, through techniques like an electroencephalogram (EEG), data analysis can help detect patterns indicating drowsiness, a major cause of fatigue that may impact tasks in various industries, including oil and gas, aviation, naval, railway, and others that involve shift work. While traditional machine learning methods, such as Multilayer Perceptron (MLP), have been explored in the literature for EEG-based drowsiness detection, advancements in computing technology have brought quantum mechanics concepts into play, offering potential advantages in computational efficiency for problem-solving. This work explores drowsiness detection via Quantum Machine Learning (QML). EEG signals are preprocessed to extract features specific to this type of data, such as Higuchi Fractal Dimension, Complexity, and Mobility, as well as statistical features such as mean, variance, root mean square, peak-to-peak, and maximum amplitude. We employ different quantum circuit architectures involving operations such as rotation gates (Ry, Rz, Ry), and entangling gates (CNOT, CZ, and iSWAP). We also combine those configurations considering different numbers of layers (1, 5, and 10). The models are trained and compared with classical MLP, considering five subjects. The main findings indicate that one subject (10) showed better results with the classic MLP model. However, for two subjects (1 and 8), iSWAP gates with 1 and 10 layers were notable, whereas for the last two subjects (5 and 6), configurations of CZ gates with 1 and 10 layers displayed the best results. This proof-of-principle study shows that QML models are suitable for analyzing EEG data related to drowsiness and can be further improved as quantum computing continues to evolve.

An explainable long short-term memory network for surgical site infection identification

BackgroundCurrently, surgical site infection surveillance relies on labor-intensive manual chart review. Recently suggested solutions involve machine learning to identify surgical site infections directly from the medical record. Deep learning is a form of machine learning that has historically performed better than traditional methods while being harder to interpret. We propose a deep learning model, a long short-term memory network, for the identification of surgical site infection from the medical record with an attention layer for explainability. MethodsWe retrieved structured data and clinical notes from the University of Utah Health System’s electronic health care record for operative events randomly selected for manual chart review from January 2016 to June 2021. Surgical site infection occurring within 30 days of surgery was determined according to the National Surgical Quality Improvement Program definition. We trained the long short-term memory model along with traditional machine learning models for comparison. We calculated several performance metrics from a holdout test set and performed additional analyses to understand the performance of the long short-term memory, including an explainability analysis. ResultsSurgical site infection was present in 4.7% of the total 9,185 operative events. The area under the receiver operating characteristic curve and sensitivity of the long short-term memory was higher (area under the receiver operating characteristic curve: 0.954, sensitivity: 0.920) compared to the top traditional model (area under the receiver operating characteristic curve: 0.937, sensitivity: 0.736). The top 5 features of the long short-term memory included 2 procedure codes and 3 laboratory values. ConclusionSurgical site infection surveillance is vital for the reduction of surgical site infection rates. Our explainable long short-term memory achieved a comparable area under the receiver operating characteristic curve and greater sensitivity when compared to traditional machine learning methods. With explainable deep learning, automated surgical site infection surveillance could replace burdensome manual chart review processes.

Research on the Application of Convolutional Neural Network in Stock Market Forecasting

With the continuous development and complexity of financial markets, stock market forecasting has become a hot topic of concern for investors and decision-makers. This study aims to explore the application of Convolutional Neural Networks (CNN) in stock market prediction, and evaluate their performance in stock index prediction by comparing them with traditional methods such as BP Neural Networks (BPNN) and Support Vector Machines (SVM). In order to further improve the performance of the model, we introduce a model that combines CNN and SVM(CNN-SVM). Experiments show that CNN-SVM model has achieved remarkable advantages in accuracy and robustness, showing the potential value of combining deep learning with traditional machine learning methods. In the empirical analysis, it is found that using CNN-SVM to predict the stock market can achieve relatively superior performance. CNN-SVM can better capture nonlinear relations and complex patterns in time series data through its excellent feature extraction ability. Compared with traditional methods, CNN-SVM shows higher accuracy, precision and recall rate, which verifies its effectiveness in the financial field. The results of this study not only provide investors with more accurate stock market forecasting tools, but also provide a new perspective for the academic research on the combination of deep learning technology in the financial field.

Fault Diagnosis of Drone Motors Driven by Current Signal Data with Few Samples

Abstract Multirotor UAVs find extensive application across diverse domains, with their motors serving as pivotal components in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the critical need for research in UAV motor fault diag-nosis. This paper focuses on quadrotor UAVs employing DC brushless motors as the research subject. The study designs fault experiments for three key components—stator, rotor, and bear-ing—based on the structural and operational characteristics of these motors. Addressing chal-lenges such as limited installation space for UAV payloads and redundant components, difficul-ties in sensor installation, high costs, and data acquisition complexities, this research adopts the current signal as the diagnostic signal. It explores a UAV motor fault diagnostic method based on the current signal. Furthermore, the study investigates a motor fault diagnosis method based on the current signal. To overcome challenges arising from the UAV's heightened sensitivity to the health of its components and the limited availability of fault data training samples, traditional machine learning and deep learning methods face difficulties in identifying representative fea-tures under small samples, often succumbing to overfitting risks and resulting in low diagnostic accuracy. To address these issues, a hybrid neural network fault diagnosis model is proposed, integrating the width learning system and convolutional neural network. The width learning system maps the original current signal into a feature space, yielding more robust and repre-sentative sample features. Subsequently, the convolutional neural network undertakes feature extraction and classification tasks. In experiments focused on current signal data-driven small-sample fault diagnosis in UAV motors, the proposed model outperforms other models used for comparison.

A Sustainable W-RLG Model for Attack Detection in Healthcare IoT Systems

The increasingly widespread use of IoT devices in healthcare systems has heightened the need for sustainable and efficient cybersecurity measures. In this paper, we introduce the W-RLG Model, a novel deep learning approach that combines Whale Optimization with Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) for attack detection in healthcare IoT systems. Leveraging the strengths of these algorithms, the W-RLG Model identifies potential cyber threats with remarkable accuracy, protecting the integrity and privacy of sensitive health data. This model’s precision, recall, and F1-score are unparalleled, being significantly better than those achieved using traditional machine learning methods, and its sustainable design addresses the growing concerns regarding computational resource efficiency, making it a pioneering solution for shielding digital health ecosystems from evolving cyber threats.

Utilizing domain knowledge: Robust machine learning for building energy performance prediction with small, inconsistent datasets

Machine learning (ML) applications often require large datasets, a requirement that can pose a major challenge in fields where data is sparse or inconsistent. To address this issue, we propose a novel approach that combines prior knowledge with data-driven methods to significantly reduce data dependency. This study represents a disentangled system compositionality knowledge by the method of Component-Based Machine Learning (CBML) in the context of energy-efficient building engineering. In this way, CBML incorporates semantic domain knowledge within the structure of a data-driven model. To understand the advantage of CBML, we conducted a case experiment to assess the effectiveness of this knowledge-encoded ML approach in scenarios with sparse data input (1 % - 0.0125 % sampling rate) and several typical ML methods. Our findings reveal three key advantages of this approach over traditional ML methods: 1) It significantly improves the robustness of ML models when dealing with extremely small and inconsistent datasets; 2) It allows for efficient utilization of data from diverse record collections; 3) It can handle incomplete data while maintaining high interpretability and reducing training time. These features offer a promising solution to the challenges associated with deploying data-intensive methods and contribute to more efficient real-world data usage. Additionally, we outline four essential prerequisites to ensure the successful integration of prior knowledge and ML generalization in target scenarios and open-sourced the code and dataset for community reproduction.

Masked Statement Classification in Conversational Text

Sarcasm can be really tricky for computer programs that analyze text to understand people’s feelings and opinions. This is because sarcasm involves saying something with the opposite meaning of what you actually feel, often to make fun of a situation. When computers analyze text, they focus on the words used rather than the intended meaning, which can lead to mistakes in how the text is categorized. Sarcasm also creates problems when trying to figure out the author’s true emotions in tasks like sentiment analysis. So, there is a need of a way to identify sarcasm in text and correct the sentiment it conveys. Recent studies have employed deep learning and machine learning methods to identify sarcasm. Deep learning is quite powerful, but traditional machine learning methods are still widely used and can work as well if trained properly. This study aims to compare different algorithms like Long Short Term Memory (LSTM), K-Nearest Neighbors (KNN), Computational Neural Network(CNN), and Artificial Neural Network(ANN) etc to see how well they work in sarcasm detection. The survey examined several systems and algorithms for detecting sarcasm, as well as any faults or drawbacks associated with these approaches. It also discusses various important challenges and future directions for sarcasm detection research in order to provide useful insights in this field