Traditional Machine Learning Approaches Research Articles

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

Medical Symptom Text Classification through NLP in Healthcare

Abstract: Medical symptom text classification through Natural Language Processing (NLP) is a rapidly evolving field that aims to leverage computational techniques to analyse and interpret vast amounts of textual data generated in healthcare settings. This paper provides a comprehensive survey of current methodologies, applications, challenges, and future directions in this domain. We begin by discussing the importance of symptom classification for improving patient outcomes, supporting clinical decisionmaking, and enhancing disease surveillance. We then review traditional machine learning approaches and advanced deep learning models, highlighting their respective strengths and limitations. Key pre-processing techniques crucial for handling medical jargon and ensuring data privacy are also examined. The paper further explores real-world applications, including clinical decision support systems, disease outbreak detection, and patient monitoring. Despite significant advancements, challenges such as data quality, model interpretability, and regulatory compliance remain. Finally, we identify emerging trends and potential future developments that could drive further innovation in NLP for healthcare. This survey aims to provide a valuable resource for researchers and practitioners seeking to understand and contribute to the field of medical symptom text classification.

Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach

Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach

RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Advancements in skin cancer classification: a review of machine learning techniques in clinical image analysis

AbstractEarly detection of skin cancer from skin lesion images using visual inspection can be challenging. In recent years, research in applying deep learning models to assist in the diagnosis of skin cancer has achieved impressive results. State-of-the-art techniques have shown high accuracy, sensitivity and specificity compared with dermatologists. However, the analysis of dermoscopy images with deep learning models still faces several challenges, including image segmentation, noise filtering and image capture environment inconsistency. After making the introduction to the topic, this paper firstly presents the components of machine learning-based skin cancer diagnosis. It then presents the literature review on the current advance in machine learning approaches for skin cancer classification, which covers both the traditional machine learning approaches and deep learning approaches. The paper also presents the current challenges and future directions for skin cancer classification using machine learning approaches.

Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques

This research utilizes advanced machine learning techniques to evaluate node vul- nerability in power grid networks. Utilizing the SciGRID and GridKit datasets, con- sisting of 479, 16,167 nodes and 765, 20,539 edges respectively, the study employs K-nearest neighbor and median imputation methods to address missing data. Cen- trality metrics are integrated into a single comprehensive score for assessing node criticality, categorizing nodes into four centrality levels informative of vulnerability. This categorization informs the use of traditional machine learning (including XG- Boost, SVM, Multilayer Perceptron) and Graph Neural Networks in the analysis. The study not only benchmarks the capabilities of these models in network analy- sis but also explores their potential in identifying critical nodes using features be- yond centrality metrics alone, enhancing their applicability in real-world scenarios. The research addresses a significant gap in effectively assessing the vulnerability of electrical networks, marked by isolated use of traditional centrality metrics and a lack of integration between these combined metrics with both tradiational and ad- vanced machine learning models. The study integrates various centrality measures into a comprehensive metric and advocates for the application of advanced ma- chine learning models, particularly GNNs. It underscores the need for larger and more complex datasets to unlock the full potential of GNNs in network vulnerabil- ity assessments. By comparing the performance of GNN models with traditional machine learning approaches across datasets of different sizes and complexities, the study provides insights into optimizing model selection for network analysis, thereby contributing significantly to the field of network vulnerability assessment.

A supervised learning algorithm based on the quasi-Bayesian method for the probability density functions and application for medical data

This study proposes a new supervised learning algorithm for probability density functions (PDFs) and effectively applies it to medical images. The proposed algorithm is demonstrated step by step, illustrated with a numerical example, and proofed the convergence. This algorithm contributes significantly to the field of recognition in four key areas. The first contribution is the improvement of determining prior probabilities by establishing a method based on the fuzzy relationship between each classified PDF and groups within the training set through cluster analysis technique. The next contribution involves developing a new measure to evaluate the level of similarity between the classified PDFs and the considered groups. Another contribution is the establishment of a new classification principle, quasi-Bayes, for PDFs. The final contribution of this study is the application of the proposed algorithm to both numerical and image data, where objects are represented as representative PDFs. Practical applications on various medical datasets with different characteristics have demonstrated the outstanding advantages of the proposed algorithm over other methods, including traditional statistics, machine learning, and deep learning approaches, based on metrics such as ACC, AUC, F1-Score, and One-way ANOVA test. Specifically, experimental results of the Skin cancer data show that the proposed algorithm achieved an ACC index of 98.024%, higher than other methods, including ResNet-50, Inception ResNet V2, and CNN with 97.33%, 97.815%, and 80.702%, respectively. Similarly, the proposed algorithm also obtained notable results for other indices such as AUC and F1-Score. Additionally, we also obtained similar results for the Brain tumor dataset.

Modeling rutting depth on RIOHTrack asphalt pavement using Circle LSTMs

In response to the critical need for accurate pavement rutting prediction in pavement design, this study introduces a novel deep-learning model, Circle LSTM, designed for the estimation of rutting depths in asphalt pavements. Leveraging data from a full-scale field accelerated road testing facility in Tongzhou, Beijing, which spans 2.038 kilometres and features 19 distinct asphalt pavements, the Circle LSTM model utilizes variables such as temperature and load axis for its predictions. Methodologically, the Circle LSTM outperforms traditional machine learning approaches by cycling through different LSTM models to enhance short-term dependence modelling, showcasing its strength in addressing nonlinear problems inherent in road engineering. Evaluation of the model on the test dataset yielded promising results, with error metrics showing an average RMSE of 2.4170, an average MAE of 2.9454, an average MAPE of 2.61%, and an average coefficient of determination (average R2) of 0.8253 for 19 different asphalt pavements. These findings underscore the Circle LSTM model’s potential not only for fast and reliable rutting depth prediction but also for broader applications in multivariate time-series forecasting tasks in road engineering and beyond.

SecureNet: Network Intrusion Detection using Machine Learning and Deep Learning Techniques

Abstract: In the ever-evolving landscape of cybersecurity, the need for robust intrusion detection systems has become paramount. This paper introduces a cutting-edge intrusion detection algorithm designed to enhance network security through the integration of advanced machine learning and deep learning methodologies. The proposed algorithm capitalizes on the strengths of both paradigms to achieve a comprehensive and adaptive approach to identifying malicious activities within a network. This research focuses on enhancing network security through the development and evaluation of a novel intrusion detection system leveraging both deep learning and traditional machine learning approaches. Utilizing the NSL-KDD dataset, we employ the Long Short-Term Memory (LSTM) model, a superior version of Recurrent Neural Networks (RNNs), and the K-Nearest Neighbors (KNN) algorithm for binary and multi-class classification of network intrusion anomalies. The LSTM model excels in capturing temporal dependencies, enabling the detection of nuanced sequential patterns, while the KNN algorithm contributes to a comprehensive classification framework. Experimental results demonstrate the effectiveness of the hybrid methodology, showcasing improved accuracy, precision, and recall compared to traditional methods. This research underscores the potential of integrating deep learning and classical machine learning techniques to bolster the capabilities of intrusion detection systems in safeguarding against evolving cyber threats

International Trade Partner Selection Model and Its Influencing Factors Based on Machine Learning

Iinternational trade partner selection model and its influencing factors based on machine learning represents a significant advancement in global business strategies. By harnessing the capabilities of machine learning algorithms, this model analyzes a multitude of variables such as market trends, economic indicators, and partner attributes to identify optimal trade partners for businesses. Factors such as geographical proximity, market stability, and cultural compatibility are incorporated into the model to provide data-driven insights into partner selection decisions. This paper investigates the application of Stacked Random Field Machine Learning (SRF-ML) in the domain of international trade partner selection. The selection of suitable trade partners is a crucial aspect of global trade operations, influencing the efficiency and success of business ventures. Traditional approaches to partner selection often rely on subjective assessments or simplistic models, which may overlook important factors and lead to suboptimal decisions. In contrast, SRF-ML offers a powerful framework for analyzing complex datasets and making informed predictions about partner suitability. Through the integration of multiple layers of random field models, SRF-ML can effectively capture intricate relationships between various attributes and provide more accurate assessments of partner compatibility. In this paper explore the performance of SRF-ML models across different datasets and scenarios, considering factors such as market size, economic stability, and logistical capability. The results demonstrate the superior performance of SRF-ML compared to traditional machine learning approaches, highlighting its potential to revolutionize partner selection processes in the realm of international trade. The results demonstrate the superior performance of SRF-ML compared to traditional machine learning approaches, with accuracy improvements ranging from 5% to 10%. By leveraging advanced feature sets and model configurations, SRF-ML enables decision-makers to make more informed and strategic decisions, ultimately enhancing the efficiency and effectiveness of global trade operations.

Application of Deep Learning for the Detection of Genetic Variations: Its Implementation in Classifying Alzheimer's Disease

Deep learning emerges as a promising technique, utilizing nonlinear transformations for feature extraction from high-dimensional datasets. However, its application encounters challenges in genome-wide association studies (GWAS) dealing with high-dimensional genomic data. This study introduces an innovative three-step method termed SWAT-CNN for the identification of genetic variants. This approach employs deep learning to pinpoint phenotype-related single nucleotide polymorphisms (SNPs), facilitating the development of precise disease classification models. In the first step, the entire genome undergoes division into non overlapping fragments of an optimal size. Subsequently, convolutional neural network (CNN) analysis is conducted on each fragment to identify phenotype-associated segments. The second step, employs a Sliding Window Association Test (SWAT), where CNN is utilized on the selected fragments to compute phenotype influence scores (PIS) and detect phenotype-associated SNPs based on these scores. The third step involves running CNN on all identified SNPs to construct a comprehensive classification model. Validation of the proposed approach utilized GWAS data from the Alzheimer’s disease Neuroimaging Initiative (ADNI), encompassing 981 subjects, including cognitively normal older adults (CN) and individuals with Alzheimer's disease (AD). Notably, the method successfully identified the widely recognized APOE region as the most significant genetic locus for AD. The resulting classification model exhibited an area under the curve (AUC) of 0.82, demonstrating compatibility with traditional machine learning approaches such as random forest and XGBoost. SWAT-CNN, as a groundbreaking deep learning-based genome-wide methodology, not only identified AD-associated SNPs but also presented a robust classification model for Alzheimer's disease, suggesting potential applications across diverse biomedical domains.

Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches

The rapid growth of e-commerce has increased the need for retailers to understand and predict customer satisfaction to support data-driven managerial decisions. This study analyzes online consumer behavior through a comparative machine learning modeling approach to forecast future customer satisfaction based on review ratings. Using a large dataset of over 100 k online orders from a major retailer, traditional machine learning models including random forest and support vector machines are benchmarked against deep learning techniques like multi-layer perceptrons. The predictive models are assessed for their ability to accurately predict customer satisfaction scores for the next orders based on key e-commerce features including delivery time, order value, and location. The findings demonstrate that the random forest model can predict future satisfaction with 92% accuracy, outperforming deep learning. The analysis further identifies core drivers of satisfaction such as delivery time and order accuracy. These insights enable retail managers to make targeted improvements, like optimizing logistics, to increase customer loyalty and revenue. This study provides a framework for leveraging predictive analytics and machine learning to unlock data-driven insights into online consumer behavior and satisfaction for superior retail decision-making. The focus on generalizable insights across a major retailer enhances the practical applicability of the machine learning approach for the retail sector.

Manifesto of Deep Learning Architecture for Aspect Level Sentiment Analysis to extract customer criticism

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

Multilingual Question Answering for Malaysia History with Transformer-based Language Model

In natural language processing (NLP), a Question Answering System (QAS) refers to a system or model that is designed to understand and respond to user queries in natural language. As we navigate through the recent advancements in QAS, it can be observed that there is a paradigm shift of the methods used from traditional machine learning and deep learning approaches towards transformer-based language models. While significant progress has been made, the utilization of these models for historical QAS and the development of QAS for Malay language remain largely unexplored. This research aims to bridge the gaps, focusing on developing a Multilingual QAS for history of Malaysia by utilizing a transformer-based language model. The system development process encompasses various stages, including data collection, knowledge representation, data loading and pre-processing, document indexing and storing, and the establishment of a querying pipeline with the retriever and reader. A dataset with a collection of 100 articles, including web blogs related to the history of Malaysia, has been constructed, serving as the knowledge base for the proposed QAS. A significant aspect of this research is the use of the translated dataset in English instead of the raw dataset in Malay. This decision was made to leverage the effectiveness of well-established retriever and reader models that were trained on English data. Moreover, an evaluation dataset comprising 100 question-answer pairs has been created to evaluate the performance of the models. A comparative analysis of six different transformer-based language models, namely DeBERTaV3, BERT, ALBERT, ELECTRA, MiniLM, and RoBERTa, has been conducted, where the effectiveness of the models was examined through a series of experiments to determine the best reader model for the proposed QAS. The experimental results reveal that the proposed QAS achieved the best performance when employing RoBERTa as the reader model. Finally, the proposed QAS was deployed on Discord and equipped with multilingual support through the incorporation of language detection and translation modules, enabling it to handle queries in both Malay and English. Doi: 10.28991/ESJ-2024-08-02-019 Full Text: PDF

Revolutionizing heart disease prediction with quantum-enhanced machine learning

The recent developments in quantum technology have opened up new opportunities for machine learning algorithms to assist the healthcare industry in diagnosing complex health disorders, such as heart disease. In this work, we summarize the effectiveness of QuEML in heart disease prediction. To evaluate the performance of QuEML against traditional machine learning algorithms, the Kaggle heart disease dataset was used which contains 1190 samples out of which 53% of samples are labeled as positive samples and rest 47% samples are labeled as negative samples. The performance of QuEML was evaluated in terms of accuracy, precision, recall, specificity, F1 score, and training time against traditional machine learning algorithms. From the experimental results, it has been observed that proposed quantum approaches predicted around 50.03% of positive samples as positive and an average of 44.65% of negative samples are predicted as negative whereas traditional machine learning approaches could predict around 49.78% of positive samples as positive and 44.31% of negative samples as negative. Furthermore, the computational complexity of QuEML was measured which consumed average of 670 µs for its training whereas traditional machine learning algorithms could consume an average 862.5 µs for training. Hence, QuEL was found to be a promising approach in heart disease prediction with an accuracy rate of 0.6% higher and training time of 192.5 µs faster than that of traditional machine learning approaches.

Analysis of traditional machine learning approaches on heart attacks prediction

Analysis of traditional machine learning approaches on heart attacks prediction

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response

Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.

Bridging expertise with machine learning and automated machine learning in clinical medicine

In this issue of the Annals, Thirunavukarasu et al.’s systematic review on the clinical performance of automated machine learning (autoML) highlights its extensive applicability across 22 clinical specialties, showcasing its potential to redefine healthcare by making artificial intelligence (AI) technologies accessible to those without advanced computational skills.1 This enables the development of effective AI models that could rival or exceed the accuracy of traditional machine learning (ML) approaches and human diagnostic methods.

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.

Estimation of total forces of jets on liquid interfaces using image processing methods

Optical imaging is a fundamental method for detecting plasma phenomena, such as plasma bubbles, plasma streamers, and self-organized plasma patterns. Therefore, data mining of images is crucial in plasma diagnostics. This work presents image processing methods for extracting substructures in plasma bubbles, streamers, and patterns. In our experiment, plasma–liquid interfaces between atmospheric pressure argon-plasma jets and NaCl solutions are quantitatively captured. After extracting accurate plasma–liquid interfaces, traditional analytic functions and machine learning approaches are used to fit curves of interfaces. Regression of machine learning method based on Gaussian process reveals many details of interfaces, but neural networks present smooth and accurate regressions. The gravitational and surface tension forces are calculated using experimental plasma–liquid interfaces. The plasma forces are estimated to be several tens of Pa. However, they increased to several hundred Pa around the inflection points of interfaces. This study extends the application of image processing to plasma diagnostics and provides target data of gas–liquid interfaces for numerical simulations.