Traditional Machine Learning Techniques Research Articles

Predicting cervical cancer risk probabilities using advanced H20 AutoML and local interpretable model-agnostic explanation techniques

Background Cancer is positioned as a major disease, particularly for middle-aged people, which remains a global concern that can develop in the form of abnormal growth of body cells at any place in the human body. Cervical cancer, often known as cervix cancer, is cancer present in the female cervix. In the area where the endocervix (upper two-thirds of the cervix) and ectocervix (lower third of the cervix) meet, the majority of cervical cancers begin. Despite an influx of people entering the healthcare industry, the demand for machine learning (ML) specialists has recently outpaced the supply. To close the gap, user-friendly applications, such as H2O, have made significant progress these days. However, traditional ML techniques handle each stage of the process separately; whereas H2O AutoML can automate a major portion of the ML workflow, such as automatic training and tuning of multiple models within a user-defined timeframe. Methods Thus, novel H2O AutoML with local interpretable model-agnostic explanations (LIME) techniques have been proposed in this research work that enhance the predictability of an ML model in a user-defined timeframe. We herein collected the cervical cancer dataset from the freely available Kaggle repository for our research work. The Stacked Ensembles approach, on the other hand, will automatically train H2O models to create a highly predictive ensemble model that will outperform the AutoML Leaderboard in most instances. The novelty of this research is aimed at training the best model using the AutoML technique that helps in reducing the human effort over traditional ML techniques in less amount of time. Additionally, LIME has been implemented over the H2O AutoML model, to uncover black boxes and to explain every individual prediction in our model. We have evaluated our model performance using the findprediction() function on three different idx values (i.e., 100, 120, and 150) to find the prediction probabilities of two classes for each feature. These experiments have been done in Lenovo core i7 NVidia GeForce 860M GPU laptop in Windows 10 operating system using Python 3.8.3 software on Jupyter 6.4.3 platform. Results The proposed model resulted in the prediction probabilities depending on the features as 87%, 95%, and 87% for class ‘0’ and 13%, 5%, and 13% for class ‘1’ when idx_value=100, 120, and 150 for the first case; 100% for class ‘0’ and 0% for class ‘1’, when idx_value= 10, 12, and 15 respectively. Additionally, a comparative analysis has been drawn where our proposed model outperforms previous results found in cervical cancer research.

Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.

Emotion Detection System for Malayalam Text using Deep Learning and Transformers

Emotion Detection System for Malayalam Text using Deep Learning and Transformers

Distributed Estimation of Support Vector Machines for Matrix Data.

Discrimination problems are of significant interest in the machine learning literature. There has been growing interest in extending traditional vector-based machine learning techniques to their matrix forms. In this article, we investigate the statistical properties of the nuclear-norm-based regularized linear support vector machines (SVMs), in particular establishing the convergence rate of the estimator in the high-dimensional setting. Furthermore, within the distributed estimation paradigm, we propose a communication-efficient estimator that can achieve the same convergence rate. We illustrate the performances of the estimators via some simulation examples and an empirical data analysis.

Large language models in complex system design

AbstractThis paper investigates the use of Large Language Models (LLMs) in engineering complex systems, demonstrating how they can support designers on detail design phases. Two aerospace cases, a system architecture definition and a CAD model generation activities are studied. The research reveals LLMs' challenges and opportunities to support designers, and future research areas to further improve their application in engineering tasks. It emphasizes the new paradigm of LLMs support compared to traditional Machine Learning techniques, as they can successfully perform tasks with just a few examples.

Email spam detection by deep learning models using novel feature selection technique and BERT

Due to the influx of advancements in technology and the increased simplicity of communication through emails, there has been a severe threat to the global economy and security due to upsurge in volume of unsolicited During the training of models, high-dimensional and redundant datasets may reduce the classification results of the model due to high memory costs and high computation. An important data processing technique is feature selection which helps in selecting relevant features and subsets of information from the dataset. Therefore, choosing efficient feature selection techniques is very important for the best performance of classification of a model. Moreover, most of the research has been performed using traditional machine learning techniques, which are not enough to deal with the huge amount of data and its variations. Also,spammers are becoming smarter with technological advancement. Therefore, there is a need for hybrid techniques consisting of deep learning and conventional algorithms to cope with these problems. We have proposed a novel scheme in this paper for email spam detection, which will result in an improved feature selection approach from the original dataset and increase the accuracy of the classifier as well. The literature has been studied to explore the efficient machine learning models that have been applied by different researchers for email spam detection and feature selection to acquire the best results. Our method, GWO-BERT, has given remarkable results with deep learning techniques such as CNN, biLSTM and LSTM. We have compared our models with RF and LSTM and used dataset: “Lingspam,” which is a publicly available dataset. With different experiments, our technique, GWO-BERT, obtained 99.14% accuracy, which is almost equal to 100 percent.

Modern techniques in detecting, identifying and classifying fruits according to the developed machine learning algorithm

Recent developments in machine vision have opened up a wide range of applications, and farming is no exception. Deep learning (DL) has a wide range of applications because of its capacity to extract robust features from photos. Shape, color, and feel of many fruit species make it difficult to discover and classify fruits. When examining the effects of artificial intelligence on fruit identification and classification, we noted that, up until 2018, the majority of approaches relied on traditional machine learning (ML) techniques, while just a few ways took use of DL techniques for recognizing fruits and categorization. In this post, we thoroughly covered the datasets that many academics utilized, the useful descriptors, the application of model, and the difficulties of utilizing DL to identify and classify fruits. Finally, we compiled the outcomes of various DL techniques used in earlier research to identify and categorize fruits. This work examines the use of models based on DL for fruit categorization and recognition in recent studies. In order to make it simpler for beginning agricultural researchers to comprehend the importance of ML in the agricultural domain, we have developed a DL model for apple categorization using the well-known dataset "Fruit 360" starting scratch.

Quantum Machine Learning: A Survey

Quantum Machine Learning (QML) is an emergent discipline that integrates the principles of quantum computing with traditional machine learning techniques, aiming to enhance the capabilities of data analysis and decision-making processes. Leveraging the unique properties, QML promises to revolutionize machine learning by offering superior processing power and computational efficiency. The synergistic approach followed by each Quantum Machine Learning Algorithm allows for the management of large databases and the execution of complex computational tasks more efficiently than classical algorithms. The integration of QML into machine learning workflows can lead to the development of advanced AI systems capable of personalized treatment recommendations, scientific discovery, and data-driven decision-making, thereby transforming the landscape of artificial intelligence and decision-making processes.

Explainable Anomaly Detection of Synthetic Medical IoT Traffic Using Machine Learning

In the context of the Internet of Things (IoT), particularly within medical facilities, the detection and categorization of Internet traffic remain significant challenges. While conventional methods for IoT traffic analysis can be applied, obtaining suitable medical traffic data is challenging due to the stringent privacy constraints associated with the health domain. To address this, this study proposes a network traffic simulation approach using an open-source tool called IoT Flock, which supports both CoAP and MQTT protocols. The tool is used to create a synthetic dataset, to simulate IoT traffic originating from various smart devices in different hospital rooms. The study shows a complete anomaly detection analysis of IoT-Flock-generated traffic, both normal and malicious, by leveraging and comparing traditional machine learning techniques, deep learning models with multiple hidden layers, and explainable artificial intelligence techniques. The results are very promising. For the binary classification, for example, the obtained accuracy is close to 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} in the case of the CoAP protocol. Good results are also obtained when the multinomial classification is performed, observing that CoAP packets are classified better than MQTT packets, even if the identification of the different MQTT packets reaches very high metrics for the most of the considered algorithms. Moreover, the obtained classification rules are also meaningful in the considered IoT context. The results indicate that IoT-Flock synthetic data can effectively be used to train and test machine and deep learning models for detecting abnormal IoT traffic in medical scenarios. This research attempts also to bridge the gap between IoT security and healthcare, providing useful insights into securing medical IoT networks in general.

Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments.

Research for Drone Target Classification Method Using Deep Learning Techniques

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.

Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models. A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital). Data from Hospital 1 were used for internal training, while data from Hospital 2 were used for external validation. Five algorithms, including traditional logistic regression (LR) and machine learning techniques (generalized linear models [GLM], random forest [RF], gradient boosting machine [GBM], deep neural network [DL], and naive Bayes [NB]), were employed to construct models predicting early lung cancer infiltration and were analyzed. The models were comprehensively evaluated through receiver operating characteristic curve (AUC) analysis based on LR, calibration curves, decision curve analysis (DCA), as well as global and individual interpretative analyses using variable feature importance and SHapley additive explanations (SHAP) plots. A total of 560 patients were used for model development in the training dataset, while a dataset comprising 243 patients was used for external validation. The GBM model exhibited the best performance among the five algorithms, with AUCs of 0.931 and 0.99 in the validation and test sets, respectively, and accuracies of 0.857 and 0.955 in the validation and test groups, respectively, outperforming other models. Additionally, the study found that nodule diameter and average CT value were the most significant features for predicting lung cancer infiltration using machine learning models. The GBM model established in this study can effectively predict the risk of infiltration in early-stage lung cancer patients, thereby improving the accuracy of lung cancer screening and facilitating timely intervention for infiltrative lung cancer patients by clinicians, leading to early diagnosis and treatment of lung cancer, and ultimately reducing lung cancer-related mortality.

Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis

This study utilized datasets from an ongoing monitoring project conducted in Wadi Dayqah Dam, the largest reservoir in Oman. The dataset comprises information on ten water quality variables collected by AAQ-RINKO device for 20 field sampling stations, encompassing different depths within the water columns. First, an in-depth data analysis was conducted to process and characterize variations in key parameters. Then, four deep learning models were developed and evaluated, including the Gated Residual Variable Selection (GRVS), Deep and Cross (DC), Deep and Wide (DW), and Base models for estimating two important water quality parameters, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a). To justify the performance of deep learning methods, they were compared to four traditional machine learning techniques: MLP, SVR, AdaBoost, and LR-Lasso models. The GRVS model emerged as the most effective method, achieving high accuracy with R2 and RMSE values of 0.98 and 0.38 for the DO parameter and 0.84, and 0.45 for the Chl-a parameter, respectively. Finally, two SHapley Additive exPlanations (SHAP) and Softmax layer model interpretation approaches were employed to highlight the influential factors affecting the predictions in the best model. Results from the SHAP analysis identified pH, depth, and temperature as the most significant variables, with mean SHAP values of 1.8, 0.75, and 0.36 for DO and 0.74, 0.37, and 0.3 for Chl-a, respectively. The data-driven framework implemented in this study holds promise for efficiently approximating hard-to-measure water quality indicators in monitoring projects using cost-effective inputs, which is particularly valuable in resource-constrained settings.

Hub‐aware random walk graph embedding methods for classification

AbstractIn the last two decades, we are witnessing a huge increase of valuable big data structured in the form of graphs or networks. To apply traditional machine learning and data analytic techniques to such data it is necessary to transform graphs into vector‐based representations that preserve the most essential structural properties of graphs. For this purpose, a large number of graph embedding methods have been proposed in the literature. Most of them produce general‐purpose embeddings suitable for a variety of applications such as node clustering, node classification, graph visualization and link prediction. In this article, we propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem. Random walk sampling strategies of the proposed algorithms have been designed to pay special attention to hubs–high‐degree nodes that have the most critical role for the overall connectedness in large‐scale graphs. The proposed methods are experimentally evaluated by analyzing the classification performance of three classification algorithms trained on embeddings of real‐world networks. The obtained results indicate that our methods considerably improve the predictive power of examined classifiers compared with currently the most popular random walk method for generating general‐purpose graph embeddings (node2vec).

SmartLaundry: A Real-Time System for Public Laundry Allocation in Smart Cities.

Smart cities facilitate the comprehensive management and operation of urban data generated within a city, establishing the foundation for smart services and addressing diverse urban challenges. A smart system for public laundry management uses artificial intelligence-based solutions to solve the challenges of the inefficient utilization of public laundries, waiting times, overbooking or underutilization of machines, balancing of loads across machines, and implementation of energy-saving features. We propose SmartLaundry, a real-time system design for public laundry smart recommendations to better manage the loads across connected machines. Our system integrates the current status of the connected devices and data-driven forecasted usage to offer the end user connected via a mobile application a list of recommended machines that could be used. We forecast the daily usage of devices using traditional machine learning techniques and deep learning approaches, and we perform a comparative analysis of the results. As a proof of concept, we create a simulation of the interaction with our system.

Unlocking the potential: CRNN-driven flexible mechanical sensors for intelligent sensing and beyond

In our rapidly evolving society, intelligent technology has become a driving force across various sectors, leading to transformative progress. This paper explores the integration of Convolutional Recurrent Neural Network (CRNN) algorithms with flexible sensors, showcasing their remarkable potential in enhancing data processing and interpretation. The synergy between CRNN and flexible sensors holds great promise in diverse domains, including health monitoring and industrial automation. Our comprehensive evaluation of the CRNN model reveals exceptional performance in classification and regression tasks, highlighting its adaptability to complex and varied sensor data. Additionally, confusion matrix analysis provides insights into multi-class classification scenarios, reinforcing the model's reliability. A comparative analysis against traditional machine learning techniques demonstrates the superiority of CRNN in handling time-series data. In conclusion, the integration of CRNN with flexible sensors is set to revolutionize intelligent sensing technology, opening new avenues for innovation and problem-solving in various industries. This collaborative approach not only enhances data accuracy and reliability but also paves the way for groundbreaking technologies and applications yet to be imagined. The future of intelligent sensing is here, and it is both exciting and promising, with CRNN-driven flexible sensors leading the way towards a more intelligent and connected world.

Assessing heterogeneous groundwater systems: Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters

This research presents an unsupervised learning approach for interpreting well-log data to characterize the hydrostratigraphical units within the Quaternary aquifer system in Debrecen area, Eastern Hungary. The study applied factor analysis (FA) to extract factor logs from spontaneous potential (SP), natural gamma ray (NGR), and resistivity (RS) logs and correlate it to the petrophysical and hydrogeological parameters of shale volume and hydraulic conductivity. This research indicated a significant exponential relationship between the shale volume and the scaled first factor derived through factor analysis. As a result, a universal FA-based equation for shale volume estimation is derived that shows a close agreement with the deterministic shale volume estimation. Furthermore, the first scaled factor is correlated to the decimal logarithm of hydraulic conductivity estimated with the Csókás method. Csókás method is modified from the Kozeny-Carman equation that continuously estimates the hydraulic conductivity. FA and Csókás method-based estimations showed high similarity with a correlation coefficient of 0.84. The use of factor analysis provided a new strategy for geophysical well-logs interpretation that bridges the gap between traditional and data-driven machine learning techniques. This approach is beneficial in characterizing heterogeneous aquifer systems for successful groundwater resource development.

Crack _ PSTU: Crack detection based on the U-Net framework combined with Swin Transformer

Cracks serve as a significant indicator of aging infrastructure. While the surfaces of traditional infrastructures are visually inspected manually, this approach is labor-intensive, highly subjective, and often surpasses the capabilities of available inspection personnel. Although some researchers have employed traditional image processing and machine learning techniques to address these challenges, the presence of irregular crack shapes, complex lighting conditions, and limitations in detecting only a single type of surface crack complicate automatic crack detection. Recent research indicates that deep learning methods are increasingly leading in image-based feature extraction, object detection, and pixel attribute analysis. This paper introduces a method, Crack_PSTU (Pre-trained Swin Transformer U-Net), which leverages the U-Net framework and Swin Transformer model for infrastructure crack classification and detection tasks. Specifically, we utilize a pre-trained Swin Transformer network as the encoder and implement the decoder using convolution, pooling, and other operations, creating a "U"-shaped model architecture. Our experiments encompassed 11,298 crack images from varied scenes, with 9603 designated for training and 1695 for validation. The findings reveal that our approach surpasses other algorithms for this dataset.

Intelligent Resource Orchestration for 5G Edge Infrastructures

The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

Probabilistic identification method for seismic failure modes of reinforced concrete beam-column joints using Gaussian process with deep kernel

Identifying the seismic failure modes of beam-column joints (BCJs) is crucial for the safety and integrity of reinforced concrete (RC) buildings or structures withstanding seismic forces. However, traditional identification methods fail to provide any indication about the uncertainties within their predictions, which is beneficial for evaluating, interpreting and improving these predictions. This study develops a probabilistic identification method for seismic failure modes of BCJs using Gaussian process (GP) with a deep kernel, which integrates the representational power of deep neural networks with the flexible structure of kernel functions to accurately represent the evolution characteristics of seismic failure modes of BCJs. Analysis results demonstrated the potential of the proposed method for improving the classification accuracy of traditional GPs, as well as its superiority over the prediction accuracy of traditional shear-resistance design methods and machine learning techniques. Furthermore, the proposed method also provides an efficient approach to estimate the uncertainties within their predictions for seismic failure modes of BCJs.