Advanced Machine Learning Algorithms Research Articles

Driverless Planning and Decision-Making based on GRU

In the wake of the swift advancement of artificial intelligence, driverless technology has progressively emerged as a revolutionary technology. The existing driverless technology primarily relies on advanced sensors, artificial intelligence and machine learning algorithms, with a view to perceiving and analyzing the surrounding environment in real time, and making driving decisions accordingly. At the present time, despite the fact that driverless technology has introduced a lot of convenience to human travel, it still encompasses a significant number of challenges, such as safety, perception technology, vehicle control, road planning, cost and other issues. In a similar vein, the safety issue is an unavoidable part of the discussion on the driverless. Driverless vehicles require robust system safety and protection mechanisms. This paper is designed to establish a data model by analyzing the NGSIM dataset, and to probe the driverless perception capability and decision-making on intelligence in behavior-aware motion perspective with GRU approach. Eventually, it was concluded that neither too many nor too few neighboring vehicles should be taken into account when forecasting the trajectory of each vehicle in the coming years, while the appropriate neighboring vehicles should be selected to achieve the best prediction results.

COUNTERFEIT CURRENCY DETECTION USING MACHINE LEARNING

This research addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN),and theefficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing. Keywords: KNN, MobileNetV2, Image Processing, Machine learning algorithms, Feature extraction.

Parts-of-Speech (PoS) Analysis and Classification of Various Text Genres

Abstract Natural language processing (NLP) has made significant leaps over the past two decades due to the advancements in machine learning algorithms. Text classification is pivotal today due to a wide range of digital documents. Multiple feature classes have been proposed for classification by numerous researchers. Genre classification tasks form the basis for advanced techniques such as native language identification, readability assessment, author identification etc. These tasks are based on the linguistic composition and complexity of the text. Rather than extracting hundreds of variables, a simple premise of text classification using only the text feature of parts-of-speech (PoS) is presented here. A new dataset gathered from Project Gutenberg is highlighted in this study. PoS analysis of each text in the created dataset was carried out. Further grouping of these texts into fictional and non-fictional texts was carried out to measure their classification accuracy using the artificial neural networks (ANN) classifier. The results indicate an overall classification accuracy of 98 and 35 % for the genre and sub-genre classification, respectively. The results of the present study highlight the importance of PoS not only as an important feature for text processing but also as a sole text feature classifier for text classification.

Analysis of control algorithms for water quality testing robots

The research background of the water quality detection robot lies in effectively monitoring and assessing the quality of water bodies to address the increasingly severe issue of water pollution. This technology, employing advanced sensors and machine learning algorithms, facilitates automated acquisition of water quality data, enabling real-time analysis and reporting of various indicators in the water bodies. Consequently, it provides crucial evidence for safeguarding the environment and human health. This article investigates the problem of posture control in water quality detection robots operating in complex underwater environments. It compares three control algorithms: the traditional PID fuzzy control algorithm, and a BP neural network-based PID algorithm. Through experimental simulation, these algorithms are evaluated based on their differences in system dynamic performance. The results indicate that the utilization of the BP neural network PID algorithm significantly enhances both stability and control precision of the robot when dealing with non-linear environments characterized by uncertainties. This algorithm combines traditional PID control with neural network technology, allowing for adaptive optimization of PID controller parameters through training the neural network. As a result, it overcomes limitations associated with traditional PID and fuzzy control algorithms while improving overall performance.

Water quality prediction: a data-driven approach exploiting advanced machine learning algorithms with data augmentation

Abstract Water quality assessment plays a crucial role in various aspects, including human health, environmental impact, agricultural productivity, and industrial processes. Machine learning (ML) algorithms offer the ability to automate water quality evaluation and allow for effective and rapid assessment of parameters associated with water quality. This article proposes an ML-based classification model for water quality prediction. The model was tested with 14 ML algorithms and considers 20 features that represent various substances present in water samples and their concentrations. The dataset used in the study comprises 7,996 samples, and the model development involves several stages, including data preprocessing, Yeo–Johnson transformation for data normalization, principal component analysis (PCA) for feature selection, and the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Performance metrics, such as accuracy, precision, recall, and F1 score, are provided for each algorithm with and without SMOTE. LightGBM, XGBoost, CatBoost, and Random Forest were identified as the best-performing algorithms. XGBoost achieved the highest accuracy of 96.31% without SMOTE and had a precision of 0.933. The application of SMOTE enhanced the performance of CatBoost. These findings provide valuable insights for ML-based water quality assessment, aiding researchers and professionals in decision-making and management.

Pharmaceutical Agents as Potential Drivers in the Development of Early-Onset Colorectal Cancer: Case-Control Study.

The incidence of early-onset colorectal cancer (EOCRC) rose abruptly in the mid 1990s, is continuing to increase, and has now been noted in many countries. By 2030, 25% of American patients diagnosed with rectal cancer will be 49 years or younger. The large majority of EOCRC cases are not found in patients with germline cancer susceptibility mutations (eg, Lynch syndrome) or inflammatory bowel disease. Thus, environmental or lifestyle factors are suspected drivers. Obesity, sedentary lifestyle, diabetes mellitus, smoking, alcohol, or antibiotics affecting the gut microbiome have been proposed. However, these factors, which have been present since the 1950s, have not yet been conclusively linked to the abrupt increase in EOCRC. The sharp increase suggests the introduction of a new risk factor for young people. We hypothesized that the driver may be an off-target effect of a pharmaceutical agent (ie, one requiring regulatory approval before its use in the general population or an off-label use of a previously approved agent) in a genetically susceptible subgroup of young adults. If a pharmaceutical agent is an EOCRC driving factor, regulatory risk mitigation strategies could be used. We aimed to evaluate the possibility that pharmaceutical agents serve as risk factors for EOCRC. We conducted a case-control study. Data including demographics, comorbidities, and complete medication dispensing history were obtained from the electronic medical records database of Maccabi Healthcare Services, a state-mandated health provider covering 26% of the Israeli population. The participants included 941 patients with EOCRC (≤50 years of age) diagnosed during 2001-2019 who were density matched at a ratio of 1:10 with 9410 control patients. Patients with inflammatory bowel disease and those with a known inherited cancer susceptibility syndrome were excluded. An advanced machine learning algorithm based on gradient boosted decision trees coupled with Bayesian model optimization and repeated data sampling was used to sort through the very high-dimensional drug dispensing data to identify specific medication groups that were consistently linked with EOCRC while allowing for synergistic or antagonistic interactions between medications. Odds ratios for the identified medication classes were obtained from a conditional logistic regression model. Out of more than 800 medication classes, we identified several classes that were consistently associated with EOCRC risk across independently trained models. Interactions between medication groups did not seem to substantially affect the risk. In our analysis, drug groups that were consistently positively associated with EOCRC included beta blockers and valerian (Valeriana officinalis). Antibiotics were not consistently associated with EOCRC risk. Our analysis suggests that the development of EOCRC may be correlated with prior use of specific medications. Additional analyses should be used to validate the results. The mechanism of action inducing EOCRC by candidate pharmaceutical agents will then need to be determined.

AR-Enabled Persistent Human-Machine Interfaces via a Scalable Soft Electrode Array.

Augmented reality (AR) is a computer graphics technique that creates a seamless interface between the real and virtual worlds. AR usage rapidly spreads across diverse areas, such as healthcare, education, and entertainment. Despite its immense potential, AR interface controls rely on an external joystick, a smartphone, or a fixed camera system susceptible to lighting. Here, an AR-integrated soft wearable electronic system that detects the gestures of a subject for more intuitive, accurate, and direct control of external systems is introduced. Specifically, a soft, all-in-one wearable device includes a scalable electrode array and integrated wireless system to measure electromyograms for real-time continuous recognition of hand gestures. An advanced machine learning algorithm embedded in the system enables the classification of ten different classes with an accuracy of 96.08%. Compared to the conventional rigid wearables, the multi-channel soft wearable system offers an enhanced signal-to-noise ratio and consistency over multiple uses due to skin conformality. The demonstration of the AR-integrated soft wearable system for drone control captures the potential of the platform technology to offer numerous human-machine interface opportunities for users to interact remotely with external hardware and software.

Evaluation of UN SDG-related formal learning activities in a university common core curriculum

Purpose Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs. Design/methodology/approach In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions. Findings The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data. Research limitations/implications The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs. Originality/value The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

Identification of novel biomarkers and immune infiltration characteristics of ischemic stroke based on comprehensive bioinformatic analysis and machine learning

BackgroundIschemic stroke (IS) is one of most common causes of disability in adults worldwide. However, there is still a lack of effective and reliable diagnostic markers and therapeutic targets in IS. Furthermore, immune cell dysfunction plays an important role in the pathogenesis of IS. Hence, in-depth research on immune-related targets in progressive IS is urgently needed. MethodsExpression profile data from patients with IS were downloaded from the Gene Expression Omnibus (GEO) database. Then, differential expression analysis and weighted gene coexpression network analysis (WGCNA) were performed to identify the significant modules and differentially expressed genes (DEGs). Key genes were obtained and used in functional enrichment analyses by overlapping module genes and DEGs. Next, hub candidate genes were identified by utilizing three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine–recursive feature elimination (SVM-RFE). Subsequently, a diagnostic model was constructed based on the hub genes, and receiver operating characteristic (ROC) curves were constructed to validate the performances of the predictive models and candidate genes. Finally, the immune cell infiltration landscape of IS was explored with the CIBERSORT deconvolution algorithm. ResultsA total of 40 key DEGs were identified based on the intersection of the DEGs and module genes, and we found that these genes were mainly enriched in the regulation of lipolysis in adipocytes, neutrophil extracellular trap formation and complement and coagulation cascades. Based on the results from three advanced machine learning algorithms, we obtained 7 hub candidate genes (ABCA1, ARG1, C5AR1, CKAP4, HMFN0839, SDCBP and TLN1) as diagnostic biomarkers of IS and developed a reliable nomogram with high predictive performance (AUC = 0.987). In addition, immune cell infiltration dysregulation was implicated in IS, and compared with those in the normal group, IS patients had increased fractions of gamma delta T cells, monocytes, M0 macrophages, M2 macrophages and neutrophils and clearly lower percentages of naive B cells, CD8 T cells, CD4+ memory T cells, follicular helper T cells, regulatory T cells (Tregs) and resting dendritic cells. Furthermore, correlation analysis indicated a significant correlation between the hub genes and immune cells in progressive IS. ConclusionIn conclusion, our study identified 7 hub genes as diagnostic biomarkers and established a reliable model to predict the occurrence of IS. Meanwhile, we explored the immune cell infiltration pattern and investigated the relationship between candidate genes and immune cells in the pathogenesis of IS. Hence, our study provides new insights into the diagnosis and treatment of IS.

A machine learning driven nomogram for predicting chronic kidney disease stages 3–5

Chronic kidney disease (CKD) remains one of the most prominent global causes of mortality worldwide, necessitating accurate prediction models for early detection and prevention. In recent years, machine learning (ML) techniques have exhibited promising outcomes across various medical applications. This study introduces a novel ML-driven nomogram approach for early identification of individuals at risk for developing CKD stages 3–5. This retrospective study employed a comprehensive dataset comprised of clinical and laboratory variables from a large cohort of diagnosed CKD patients. Advanced ML algorithms, including feature selection and regression models, were applied to build a predictive model. Among 467 participants, 11.56% developed CKD stages 3–5 over a 9-year follow-up. Several factors, such as age, gender, medical history, and laboratory results, independently exhibited significant associations with CKD (p < 0.05) and were utilized to create a risk function. The Linear regression (LR)-based model achieved an impressive R-score (coefficient of determination) of 0.954079, while the support vector machine (SVM) achieved a slightly lower value. An LR-based nomogram was developed to facilitate the process of risk identification and management. The ML-driven nomogram demonstrated superior performance when compared to traditional prediction models, showcasing its potential as a valuable clinical tool for the early detection and prevention of CKD. Further studies should focus on refining the model and validating its performance in diverse populations.

Review of machine learning and deep learning models for toxicity prediction.

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Machine Learning in Robotics with Fog/Cloud Computing and IoT

Robotics has been transformed by machine learning (ML), enabling intelligent and adaptive autonomous systems. By delivering massive computational resources and real-time data, fog/cloud computing and the Internet of Things boost ML-based robotics. Intelligent and linked robotics have emerged from fog/cloud computing, IoT, and machine learning. Robots using distributed computing, real-time IoT data, and advanced machine learning algorithms could alter industries and improve automation. To maximize its potential, this revolutionary combination must overcome several obstacles. This paper discusses the benefits and drawbacks of integrating technologies. It offer rapid model training and deployment for robots ML algorithms like deep learning and reinforcement learning. Case studies demonstrate how this combination might enhance robotics across industries. This study discusses the benefits and drawbacks of fog/cloud computing, IoT, and machine learning in robots. We propose solutions for security and privacy, resource management, latency and bandwidth, interoperability, energy efficiency, data quality, and bias. By proactively addressing these difficulties, we can establish a secure, efficient, and privacy-conscious robotic ecosystem where robots seamlessly interact with the physical world, improving productivity, safety, and human-robot collaboration. As these technologies progress, appropriate integration and ethical principles are needed to maximize their benefits to society.

Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

Cricket player selection is a crucial task for both national teams and franchise leagues. Traditionally, selectors rely on their experience and knowledge to evaluate a player's physical fitness, batting, and bowling performance. However, with the advancements in machine learning algorithms, it is possible to automate and improve the selection process. In this research, a Machine Learning (ML)-based approach is proposed for cricket player selection. This approach uses a combination of physical fitness data, batting and bowling statistics, and other relevant metrics to create a comprehensive player profile. Then three ML algorithms-linear regression, support vector regression, and random forests-are employed to identify the most promising players. Data is collected on a large number of cricket players and their performance in national and franchise leagues from the two most prominent cricket websites, espncricinfo, and cricbuzz, to evaluate the proposed approach. Then the proposed approach trained and tested the ML models on this data and compared their accuracy and performance. Based on the performance scores obtained from these models, two squads are selected for the national team and one squad for each franchise league team. The results demonstrate that the proposed approach can significantly improve the selection process and identify players with high potential. Furthermore, results found that the support vector regression algorithm outperformed other ML models in terms of prediction and player selection.

AI Content Generation Technology based on Open AI Language Model

The Open AI language model is a powerful tool for generating AI content. A Large amount of text data is trained through the language model, which can generate new text that is similar in style and tone to the training data. This language model can assist writers in generating high-quality content by offering suggestions and insights for improving language usage, sentence structure, and overall readability. This study represents the development of a content generation tool based on the open AI language model by utilising GPT 3 in the backend as an API to generate the necessary information for the model. With the help of this tool, businesses and individuals can produce high-quality, engaging content more efficiently than ever before. This content generation tool uses a recurrent neural network (RNN) architecture, which enables it to make more accurate predictions than rule-based chatbots. All the features, like Facebook ads, LinkedIn posts, Amazon product descriptions, blogs, company bios, chat bots, and so on, will be presented in the dashboard. This tool is powered by advanced machine learning algorithms that can analyse and understand natural language, allowing them to produce content that is grammatically correct, free of errors, and tailored to specific audiences. They can also help optimize content for search engines, ensuring that it reaches a wider audience and generates more traffic with fine-tuning templates.

PREDICTION OF CHRONIC KIDNEY DISEASE

Chronic Kidney Disease is a prevalent and weakening medical condition with a substantial impact on public health. Early detection and prediction reduces its adverse effects and improve patient outcomes. In this project, wepropose a comprehensive predictive analysis framework utilizing advanced machine learning algorithms to forecast the onset and progression of the disease. The dataset comprises a diverse range of clinical, population, and laboratory features collected from a group of chronic kidney disease patients over a defined period. Preprocessing techniques are employed to handle missing values and normalize data. Subsequently, various machine learning algorithms, including Random Forest, Support Vector Machine, and Gradient Boosting , are trained on the processed dataset to learn complex patterns and relationships within the data. Additionally, feature importance analysis is undertaken to identify the most influential variables contributing to prediction accuracy. The machine learning algorithms can effectively aid clinicians in predicting chronic kidney diseases, enabling timely interventions and personalized treatment plans. This research contributes to the growing knowledge on the application of machine learning in healthcare, particularly in chronic disease prediction.

Empowering Business Strategies advanced machine learning algorithms for precise sales

Abstract Machine learning (ML) is rapidly developing field that has the potential to revolutionize industries, including sales. Advanced ML algorithms can be used to empower businesses with precise sales data and insights, helping them to makebetter decisions and improve their sales performance. This paper discusses how advanced ML algorithms can be used to improve sales in a number of ways, including sales forecasting, lead generation, customer segmentation, and upsell and Cross - sell opportunities. It also provides examples of businesses that are already using advanced ML algorithms to improve their sales performance. The paper concludes with a number of recommendations for businesses that are looking to start using advanced ML algorithms to improve their sales performance.

Efficient Classification of Marine Debris using SVM with Noise Removal and Feature Extraction Techniques with Improved Performances

Marine debris is a significant environmental issue, necessitating the development of precise and efficient technologies for classifying and reducing its impact. This research tries to address the issues of image noise in ocean trash classification using four alternative filters the Gaussian, Bilateral, Mean, and Alpha-Trimmed Mean (ATM) filters for noise reduction and SVM for classification. The accuracy of categorization algorithms can be considerably impacted by the presence of noise in photos of maritime debris. In this study, we suggest a two-pronged strategy: To effectively decrease noise and improve the quality of the input photos, we first apply filters. These filters were chosen with care to protect significant details while eliminating extraneous noise. Second, we classify the debris into various categories based on its visual attributes using SVM, a powerful ML technique. The ATM filter significantly reduces noise and improves the clarity of photographs of ocean trash, according to the results of our experiments. This work proposes a novel approach for classifying marine debris using advanced machine learning algorithms. For improved classification accuracy, we suggest combining Support Vector Machines (SVM) with Adaptive Thresholding Mean (ATM) filtering and Histogram of Oriented Gradients (HOG) feature extraction. According to this research, the ATM filter is a promising option for noise reduction in ocean trash imaging, potentially increasing the precision of subsequent classification algorithms and assisting in efficient environmental monitoring and marine ecosystem conservation efforts. The language used for execution is Python.

Brain-Storming to Detect Brain Disorders

Abstract: Brain disorders represent a significant and growing global health challenge, encompassing a spectrum of conditions ranging from neurodegenerative diseases, like Alzheimer's and Parkinson's, to various neuropsychiatric disorders. Timely and accurate detection of brain disorders is crucial for enabling prompt interventions, tailoring appropriate treatments, and improving the overall quality of life for affected individuals. This research paper explores an innovative and interdisciplinary approach to enhance the process of brain disorder detection through the integration of brainstorming techniques and advanced machine learning algorithms. The research delves into the adaptability and effectiveness of brainstorming techniques for brain disorder detection, leveraging diverse data sources such as clinical records, genetic information, and patient histories. The study presents various brainstorming techniques that are utilised to analyse, identify and arrive at the best possible methodology that can be used for the detection of brain disorders.

AI Based Asthma Recognition System Using Respiratory Sound

Abstract: Asthma is a chronic respiratory condition that affects millions of people worldwide, leading to significant healthcare costs and a reduced quality of life for affected individuals. Timely and accurate detection of asthma exacerbations is crucial for effective management and intervention. Traditional methods of asthma diagnosis rely heavily on clinical assessments, which may not always provide real-time, objective data for prompt action. The aim of this project is to develop an AI-based asthma detection system that leverages the analysis of respiratory sound patterns. Current diagnostic tools often lack the ability to capture subtle changes in respiratory sounds that could indicate the onset or worsening of asthma symptoms. By employing advanced machine learning algorithms, this system aims to identify distinctive patterns and anomalies in respiratory sounds associated with asthma, enabling early detection and intervention.