Role Of Machine Learning Algorithms Research Articles

Impact of Artificial Intelligence in Companies Marketing Strategies

Impact of Artificial Intelligence in Companies Marketing Strategies

Mobile Malware Detection using Machine Learning Classifiers

In today world the mobile malware shows the significant threat to the security and privacy of the society using smartphones. These malware aims to access the sensitive data and harm the devices of users. This paper conducts a comprehensive comparison between the various machine learning and traditional methods for mobile malware detection based on the research papers published by the authors. Signature-based detection depends upon the predefined and common patterns, while the anomaly based techniques analyse the deviation from the regular normal behaviour. This study discusses the strengths and limitations of different approaches and highlights the need for adopting the malware detection methods to fight the growing threats. It also examines the role of machine learning algorithms, like Decision Trees, Random Forests, Convolutional Neural Networks, Support Vector Machines, and Naïve Bayes, for better malware detection. Latest findings and research highlights the importance of the continuing innovation to fight the emerging threat to the user privacy, data and security due to malwares. Keywords: Mobile Malware, Artificial Intelligence, Virus, Signature-based Detection, Machine Learning

Distributed Architectures for Big Data Analytics in Cloud Computing: A Review of Data-Intensive Computing Paradigm

Big Data challenges are prevalent in various fields, including economics, business, public administration, national security, and scientific research. While it offers opportunities for productivity and scientific breakthroughs, it also presents challenges in data capture, storage, analysis, and visualization. This paper provides a comprehensive overview of Big Data applications, opportunities, challenges, and current techniques and technologies to address these issues. This study presents a system for managing big data resources using cloud for the development of data-intensive applications. It addresses even the challenges related to technologies that combine cloud computing with other allied technologies and devices. In addition, the increasing volume, velocity, and variety of data in the era of Big Data necessitate advanced methods for data processing and management. This study delves into the intricacies of data scalability, real-time processing, and the integration of diverse data types. Furthermore, it explores the role of machine learning algorithms and artificial intelligence in extracting meaningful insights from massive datasets.

Electromagnetic shielding material database and machine learning preparing for laser cladding FeCo-based alloy

The compilation and preprocessing of electromagnetic shielding material databases using machine learning techniques are pivotal in contemporary materials science and engineering. These materials play a crucial role in diverse applications such as electronics, telecommunications, aerospace, and automotive industries, necessitating effective attenuation of electromagnetic interference (EMI). This paper underscores the significance of comprehensive databases in organizing material properties systematically, facilitating the identification of novel materials with enhanced shielding effectiveness. It explores the role of machine learning algorithms in predictive modeling and data analysis, expediting the screening process of candidate materials and uncovering hidden correlations within complex datasets. However, the efficacy of machine learning techniques relies heavily on the quality of input data and preprocessing steps. Thus, this paper discusses methodologies for collecting material data, challenges in data curation and integration, and common preprocessing techniques such as data cleaning, feature extraction, and normalization. The integration of electromagnetic shielding material databases with machine learning preprocessing holds great promise for FeCo-based alloy laser cladding manufacturing and design, leading to innovative solutions for electromagnetic interference mitigation across various technological domains.

The Influence of AI: The Revolutionary Effects of Artificial Intelligence in Healthcare Sector

The application of artificial intelligence (AI) in healthcare is growing as it becomes more prevalent in modern business and everyday life. It is frequently regarded as a significant technological advancement in the present period. In recent times, the fields of artificial intelligence (AI) and big data analytics have been utilised in the domain of mobile health (m-health) to establish a highly efficient healthcare system. Modern medical research utilises diverse and poorly understood data, including electronic health records (EHRs), medical imaging, and complex language that is widely unorganised. The growth of mobile applications, together with healthcare systems, is a significant factor leading to the presence of disorganised and unstructured datasets. The enhanced accessibility of diverse datasets and advanced computer techniques like machine learning can enable researchers to usher in a new era of highly efficient genetic therapy. This review paper has clarified the role of machine learning algorithms in healthcare systems.

A Literature Survey on AI Health Care System

The integration of Artificial Intelligence (AI) in healthcare systems has witnessed remarkable progress in recent years, revolutionizing the landscape of patient care, diagnostics, and medical research. This paper provides a comprehensive survey of the diverse applications, challenges, and benefits of AI in healthcare. We explore the role of machine learning algorithms and natural language processing in enhancing diagnosis and treatment planning. The utilization of AI for predictive analytics, personalized medicine, and patient management is discussed, showcasing its potential to improve healthcare outcomes and reduce costs. Additionally, we address ethical considerations, data privacy concerns, and regulatory frameworks that accompany the implementation of AI in healthcare. Through an extensive literature review, this paper aims to offer insights into the current state of AI in healthcare, highlighting key trends and future directions for research and development in this rapidly evolving field

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review.

This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.

AI in Autonomous Vehicles

The integration of Artificial Intelligence (AI) in autonomous vehicles represents a transformative leap towards safer, more efficient, and technologically advanced transportation systems. This abstract provides a comprehensive overview of the dynamic landscape where AI converges with autonomous vehicles, examining the synergies, challenges, and far-reaching implications of this groundbreaking integration AI-Powered Perception and Decision-Making: Delving into the technological core, the abstract explores how AI empowers autonomous vehicles with sophisticated perception systems, such as computer vision and sensor fusion. It discusses the role of machine learning algorithms in real-time decision-making, enabling vehicles to adapt to dynamic road conditions and unforeseen circumstances. Challenges in Autonomy: Recognizing the complexity of autonomous systems, the abstract addresses challenges such as handling edge cases, ensuring robustness against adversarial attacks, and navigating regulatory and ethical considerations. It emphasizes the importance of addressing these challenges to foster public trust and acceptance of autonomous vehicles. Human-AI Interaction in Autonomous Vehicles: Examining the interface between humans and AI-driven vehicles, the abstract discusses the importance of designing intuitive and trustworthy communication channels. It explores advancements in natural language processing and gesture recognition, fostering seamless collaboration between humans and autonomous systems. Regulatory Landscape and Ethical Considerations: Recognizing the pivotal role of regulations, the abstract discusses the evolving regulatory landscape for autonomous vehicles. It delves into ethical considerations surrounding AI decisions in critical situations, underscoring the need for a harmonized approach to ensure responsible AI deployment in autonomous driving scenarios.

Evaluation of machine learning algorithms' performance in digital transformations: a comparative analysis

In the realm of digital transformations, the role of machine learning algorithms stands as a critical catalyst for innovation and progress. The integration of machine learning algorithms has become integral in driving digital transformations across various industries. This research paper embarks on a comparative analysis, meticulously evaluating the performance of various machine learning algorithms in the context of driving and shaping digital transformations. Leveraging diverse datasets and real-world case studies, this study delves into the efficacy, adaptability, and limitations of prominent machine learning techniques. Through methodical experimentation and rigorous assessment, this research endeavors to offer valuable insights into the selection and optimization of these algorithms. By shedding light on their strengths and weaknesses, this analysis aims to empower organizations and decision-makers, enabling them to make informed choices when integrating machine learning into their digital transformation strategies. Ultimately, this study seeks to provide a comprehensive understanding of the landscape, assisting in maximizing the potential and impact of machine learning in advancing digital transformations across various industries.

Review to unfold the role of Machine Learning Algorithms in Natural Language Processing

This research review has been written to explore the role of machine learning algorithms in natural language processing (NLP), a computer field that offers human-like comprehension of virtual text. The main motive of the article is to reveal the participation of machine learning (ML) in NLP and its scope in coherent fields. In this study, the researchers have used a systematic literature review approach to explore the role of ML algorithms in NLP. Highlighting the techniques of ML algorithms as supervised, unsupervised, semi-supervised, and reinforcement methods, this study discloses the connectivity with morphological, semantic, syntactic, pragmatic, and discourse analysis in NLP. The article describes that ML/NLP has immense applications in different fields where several tools of ML/NLP are utilized. The researchers have studied the use of chatbots, text summarization tools, web scrapping, sentimental analysis in the social media stock market, medical field disease detection, and fraud detection. ML’s role and contributions to the progressions are detained in current studies. A distinct comparison has been made between the past present and future of ML in NLP. ML algorithms and applications with examples are disclosed in the classification of logistic regression, SVM, Naive Bayes, K-Nearest Neighbor, and decision tree. Data distortion, interpretation of the research findings, and contextual ambiguity are visibly determined as challenges and discussed in the study. This writing opens up to the recent advancements and advantages in multiple areas correspondingly. The discussions and findings expose the role of ML in opening the gateway to the profound revolutionary search engines, algorithms, and multiple techniques through the development of technology and its evolution to adapt to new language differences, slang, and changes in language use.

Prediction and Interpretation of Total N and Its Key Drivers in Cultivated Tropical Peat using Machine Learning and Game Theory

Currently, there is a growing interest among research communities in the development of statistical learning-based pedotransfer functions/PtFs to predict mineral soil nutrients; however, similar studies in peatlands are relatively rare. Moreover, extracting meaningful information from these ‘black-box’ models is crucial, particularly concerning their algorithmic complexity and the non-linear nature of the soil covariate interrelationships. This study employed the Pulunggono (2022a) dataset and the bootstrapping method, to (1) develop and evaluate seven PtF models, including both general linear models (GLM) and machine learning (ML) regressors for estimating total nitrogen (N) in tropical peat that has been drained and cultivated for oil palm (OP) in Riau, Indonesia and (2) explaining model functioning by incorporating Shapley Additive Explanation (SHAP), a tool derived from coalitional game theory. This study demonstrated the superior predictive performance of ML-based PtFs in estimating total N compared to GLM algorithms. The top-performing algorithms for PtF models were identified as GBM, XGB, and Cubist. The SHAP method revealed that sampling depth and organic C were consistently identified as the most important covariates across all models, irrespective of their algorithmic capabilities. Additionally, ML algorithms identified the total Fe, pH, and bulk density (BD) as significant covariates. Local explanations based on Shapley values indicated that the behavior of PtF-based algorithms diverged from their global explanations. This study emphasized the critical role of ML algorithms and game theory in accurately predicting total N in peatlands subjected to drainage and cultivation for OP and explaining their model behavior in relation to soil biogeochemical processes.

Predicting Infectious Disease Outbreaks with Machine Learning and Epidemiological Data

Over the past several years, there has been a notable shift in the international public health arena, mostly driven by the use of machine learning methodologies and epidemiological data for the purpose of forecasting and controlling outbreaks of infectious diseases. This study explores the changing paradigm of disease outbreak prediction by examining current advancements and emerging patterns in the field of machine learning and epidemiology. In this paper, we explore the complex procedure of forecasting infectious disease outbreaks, a task of significant significance for global public health authorities. This paper examines the crucial role of machine learning algorithms in this undertaking, elucidating their capacity to analyze extensive and heterogeneous datasets in order to produce significant insights and predictions Our inquiry spans multiple facets of this complex topic. This study examines the transformative impact of machine learning models, namely deep learning and ensemble approaches, on the field. The individuals in question have exhibited remarkable proficiency in recognizing patterns, establishing correlations, and formulating predictions by utilizing past data. Consequently, this has greatly contributed to the prompt identification and readiness for potential outbreaks. Moreover, our study involves the incorporation of epidemiological data, including case reports, genetic sequencing, and population dynamics, into the machine learning architecture. This study investigates the enhanced predictive accuracy and improved comprehension of disease dynamics resulting from the integration of data-driven models and expert knowledge from the field of epidemiology. The integration of different approaches is of utmost importance when it comes to effectively tackling the distinct characteristics and problems presented by diverse infectious illnesses. Additionally, the research emphasizes the significance of incorporating a wide range of data sources, including not only data related to human health, but also environmental factors, socio-economic metrics, and patterns of human mobility. Non-conventional data sources provide essential contextual information for comprehending the dynamics of disease transmission, hence enhancing the robustness and comprehensiveness of forecasts.

Evaluation of Supervised Machine Learning Algorithms and Computational Structural Validation of Single Nucleotide Polymorphisms Related to Acute Liver Injury with Paracetamol.

To identify single nucleotide polymorphisms (SNPs) of paracetamol-metabolizing enzymes that can predict acute liver injury. Paracetamol is a commonly administered analgesic/antipyretic in critically ill and chronic renal failure patients and several SNPs influence the therapeutic and toxic effects. To evaluate the role of machine learning algorithms (MLAs) and bioinformatics tools to delineate the predictor SNPs as well as to understand their molecular dynamics. A cross-sectional study was undertaken by recruiting critically ill patients with chronic renal failure and administering intravenous paracetamol as a standard of care. Serum concentrations of paracetamol and the principal metabolites were estimated. Following SNPs were evaluated: CYP2E1*2, CYP2E1_-1295G>C, CYP2D6*10, CYP3A4*1B, CYP3A4*2, CYP1A2*1K, CYP1A2*6, CYP3A4*3, and CYP3A5*7. MLAs were used to identify the predictor genetic variable for acute liver failure. Bioinformatics tools such as Predict SNP2 and molecular docking (MD) were undertaken to evaluate the impact of the above SNPs with binding affinity to paracetamol. CYP2E1*2 and CYP1A2*1C genotypes were identified by MLAs to significantly predict hepatotoxicity. The predictSNP2 revealed that CYP1A2*3 was highly deleterious in all the tools. MD revealed binding energy of -5.5 Kcal/mol, -6.9 Kcal/mol, and -6.8 Kcal/mol for CYP1A2, CYP1A2*3, and CYP1A2*6 against paracetamol. MD simulations revealed that CYP1A2*3 and CYP1A2*6 missense variants in CYP1A2 affect the binding ability with paracetamol. In-silico techniques found that CYP1A2*2 and CYP1A2*6 are highly harmful. MD simulations revealed CYP3A4*2 (A>G) had decreased binding energy with paracetamol than CYP3A4, and CYP3A4*2(A>T) and CYP3A4*3 both have greater binding energy with paracetamol. Polymorphisms in CYP2E1, CYP1A2, CYP3A4, and CYP3A5 significantly influence paracetamol's clinical outcomes or binding affinity. Robust clinical studies are needed to identify these polymorphisms' clinical impact on the pharmacokinetics or pharmacodynamics of paracetamol.

Clinical Informatics in Critical Care Medicine.

Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to consider every accessible data point when making the quick decisions necessary at the point of care. Consequently, Clinical Informatics offers a natural partnership to improve the care for critically ill patients. The last two decades have seen a significant evolution in the role of Clinical Informatics in Critical Care Medicine. In this review, we will discuss how Clinical Informatics improves the care of critically ill patients by enhancing not only data collection and visualization but also bedside medical decision making. We will further discuss the evolving role of machine learning algorithms in Clinical Informatics as it pertains to Critical Care Medicine.

Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders.

We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset. The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE. This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score.

Developing Machine Learning Models for Catalysts in Oxidative Dehydrogenation of n‐butane

Abstract1,3‐butadiene is widely used as a raw material in the synthesis of rubber and automobile tires. In this study, machine learning models were utilized to predict the performance of catalysts in the oxidative dehydrogenation of n‐butane to 1,3‐butadiene., The datasets consisted of 304 data points generated from experiments conducted in a fixed‐bed reactor in the temperature range of 400– 500 °C and O2/C4 molar feed ratio of 1–4 mol/mol, using metal oxides of Nickel (Ni), Iron (Fe), Cobalt (Co), Bismuth (Bi), Molybdenum (Mo), Manganese (Mn) and Tungsten (W) supported on gamma alumina catalysts. Repeated 10‐fold cross‐validation was used, with 70 % of the datasets randomly selected for training and optimizing the models, whereas the remaining 30 % were used for testing the models. Among the various models examined, support vector machine with radial basis function (SVMR) model had the best coefficient of determination (R2) performance for the n‐butane conversion (training set: 98.3 % vs. test set: 88.3 %) and 1,3‐butadiene selectivity (training set: 94.0 % vs. test set: 88.6 %). In addition, the models had the lowest training and test mean absolute errors (MAE) and root mean square errors (RMSE). This study highlights the notable role of machine learning algorithms in enhancing the prediction of n‐butane oxidative dehydrogenation catalyst performance, thus promoting the development of more effective catalyst designs that exhibit high activity and selectivity, contributing positively to the field of heterogeneous catalysis. Notably, a user‐friendly application incorporating the developed machine learning models was developed, providing an invaluable tool for the real‐time on‐site prediction of catalyst performance in the oxidative dehydrogenation of n‐butane to 1,3‐butadiene. This application is a significant advancement, facilitating swift and accurate catalyst performance prediction, fostering more efficient catalyst design, streamlining, and revolutionizing the rubber and automobile tire synthesis industries.

Role of Machine Learning Algorithms for Wind Power Generation Prediction in Renewable Energy Management

The electrical energy demand is growing every day. Fossil fuel-based electrical power generation pollutes the environment. So, to fulfil the electrical energy demand, clean renewable energy sources like solar and wind are emerging as viable alternatives. But the amount of power generated from solar and wind energy sources is varying in magnitude and is intermittent in nature. So, predicting solar and wind power generation is indispensable for efficient planning and controlling of a renewable energy system. This paper aims to identify a machine learning model suitable for time-series prediction of wind power and also surveys the predominantly existing machine learning followed by the deep learning approaches. The main objective of this study is to solve a real-life problem in the renewable energy sector by accurately estimating the amount of power generation production per hour by applying machine learning techniques using historical wind power energy production data. Hence, historical wind data set with input parameters like wind speed, wind direction air temperature, pressure, and output parameters of wind power generation is considered for training and testing using mean absolute error for evaluating the prediction accuracy. Layers from simple to complex deep layers are trained and tested for prediction accuracy, i.e. baseline, linear, dense, multidense, convolution neural networks, and long short-term memory. Investigating further, LSTM and Residual LSTM prove to have higher mean absolute prediction accuracy of 0.0987 and 0.0958. This eventually enlightens the wind power forecasting models and new paths for further research applications.

Investigating the Role of Machine Learning Algorithms in Predicting Sepsis using Vital Sign Data

Investigating the Role of Machine Learning Algorithms in Predicting Sepsis using Vital Sign Data

A digitális technológia kínálta új lehetőségek a melanoma malignum szűrésében.

In Eastern and Central Europe, Hungary has the third highest incidence of melanoma after the Czech Republic and Slovakia. In order to improve the efficiency of melanoma diagnostics and screening, several technological developments have been made in recent years, one is the integrated automated total body mapping and digital dermatoscopy. The aim of this targeted literature review is to demonstrate the potential of new digital technologies developed for total body mapping in relation to melanoma screening. Targeted literature review was performed to collect information. We searched in publicly available databases for guidelines and recommendations as well as scientific publications on screening and early detection of melanoma. We identified 15 international guidelines and recommendations summarizing screening and early detection of melanoma. We did not find any Hungarian guidelines on melanoma screening. We analyzed 2 clinical, 5 observational studies and 8 systematic reviews on the effectiveness of digital dermatoscopy and automated total body mapping. We found 10 studies that investigated the complementary role of machine learning algorithms in the early diagnosis of melanoma. Digital dermatoscopy and total body mapping can help identify skin malignancies or new melanoma at high-risk individuals. Due to the lack of reliable data of melanoma screening programmes on cancer mortality, population-based melanoma screening is not recommended by international guidelines. International guidelines highlight the role of automated total body mapping and sequential digital dermatoscopic imaging in screening of individuals at increased risk of melanoma. Orv Hetil. 2022; 163(49): 1943-1951.

Towards solving NLP tasks with optimal transport loss

Loss functions are essential to computing the divergence of a model’s predicted distribution from the ground truth. Such functions play a vital role in machine learning algorithms as they steer the learning process. Most common loss functions in natural language processing (NLP), such as Kullback–Leibler (KL) and Jensen–Shannon (JS) divergences, do not base their computations on the properties of label coordinates. Label coordinates can help encode the inter-label relationships. For the sentiment classification task, strongly positive sentiment is closer to positive than strongly negative sentiment. Incorporating such information in the computations of the probability divergence can facilitate the model’s learning dynamics.In this work, we study an under-explored loss function in NLP — Wasserstein Optimal Transport (OT) — which takes label coordinates into account and thus allows the learning algorithm to incorporate inter-label relations. However, the limited applications of OT-based loss owe to the challenges in defining quality label coordinates. We explore the current limitations of learning with OT and provide an algorithm that jointly learns label coordinates with the model parameters. We show the efficacy of OT on several text classification tasks such as sentiment analysis and emotion recognition in conversation. We also discuss the limitations of the approach. The source codes pertaining to this work are publicly available at: https://github.com/declare-lab/NLP-OT.