Analysis Algorithm Research Articles

Gene signatures for cancer research: A 25-year retrospective and future avenues.

Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.

Informatization of the processes of administration of the quality of medical services in surgical hospitals

Among the various forms of quality control of the provision of medical care, self-control and internal control of the quality of medical services are the most effective and significant. Currently, there is no comprehensive information technology and algorithm for recording data on the provision of medical services and determining their quality. The goal is to develop and analyze the effectiveness of business processes and an algorithm for quality control of the performance of medical services in a surgical health care institution by establishing their daily accounting in the information system, control of indicators characterizing the safety and quality of their performance, as well as an opportunity to assess the dynamics changes in indicators for making management decisions. In the study, an analysis of literary sources was carried out, business processes were formulated, and on the materials of a retrospective analysis algorithms for the use of information technologies were developed based on the methods of generalization, comparison, content analysis, system-analytical and logical analysis, a graphic analysis was performed to increase the clarity of the presentation of the material. The research was approved by the Biomedical Ethics Committee. For the first time, as a result of the work of the internal quality commission, daily and summarized forms of reports on the work of the medical center, which have five sections are obtained. The statistical indicators of the operation of the surgical unit for any period are fixed, formed in an automated mode and analyzed. Clinical data characterizing all prescribed procedures applied are subject to analysis, and the results provide an opportunity to isolate from the records the disorders and complications reflected in the reports. The quality control of the use of antibiotics, the frequency of their use for preventive and therapeutic purposes is also carried out. The section that contains multivariate epidemiological control of the work of the treatment facility, as well as the section that shows the quality of medical record keeping and patient care is important. Together with the inpatient chart, e-mails of medical appointments are analyzed, patient falls are recorded, appointments and monitoring of patient care, pain assessment after surgery, allergic reactions are recorded, as well as a retrospective audit of the charts regarding the justification of the appointment of antimicrobial therapy. Control of patient safety and the quality of the provided medical services requires constant verification of their implementation and paying attention to be up to standards, the occurrence of deviations and complications. The application of a specially developed algorithm based on information technologies, aimed at daily accounting of medical services and obtaining data on deviations in their application, makes it possible to make urgent management decisions and influence the improvement of the qualifications of personnel who provide such services.

Retraction Note: Data analysis algorithm for internet of things based on federated learning with optical technology

Retraction Note: Data analysis algorithm for internet of things based on federated learning with optical technology

Comparative Analysis of Machine Learning Techniques for Sentiment Classification

Abstract—Sentiment analysis is an important part of natural language processing (NLP) and has many applications in social media, e-commerce, and other fields. This study aims to provide a clear distinction between these methodologies by offering a structured overview of sentiment analysis and the variety of techniques used in its execution. The article examines different machine learning algorithms for sentiment analysis and high- lights their advantages and disadvantages by drawing on credible prior research on the subject. Additionally, the study provides a tabular comparison of different machine learning methods by selecting suitable parameters. Keywords: Decision Tree, Support Vector Machine (SVM), Random Forest, Convolution neural network (CNN), Neural network, Long Short-Term Memory Networks (LSTM), BERT

CTPAD: an interactive web application for comprehensive transcriptomic profiling in allergic diseases

BackgroundAllergic diseases are systemic chronic inflammatory diseases associated with multiorgan damage and complex pathogenesis. Several studies have revealed the association of gene expression abnormalities with the development of allergic diseases, but the biomedical field still lacks a public platform for comprehensive analysis and visualization of transcriptomic data of allergic diseases.ObjectiveThe aim of the study is to provide a comprehensive web tool for multiple analysis in allergic diseases.MethodsWe retrieved and downloaded human and mouse gene expression profile data associated with allergic diseases from the Gene Expression Omnibus (GEO) database and standardized the data uniformly. We used gene sets obtained from the MSigDB database for pathway enrichment analysis and multiple immune infiltration algorithms for the estimation of immune cell proportion. The basic construction of the web pages was based on the Shiny framework. Additionally, more convenient features were added to the server to improve the efficiency of the web pages, such as jQuery plugins and a comment box to collect user feedback.ResultsWe developed CTPAD, an interactive R Shiny application that integrates public databases and multiple algorithms to explore allergic disease-related datasets and implement rich transcriptomic visualization capabilities, including gene expression analysis, pathway enrichment analysis, immune infiltration analysis, correlation analysis, and single-cell RNA sequencing analysis. All functional modules offer customization options and can be downloaded in PDF format with high-resolution images.ConclusionsCTPAD largely facilitates the work of researchers without bioinformatics background to enable them to better explore the transcriptomic features associated with allergic diseases. CTPAD is available at https://smuonco.shinyapps.io/CTPAD/.

Detection Methods for Pine Wilt Disease: A Comprehensive Review.

Pine wilt disease (PWD), caused by the nematode Bursaphelenchus xylophilus, is a highly destructive forest disease that necessitates rapid and precise identification for effective management and control. This study evaluates various detection methods for PWD, including morphological diagnosis, molecular techniques, and remote sensing. While traditional methods are economical, they are limited by their inability to detect subtle or early changes and require considerable time and expertise. To overcome these challenges, this study emphasizes advanced molecular approaches such as real-time polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), and loop-mediated isothermal amplification (LAMP) coupled with CRISPR/Cas12a, which offer fast and accurate pathogen detection. Additionally, DNA barcoding and microarrays facilitate species identification, and proteomics can provide insights into infection-specific protein signatures. The study also highlights remote sensing technologies, including satellite imagery and unmanned aerial vehicle (UAV)-based hyperspectral analysis, for their capability to monitor PWD by detecting asymptomatic diseases through changes in the spectral signatures of trees. Future research should focus on combining traditional and innovative techniques, refining visual inspection processes, developing rapid and portable diagnostic tools for field application, and exploring the potential of volatile organic compound analysis and machine learning algorithms for early disease detection. Integrating diverse methods and adopting innovative technologies are crucial to effectively control this lethal forest disease.

Evaluation of the Multiple Regression Analysis Algorithm on Stock Market Prediction

Financial time series is one of the most challenging applications of modern time series forecasting. The financial time series is closely related to noise, non-stationary, and deterministic chaos. The characteristics suggest that no complete information can be obtained from the past behavior of financial markets to fully capture the dependency between future prices and that of the past. The data collection method was collected from the Stock Market Online Application "MetaTrader version 4" type "Daily" with a time range from "03/09/2001 to 25/07/2012", as many as 2052 data", with the attributes "Date, Open, High, Low, Close, Volume" with the main attribute "Close" using the Support vector machine algorithm, artificial neural network, and multiple linear regression. The conclusion of the value that is close to the series value is the value by testing on the support vector machine algorithm, with the parameter for the RMSE value that is close to the "0" value obtained from the measurement results on the SVM algorithm on the RBF kernel (radial base function) with a value of "gamma" γ = 100 with the value of RMSE = 0.000, and SE = 0.000. with prediction accuracy error = 0.976

An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

ABSTRACTAiming at the problem that the existing ultra‐short‐term wind power prediction methods lack consideration of the spatial correlation characteristics of wind farms, resulting in insufficient prediction accuracy, an ultra‐short‐term wind power prediction method based on spatiotemporal characteristics fusion is proposed in this article. First, the fluctuation difference of the time window of wind power is input into the K‐means clustering algorithm to cluster wind farms into several clusters based on power fluctuation similarity. Then, the principal component analysis algorithm is used to reduce the dimensionality of numerical weather prediction data combinations in different regions to reduce the impact of redundant information on modeling accuracy. Finally, a convolutional long‐short‐term memory neural network is designed to extract spatiotemporal features of wind power data and output prediction results. The experimental verification on 18 wind farms in a province in China shows that the proposed wind power prediction method has an average root mean square error of only 0.1257 and has certain applicability.

Exploring the application of synchronous analysis of physiological signals and facial expressions in emotion recognition

In today's increasingly frequent human-computer interaction, the accurate recognition and response of intelligent systems to human emotions has become the key to improving the user experience. This paper aims to explore the application of physiological signals and facial expression synchronization analysis in emotion recognition. This paper adopts the method of literature review, and summarizes the acquisition techniques of physiological signals and facial expressions, synchronous analysis methods, and their application cases in emotion recognition. At the same time, the limitations and challenges of the existing technology are discussed. The review shows that the synchronous analysis of physiological signals and facial expressions has important application value in the field of emotion recognition, especially in mental health monitoring, educational feedback, and customer service automation. However, the practical application of the technology still faces many challenges, including the real-time nature of data collection, the adaptability of individual differences, and other identification problems. Therefore, future research needs to further optimize data acquisition technology and develop more accurate and personalized analysis algorithms.

Optimal sizing of battery energy storage system (BESS) for multiple applications using regression analysis and deep sleep optimizer algorithm

The multifunctional applications of battery energy storage system in a power system network will reduce the significant slack time of use as evident in many single-based applications. In order to deploy BESS for multiple applications, it is of utmost importance that the optimal size for the desired multiple functions, firstly be determined. This work proposes a novel methodology for the optimal sizing of battery energy storage system for frequency support, power loss minimization and voltage deviation mitigations. The suggested sizing methodology takes into account the level of penetration of the renewable energy sources in the power network. Regression analysis is used for mathematical formulations while Deep Sleep Heuristic algorithms in MATLAB environment is used for the optimization process for BESS optimal size. The robustness of the proposed method was tested by using IEEE modified 39-bus system. Simulation results show that with the BESS optimal size integrated into the network, voltage deviations were mitigated by about 20 % and power losses were reduced from 65.3 MW to 59.68 MW. Also, the system frequency nadir during the outage of the largest single generating, was sustained at 59.60 Hz whereas, without BESS it was 59.15 Hz. This is critical because such a frequency decline may activate the underfrequency load shedding relays.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

A Novel YOLOv10-DECA Model for Real-Time Detection of Concrete Cracks

The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of attention mechanism modules and the lack of explanation regarding how these mechanisms influence the model’s decision-making process to improve accuracy. To address these issues, a novel Dynamic Efficient Channel Attention (DECA) module is proposed in this study, which is designed to enhance the performance of the YOLOv10 model in concrete crack detection, and the effectiveness of this module is visually demonstrated through the application of interpretable analysis algorithms. In this paper, a concrete dataset with a complex background is used. Experimental results indicate that the DECA module significantly improves the model’s accuracy in crack localization and the detection of discontinuous cracks, outperforming the existing Efficient Channel Attention (ECA). When compared to the similarly sized YOLOv10n model, the proposed YOLOv10-DECA model demonstrates improvements of 4.40%, 3.06%, 4.48%, and 5.56% in precision, recall, mAP50, and mAP50-95 metrics, respectively. Moreover, even when compared with the larger YOLOv10s model, these performance indicators are increased by 2.00%, 0.04%, 2.27%, and 1.12%, respectively. In terms of speed evaluation, owing to the lightweight design of the DECA module, the YOLOv10-DECA model achieves an inference speed of 78 frames per second, which is 2.5 times faster than YOLOv10s, thereby fully meeting the requirements for real-time detection. These results demonstrate that an optimized balance between accuracy and speed in concrete crack detection tasks has been achieved by the YOLOv10-DECA model. Consequently, this study provides valuable insights for future research and applications in this field.

Identification of a potential sialylation-related pattern for the prediction of prognosis and immunotherapy response in small cell lung cancer.

Our study aimed to establish a novel system for quantifying sialylation patterns and comprehensively analyze their relationship with immune cell infiltration (ICI) characterization, prognosis, and therapeutic sensitivity in small cell lung cancer (SCLC). We conducted a thorough assessment of the sialylation patterns in 100 patients diagnosed with SCLC. Our primary focus was on analyzing the expression levels of 7 prognostic sialylation-related genes. To evaluate and quantify these sialylation patterns, we devised a sialylation score (SS) using principal component analysis algorithms. Prognostic value and therapeutic sensitivities were then evaluated using multiple methods. The GSE176307 was used to verify the predictive ability of SS for immunotherapy. Our study identified 2 distinct clusters based on sialylation patterns. Sialylation cluster B exhibited a lower level of induced ICI therapy and immune-related signaling enrichment, which was associated with a poorer prognosis. Furthermore, there were significant differences in prognosis, response to targeted inhibitors, and immunotherapy between the high and low SS groups. Patients with high SS were characterized by decreased immune cell infiltration, chemokine and immune checkpoint expression, and poorer response to immunotherapy, while the low SS group was more likely to benefit from immunotherapy. This work showed that the evaluation of sialylation subtypes will help to gain insight into the heterogeneity of SCLC. The quantification of sialylation patterns played a non-negligible role in the prediction of ICI characterization, prognosis and individualized therapy strategies.

Investigation of age-hardening behaviour of Al alloys via feature screening-assisted machine learning

Age hardening stands as a crucial strengthening process for aluminium (Al) alloys. However, determining the optimal ageing conditions has traditionally relied on resource-intensive trial-and-error methods. To streamline the design of Al alloys, this study introduces a novel feature screening-assisted machine learning (ML) approach to explore age-hardening behaviour across varying compositions and heat treatment parameters, focusing on yield tensile strength (YTS), ultimate tensile strength (UTS), and elongation (EL). A comprehensive pool of features, incorporating alloy composition and fundamental elemental properties, was generated to provide metallurgical insights for ML predictions. Subsequently, feature screening methodologies, including correlation analysis, feature elimination, and multi-objective genetic algorithm (MOGA), were employed to identify key features from the vast feature pool, balancing model complexity and prediction accuracy. Employing support vector regression models trained on these optimized feature sets, we demonstrate enhanced prediction accuracy for strength properties while maintaining model simplicity for EL, with minimal impact on accuracy. In addition, experimental results further validate the method's ability to reproduce ageing curves across all three mechanical properties for both commercialized and newly designed Al alloys.

Rapid identification of cod authenticity based on hyperspectral imaging technology

The high economic value of Atlantic cod makes it prone to fraudulent activities in the market, thus achieving rapid and non-destructive identification of its authenticity has practical significance. This study investigated the hyperspectral imaging (HSI) systems with a Vis-NIR (400 – 1000 nm) and SWIR (900 – 1700 nm) spectral range, for determining the authenticity of Atlantic cod fillets in two frozen and thawed sample states. Results found that the model effect of Vis-NIR data was generally better than SWIR data. Random forest (RF) and Linear discriminant analysis (LDA) models of Vis-NIR data achieved 100 % accuracy. Variable screening algorithms of Successive projections algorithm (SPA) and Variable combination population analysis- iteratively retaining informative variables (VCPA-IRIV) maintained 100 % accuracy of the LDA model at VIS-NIR wavebands while simplifying the data operation burden. Overall, this study suggests that HSI is a promising solution for rapid and non-destructive detection of Atlantic cod authenticity.

Automatic Processing of Bibliometric Data: Newest Literature Analysis Algorithms

Automated processing of bibliometric data has become an integral element in understanding and measuring the impact of scientific research as well as trends in the academic literature. The purpose of this study was to evaluate and compare current literature analysis algorithms to identify advances in automated processing of bibliometric data. This study explores the use of the latest algorithms in literature analysis which includes identification of research themes, research network analysis, citation impact evaluation, and development of literature recommendation algorithms. This research exposes innovative applications that utilize the latest technologies in text processing and data analysis to assist researchers, libraries, and information professionals in revealing deep insights from the latest scientific literature. The results of this study illustrate that the automated processing of bibliometric data with the latest algorithms paves the way for a deeper understanding of the development of scientific research, its impact in the scientific community as well as improving bibliometric analysis methods and tools by integrating the latest algorithms, so that scientific research and trends can be identified more accurately and relevantly. The study could use more sophisticated multifaceted techniques to analyze simple productivity measures based on article citations. This research is also able to recognize and describe the development of new science and technology.

IHBOT: An Intelligent and Hybrid Model for Investigation and Classification of IoT Botnet

The Internet of Things (IoT) is revolutionizing the technological market with exponential growth year wise. This revolution of IoT applications has also brought hackers and malware to gain remote access to IoT devices. The security of IoT systems has become more critical for consumers and businesses because of their inherent heterogenous design and open interfaces. Since the release of Mirai in 2016, IoT malware has gained an exponential growth rate. As IoT system and their infrastructure have become critical resources that triggers IoT malware injected by various shareholders in different settings. The enormous applications cause flooding of insecure packets and commands that fueled threats for IoT applications. IoT botnet is one of the most critical malwares that keeps evolving with the network traffic and may harm the privacy of IoT devices. In this work, we presented several sets of malware analysis mechanisms to understand the behavior of IoT malware. We devise an intelligent and hybrid model (IHBOT) that integrates the malware analysis and distinct machine learning algorithms for the identification and classification of the different IoT malware family based on network traffic. The clustering mechanism is also integrated with the proposed model for the identification of malware families based on similarity index. We have also applied YARA rules for the mitigation of IoT botnet traffic.

A noise resilient multi-step ahead deep learning forecasting technique for solar energy centered generation of green hydrogen

Generation of green hydrogen based on solar energy is significantly dependent upon photovoltaic (PV) panel energy, which is reliant on the stochastic and intermittent global horizontal irradiance (GHI). Further, if inconsistent power output from PV panels due to influence of noise interferes with continuous supply of energy needed for electrolysis process, then reliable generation of green hydrogen may be impacted. Therefore, in this study, preliminary fast fourier transform (FFT) technique for noise removal is utilized and thereafter, singular spectrum analysis (SSA) algorithm is integrated with Gated Recurrent Unit (GRU) in order to enhance the accuracy during forecasting of green hydrogen reliant on GHI at multiple time steps. The suggested methodology is assessed using GHI dataset for Jaipur site (Rajasthan, India) obtained from National Institute of Wind Energy (NIWE) portal. The monthly average prediction for solar energy centered green hydrogen generation by employing the proposed methodology at Jaipur location corresponds between 0.010 kg/m2 (10 × 103 kg/km2) to 0.020 kg/m2 (20 × 103 kg/km2) in diverse circumstances. The suggested forecasting algorithm provides a reliable and precise methodology that will significantly supports low-carbon economy goals.

A Machine Learning Approach for Sentiment Analysis Using Social Media Posts

Sentiment analysis on Twitter provides organizations and persons with quick and effective instrument to observe the public's perceptions of them and their competition. A modest number of assessment datasets have been produced in recent years to check the efficiency of sentiment analysis algorithms on Twitter. Researchers offer a review of eight publicly accessible as well as manually annotated assessment datasets for analyzing Twitter sentiment in this research. As a result of this evaluation, we demonstrate that is a widespread weakness of many when using these datasets performing at sentiment analysis the objective (entity) level is indeed the absence of different sentiment classifications across tweets as well as the objects contained in them.[1], As an example all of that "I love my iPhone but I despise my iPad." Could be marked with a made-by-mixing classify however the object iPhone contained within this Twitter post should be annotated with just a label with an optimism. To get around this restriction and enhance existing assessment We have datasets that provide STS-Gold a novel assessment of datasets in which tweets or objects (entities) remain tagged separately hence might show alternative opinion labels. Though research furthermore compares the various datasets on multiple characteristics such as an entire quantity of posts as well as vocabulary size and sparsity.[2] In addition, look at pair by pair relationships between these variables and how they relate to sentiment classifier performance on various data. In this study we used five different classifiers and compared them and, in our experiment, we found that the bagging ensemble classifier performed best among them and have an accuracy level of 94.2% for the GASP dataset and 91.3% for the STS-Gold dataset.

A novel iterative algorithm for nonlinear inelastic dynamic analysis of truss structures; MCB-Spline+Sp time integration method

The focus of this work is on the development of a novel algorithm for the nonlinear dynamic analysis of structures. In the sake of a general and easy-to-implement solution method, the tangential stiffness matrix is calculated by combining the Cauchy-Riemann equations derived from the differentiation of complex functions and using the nodal displacement perturbation vector. An implicit direct time integration method based on a cubic B-Spline is integrated with the incremental-iterative method using a quadrature rule based on the interpolation of spline functions, which is referred to as the MCB-Spline+Sp time integration method. A step-by-step algorithm for developing a computer code to implement this method is provided. The effectiveness of the proposed approach is evaluated through several nonlinear time history analyses, which demonstrate its accuracy and efficiency by observing less computational efforts and fast convergence rate. Moreover, a comparison is made between the developed method and well-established techniques such as the Newmark and Standard-Bathe time integration methods in combination with the iterative Newton-Raphson method.