Data Analysis Algorithms Research Articles

Home Air Quality Monitoring System

Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.

INTEGRATION OF ARTIFICIAL INTELLIGENCE IN HOTEL SERVICES: TRENDS AND DEVELOPMENTS (THE ISRAELI CASE)

The integration of artificial intelligence in hotel services is an important and rapidly developing field. The article "Integration of Artificial Intelligence in Hotel Services: Trends and Developments" explores the current possibilities and trends in utilizing advanced artificial intelligence technologies in the hotel industry. The article discusses the use of smart algorithms for data analysis, the implementation of robots and smart technologies to enhance guest experiences, the utilization of support systems and personalized self-service, and the improvement of management and operational processes in hotels . It presents examples and research from the industry that demonstrate the benefits of leveraging AI and machine learning in hotel services and offers scenarios and tools for successful implementation of this advanced technology in the field. The article provides an in-depth and up-to-date insight into the use of artificial intelligence in the hotel industry and offers ideas and recommendations for experts and businesses in the field to effectively implement this technology in hotel operations.

The New Mixed Teaching Exploration of Low-Voltage Electrician Training Course under the Background of New Engineering

In the context of New Engineering, the exploration of a novel blended teaching approach for low-voltage electrician training courses aims to address the challenges of modern engineering education by integrating New Engineering concepts and modern teaching technologies to achieve comprehensive innovation in course content and teaching methods. This strategy leverages the advantages of combining online and offline learning to provide a diverse and personalized learning experience, thereby enhancing students' knowledge and practical skills. Online course resources, including interactive courseware, virtual experiments, and simulations, enrich the learning content and increase students' interest and engagement. Offline classes focus on interactivity and practical application through discussions, case studies, and project collaborations, helping students deepen their understanding and apply their knowledge. Flexible scheduling allows students to learn at their own pace, improving learning efficiency and effectiveness. Personalized learning path design, through preliminary surveys and assessments, helps students set individualized learning goals. Utilizing big data analysis and intelligent recommendation algorithms, the system provides the most suitable learning resources. Students can autonomously adjust their learning paths, fostering self-directed learning, while teachers offer targeted guidance and support to ensure the successful completion of learning tasks. Interactive and collaborative learning environments are strengthened through online and offline interaction mechanisms, group collaborative learning, and the use of various interactive tools, enhancing classroom interaction and participation. The establishment of online learning communities promotes resource sharing and experience exchange, creating a positive learning atmosphere. Assessment and feedback employ formative and practical assessments through online tests, assignments, and project work, comprehensively reflecting students' learning progress and outcomes. Multi-dimensional feedback mechanisms and data-driven evaluation provide detailed learning analysis and improvement suggestions for both teachers and students, enhancing teaching effectiveness.

Efficient vibration analysis system using empirical mode decomposition residual signal and multi-axis data

The empirical mode decomposition (EMD) method is a technique that recursively decomposes an input signal into intrinsic mode functions (IMFs) by residual signals, primarily for identifying desirable features. The suggested algorithm observes the residual signal instead of the IMF, which lowers the computing load. The study introduces a new method for detecting bearing faults by enhancing signal extraction from sensor data using EMD and multi-axis feature extraction. This method streamlines the process by filtering out high-frequency noise and correlating residual signal information with analysis. The approach also enhances the signal-to-noise ratio (SNR) and feature signature identification using digital signal processing (DSP) techniques. The algorithm for vibration data analysis is tested for bearing failures, identifying shaft frequency and inner race bearing faults, which can be implemented in parallel. For the inner race fault bearing analysis, two-level EMD with a residual signal generates output similar to five-iteration EMD, saving 60% of computations. The use of spectral multiplication to multi-axis data processing produced a rise in the SNR of 18.32 dB to 20.92 dB for Y-axis and X-axis input, respectively. When compared to the single-axis IMF data computation, 20% fewer iterations are needed overall. A single-level EMD is adequate for calculating the rotational frequency of a healthy bearing. For the Y- and X-axis input, multi-axis analysis increases SNR by 10.68 dB and 13.14 dB, accordingly. This comprehensive strategy reduces computational complexity, improves fault detection accuracy, and minimizes noise impact, making it a promising solution for bearing fault detection.

Emerging Machine Learning-Based Data Analysis Techniques and Algorithms for Exploiting 4D-STEM Datasets

Emerging Machine Learning-Based Data Analysis Techniques and Algorithms for Exploiting 4D-STEM Datasets

An accurate slicing method for dynamic time warping algorithm and the segment-level early abandoning optimization

Time series data analysis algorithms have been gaining significant importance in the research community. Extensive studies have confirmed that Dynamic Time Warping (DTW) is the best distance measure in time series analysis across multiple domains. However, DTW is a time-consuming algorithm with quadratic time complexity, which limits its widespread adoption. In this paper, we proposed a novel slicing mechanism for DTW, called Partial Dynamic Time Warping (PDTW). PDTW is capable of dividing a complete DTW calculation into multiple independent partial calculations. The proposed PDTW ensures very consistent alignments with the original Constrained DTW (CDTW) by incorporating additional data windows for each pairwise time series. On the basis of PDTW, we also present Early Abandoning Dynamic Time Warping (EADTW) technique, which saves a substantial amount of computing time by abandoning superfluous calculations of unnecessary segments in the time series classification task. Large-scale experimental results on 96 datasets from the UCR archive show the effectiveness of PDTW and EADTW. The cumulative sum of PDTW distances for segments is nearly identical to the CDTW distance, and in many cases, it is exactly equal. This remarkable characteristic of PDTW indicates its immense potential for further development and optimization of DTW, such as parallelization. In classification tasks, EADTW is 1.99 times faster than CDTW on average, with a maximum speedup of 2.89 times, while maintaining accuracy. Additionally, it enhances accuracy by 0.93% and improves speed by 2.34 times under the accuracy priority parameter.

Implementation and research of gesture recognition in HCI field

Since the first Neural Network computer was made by Marvin Minsky and his schoolmates in 1951, the development of artificial intelligence (AI)has undergone various and huge changes. It generally evolves into a field that has infinite possibilities and a major branch of computer science that can not be ignored. With the trend of computerization and the rapid evolution of the Internet of Things (IoT), gesture recognition emerged. Various prototypes are blossoming in the laboratories, and some of them have become certain products that have practical applications in later days. At the same time, more and more deep learning technology has been applied to gesture recognition systems that greatly improve the quality of the service. The purpose of this review is to summarize and analyze the existing algorithms of Dynamic Gesture Recognition systems, which have several different methods that are based on multiple signal extractors. This study focuses on the 3D Convolutional Neural Network(CNN), which plays an important role in the Dynamic Gesture Recognition optimization algorithm and data analysis algorithm. In this paper, we reviewed past papers in the gesture recognition field, which include the sEMG method, microwave method, and vision recognition method. During the process of data collecting and summarizing the past papers, we mainly focused on the accuracy differences between recognition methods and the efficiency differences in data processing algorithms. (CNN-based, LSTM based, etc.) Then, we analyzed the main difficulties and challenges of these methods, which are briefly listed in the Introduction part. Data processing algorithms are also being studied, and a horizontal comparison between CNN-based, LSTM-based, and transformer-based algorithms is also being made. Besides this, for those problems that already have a reliable solution, we also summarized the possible solutions and listed them out. We found that the gesture recognition system has already been systematically studied and is partly used in some fields. However, the algorithm and modeling methods can still be optimized and it also needs further study to be more widely used.

A DNN-based diagnosis on autism spectrum disorder in children

The prevalence of autism spectrum disorder (ASD) witnesses a sharp increasing in recent years, and early diagnosis and intervention of ASD are critically needed. This study explored the efficacy of Deep Neural Networks (DNN) in diagnosing ASD among children aged 0 to 10. Utilizing the latest dataset derived from the ASDTests mobile application, which encompasses behavioral characteristics of over 2,000 children, we implemented a DNN model to capture complex non-linear patterns indicative of ASD. The results of comparative analysis with traditional machine learning models revealed DNN's superior accuracy in predicting ASD, indicating that the DNN achieved a significant improvement in identifying minority classes post-imbalance learning treatment. The promising results, including the 99.55% accuracy rate, paved the way for future investigations into integrating DNN with multimodal data analysis and other advanced algorithms to enhance early diagnostic processes and intervention strategies for ASD.

Explainability analysis: An in-depth comparison between Fuzzy Cognitive Maps and LAMDA

Currently, it has become very relevant that machine learning techniques can provide an explanation of the results they generate, which is even more relevant in certain domains, called critical, such as health and energy, among others. For this reason, several explainability methods have been generated in the literature. Some of them are Local Interpretable Model-Agnostic Explanations (LIME) and Feature Importance, which have been used in a wide range of problems. The objective of this work is to analyze the explainability capacity of the Learning Algorithm for Multivariate Data Analysis (LAMDA) and Fuzzy Cognitive Maps (FCM), which have been used with great interest due to their interpretability, management of uncertainty during the inference process, simplicity in its use, among other things. For the development of this work, data from two critical domains have been considered, health (COVID-19 and Dengue datasets) and energy (energy price dataset), for which prediction/classification models have been developed using LAMDA and FCM techniques. Afterward, two explainability techniques were used to analyze the explainability provided by each method, one based on the LIME method and the other on the feature importance method, the latter adapted to our work by being based on the permutation of values. Finally, the work proposes two new explainability methods, one based on causal inference and the other on the degrees of membership of the variables to the classes. The latter, in particular, allows doing an explainability analysis by class. The new explainability methods reproduce the results of well-known explainability methods in the literature such as LIME and feature importance, with less execution cost, and also, with an explainability analysis by class. This work opens the doors to new work on class explainability. Furthermore, we see that machine learning approaches based on causal or fuzzy relationships are quite self-explanatory, but specific explainability methods such as those we propose in the work allow us to study particular aspects, such as highly important variables, which general explainability methods do not allow us to do, such as LIME and feature importance.

Preface

From 22nd to 24th December 2023, the 2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA 2023) was held in Sanya, China via virtual form. The Conference attracted about 150 researchers, engineers, teachers and students worldwide working in or interested in the fields related to computational modeling, simulation and data analysis. The general objectives of the CMSDA 2023 were: To generate an adequate academic and scientific forum to present the work and experiences of researchers/teachers/students, contributing, in this way, to the production of new knowledge in the fields of computational modeling and data analysis. To create a path of establishing certain relationships for the speakers, authors and listeners, and providing opportunities for collaboration among the universities and institutions, in order to promote scientific research activities and develop relevant technologies. The subject areas in which the accepted papers were presented cover but are but not limited to: Perceptual Issues in Visualization and Modeling; Mathematical and Numerical Methods in Simulation and Modeling; Parallel and Distributed Computing Simulation; Big Data Visual Analysis; Data Analysis Algorithms and Systems, etc. We have invited renowned professors at home and abroad in these academic fields who shared with the attendees the latest innovations and research results in the domains of computational modeling, data mining and analysis. The Conference mainly featured keynote speeches by renowned experts and presentations of peer-reviewed papers by the authors. Among them, Prof. Daowen Qiu (Sun Yat-Sen University, China) shared his research on Distributed Quantum Algorithm for Simon’s Problem. Limited by today’s physical devices, quantum circuits are usually noisy and difficult to be designed deeply. The novel computing architecture of distributed quantum computing is expected to reduce the noise and depth of quantum circuits. And in his study, he presented the Simon’s problem in distributed scenarios and designed a distributed quantum algorithm to solve the problem. The algorithm proposed by him has the advantage of not only exponential acceleration compared with the classical distributed computing, but also square acceleration compared with the best distributed quantum algorithm proposed before. The Conference Proceedings was made so fruitful with all the excellent contributions collected from both domestic and international channels including the wonderful presentations in the Conference. It is a pleasure to thank the organizers, sponsors and all the participants and contributors for making the Conference possible and interesting. And our appreciation also to the members of Journal of Physics: Conference Series for providing us help in publishing this paper volume. The Committee of CMSDA 2023 List of Committee Member is available in this pdf.

Intelligent Decision Support System in Healthcare using Machine Learning Models

Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems.

Predictive analytics on artificial intelligence in supply chain optimization

AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced response time, enhanced logistics route optimization, improved demand planning as well as real-time inventory control. Thus, the idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics. SCO-AI implementation has seen significant improvements in inventory turnover rate, rates of on-time delivery as well as overall supply chain costs. In this period of high business turbulence, such kind of research builds up the robustness of a given supply chain while at the same time minimizing operational risks by means of simulations and case studies

Deep Learning-Based Analysis of Emotional and Content Relevance Between Bullet Screens and Subtitles as Movie Narrative Medium

This study borrows elements of film text in the field of film studies to develop the potential of data analysis using AI algorithms, while aiming to explore the emotional and content relevances between the two narrative mediums in film, bullet screens and subtitles, as well as the narrative effect variations that they bring about. The film sample Forever Young was analyzed by deep learning, data analysis, and knowledge discovery algorithms and tools. The BERT model was used to quantify the intensity of emotions and conduct fine-grained emotional classification of film characters. Emotion curves are plotted based on time series data. Thematic words are extracted by the LDA algorithm, and text similarity is computed using Word2Vec. The study found differences in emotional expression between bullet screens and subtitles, and they also exhibit strong emotional and content relevances. Yet, this correlation is not only influenced by temporal deviation factors but also by specific emotional types, different stages of story development, and the collective emotions of online views. The conclusion suggests that the effective collection of plot metronomes in film narratives helps reveal crucial plot nodes and emotional trends in films that cause obvious emotional changes, and placing them appropriately is fundamental for continuously attracting and retaining viewers’ attention. The interaction between bullet comments and subtitles is not only about emotional expression but also enhances the effectiveness of the film narrative. Overall, this study demonstrates the importance of database logic and technology in understanding and expressing film emotions and meanings, and providing new ideas and methods for enhancing film narrative effectiveness.

PLAN: Variance-Aware Private Mean Estimation

Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy and utility is well understood in the worst case, many datasets exhibit structure that could potentially be exploited to yield better algorithms. In this paper we present Private Limit Adopted Noise (PLAN), a family of differentially private algorithms for mean estimation in the setting where inputs are independently sampled from a distribution D over R^d, with coordinate-wise standard deviations σ in R^d. Similar to mean estimation under Mahalanobis distance, PLAN tailors the shape of the noise to the shape of the data, but unlike previous algorithms the privacy budget is spent non-uniformly over the coordinates. Under a concentration assumption on D, we show how to exploit skew in the vector σ, obtaining a (zero-concentrated) differentially private mean estimate with l_2 error proportional to ||σ||_1. Previous work has either not taken σ into account, or measured error in Mahalanobis distance --- in both cases resulting in l_2 error proportional to sqrt{d}||σ||_2, which can be up to a factor sqrt{d} larger. To verify the effectiveness of PLAN, we empirically evaluate accuracy on both synthetic and real-world data.

Implementation of an Artificial Intelligence Based Learning Management System for Adaptive Learning

In the world of modern education, the need for adaptive and personalized learning systems is becoming increasingly important to accommodate the unique learning needs of each individual. Implementing artificial intelligence (AI) in learning management systems offers great potential for optimizing adaptive learning experiences, where the system automatically adjusts learning content and approaches based on student profiles and responses. This research aims to develop and implement an artificial intelligence-based learning management system to personalize learning. The primary objective is to assess the effectiveness of the system in improving student engagement and learning outcomes and identify factors that influence its success. The method includes developing a system using machine learning algorithms for real-time analysis of student learning data. This research uses a mixed design that combines quantitative and qualitative data to analyze learning outcomes and understand user perceptions of the system. The research results show that this AI-based learning management system successfully increases student engagement and provides effective personalization of learning. Data shows improvements in grades and understanding of the material, especially in concepts students previously found difficult. Implementing an artificial intelligence-based learning management system has proven effective in supporting adaptive learning. This system improves learning outcomes and provides new insights into how AI technology can effectively integrate into educational contexts to support diverse and dynamic learning needs. The research also identified several challenges, including the need for more significant and varied training data and smoother integration with school curricula.

Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis.

Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct from discrete sequences, which limits the application of deep learning-based algorithms in data analysis due to the lack of discretization. Herein, a model called fragment-fusion transformer is proposed, which integrates the discrete fragmentation of continuous spectra based on their intrinsic characteristics with the extraction of intrafragment features and the fusion of interfragment features. The model integrates the intrinsic feature-based fragmentation of spectra with transformer, constructing the fragment transformer block for feature extraction within fragments. Interfragment information is combined through the pyramid design structure to improve the model's receptive field and fully exploit the spectral properties. During the pyramidal fusion process, the information gain of the final extracted features in the spectrum has been enhanced by a factor of 9.24 compared to the feature extraction stage within the fragment, and the information entropy has been enhanced by a factor of 13. The fragment-fusion transformer achieved a spectral recognition accuracy of 94.5%, which is 4% higher compared to the method without fragmentation and fusion processes on the test set of cell Raman spectroscopy identification experiments. In comparison to common spectral classification models such as KNN, SVM, logistic regression, and CNN, fragment-fusion transformer has achieved 4.4% higher accuracy than the best-performing CNN model. Fragment-fusion transformer method has the potential to serve as a general framework for discretization in the field of continuous spectral data analysis and as a research tool for analyzing the intrinsic information within spectra.

Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage-as-a-Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba’s public IaaS cloud products using both real-world Yahoo S2 data and synthesized datasets.

Robust Quantification of Live-Cell Single-Molecule Tracking Data for Fluorophores with Different Photophysical Properties.

High-speed single-molecule tracking in live cells is becoming an increasingly popular method for quantifying the spatiotemporal behavior of proteins in vivo. The method provides a wealth of quantitative information, but users need to be aware of biases that can skew estimates of molecular mobilities. The range of suitable fluorophores for live-cell single-molecule imaging has grown substantially over the past few years, but it remains unclear to what extent differences in photophysical properties introduce biases. Here, we tested two fluorophores with entirely different photophysical properties, one that photoswitches frequently between bright and dark states (TMR) and one that shows exceptional photostability without photoswitching (JFX650). We used a fusion of the Escherichia coli DNA repair enzyme MutS to the HaloTag and optimized sample preparation and imaging conditions for both types of fluorophore. We then assessed the reliability of two common data analysis algorithms, mean-square displacement (MSD) analysis and Hidden Markov Modeling (HMM), to estimate the diffusion coefficients and fractions of MutS molecules in different states of motion. We introduce a simple approach that removes discrepancies in the data analyses and show that both algorithms yield consistent results, regardless of the fluorophore used. Nevertheless, each dye has its own strengths and weaknesses, with TMR being more suitable for sampling the diffusive behavior of many molecules, while JFX650 enables prolonged observation of only a few molecules per cell. These characterizations and recommendations should help to standardize measurements for increased reproducibility and comparability across studies.

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Sarcopenia diagnosis in patients with colorectal cancer: a review of computed tomography-based assessments and emerging ways to enhance practicality.

Traditionally, cancer treatment has focused on the stages of the disease; however, recent studies have highlighted the importance of considering the overall health status of patients in the prognosis of cancer. Loss of skeletal muscle, known as sarcopenia, has been found to significantly affect outcomes in many different types of cancers, including colorectal cancer. In this review, we discuss the guidelines for diagnosing sarcopenia, with a specific focus on CT-based assessments. Many groups worldwide, including those in Europe and Asia, have introduced their own diagnostic guidelines for sarcopenia. Seemingly similar yet subtle discrepancies, particularly in the cutoff values used, limit the use of these guidelines in the general population, warranting a more universal guideline. Although CT-based measurements, such as skeletal muscle index and radiodensity, have shown promise in predicting outcomes, the lack of standardized values in these measurements hinders their universal adoption. To overcome these limitations, innovative approaches are being developed to assess changes in muscle mass trajectories and introduce new indices, such as skeletal and appendicular muscle gauges. Additionally, machine learning models have shown superior performance in predicting sarcopenic status, providing an alternative to CT-based diagnosis, particularly after surgery. CT has tremendous benefits and a significant role in visually as well as quantitatively retrieving information on patient body composition. In order to compensate for the limitation of standard cutoff value, 3-dimensional analysis of the CT, artificial intelligence-based body composition analysis, as well as machine learning algorithms for data interpretation and analysis have been proposed and are being utilized. In conclusion, despite the varying definitions of sarcopenia, CT-based measurements coupled with machine-learning models are promising for evaluating patients with cancer. Standardization efforts can improve diagnostic accuracy, reduce the reliance on CT examinations, and make sarcopenia assessments more accessible in clinical settings.