Classification Method Research Articles

A Novel Rainfall Classification for Mapping Rainwater Harvesting: A Case Study in Kalar, Iraq

Increasing water demand driven by population growth and climate change strains water resources, especially in arid regions. The effectiveness of rainwater harvesting (RWH) as a viable solution is contingent upon the meticulous selection of appropriate sites. Contemporary efforts have increasingly utilized Geographic Information Systems (GIS) and remote sensing technologies to optimize the identification of ideal locations for implementing RWH infrastructure. However, inconsistencies in rainfall classification methodologies can compromise the accuracy of the resulted suitability maps. Consequently, a standardized approach to grading rainfall depth for mapping RWH sites becomes imperative. This study presents an innovative rainfall classification method tailored for both micro and macro catchment areas, offering a reliable and adaptable approach to rainfall analysis. By refining classification criteria, this method aims to improve the consistency and precision of RWH mapping, addressing a gap in existing methodologies and providing a more standardized approach. Through the application of FAHP and Fuzzy overlay techniques in ArcGIS 10.4, the study compares traditional rainfall classification with the proposed new classification method to assess RWH suitability in Kalar. The comparison highlights that the new rainfall classification-based map yielded higher accuracy and realism compared to traditional methods.

A New Approach to Rural Classification Based on the Filter-Method System: An Empirical Study in Nanning, South China

Rural revitalization is a strategic plan to address sustainable rural development in China and is an important revitalization task for Chinese villages. Rural classification is a key strategy for clarifying the direction and positioning of rural development and providing scientific policy decision-making Based on the findings of previous research on rural classification, we propose a new method for rural classification—the filter-method classification system. The operational steps of the classification are as follows: determining the type of villages, implementing the qualitative filter-method, implementing the quantitative filter-method, testing and feedback, and formulating the development guidelines. A total of 1425 villages in Nanning, the capital city of Guangxi, were classified. The classification results show that villages in Nanning city can be divided into four primary categories and eight secondary categories. The accuracy of the primary categories in the classification results was tested to verify the scientific objectivity and applicability of the classification idea and mode. Finally, development suggestions are presented based on the results of the classification of villages. The study results provide a reference for further rural classification work to help rural revitalization and improve the rural classification methodology and the scientific objectivity of classification. It also provides the basis for sustainable monitoring of rural development.

ЧИННИКИ, ЯКІ ОБУМОВЛЮЮТЬ ВИНИКНЕННЯ НЕПРАЦЮЮЧИХ (ПРОБЛЕМНИХ) АКТИВІВ У БАНКІВСЬКІЙ СФЕРІ В СУЧАСНИХ УМОВАХ

Today, Ukraine is in an extremely difficult economic, social and political situation, which in turn determines the need to ensure the effective functioning of all spheres of the state's life, especially its financial sector. An important element of the latter is the banking system, which in the conditions of a full-scale war faced a large number of risks, among which a special place is given to non-working (problem) assets. At the same time, it should be noted that the emergence of non-performing (problem) assets in the banking sector is determined by a number of factors of a diverse nature. Objective. The article, based on the analysis of the scientific views of scientists, the norms of the current legislation and the practice of its implementation, highlights the factors that cause the emergence of non-performing (problem) assets in the banking sector in modern conditions. Methods. In the process of preparing a scientific study, a number of both general and special methods of scientific knowledge were used. Thus, the analytical method and the method of documentary analysis were used in order to identify the problems causing the emergence of (problem) assets in the banking sector. The method of systematization and classification was used in order to form the author's vision of the factors that cause the emergence of non-performing (problem) assets in the banking sector. Results. It is argued that the factors causing the emergence of non-performing (problem) assets in the banking sector should be divided into two large groups: 1) external, in particular: a) regulatory and legal; b) political; c) financial and economic; d) safe; e) demographic; 2) internal factors, which include: a) organizational and managerial factors; b) personnel; c) informational and analytical. Scientific novelty. The scientific novelty of the research lies in the fact that the theoretical approach to the definition of the key factors causing the emergence of non-performing (problem) assets in the banking sector in modern conditions was further elaborated in it. Practical significance. To solve the theoretical and practical problems causing the emergence of non-performing (problem) assets in the banking sector in modern conditions. Keywords: factors, banks, banking activity, non-performing, problem assets, financial sphere, banking sphere.

Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method

Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method

Kalman filtering-based decision threshold dynamic optimization in photonics-aided millimeter-wave systems

We propose a novel, to the best of our knowledge, decision threshold dynamic optimization (DTDO) method based on Kalman filtering to mitigate the nonlinear effect impacts of the time-domain jitter and saturation distortion on a bit error rate (BER) performance. This dynamic optimization method was validated in a photonics-aided W-band millimeter-wave system. Compared to nonlinear algorithms and clustering classification methods, DTDO offers lower complexity and superior dynamic tracking capabilities when optimizing the BER performance in photonics-aided systems. The four-level pulse amplitude modulation (PAM4) signal, after being transmitted over a 50-m W-band link, was equalized using a low-complexity filter, maintaining an exceptionally low BER. This approach effectively leverages the inherent low-complexity advantages of single-carrier transmission over the link.

Prediction of Lung Cancer Disease Using Machine Learning Techniques

The pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML). One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of this work is to evaluate supervised learning algorithms, explain SML classification methodologies, and identify the most effective classification algorithm given the available data. Two distinct machine learning (ML) techniques were examined: Random Forest (RF) and Neural Networks (NN). The algorithms were implemented using Python for knowledge analysis. For the categorization, 310 cases from a lung cancer data set were employed, with 15 features serving as independent variables and one serving as the dependent variable. In comparison to NN classification methods, RF was found to be the algorithm with the highest precision and accuracy, according to the results. The study reveals that while the kappa statistic and mean square error (MSE) are factors on the one hand, the time required to create a model and precision (accuracy) are factors on the other. Consequently, to have supervised predictive ML algorithms need to be precise, accurate, and minimum error. Thus, as a consequence of the research, we are currently at this analysis. The categorizing of NNs accuracy is 0.75 the MSE is 0.25, The RF classification accuracy is 0.89 and the MSE is 0.21.

Classification of Dog Breeds Using Convolutional Neural Network Models and Support Vector Machine

When classifying breeds of dogs, the accuracy of classification significantly affects breed identification and dog research. Using images to classify dog breeds can improve classification efficiency; however, it is increasingly challenging due to the diversities and similarities among dog breeds. Traditional image classification methods primarily rely on extracting simple geometric features, while current convolutional neural networks (CNNs) are capable of learning high-level semantic features. However, the diversity of dog breeds continues to pose a challenge to classification accuracy. To address this, we developed a model that integrates multiple CNNs with a machine learning method, significantly improving the accuracy of dog images classification. We used the Stanford Dog Dataset, combined image features from four CNN models, filtered the features using principal component analysis (PCA) and gray wolf optimization algorithm (GWO), and then classified the features with support vector machine (SVM). The classification accuracy rate reached 95.24% for 120 breeds and 99.34% for 76 selected breeds, respectively, demonstrating a significant improvement over existing methods using the same Stanford Dog Dataset. It is expected that our proposed method will further serve as a fundamental framework for the accurate classification of a wider range of species.

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.

Salicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate. A trial and validation set of 96 and 24 plants from 2 populations confirmedthe efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding. The CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

A Hybrid Fuzzy Evaluation Method for Quantitative Risk Classification of Barrier Lakes Based on an AHP Method Extended by D Numbers

The risk classification of barrier lakes is the key to conducting emergency treatment in a scientific manner. However, risk classification faces difficulties such as a short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly, and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an eight-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lakes and intelligent identification of particles on the surface of barrier bodies were developed, which realized the rapid acquisition of eight-factor evaluation index information in an emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of eight factors. The risk assessment on 15 barrier lakes such as Tangjiashan barrier lake shows that the conclusions drawn for the risk classification method proposed in this paper are basically consistent with those of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss impacts on the risk level of barrier lakes and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

Spatial and Temporal Analysis of Severe Dust Storm in Iraq in May 2022

Dust storms are a common ecological occurrence in many world‘s countries, mainly in dry and semi-dry parts. Dust storms tremendously influence human health, the environment, the climate, and numerous social aspects. In this paper, spatial and temporal analysis, metrological triggers, and trajectory, dust exporting areas of a severe dust storm that occurred in Iraq on May 16, 2022, were investigated. The dust storm's backward trajectory was determined using HYSPLIT model, which is then compared with MODIS and Meteosat satellite images. The weather is then analyzed using the NCEP/NCAR Reanalysis model, and the approximate area of these sources was determined using Landsat 8 satellite image classification method. The results revealed that the HYSPLIT model trajectory of the dust storm agreed with MODIS and Meteosat satellite visuals. The primary dust storm sources and their areas are identified. The first source is from the shared border region between Syria (Rif-Dimasshq) and Jordan (north of Al-Ruwaished), with an area of about 775 km2. The second is from the northwestern regions of Iraq, specifically north of Anbar and south of Nineveh, with an area of about 905 km2.

A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method

Annually, many people worldwide lose their lives due to breast cancer, making it one of the most prevalent cancers in the world. Since the disease is becoming more common, early detection of breast cancer is essential to avoiding serious complications and possibly death as well. This research provides a novel Breast Cancer Discovery (BCD) strategy to aid patients by providing prompt and sensitive detection of breast cancer. The two primary steps that form the BCD are the Breast Cancer Discovery Step (BCDS) and the Pre-processing Step (P2S). In the P2S, the needed data are filtered from any non-informative data using three primary operations: data normalization, feature selection, and outlier rejection. Only then does the diagnostic model in the BCDS for precise diagnosis begin to be trained. The primary contribution of this research is the novel outlier rejection technique known as the Combined Outlier Rejection Technique (CORT). CORT is divided into two primary phases: (i) the Quick Rejection Phase (QRP), which is a quick phase utilizing a statistical method, and (ii) the Accurate Rejection Phase (ARP), which is a precise phase using an optimization method. Outliers are rapidly eliminated during the QRP using the standard deviation, and the remaining outliers are thoroughly eliminated during ARP via Binary Harris Hawk Optimization (BHHO). The P2S in the BCD strategy indicates that data normalization is a pre-processing approach used to find numeric values in the datasets that fall into a predetermined range. Information Gain (IG) is then used to choose the optimal subset of features, and CORT is used to reject incorrect training data. Furthermore, based on the filtered data from the P2S, an Ensemble Classification Method (ECM) is utilized in the BCDS to identify breast cancer patients. This method consists of three classifiers: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Wisconsin Breast Cancer Database (WBCD) dataset, which contains digital images of fine-needle aspiration samples collected from patients’ breast masses, is used herein to compare the BCD strategy against several contemporary strategies. According to the outcomes of the experiment, the suggested method is very competitive. It achieves 0.987 accuracy, 0.013 error, 0.98 recall, 0.984 precision, and a run time of 3 s, outperforming all other methods from the literature.

A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning

Epilepsy is a chronic neurological disorder that poses significant challenges to patients and their families. Effective detection and prediction of epilepsy can facilitate patient recovery, reduce family burden, and streamline healthcare processes. Therefore, it is essential to propose a deep learning method for efficient detection and prediction of epileptic electroencephalography (EEG) signals. This paper reviews several key aspects of epileptic EEG signal processing, focusing on epilepsy detection and prediction. It covers publicly available epileptic EEG datasets, preprocessing techniques, feature extraction methods, and deep learning-based networks used in these tasks. The literature is categorized based on patient independence, distinguishing between patient-independent and non-patient-independent studies. Additionally, the evaluation methods are classified into general classification indicators and specific epilepsy prediction criteria, with findings organized according to the prediction cycles reported in various studies. The review reveals several important insights. Despite the availability of public datasets, they often lack diversity in epilepsy types and are collected under controlled conditions that may not reflect real-world scenarios. As a result, signal preprocessing methods tend to be limited and may not fully represent practical conditions. Feature extraction and network designs frequently emphasize fusion mechanisms, with recent advances in Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) showing promising results, suggesting that new network models warrant further exploration. Studies using patient-independent data generally produce better results than those relying on non-patient-independent data. Metrics based on general classification methods typically perform better than those using specific epilepsy prediction criteria, though future research should focus on the latter for more accurate evaluation. Epilepsy prediction cycles are typically kept under 1 h, with most studies concentrating on intervals of 30 min or less.

Backdoor Attacks and Defenses Targeting Multi-Domain AI Models: A Comprehensive Review

Since the emergence of security concerns in artificial intelligence (AI), there has been significant attention devoted to the examination of backdoor attacks. Attackers can utilize backdoor attacks to manipulate model predictions, leading to significant potential harm. However, current research on backdoor attacks and defenses in both theoretical and practical fields still has many shortcomings. To systematically analyze these shortcomings and address the lack of comprehensive reviews, this paper presents a comprehensive and systematic summary of both backdoor attacks and defenses targeting multi-domain AI models. Simultaneously, based on the design principles and shared characteristics of triggers in different domains and the implementation stages of backdoor defense, this paper proposes a new classification method for backdoor attacks and defenses. We use this method to extensively review backdoor attacks in the fields of computer vision and natural language processing, and also examine the current applications of backdoor attacks in audio recognition, video action recognition, multimodal tasks, time series tasks, generative learning, and reinforcement learning, while critically analyzing the open problems of various backdoor attack techniques and defense strategies. Finally, this paper builds upon the analysis of the current state of AI security to further explore potential future research directions for backdoor attacks and defenses.

Fine construction of gene coexpression network analysis using GTOM and RECODE detected a critical module of neuroblastoma stages 4 and 4S.

Stage 4 neuroblastoma (NBL), a solid tumor of childhood, has a poor prognosis. Despite intensive molecular genetic studies, no targetable gene abnormalities have been identified. Stage 4S NBL has a characteristic of spontaneous regression, and elucidation of the mechanistic differences between stages 4 and 4S may improve treatment. Conventional NBL studies have mainly focused on the detection of abnormalities in individual genes and have rarely examined abnormalities in gene networks. While the gene coexpression network is expected to contribute to the detection of network abnormalities, the fragility of the network due to data noise and the extraction of arbitrary topological structures for the high-dimensional network are issues. The present paper concerns the classification method of stages 4 and 4S NBL patients using highly accurate gene coexpression network analysis based on RNA-sequencing data of transcription factors (TFs). In particular, after applying a noise reduction method RECODE, generalized topological overlapping measure (GTOM), which weighs the connections of nodes in the network structure, succeeded in extracting a cluster of TFs that showed high classification performance for stages 4 and 4S. In addition, we investigated how these clusters correspond to clinical information and to TFs which control the normal adrenal tissue and NBL characters. A clustering method is presented for finding intermediate-scale clusters of TFs that give considerable separation performance for distinguishing between stages 4 and 4S. It is suggested that this method is useful as a way to extract factors that contribute to the separation of groups from multiple pieces of information such as gene expression levels.

Deep Learning for Urban Tree Canopy Coverage Analysis: A Comparison and Case Study

Urban tree canopy (UTC) coverage, or area, is an important metric for monitoring changes in UTC over large areas within a municipality. Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. This study seeks to compare three urban tree canopy cover classifiers by testing a deep learning U-Net convolutional neural network (CNN), support vector machine learning classifier (SVM) and a random forests machine learning classifier (RF) on cost-free 2012 aerial imagery over a small southern USA city and midsize, growing southern USA city. The results of the experiment are then used to decide the best classifier and apply it to more recent aerial imagery to determine canopy changes over a 10-year period. The changes are subsequently compared visually and statistically with recent urban heat maps derived from thermal Landsat 9 satellite data to compare the means of temperatures within areas of UTC loss and no change. The U-Net CNN classifier proved to provide the best overall accuracy for both cities (89.8% and 91.4%), while also requiring the most training and classification time. When compared spatially with city heat maps, city periphery regions were most impacted by substantial changes in UTC area as cities grow and the outer regions get warmer. Furthermore, areas of UTC loss had higher temperatures than those areas with no canopy change. The broader impacts of this study reach the urban forestry managers at the local, state/province, and national levels as they seek to provide data-driven decisions for policy makers.

Prediction model and technical and tactical decision analysis of women's badminton singles based on machine learning.

In the Paris Olympic cycle, South Korean women's athlete An Se-young rose to the top of the 2023 BWF Olympic points with a win rate of 89.5%. With An Se-young as the subject, this paper aims to carry out technical and tactical analysis of women's badminton singles and formulate a prediction model based on machine learning. Firstly, An's technical and tactical statistics are analyzed and presented in a proposed "three-stage" data classification method. Secondly, we improve our "three-stage" machine learning dataset using video analysis of 10 matches (21 point games) where An Se-young faced off against four other players ranked in the top five of the World Badminton Federation (BWF) in week 44 of 2023. Finally, we establish a prediction model for the scoring and losing of points in the women's badminton singles based on the 'Decision tree', 'Random forest', 'XGBoost', 'Support vector' and 'K-proximity' algorithms, and analyze the effectiveness of this model. The results show that the improved data classification is reasonable and can be used to predict the final score of a match. When the support vector machine uses the RBF function kernel, the accuracy reaches its highest at 87.5%, and the consistency of this prediction model is strong. An's playstyle is sustained and unified; she does not seek continuous pressure, but rather exploits and maximizes her aggression following any mistake made by her opponents, immediately utilizing assault methods such as kills or dives, often resulting in the conversion of points during the subsequent 2-3 strikes.

STRUCTURE-ACTIVITY RELATIONSHIP OF PSYCHOACTIVE CANNABINOIDS: A CHEMOMETRIC EXPERIMENT FOR UNDERGRADUATE AND POSTGRADUATE STUDENTS IN CHEMICAL EDUCATION

Undergraduate and postgraduate Chemistry Education students had to develop the experiment in four (4) stages. Twenty-two (22) cannabinoids (Δ9-THC and derivatives) with different degrees of psychoactivity, previously scrutinized by our research group with the approaches: molecular electrostatic potential (MEP) and interaction with the CB1 receptor (CARDOSO-FILHO et al., 2004), are investigated through Chemometrics. Exploratory Analysis: Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). E Classification Methods: K-Nearest Neighbor (KNN) method, Soft Independent Modeling Class Analogy (SIMCA) method, and Stepwise Discriminant Analysis (SDA) were employed to reduce the dimensionality of the data matrix and investigate which descriptors are responsible for the classification between the most active cannabinoids (mac), pKi ≥ 6.72, and the least active cannabinoids (lac), pKi < 6.72, according to the hypothesis previously reported (Cardoso-Filho et al., 2024). The investigation with PCA, HCA, KNN, SIMCA, and SDA showed that the descriptors LUMO+1 energy, Geary autocorrelation of lag 6 weighted by van der Waals volumes (GATS6V), hydration energy (EH), atomic charge on the atom of C4 (qC4), and molecular representation of structure based on the electron diffraction, code of signal 3, unweighted (MOR03u) are responsible for separating cannabinoids according to their degrees of psychoactivity. The insights accumulated and the models built in the research – PCA chemometric model, HCA chemometric model, KNN chemometric model, SIMCA chemometric model, and SDA chemometric model – will be able to support the design of new potentially psychoactive Δ9-THC derivatives.

A method for evaluating ecological quality levels and trends using natural evolution time series.

Ecological quality assessment serves as a valuable tool for impartially measuring the effects of development and utilization activities on ecosystems, forming the bedrock of ecological governance. The ecological quality evaluation method, relying on a reference frame, takes into consideration the comparability of ecological quality, thus guaranteeing the presence of external benchmarks for the assessment results. However, present research on absolute assessment of ecological quality is limited, as the majority of existing studies focus on large-scale regions, and overlooking regional differences in smaller-scale regions to a certain extent. Therefore, to achieve a comprehensive, convenient, objective, and accurate ecological quality evaluation, this study established a model for regional ecological quality assessment through remote sensing. This model was based on the analysis of natural evolutionary features and was applied to assess the ecological quality in the largest ecosystem within the arid area of the Ningxia Hui Autonomous Region (NHAR) - the grassland ecosystem. For this assessment, the Modified soil-adjusted vegetation index (MSAVI), the wetness components of tasseled cap transformations (WET), and net primary productivity (NPP) were selected as indicators of soil and water conservation capacity, water source conservation capacity, and carbon sequestration capacity, respectively. The evaluation units were established according to the geomorphic types, and the improved derivative dynamic time warping (DDTW) algorithm was used to compare the conformity between the time series of evaluation units and natural evolution time series, pixel by pixel. The ecological quality was classified into five levels (levels 1, 2, 3, 4, and 5) using the natural breaks (Jenks) classification method. The Mann-Kendall trend test and the Sen's slope are used to analyze the trend of the conformity measurement results of the ecological quality each year at a significance level of 95%. The main results are as follows: (1) The indicators selected in the study are mostly independent of each other within each evaluation unit, with a correlation (0.552) observed only between MSAVI and WET in plain areas. (2) Spatial heterogeneity exists in the natural evolution characteristics, with significant variations in the similarity between the reference frames of different evaluation units. There is no strict rule linking the similarity results to the elevation of the terrain. (3) From the perspective of the evolutionary process and intensity, the soil and water conservation capacity, water source conservation capacity, and carbon sequestration capacity in the study area primarily fell within level 2. The advantageous level of each evaluation unit was also primarily at level 2. The overall trend of these three capacities demonstrated stability and remained constant. In summary, our assessment indicated that the overall quality of grassland ecosystems in the arid areas of the NHAR was relatively stable and superior. This method we employed has broad applicability and can serve as a general framework for ecological quality assessments. It can be adapted for ecological restoration assessments in other regions by fine-tuning regional parameters, offering valuable insights for regional ecological management.

AI-Powered Geotechnics: Enhancing Rock Mass Classification for Safer Engineering Practices

AbstractRock mass classification is fundamental for evaluating rock mass quality, essential for stability analysis and geotechnical design. Traditional classification methods are limited by joint observation technology, which typically gathers joint information from one-dimensional or two-dimensional perspectives, failing to comprehensively capture three-dimensional joint occurrences. This often necessitates empirical formulas for joint distribution, resulting in less precise joint parameter calculations. This paper reviews 44 seminal articles on rock engineering classification in construction and subterranean projects, tracing the evolution from foundational methods like Rock Quality Designation, Rock Mass Rating, Q-system, Basic Quality, and Hydropower Classification to contemporary techniques. It highlights the transformative impact of data science, particularly artificial intelligence, on rock engineering. The analysis reveals 73 distinct algorithms used 162 times in literature, with Support Vector Machines Support, Vector Regression, K-means clustering, K-Nearest Neighbors, Artificial Neural Networks and Random Forest being the most successful. This paper examines each method's advantage and limitations, discussing the challenges of algorithm deployment in the scientific community. The findings underscore the integration of machine learning and meta-heuristic optimization methods in rock engineering classification, offering valuable insights for future research and applications.

Abstract IA026: Methods for tumor subtype classification using cell-free DNA

Abstract Accurately diagnosing the tumor histological subtype is crucial for clinical care. Changes in subtype can emerge during treatment resistance. However, obtaining a biopsy to determine the tumor histology presents challenges for patients with advanced cancers. Circulating tumor DNA (ctDNA) is a non-invasive 'liquid biopsy' solution that overcomes this limitation of tissue accessibility. Current ctDNA approaches primarily focuses on detecting genetic mutations, but these alterations alone may not fully delineate tumor histological subtypes or explain treatment resistance. Recent advancements highlight the value of epigenetic information obtained from profiling nucleosome positioning and accessibility in ctDNA. I will discuss how we applied these concepts to develop computational methods for predicting transcriptional activity and classifying tumor subtypes and phenotypes in breast, prostate, and lung cancers. These tools have applications in monitoring treatment-induced phenotype changes and expand on the utility of ctDNA for precision oncology. Citation Format: Gavin Ha. Methods for tumor subtype classification using cell-free DNA [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA026.