K-nearest Neighbor Research Articles

Water Level Classification for Detect Flood Disaster Status Using KNN and SVM

Flooding occurs when the water's surface elevation exceeds the average level, overflowing river water and creating inundation in low-lying areas. Early warning for potential floods significantly reduces losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The study results indicate that the SVM algorithm outperforms the KNN algorithm. The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.

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.

Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas

Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

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.

Robust Human Activity Recognition for Intelligent Transportation Systems Using Smartphone Sensors: A Position-Independent Approach

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies.

Classification-Based Parameter Optimization Approach of the Turning Process

The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.

Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification

The sustainable development of agriculture is closely related to the adoption of precision agriculture techniques, and accurate crop classification is a fundamental aspect of this approach. This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. These features were combined with five machine learning models: random forest (RF), support vector machine (SVM), k-nearest neighbour (KNN) based, classification and regression tree (CART) and artificial neural network (ANN). The results show that the Random Forest model consistently outperforms the other models, with an overall accuracy (OA) of over 97% and a significantly higher Kappa coefficient. Fusion of RGB images and multispectral data improved the accuracy by 1–4% compared to using a single data source. Our feature importance analysis showed that band values and vegetation indices had the greatest impact on classification results. This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques.

Distinguishing lymphoma from benign lymph node diseases in fever of unknown origin using PET/CT radiomics

BackgroundA considerable portion of patients with fever of unknown origin (FUO) present concomitant lymphadenopathy. Diseases within the spectrum of FUO accompanied by lymphadenopathy include lymphoma, infections, and rheumatic diseases. Particularly, lymphoma has emerged as the most prevalent etiology of FUO with associated lymphadenopathy. Distinguishing between benign and malignant lymph node lesions is a major challenge for physicians and an urgent clinical concern for patients. However, conventional imaging techniques, including PET/CT, often have difficulty accurately distinguishing between malignant and benign lymph node lesions. This study utilizes PET/CT radiomics to differentiate between lymphoma and benign lymph node lesions in patients with FUO, aiming to improve diagnostic accuracy.ResultsData were collected from 204 patients who underwent 18F-FDG PET/CT examinations for FUO, including 114 lymphoma patients and 90 patients with benign lymph node lesions. Patients were randomly divided into training and testing groups at a ratio of 7:3. A total of 15 effective features were obtained by the least absolute shrinkage and selection operator (LASSO) algorithm. Machine learning models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) algorithms. In the training group, the area under the curve (AUC) values for predicting lymphoma and benign cases by LR, SVM, RF, and KNN models were 0.936, 0.930, 0.998, and 0.938, respectively. There were statistically significant differences in AUC between the RF and other models (all P < 0.001). In the testing group, the AUC values for the four models were 0.860, 0.866, 0.915, and 0.891, respectively, with no statistically significant differences observed among them (all P > 0.05). The decision curve analysis (DCA) curves of the RF model outperformed those of the other three models in both the training and testing groups.ConclusionsPET/CT radiomics demonstrated promising performance in discriminating lymphoma from benign lymph node lesions in patients with FUO, with the RF model showing the best performance.

MRI Radiomics-Based Machine Learning to Predict Lymphovascular Invasion of HER2-Positive Breast Cancer.

This study aims to develop and prospectively validate radiomic models based on MRI to predict lymphovascular invasion (LVI) status in patients with HER2-positive breast cancer. A total of 225 patients with HER2-positive breast cancer who preoperatively underwent breast MRI were selected, forming the training set (n = 99 LVI-positive, n = 126 LVI-negative). A prospective validation cohort included 130 patients with breast cancer from the Affiliated Zhongshan Hospital of Dalian University (n = 57 LVI-positive, n = 73 LVI-negative). A total of 390 radiomic features and eight conventional radiological characteristics were extracted. For the optimum feature selection phase, the LASSO regression model with tenfold cross-validation (CV) was employed to identify features with non-zero coefficients. The conventional radiological (CR) model was determined based on visual morphological (VM) features and the optimal radiomic features correlated with LVI, identified through multivariate logistic analyses. Subsequently, various machine learning (ML) models were developed using algorithms such as support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting machine (GBM), and random forest (RF). The performance of ML and CR models. The results show that the AUC of the CR model in the training and validation sets were 0.81 (95% confidence interval [CI], 0.74-0.86) and 0.82 (95% CI, 0.69-0.89), respectively. The ML model achieved the best performance, with AUCs of 0.96 (95% CI, 0.99-1.00) in the training set and 0.95 (95% CI, 0.89-0.96) in the validation set. There were significant differences between the CR and ML models in predicting LVI status. Our study demonstrated that the machine learning models exhibited superior performance in predicting LVI status based on pretreatment MRI compared to the CR model, which does not necessarily rely on a priori knowledge of visual morphology.

RF sensing enabled tracking of human facial expressions using machine learning algorithms

Automatic analysis of facial expressions has emerged as a prominent research area in the past decade. Facial expressions serve as crucial indicators for understanding human behavior, enabling the identification and assessment of positive and negative emotions. Moreover, facial expressions provide insights into various aspects of mental activities, social connections, and physiological information. Currently, most facial expression detection systems rely on cameras and wearable devices. However, these methods have drawbacks, including privacy concerns, issues with poor lighting and line of sight blockage, difficulties in training with longer video sequences, computational complexities, and disruptions to daily routines. To address these challenges, this study proposes a novel and privacy-preserving human behavior recognition system that utilizes Frequency Modulated Continuous Wave (FMCW) radar combined with Machine Learning (ML) techniques for classifying facial expressions. Specifically, the study focuses on five common facial expressions: Happy, Sad, Fear, Surprise, and Neutral. The recorded data is obtained in the form of a Micro-Doppler signal, and state-of-the-art ML models such as Super Learner, Linear Discriminant Analysis, Random Forest, K-Nearest Neighbor, Long Short-Term Memory, and Logistic Regression are employed to extract relevant features. These extracted features from the radar data are then fed into ML models for classification. The results show a highly promising classification accuracy of 91%. The future applications of the proposed work will lead to advancements in technology, healthcare, security, and communication, thereby improving overall human well-being and societal functioning.

Accurate felt-tip pen brands classification based on a convolutional neural network using data augmentation.

Ink analysis played an important role in document examination, but the limited dataset made it difficult for many algorithms to distinguish inks accurately. This article aimed to evaluate the feasibility of two data augmentation (DA) methods, Gaussian noise data augmentation (GNDA) and extended multiplicative signal augmentation (EMSA), for the classification of felt-tip pen ink brands. Four brands of felt-tip pens were analyzed using FT-IR spectroscopy. Five classification models were used, convolutional neural network (CNN), K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The results showed that the datasets generated by GNDA and EMSA are similar to the original datasets and have some diversity. The EMSA method had optimal classification results when combined with CNN, with classification accuracy (ACC), precision (PRE), recall (REC) and F1 score reaching 99.86%, 99.87%, 99.86%, 99.86%, and 99.86%, compared with GNDA-CNN method (ACC = 80.90%, PRE = 87.34%, REC = 81.62%, F1 score = 79.23%). This study shows that when raw spectral data is small, DA methods can be combined with neural network models to identify ink brands effectively.

Comparison of automatic and physiologically-based feature selection methods for classifying physiological stress using heart rate and pulse rate variability indices.

This study evaluates the effectiveness of four machine Learning algorithms in classifying physiological stress using Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) time series, comparing an automatic feature selection based on Akaike's criterion to a physiologically-based feature selection approach. Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors and Random Forest were applied on ten HRV and PRV indices from time, frequency and information domains, selected with the two feature selection approaches. Data were collected from 127 healthy individuals during different stress conditions (rest, postural and mental stress). Our results highlight that, while specific stress classification is feasible, distinguishing between postural and mental stress remains challenging. The used classifiers exhibited similar performance, with automatic Akaike Information Criterion-based feature selection proving overall better than the physiology-driven approach. Additionally, PRV-based features performed comparably to HRV-based ones, indicating their potential in outpatient monitoring using wearable devices. The obtained findings help to determine the most relevant HRV/PRV features for stress classification, potentially useful to highlight different physiological mechanisms involved during both challenges accompanied by a shift in the sympathovagal balance. The proposed approach may have implications for advancing stress assessment methodologies in clinical settings and real-world contexts for well-being evaluation.

Study on the Identification of Brown Rice Storage Year Based on Fluorescence Spectral Fusion Technique

The storage time of rice determines its quality and nutritional value, and the longer the storage time, the greater the impact. In this study, different excitation wavelengths (405 nm, 365 nm, 310 nm) were used to detect the fluorescence spectrum of “Dongdao 12” brown rice. Support vector machine (SVM), K-nearest neighbor (KNN), and wide neural network (WNN) were used for modeling and analysis. Under the excitation of 310 nm, the accuracy of WNN classification is up to 99.2%. In order to reduce the scattering effect and other interference in the data, multiplicative scatter correction (MSC), standard normal variable (SNV), and Savitzky–Goray smoothing (SG) preprocessing methods were used. The results showed that SG + KNN classification achieved an accuracy of 99.3% under 310 nm excitation. In order to further improve the classification accuracy, the original spectrum and the preprocessed spectrum under different excitation light sources were fused. The classification accuracy of all methods was improved, and the original data fusion was combined with the WNN model to reach 100%. It shows that fluorescence spectroscopy has excellent potential in identifying rice storage years.

Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients.

Aim: Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role of IPI for contrast-induced nephropathy (CIN) development in non-ST segment elevation myocardial infarction patients using a nomogram and performing machine learning (ML) algorithms.Materials & methods: A total of 178 patients with CIN (+) and 1511 with CIN (-) were included.Results: CIN (+) patients had higher IPI levels, and IPI was independently associated with CIN. A risk prediction nomogram including IPI had a higher predictive ability and good calibration. Naive Bayes and k-nearest neighbors were the best ML algorithms for the prediction of CIN patients.Conclusion: IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms.

Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions

The field of physically interactive electronic games is rapidly evolving, driven by the fact that it combines the benefits of physical activities and the attractiveness of electronic games, as well as advancements in sensor technologies. In this paper, a new game was introduced, which is a special version of Bubble Soccer, which we named Q-eBall. It creates a dynamic and engaging experience by combining simulation and physical interactions. Q-eBall is equipped with a fall detection system, which uses an embedded electronic circuit integrated with an accelerometer, a gyroscopic, and a pressure sensor. An evaluation of the performance of the fall detection system in Q-eBall is presented, exploring its technical details and showing its performance. The system captures the data of players’ movement in real-time and transmits it to the game controller, which can accurately identify when a player falls. The automated fall detection process enables the game to take the required actions, such as transferring possession of the visual ball or applying fouls, without the need for manual intervention. Offline experiments were conducted to assess the performance of four machine learning models, which were K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM), for falls detection. The results showed that the inclusion of pressure sensor data significantly improved the performance of all models, with the SVM and LSTM models reaching 100% on all metrics (accuracy, precision, recall, and F1-score). To validate the offline results, a real-time experiment was performed using the pre-trained SVM model, which successfully recorded all 150 falls without any false positives or false negatives. These findings prove the reliability and effectiveness of the Q-eBall fall detection system in real time.

Explainable machine learning methods to predict postpartum depression risk

Postpartum depression (PPD) is a type of depression that mothers have following childbirth due to hormonal changes, psychological transition to parenting, and exhaustion. This depression strikes either during/or in the first year following childbirth. It is also a frequently disregarded medical condition that must be treated right away as it might have major repercussions. Machine learning (ML) and artificial intelligence (AI) are tools that healthcare professionals can utilize to anticipate this condition more rapidly and correctly. Consequently, we have demonstrated how to use explainable artificial intelligence (XAI) methods and heterogeneous classifiers to predict postpartum depression in mothers who have recently given birth. The K-Nearest Neighbor (KNN) model and the customized stack model outperformed all other classifiers. KNN model obtained 97% accuracy, 98% recall, and 95% precision and the stack model obtained 97% accuracy, 100% recall, and 94% precision, respectively. A set of frameworks and resources known as explainable artificial intelligence (XAI) facilitates the comprehension and interpretation of predictions made by machine learning algorithms. Four distinct XAI techniques: ELI5, Shapley Additive Values (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchor – have been used to interpret the model predictions. Explainability, interpretability, accountability, and transparency are crucial parameters of XAI, ensuring that machine learning models provide understandable and trustworthy results to users and stakeholders. The goal of this interdisciplinary research is to develop an automated diagnosis framework with tools that can transform therapy for postpartum depression leading to suicide attempts and empower medical professionals to offer mothers individualized, high-quality care.

Identifying runtime libraries in statically linked linux binaries

Vulnerabilities in unpatched applications can originate from third-party dependencies in statically linked applications, as they must be relinked each time to take advantage of libraries that have been updated to fix any vulnerability. Despite this, malware binaries are often statically linked to ensure they run on target platforms and to complicate malware analysis. In this sense, identification of libraries in malware analysis becomes crucial to help filter out those library functions and focus on malware function analysis. In this paper, we introduce MANTILLA, a system for identifying runtime libraries in statically linked Linux-based binaries. Our system is based on radare2 to identify functions and extract their features (independent of the underlying architecture of the binary) through static binary analysis and on the K-nearest neighbors supervised machine learning model and a majority rule to predict final values. MANTILLA is evaluated on a dataset consisting of binaries built for different architectures (MIPSeb, ARMel, Intel x86, and Intel x86-64) and different runtime libraries (uClibc, glibc, and musl), achieving very high accuracy. We also evaluate it in two case studies. First, using a dataset of binary files belonging to the binutils collection and second, using an IoT malware dataset. In both cases, good accuracy results are obtained both in terms of runtime library detection (94.4% and 95.5%, respectively) and architecture identification (100% and 98.6%, respectively).

WKNN-Based Wi-Fi Fingerprinting with Deep Distance Metric Learning via Siamese Triplet Network for Indoor Positioning

Weighted k-nearest neighbor (WKNN)-based Wi-Fi fingerprinting is popular in indoor location-based services due to its ease of implementation and low computational cost. KNN-based methods rely on distance metrics to select the nearest neighbors. However, traditional metrics often fail to capture the complexity of indoor environments and have limitations in identifying non-linear relationships. To address these issues, we propose a novel WKNN-based Wi-Fi fingerprinting method that incorporates distance metric learning. In the offline phase, our method utilizes a Siamese network with a triplet loss function to learn a meaningful distance metric from training fingerprints (FPs). This process employs a unique triplet mining strategy to handle the inherent noise in FPs. Subsequently, in the online phase, the learned metric is used to calculate the embedding distance, followed by a signal-space distance filtering step to optimally select neighbors and estimate the user’s location. The filtering step mitigates issues from an overfitted distance metric influenced by hard triplets, which could lead to incorrect neighbor selection. We evaluate the proposed method on three benchmark datasets, UJIIndoorLoc, Tampere, and UTSIndoorLoc, and compare it with four WKNN models. The results show a mean positioning error reduction of 3.55% on UJIIndoorLoc, 16.21% on Tampere, and 16.49% on UTSIndoorLoc, demonstrating enhanced positioning accuracy.

Longitudinal FDG-PET Radiomics for Early Prediction of Treatment Response to Chemoradiation in Locally Advanced Cervical Cancer: A Pilot Study.

Objectives: This study aimed to assess the capacity of longitudinal FDG-PET radiomics for early distinguishing between locally advanced cervical cancer (LACC) patients who responded to treatment and those who did not. Methods: FDG-PET scans were obtained before and midway through concurrent chemoradiation for a study cohort of patients with LACC. Radiomics features related to image textures were extracted from the primary tumor volumes and stratified for relevance to treatment response status with the aid of random forest recursive feature elimination. Predictive models based on the k-nearest neighbors time series classifier were developed using the top-selected features to differentiate between responders and non-responders. The performance of the developed models was evaluated using receiver operating characteristic (ROC) curve analysis and n-fold cross-validation. Results: The top radiomics features extracted from scans taken midway through treatment showed significant differences between the two responder groups (p-values < 0.0005). In contrast, those from pretreatment scans did not exhibit significant differences. The AUC of the mean ROC curve for the predictive model based on the top features from pretreatment scans was 0.8529, while it reached 0.9420 for those derived midway through treatment scans. Conclusions: The study highlights the potential of longitudinal FDG-PET radiomics extracted midway through treatment for predicting response to chemoradiation in LACC patients and emphasizes that interim PET scans could be crucial in personalized medicine, ultimately enhancing therapeutic outcomes for LACC.

A deep neural network framework with Analytic Continuation for predicting hypervelocity fragment flyout from satellite explosions

Hypervelocity breakup events contribute to the rapidly growing population of space debris orbiting Earth. To ensure a safe environment for future space missions, it is crucial to accurately characterize resulting fragments, which constitute a major portion of the orbital population but are often too small for conventional tracking methods. Currently, the Space Surveillance Network tracks satellites and debris within a specific altitude range and releases the data publicly as Two-Line Elements, though these datasets are limited in both size and accuracy. To supplement Two-Line Element data, this paper presents a novel Deep Neural Network approach to estimate orbital elements of hypervelocity fragments resulting from simulated satellite explosions. It employs initial conditions collected from realistic terrestrial explosions considered to be relative to 5 different detonation points on the polar orbit of the weather satellite, NOAA-16, to model the initial states of debris fragments immediately following an explosion. The debris trajectories are then propagated using a high-precision semi-analytic integration method, Analytic Continuation, considering J2−J6 zonal gravitational terms and Earth’s atmospheric drag perturbations. The collected data is used to train a Deep Neural Network for each of the orbital elements. Finally, a testing data set is used to validate the results, finding that this technique accurately estimates most of the orbital elements, but is not suited to predict the true anomaly of the fragments. Therefore, K-Nearest Neighbor regression is utilized instead to predict the nature of the final orbital element.