Performance Metrics Research Articles

Using Learning from Demonstration (LfD) to perform the complete apple harvesting task

Learning from Demonstration (LfD) can be used to make it easier for robots to learn to perform tasks such as apple harvesting. The required apple harvesting motions can be split into four steps: 1. Approaching, 2. Grasping, 3. Detaching, 4. Placing. So far, only parts of the apple harvesting task have been learned using LfD. In our work, we learned an abstracted version of the complete apple harvesting task using a cube. We used two methods and tested the sensitivity to model parameters and the number of demonstrations. We selected (1) the Gaussian Mixture Model (GMM) with Gaussian Mixture Regression (GMR) and (2) the Hidden Markov Model (HMM) with Linear Quadratic Regression (LQR). Both methods make use of Task Parameterization (TP), which allows models to combine multiple phases from the apple harvesting task. Combining multiple harvesting phases is important for effective LfD. We analysed the performance of these algorithms on four performance metrics, namely the grasp pose, the detachment motion, the place pose, and the success rate when using the real robot. A high grasp pose accuracy was achieved, specifically a position error of 0.004 and an orientation error of 0.004 for the GMM with GMR, trained with 100 demonstrations and 9 Gaussian components. The detachment motion was performed but with a reduced range. The model that performed the best was the GMM with GMR, trained with 60 demonstrations and 5 Gaussian components. A high place pose accuracy was achieved, specifically a position error of 0.003 and an orientation error of 0.003 for the GMM with GMR, trained with 100 demonstrations and 15 Gaussian components. The task was executed by a real robot successfully, resulting in a success rate of 67% for HMM with LQR, trained with 40 demonstrations, 5 states, and a scaling factor of 1.0. For the number of components, no clear relation was found. The same was true for the number of states in the HMM with LQR. For the scaling factor, a clear relation could be found. For this task, a value in the range of -1.0 to 1.0 should be used. For the number of demonstrations, there was an optimum of 40 demonstrations. Further improvements are needed to deal with challenges such as apple shift and detecting detachment of fruits.

An exhaustive review of the stream ciphers and their performance analysis

The number of internet of things (IoT) applications has increased, which has increased the demand for low-resource gadgets. The data produced by these devices must be protected to guarantee security. The devices operate in conditions with limited space, computational power, memory, and energy. High-security standards are difficult to achieve with limited resources. The detailed analysis of various stream ciphers and their performance metrics is reviewed in this manuscript. The functionality of the stream ciphers is categorized and thoroughly discussed based on both the hardware and software viewpoints. The security attacks and their countermeasure methods using stream ciphers are discussed. The performance metrics of most hardware-based stream ciphers, including the ECRYPT stream cipher project (eSTREAM) ciphers, are discussed. Each hardware stream cipher design highlights the hardware constraints such as chip area, frequency, throughput, and hardware efficiency. The work also highlights the various applications using these stream ciphers. The current trends using these stream ciphers are discussed with futuristic goals.

Melanoma image synthesis: a review using generative adversarial networks

Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.

Frequency reconfigurable microstrip patch antenna for multiband applications

Wireless communication technology is well-established, and several antennas have been developed and produced specifically for this purpose. However, antenna performance and communication system development need to be enhanced in order to adapt to the present era. The performance of the antenna is significantly influenced by its design. Thus, this work produced a novel wideband antenna design via the use of a frequency reconfigurable approach. In the recommended study, microstrip patch antennas (MPAs) were used in wideband applications to switch frequencies using shunt-series microelectromechanical systems (MEMS). The suggested antenna, which has two switches built into it, is tested in ON-ON, OFF-ON, and OFF-OFF switching scenarios. Radiation pattern, voltage standing wave ratio (VSWR), gain, bandwidth, and return loss are among the antenna performance metrics used to assess the suggested antenna's performance in each switching situation. The simulation findings suggest that the optimal antenna design for usage in wireless communication systems is one that works well with a shunt-series MEMS switch.

Subjective and Objective Monitoring Markers: Are They Related to Game Performance Indicators in Elite Female Volleyball Players?

This cohort study aimed to investigate the relationship between subjective (wellness and internal training load [ITL]) and objective (neuromuscular fatigue) monitoring markers and performance aspects (reception quality [RQ] and attack efficiency [AE]) in professional female volleyball players. The study was conducted over an 8-week period during the final mesocycle of the competitive phase. A total of 24 training sessions and 10 matches were included in the analysis. Subjective measures of wellness and ITL were assessed, and neuromuscular fatigue was evaluated using countermovement-jump (CMJ) height. RQ and AE were determined based on game statistics. The study found a positive relationship between wellness and RQ, particularly affecting outside hitters and liberos. ITL showed a positive association with AE, primarily impacting outside hitters, opposite hitters, and middle blockers. Additionally, ITL demonstrated a negative correlation with RQ, mainly affecting outside hitters and liberos. CMJ performance was associated with AE, where a decrease in CMJ height was linked to reduced AE. The findings highlight the importance of considering players' wellness scores in training and match strategies for different positions. Careful management of training loads, considering both physical and technical demands, is crucial for optimizing performance outcomes. Monitoring neuromuscular fatigue, as indicated by CMJ performance, is particularly relevant for outside hitters, opposite hitters, and middle blockers involved in attack actions. Coaches, trainers, and sports practitioners can use these insights to develop position-specific training protocols and implement effective strategies for maintaining or improving performance metrics under various stressors.

A Spreadsheet-Based Redox Flow Battery Cell Cycling Model Enabled by Closed-Form Approximations

Abstract The complex interplay between numerous parasitic processes—voltage losses, crossover, decay—challenges interpretation of cycling characteristics in redox flow batteries (RFBs). Mathematical models offer a means to predict cell performance prior to testing and to interpret experimentally measured cycling data, however most implementations require extensive domain expertise, programming knowledge, and/or computational resources. Here, we expand on our previously developed zero-dimensional modeling framework by deriving closed-form expressions for key performance metrics. The resulting closed-form model streamlines the computational structure and allows for spreadsheet modeling of cell cycling behavior, which we highlight by developing a simulation package in Microsoft® Excel®. We then apply this model to analyze previously published experimental data from our group and others, highlighting its utility in numerous diagnostic configurations—bulk electrolysis, compositionally unbalanced symmetric cell cycling, and full cell cycling. Given the accessibility of this modeling toolkit, it has potential to be a widely deployable tool for RFB research and education, aiding in data interpretation and performance prediction.

Use of natural language processing techniques to predict patient selection for total hip and knee arthroplasty from radiology reports

AimsTo examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports.MethodsData pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.ResultsFor THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts.ConclusionThe use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts.Cite this article: Bone Joint J 2024;106-B(7):688–695.

Population assessment of health system performance in 16 countries.

To demonstrate how the new internationally comparable instrument, the People's Voice Survey, can be used to contribute the perspective of the population in assessing health system performance in countries of all levels of income. We surveyed representative samples of populations in 16 low-, middle- and high-income countries on health-care utilization, experience and confidence during 2022-2023. We summarized and visualized data corresponding to the key domains of the World Health Organization universal health coverage framework for health system performance assessment. We examined correlation with per capita health spending by calculating Pearson coefficients, and within-country income-based inequities using the slope index of inequality. In the domain of care effectiveness, we found major gaps in health screenings and endorsement of public primary care. Only one in three respondents reported very good user experience during health visits, with lower proportions in low-income countries. Access to health care was rated highest of all domains; however, only half of the populations felt secure that they could access and afford high-quality care if they became ill. Populations rated the quality of private health systems higher than that of public health systems in most countries. Only half of respondents felt involved in decision-making (less in high-income countries). Within countries, we found statistically significant pro-rich inequalities across many indicators. Populations can provide vital information about the real-world function of health systems, complementing other system performance metrics. Population-wide surveys such as the People's Voice Survey should become part of regular health system performance assessments.

Enhancing Energy Efficiency in Telehealth IoT through MultiObjective Optimization on a Hybrid Fog/Cloud Computing Platform

In the rapidly evolving field of Telehealth Internet of Things (IoT), the pursuit of energy-efficient solutions that coexist with optimal system performance is a critical concern. This paper introduces a novel approach to address this challenge by integrating multi-objective optimization techniques within a hybrid fog/ cloud computing platform. Building upon established research on a fog-based telehealth model, this study extends its investigation to encompass a broader spectrum of performance metrics, including energy efficiency, response time, throughput, and resource utilization. The study employs well-established multiobjective optimization algorithms, specifically NSGA-II (Non-dominated Sorting Genetic Algorithm II) and SPEA2 (Strength Pareto Evolutionary Algorithm 2), to construct a comprehensive optimization framework. An intricate objective function is meticulously formulated to quantify the trade-offs between energy efficiency and other key performance metrics, facilitating the identification of Pareto-optimal solutions. The resulting Pareto front offers illuminating insights into the nuanced interplay between energy efficiency and performance attributes, providing decision-makers with tailored options that cater to their specific priorities. Rigorous evaluation of these solutions through simulated experiments reveals a harmonious landscape where energy-saving imperatives coalesce harmoniously with response time, throughput, and resource utilization goals. The implications of this multi-objective optimization approach are analyzed in depth, underscoring its potential to reshape optimization paradigms for Telehealth IoT deployments within a fog/cloud hybrid platform. This research represents a pioneering stride towards reconciling energy efficiency and performance in Telehealth IoT systems, offering a nuanced perspective for informed decision-making and a sustainable future for energy-saving initiatives in Telehealth IoT applications

HumourHindiNet: Humour detection in Hindi web series using word embedding and convolutional neural network

Humour is a crucial aspect of human speech, and it is, therefore, imperative to create a system that can offer such detection. While data regarding humour in English speech is plentiful, the same cannot be said for a low-resource language like Hindi. Through this article, we introduce two multimodal datasets for humour detection in the Hindi web series. The dataset was collected from over 500 minutes of conversations amongst the characters of the Hindi web series Kota-Factory and Panchayat . Each dialogue is manually annotated as Humour or Non-Humour. Along with presenting a new Hindi language-based Humour detection dataset, we propose an improved framework for detecting humour in Hindi conversations. We start by preprocessing both datasets to obtain uniformity across the dialogues and datasets. The processed dialogues are then passed through the Skip-gram model for generating Hindi word embedding. The generated Hindi word embedding is then passed onto three convolutional neural network (CNN) architectures simultaneously, each having a different filter size for feature extraction. The extracted features are then passed through stacked Long Short-Term Memory (LSTM) layers for further processing and finally classifying the dialogues as Humour or Non-Humour. We conduct intensive experiments on both proposed Hindi datasets and evaluate several standard performance metrics. The performance of our proposed framework was also compared with several baselines and contemporary algorithms for Humour detection. The results demonstrate the effectiveness of our dataset to be used as a standard dataset for Humour detection in the Hindi web series. The proposed model yields an accuracy of 91.79 and 87.32 while an F1 score of 91.64 and 87.04 in percentage for the Kota-Factory and Panchayat datasets, respectively.

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique

Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.

Galaxy merger challenge: A comparison study between machine learning-based detection methods

Aims. Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. Our aim is to benchmark the relative performance of merger detection methods based on machine learning (ML). Methods. We explore six leading ML methods using three main datasets. The first dataset consists of mock observations from the IllustrisTNG simulations, which acts as the training data and allows us to quantify the performance metrics of the detection methods. The second dataset consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data to those employed for training. The third dataset is composed of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. We also compare mergers and non-mergers detected by the different methods with a subset of HSC-SSP visually identified galaxies. Results. For the simplest binary classification task (i.e. mergers vs. non-mergers), all six methods perform reasonably well in the domain of the training data. At the lowest redshift explored 0.1 < ɀ < 0.3, precision and recall generally range between ~70% and 80%, both of which decrease with increasing ɀ as expected (by ~5% for precision and ~10% for recall at the highest ɀ explored 0.76 < ɀ < 1.0). When transferred to a different domain, the precision of all classifiers is only slightly reduced, but the recall is significantly worse (by ~20–40% depending on the method). Zoobot offers the best overall performance in terms of precision and F1 score. When applied to real HSC observations, different methods agree well with visual labels of clear mergers, but can differ by more than an order of magnitude in predicting the overall fraction of major mergers. For the more challenging multi-class classification task to distinguish between pre-mergers, ongoing-mergers, and post-mergers, none of the methods in their current set-ups offer good performance, which could be partly due to the limitations in resolution and the depth of the data. In particular, ongoing-mergers and post-mergers are much more difficult to classify than pre-mergers. With the advent of better quality data (e.g. from JWST and Euclid), it is of great importance to improve our ability to detect mergers and distinguish between merger stages.

Increasing Gender Integration inU.S. Marine Corps Recruit Training: Policy Recommendations andRationales From an Interdisciplinary Study.

This article describes alternate models and policy recommendations created by an interdisciplinary team of researchers to increase gender integration at U.S. Marine Corps (USMC) recruit training. The USMC requested a study to analyze current approaches to gender integration at recruit training and provide alternate models that maximize integration, while continuing to train marines to established standards. USMC remains the only service that segregates recruits by gender at the lowest unit level (e.g., platoon) in recruit training and maintains gender-segregated drill instructor teams (i.e., same-gender teams train platoons of same-gender recruits). A mixed-method, interdisciplinary approach was used to capture multiple perspectives and informed recommendations and alternate models for gender integration. The team studied select USMC, army, navy, air force, and coast guard recruit training locations, between June and November 2021. At each site, the team collected qualitative, quantitative, and administrative data as well as physical performance metrics and human performance outcomes. Study participants included recruits, drill instructors, training cadre, service leaders, and subject matter experts on gender integration in military services. Each alternate model was designed to maximize the feasibility of implementation within current USMC training conditions. The research team developed three alternate models and several policy recommendations. Alternate model 1 proposed a methodology for mixed-gender drill instructor teams within the Integrated Company model, USMC's current integrated training model. Alternate model 2 proposed an Integrated Company Plus model which increases the number and types of gender-integrated training events at or below the platoon level within the Integrated Company model. Alternate model 3 outlined an integrated platoon model where recruits fall out into integrated training platoons after their morning routine, with two options of varying integration based on USMC priorities for integration. The team also proposed a set of policy recommendations independent of the three alternate models to support current and future gender integration efforts at the Marine Corps Recruit Depots. Gender-integrated military training has been shown to positively alter perceptions and evaluations of women in military settings over detrimental aspects developed by gender-segregated training. The study team recommended USMC train recruits in the Integrated Company model with mixed-gender drill instructor teams (alternate model 1) and integrate more training events following the priority tiers outlined in the Integrated Company plus model (alternate model 2). The combined execution of these two alternate models would provide USMC recruits increased exposure to direct, sustained training from opposite-gender drill instructors and deliver intentional training opportunities for male and female recruits to work together and interact in meaningful ways. The integrated platoon model (alternate model 3) would offer USMC recruits the most direct exposure to training and working with members of the opposite gender, but it requires substantial changes to current logistics, accountability, and training procedures.

A Comprehensive Study of Walmart Sales Predictions Using Time Series Analysis

This article presents a comprehensive study of sales predictions using time series analysis, focusing on a case study of Walmart sales data. The aim of this study is to evaluate the effectiveness of various time series forecasting techniques in predicting weekly sales data for Walmart stores. Leveraging a dataset from Kaggle comprising weekly sales data from various Walmart stores around the United States, this study explores the effectiveness of time series analysis in forecasting future sales trends. Various time series analysis techniques, including Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), Prophet, Exponential Smoothing, and Gaussian Processes, are applied to model and forecast Walmart sales data. By comparing the performance of these models, the study seeks to identify the most accurate and reliable methods for forecasting retail sales, thereby providing valuable insights for improving sales predictions in the retail sector. The study includes an extensive exploratory data analysis (EDA) phase to preprocess the data, detect outliers, and visualize sales trends over time. Additionally, the article discusses the partitioning of data into training and testing sets for model evaluation. Performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized to compare the accuracy of different time series models. The results indicate that Gaussian Processes outperform other models in terms of accuracy, with an RMSE of 34,116.09 and an MAE of 25,495.72, significantly lower than the other models evaluated. For comparison, ARIMA and SARIMA models both yielded an RMSE of 555,502.2 and an MAE of 462,767.3, while the Prophet model showed an RMSE of 567,509.2 and an MAE of 474,990.8. Exponential Smoothing also performed well with an RMSE of 555,081.7 and an MAE of 464,110.5. These findings suggest the potential of Gaussian Processes for accurate sales forecasting. However, the study also highlights the strengths and weaknesses of each forecasting methodology, emphasizing the need for further research to refine existing techniques and explore novel modeling approaches. Overall, this study contributes to the understanding of time series analysis in retail sales forecasting and provides insights for improving future forecasting endeavors.

A ROBUST ENSEMBLE SEGMENTATION APPROACH AND OPPOSITION-BASED RAIN OPTIMIZATION ALGORITHM FOR ENHANCING ACUTE LYMPHOBLASTIC LEUKEMIA (ALL) DETECTION

Generally, leukemia is one of the blood cancers that can lead to death. A huge amount of immature WBCs in the bone marrow affect the healthy cells and this has become the leading cause of leukemia disease. Moreover, “Acute Lymphoblastic Leukemia (ALL)” is a type of blood cancer that is generally categorized with a huge amount of immature lymphocytes that are given as blast cells. But, the analysis of this model is dependent, boring as well as time-consuming based on the skills of hematologists. So, there is a requirement for the most desirable techniques to tackle these restrictions. Additionally, the accurate and automated diagnosis of ALL is a crucial task. In this case, an automated detection model of ALL using ensemble segmentation and a heuristic-assisted layer-improved hybrid deep learning approach is implemented. In the initial stage, the raw images are collected from benchmark datasets. Further, the image pre-processing is undergone by contrast enhancement and filtering process. After pre-processing, the image segmentation is carried out by ensemble segmentation, which is acquired by the region growing, K-medoids, and Fuzzy C-Means (FCM). From the segmented images, based on the mutual information, the pixel is selected optimally, in which the optimal pixel is identified by Opposition-based Rain Optimization Algorithm (OROA). Subsequently, the optimal pixels are fed as input to the Layer Improved Hybrid DenseNet and ResNet (LIH-DRNet) model that is constructed with DenseNet and ResNet, where some hyperparameters are tuned optimally by the improved ROA. At last, the performance is evaluated with diverse performance metrics. Thus, the findings reveal that the developed hybrid deep learning method achieves a higher detection rate to ensure the effectiveness of the model.

Lifetime reliability modeling on EMC performance of digital ICs influenced by the environmental and aging constraints: A case study

This paper aims to develop the lifetime reliability model on electromagnetic compatibility (EMC) performance of the Atmel Attiny85 microcontroller integrated circuit (IC) chip samples, depending on the observed variation of the conducted immunity to the electromagnetic interference imposed by the combined influence of various environmental and aging (i.e., thermal and electrical voltage stress) constraints. A constant-stress accelerated degradation tests plan was designed and implemented by applying different constant thermal (i.e., 70 and 110 °C) and electrical voltage (i.e., 4 and 5 V) stress magnitude levels simultaneously in various multiple stress combinations. Direct power injection (DPI) conducted immunity tests were performed in nominal condition on all the programmed device under test (DUT) samples in both the fresh and aged states at various stress time duration. The best-fit EMC degradation paths were generated using regression analysis, followed by evaluating the pseudo time-to-failure (TTF) data and estimating the unknown parameters of the developed degradation path model. The performance metrics for lifetime reliability were evaluated by combining the Weibull distribution function with the generalized Eyring accelerated life test model. The maximum likelihood estimation method was utilized to estimate the relevant reliability model parameters. The developed reliability model was found to have the capability to estimate the electromagnetic unreliability against the lifetime TTF data of all the selected DUT samples with good precision and acceptable accuracy in both nominal and aging stress conditions. It is demonstrated that the non-failure probability of the DUT samples would remain at 1 for the first 1200 h, and that, under nominal conditions, the prediction of corresponding TTF data for all of those IC samples would fluctuate between 1400 and 1600 h.

Physical unclonable function using photonic spin Hall effect

This study presents a novel method leveraging surface wave-assisted photonic spin Hall effect (PSHE) to construct physical unclonable functions (PUFs). PUFs exploit inherent physical variations to generate unique Challenge–Response pairs, which are critical for hardware security and arise from manufacturing discrepancies, device characteristics, or timing deviations. We explore PSHE generation-based PUF design, expanding existing design possibilities. With recent applications in precise sensing and computing, PSHE offers promising performance metrics for our proposed PUFs, including an inter-Hamming distance of 47.50% , an average proportion of unique responses of 62.5% , and a Pearson correlation coefficient of − 0.198. The PUF token demonstrates robustness to simulated noise. Additionally, we evaluate security using a machine learning-based attack model, employing a multi-layer perceptron (MLP) regression model with a randomized search method. The average accuracy of successful attack prediction is 9.70% for the selected dataset. Our novel PUF token exhibits high non-linearity due to the PSHE effect, resilience to MLP-based attacks, and sensitivity to process variation.

Feature Engineering for a MIL-STD-1553B LSTM Autoencoder Anomaly Detector

The MIL-STD-1553B data bus protocol is used in both civilian and military aircraft to enable communications between subsystems. These interconnected subsystems are responsible for core services such as communications, flow of instrument data and aircraft control. With aircraft modernization, threat vectors are introduced through increased inter-connectivity internal and external to the aircraft. The resulting potential for exploitation introduces a requirement for an intrusion detection capability in order to maintain the integrity, availability and reliability of data transmitted using the MIL-STD-1553B protocol, safety of the aircraft and overall, to achieve mission assurance. Research in recent years has investigated signature, statistical and machine learning based solutions to detect attacks on MIL-STD-1553B buses. Of the different techniques, those based on machine learning have shown extremely good results. The aim of this research is to improve the performance of an existing Long Short-Term Memory Auto-Encoder by refining the feature engineering phase of its pipeline. The improvement in the detector’s overall effectiveness was accomplished through feature engineering focused on feature generation and selection. Five different attack datasets were used as the starting point, consisting of four different denial of service attacks and one data integrity attack. From initial feature extraction of 155 features, two feature generation techniques were employed to create over 38,000 features as a starting point. Using five different MIL-STD-1553B datasets and three feature selection techniques, fifteen different Long Short-Term Memory Auto-Encoder models were created, trained and evaluated using common performance metrics and compared to those of the original anomaly detector. This research demonstrated marked performance improvement achieved by the feature engineering refinements made in comparison to those of the original model. Equally important, this research also showed a significant reduction in the number of features required to achieve this performance gain. In the context of miliary air operations, the ability to improve detection capabilities with less data is important to the technical solutions that contribute to the achievement of cyber mission assurance.

Assessment of spatial air quality on the East Coast of Peninsular Malaysia utilizing environmetric techniques.

The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p < 0.0001). Principal component analysis (PCA) was conducted to validate the pattern of air quality variables in relation to the identified clusters (HPC, MPC, and LPC). The results showed that two verifactors (VFs) were extracted in HPC and LPC, while three VFs were identified in MPC. The cumulative variance explained by the PCA for HPC, MPC, and LPC was 69.43%, 82.32%, and 62.16%, respectively. Finally, an artificial neural network (ANN) was used to forecast the air pollutant index (API) levels, using the R2 and RMSE performance metrics. The PCA-MLP Model A yielded an R2 value of 0.8470 and an RMSE of 6.6470, while PCA-MLP Model B achieved an R2 value of 0.8591 and an RMSE of 6.3000, both indicating a significant and strong correlation.

A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm

Microseismic data are widely employed for assessing rockburst risks; however, significant disparities exist in the monitoring capabilities of seismic networks across different mines, and none can capture a complete dataset of microseismic events. Such differences introduce unfairness when applying the same methodologies to evaluate rockburst risks in various mines. This paper proposes a method for assessing the monitoring capability of seismic networks applicable to heterogeneous media in mines. It achieves this by integrating three gradient boosting algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). Initially, the isolation forest algorithm is utilized for preliminary data cleansing, and feature engineering is constructed based on the relative locations of event occurrences to monitoring stations and the working face. Subsequently, the optimal hyperparameters for three models are searched for using 8508 microseismic events from the a Coal Mine in eastern China as samples, and 18 sub-models are trained. Model weights are then determined based on the performance metrics of different algorithms, and an ensemble model is created to predict the monitoring capability of the network. The model demonstrated excellent performance on the training and test sets, achieving log loss, accuracy, and recall scores of 7.13, 0.81, and 0.76 and 6.99, 0.80, and 0.77, respectively. Finally, the method proposed in this study was compared with traditional approaches. The results indicated that, under the same conditions, the proposed method calculated the monitoring capability of the key areas to be 11% lower than that of the traditional methods. The reasons for the differences between these methods were identified and partially explained.