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Rogue waves are reported to wreck ships and claim lives. The prompt detection of their presence is difficult due to their small footprint and unpredictable emergence. The retrieval of sea surface height via remote sensing techniques provides a viable solution for detecting rogue waves. However, conventional synthetic aperture radar (SAR) techniques are ineffective at retrieving the surface height profile of rogue waves in real time due to nonlinearity between surface height and normalized radar cross-section (NRCS), which is not obvious in the absence of rogue waves. In this work, a cross-track interferometric SAR (XTI-SAR) imaging approach is proposed to detect elusive rogue waves over a wide area, with sea-surface profiles embedding rogue waves simulated using a probability-based model. The performance of the proposed imaging approach is evaluated in terms of errors in the position and height of rogue-wave peaks, the footprint area of rogue waves, and a root-mean-square error (RMSE) of the sea-surface height profile. Different rogue-wave events under different wind speeds are simulated, and the reconstructed height profiles are analyzed to determine the proper ranges of look angle, baseline, and mean-filter size, among other operation variables, in detecting rogue waves. The proposed approach is validated by simulations in detecting a rogue wave at a spatial resolution of 3 m × 3 m and height accuracy of decimeters.

The procurement process is one of the most important phases in any project life cycle, particularly when it comes to selecting the right contractor for the job. Awarding the contract to the best bid proposal is a critical step to ensure the greatest value. BIM has been recognized as not only a geometric modelling of buildings, but also, it facilitates the different stages in management of construction projects. The purpose of this paper is to study the impact of using Building Information Modeling (BIM) in the tendering process from the contractor’s perspective, based on a probability model able to predict winning probability, regardless of relative weight. The main objective of this research is to measure the likelihood of winning a tender in the case of implementing BIM strategy, compared with contractors who do not use BIM. The research uses a literature review, surveys, and interviews with experts to develop a model that predicts the probability of winning a contract; this is determined by measuring the BIM impact on each selection criterion in a multicriteria selection process using the Analytical Hierarchy Process (AHP) to develop a probability-based model. The results of the survey and the interview show that BIM strategy has a variant influence on the score the contractor could have on each of them raising the probability of winning the tender. The main result of this paper is the property-based model, which is able to predict BIM winning probability regardless of relative weight, which can be applied in any country. Nonetheless, the Saudi case study shows that utilizing BIM when proposing could increase the winning probability by up to 9.42% in the case of Quality-Based Selection (QBS), and to 5.5% in the case of Cost-Based Selection (CBS).

In this paper, Drone-Assisted-Cooperation (DAC) for multi-cluster disaster scenarios have been considered, where the drone serves as an aerial relay among the ground users (GUs) belonging to each cluster. In particular, the probability-based statistical channel model is considered for assigning air-to-ground (A2G) links and Rayleigh channel distribution among GUs for multi-cluster DAC systems. In this proposal, we assume the probability-based statistical channel model among A2G links to be statistically independent of each other and dependent on the drone height and other surrounding environmental parameters. The system performance for the proposed scenario is evaluated using amplify-and-forward (AF) relaying at the drone and considering maximal ratio combining as a signal-combining approach at the destination.Two algorithms have also been developed in this study, one for modelling the A2G links and another for calculating the performance metrics of the proposal. The analytical framework has been developed for the proposed work and performance has been compared with the existing Rayleigh and Rician channel model. Furthermore, for a given drone height, the AF-noise component and variance of the AF-noise component have also been evaluated. However, the actual performance in the case of the proposed work comes over the existing state-of-the-art (Rayleigh and Rician fading distribution environment) in practical scenarios.

Continuous K -nearest skyline query ( CKNSQ ) is an important query in the spatio-temporal databases. Given a query time interval [ t s , t e ] and a moving query object q , a CKNSQ is to retrieve the K -nearest skyline points of q at each time instant within [ t s , t e ]. Different from the previous works, in this paper we devote to overcoming the specific assumptions that each object is static in road networks and has the certain dimensional values. We focus on processing the CKNSQ over moving objects with uncertain dimensional values in Euclidean space and the velocity of each object (including the query object) varies within a known range. As the uncertainty is involved, such a query is called the continuous possible K-nearest skyline query ( CPKNSQ ). We first discuss the difficulties raised by the uncertainty of moving objects and then propose the CPKNSQ algorithm operated with a data-partitioning index, the uncertain TPR-tree (UTPR-tree), to efficiently answer the CPKNSQ . Moreover, we design a probability-based model to quantify the possibility of each object being the query result. Finally, extensive experiments using the synthetic and real datasets demonstrate the effectiveness and the efficiency of the proposed approaches.

In the current world, the disorders occurring in dermatological images are among the foremost widespread diseases. Despite being common, its identification is tremendously hard because of the complexities like skin tone and color variation due to the presence of hair regions. Therefore the type of skin disease prediction is not accurately achieved in many pieces of research. To deal with mentioned concerns, a novel optimal probability-based deep neural network is proposed to assist medical professionals in appropriately diagnosing the type of skin disease. Initially, the input dataset is fed into the pre-processing stage, which helps to remove unwanted contents in the image. Afterward, features extracted for all the pre-processed images are subjected to the proposed Optimal Probability-Based Deep Neural Network (OP-DNN) for the training process. This classification algorithm classifies incoming clinical images as different skin diseases with the help of probability values. While learning OP-DNN, it is essential to determine the optimal weight values for reducing the training error. For optimizing weight in OP-DNN structure, an optimization approach is implemented in this research. For that, whale optimization is utilized because it works faster than other methods. The proposed multi-type skin disease prediction model is implemented in MatLab software and achieved 95% of accuracy, 0.97 of specificity, and 0.91 of sensitivity. This exposes the superiority of the proposed multi-type skin disease prediction model using an effective OP-DNN based feature extraction approach to attain a high accuracy rate and also it predict several kinds of skin disease than the previous models, which can protect the patients survives as well as can assist the physicians in making a decision certainly.

Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.

The requirements for modern storage systems are steadily increasing due to limited space, cost, time, and personnel. Robotic compact storage and retrieval systems (RCS/RS), where containers are stacked and arranged in a block layout with robots operating from above, offer a promising solution. Some systems benefit from a self-sorting effect, where robots relocate previously moved containers after accessing non-directly accessible ones, resulting in demand-based sorted stacks. Despite various analytical models for automated storage systems, RCS/RS remain under-researched. Apart from two distinct papers on performance evaluation, there are no general, fast, and easy-to-use tools to assess system throughput under demand-based access patterns. Additionally, the performance benefits of self-sorting have not yet been studied. This paper presents an analytical approach to predict RCS/RS performance using a class-based access structure. A discrete event simulation validates the model, and an optimization example demonstrates the model's broad applicability and ease of use.

In this paper, a novel combined fatigue life reliability analysis model is constructed from the perspective of inverse analysis of Manson-Coffin equation. By the derived equivalent threshold of low cycle fatigue life, the failure event that the combined fatigue life is less than or equal to the presupposed threshold is equivalently transformed into the event that the actual strain range in the low cycle fatigue mode is larger than or equal to the inverse strain range threshold. The inverse strain range threshold corresponds to the equivalent threshold of low cycle fatigue life derived by the presupposed threshold of combined fatigue life. Then, the inverse strain range-based limit state function is constructed to analyze the fatigue life reliability, where solution of the exponential Manson-Coffin equation which is used to determine the low cycle fatigue life is avoided. A combination of the inverse strain range-based limit state function and adaptive Kriging (AK) model is constructed first to estimate the combined fatigue life reliability where the AK model directly surrogates the inverse strain range-based limit state function, and this algorithm is defined as a full-surrogate algorithm. The inverse strain range-based limit state function consists of two nested parts. The first part is the structural analysis which is usually an implicit function and the second part is the life analysis which is usually an explicit function. In this regard, another combination of the inverse strain range-based limit state function and AK model is constructed to estimate the combined fatigue life reliability, where the AK model only surrogates a part of inverse strain range-based limit state function, i.e., the implicit structural analysis part, and this algorithm is regarded as a semi-surrogate algorithm. Two aero-engine structures are analyzed to validate the effectiveness of the proposed method.

Background and Objectives: Investigating the adherence to antihypertensive medication and identifying patients with low adherence allows targeted interventions to improve therapeutic outcomes. Artificial intelligence (AI) offers advanced tools for analyzing medication adherence data. This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. Materials and Methods: A cross-sectional, multi-center study was conducted on 3095 hypertensive patients from community pharmacies. A structured questionnaire was administered, collecting data on sociodemographic factors, medical history, self-monitoring behaviors, and informational exposure, alongside medication adherence measured using the Romanian-translated and validated ARMS (Adherence to Refills and Medications Scale). Five machine learning models were developed to predict non-adherence, defined by ARMS quartile-based thresholds. The models included Logistic Regression, Random Forest, and boosting algorithms (CatBoost, LightGBM, and XGBoost). Models were evaluated based on their ability to stratify patients according to adherence risk. Results: A total of 79.13% of respondents had an ARMS Score ≥ 15, indicating a high prevalence of suboptimal adherence. Better adherence was statistically associated (adjusted for age and sex) with more frequent blood pressure self-monitoring, a reduced salt intake, fewer daily supplements, more frequent reading of medication leaflets, and the receipt of specific information from pharmacists. Among the ML models, CatBoost achieved the highest ROC AUC Scores across the non-adherence classifications, although none exceeded 0.75. Conclusions: Several machine learning models were developed and validated to estimate levels of medication non-adherence. While the performance was moderate, the results demonstrate the potential of AI in identifying and stratifying patients by adherence profiles. Notably, to our knowledge, this study represents the first application of permutation and SHapley Additive exPlanations feature importance in combination with probability-based adherence stratification, offering a novel framework for predictive adherence modelling.

The classification of fish species has become an essential task for marine ecologists and biologists for the estimation of large quantities of fish variants in their own environment and also to supervise their population changes. Different conventional classification is expensive, time-consuming, and laborious. Scattering and absorption of light in deep sea atmosphere achieves a very low-resolution image and becomes highly challenging for the recognition and classification of fish variants. Then, the performance rate of existing computer vision methods starts to reduce underwater because of highly indistinct features and background clutter of marine species. The attained classification issues can be resolved using deep structured models, which are highly recommended to enhance the performance rate in fish species classification. But, only a limited amount of fish datasets is available, which makes the system more complex, and also, they need enormous amounts of datasets to perform training. So, it is essential to develop an automated and optimized system to detect, categorize, track, and minimize manual interference in fish species classification. Thus, this paper aims to suggest a new fish species classification model by the optimized recurrent neural network (RNN) and feature fusion. Initially, standard underwater images are acquired from a standard database. Then, the gathered images are pre-processed for cleaning and enhancing the quality of images using “contrast limited adaptive histogram equalization (CLAHE) and histogram equalization”. Then, the deep feature extractions are obtained using DenseNet, MobileNet, ResNet, and VGG16, where the gathered features are given to the new phase optimal feature selection. They are performed with a new heuristic algorithm called “modified mating probability-based water strider algorithm (MMP-WSA)” that attains the optimal features. Further, the optimally selected features are further fed to the feature fusion process, where the feature fusion is carried out using the adaptive fusion concept. Here, the weights are tuned using the designed MMP-WSA. In addition, the fused features are sent to the classification phase, where the classification is performed using developed FishRNFuseNET, in which the parameters of the RNN are tuned by developed MMP-WSA for getting accurate classified outcomes. The proposed method is an effective substitute for time-consuming and strenuous approaches in human identification by professionals, and it turned as a benefit to monitor the biodiversity of fish in their place.