Selection Algorithm Research Articles

A Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption

Existing methods for optimizing the LLVM compiler face challenge in quickly obtaining high-quality solutions due to the large and discrete optimization space of option sequences, time-consuming energy consumption evaluation, and complex interactions among options. To address these challenges, we present a Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption (TSOMFEC). In the first stage, we propose an Option Selection algorithm based on Pattern Mining (OSPM). OSPM integrates a pattern mining algorithm and CRC algorithm to precisely preselect options, with the goal of reducing the search space of option sequences while guaranteeing optimization quality. In the second stage, we present an Option Sequence Evolutionary Algorithm (OSOA). OSOA encodes options and their positions in the sequence as individual items and then constructs an item interaction graph to capture complex interactions among different items. The item interaction graph is utilized in a genetic algorithm to reduce actual energy consumption evaluations and rapidly obtain high-quality option sequences. We evaluate the performance of TSOMFEC by comparing it with four state-of-the-art methods across eight typical embedded programs in terms of solution quality and optimization time. Furthermore, we validate the effectiveness of both OSPM and item interaction graph. The experimental results demonstrate that TSOMFEC not only achieves the best solution quality but also spends the least optimization time. It obtains average energy consumption improvement percentages relative to the -O3 level of 0.075, along with average optimization time of 3178 s. Moreover, the effectiveness of both OSPM and the item interaction graph has been validated.

Key nodes of misinformation source inference: A message-passing-based approach

The misinformation spreading in social networks causes unpredictable damage to the networked system, thus inferring the misinformation source is an important research topic in the field of network science and security. Many source inference algorithms have been proposed to find the most likely propagation source through observable snapshot. However, under limited observable conditions, observing different nodes states markedly affects the algorithm’s effectiveness. Yet, we still lack relevant research on which nodes can more accurately assist us in completing source inference. Here, we propose the heuristic message-passing-based algorithm to find the key nodes that can maximize the accuracy of source inference, which uses the average rank of the source in the message-passing method as a measure and performs continuous annealing on this basis to update the set. As a comparison, we propose random selection algorithm as the basic, high-eigenvalue algorithm and high-degree algorithm focused on centrality, and basic message-passing-based algorithm from the perspective of energy entropy in message passing. Through extensive numerical simulation on artificial and real-world networks, compared with other four algorithms, our heuristic message-passing-based algorithm finds the optimal key node set that can more accurately complete source inference. Moreover, it has over 8% higher inference accuracy than other methods in low visibility situations especially.

A Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a radiomics framework for preoperative MRI-based prediction of IDH mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using Random Forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive (TCIA, 227 patients), the University of California San Francisco Preoperative Diffuse Glioma MRI dataset (UCSF, 495 patients), and the Erasmus Glioma Database (EGD, 456 patients)) and internal datasets collected from UT Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best-performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best-performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Published under a CC BY 4.0 license.

Wear particle image analysis: feature extraction, selection and classification by deep and machine learning

Purpose This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis. Design/methodology/approach Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families. Findings From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy. Originality/value The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

Cognitive pairwise comparison forward feature selection with deep learning for astronomical object classification with sloan digital sky survey

This paper proposes a hybrid approach integrating the expert knowledge judgment approach using the Cognitive Pairwise Comparison (CPC) to the Deep Learning, a modern classification approach, for astronomic object classification. The astronomic data with ten thousand samples retrieved from Sloan Digital Sky Survey Sky Server Data Release 15 (SDSS SkyServer DR 15) are used for this study. The CPC is an approach to elicit and encode expert knowledge in the format of a Pairwise Opposite Matrix (POM) to evaluate expert preferences for the features. A forward feature selection algorithm taking the expert choices using CPC for the ordered features is used for the feature selection for the deep learning algorithm to build a heuristic training model based on the astronomic data. Whilst the accuracy of the case of improper feature selection is just 37.1%, the proposed hybrid approach can obtain a very high accuracy of 97.9% for the classification of the astronomic object using the eight scaled features (u, g, r, i, z redshift, ra, dec). To extend this research, the proposed CPC can be used as a human-centered tool to be applied to other areas of data sciences.

Features of development of mathematical model electric motorcycle thermostating system

BACKGROUND: To maintain the required temperature range of electrical components of electric vehicles, it is necessary to use thermostating systems. The developing mathematical model of the temperature control system allows, even at the design stage of the vehicle, to determine the necessary parameters of the temperature control system, select algorithms for controlling the system, and calibrate the developed algorithm to ensure the necessary heat removal from electrical components. AIMS: Creation of a mathematical model of the electric motorcycle thermostating system, aimed at determining the potential possibilities of optimizing the system by organizing the air flow through the radiator. METHODS: The mathematical model is implemented in the AVL Cruise software environment. The characteristics of the components included in the model were obtained as a result of laboratory studies and supplemented with power equilibrium equations at the inputs and outputs, taking into account their efficiency. RESULTS: Using the developed model of the radiator of the thermostating system, the temperature graphs of the electric motor and inverter of the electric motorcycle during the driving cycle were obtained. It is noted that the applied temperature control system allows to maintain the temperature of electric components in the specified range. CONCLUSIONS: The mathematical model of the radiator of the thermostating system proposed in this paper allows to carry out calculations of the system at the stage of design of electric vehicles. The model also allows for further development in order to more fully determine the parameters of the thermostating system, including the thermal efficiency.

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

Selection of relay nodes in Bluetooth Mesh network for smart home application

Bluetooth mesh (BM) is increasingly growing and appearing in Internet of Things (IoT) systems. BM uses a managed flooding mechanism to transmit the message. Nodes with a relay feature will receive and forward the message to neighboring nodes, so that the message can be spread to the whole network. The default configuration of BM is that all nodes are relays, ensuring that every node in the network can connect to each other. However, when all nodes act as relays, it results in a lot of messages being transmitted through the network at a time, which increases the possibility of packet collisions, thereby reducing the packet delivery rate (PDR) and increasing the end-to-end delay (E2E delay). These issues will greatly affect the user experience, especially in smart home systems where users interact near the device. Therefore, it is necessary to choose the relay nodes in the network appropriately to improve the quality of the network. This paper will select and analyze some relay node selection algorithms in the BM network applied in the smart home system, and compare them with the default configuration of the network (all relays) in terms of PDR and E2E delay criteria.

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS’s performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS’s positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites’ signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC’s performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy.

Enhanced CH selection and energy efficient routing algorithm for WSN

Enhanced CH selection and energy efficient routing algorithm for WSN

Wi‐Fi fingerprint‐based indoor location: Multiple fingerprints and multiple classifiers with two‐layer fusion weights

SummaryIndoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM.

An Analytics of Sleep Apnea Classification using Caswideresnet Algorithm

Sleep apnea is a common yet serious sleep disorder that affects millions of individuals worldwide. Timely and accurate detection of sleep apnea can significantly improve patient outcomes and quality of life. In this study, we propose an advanced approach for sleep apnea classification and prediction using the CasWideResNet algorithm. Our methodology includes preprocessing steps such as Z-score normalization to standardize the data and improve algorithm performance. Additionally, we employ an improved Pan-Tompkins algorithm for feature selection, which helps in identifying relevant features from physiological signals such as electrocardiogram (ECG) and oxygen saturation (SpO2) data. The CasWideResNet algorithm, known for its deep learning capabilities, is utilized for the classification and prediction tasks. This algorithm leverages the power of deep convolutional neural networks (CNNs) to automatically learn discriminative features from the input data and make accurate predictions. By integrating Z-score normalization, improved feature selection, and the CasWideResNet algorithm, our proposed system aims to achieve high accuracy in detecting and predicting sleep apnea episodes. We evaluate the performance of our approach using a comprehensive dataset containing a diverse range of sleep apnea cases. Our results demonstrate the effectiveness of Z-score normalization in improving data quality and the role of the improved Pan-Tompkins algorithm in selecting informative features.

A secured cloud‐medical data sharing with A‐BRSA and Salp ‐Ant Lion Optimisation Algorithm

AbstractSharing medical data among healthcare providers, researchers, and patients is crucial for efficient healthcare services. Cloud‐assisted smart healthcare (s‐healthcare) systems have made it easier to store EHRs effectively. However, the traditional encryption algorithms used to secure this data can be vulnerable to attacks if the encryption key is compromised, posing a security threat. A secured cloud‐based medical data‐sharing system is proposed using a hybrid encryption model called A‐BRSA, which combines attribute‐based encryption (ABE) and B‐RSA encryption. The system utilises the Salp‐Ant Lion Optimisation Algorithm for optimal key selection. The encrypted data is stored in the cloud and transmitted to the recipient, where it is decrypted using A‐BRSA‐based decryption. The study measures turnaround time, encryption time, decryption time, and restoration efficiency to evaluate the system's performance. The results demonstrate the effectiveness of the A‐BRSA model in ensuring secure medical data sharing in cloud‐based s‐healthcare systems.

The role and novel use of natural killer cells in graft-versus-leukemia reactions after allogeneic transplantation

Allogeneic hematopoietic cell transplantation (HCT) has transformed over the past several decades through enhanced supportive care, reduced intensity conditioning (RIC), improved human leukocyte antigen (HLA) typing, and novel graft-versus-host disease (GVHD)-prevention and treatment strategies. Most notably, the implementation of post-transplantation cyclophosphamide (PTCy) has dramatically increased the safety and availability of this life-saving therapy. Given reductions in nonrelapse mortality (NRM) with these advances, the HCT community has placed even greater emphasis on developing ways to reduce relapse - the leading cause of death after HCT. When using RIC HCT, protection from relapse relies predominantly on graft-versus-leukemia (GVL) reactions. Donor lymphocyte infusion (DLI), adoptive cellular therapy, checkpoint inhibition, and post-HCT maintenance strategies represent approaches under study that aim to augment or synergize with the GVL effects of HCT. Optimizing donor selection algorithms to leverage GVL represents another active area of research. Many of these strategies seek to harness the effects of T cells, which for decades were felt to be the primary mediators of GVL and the focus of investigation in relapse reduction. However, there is growing interest in capitalizing on the ability of natural killer (NK) cells to yield potent anti-tumor effects. A potential advantage of NK cell-based approaches over T cell-mediated is the potential to reduce NRM in addition to relapse. By decreasing infection, without increasing the risk of GVHD, NK cells may mitigate NRM, while still yielding relapse reduction through identification and clearance of cancer cells. Most T cell-focused relapse-prevention strategies must weigh the benefits of relapse reduction against the increased risk of NRM from GVHD. In contrast, NK cells have the potential to reduce both, potentially tipping the scales significantly in favor of survival. Here, we will review the role of NK cells in GVL, optimization of NK cell match or mismatch, and burgeoning areas of research in NK cell therapy such as adoptive transfer and chimeric antigen receptor (CAR) NK cells.

Retraction Note: Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism

Retraction Note: Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions

ObjectiveThe aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics.MethodsA systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated.ResultsThis study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83–0.92) and 0.79 (95% CI 0.72–0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1–5.7), 0.15 (95% CI: 0.10–0.22), and 29.0 (95% CI 15–55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87–0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes.ConclusionMRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients’ medical outcomes and quality of life.

An Improved Northern Goshawk Optimization Algorithm for Feature Selection

An Improved Northern Goshawk Optimization Algorithm for Feature Selection

A differential evolution framework based on the fluid model for feature selection

Feature selection in machine learning is a crucial step to effectively address the issue of feature redundancy in classification problems. Numerous feature selection algorithms have been developed to minimize the number of features, reduce computational cost, and improve classification accuracy. Differential evolution algorithms have the advantage of being simple in structure, robust, fast in convergence, and frequently used to solve feature selection problems. However, it is worth noting that differential evolution algorithms are susceptible to local optimum and stagnation issues, particularly when applied to high-dimensional data. To address this issue, in this study, we propose a differential evolution framework based on the fluid model, named DEF-FM, for feature selection. DEF-FM has the capability to speed up the convergence of differential evolution algorithms and alleviate the effects of local optima. The proposed framework is validated and compared against eight popular differential evolution algorithms using 12 publicly available benchmark datasets and experimental results unequivocally demonstrate the superiority of the proposed framework.

An Improved Genetic Method for Satellite Selection in Multi-Global Navigation Satellite System Positioning for Mobile Robots

An Improved Genetic Method for Satellite Selection in Multi-Global Navigation Satellite System Positioning for Mobile Robots

Dynamic Scheduling and Power Allocation with Random Arrival Rates in Dense User-Centric Scalable Cell-Free MIMO Networks

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency.