Rank Selection Research Articles

Machine Learning-Based Reconnaissance Bee Colony with Custom AES & AE for Robust IoT Data Security and Transmission

Objectives: To propose a robust method to enhance data security and transmission in IoT to address the challenges in real-time data transmission and create a comprehensive security solution. The new method takes credit for the machine learning-based bioinspired method to minimize data loss and maximize end-to-end data transmission rate. Methods: Custom-AES with Avalanche Effect (AE) and Machine Learning-based Reconnaissance Bee Colony are employed to secure the data with enhanced encryption by creating a multi-layered IoT security solution. Key stretching is used to provide high resistance to cryptographic attacks. In order to optimize threat detection, Pareto-based selection and dynamic parameter tuning with ML-RBC are used. To improve attack resilience, rank, and tournament selection methods are employed to target DDoS and MMIT attacks with a minimized encryption time. The NSL-KDD IoT network dataset is used to test the proposed CAES-AE with the ML-RBC model using a network simulator. To assess the performance of the proposed work, the results are compared with prevailing IoT data transmission techniques such as ERSA, QoS-BFT, AES, Custom AES, and RBC. Findings: The suggested CAES-AE with ML-RBC data security and transmission method attains the promising results of 97% authentication success rate, 6 seconds response time to security actions, 96% vulnerability detection speed, 97% data encryption strength, 5 seconds incident response time, and 97.8% network performance (energy efficiency & lifetime), which is higher than earlier methods. Novelty: This research provides a secured data transmission method in an IoT environment to address the real-time data transmission challenges faced in sensor-based networks. This will function as a multi-layered IoT security model that optimizes real-time attack detection and resilience. The performance metrics are evaluated with multiple packet transfer rates to boost the network performance. Keywords: Machine Learning, Advanced Networking, Data Security, Secured Data Transmission, Bio­Inspired Algorithm

Class-specific feature selection for enhancing explainability in ensemble classification models

AbstractFeature selection techniques aim at finding a relevant subset of features that perform equally or better than the original set of features at explaining the behavior of data. Typically, features are extracted from feature ranking or subset selection techniques, and the performance is measured by classification or regression tasks. However, while selected features may not have equal importance for the task, they do have equal importance for each class. The fundamental idea of the class-specific concept resides in the understanding that the significance of each feature can vary from one class to another. This contrasts with the traditional class-independent approach, which evaluates the importance of attributes collectively for all classes. For example, in tumor prediction scenarios, each type of tumor (class) may be associated with a distinct subset of relevant features. These features possess significant discriminatory power, enabling the differentiation of one tumor type from others (classes). This class-specific perspective offers a more effective approach to classification tasks by recognizing and leveraging the unique characteristics of each class. A novel deep one-versus-each strategy is introduced, which offers advantages from the point of view of explainability (feature selection) and decomposability (classification). In addition, the class-specific relevance matrix is presented, from which some more sophisticated classification schemes can be derived, such as the three-layer class-specific scheme. These schemes have the great advantage to combine independent classification units (e.g., neural networks) that use a reduced number of features to target each class. The potential for further advancements in this area is wide and will open new horizons for exploring novel research directions in interdisciplinary fields, particularly in complex, multiclass hyperdimensional contexts (e.g., in genomics).

Path Algebra-Driven Classification Solution to Realize User-Centric Performance-Oriented Virtual Network Embeddings

The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. This research offers a new evaluation mechanism to successfully replace the aforementioned traditional path ranking and path selection techniques. Specifically, the proposed framework is integrated with optimization-oriented metrics, each indicating a unique aspect of performance for evaluating candidate network paths. The deployed metrics are then algebraically synthesized to provide a distinctive multidimensional description of the examined substrate resources. These primary and composite metrics adhere to the fundamental monotonicity and isotonicity properties of a Path Algebra; hence, the validity and optimality of the proposed evaluation mechanism is guaranteed by design. To tackle the complexity created by the variety of human-centric customization, a novel methodology that analyzes and determines the weighted influence of the synthesized metrics depending on the characteristics of the served user-centric application is also introduced. The chosen suitable weights address performance-oriented mission-critical tailored objectives for adaptive optimizations. Its innovative algebraic design allows it to successfully describe and rank candidate paths in a versatile way, whether in legacy or modern architectures. The experimental data of the first scenario show that 62.5% and 50% of highlighted path evaluations proposed by the shortest path and unidimensional constraint strategies, respectively, suffer from moderate performance-oriented values compared to the proposed framework. Likewise, the results of the second examined scenario reveal that the proposed composite metric yields more suitable path rankings by 50% in contrast to its traditional counterparts, rendering the contested evaluation mechanisms obsolete.

Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity.

This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time-series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes intersubject similarities and differences between two predetermined groups by considering a combination of group spatial information and individual temporal dynamics. Furthermore, it enables the identification of intrasubject similarities and differences over time by employing different model configurations for each subject. Methodologically, the framework combines a novel principal angle-based rank selection algorithm and a noniterative integrative analysis framework. Inspired by simultaneous component analysis, this approach also reconstructs identifiable latent factor series with flexible covariance structures. The performance of the GRIDY models is evaluated through simulations conducted under various scenarios. An application is also presented to compare resting-state functional MRI data collected from multiple subjects in autism spectrum disorder and controlgroups.

HSMAOA: An enhanced arithmetic optimization algorithm with an adaptive hierarchical structure for its solution analysis and application in optimization problems

The Arithmetic Optimization Algorithm (AOA) has recently gained significant attention as a novel meta-heuristic algorithm. However, it faces challenges such as premature convergence and entrapment in local optima when addressing complex optimization problems. To overcome these limitations, this paper proposes an enhanced AOA, termed the Self-Adaptive Hierarchical Arithmetic Optimization Algorithm (HSMAOA). The proposed method integrates three key strategies: Firstly, a spiral-guided random walk mechanism is introduced to improve global search ability. Secondly, a novel adaptive hierarchy leader and follower mechanism is proposed, which establishes a complete multi-branch tree hierarchy with decreasing branching degrees within the population, thereby increasing information exchange among population individuals to escape local optima. Finally, a differential mutation strategy based on ranked selection is introduced to enhance candidate solution quality. HSMAOAʼs performance was evaluated on the CEC2022 test suite against some state-of-the-art algorithms. Results, including optimization accuracy analysis, convergence curves, and various statistical tests, demonstrate HSMAOAʼs superior optimization capability and robustness. In addition, tests on eight engineering structure optimization problems, including the pressure vessel design problem, the multiple disk clutch brake design problem, and the step-cone pulley problem, and so forth, further validate its effectiveness. Thus, HSMAOA shows strong competitiveness in complex optimization tasks and potential for a wide range of applications, and is an advantageous and promising alternative solution for optimization problems.

What Military Applicants Value When Choosing a Residency in Gynecologic Surgery and Obstetrics.

Military medical students participate in a separate residency match program, distinct from their civilian counterparts. There is limited information regarding factors that applicants find important when selecting a residency program in Gynecologic Surgery and Obstetrics (GSO). We aimed to identify factors that influence applicant program selection. A voluntary, anonymous, 45-question survey was distributed to military candidates pursuing GSO residency training in the fall of 2023. Participants were asked to rate the factors they considered most important when ranking residency programs. Thirty-seven of the sixty (61.7%) applicants completed the survey. The culture of the residency program was deemed "important" by most participants (94.6%), followed by surgical volume (70.3%) and obstetric volume (70.3%). When asked to rank selection factors, the culture of the residency program, geographic location, and surgical volume were among the top 5 influential factors for the majority of applicants. Participants considered the following factors "not important": Salary (military pay versus civilian salary) (43.2%), research opportunities (32.4%), and availability of abortion training (21.6%). Most military applicants (62.2%) stated that abortion restrictions would not affect program selection, while 37.8% reported that this would affect their rankings. Gynecologic Surgery and Obstetrics military applicants deemed the residency program's culture, surgical volume, and obstetrical volume to be the most important factors impacting residency program selection. Residency programs can use these findings to promote program strengths, improve program shortfalls, and guide applicant recruitment.

An efficient detection method for infrared dim small target via totally factor group-sparse framework

Many infrared dim small target detection models based on the low-rank and sparse decomposition (LRSD) achieve satisfactory performance. However, rank surrogate approximations in norm models frequently require complex singular value decomposition (SVD) iterations that are computationally expensive. Factorization models may occur performance degradation impacted by inappropriate rank selection. This paper aims to balance efficiency and accuracy through the factor group-sparse framework. Inspired by the factored factorization of Schatten p-norm, a bridge between the norm and factorization models is established. It enables the model to avoid complex SVD iterations and appropriate rank selection. Furthermore, this paper defines a multi-directional weighted L1 norm to characterize targets, which enhances the suppression of target-like sparse noise. Then, a proximal alternating minimization (PAM)-based optimization framework is used to solve the proposed model. Finally, comparative experiments on three public datasets demonstrate that the proposed model outperforms other state-of-the-art low-rank methods in terms of efficiency and accuracy.

Importance estimate of features via analysis of their weight and gradient profile

Understanding what is important and redundant within data can improve the modelling process of neural networks by reducing unnecessary model complexity, training time and memory storage. This information is however not always priorly available nor trivial to obtain from neural networks. There are existing feature selection methods which utilise the internal working of a neural network for selection, however further analysis and interpretation of the input features’ significance is often limiting. We propose an approach that offers an extension that estimates the significance of features by analysing the gradient descent of a pairwise layer within a model. The changes that occur with the weights and gradients throughout training provide a profile that can be used to better understand the importance hierarchy between the features for ranking and feature selection. Additionally, this method is transferable to existing fully or partially trained models, which is beneficial for understanding existing or active models. The proposed approach is demonstrated empirically with a study which uses benchmark datasets from libraries such as MNIST and scikit-feat, as well as a simulated dataset and an applied real world dataset. This is verified with the ground truth where available, and if not, via a comparison of fundamental feature selection methods, which includes existing statistical based and embedded neural network based feature selection methods through the methodology of Reduce and Retrain.

A fix-propagate-repair heuristic for mixed integer programming

AbstractWe describe a diving heuristic framework based on constraint propagation for mixed integer linear programs. The proposed approach is an extension of the common fix-and-propagate scheme, with the addition of solution repairing after each step. The repair logic is loosely based on the WalkSAT strategy for boolean satisfiability. Different strategies for variable ranking and value selection, as well as other options, yield different diving heuristics. The overall method is relatively inexpensive, as it is basically LP-free: the full linear programming relaxation is solved only at the beginning (and only for the ranking strategies that make use of it), while additional, typically much smaller, LPs are only used to compute values for the continuous variables (if any), once at the bottom of a dive. While individual strategies are not very robust in finding feasible solutions on a heterogeneous testbed, a portfolio approach proved quite effective. In particular, it could consistently find feasible solutions in 189 out of 240 instances from the public MIPLIB 2017 benchmark testbed, in a matter of a few seconds of runtime. The framework has also been implemented inside the commercial MIP solver Xpress and shown to give a small performance improvement in time to optimality on a large internal heterogeneous testbed.

Ranking and Contextual Selection

Context-Sensitive Simulation-Based Decisions When There Is No Time to Simulate Stochastic simulation is a powerful tool for discovering system design decisions that are the best possible (optimal) when averaged over real-world uncertainty. However, in applications such as personalized medicine and web content optimization, even better decisions can be made if they are tailored to specific, contemporaneous covariate information, such as patient health history and user reading habits. Unfortunately, in these and similar applications, there is no time to perform a refined simulation optimization. In “Ranking and Contextual Selection,” Keslin, Nelson, Pagnoncelli, Plumlee, and Rahimian use off-the-shelf simulation optimization methods to create a database of covariates and associated decisions that form a covariate-to-decision classifier and an upper confidence bound on its optimality gap when applied to covariates not in the database. A realistic example of web page assortment optimization is presented using a data set from Yahoo!.

FuBay: An Integrated Fusion Framework for Hyperspectral Super-Resolution Based on Bayesian Tensor Ring.

Fusion with corresponding finer-resolution images has been a promising way to enhance hyperspectral images (HSIs) spatially. Recently, low-rank tensor-based methods have shown advantages compared with other kind of ones. However, these current methods either relent to blind manual selection of latent tensor rank, whereas the prior knowledge about tensor rank is surprisingly limited, or resort to regularization to make the role of low rankness without exploration on the underlying low-dimensional factors, both of which are leaving the computational burden of parameter tuning. To address that, a novel Bayesian sparse learning-based tensor ring (TR) fusion model is proposed, named as FuBay. Through specifying hierarchical sprasity-inducing prior distribution, the proposed method becomes the first fully Bayesian probabilistic tensor framework for hyperspectral fusion. With the relationship between component sparseness and the corresponding hyperprior parameter being well studied, a component pruning part is established to asymptotically approaching true latent rank. Furthermore, a variational inference (VI)-based algorithm is derived to learn the posterior of TR factors, circumventing nonconvex optimization that bothers the most tensor decomposition-based fusion methods. As a Bayesian learning methods, our model is characterized to be parameter tuning-free. Finally, extensive experiments demonstrate its superior performance when compared with state-of-the-art methods.

Low-rank sparse fully-connected tensor network for tensor completion

Fully-connected tensor network (FCTN) has recently drawn lots of attention in tensor completion due to its full description of all correlations between any two modes. However, the FCTN model has multiple ranks, and existing methods often ignore the difficulty brought about by rank selection, especially when the model is rank-sensitive. To overcome this drawback, this paper proposed a low-rank sparse FCTN for tensor completion. Specifically, we theoretically show that, for a tensor with FCTN structure, its subtensor can be represented as a coefficient sum of basic tensors, where the coefficients are remained in the FCTN’s factors. This means that factors’ sparsity can improve the robustness of FCTN to larger rank selection. Moreover, according to the relation between the target tensor and its FCTN’s factors, low-rank constrain is used to enhance rank robustness. Lastly, we optimize the proposed model by the alternating direction method of multipliers (ADMM) algorithm. Experimental results show that the low-rank sparse constraint effectively improves the rank robustness of the FCTN model, and achieves excellent results compared with other state-of-the-art completion methods.

CT-based whole lung radiomics nomogram for identification of PRISm from non-COPD subjects

BackgroundPreserved Ratio Impaired Spirometry (PRISm) is considered to be a precursor of chronic obstructive pulmonary disease. Radiomics nomogram can effectively identify the PRISm subjects from non-COPD subjects, especially when during large-scale CT lung cancer screening.MethodsTotally 1481 participants (864, 370 and 247 in training, internal validation, and external validation cohorts, respectively) were included. Whole lung on thin-section computed tomography (CT) was segmented with a fully automated segmentation algorithm. PyRadiomics was adopted for extracting radiomics features. Clinical features were also obtained. Moreover, Spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking and least absolute shrinkage and selection operator (LASSO) classifier were adopted to analyze whether radiomics features could be used to build radiomics signatures. A nomogram that incorporated clinical features and radiomics signature was constructed through multivariable logistic regression. Last, calibration, discrimination and clinical usefulness were analyzed using validation cohorts.ResultsThe radiomics signature, which included 14 stable features, was related to PRISm of training and validation cohorts (p < 0.001). The radiomics nomogram incorporating independent predicting factors (radiomics signature, age, BMI, and gender) well discriminated PRISm from non-COPD subjects compared with clinical model or radiomics signature alone for training cohort (AUC 0.787 vs. 0.675 vs. 0.778), internal (AUC 0.773 vs. 0.682 vs. 0.767) and external validation cohorts (AUC 0.702 vs. 0.610 vs. 0.699). Decision curve analysis suggested that our constructed radiomics nomogram outperformed clinical model.ConclusionsThe CT-based whole lung radiomics nomogram could identify PRISm to help decision-making in clinic.

Homotopy properties of the complex of frames of a unitary space

AbstractLet be a finite‐dimensional vector space equipped with a nondegenerate Hermitian form over a field . Let be the graph with vertex set the one‐dimensional nondegenerate subspaces of and adjacency relation given by orthogonality. We give a complete description of when is connected in terms of the dimension of and the size of the ground field . Furthermore, we prove that if , then the clique complex of is simply connected. For finite fields , we also compute the eigenvalues of the adjacency matrix of . Then, by Garland's method, we conclude that for all , where is a field of characteristic 0, provided that . Under these assumptions, we deduce that the barycentric subdivision of deformation retracts to the order complex of the certain rank selection of that is Cohen–Macaulay over . Finally, we apply our results to the Quillen poset of elementary abelian ‐subgroups of a finite group and to the study of geometric properties of the poset of nondegenerate subspaces of and the poset of orthogonal decompositions of .

Fast audio signal denoising with low-rank approximation using windowed singular value decomposition of frequency division

The method of constructing Hankel matrices and using low-rank approximation has been demonstrated to be an effective approach to audio denoising. However, the significant computational complexity and the trade-off between the denoising quality and the loss of effective signal remain open issues. This paper proposes a denoising method based on frequency-divided Windowed Singular Value Decomposition (WSVD) and exploits the low-rank characteristics and frequency features commonly found in audio signals including speech and music recordings. The method incorporates an improved Lanczos Bidiagonalization algorithm to accelerate the singular value decomposition with low error and high tolerance. Furthermore, techniques are added at the window junctions to maintain the continuity and smoothness of the final audio, thus achieving denoising efficiently and effectively. This paper also assesses the influence of window segmentation length, main frequency domain characteristics, rank selection of Hankel matrix and characteristics of different noises on the final denoising effect. Finally, the denoising algorithm's robustness and effectiveness are validated through simulations and experiments.

Hybrid Tasmanian Devil and Elephant Herding Model-Based Optimal Cluster Head Selection and Routing in MANET

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes.

Explainable Authorship Identification in Cultural Heritage Applications

While a substantial amount of work has recently been devoted to improving the accuracy of computational Authorship Identification (AId) systems for textual data, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions. This substantially hinders the practical application of AId methods, since the predictions returned by such systems are hardly useful unless they are supported by suitable explanations. In this article, we explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId, with a focus on explanations addressed to scholars working in cultural heritage. In particular, we assess the relative merits of three different types of XAI techniques (feature ranking, probing, factual and counterfactual selection) on three different AId tasks (authorship attribution, authorship verification and same-authorship verification) by running experiments on real AId textual data. Our analysis shows that, while these techniques make important first steps towards XAI, more work remains to be done to provide tools that can be profitably integrated into the workflows of scholars.

The need for “Considered Estimation” versus “Conservative Estimation” when ranking or comparing predictors of job performance

AbstractA recent attempt to generate an updated ranking for the operational validity of 25 selection procedures, using a process labeled “conservative estimation” (Sackett et al., 2022), is flawed and misleading. When conservative estimation's treatment of range restriction (RR) is used, it is unclear if reported validity differences among predictors reflect (i) true differences, (ii) differential degrees of RR (different u values), (iii) differential correction for RR (no RR correction vs. RR correction), or (iv) some combination of these factors. We demonstrate that this creates bias and introduces confounds when ranking (or comparing) selection procedures. Second, the list of selection procedures being directly compared includes both predictor methods and predictor constructs, in spite of the substantial effect construct saturation has on validity estimates (e.g., Arthur &amp; Villado, 2008). This causes additional confounds that cloud comparative interpretations. Based on these, and other, concerns we outline an alternative, “considered estimation” strategy when comparing predictors of job performance. Basic tenets include using RR corrections in the same manner for all predictors, parsing validities of selection methods by constructs, applying the logic beyond validities (e.g., ds), thoughtful reconsideration of prior meta‐analyses, considering sensitivity analyses, and accounting for nonindependence across studies.

Enhancement of Harris Hawks optimization applied in path planning for an indoor navigation mobile application

The research focuses on the enhancement of Harris Hawks Optimization (HHO) algorithm in achieving an efficient path planning, specifically in indoor environments. It solves problems encountered in HHO; firstly, the exploration operator of HHO is modified to incorporate the survival of the fittest principle, this ensures a controlled diversity by using an Exponential Ranking selection method. The method aims to guide the algorithm to find a more optimal solution while allowing it to search for alternative paths. Secondly, Linear Path Strategy is used to reduce the number of nodes in the paths, therefore minimizing its length and simplifying trajectories. In addition, Linear Path Strategy aims to create smoother paths and avoid obstacles, improving the overall performance of the algorithm. Lastly, general multi-objective formula is defined to evaluate path length and travel time. Considering these factors established a balanced evaluation metric, which provided a detailed assessment of path quality for scenarios like indoor navigation. Comparative analysis was done, and results highlighted the effectiveness of EHHO in generating better paths, in comparison with Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), and with the current HHO algorithm. It outperformed the algorithms compared in terms of path length, travel time, and efficiency of the execution, especially in a complex environment. In conclusion, EHHO algorithm showed promising results in providing shorter, faster, and more efficient routes, with potential applications across different domains that requires optimal path planning solutions.

Cross-validation to select the optimum rank for a reduced-rank approximation to multivariate data

ABSTRACT In this paper we consider the Gabriel form of cross-validation (CV) and we investigate how to estimate the optimum rank for lower rank approximations of any dataset that can be written in matrix form, with particular application in multivariate analysis and in the analysis of multienvironment trials. The literature related to the method suggests that it can produce overfitting and poor-quality predictions, characteristics that result in overestimation of the rank. Because of this, it is proposed to change the rank selection criterion, testing thirteen statistics both in the original method and in four proposed extensions that seek to solve the above problems. A comparison is made with two gold standard methods for CV through a simulation study and through the analysis of seventeen real datasets, two of which are general multivariate and fifteen are from experiments with genotype-by-environment interaction. It is concluded that from a predictive point of view, the highest accuracy in estimating the rank is obtained by using a regularized singular value decomposition.