Optimal Selection Process Research Articles

Does basic physician trainee selection predict successful progression in the Royal Australasian College of Physicians adult medicine training programme? A retrospective cohort study.

The optimal selection process for basic physician trainees (BPTs) is unclear. To assess an adult BPT selection process, including a standardised recruitment interview assessing clinical decision-making and situational judgement. A retrospective multi-year cohort study of applicants who were selected for interview during the annual recruitment for 2011-2015 clinical years in an adult basic physician training network in NSW. The predictive capacity of the final recruitment assessment score was compared with success in the Royal Australasian College of Physicians (RACP) adult medicine clinical examination within 2 years. Four hundred thirty-six applicants were selected for interview (84-91 in each year). Summary mean post-interview scores ranged from 1.25 to 10, with the median ranging between 7 and 8. Median scores were higher in those who passed the examination than those who did not (7.75 vs 6.43, P < 0.0001). Those who did not sit for the examination in the relevant year or never sat for the examination had similar median scores (7 vs 6.75, P = 0.11). Those not deemed eligible for recruitment were less likely to pass the clinical examination within 2 years than those on the eligibility list (odds ratio (OR) = 0.38, 95% confidence interval (CI) = 0.25-0.57). Applicants with mean scores lower than 8 were less likely to pass than those in the top band (8.1-10); scores between 6.1 and 8 OR 0.42 (95% CI = 0.26-0.68), 4.1-6 OR 0.23 (95% CI = 0.13-0.41) and 2.1-4 OR 0.24 (95% CI = 0.10-0.60). The predictive capacity of the selection process for success in the RACP clinical examination within the minimum time period is high.

Heuristic Forest Fire Detection Using the Deep Learning Model with Optimized Cluster Head Selection Technique

Disaster prediction systems enable authorities and communities to identify and understand the risks associated with various natural and man‐made disasters. Disaster prediction systems are essential tools for enhancing public safety, reducing the impact of disasters, and enabling more informed and strategic decision‐making across various sectors. Their development and implementation represent a crucial aspect of modern disaster risk management and resilience systems. This novel technique introduces a modern approach to forest fire prediction by integrating a deep learning model with an optimized cluster head selection technique. The major goal is to augment the accuracy and efficiency of forest fire prediction, leveraging the capabilities of advanced machine learning algorithms and optimized sensor network management. The proposed system comprises two core components: a deep learning model for predictive analysis and an optimized selection process for cluster heads in sensor networks. The deep learning model utilizes various environmental data parameters such as humidity, wind speed, temperature and former fire incidents. These parameters are processed through a sophisticated neural network architecture designed to identify patterns and correlations that signify the likelihood of a forest fire. The model is trained on historical data to improve its predictive accuracy, and its performance is continuously evaluated against new data. Simultaneously, the optimized cluster head selection using the cat‐mouse optimization technique plays a crucial role in efficiently managing sensor networks deployed in forests. The integration of these two components results in a robust system capable of predicting forest fires with high precision. The system not only assists in early detection and timely alerts but also contributes to the strategic planning of firefighting and resource allocation efforts. This approach has the prospective to significantly lessen the impact of forest fires, thereby protecting ecosystems and communities.

A General Evaluation System for Optimal Selection Performance of Radar Clutter Model

The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.

Performance Analysis of Multi-Relay Assisted IoT Networks in Mixed Fading Environments

This study delves into the realm of multi-relay assisted Internet of Things (IoT) networks within the context of mixed fading environments. Here, data transmission from the source to the destination is facilitated through a configuration involving multiple decode-and-forward relays. Specifically, our investigation revolves around mixed fading environments characterized by the first-hop relaying links conforming to a uniform distribution, while the second-hop relaying links exhibit Rayleigh fading. To bolster the overall efficacy of the network, we introduce two distinct relay selection criteria. The first criterion entails an optimal selection process hinging on the identification of the most proficient relay. This determination relies upon the evaluation of dual-hop relaying links. In contrast, the second criterion adopts a sub-optimal selection approach by singling out the optimal relay solely based on characteristics associated with the second-hop relaying links. The performance evaluation of the two aforementioned relay selection criteria entails the derivation of analytical expressions governing the system's outage probability. To validate the theoretical underpinnings presented in this research, we supplement our analysis with simulation results. Notably, our findings underscore the efficacy of augmenting network performance by augmenting the number of relays within the network topology.

Automating the Optimal Selection Process of Subassembly Sections of a Modular Spreader Beam Used in Lifting Operations

Spreader beams used in lifting operations undergo a purely compressive load to spread apart the ends of a sling which enables large payloads to be lifted from a single point, such as a crane hook, without damage. A modular spreader beam can be made using subcomponents of different standard sizes to create a spreader beam of any length, making them more versatile and cost-effective than non-modular spreader beams. However, while the manual calculation and selection of an optimum number of subsections for a single beam is straightforward, the process for the multiple range of spreader beam is very challenging and is labour-intensive in a lifting company. The main aim of this study was to develop an automated system for determining the optimal configuration of the modular spreader beam which leads to increasing the efficiency of the lifting company through saving the associated labour and time costs. The automated system is underpinned by designing an algorithm based on a dynamic programming optimisation search to test every possible configuration and return the optimal configuration. Hence, the main novelty in this study is the development of a computer-based system to automate the selection process of the modular beam’s subsections, which generates an optimal package immediately to create different lengths with the fewest sections needed for a lifting operation. Eventually, the process of generating quotation for clients can be significantly accelerated while the risk of human errors can be also eliminated.

Critical Concurrent Feature Selection and Enhanced Heterogeneous Ensemble Learning Approach for Fault Detection in Industrial Processes

Machine learning approaches have been successfully applied to fault detection in complex industrial processes, while it is still challenging to design an optimal feature space and a stable model for various faults. To solve this problem, a hybrid approach with critical concurrent feature selection and enhanced heterogeneous ensemble learning is proposed. First, raw process variables from field instruments and slow features obtained by simple linear transformation are ingeniously combined to construct the concurrent feature space so as to contain static and dynamic information. Then, the critical concurrent feature space of each individual learner is automatically obtained by the optimal selection process, and the feature diversity of ensemble is realized simultaneously. Finally, an enhanced heterogeneous ensemble model is constructed by different optimized individual learners, which effectively improves the classification accuracy and stability in fault detection. The performance of the proposed approach is evaluated in a simulated Tennessee Eastman benchmark and a real-word three-phase flow process. The experimental results illustrate that the performance of each optimized individual learner outperforms classic random forest algorithm. The generalization and stability of the ensemble model are further improved. Compared with traditional classification algorithms, the proposed approach achieves superior performance with accuracy that exceeds 89% for the Tennessee Eastman process and exceeds 88% for the three-phase flow process respectively. Additionally, the selected critical concurrent features indicate that both static and dynamic information play important roles in fault detection of industrial processes.

Selection of the Best Swimming Athletes using MCDM-AHP and VIKOR Methods

The process of selecting the best swimming athletes is carried out in several test stages, the first is the ability in the four basic swimming styles which often contested in international competitions and the second test is the basic physical abilities possessed by a number of swimming athletes. The tests related to the swimming style consist of breaststroke swimming, butterfly swimming, backstroke swimming and crawlstroke swimming, while the plyometrics test consists of banded knee jump, squat jump, jump to side, and dept jump, Due to the large number of selections, a test is required for every athlete. The purpose of this selection is to find the best swimming athletes who will be competed in the international swimming class event. The nine athletes of millenium aquatic swimming club that were selected previously, they are the forerunners of the selected swimming athletes and will be evaluated on a representative basis, which is the best among the nine athletes. The method used in the evaluation and selection process uses two continuous methods, namely the AHP and VIKOR methods. From the results selection assessment, it was found that the best three of the nine nominations selected, the first position selected was AT2 with an index 0.00, the second position was AT8 with an index 0.25 and the third position was AT7 with an index 0.61. Thus it can be concluded that the AHP and VIKOR methods can be used as decision support to determine optimally in the optimal selection process for swimming athletes.

Agent-Based Autonomic Semantic Context-Aware Platform for Smart Health Monitoring and Disease Detection

Abstract Nowadays, mobile applications have been widely used in various health and social domains. While there are large number of smart devices that connect and transmit their context data in free manner, these mobile applications have limitations regarding the increasing number of heterogeneous context data sent by devices, conducting the problem of identifying user-centric situations and providing services in real-time. Many smart mobile health systems were proposed to ensure context-aware personal health monitoring and diseases detection, while most of them are failed to ensure a right level of dynamicity and enough flexibility for assisting users everywhere, anytime, through widespread sensors and mobile devices. In fact, it becomes necessary to rethink a new way to minimize the response time combining competitive agents with semantic-based situation reasoning strategy. In this paper, we introduce a novel agent-based platform with three-layered ontology for the semantic description and parallel management of services selection approach dedicated to context-aware health mobile applications. In addition, we propose an innovative parallel services discovery and optimal selection process, which involved a set of filtered and classified semantic health multipath according to user’s context and preferences with consistent mobility of users and limited resources (low battery, processing capabilities, memory and others). Experimental results show the effectiveness of the proposed approach as it includes the semantic service information on cooperative agents. In addition, the proposed approach ensures fast response time and prolongs the continuity of the mobile application.

The optimized selection strategy of crowdsourcing members in cloud-based design and manufacturing platform

Crowdsourcing services and cloud-based design and manufacturing platforms have been combined into an emerging network service model. Due to the diversity and differences of crowdsourcing members in the platform, member optimized selection process is uncertain and discrete, and it is difficult to find the most satisfying solution. In view of this, this article proposed an optimized selection strategy of crowdsourcing members to help users select reasonable crowdsourcing members to form the cooperative team with the most satisfaction. The research steps in this article are as follows. The first step is to analyze the characteristics of crowdsourcing services and crowdsourcing members in the cloud-based design and manufacturing platform. In the second step, according to the characteristics of crowdsourcing members, the evaluation index and target variables of members in the optimized process are proposed, the optimized selection system is established, and the calculation based on each target variable is given. In the third step, the decision-making model is established based on the optimized selection index system. The model decomposes the task-oriented global optimized selection indexes for crowdsourcing members into subtask-oriented local optimized selection indexes, and the gray relational analysis method is used to solve the model. Finally, the effectiveness of the proposed method is verified by taking the crowdsourcing member optimized selection process in the medical product research and development task as an example.

Effect of process parameters on mechanical properties of VeroBlue material and their optimal selection in PolyJet technology

PolyJet technology is one of the additive manufacturing technologies, which can produce complex geometries with variety of textures. The 3D printed complex geometry parts need better mechanical behaviour. The mechanical properties of a fabricated product depend on several process parameters such as build orientation, layer thickness, material and surface finish. This paper aims to study the effect of printing mode and type of surface finish on the mechanical properties of VeroBlue material used in PolyJet technology. The tensile, flexural and shore hardness tests are carried out to determine the mechanical response of the fabricated specimen. Four different combinations are derived from printing mode (high quality (HQ) and high speed (HS)) and finish type (matte (M) and glossy (G)). Findings indicate the HS-G specimens have better mechanical property and are faster in production and cheaper than HQ-M, HQ-G and HS-M. Highest average tensile strength of the HS-G (49.77 MPa) is deviated by 11.19% from standard value. Tensile specimens of HS-G save 60.86% of printing time and 14.72% of cost than HQ-G. This research paper provides a unique way of meeting optimal selection process parameters. Finally, a case study was carried out for the selected application with optimized process parameters.

Using Microbiological Data Analysis to Tackle Antibiotic Resistance of Klebsiella Pneumoniae.

Optimal antibiotic use for the treatment of nosocomial infections plays a central role in the effort to control the rapidly increasing prevalence of multidrug-resistant bacteria. Antibiotic selection should be based on accurate knowledge of local susceptibility rates. Traditional methods of resistance reporting, which are in routine use by microbiology laboratories could be enhanced by using statistically significant results. We present a method of reporting based on antibiotic susceptibility data analysis which offers an accurate tool that reduces clinician uncertainty and enables optimization of the antibiotic selection process.

An Optimal Online Method of Selecting Source Policies for Reinforcement Learning

Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In this paper, we develop an optimal online method to select source policies for reinforcement learning. This method formulates online source policy selection as a multi-armed bandit problem and augments Q-learning with policy reuse. We provide theoretical guarantees of the optimal selection process and convergence to the optimal policy. In addition, we conduct experiments on a grid-based robot navigation domain to demonstrate its efficiency and robustness by comparing to the state-of-the-art transfer learning method.

A Feature Selection process Optimization in multi-class Miner for Stream Data Classification

Multi-class miner resolves the problem of feature evaluation, data drift and concept evaluation of stream data classification. The process of stream data classification in multi-class miner based on ensemble technique of clustering and classification on feature evaluation technique. The process of feature evaluation technique faced a problem of correct point selection of cluster centre for the process of data grouping. For the proper selection of features point we used optimization technique for feature selection process. The feature selection process based on advance genetic algorithm (AGA). The advance genetic algorithm poses a process of feature point for neighbour class detection for finding a correct point in classification. Our proposed algorithm tested on some well know data set provided by UCI machine learning repository. Our empirical evaluation result shows that better result in comparison of multi-class miner for stream data classification.

Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection

A company must purchase a lot of diverse components and raw material from different upstream suppliers to manufacture or assemble its products. Under this situation the supplier selection has become a critical issue for the purchasing department.The selection of suppliers depends on number of criteria and the challenge is to optimize selection process based on critical criteria and select the best supplier(s). During supplier selection process initial screening of potential suppliers from a large set is vital and the determination of prospective supplier is largely dependent on the criteria chosen of such pre-qualification. In the literature, many judgments based methods are proposed and derived criteria selection from the opinion of either the customers or the experts. All these techniques use the knowledge and experience of the decision makers. These methods inherit certain degree of uncertainty due to complex supply chain structure. The extraction of hidden knowledge is one of the most important tools to address such uncertainty and data mining is one such concept to account for such uncertainty and it has been found applicable in many scenarios. The proposed research aims to introduce a data mining approach, to discover the hidden relationships among the supplier's pre-qualification data with the overall supplier rating that have been derived after observation of previously executed work for a period of time. It provides an overview that how supplier's initial strength influence its final work performance.

AHP-Based Evaluation Model for Optimal Selection Process of Patching Materials for Concrete Repair: Focused on Quantitative Requirements

The process of selecting a repair material is a typical one of multi-criteria decision-making (MCDM) problems. In this study Analytical Hierarch Process was applied to solve this MCDM problem. Many factors affecting a process to select an optimal repair material can be classified into quantitative and qualitative requirements and this study handled only quantitative items. Quantitative requirements in the optimal selection model for repair material were divided into two parts, namely, the required chemical performance and the required physical performance. The former is composed of alkali-resistance, chloride permeability and electrical resistivity. The latter is composed of compressive strength, tensile strength, adhesive strength, drying shrinkage, elasticity and thermal expansion. The result of the study shows that this method is the useful and rational engineering approach in the problem concerning the selection of one out of many candidate repair materials even if this study was limited to repair material only for chloride-deteriorated concrete.

Joint robustness and security enhancement for feature-based image watermarking using invariant feature regions

Local image features have been widely applied in feature-based watermarking schemes. The feature invariance is exploited to achieve robustness against attacks, but the leakage of information about hidden watermarks from publicly known locations and sizes of features are often unconsidered in security. This paper, therefore, proposes a novel image watermarking approach, which adopts invariant feature regions to jointly enhance its robustness and security. Initially, circular feature regions are determined by the scale-adapted auto-correlation matrix and the Laplacian-of-Gaussian operation. Leakage of secret information is also controlled carefully during feature detection procedure. An optimal selection process formulated as a multidimensional knapsack problem is then proposed to select robust non-overlapping regions from those circular feature regions to resist various attacks. This process is implemented by a genetic algorithm-based approach, and incorporates randomization to mitigate the security risk. Finally, each selected region is normalized to obtain a geometrically invariant feature region, and embedded with a region-dependent watermark to overcome the weakness of multiple-redundant watermarks. The evaluation results based on the StirMark benchmark present the proposed scheme can tolerate various attacks, including noise-like signal processing and geometric distortions. A security analysis in terms of differential entropy also confirms the security improvement of the proposed method.

An Analysis of an Optimal Selection Process for Characteristics and Technical Performance of Baseball Pitchers

Selection of a qualified pitcher has relied previously on qualitative indices; here, both quantitative and qualitative indices including pitching statistics, defense, mental skills, experience, and managers' recognition were collected, and an analytic hierarchy process was used to rank baseball pitchers. The participants were 8 experts who ranked characteristics and statistics of 15 baseball pitchers who comprised the first round of potential representatives for the Chinese Taipei National Baseball team. The results indicated a selection rate that was 91% consistent with the official national team roster, as 11 pitchers with the highest scores who were recommended as optimal choices to be official members of the Chinese Tai-pei National Baseball team actually participated in the 2009 Baseball World Cup. An analytic hierarchy can aid in selection of qualified pitchers, depending on situational and practical needs; the method could be extended to other sports and team-selection situations.

Assessing the Assignation of Public Subsidies: Do the Experts Choose the Most Efficient R&amp;D Projects?

The implementation of public programs to support business R&D projects requires the establishment of a selection process. This selection process faces various difficulties, which include the measurement of the impact of the R&D projects as well as selection process optimization among projects with multiple, and sometimes incomparable, performance indicators. To this end, public agencies generally use the peer review method, which, while presenting some advantages, also demonstrates significant drawbacks. Private firms, on the other hand, tend toward more quantitative methods, such as Data Envelopment Analysis (DEA), in their pursuit of R&D investment optimization. In this paper, the performance of a public agency peer review method of project selection is compared with an alternative DEA method.

Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach

Information technology (IT) is a tool crucial for enterprises to achieve a competitive advantage and organizational innovation. A critical aspect of IT management is the decision whereby the best set of IT projects is selected from many competing proposals. The optimal selection process is a significant strategic resource allocation decision that can engage an organization in substantial long-term commitments. However, making such decisions is difficult because there are lots of quantitative and qualitative factors to be considered in evaluation process. This paper has two main contributions. Firstly, it combines two well-established managerial methodologies, balanced scorecard (BSC) and data envelopment analysis (DEA), and proposes a new approach for IT project selection. This approach uses BSC as a comprehensive framework for defining IT projects evaluation criteria and uses DEA as a nonparametric technique for ranking IT projects. Secondly, this paper introduces a new integrated DEA model which identifies most efficient IT project by considering cardinal and ordinal data. Applicability of proposed approach is illustrated by using real world data of Iran Ministry of Science, Research and Technology.

A central limit theorem for the optimal selection process for monotone subsequences of maximum expected length

This article provides a refinement of the main results for the monotone subsequence selection problem, previously obtained by Bruss and Delbaen (Stoch. Proc. Appl. 96 (2001) 313). Let ( N s ) s ⩾ 0 be a Poisson process with intensity 1 defined on the positive half-line. Let T 1 , T 2 , … be the corresponding occurrence times, and let ( X k ) k = 1 , 2 , … be a sequence of i.i.d. uniform random variables on [0,1], independent of the T j 's. We observe the ( T k , X k ) sequentially. Call X k the observed value at time T k . For a given horizon t , consider the objective to select in sequential order, without recall on preceding observations, a subsequence of monotone increasing values of maximal expected length. Let L t t be the random number of selected values under the optimal strategy. Extending the objective of our first paper the main goal of the present paper is to understand the whole process ( L u t ) 0 ⩽ u ⩽ t , where the random variable L u t denotes the number of the selected values up to time u under the t -optimal strategy. We show that this process obeys, under suitable normalization, a Central Limit Theorem. In particular, we show that this holds in a more complete sense than one would expect. The problem of interdependence of this process with two other processes studied before is overcome by the simultaneous study of three associated martingales. This analysis is based on refined martingale methods, and a non-negligible level of technical sophistication seems unavoidable. But then, the results are rewarding. We not only get the “right” functional Central Limit Theorem for t tending to infinity but also the (singular) covariance matrix of the three-dimensional process summarizing the interacting processes. We feel there is no other way to understand these interactions, and believe that this adds value to our approach.