Sorting Algorithm Research Articles

Healthy Kai (Food) Checker Web-Based Tool to Support Healthy Food Policy Implementation: Development and Usability Study

Background Public health programs and policies can positively influence food environments. In 2016, a voluntary National Healthy Food and Drink Policy was released in New Zealand to improve the healthiness of food and drinks for hospital staff and visitors. However, no resources were developed to support policy implementation. Objective This study aimed to design, develop, and test a new web-based tool to support food providers implementing the National Healthy Food and Drink Policy in New Zealand. Methods The Double Diamond model, a structured framework with 4 design phases, was used to design and develop a web-based tool. Findings from our previous research, such as (1) systematic review of barriers and facilitators to workplace healthy food policy implementation; (2) scoping review of current tools and resources available in New Zealand, Australia, and Canada; (3) interviews with food providers and public health nutrition professionals; and (4) food and drink availability audit results in New Zealand hospitals were used in the “Discover” (understanding of current gaps) and “Define” (prioritizing functions and features) phases. Subsequent phases focused on generating ideas, creating prototypes, and testing a new web-based tool using Figma, a prototyping tool. During the “Develop” phase, project stakeholders (11 public health nutrition professionals) provided feedback on the basic content outline of the initial low-fidelity prototype. In the final “Deliver” phase, a high-fidelity prototype resembling the appearance and functionality of the final tool was tested with 3 end users (public health nutrition professionals) through interactive interviews, and user suggestions were incorporated to improve the tool. Results A new digital tool, Healthy Kai (Food) Checker—a searchable database of packaged food and drink products that classifies items according to the Policy’s nutritional criteria—was identified as a key tool to support Policy implementation. Of 18 potential functions and features, 11 were prioritized by the study team, including basic and advanced searches for products, sorting list options, the ability to compile a list of selected products, a means to report products missing from the database, and ability to use on different devices. Feedback from interview participants was that the tool was easy to use, was logical to navigate, and had an appealing color scheme. Suggested visual and usability improvements included ensuring that images represented the diverse New Zealand population, reducing unnecessary clickable elements, adding information about the free registration option, and including more frequently asked questions. Conclusions Comprehensive research informed the development of a new digital tool to support implementation of the National Healthy Food and Drink Policy. Testing with end users identified features that would further enhance the tool’s acceptability and usability. Incorporation of more functions and extending the database to include products classified according to the healthy school lunches program policy in the same database would increase the tool’s utility.

A reliable unmanned aerial vehicle multi-ship tracking method.

As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively. To mitigate the impact of limited ship data on model training, transfer learning techniques are employed to enhance the YOLOv7 model's performance. Additionally, the integration of the SimAM attention mechanism within the YOLOv7 detection model improves feature representation by emphasizing salient features and suppressing irrelevant information, thereby boosting detection capabilities. The inclusion of the partial convolution (PConv) module further enhances the detection of irregularly shaped or partially occluded targets. This module minimizes the influence of invalid regions during feature extraction, resulting in more accurate and stable features. The implementation of PConv not only improves detection accuracy and speed but also reduces the model's parameters and computational demands, making it more suitable for deployment on computationally constrained UAV platforms. Furthermore, to address issues of false negatives during clustering in the Deep SORT algorithm, the IOU metric is replaced with the DIOU metric at the matching stage. This adjustment enhances the matching of unlinked tracks with detected objects, reducing missed detections and improving the accuracy of target tracking. Compared to the original YOLOv7+Deep SORT model, which achieved an MOTA of 58.4% and an MOTP of 78.9%, the enhanced system achieves a MOTA of 65.3% and a MOTP of 81.9%. This represents an increase of 6.9% in MOTA and 3.0% in MOTP. After extensive evaluation and analysis, the system has demonstrated robust performance in ship monitoring scenarios, offering valuable insights and serving as a critical reference for ship surveillance tasks.

Bayesian Learning Based Elitist Nondominated Sorting Algorithm for a Kind of Multi-objective Integrated Production Scheduling and Transportation Problem

This paper focuses on a kind of multi-objective integrated production scheduling and transportation problem (MIPSTP), which is encountered in many real-life industrial processes. MIPSTP contains a production stage for processing products and a transportation stage for transporting products. In MIPSTP, we consider a dedicated integration of distributed scheduling and transportation regarding the two stages, arising from a practical project for a cooperative iron and steel company. The objective of MIPSTP is to minimize the total completion time of each factory and total transportation cost. To solve the problem, a Bayesian learning-based elitist nondominated sorting algorithm (BLENSA) is proposed. The primary highlights of this work are two-fold: 1) new solution structures of MIPSTP and 2) novel search model of BLENSA. For the solution structures, we propose for the first time the mathematical description of MIPSTP, accounting for several features in applications and so is different from conventional scheduling problems. For the search model of BLENSA, we propose a Bayesian learning-based probability model to learn valuable knowledge about nondominated solutions. Thereby, BLENSA combines the Bayesian learning-based probability model to produce a candidate population (CP) and the nondominated sorting method to generate a main population (MP). Next, we propose a greedy competition strategy to construct MP from CP and MP for the next generation. Results of experiments on 57 test instances and a real-life case study demonstrate the effectiveness and practical values of BLENSA.

Evolutionary architecture search for generative adversarial networks using an aging mechanism-based strategy

Generative Adversarial Networks (GANs) have emerged as a key technology in artificial intelligence, especially in image generation. However, traditionally hand-designed GAN architectures often face significant training stability challenges, which are effectively addressed by our Evolutionary Neural Architecture Search (ENAS) algorithm for GANs, named EAMGAN. This one-shot model automates the design of GAN architectures and employs an Operation Importance Metric (OIM) to enhance training stability. It also incorporates an aging mechanism to optimize the selection process during architecture search. Additionally, the use of a non-dominated sorting algorithm ensures the generation of Pareto-optimal solutions, promoting diversity and preventing premature convergence. We evaluated our method on benchmark datasets, and the results demonstrate that EAMGAN is highly competitive in terms of efficiency and performance. Our method identified an architecture achieving Inception Scores (IS) of 8.83±0.13 and Fréchet Inception Distance (FID) of 9.55 on CIFAR-10 with only 0.66 GPU days. Results on the STL-10, CIFAR-100, and ImageNet32 datasets further demonstrate the robust portability of our architecture.

ExpoSort : Breaking the quasi-polynomial-time barrier for reluctant sorting

We introduce the algorithm ExpoSort, a groundbreaking method that sorts an array of n numbers in a spectacularly inefficient Θ(2 n ) time. ExpoSort proudly claims the title of the first reluctant algorithm to decisively surpass the quasi-polynomial running time Ω( n log n/(2+ε) ) of the notoriously sluggish SlowSort algorithm by Broder and Stolfi [ACM SIGACT News, 1984]. In the ongoing quest for the slowest possible sort, ExpoSort redefines what it means to take one's time. Remarkably, ExpoSort achieves this feat with one of the simplest pseudocodes among all known sorting algorithms. However, a slight modification-merely moving one recursive call inside an if statement- transforms ExpoSort into an astonishingly well-camouflaged variant of the classic InsertionSort with best- and worst-case running times of Θ(n) and Θ(n 3 ), respectively. This dual nature of ExpoSort serves as a reminder of the utmost care required when crafting pessimal algorithms, where a slight lapse in judgment could result in accidentally producing an embarrassingly practical algorithm.

Тұрмыстық қатты қалдықтардың сапалық және сандық құрамын зерттеу

Based on the fractional composition of municipal solid waste, it is possible to divide the objects of their collection into low-component and multi-component ones. Small-scale objects for the collection of municipal solid waste are trade and public catering enterprises. Municipal solid wastes of these facilities contain waste paper, glass, plastic, polyethylene, and non-ferrous metals. The main part of these wastes from these facilities, 75% of the volume, can be sorted and processed. This is due to the high volume and low moisture content of the waste collected at these facilities. High humidity and a large amount of composite materials of municipal solid wastes complicate sorting. The last factor presents a sorting problem. Multicomponent municipal solid waste is present at landfills mainly in the following combinations: paper-polyethylene-foil, metal-paper, metal-plastic, metal-plastic-ceramic, polyethylene- plastic, paper-polyethylene, textile-plastic, textile-plastic-rubber.

Optimizing Heap Sort for Repeated Values: A Modified Approach to Improve Efficiency in Duplicate-Heavy Data Sets

Sorting algorithms are critical to various computer science applications, including database management, big data analytics, and real-time systems. While Heap Sort is a widely used comparison-based sorting algorithm, its efficiency significantly diminishes when dealing with data sets containing a high volume of duplicate values. To address this limitation, this paper introduces a modified Heap Sort algorithm optimized for duplicate-heavy data. The proposed modification detects and handles duplicate values more efficiently by reducing unnecessary comparisons and swaps at the root of the heap and restructuring the heap more strategically. Experimental results demonstrate that the modified Heap Sort achieves up to a 15% reduction in sorting time, a 30% decrease in the number of swaps, and a 10% reduction in comparisons when tested on data sets with varying levels of duplication. These improvements highlight the enhanced computational efficiency and scalability of the modified algorithm in duplicate-heavy data scenarios. This advancement offers significant potential for improving sorting performance in practical domains such as big data analytics, database operations, and real-time data processing.

A Novel, Direct Matrix Solver for Supersonic Boundary Element Method Systems

For problems with very fine surface meshes, typically the most time-consuming step of a boundary element method (BEM, also called a panel method) is solving the final linear system of equations. Many have already studied how to efficiently solve the dense, asymmetric systems which arise in elliptic BEMs. However, this has not been studied for a supersonic aerodynamic BEM, for which the governing PDE is hyperbolic. Due to this hyperbolic character, the matrix equation which arises from a supersonic BEM has a large number of identically zero elements. But the resulting linear system of equations is also not sparse in the standard sense. Hence, the efficient solution of the linear system of equations arising in a supersonic BEM is here considered. A novel sorting algorithm is developed whereby the non-zero elements may be arranged into a useful structure with minimal cost. A novel direct solution method is developed here based on fast Givens rotations and the QR decomposition. This novel solver leverages the unique matrix structure to solve the supersonic system of equations more quickly than traditional direct methods. This novel method is then compared to other direct and iterative matrix solvers and is shown to be more robust than iterative solvers and more efficient than other direct solvers, with a computational time complexity of approximately O(N2.5).

Repeated K-means Clustering Algorithm For Radar Sorting

In modern electronic warfare, a number of radar emitters are in operation, causing radar receivers to receive high-density signal pulses that occur simultaneously. To analyze the radar signals more accurately and identify enemies, the sorting process of high-density radar signals is very important before analysis. Recently, machine learning algorithms, specifically K-means clustering, are the subject of research aimed at improving the accuracy of radar signal sorting. One of the challenges faced by these studies is that the clustering results can vary depending on how the initial points are selected and how many clusters number are set. This paper introduces a repeated K-means clustering algorithm that aims to accurately cluster all data by identifying and addressing false clusters in the radar sorting problem. To verify the performance of the proposed algorithm, experiments are conducted by applying it to simulated signals that are generated by a signal generator.

Deep Vision Computing-Driven Realtime Multitarget Tracking for Intelligent Industrial Aquaculture Management

Nowadays, intelligent industrial aquaculture management has been a more universal demand in digital society. Deep learning-based vision computing technique can provide much potential for such applications. As a result, this paper proposes a deep vision computation-driven realtime multitarget tracking approach for this purpose. It is a two-stage method, in which object detection is used in the first stage and target tracking is used in the second stage. First, the improved YOLOv7 model is utilized to train and identify the preprocessed data set to achieve accurate detection of fish targets. Then, a tracking algorithm, named SORT, is utilized to conduct an in-depth analysis of fish images to achieve continuous tracking of fish targets. Thus, further management affairs can be realized upon the basis of such conditions. Experimental results show that the improved YOLOv7 model achieves a high accuracy of 92% on the target detection task, and the Sort algorithm maintains a high degree of tracking accuracy and low tracking errors between consecutive frames. By combining these two methods, the daily behavior of fishes can be accurately detected and tracked in real time. In addition, the research also explores how to combine tracking results with breeding decisions to promote the development of breeding management in an intelligent direction.

Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing

The advancement of the Internet of Autonomous Vehicles has facilitated the development and deployment of numerous onboard applications. However, the delay-sensitive tasks generated by these applications present enormous challenges for vehicles with limited computing resources. Moreover, these tasks are often interdependent, preventing parallel computation and severely prolonging completion times, which results in substantial energy consumption. Task-offloading technology offers an effective solution to mitigate these challenges. Traditional offloading strategies, however, fall short in the highly dynamic environment of the Internet of Vehicles. This paper proposes a task-offloading scheme based on deep reinforcement learning to optimize the strategy between vehicles and edge computing resources. The task-offloading problem is modeled as a Markov Decision Process, and an improved twin-delayed deep deterministic policy gradient algorithm, LT-TD3, is introduced to enhance the decision-making process. The integration of LSTM and a self-attention mechanism into the LT-TD3 network boosts its capability for feature extraction and representation. Additionally, considering task dependency, a topological sorting algorithm is employed to assign priorities to subtasks, thereby improving the efficiency of task offloading. Experimental results demonstrate that the proposed strategy significantly reduces task delays and energy consumption, offering an effective solution for efficient task processing and energy saving in autonomous vehicles.

Teaching the Bubble Sort Algorithm Using CS Unplugged Activities at the K-12 Level

Teaching the Bubble Sort Algorithm Using CS Unplugged Activities at the K-12 Level

Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria

Deriving a representative model using value function-based methods from the perspective of preference disaggregation has emerged as a prominent and growing topic in multi-criteria sorting (MCS) problems. A noteworthy observation is that many existing approaches to learning a representative model for MCS problems traditionally assume the monotonicity of criteria, which may not always align with the complexities found in real-world MCS scenarios. Consequently, this paper proposes some approaches to learning a representative model for MCS problems with non-monotonic criteria through the integration of the threshold-based value-driven sorting procedure. To do so, we first define some transformation functions to map the marginal values and category thresholds into a UTA-like functional space. Subsequently, we construct constraint sets to model non-monotonic criteria in MCS problems and develop optimization models to check and rectify the inconsistency of the decision maker’s assignment example preference information. By simultaneously considering the complexity and discriminative power of the models, two distinct lexicographic optimization-based approaches are developed to derive a representative model for MCS problems with non-monotonic criteria. Eventually, we offer an illustrative example and conduct comprehensive simulation experiments to elaborate the feasibility and validity of the proposed approaches.

A Dilemma-Based Learning-to-Rank Approach for Generative Design in Urban Architectural Regeneration

Continuous urbanization and climate change degrade urban living conditions. Nature-based solutions in architectural and urban design offer promising remedies but are often hindered by time, cost, and early design phase challenges. To address this, we present a Generative Design System framework utilizing AI-generated images and learning-to-rank algorithms. This system generates numerous image solutions to inspire architects and urban planners, significantly accelerating early design stages. To manage the overwhelming volume of images, we introduce a dilemma-based learning approach that employs learning-to-rank and smart bubble sorting algorithms to prioritize images based on user preference. A case study demonstrates the framework’s potential, providing valuable insights into its application, benefits, and limitations in urban design.

Deep learning-based spike sorting: a survey.

Objective.Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.Approach.Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e. models that detect spikes and extract features or do classification within a single network, are included.Main results.Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on application-specific integrated circuits and field programmable gate arrays. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.Significance.This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.

Collaborative Scheduling of Mass Rescue Operations at Sea

The continued growth of the global cruise market has posed a major challenge to maritime search and rescue, as available rescue resources are limited compared to the scale of passenger ship disasters. To bridge the gap between the number of passengers in distress and the availability of rescue resources, this paper develops a two-stage Mass Rescue Operation (MRO) decision support model (MRO model) to fully utilize the available multiple rescue resources. Based on the combinatorial optimization theory, in the MRO model we consider the rescue capacity of multiple rescue resources and the synergy between them, the accident types and the marine environment conditions to optimize two objectives (rescue time and number of rescue resources dispatched) in two stages. In the first stage, the objective is to minimize the rescue time by Classical Selection Sort Algorithm. In the second stage, the rescue time and the number of rescue resources are simultaneously optimized by Simulated Annealing Arithmetic (SAA) integrated with Genetic Algorithm (GA). Furthermore, considering the actual role of helicopters in MROs, the MRO model is enhanced to schedule helicopters mandatorily or non-mandatorily. Finally, the MRO model was verified by simulating accidents in the Taiwan Strait. The simulation results show that compared with the first stage, the rescue time in the second stage model is saved by up to 16.18% and the number of rescue resources is reduced by up to 45.16%.

Efficiency of sorting algorithms in typescript

Efficiency of sorting algorithms in typescript

Enhancing Bucket Sort Efficiency: A Combinatorial Approach to Algorithm Optimization

Abstract: The efficiency of sorting algorithms is critical to numerous computational processes, including data management, search operations, and real-time applications. Among these algorithms, Bucket Sort is notable for its linear-time performance when data is uniformly distributed over a defined range. However, its efficiency suffers significantly with non-uniform data distributions, resulting in unbalanced buckets and increased sorting time. In our research, we explore how combinatorial algorithms can enhance Bucket Sort’s efficiency by optimizing bucket partitioning, dynamic allocation, and internal sorting strategies. Through rigorous mathematical proofs and analysis, we demonstrate that the application of combinatorial techniques yields substantial improvements in average-case performance, particularly in handling skewed data distributions. Practical case studies in large-scale data processing illustrate the adaptability and effectiveness of these optimizations, supporting their potential in real-world applications.

Design of intelligent algorithm for object search based on IoT digital images

With the development of artificial intelligence, traditional object search and image recognition have been replaced by the Internet of Things and artificial intelligence. However, traditional object search algorithms often lack accuracy and low precision. Therefore, this study proposes a new intelligent encryption algorithm to address the issues of insufficient accuracy in object search algorithms and image recognition algorithms. The new algorithm ensures the security of user data and the response efficiency of the model during the conversation process by integrating fully homomorphic encryption technology and dynamic sparse attention mechanism. The dynamic sparse attention mechanism introduced simultaneously improves the model's ability to handle long sequence data by dynamically adjusting attention weights. Experimental results showed that the precision of the proposed algorithm was 0.05 % higher than that of random algorithms and 0.19 % higher than that of sorting algorithms. The recall rate of the proposed algorithm was 0.14 % higher than that of random algorithms and 0.16 % higher than that of sorting algorithms. The research algorithm can identify objects with certain characteristics and is suitable for specific environments, greatly reducing the probability of data leakage in object search and providing new ideas for research in this field.

Occluded vehicle tracking by integrating full-scale features and trajectory correction

In order to improve the tracking drift and identity switching (IDs) problems caused by occlusion in vehicle tracking, a method for tracking occluded vehicles that integrates full-scale features and trajectory correction is proposed based on the Deep SORT algorithm. Firstly, a full-scale feature extraction network is introduced to extract the features of different scales of the target and realize adaptive dynamic fusion to enhance the target appearance characteristics. Then, a trajectory correction algorithm is proposed to repair the tracking trajectory during occlusion, and the Kalman filter parameters are updated to reduce the accumulated linear error during occlusion and optimize the target motion characteristics. Finally, the occluded vehicle is tracked by combining appearance features and motion features. The feasibility of the proposed method is verified by ablation experiments and visualization analysis. The experimental results on the KITTI dataset show that compared with the existing typical methods, the proposed method achieves the highest comprehensive score of 78.13 and the lowest number of IDs of 192, which effectively improves the IDs problem in occluded vehicle tracking and improves the robustness of vehicle tracking.