Alternative Algorithm Research Articles

Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces.

To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications.

WASP: weighted average of sequential projections for ptychographic phase retrieval

We introduce the weighted average of sequential projections, or WASP, an algorithm for ptychography. Using both simulations and real-world experiments, we test this new approach and compare performance against several alternative algorithms. These tests indicate that WASP effectively combines the benefits of its competitors, with a rapid initial convergence rate, robustness to noise and poor initial conditions, a small memory footprint, easy tuning, and the ability to reach a global minimum when provided with noiseless data. We also show how WASP can be parallelised to split operation across several different computation nodes.

Boosting cuckoo optimization algorithm via Gaussian mixture model for optimal power flow problem in a hybrid power system with solar and wind renewable energies

This paper presents a novel approach, the Gaussian Mixture Method-enhanced Cuckoo Optimization Algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, emissions, network loss, and voltage deviation. GMMCOA integrates the strengths of COA and GMM while mitigating their individual limitations. While COA offers robust search capabilities, it suffers from initial parameter dependency and the risk of getting trapped in local optima. Conversely, GMM delivers high-speed performance but requires guidance to identify the best solution. By combining these methods, GMMCOA achieves an intelligent approach characterized by reduced parameter dependence and enhanced convergence speed. The effectiveness of GMMCOA is demonstrated through extensive testing on both the IEEE 30-bus and the large-scale 118-bus test systems. Notably, for the 118-bus test system, GMMCOA achieved a minimum cost of $129,534.7529 per hour and $103,382.9225 per hour in cases with and without the consideration of renewable energies, respectively, surpassing outcomes produced by alternative algorithms. Furthermore, the proposed method is benchmarked against the CEC 2017 test functions. Comparative analysis with state-of-the-art algorithms, under consistent conditions, highlights the superior performance of GMMCOA across various optimization functions. Remarkably, GMMCOA consistently outperforms its competitors, as evidenced by simulation results and Friedman examination outcomes. With its remarkable performance across diverse functions, GMMCOA emerges as the preferred choice for solving optimization problems, emphasizing its potential for real-world applications.

A Stochastic Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Equality-Constrained Optimization

A stochastic algorithm is proposed, analyzed, and tested experimentally for solving continuous optimization problems with nonlinear equality constraints. It is assumed that constraint function and derivative values can be computed but that only stochastic approximations are available for the objective function and its derivatives. The algorithm is of the sequential quadratic optimization variety. Distinguishing features of the algorithm are that it only employs stochastic objective gradient estimates that satisfy a relatively weak set of assumptions (while using neither objective function values nor estimates of them) and that it allows inexact subproblem solutions to be employed, the latter of which is particularly useful in large-scale settings when the matrices defining the subproblems are too large to form and/or factorize. Conditions are imposed on the inexact subproblem solutions that account for the fact that only stochastic objective gradient estimates are employed. Convergence results are established for the method. Numerical experiments show that the proposed method vastly outperforms a stochastic subgradient method and can outperform an alternative sequential quadratic programming algorithm that employs highly accurate subproblem solutions in every iteration. Funding: This material is based upon work supported by the National Science Foundation [Awards CCF-1740796 and CCF-2139735] and the Office of Naval Research [Award N00014-21-1-2532].

Multi-view and Multi-order Graph Clustering via Constrained [formula omitted]-norm

The graph-based multi-view clustering algorithms achieve decent clustering performance by consensus graph learning of the first-order graphs from different views. However, the first-order graphs are often sparse, lacking essential must-link information, which leads to suboptimal consensus graph. While high-order graphs can address this issue, a two-step strategy involving the selection of a fixed number of high-order graphs followed by their fusion may result in information loss or redundancy, restricting the exploration of high-order information. To address these challenges, we propose Multi-view and Multi-order Graph Clustering via Constrained l1,2-norm (MoMvGC), which mitigates the impact of graph sparsity on multi-view clustering. By innovatively designing constrained l1,2-norm, the model ingeniously integrates the selection of multi-order graphs and corresponding weight learning into a unified framework. Furthermore, MoMvGC not only enable sparse selection of multi-order graphs but also simultaneous selection of views. Afterwards, we design an efficient alternative optimization algorithm to solve the optimization problems in MoMvGC. The proposed model achieves state-of-the-art clustering performance on nine real-world datasets, with particularly notable improvements observed on the MSRC dataset, where the clustering accuracy is increased by 5.24% compared to the best baseline. Comprehensive experiments demonstrate the effectiveness and superiority of our model. The code is available at https://github.com/haonanxin/MoMvGC_code.

Aided design decision-making framework for engineering projects considering cost and social benefits

PurposeCurrently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.Design/methodology/approachA database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.FindingsThe case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.Originality/valueThe framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

Speed Sensor-less Control of Induction Motor based on Super-helical Sliding Mold

Background: With the continuous progress of technology and the development of shield enterprises, higher requirements are put forward for speed estimation and system stability in the sensorless control system of induction motors in shield machines. Method: In order to solve the problems of velocity observation error and sliding mode vibration of the induction motor velocity observation method based on sliding mold observer, an improved superhelical sliding mold observer is proposed by combining the currently known invention patents and the information based on the induction motor control. Result: The open square of the current error value is introduced into the continuous function of the sliding variable of the observer, and two different functions are used in the boundary layer to replace the sign function, which reduces the sliding mode noise and vibration problems, and the stability of the observer is proved by the Lyapunov stability theory. After that, a phase-locked loop alternative model-referenced adaptive algorithm is used as the rotational speed observation link, which solves the problem that the conventional model-referenced adaptive algorithm relies on the current-modeled magnetic chain observer. Conclusion: The improved super-helical sliding mode observer and phase-locked loop control method effectively reduce the system vibration and speed estimation error of the motor under different speeds and loading conditions, and the observed currents are smoother, which improves the stability of the system. Simulation and experimental results under different working conditions show the effectiveness of the proposed method.

An Al-BERT-Bi-GRU-LDA algorithm for negative sentiment analysis on Bilibili comments

The number of online self-learning users has been increasing due to the promotion of various lifelong learning programs. Unstructured commentary text related to their real learning experience regarding the learning process is generally published by users to show their opinions and complaints. The article aims to utilize the dataset of real text comments of 10 high school mathematics courses participated by high school students in the Bilibili platform and construct a hybrid algorithm called the Artificial Intelligence-Bidirectional Encoder Representations from Transformers (BERT) + Bidirectional Gated Recurrent Unit (BiGRU) and linear discriminant analysis (LDA) to crunch data and extract their sentiments. A series of tests regarding algorithm comparison were conducted on the educational review datasets. Comparative analysis found that the proposed algorithm achieves higher precision and lower loss rates than other alternative algorithms. Meanwhile, the loss ratio of the proposed algorithm was kept at a low level. At the topic mining level, the topic clustering of negative comments found that the barrage content was very messy and the complexity of the course content was generally reported by the students. Some problems related to videos were also mentioned. The outcomes are promising that the fundamental issues underlined by the students can be effectively resolved to improve curriculum and teaching quality.

Research on financial risk screening of listed companies based on clustering algorithm

The ability to recognize financial fraud activity in listed firms has grown in importance and is a constant source of worry in both academics and business. The A-share listed businesses in the biological and pharmaceutical industries that have faced penalties from the China Securities Regulatory Commission during the previous five years are used as samples in this study. Using Kangmei Pharmaceutical as an example, 24 characteristics are chosen using the fraud triangle theory, and testing and analysis are conducted using a Random Forest classification algorithm model in conjunction with SMOTE Oversampling technology. The findings show that it is more beneficial to use numerous feature sets to develop models or to construct models with many alternative algorithms for financial fraud screening studies, as opposed to merely classifying organizations into fraudulent and non-fraudulent categories.

Faster space-efficient STR-IC-LCS computation

One of the most fundamental method for comparing two given strings A and B is the longest common subsequence (LCS), where the task is to find (the length) of an LCS of A and B. In this paper, we deal with the STR-IC-LCS1 problem which is one of the constrained LCS problems proposed by Chen and Chao [J. Comb. Optim, 2011]. A string Z is said to be an STR-IC-LCS of three given strings A, B, and P, if Z is a longest string satisfying that (1) Z includes P as a substring and (2) Z is a common subsequence of A and B. We present three efficient algorithms for this problem: First, we begin with a space-efficient solution which computes the length of an STR-IC-LCS in O(n2) time and O((ℓ+1)(n−ℓ+1)) space, where ℓ is the length of an LCS of A and B of length n. When ℓ=O(1) or n−ℓ=O(1), then this algorithm uses only linear O(n) space. Second, we present a faster algorithm that works in O(nr/log⁡r+n(n−ℓ+1)) time, where r is the length of P, while retaining the O((ℓ+1)(n−ℓ+1)) space efficiency. Third, we give an alternative algorithm that runs in O(nr/log⁡r+n(n−ℓ′+1)) time with O((ℓ′+1)(n−ℓ′+1)) space, where ℓ′ denotes the STR-IC-LCS length for input strings A, B, and P.

Optic Cup and Optic Disc Segmentation Based on improved TransUnet

Glaucoma ranks as the second leading cause of blindness globally, surpassed only by cataracts. It inflicts irreversible harm to the optic nerve, and once vision is lost, restoration is unattainable for life[1]. Therefore, early detection of glaucoma is imperative.The cup-to-disc ratio serves as a primary diagnostic tool for identifying glaucoma. Generally, an excessively large cup-to-disc ratio in fundus photographs strongly suggests glaucoma. However, human error in diagnosing fundus photographs by clinicians is prevalent, leading to time-consuming, labor-intensive, expensive, and potentially inaccurate diagnoses during extensive screenings.To combat this challenge, an enhanced TransUnet-based method is proposed for optic cup and optic disc segmentation, aiding clinicians in large-scale glaucoma screenings. The model incorporates the Focus structure and CBAM structure. The Focus structure addresses information loss during single downsampling of images, preserving more data without changing image dimensions. Meanwhile, the CBAM structure integrates channel and spatial attention, enhancing the model's ability to extract features.On the ORIGA-650 dataset, this enhanced method achieved a mean Dice coefficient of 0.984 for the disc and 0.947 for the cup, along with a mean IOU value of 0.968 for the disc and 0.902 for the cup. Compared to alternative algorithms, the segmentation results exhibit superior accuracy, showcasing the efficacy of our proposed model.

Prediction of Student Performance Using Random Forest Combined With Naïve Bayes

Abstract Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model’s interpretability diminishes as the ensemble’s complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches.

Performance Degradation Estimation Mechanisms for Networked Control Systems Under DoS Attacks and its Application to Autonomous Ground Vehicle.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

Dual-blend insight recommendation system for e-commerce recommendations and enhance personalization

E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the internet. This digital transaction model has revolutionized the way businesses operate and consumers shop. In response to the burgeoning complexity of e-commerce datasets, this work addresses the need for advanced recommendation systems. This work introduces the dual-blend insight recommendation system (DIRS) model for personalized e-commerce recommendation system. The DIRS model involves dataset loading, preprocessing, and feature extraction, enabling training with recurrent neural network (RNN) and Bayesian personalized ranking (BPR) models. Recommendations are generated based on user-defined functions, i.e., location and session, and evaluation metrics such as hit rate (HR) and mean reciprocal rate (MRR) highlight DIRS’s superior performance. The model is evaluated using the Tmall dataset. Results reveal DIRS consistently outperforms alternative algorithms, showcasing its effectiveness in 10k and 20k recommendation sets. This study provides valuable insights into optimizing e-commerce recommendations, emphasizing DIRS as a powerful model for enhancing user experience and engagement.

Application of the FP-Growth Algorithm in Consumer Purchasing Pattern Analysis

Technology is currently used in various ways, one of which is businesses engaged in selling daily products. The right marketing strategy makes knowledge of consumer shopping patterns important to study because consumers are the main actors in carrying out transactions. The more diverse the types of goods sold in a company, the more diverse the resulting consumer spending patterns will be. Data mining is an analysis process that is carried out automatically on complex and large amounts of data to obtain patterns or trends that are generally not realized. The FP-Growth algorithm is an alternative algorithm that can be used to determine the data set that appears most frequently (frequent itemset) in a data set. The method used in this research is the FP-Growth method which is implemented in the PHP programming language and MySQL as the database. Designing a data mining program using the FP-Growth method can analyze and manage consumer purchasing patterns based on goods purchased simultaneously. The data processed in this research is transaction data that has been processed into information so as to gain knowledge in calculating stock of goods sourced from the owner of Toko Asra. From testing this method, results were obtained from the 10 transactions in December 2021, by limiting the minimum support value to 0.2 and minimum confidence to 0.75, 33 patterns of consumer shopping habits were obtained, meaning that 33 products were most frequently purchased by consumers. Designing a data mining program using the FP-Growth method can help analyze consumer purchasing patterns based on items purchased simultaneously. The results of frequent itemset calculations can help find a sequence of combinations that can be used as product recommendations in business decision

Label relaxation and shared information for multi-label feature selection

Due to the rapid growth of labels and high-dimensional data, multi-label feature selection has attracted increasing attention. However, two common issues are ignored by existing multi-label feature selection methods: 1) The pseudo-label matrix is constructed by using logical label matrix directly. 2) There is an imbalance of label density in multi-label data. To tackle the mentioned issues, we innovatively propose a new method named Label relaxation and Shared information for Multi-label Feature Selection (LSMFS). Specifically, LSMFS combines a logical label matrix with a non-negative label relaxation matrix to fit a pseudo-label matrix, which is used for learning the correlations of class labels. LSMFS uses the feature weight matrix to capture a shared information of different related labels, which mitigates the influence of low-density labels on feature selection. The above principles are transformed into the objective function of LSMFS, and an alternate iterative optimization algorithm is developed to solve the objective function. Experiments on multi-label datasets from different domains have demonstrated the effectiveness of the proposed method LSMFS. Code is released at https://github.com/HQUF/LSMFS.

Analysis of Gradient Boosting, XGBoost, and CatBoost on Mobile Phone Classification

In the ever-evolving landscape of mobile phone technology, accurately classifying device specifications is paramount for market analysis and consumer decision-making. This research conducts a comprehensive analysis of mobile phone specification classification using three prominent machine learning algorithms: Gradient Boosting, XGBoost, and CatBoost. Through meticulous dataset acquisition and preprocessing steps, including resolution normalization and price categorization, features essential for classification analysis were standardized. Robust cross-validation techniques were employed to assess model performance effectively. The study demonstrates the significant impact of normalization techniques on improving model performance across all algorithms and fold variations. CatBoost consistently emerges as the top-performing algorithm, followed closely by XGBoost, with Gradient Boosting displaying respectable performance. Notably, CatBoost consistently achieves the highest AUC values and accuracy scores, demonstrating superior performance in accurately classifying mobile phone specifications. These findings underscore the importance of preprocessing methods and algorithm selection in achieving optimal classification results. For mobile phone manufacturers, leveraging machine learning algorithms for effective classification can inform product development strategies, optimizing offerings based on consumer preferences. Similarly, for data analysts, employing appropriate preprocessing techniques and algorithmic approaches can lead to more accurate predictions and informed decision-making. Future research avenues include exploring advanced preprocessing methods, investigating alternative algorithms, and incorporating additional features or datasets to enrich the classification process. Overall, this research contributes to understanding mobile phone specification classification through machine learning methodologies, offering actionable insights for industry practitioners and researchers to address evolving market dynamics and consumer preferences.

An improved hunger game search optimizer based IoT task scheduling in cloud–fog computing

Due to the rapid expansion of Internet of Things (IoT)-related applications, the utilization of cloud services is experiencing significant growth. Although cloud computing has proven its effectiveness in processing and storing data for various applications, it faces challenges in addressing certain requirements, such as the growing need for real-time or latency-sensitive applications and the limitations of network bandwidth. As a result, fog computing is often seen as a supplementary paradigm to cloud computing, providing additional capabilities and benefits to the traditional cloud paradigm, aiming to extend cloud services to edge devices and end-users. However, the limited capabilities of fog nodes require lighter tasks while other tasks that need more processing time are processed in the cloud. In the present research paper, we propose a novel algorithm that is customized for task scheduling within the context of cloud–fog computing on the Internet of Things (IoT) framework. Our approach builds upon the Hunger Game Search algorithm (HGS) as its foundation. To improve the exploitative capabilities of the HGS, our proposed method, called HGSMPA, incorporates the Marine Predator Algorithm (MPA). Through experimental evaluation using various workload traces, we have demonstrated the efficacy of HGSMPA. The findings reveal that HGSMPA surpasses alternative algorithms in terms of reducing energy consumption and minimizing the makespan time. Specifically, The empirical evaluation indicates that HGSMPA can reduce the makespan time by 19.31% for synthetic workloads and by 17.47% for real workloads as compared to similar scheduling algorithms. Moreover, HGSMPA can reduce energy consumption by 14.72% for synthetic workloads and by 17.68% for real workloads as compared to other methods.

Modeling and evaluation of the permeate volume in membrane desalination processes using machine-learning techniques

Machine learning methodologies are gaining significant recognition as an effective approach for tackling and modelling challenges related to membranes. This study delves into the utilization of machine learning algorithms to forecast the quality of reverse osmosis (RO) water. Specifically, we conduct a comparative analysis of four popular algorithms: decision tree, random forest, support vector machine (SVM), and K-nearest neighbours (KNN). Our dataset comprises essential water quality evaluation features such as temperature, pH, and conductivity. Using these features, we train and test our models, evaluating their performance with metrics like accuracy and root-mean-squared error (RMSE). The outcomes indicate that all four algorithms perform admirably in predicting RO water quality, achieving accuracy scores ranging from 80 % to 95 %. Notably, KNN stands out with the highest accuracy score of 95 %, establishing it as the most effective algorithm for this task. Besides its performance, KNN's simplicity of implementation and interpretability make it a pragmatic choice for real-world applications. This study serves as compelling evidence of the potential of machine learning algorithms for forecasting RO water quality, particularly highlighting KNN's effectiveness in this context. To further enhance the accuracy of RO water quality prediction, future research could explore the inclusion of other features or alternative algorithms.

Categorization of Health Insurance Coverage Type from Survey Questions after Health Reform: The Case of the Current Population Survey

Measuring health insurance in surveys has always been challenging, and the Affordable Care Act (ACA) introduced considerable ambiguities. For example, the public/private line was blurred with the introduction of marketplace coverage, which is considered private coverage even though in some cases, it is partially or fully subsidized by the government. This study uses a rigorous design where administrative records are linked to survey data. We compare alternative algorithms that employ survey data points found in several major national surveys to categorize coverage type, focusing on the very difficult challenge of separating private marketplace coverage from public coverage. This is important, given researchers’ and policymakers’ need to produce estimates of public versus private coverage from survey data. Results indicate that integrating a data point on plan name reduces a more simplistic algorithm’s overestimation of marketplace coverage and results in significant improvements in accurate categorization across public and private coverage types.