Efficient Algorithm Research Articles

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

In general, uncapacitated exam timetabling is conducted manually, which can be time-consuming. Many studies aim to automate and optimize uncapacitated exam timetabling. However, pinpointing the most efficient algorithm is challenging since most studies assert that their algorithms surpass previous ones. To identify the optimal algorithm, this research evaluates the performance of four algorithms: Hill climbing (HC), simulated annealing (SA), great deluge (GD), and tabu search (TS) in addressing the exam timetabling problem. The Kempe chain operator’s influence on optimization solutions is also examined. A simple random method is employed to select the low-level heuristic (LLH). The Carter (Toronto) dataset served as the test material, with each algorithm undergoing 200,000 iterations for comparison. The results indicate that the TS algorithm is superior, providing the best solution in 13 instances. The use of a tabu list enhanced the search process’s efficiency by preventing redundant modifications. The Kempe chain LLH exhibited a tendency towards achieving better solutions.

Network-based representations and dynamic discrete choice models for multiple discrete choice analysis

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such as the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.

Design and analysis of face recognition system based on VGGFace-16 with various classifiers

This research presents a face recognition system based on different classifiers that deal with various face positions. The proposed system involves the extraction of features through the VGG-Face-16 deep neural network, which only extracts essential features of input images, leading to an improved recognition step and enhanced algorithm efficiency, while the recognition involves the radial basis function in support vector machine (SVM) classifier and evaluate the performance of the system. Also, the system is designed and implemented later by using other classifiers; they are K-neareste2 neighbour (KNN) classifiers, logistic regression (LR), gradient boosting (XGBoost), decision tree classifier (DT) and Naive Bayes classifier (NB). The proposed algorithm was tested with the four face databases: AT&T, PINs Face, linear friction welding (LFW) and real database. The database was divided into two groups: One contains a percentage of images that are used for training and the second contains a percentage of images (remainder) which was used for testing. The results show that the classification by RBF in SVM has the highest recognition rate in the case of using small, medium and large databases; it was 100% in AT&T and Real database, while its efficiency appears to be lower when using large-size databases whereas it is 96% in PINs database and 60.1% in LFW database.

Cost optimization of electricity in energy storage system by dynamic programming

This paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement.

Blockchian Based Hybrid Consensus Algorithm for Data Security in Edge Computing

Abstract: Edge computing has emerged as a paradigm to process data closer to its source, enabling low-latency and real-time applications. However, the distributed and heterogeneous nature of edge environments poses significant challenges for ensuring data security and integrity. Blockchain technology offers a promising solution by providing a decentralized and immutable ledger for recording transactions. This abstract proposes a consensus algorithm tailored for securing data in edge computing environments using blockchain technology. The proposed consensus algorithm leverages a hybrid approach combining Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT). PoA ensures that only trusted nodes within the edge network are eligible to participate in the consensus process, mitigating the risk of malicious attacks. PBFT enhances the efficiency and scalability of the consensus algorithm by allowing fast decision-making among a subset of nodes, thereby reducing latency in data validation and verification. Furthermore, smart contracts are employed to enforce data access control and execute predefined rules for data processing and sharing. These smart contracts facilitate secure and transparent interactions among edge devices, ensuring that only authorized parties can access and manipulate sensitive data. The integration of blockchain technology with edge computing offers several benefits, including enhanced data security, traceability, and accountability. By employing a tailored consensus algorithm, edge computing environments can effectively mitigate security threats while maintaining low-latency data processing capabilities. Future research directions may explore optimization techniques to further improve the performance and scalability of the proposed solution in large-scale edge networks.

Exploring Sentiment Analysis across Text, Audio, and Video: A Comprehensive Approach and Future Directions

Abstract: This study presents a comprehensive exploration of sentiment analysis techniques across text, audio, and video modalities. Leveraging natural language processing (NLP), speech recognition, and computer vision algorithms, the research demonstrates the versatility and adaptability of sentiment analysis across diverse data sources. The necessity of such an approach lies in its ability to provide deeper insights into user emotions and opinions expressed in various mediums, including written text, spoken language, and visual content. Moreover, the study highlights the importance of sentiment analysis in understanding customer feedback, market trends, social media sentiments, and sentiment-aware recommendation systems. Future directions include advancing algorithmic accuracy and efficiency, integrating multimodal fusion techniques, and exploring applications in diverse domains, thereby paving the way for enhanced sentiment analysis capabilities and broader realworld applications

Sensorless speed controller of an induction motor with MRAS-based model predictive control

Speed sensors play critical role in induction motor (IM) control systems. However, the use of these sensors cause several drawbacks such as increasing the overall cost, decreased reliability on control and increased noise problems in data acquisition. The sensorless control methods also have been proposed in control systems of IM to increase the overall performance. On the other hand, model predictive controllers (MPCs) which is one of the most recently used controller methods require the speed measurement to perform speed and torque control of an IM. In addition to control method, the topology of voltage source inverter (VSI) affects the overall efficiency of the IM drive because of its losses and total harmonic distortion (THD) ratios delivered by the topology. The main proposal of this study is to implement an IM drive built with a three-level active neutral point clamped (3L-ANPC) voltage source inverter (VSI) and to propose an MPC that eliminates sensor requirement in speed control of IM. The proposed IM drive takes benefits of multilevel inverter topology, which reduces the torque ripple, enables low switching frequency and provides decreased harmonic distortion. The switching pulses of ANPC VSI are generated by the proposed sensorless MPC method for controlling the torque of IM. The modelling and experimental verification of the ANPC based IM drive have been carried out and the sensorless MPC controller has been validated in this study. There two types of model reference adaptive system (MRAS) have been proposed to ensure precise estimation of IM speed that should be supplied to MPC controller. These MRAS methods are titled as the rotor flux based model reference adaptive system (MRASF), and the stator current based model reference adaptive system (MRASCC) according to estimation references. The proposed MPC-based IM drive has been analyzed under variable speed and torque circumstances to detect the efficiency and reliability of MRASF and MRASCC algorithms. The efficiency indicators such as flux and torque responses, and speed control of the suggested MPC IM drive are validated with experimental analysis. The results of revealed that the IM effectively tracks the reference speed and produces the required torque depending to the precise control of proposed MPC algorithm.

A novel geometric-based bistatic range grouping algorithm for multi-target localization in distributed MIMO radar systems

In this paper, a novel bistatic range grouping algorithm is proposed in order to localize multiple targets in distributed multiple-input multiple-output (MIMO) radar systems. In a distributed MIMO radar, target localization is performed based on bistatic ranges, the distances between antennas through a target, without directional information, and grouping bistatic ranges with respect to the target is a crucial step to estimate multiple targets efficiently. In contrast to the existing grouping schemes that use tentative target estimation or complicated joint bistatic range grouping and target estimation, the proposed algorithm uses only the geometric property of bistatic ranges, resulting in computationally efficient and robust grouping algorithm without noise enhancement, especially for realistic noisy environments where other existing grouping algorithms often fail. The performance of the proposed scheme is confirmed by evaluating the probability of miss detection and the probability of false alarm through numerical simulations for various environments including indirect paths and blocked paths. The root mean square error performance of the multi-target localization using the proposed bistatic grouping algorithm is also presented for realistic noisy environments.

Efficient Path Finding for Emergency Vehicles: A Comparative Exploration of Sparrow, Monkey and Frog Search Algorithms

Efficient path finding for emergency vehicles is essential for saving lives by taking the optimal path from source to destination point that takes less time and covered less area by avoid traffic and any obstacles. There has been a lot of research is completed for finding optimal routes for emergency vehicles using different algorithms like Dijkstra, A* and genetic algorithms. In this research work has been done by using three bio-inspired algorithms such as Sparrow, Monkey and Frog search Algorithms for finding optimal ambulance routes. The objective not only to find the optimal routes but also presents a performance comparison graph of these path planning algorithms, where it finds the most efficient algorithms in terms of path length and time-taken efficiency of these three algorithm and shows the strengths and weakness of each methods in the ambulance routing and any emergency system. Keywords: Emergency services, Path Finding, Ambulance routing, Sparrow search algorithm, Monkey search algorithm, Frog search algorithm.

An Intrusion Detection Model Based on Extra Trees Algorithm with Dimensionality Reduction and Oversampling

With the advancement of the university information process, more and more application systems are running on the campus network, and the information system becomes larger and more complex. With the rapid growth of network users and the popularization and deepening of computer knowledge, the campus network has been transformed from an experimental network for education and scientific research into an operational network that attaches equal importance to education, scientific research, and service. As the most important transmission carrier of digital information, how to ensure its security has become an urgent issue for colleges and universities. Therefore, this paper uses advanced intrusion detection technology to design the corresponding model to solve the security problem of the campus network. When traditional machine learning algorithms train network intrusion data sets, they are prone to problems such as too many feature dimensions, overfitting, and imbalance of data sets, which lead to lower accuracy and low time efficiency of intrusion detection algorithms. In order to solve the above problems, this paper proposes an intrusion detection model based on Extra Trees, which uses linear discriminant analysis to reduce the dimension of data, then uses oversampling to reduce the influence of imbalance of sample categories of the network intrusion dataset, and finally uses Extra Trees algorithm to train the model. The experimental results show that after LDA reduction and oversampling, using the Extra Trees classification model can improve the overall recognition performance of imbalanced data sets under multi-classification and satisfy the network intrusion detection.

A unifying quantum speed limit for time-independent Hamiltonian evolution

Quantum speed limit (QSL) is the study of fundamental limits on the evolution time of quantum systems. For instance, under the action of a time-independent Hamiltonian, the evolution time between an initial and a final quantum state obeys various mutually complementary lower bounds. They include the Mandelstam–Tamm (MT) bound, the Margolus–Levitin (ML) bound, the Luo–Zhang bound, the Lee–Chau (LC) bound together with the dual ML bound introduced by Ness and coworkers. Here we show that the MT bound can be obtained by optimizing the LC bound over a certain parameter. More importantly, we report a QSL that includes all the above bounds as special cases before optimizing over the physically meaningless reference energy level of a quantum system. This unifying bound depends on a certain parameter p. For any fixed p, we find all pairs of time-independent Hamiltonian and initial pure quantum state that saturate this unifying bound. More importantly, these pairs allow us to compute this bound accurately and efficiently using an oracle that returns certain pth moments related to the absolute value of energy of the quantum state. Moreover, this oracle can be simulated by a computationally efficient and accurate algorithm for finite-dimensional quantum systems as well as for certain infinite-dimensional quantum states with bounded and continuous energy spectra. This makes our computational method feasible in a lot of practical situations. We further compare the performance of this bound for the case of a fixed p as well as the case of optimizing over p with existing QSLs. We find that if the dimension of the underlying Hilbert space is ≲2000 , our unifying bound optimized over p can be computed accurately in a few minutes using Mathematica code with just-in-time compilation in a typical desktop. Besides, this optimized unifying QSL is at least as good as all the existing ones combined and can occasionally be a few percent to a few times better.

A flexible time-varying coefficient rate model for panel count data.

Panel count regression is often required in recurrent event studies, where the interest is to model the event rate. Existing rate models are unable to handle time-varying covariate effects due to theoretical and computational difficulties. Mean models provide a viable alternative but are subject to the constraints of the monotonicity assumption, which tends to be violated when covariates fluctuate over time. In this paper, we present a new semiparametric rate model for panel count data along with related theoretical results. For model fitting, we present an efficient EM algorithm with three different methods for variance estimation. The algorithm allows us to sidestep the challenges of numerical integration and difficulties with the iterative convex minorant algorithm. We showed that the estimators are consistent and asymptotically normally distributed. Simulation studies confirmed an excellent finite sample performance. To illustrate, we analyzed data from a real clinical study of behavioral risk factors for sexually transmitted infections.

An efficient exact algorithm for planted motif search on large DNA sequence datasets.

DNA motif is the pattern shared by similar fragments in DNA sequences, which plays a key role in regulating gene expression, and DNA motif discovery has become a key research topic. Exact planted (l,d)-motif search (PMS) is one of the motif discovery approaches, which aims to find from t sequences all the (l,d)-motifs that are motifs of l length appearing in at least qt sequences with at most d mismatches. The existing exact PMS algorithms are only suitable for small datasets of DNA sequences. The development of high-throughput sequencing technology generates vast amount of DNA sequence data, which brings challenges to solving exact PMS problems efficiently. Therefore, we propose an efficient exact PMS algorithm called PMmotif for large datasetsof DNA sequences, after analyzing the time complexity of the existing exact PMS algorithms. PMmotif finds (l,d) -motifs with strategy by searching the branches on the pattern tree that may contain (l,d) -motifs. It is verified by experiments that the running time ratio of the existing excellentPMS algorithmstoPMmotif isbetween14.83and 58.94. In addition, for the first time, PMmotif can solve the (15,5) and(17,6) challenge problem instances on large DNA sequence datasets within 24 hours.

Research on Optimization of International Multimodal Transportation Routes Based on Uncertainty Theory

Delays in international multimodal marine transport can result in subsequent connection delays, leading to prolonged transportation time and increased uncertainty. To identify more reliable and cost-effective robust transportation routes, this study establishes a constrained planning model for multimodal transportation routes. Employing uncertainty theory, the model is transformed into a deterministic equivalent class model and ultimately incorporates an adaptive differential evolution (ADE) algorithm. Results of a case study indicate that: (i) the proposed model, compared with deterministic models, exhibits greater robustness and better aligns with practical transportation scenarios, resulting in substantial reductions in actual transportation time and cost; (ii) the solution efficiency of the ADE algorithm surpasses that of the genetic algorithm and Dijkstra algorithm; (iii) the start time of transportation and the confidence level of uncertainty also play crucial roles in influencing route selection. Therefore, decision-makers should consider a multifaceted approach when formulating transportation routes.

An efficient iterative procedure in hyperbolic space and application to non-linear delay integral equation

An efficient iterative procedure in hyperbolic space and application to non-linear delay integral equation

Improved stiff string torque and drag prediction using a computationally efficient contact algorithm

ABSTRACT Due to the intermittent contact with the wellbore, determining torque and drag for deviated wells is difficult. Most models have ignored drill string stiffness and assumed continual contact to simplify derivation. However, the accuracy of these ‘soft-string’ models is restricted, especially at high dogleg severities. On the other hand, most ‘stiff-string’ models rely on computationally intensive approaches or continuous contact assumptions. To mitigate these issues, a computationally efficient penalty-based wellbore contact algorithm has been developed based on vector calculation, which at most requires two dot products and three arithmetic operations to determine contact locations. This algorithm is incorporated into a 3D multibody dynamics (MBD) model, which utilizes rigid drill-string segments based on the Newton-Euler formulation, connected via axial, shear, torsional, and bending springs to capture drill string flexibility. This model performs simulations faster than real-time and has been validated using surface measurements from a completed well.

Enhancing computational thinking and spatial reasoning skills in gamification programming learning: A comparative study of tangible, block and paper‐and‐pencil tools

AbstractTangible programming tools have become a mainstream teaching aid in gamification programming learning (GPL) due to their interactivity and ability to enhance novice learners' computational thinking and spatial reasoning skills. However, comparing the relative efficacy of different programming tools that simultaneously support these skills was not adequately explored. This study designed and evaluated three programming tools: the tangible programming tool (TPG), which uses real touchable objects; the block programming tool (BPG), which employs virtual programming blocks and 3D game scenarios; and the paper‐and‐pencil programming tool (PPG), which uses paper and pen to draw. The study involved 112 seventh‐grade students from three natural classes: Class A (TPG, n1=37), Class B (BPG, n2=38), and Class C (PPG, n3=37). These students completed four gamification programming tasks and CT skills, spatial reasoning skills, enjoyment, cognitive load and GPL task list measurements. The results indicated that the tangible programming tool led to lower cognitive load, significant improvement in spatial reasoning skills and better abstraction and problem decomposition skills. The block programming tool provided a more enjoyable experience and facilitated students' algorithm design and efficiency. The paper‐and‐pencil programming tool was found to be less effective in improving spatial reasoning skills. This study's findings can help programming educators cultivate students' thinking skills and improve their learning experience by effectively selecting the most appropriate programming tools.

Generalized fused Lasso for grouped data in generalized linear models

Generalized fused Lasso (GFL) is a powerful method based on adjacent relationships or the network structure of data. It is used in a number of research areas, including clustering, discrete smoothing, and spatio-temporal analysis. When applying GFL, the specific optimization method used is an important issue. In generalized linear models, efficient algorithms based on the coordinate descent method have been developed for trend filtering under the binomial and Poisson distributions. However, to apply GFL to other distributions, such as the negative binomial distribution, which is used to deal with overdispersion in the Poisson distribution, or the gamma and inverse Gaussian distributions, which are used for positive continuous data, an algorithm for each individual distribution must be developed. To unify GFL for distributions in the exponential family, this paper proposes a coordinate descent algorithm for generalized linear models. To illustrate the method, a real data example of spatio-temporal analysis is provided.

The multicenter ablate-by-LAW study: personalized paroxysmal atrial fibrillation ablation according to the local left atrial wall thickness

Abstract Background Personalized radiofrequency (RF) catheter ablation for paroxysmal atrial fibrillation (PAF), tailoring the ablation index (AI) to the local left atrial wall thickness (LAWT), has proven to be an efficient procedure with high arrhythmia-free survival rates. However, to date, only monocentric experiences have been reported in the literature. Purpose The present study sought to report a multicenter experience in order to assess the safety, efficacy, and efficiency, of a LAWT-guided personalized pulmonary vein isolation (PVI) approach for PAF ablation (ClinicalTrials.gov, NCT04218604). Methods We conducted an international multicenter prospective study at five tertiary hospitals (two in Spain, one in Italy, one in Germany, and one in the United States). Consecutive patients referred for first-time RF catheter ablation of PAF were prospectively enrolled from November 2020 to September 2022. In all patients a three-dimensional map of LAWT were obtained from preprocedural multidetector computed tomography (MDCT)-derived data postprocessing. LAWT was categorized into 1 mm layers and AI was titrated according to the LAWT. All procedures were performed under general anesthesia, utilizing high-rate low-tidal volume (HR-LTV) ventilation (Figure 1). All antiarrhythmic drugs were discontinued following the 3-month blanking period. The primary endpoint was freedom from any documented sustained atrial arrhythmia at 12-month follow-up, regardless of symptoms. Results One hundred and nine patients (60.1±9.4 years, 64.2% male) were prospectively enrolled. The mean LA diameter was 38.7±5.8 mm and the mean left ventricular ejection fraction was 60.7±4.7%. The median procedure time was 61.7 minutes (IQR 48.4–83.8), and the median RF application time was 13.9 minutes (IQR 12.3–16.8). The mean number of VisiTags was 63.2 ± 18. First-pass PVI was achieved in the 83.5% of patients. The median fluoroscopy time was 61.5 seconds (IQR 22.5–197.5). In 94 (86.2%) patients, ablation was performed with a single femoral venous puncture. At 12-month follow-up, the rate of freedom from any documented atrial arrhythmia was 93.4% (Figure 1). Two (1.8%) patients died during follow-up, one from unknown cause three months after the procedure, and another from complications following multiple trauma after falling from a ladder. One patient (0.9%) developed femoral artery pseudoaneurysm, with no other serious procedure-related complications. Conclusion The Ablate-by-LAW multicenter study proved that a personalized PAF ablation protocol, tailoring the AI to the local LAWT is highly efficient and achieved a high acute first-pass PVI rates. The study reported a high 12-month survival-free from any documented sustained atrial arrhythmias, exceeding 90%, and an exceptionally low incidence of serious procedure-related complications.

An efficient and universal parallel algorithm for high-dimensional quantum dynamics in poly-atomic reactions.

This study presents a parallel algorithm for high-dimensional quantum dynamics simulations in poly atomic reactions, integrating distributed- and shared-memory models. The distributions of the wave function and potential energy matrix across message passing interface processes are based on bundled radial and angular dimensions, with implementations featuring either two- or one-sided communication schemes. Using realistic parameters for the H + NH3 reaction, performance assessment reveals linear scalability, exceeding 90% efficiency with up to 600 processors. In addition, owing to the universal and concise structure, the algorithm demonstrates remarkable extensibility to diverse reaction systems, as demonstrated by successes with six-atom and four-atom reactions. This work establishes a robust foundation for high-dimensional dynamics studies, showcasing the algorithm's efficiency, scalability, and adaptability. The algorithm's potential as a valuable tool for unraveling quantum dynamics complexities is underscored, paving the way for future advancements in the field.