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Riyad, Wahidul Alam. "Comparative Evaluation of Numerous Optimization Algorithms for Compiling Travel Salesman Problem". Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25.07.2020): 877–83. http://dx.doi.org/10.5373/jardcs/v12sp7/20202178.
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Thompson, William J. "Numerous Neat Algorithms for the Voigt Profile Function". Computers in Physics 7, nr 6 (1993): 627. http://dx.doi.org/10.1063/1.4823236.
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Ambhika, Ms. "Cost-effective Numerous Kernel Learning Algorithms using Low-Rank Interpretation". International Journal for Research in Applied Science and Engineering Technology 8, nr 6 (30.06.2020): 136–49. http://dx.doi.org/10.22214/ijraset.2020.6021.
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Geetanjli. "Study of Power Control in CDMA System on the basis of optimization Technique". International Journal of Advanced Research in Computer Science and Software Engineering 7, nr 8 (30.08.2017): 126. http://dx.doi.org/10.23956/ijarcsse.v7i8.38.
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The power control in CDMA systems, grant numerous users to share resources of the system uniformly between each other, leading to expand capacity. With convenient power control, capacity of CDMA system is immense in contrast of frequency division multiple access (FDMA) and time division multiple access (TDMA). If power control is not achieved numerous problems such as the near-far effect will start to monopolize and consequently will reduce the capacity of the CDMA system. However, when the power control in CDMA systems is implemented, it allows numerous users to share resources of the system uniformly between themselves, leading to increased capacity For power control in CDMA system optimization algorithms i.e. genetic algorithm & particle swarm algorithm can be used which regulate a convenient power vector. These power vector or power levels are dogged at the base station and announce to mobile units to alter their transmitting power in accordance to these levels. The performances of the algorithms are inspected through both analysis and computer simulations, and compared with well-known algorithms from the literature.
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Gahman, Nicholas, i Vinayak Elangovan. "A Comparison of Document Similarity Algorithms". International Journal of Artificial Intelligence & Applications 14, nr 2 (30.03.2023): 41–50. http://dx.doi.org/10.5121/ijaia.2023.14204.
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Document similarity is an important part of Natural Language Processing and is most commonly used forplagiarism-detection and text summarization. Thus, finding the overall most effective document similarity algorithm could have a major positive impact on the field of Natural Language Processing. This report setsout to examine the numerous document similarity algorithms, and determine which ones are the mostuseful. It addresses the most effective document similarity algorithm by categorizing them into 3 types ofdocument similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-basedalgorithms. The most effective algorithms in each category are also compared in our work using a series of benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
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Tsagris, Michail, i Ioannis Tsamardinos. "Feature selection with the R package MXM". F1000Research 7 (20.09.2018): 1505. http://dx.doi.org/10.12688/f1000research.16216.1.
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Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R as a package. The R package MXM is such an example, which not only offers a variety of feature selection algorithms, but has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models to plug into the feature selection algorithms; c) it includes an algorithm for detecting multiple solutions (many sets of equivalent features); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R. In this paper we qualitatively compare MXM with other relevant packages and discuss its advantages and disadvantages. We also provide a demonstration of its algorithms using real high-dimensional data from various applications.
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Villuendas-Rey, Yenny, Eley Barroso-Cubas, Oscar Camacho-Nieto i Cornelio Yáñez-Márquez. "A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms". Mathematics 9, nr 7 (6.04.2021): 786. http://dx.doi.org/10.3390/math9070786.
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Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.
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Dey, Nilanjan, i Amira S. Ashour. "Antenna Design and Direction of Arrival Estimation in Meta-Heuristic Paradigm". International Journal of Service Science, Management, Engineering, and Technology 7, nr 3 (lipiec 2016): 1–18. http://dx.doi.org/10.4018/ijssmet.2016070101.
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Antennas are considered as a significant component in any wireless system. There are numerous factors and constraints that affect its design. Therefore, recently several algorithms are developed to allow the designers optimize the antenna with respect to numerous different criteria, general constraints and the desired performance characteristics. In recent years there has been an increasing attention to some novel evolutionary techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria-Foraging (BF), Biogeography Based Optimization (BBO), and Differential Evolution (DE) that used for antenna optimization. The current study discussed three popular population-based meta-heuristic algorithms for optimal antenna design and direction of arrival estimation. Basically, single and multi-objective population-based meta-heuristic algorithms are included. Besides hybrid methods are highlighted. This paper reviews antenna array design optimization as well as direction of arrival optimization problem for different antennas configurations.
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Ge, Lina, Jie Wang i Guifen Zhang. "Survey of Consensus Algorithms for Proof of Stake in Blockchain". Security and Communication Networks 2022 (29.05.2022): 1–13. http://dx.doi.org/10.1155/2022/2812526.
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As the core of blockchain technology, the consensus algorithm directly affects the security, stability, and decentralisation of the blockchain and numerous other important characteristics. Choosing an appropriate consensus algorithm for different scenarios is currently a challenge in the implementation of blockchain applications. This paper classifies the improvement schemes of proof of stake (PoS) into three categories: PoS-based consensus algorithms, PoS- and PoW-based consensus algorithms, and PoS- and BFT-based consensus algorithms. First, the study introduces the PoS and PoS consensus algorithm variants and then summarises the core ideas, effects, advantages, and disadvantages of these algorithms. Subsequently, the performances of the improved algorithms are compared. Finally, the main improved methods are summarised, and the most common network security attacks are discussed. The study lays a foundation for the main improvement directions of PoS in the future, hoping to provide a reference for researchers to help them select and design consensus algorithms in different application scenarios while also helping the evolution of consensus algorithms and the implementation of blockchain applications.
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Hendry Leo Kanickam, S., i L. Jayasimman. "Comparative Analysis of Hash Authentication Algorithms and ECC Based Security Algorithms in Cloud Data". Asian Journal of Computer Science and Technology 8, nr 1 (5.02.2019): 53–61. http://dx.doi.org/10.51983/ajcst-2019.8.1.2118.
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Cloud computing is ensuring the security of stored data in cloud computing servers is one of the mainly demanding issues. In Cloud numerous security issues arises such as authentication, integrity and confidentiality. Different encryption techniques attempt to overcome these data security issues to an enormous extent. Hashing algorithm plays an important role in data integrity, message authentication, and digital signature in modern information security. For security purpose using encryption algorithm like ECC (Elliptic Curve Cryptography) and Authentication of data integrity using hashing algorithms like MD5, and SHA-512. This combination method provides data security, authentication and verification for secure cloud computing.
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Hamzaj, Genc, Artan Mazrekaj i Isak Shabani. "Applying Optimized Algorithms and Technology for Interconnecting Big Data Resources in Government Institutions". International Journal of Online and Biomedical Engineering (iJOE) 19, nr 08 (27.06.2023): 4–18. http://dx.doi.org/10.3991/ijoe.v19i08.39661.
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The quality of the data in core electronic registers has constantly decreased as a result of numerous errors that were made and inconsistencies in the data in these databases due to the growing number of databases created with the intention of providing electronic services for public administration and the lack of the data harmonization or interoperability between these databases.Evaluating and improving the quality of data by matching and linking records from multiple data sources becomes exceedingly difficult due to the incredibly large volume of data in these numerous data sources with different data architectures and no unique field to create interconnection among them.Different algorithms are developed to treat these issues and our focus will be on algorithms that handle large amounts of data, such as Levenshtein distance (LV) algorithm and Damerau-Levenshtein distance (DL) algorithm.In order to analyze and evaluate the effectiveness and quality of data using the mentioned algorithms and making improvements to these algorithms, through this paper we will conduct experiments on large data sets with more than 1 million records.
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Sandu, Iuliana, Menno Wiersma i Daphne Manichand. "Time to audit your AI algorithms". Maandblad voor Accountancy en Bedrijfseconomie 96, nr 7/8 (16.09.2022): 253–65. http://dx.doi.org/10.5117/mab.96.90108.
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Undoubtedly, the use of algorithms, and Artificial Intelligence (AI) algorithms in particular, has numerous benefits. Fields such as finance, healthcare, automotive, education, and recruitment, to name a few, have demonstrated successful application of AI algorithms. Conversely, cases of bad algorithms abound and lead to lost revenue, discrimination, disinformation, or even bodily harm. Currently, we have surpassed the stage of just observing bad algorithms. New European regulations governing AI force organizations to manage the risks introduced by algorithms and convince the public about the proper functioning of algorithms. In this context, can algorithms be rigorously audited to build public trust and if yes, how? This article aims to answer these questions by building on an auditing framework for model risk management that controls for the novelty introduced by AI algorithms while connecting AI algorithm audit with internal audit terminology.
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Sivakumar, Ramah, i Dr J.G.R. Sathiaseelan. "An enhanced constraint based technique for frequent itemset mining in transactional databases". International Journal of Engineering & Technology 7, nr 2.22 (20.04.2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.22.11807.
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Mining frequent patterns is one of the wide area of research in recent times as it has numerous social applications. Variety of frequent patterns finds usage in diverse applications and the research to mine those in an optimized way is an important aspect under consideration. So far, many algorithms had been proposed for mining frequent itemsets and each has their own pros and cons. The basic algorithms used in the process are Apriori, Fpgrowth and Eclat. Many enhancements of these algorithms are ongoing process in recent times. In this paper, an enhanced Varied Support Frequent Itemset (VSFIM) algorithm is proposed which is an enhancement of FPGrowth algorithm. Unique minimum support for each item in the transaction is provided and then mining is done in the proposed approach. The performance of the proposed algorithm is tested with existing algorithms. It is found that VSFIM outperformed the existing algorithms in both processing time and space utilization.
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Shankar, Rajendran, Narayanan Ganesh, Robert Čep, Rama Chandran Narayanan, Subham Pal i Kanak Kalita. "Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization". Processes 10, nr 3 (21.03.2022): 616. http://dx.doi.org/10.3390/pr10030616.
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The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.
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Bulitko, Vadim. "Per-Map Algorithm Selection in Real-Time Heuristic Search". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 12, nr 1 (25.06.2021): 143–48. http://dx.doi.org/10.1609/aiide.v12i1.12882.
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Real-time heuristic search is suitable for time-sensitive pathfinding and planning tasks when an AI-controlled non-playable character must interleave its planning and plan execution. Since its inception in the early 90s, numerous real-time heuristic search algorithms have been proposed. Many of the algorithms also have control parameters leaving a practitioner with a bewildering array of choices. Recent work treated the task of algorithm and parameter selection as a search problem in itself. Such automatically found algorithms outperformed previously known manually designed algorithms on the standard video-game pathfinding benchmarks. In this paper we follow up by selecting an algorithm and parameters automatically per map. Our sampling-based approach is efficient on the standard video-game pathfinding benchmarks. We also apply the approach to per-problem algorithm selection and while it is effective there as well, it is not practical. We offer suggestions on making it so.
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Zheng, Hexin. "False Vision Graphics in Logo Design Based on Artificial Intelligence in the Visual Paradox Environment". Journal of Environmental and Public Health 2022 (13.09.2022): 1–9. http://dx.doi.org/10.1155/2022/1832083.
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There are numerous fresh ways of thinking and creativity as a result of the ongoing development of art design. How do you create a fantastic logo that has extraordinary artistic effects? The incorrect interpretation of artistic graphics can cause a special visual phenomenon known as the contradiction between visual images and objective reality, which causes people’s visual systems to produce points of interest. Wrong vision graphics, or visual paradox, can give people a wonderful experience that transcends time and space and reveals the enigmatic beauty that is concealed in the natural order, which is in line with the design goals of sign art. The integration of design and artificial intelligence has drawn a lot of attention as science and technology have advanced. Designing an intelligent and comfortable user experience is more important than ever, especially given the world’s growing digital demand. The technical limitations of algorithm black boxes exacerbate the power disparity between humans and algorithms, which is inherent in artificial algorithms. According to its research, artificial intelligence’s algorithm is responsible for roughly 60% of its calculations. Random forest technology’s current benefits and drawbacks are contrasted with those of popular algorithms, and a clear vision of its potential applications in the future is presented. The artificial algorithm’s recommended operation mechanism chooses the algorithm’s nontechnical neutrality and value orientation.
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Bouhmala, Noureddine, i Ole-Christoffer Granmo. "Stochastic Learning for SAT- Encoded Graph Coloring Problems". International Journal of Applied Metaheuristic Computing 1, nr 3 (lipiec 2010): 1–19. http://dx.doi.org/10.4018/jamc.2010070101.
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The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.
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RAJASEKARAN, SANGUTHEVAR, i VAMSI KUNDETI. "SPECTRUM BASED TECHNIQUES FOR GRAPH ISOMORPHISM". International Journal of Foundations of Computer Science 20, nr 03 (czerwiec 2009): 479–99. http://dx.doi.org/10.1142/s0129054109006693.
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The graph isomorphism problem is to check if two given graphs are isomorphic. Graph isomorphism is a well studied problem and numerous algorithms are available for its solution. In this paper we present algorithms for graph isomorphism that employ the spectra of graphs. An open problem that has fascinated many a scientist is if there exists a polynomial time algorithm for graph isomorphism. Though we do not solve this problem in this paper, the algorithms we present take polynomial time. These algorithms have been tested on a good collection of instances. However, we have not been able to prove that our algorithms will work on all possible instances. In this paper, we also give a new construction for cospectral graphs.
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Melnic, Vladimir. "ATTITUDE CONTROL OF THE NANOSATELLITE USING A HYBRID FUZZY ALGORITHM BY MEANS OF THE REACTION WHEELS". Journal of Engineering Science 30, nr 2 (1.07.2023): 104–16. http://dx.doi.org/10.52326/jes.utm.2023.30(2).09.
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Numerous application fields of automation the industrial processes have demonstrated favorable outcomes with modern control algorithms that rely on artificial intelligence. Therefore, some researchers have endeavored to implement these algorithms in the attitude control of satellites by evaluating their performance in simulated scenarios. However, due to the associated cost and risks, there is a scarcity of experimental data available for testing new attitude control algorithms on a real satellite. To address this issue, simulation of satellite positioning based on the dynamic model of the satellite with reaction wheels was carried out to perform a comparative analysis of a hybrid proportional integral and derivative (PID) fuzzy control algorithm and a classical PID control algorithm, regarding the analysis of the obtained performances. The obtained results indicate that using a Hybrid Fuzzy PID control algorithm produces better performances, than the PID control algorithm.
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Abuhamdah, Anmar, i Mohammed Al-Shabi. "HYBRID LOAD BALANCING ALGORITHM FOR FOG COMPUTING ENVIRONMENT". International Journal of Software Engineering and Computer Systems 8, nr 1 (24.01.2022): 11–21. http://dx.doi.org/10.15282/ijsecs.8.1.2022.2.0092.
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Fog computing has become a new trend in the Internet of things domain and cloud computing applications. It is a novel model to achieve the availability, flexibility and better responding time. In spite of that, there is so many challenges facing computing environments such as the misuse of the resources and load-balancing between them, which has a major effect on performance. The requirement of effective and robust load-balancing algorithms is one of the most significant interest in this field. Many researchers suggested various load-balancing algorithms in fog computing, but there is still inefficiency in the system performance and misalignment in load-balancing. This paper will provide a description of numerous concepts such as computing fog, fog nodes, load balancing and then we recommend a load-balancing algorithm to enhance the fog-computing environment performance, which is a hybrid algorithm benefits from the optimizing processing time (OPT) algorithms. In order to explore the proposed algorithm performance, a comparison made with other algorithms. Results indicates that using the proposed optimizing processing time algorithm in load-balancing algorithm has superior response and processing time than the compared algorithms to user requests, and the data total cost centre’s as well.
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Tarnaris, Konstantinos, Ioanna Preka, Dionisis Kandris i Alex Alexandridis. "Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study". Electronics 9, nr 4 (21.04.2020): 675. http://dx.doi.org/10.3390/electronics9040675.
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The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.
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Ośródka, Katarzyna, i Jan Szturc. "Improvement in algorithms for quality control of weather radar data (RADVOL-QC system)". Atmospheric Measurement Techniques 15, nr 2 (19.01.2022): 261–77. http://dx.doi.org/10.5194/amt-15-261-2022.
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Abstract. Data from weather radars are commonly used in meteorology and hydrology, but they are burdened with serious disturbances, especially due to the appearance of numerous non-meteorological echoes. For this reason, these data are subject to advanced quality control algorithms. The paper presents a significant improvement of the RADVOL-QC system made necessary by the appearance of an increasing number of various disturbances. New algorithms are mainly addressed to the occurrence of clutter caused by wind turbines (DP.TURBINE algorithm) and other terrain obstacles (DP.NMET algorithm) as well as various forms of echoes caused by the interaction of a radar beam with RLAN signals (set of SPIKE algorithms). The individual algorithms are based on the employment of polarimetric data as well as on the geometric analysis of echo patterns. In the paper the algorithms are described along with examples of their performance and an assessment of their effectiveness, and finally examples of the performance of the whole system are discussed.
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Arowolo, Micheal Olaolu, Marion O. Adebiyi, Ayodele A. Adebiyi i Olatunji J. Okesola. "Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms". Indonesian Journal of Electrical Engineering and Computer Science 21, nr 2 (1.02.2021): 1073. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1073-1081.
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<p>Malaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%.</p>
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PATHAK, SUDIPTA, SANGUTHEVAR RAJASEKARAN i MARIUS NICOLAE. "EMS1: AN ELEGANT ALGORITHM FOR EDIT DISTANCE BASED MOTIF SEARCH". International Journal of Foundations of Computer Science 24, nr 04 (czerwiec 2013): 473–86. http://dx.doi.org/10.1142/s0129054113500159.
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Motifs are biologically significant patterns found in DNA/protein sequences. Given a set of biological sequences, the problem of identifying the motifs is very challenging. This problem has been well studied in computational biology. Identifying motifs through experimental processes is extremely expensive and time consuming. This is one of the factors influencing computational biologists to come up with novel computational methods to predict motifs. Several motif models have been proposed in the literature and for each model numerous algorithms have been devised. Three popular motif models are (l, d)-motif search or Planted Motif Search (PMS), Simple Motif Search (SMS), and Edit-distance based Motif Search (EMS). For PMS and SMS several algorithms have been proposed and implemented. On the other hand, even though some algorithms exist in the literature for the problem of EMS, no implementations of these algorithms are known. This is mainly because the proposed algorithms are complex. In this paper we present an elegant algorithm for EMS. We have implemented this algorithm and compared it against 14 other algorithms in terms of sensitivity and specificity. Our experimental results indicate that the new algorithm is very competitive in practice.
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Salkanovic, Alen, Sandi Ljubic, Ljubisa Stankovic i Jonatan Lerga. "Analysis of Cryptography Algorithms Implemented in Android Mobile Application". Information Technology and Control 50, nr 4 (16.12.2021): 786–807. http://dx.doi.org/10.5755/j01.itc.50.4.29464.
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This paper evaluates the performances of numerous encryption algorithms on mobile devices running the Android operating system. The primary objective of our research was to measure and compare the relative performances of tested algorithm implementations (Data Encryption Standard (DES), 3DES, Advanced Encryption Standard (AES), ChaCha20, Blowfish, and Rivest Cipher 4 (RC4)) on the Android platform. The algorithms were compared in terms of CPU utilization by measuring the time required to encrypt and decrypt variable size text files. Besides evaluating the six common symmetric encryption ciphers, a comparison has been conducted for several Password-Based Encryption (PBE) algorithms. Diverse cipher transformations were evaluated for each algorithm by utilizing various feedback modes and padding schemes. Two smartphone devices were used for testing, with different versions of the Android operating system and hardware specifications. The summarized performance outcomes for various cipher transformations are presented to demonstrate the effectiveness of each algorithm.
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Abdalla, Ahmed, Suhad Mohammed, Abdelazeim Abdalla, Tang Bin i Mohammed Ramadan. "A Study of a various Acoustic Beamforming Techniques Using a Microphone Array". Journal of Communications Technology, Electronics and Computer Science 1 (22.10.2015): 7. http://dx.doi.org/10.22385/jctecs.v1i0.3.
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In this paper, A study of numerous acoustic beamforming algorithms is carried out. Beamforming algorithms are techniques utilize to determine the Direction of arrival of (DOA) the speech signals while suppress out the corresponding noises and interferences. The simple delay and sum beamformer technique which use the constrained least mean squares (LMS) filter for spatial filtering is firstly investigated. Secondly, a constrained least mean square algorithm (also known as Frost Beamformer) is considered. The beamformer algorithms are simulated in MATLAB and therefore, the simulation results indicate that there a significant enhancement in the Signal-to-Noise-Ratio (SNR) for frost beamformer as compared to the simple delay and sum beamformer.
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Yao, Jinyan, Yongbai Sha, Yanli Chen i Xiaoying Zhao. "A Novel Ensemble of Arithmetic Optimization Algorithm and Harris Hawks Optimization for Solving Industrial Engineering Optimization Problems". Machines 10, nr 8 (24.07.2022): 602. http://dx.doi.org/10.3390/machines10080602.
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Recently, numerous new meta-heuristic algorithms have been proposed for solving optimization problems. According to the Non-Free Lunch theorem, we learn that no single algorithm can solve all optimization problems. In order to solve industrial engineering design problems more efficiently, we, inspired by the algorithm framework of the Arithmetic Optimization Algorithm (AOA) and the Harris Hawks Optimization (HHO), propose a novel hybrid algorithm based on these two algorithms, named EAOAHHO in this paper. The pinhole imaging opposition-based learning is introduced into the proposed algorithm to increase the original population diversity and the capability to escape from local optima. Furthermore, the introduction of composite mutation strategy enhances the proposed EAOAHHO exploitation and exploration to obtain better convergence accuracy. The performance of EAOAHHO is verified on 23 benchmark functions and the IEEE CEC2017 test suite. Finally, we verify the superiority of the proposed EAOAHHO over the other advanced meta-heuristic algorithms for solving four industrial engineering design problems.
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Frackiewicz, Mariusz, Henryk Palus i Daniel Prandzioch. "Superpixel-Based PSO Algorithms for Color Image Quantization". Sensors 23, nr 3 (18.01.2023): 1108. http://dx.doi.org/10.3390/s23031108.
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Nature-inspired artificial intelligence algorithms have been applied to color image quantization (CIQ) for some time. Among these algorithms, the particle swarm optimization algorithm (PSO-CIQ) and its numerous modifications are important in CIQ. In this article, the usefulness of such a modification, labeled IDE-PSO-CIQ and additionally using the idea of individual difference evolution based on the emotional states of particles, is tested. The superiority of this algorithm over the PSO-CIQ algorithm was demonstrated using a set of quality indices based on pixels, patches, and superpixels. Furthermore, both algorithms studied were applied to superpixel versions of quantized images, creating color palettes in much less time. A heuristic method was proposed to select the number of superpixels, depending on the size of the palette. The effectiveness of the proposed algorithms was experimentally verified on a set of benchmark color images. The results obtained from the computational experiments indicate a multiple reduction in computation time for the superpixel methods while maintaining the high quality of the output quantized images, slightly inferior to that obtained with the pixel methods.
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Zhu, Simin, Wenya Wu i Chuanjie Yang. "PepWise: Peptide Identification Algorithms for Tandem Mass Spectrometry Based on the Weight of Pair Amino Acid Fracture". Highlights in Science, Engineering and Technology 14 (29.09.2022): 231–38. http://dx.doi.org/10.54097/hset.v14i.1772.
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Tandem mass spectrometry is the core of the high-throughput techniques for protein identification. Abundant of MS/MS data can be generated and need to be interpreted, although numerous of peptide identification algorithms have been proposed, most well-known algorithms have been prevailingly employed to predict fragment m/z value to assign peptide sequences to spectrum, such as X!Tandem, OMSSA, Sequest, SQID and ProVerB incorporate intensity information into algorithms to assist peptide identification. Hence, we can easily know, different algorithms would use different information from the same MS data sets. Here we describe a novel protein algorithm based on the weight of pair amino acid fracture, named PepWise, compared with Mascot, Sequest at 1% False Discovery Rate (FDR), which verified the more accuracy, robustness and compatibility.
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Naga Chakravarthi Manepalli, Venkata, i Chandramohan Bhuma. "Optimization Algorithms based compensation of mismatches in Time interleaved Analog to Digital Converters". International journal of electrical and computer engineering systems 13, nr 3 (19.04.2022): 237–44. http://dx.doi.org/10.32985/ijeces.13.3.8.
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Time interleaved analog to digital converters (TIADCs) play a significant role in signal processing wherever higher sampling rates are required. However, TIADCs suffer from various mismatches like sampling time, dc offset, gain, bandwidth etc. This results in generation of erroneous signal. Numerous methods were proposed for estimation of these mismatches and for correction of the erroneous signal. Optimization algorithms like genetic algorithm (GA), differential evolution (DE) algorithm were also used for estimation of mismatches. An overview of these algorithms and their performance comparison, with respect to various signal quality metrics like signal to noise ratio (SNR), signal to noise, distortion ratio (SNDR) etc, is given in this article.
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Goldman, Geoffrey H. "Analysis of turbulence and scattering effects on detecting elevated sources with a microphone array". Journal of the Acoustical Society of America 151, nr 4 (kwiecień 2022): A135. http://dx.doi.org/10.1121/10.0010897.
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Sound waves propagating in urban environments are affected by turbulence, scattering from numerous structures, and atmospheric refraction. These effects can be incorporated into beamforming algorithms but at the cost of increased processing time and algorithm complexity. An analysis of incorporating refraction effects into the beamformers for a simplified scenario is performed on detection algorithms based on four generalized beamforming algorithms. Simulated results for beamforming algorithms implemented with no model mismatch error are compared to results for classical beamforming algorithms implemented with model mismatch errors generated by atmospheric refraction. Their relative performance is evaluated using the area under the receiver operating characteristic curve statistic calculated with Monte Carlo simulations. The results processed with data that include effects from turbulence and rough surface scattering show that there is no significant improvement in detection performance obtained by including refraction effects into the beamforming algorithms over a wide range of frequencies. The results indicate that complex beamforming algorithms are not required for many urban acoustic applications.
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Narayanan, Rama Chandran, Narayanan Ganesh, Robert Čep, Pradeep Jangir, Jasgurpreet Singh Chohan i Kanak Kalita. "A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications". Mathematics 11, nr 10 (15.05.2023): 2301. http://dx.doi.org/10.3390/math11102301.
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In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.
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Cruz, Helena, Mário Véstias, José Monteiro, Horácio Neto i Rui Policarpo Duarte. "A Review of Synthetic-Aperture Radar Image Formation Algorithms and Implementations: A Computational Perspective". Remote Sensing 14, nr 5 (4.03.2022): 1258. http://dx.doi.org/10.3390/rs14051258.
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Designing synthetic-aperture radar image formation systems can be challenging due to the numerous options of algorithms and devices that can be used. There are many SAR image formation algorithms, such as backprojection, matched-filter, polar format, Range–Doppler and chirp scaling algorithms. Each algorithm presents its own advantages and disadvantages considering efficiency and image quality; thus, we aim to introduce some of the most common SAR image formation algorithms and compare them based on these two aspects. Depending on the requisites of each individual system and implementation, there are many device options to choose from, for instance, FPGAs, GPUs, CPUs, many-core CPUs, and microcontrollers. We present a review of the state of the art of SAR imaging systems implementations. We also compare such implementations in terms of power consumption, execution time, and image quality for the different algorithms used.
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Yu, Yue, i Zi Ye. "Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players". Journal of Healthcare Engineering 2021 (24.11.2021): 1–10. http://dx.doi.org/10.1155/2021/3461648.
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It is important to predict the potential harm to the knee joint in order to prevent football players from inflicting numerous injuries to the knee during activity. Numerous professionals have been drawn to this subject, and many viable prediction systems have been developed. Prediction of potential knee joint injury is critical to effectively avoid knee joint injury during exercise. The current prediction algorithms are mainly implemented through expert interviews, medical reports, and historical documents. The algorithms have problems with low prediction accuracy or precision values. There is a need to understand more knee injury factors and improve the prediction accuracy; hence, the intelligent prediction algorithm for potential injury of knee joints of football players is proposed in this paper. Firstly, the characteristics of the knee joint injury and the injury factors of the football players are gathered and analyzed. Then, the damage is predicted by the similarity measurement. The experimental results show that the proposed algorithm has higher prediction accuracy and shorter time. According to the findings of a survey that collected healthcare data, several key factors contribute to football knee injuries. To a degree, this algorithm can predict the likelihood of a football player’s knee injury.
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Sergienko, Ivan, Vladimir Shylo, Valentyna Roshchyn i Petro Shylo. "The Efficiency of Discrete Optimization Algorithm Portfolios". Cybernetics and Computer Technologies, nr 2 (30.06.2021): 5–12. http://dx.doi.org/10.34229/2707-451x.21.2.1.
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Introduction. Solving large-scale discrete optimization problems requires the processing of large-scale data in a reasonable time. Efficient solving is only possible by using multiprocessor computer systems. However, it is a daunting challenge to adapt existing optimization algorithms to get all the benefits of these parallel computing systems. The available computational resources are ineffective without efficient and scalable parallel methods. In this connection, the algorithm unions (portfolios and teams) play a crucial role in the parallel processing of discrete optimization problems. The purpose. The purpose of this paper is to research the efficiency of the algorithm portfolios by solving the weighted max-cut problem. The research is carried out in two stages using stochastic local search algorithms. Results. In this paper, we investigate homogeneous and non-homogeneous algorithm portfolios. We developed the homogeneous portfolios of two stochastic local optimization algorithms for the weighted max-cut problem, which has numerous applications. The results confirm the advantages of the proposed methods. Conclusions. Algorithm portfolios could be used to solve well-known discrete optimization problems of unprecedented scale and significantly improve their solving time. Further, we propose using communication between algorithms, namely teams and portfolios of algorithm teams. The algorithms in a team communicate with each other to boost overall performance. It is supposed that algorithm communication allows enhancing the best features of the developed algorithms and would improve the computational times and solution quality. The underlying algorithms should be able to utilize relevant data that is being communicated effectively to achieve any computational benefit from communication. Keywords: Discrete optimization, algorithm portfolios, computational experiment.
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Boggs, Paul T., i Jon W. Tolle. "Sequential Quadratic Programming". Acta Numerica 4 (styczeń 1995): 1–51. http://dx.doi.org/10.1017/s0962492900002518.
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Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation, both commercial and public-domain SQP algorithms have been developed and used to solve a remarkably large set of important practical problems. Recently large-scale versions have been devised and tested with promising results.
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Park, Rae-Jun, Kyung-Bin Song i Bo-Sung Kwon. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning". Energies 13, nr 10 (21.05.2020): 2640. http://dx.doi.org/10.3390/en13102640.
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Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.
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Párizs, Richárd Dominik, Dániel Török, Tatyana Ageyeva i József Gábor Kovács. "Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction". Sensors 22, nr 7 (1.04.2022): 2704. http://dx.doi.org/10.3390/s22072704.
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One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8–10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.
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García-Monzó, Alejandro, Héctor Migallón, Antonio Jimeno-Morenilla, José-Luis Sánchez-Romero, Héctor Rico i Ravipudi Rao. "Efficient Subpopulation Based Parallel TLBO Optimization Algorithms". Electronics 8, nr 1 (23.12.2018): 19. http://dx.doi.org/10.3390/electronics8010019.
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A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuristic algorithms allow problems to be solved more quickly than deterministic methods. The computational time required to obtain the optimum (or near optimum) value of a cost function is a critical aspect of scientific applications in countless fields of knowledge. Therefore, we proposed efficient algorithms parallel to Teaching-learning-based optimization algorithms. TLBO is efficient and free from specific parameters to be tuned. The parallel proposals were designed with two levels of parallelization, one for shared memory platforms and the other for distributed memory platforms, obtaining good parallel performance in both types of parallel architectures and on heterogeneous memory parallel platforms.
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Nikolaidis, Dimitris, Panos Groumas, Christos Kouloumentas i Hercules Avramopoulos. "Novel Benes Network Routing Algorithm and Hardware Implementation". Technologies 10, nr 1 (25.01.2022): 16. http://dx.doi.org/10.3390/technologies10010016.
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Benes/Clos networks constitute a particularly important part of interconnection networks and have been used in numerous areas, such as multi-processor systems, data centers and on-chip networks. They have also attracted great interest in the field of optical communications due to the increasing popularity of optical switches based on these architectures. There are numerous algorithms aimed at routing these types of networks, with varying degrees of utility. Linear algorithms, such as Sun Tsu and Opferman, were historically the first attempt to standardize the routing procedure of this types of networks. They require matrix-based calculations, which are very demanding in terms of resources and in some cases involve backtracking, which impairs their efficiency. Parallel solutions, such as Lee’s algorithm, were introduced later and provide a different answer that satisfy the requirements of high-performance networks. They are, however, extremely complex and demand even more resources. In both cases, hardware implementations reflect their algorithmic characteristics. In this paper, we attempt to design an algorithm that is simple enough to be implemented on a small field programmable gate array board while simultaneously efficient enough to be used in practical scenarios. The design itself is of a generic nature; therefore, its behavior across different sizes (8 × 8, 16 × 16, 32 × 32, 64 × 64) is examined. The platform of implementation is a medium range FPGA specifically selected to represent the average hardware prototyping device. In the end, an overview of the algorithm’s imprint on the device is presented alongside other approaches, which include both hard and soft computing techniques.
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Cazacu, Razvan. "Matlab Framework for Image Processing and Feature Extraction Flexible Algorithm Design". Proceedings 63, nr 1 (24.01.2021): 72. http://dx.doi.org/10.3390/proceedings2020063072.
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Image processing and the analysis of images in order to extract relevant data is an ever-growing topic of research. Although there are numerous methods readily available, the task of image preprocessing and feature extraction requires developing specific algorithms for specific problems by combining different functions and tweaking their parameters. This paper proposes a framework that allows the flexible construction of image processing algorithms. Its user interface and architecture are designed to ease and speed up the process of algorithm creation and testing as well as serve as an application for the use of these algorithms by end users. The framework was built in Matlab and makes use of its integrated Image Processing toolbox.
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Tan, Xicheng, Liping Di, Yanfei Zhong, Nengcheng Chen, Fang Huang, Jinchuan Wang, Ziheng Sun i Yahya Khan. "Distributed Geoscience Algorithm Integration Based on OWS Specifications: A Case Study of the Extraction of a River Network". ISPRS International Journal of Geo-Information 8, nr 1 (28.12.2018): 12. http://dx.doi.org/10.3390/ijgi8010012.
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To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges.
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TASOULIS, D. K., i M. N. VRAHATIS. "UNSUPERVISED CLUSTERING USING FRACTAL DIMENSION". International Journal of Bifurcation and Chaos 16, nr 07 (lipiec 2006): 2073–79. http://dx.doi.org/10.1142/s021812740601591x.
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Clustering can be defined as the process of "grouping" a collection of objects into subsets or clusters. The clustering problem has been addressed in numerous contexts and by researchers in different disciplines. This reflects its broad appeal and usefulness as an exploratory data analysis approach. Unsupervised clustering algorithms have been developed to address real world problems in which the number of clusters present in the dataset is unknown. These algorithms approximate the number of clusters while performing the clustering procedure. This paper is a first step towards the development of unsupervised clustering algorithms capable of identifying clusters within clusters. To this end, an unsupervised clustering algorithm is modified so as to take into consideration the fractal dimension of the data. The experimental results indicate that this approach can provide further qualitative information compared to the unsupervised clustering algorithm.
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Gulayeva, N. M., i S. A. Yaremko. "EXPERIMENTAL ANALYSIS OF MULTINATIONAL GENETIC ALGORITHM AND ITS MODIFICATIONS". Radio Electronics, Computer Science, Control, nr 2 (3.07.2021): 71–83. http://dx.doi.org/10.15588/1607-3274-2021-2-8.
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Context. Niching genetic algorithms are one of the most popular approaches to solve multimodal optimization problems. When classifying niching genetic algorithms it is possible to select algorithms explicitly analyzing topography of fitness function landscape; multinational genetic algorithm is one of the earliest examples of these algorithms. Objective. Development and analysis of the multinational genetic algorithm and its modifications to find all maxima of a multimodal function. Method. Experimental analysis of algorithms is carried out. Numerous runs of algorithms on well-known test problems are conducted and performance criteria are computed, namely, the percentage of convergence, real (global, local) and fake peak ratios; note that peak rations are computed only in case of algorithm convergence. Results. Software implementation of a multinational genetic algorithm has been developed and experimental tuning of its parameters has been carried out. Two modifications of hill-valley function used for determining the relative position of individuals have been proposed. Experimental analysis of the multinational genetic algorithm with classic hill-valley function and with its modifications has been carried out. Conclusions. The scientific novelty of the study is that hill-valley function modifications producing less number of wrong identifications of basins of attraction in comparison with classic hill-valley function are proposed. Using these modifications yields to performance improvements of the multinational genetic algorithm for a number of test functions; for other test functions improvement of the quality criteria is accompanied by the decrease of the convergence percentage. In general, the convergence percentage and the quality criterion values demonstrated by the algorithm studied are insufficient for practical use in comparison with other known algorithms. At the same time using modified hill-valley functions as a post-processing step for other niching algorithms seems to be a promising improvement of performance of these algorithms.
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Allugunti, Viswanatha Reddy, Dr Elango NM i Dr Kishor Kumar Reddy C. "Internet of Things Based Early Detection of Diabetes Using Machine Learning Algorithms: Dpa". International Journal of Innovative Technology and Exploring Engineering 8, nr 10 (30.08.2019): 1443–47. http://dx.doi.org/10.35940/ijitee.a1013.0881019.
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This paper introduces a new decision tree algorithm Diabetes Prediction Algorithm (DPA), for the early prediction of diabetes based on the datasets. The datasets are collected by using Internet of Things (IOT) Diabetes Sensors, comprises of 15000 records, out of which 11250 records are used for training purpose and 3750 are used for testing purpose. The proposed algorithm DPA yielded an accuracy of 90.02 %, specificity of 92.60 %, and precision of 89.17% and error rate of 9.98%. further, the proposed algorithm is compared with existing approaches. Currently there are numerous algorithms available which are not complete accurate and DPA helps.
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Tsagris, Michail, i Ioannis Tsamardinos. "Feature selection with the R package MXM". F1000Research 7 (30.09.2019): 1505. http://dx.doi.org/10.12688/f1000research.16216.2.
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Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications.
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Shieh, Chin-Shiuh, Thanh-Tuan Nguyen, Wan-Wei Lin, Dinh-Cuong Nguyen i Mong-Fong Horng. "Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems". Applied Sciences 12, nr 19 (30.09.2022): 9867. http://dx.doi.org/10.3390/app12199867.
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The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired metaheuristic method named Coral Reef Optimization in conjunction with two local search techniques, Simulated Annealing (SA) and Variable Neighborhood Search (VNS), with significant performance and finding-solutions speed enhancement. The two-hybrid algorithms’ performance is evaluated by solving JSSP of various sizes. The findings demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms.
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Yang, Xu, Chao Song, Mengdi Yu, Jiqing Gu i Ming Liu. "Distributed Triangle Approximately Counting Algorithms in Simple Graph Stream". ACM Transactions on Knowledge Discovery from Data 16, nr 4 (31.08.2022): 1–43. http://dx.doi.org/10.1145/3494562.
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Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, the problem of counting global and local triangles in a graph stream has been widely studied, and numerous triangle counting steaming algorithms have emerged. To improve the throughput and scalability of streaming algorithms, many researches of distributed streaming algorithms on multiple machines are studied. In this article, we first propose a framework of distributed streaming algorithm based on the Master-Worker-Aggregator architecture. The two core parts of this framework are an edge distribution strategy, which plays a key role to affect the performance, including the communication overhead and workload balance, and aggregation method, which is critical to obtain the unbiased estimations of the global and local triangle counts in a graph stream. Then, we extend the state-of-the-art centralized algorithm TRIÈST into four distributed algorithms under our framework. Compared to their competitors, experimental results show that DVHT-i is excellent in accuracy and speed, performing better than the best existing distributed streaming algorithm. DEHT-b is the fastest algorithm and has the least communication overhead. What’s more, it almost achieves absolute workload balance.
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Guo, Shuman, Shichang Wang, Zhenzhong Yang, Lijun Wang, Huawei Zhang, Pengyan Guo, Yuguo Gao i Junkai Guo. "A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving". Applied Sciences 12, nr 21 (23.10.2022): 10741. http://dx.doi.org/10.3390/app122110741.
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Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms have evolved in recent years as a result of deep learning’s outstanding performance in the field of visual object tracking. There have been a number of evaluations on individual sub-problems, but none that cover the challenges, datasets, and algorithms associated with visual multi-object tracking in autonomous driving scenarios. In this research, we present an exhaustive study of algorithms in the field of visual multi-object tracking over the last ten years, based on a systematic review approach. The algorithm is broken down into three groups based on its structure: methods for tracking by detection (TBD), joint detection and tracking (JDT), and Transformer-based tracking. The research reveals that the TBD algorithm has a straightforward structure, however the correlation between its individual sub-modules is not very strong. To track multiple objects, the JDT technique combines multi-module joint learning with a deep network framework. Transformer-based algorithms have been explored over the past two years, and they have benefits in numerous assessment indicators, as well as tremendous research potential in the area of multi-object tracking. Theoretical support for algorithmic research in adjacent disciplines is provided by this paper. Additionally, the approach we discuss, which uses merely monocular cameras rather than sophisticated sensor fusion, is anticipated to pave the way for the quick creation of safe and affordable autonomous driving systems.
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Mir, Raja Tawseef Ahmad. "Machine Learning Algorithms and Its Applications: A Survey". International Journal for Research in Applied Science and Engineering Technology 10, nr 6 (30.06.2022): 3467–69. http://dx.doi.org/10.22214/ijraset.2022.44672.
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Abstract: In todays present world lots of microelectronic statistics is created in apiece and every field. The data that we obtain contains valuable info to forecast the future. Owing to the enormous in magnitude, the physical forecasting gives a intricate chore to humans. To overwhelm this problem, the data model is made in such a way so that it can predict the future by the situation with the aid of training data and test datasets. To Pullman the machine or the data model, numerous types of machine learning algorithms (ML) and tools are available. This paper will emphasis on the review of the few machine learning algorithms(ML) and methods used in numerous applications and domains in a detailed manner