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Journal articles on the topic 'Computational Supremacy'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Harrow, Aram W., and Ashley Montanaro. "Quantum computational supremacy." Nature 549, no. 7671 (2017): 203–9. http://dx.doi.org/10.1038/nature23458.

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Morimae, Tomoyuki, and Suguru Tamaki. "Fine-grained quantum computational supremacy." quantum Information and Computation 19, no. 13&14 (2019): 1089–115. http://dx.doi.org/10.26421/qic19.13-14-2.

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(pp1089-1115) Tomoyuki Morimae and Suguru Tamaki doi: https://doi.org/10.26421/QIC19.13-14-2 Abstracts: Output probability distributions of several sub-universal quantum computing models cannot be classically efficiently sampled unless some unlikely consequences occur in classical complexity theory, such as the collapse of the polynomial-time hierarchy. These results, so called quantum supremacy, however, do not rule out possibilities of super-polynomial-time classical simulations. In this paper, we study ``fine-grained" version of quantum supremacy that excludes some exponential-time classica

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Kocharovsky, Vitaly. "Universal nature of quantum supremacy." Journal of Physics: Conference Series 2894, no. 1 (2024): 012002. http://dx.doi.org/10.1088/1742-6596/2894/1/012002.

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Abstract We disclose the universal nature of computational #P-hardness and quantum supremacy of quantum many-body systems. We do so by means of the new powerful technique (the hafnian master theorem) that allows one to address the #P-hard problems systematically. We consider a generic example of many-body interacting systems – a trapped BEC-gas of interacting Bose atoms, apply the hafnian master theorem and refer to the Toda’s theorem on a #P-complete oracle.

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Dalzell, Alexander M., Aram W. Harrow, Dax Enshan Koh, and Rolando L. La Placa. "How many qubits are needed for quantum computational supremacy?" Quantum 4 (May 11, 2020): 264. http://dx.doi.org/10.22331/q-2020-05-11-264.

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Quantum computational supremacy arguments, which describe a way for a quantum computer to perform a task that cannot also be done by a classical computer, typically require some sort of computational assumption related to the limitations of classical computation. One common assumption is that the polynomial hierarchy (PH) does not collapse, a stronger version of the statement that P≠NP, which leads to the conclusion that any classical simulation of certain families of quantum circuits requires time scaling worse than any polynomial in the size of the circuits. However, the asymptotic nature of

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Zhang, Han. "Quantum Entanglement and Qubit Interactions: The Key to Quantum Supremacy." Theoretical and Natural Science 41, no. 1 (2024): 115–21. http://dx.doi.org/10.54254/2753-8818/41/2024ch0156.

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Abstract. Quantum computing operates in a fundamentally different way from classical computing by harnessing the principles of quantum mechanics to process information. Quantum supremacy is achieved when a quantum computer can solve problems that are beyond the capabilities of classical systems, including the human brain, showcasing its superior processing power. To attain quantum supremacy, quantum entanglement and qubit interactions play a pivotal role. Quantum entanglement occurs when qubits are interconnected in a manner where the state of one qubit directly influences the state of others,

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Sarpeshkar, Rahul, and Thomas E. Kurtz. "Quantum Computing: Redefining Computational Limits for the Next Era." International Journal of Innovative Computer Science and IT Research 1, no. 01 (2025): 1–6. https://doi.org/10.63665/ijicsitr.v1i01.02.

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Quantum computing is poised to revolutionize the world of computation by har nessing the principles of quantum mechanics to solve problems that are beyond the capabilities of classical computers. Unlike traditional binary computing, which relies on bits that exist as 0s or 1s, quantum computing operates using qubits, which leverage superposition, entanglement, and quantum parallelism to perform computations exponentially faster. This transformative technology has applications in cryptography, artificial intelligence, materials science, and complex system simulations, offering unprecedented com

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Rahul, Sarpeshkar Thomas E. Kurtz. "Quantum Computing: Redefining Computational Limits for the Next Era." International Journal of Innovative Computer Science and IT Research 01, no. 01 (2025): 10–15. https://doi.org/10.5281/zenodo.15151970.

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Quantum computing is poised to revolutionize the world of computation by harnessing the principles of quantum mechanics to solve problems that are beyond the capabilities of classical computers. Unlike traditional binary computing, which relies on bits that exist as 0s or 1s, quantum computing operates using qubits, which leverage superposition, entanglement, and quantum parallelism to perform computations exponentially faster. This transformative technology has applications in cryptography, artificial intelligence, materials science, and complex system simulations, of

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Li, Riling, Bujiao Wu, Mingsheng Ying, Xiaoming Sun, and Guangwen Yang. "Quantum Supremacy Circuit Simulation on Sunway TaihuLight." IEEE Transactions on Parallel and Distributed Systems 31, no. 4 (2020): 805–16. http://dx.doi.org/10.1109/tpds.2019.2947511.

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Bentivegna, Marco, Nicolò Spagnolo, Chiara Vitelli, et al. "Experimental scattershot boson sampling." Science Advances 1, no. 3 (2015): e1400255. http://dx.doi.org/10.1126/sciadv.1400255.

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Boson sampling is a computational task strongly believed to be hard for classical computers, but efficiently solvable by orchestrated bosonic interference in a specialized quantum computer. Current experimental schemes, however, are still insufficient for a convincing demonstration of the advantage of quantum over classical computation. A new variation of this task, scattershot boson sampling, leads to an exponential increase in speed of the quantum device, using a larger number of photon sources based on parametric down-conversion. This is achieved by having multiple heralded single photons b

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Jain, Shilpa, Prakash C. Mathpal, Dinesh Bisht, and Phool Singh. "A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting." Cybernetics and Information Technologies 18, no. 1 (2018): 3–10. http://dx.doi.org/10.2478/cait-2018-0001.

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Abstract This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central tendencies mean and variance. This study gives a solution to the critical issue in designing of fuzzy systems, number of fuzzy sets. Proposed computational method helps in finding intervals and thereby fuzzy sets for fuzzy time series forecasting. Proposed computational method is implemented on the authentic data for the enrolments of University of Alabama, which is considered as benchmark problem in the field of fuzzy time series. The

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Muraleedharan, Gopikrishnan, Akimasa Miyake, and Ivan H. Deutsch. "Quantum computational supremacy in the sampling of bosonic random walkers on a one-dimensional lattice." New Journal of Physics 21, no. 5 (2019): 055003. http://dx.doi.org/10.1088/1367-2630/ab0610.

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Bentivegna, Marco, Nicolò Spagnolo, Chiara Vitelli, et al. "Bayesian approach to Boson sampling validation." International Journal of Quantum Information 12, no. 07n08 (2014): 1560028. http://dx.doi.org/10.1142/s021974991560028x.

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The Boson sampling problem consists in sampling from the output probability distribution of a bosonic Fock state, after it evolves through a linear interferometer. There is strong evidence that Boson sampling is computationally hard for classical computers, while it can be solved naturally by bosons. This has led it to draw increasing attention as a possible way to provide experimental evidence for the quantum computational supremacy. Nevertheless, the very complexity of the problem makes it hard to exclude the hypothesis that the experimental data are sampled from a different probability dist

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Vashisth, Shubham, Ishika Dhall, and Garima Aggarwal. "Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis." Journal of Intelligent Systems 30, no. 1 (2021): 998–1013. http://dx.doi.org/10.1515/jisys-2020-0089.

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Abstract The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. This study illustrates the working of the qu

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Kocharovsky, Vitaly V., Vladimir V. Kocharovsky, William D. Shannon, and Sergey V. Tarasov. "Towards the Simplest Model of Quantum Supremacy: Atomic Boson Sampling in a Box Trap." Entropy 25, no. 12 (2023): 1584. http://dx.doi.org/10.3390/e25121584.

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We describe boson sampling of interacting atoms from the noncondensed fraction of Bose–Einstein-condensed (BEC) gas confined in a box trap as a new platform for studying computational ♯P-hardness and quantum supremacy of many-body systems. We calculate the characteristic function and statistics of atom numbers via the newly found Hafnian master theorem. Using Bloch–Messiah reduction, we find that interatomic interactions give rise to two equally important entities—eigen-squeeze modes and eigen-energy quasiparticles—whose interplay with sampling atom states determines the behavior of the BEC ga

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Mittal, Meera. "Quantum Computing and Information: Recent Developments and Future Prospects." Journal of Quantum Science and Technology 1, no. 2 (2024): 12–17. http://dx.doi.org/10.36676/jqst.v1.i2.10.

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Recent advancements in quantum computing have sparked significant interest due to their potential to revolutionize various fields, from cryptography to optimization problems. This paper reviews recent developments in quantum computing and quantum information theory, focusing on breakthroughs in qubit stability, error correction codes, and quantum algorithm design. Key achievements include the demonstration of quantum supremacy in specific tasks and progress towards scalable quantum processors. Looking forward, the prospects of quantum computing in solving complex computational problems and its

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Hangleiter, Dominik, Juan Bermejo-Vega, Martin Schwarz, and Jens Eisert. "Anticoncentration theorems for schemes showing a quantum speedup." Quantum 2 (May 22, 2018): 65. http://dx.doi.org/10.22331/q-2018-05-22-65.

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One of the main milestones in quantum information science is to realise quantum devices that exhibit an exponential computational advantage over classical ones without being universal quantum computers, a state of affairs dubbed quantum speedup, or sometimes "quantum computational supremacy". The known schemes heavily rely on mathematical assumptions that are plausible but unproven, prominently results on anticoncentration of random prescriptions. In this work, we aim at closing the gap by proving two anticoncentration theorems and accompanying hardness results, one for circuit-based schemes,

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Alshamrani, Ahmad M., Akash Saxena, Shalini Shekhawat, Hossam M. Zawbaa, and Ali Wagdy Mohamed. "Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction." Processes 11, no. 6 (2023): 1655. http://dx.doi.org/10.3390/pr11061655.

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Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). Firs

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Tiwari, Sunil. "STUDY OF PARALLEL COMPUTING STRUCTURE AND CIRCUIT ANALYSIS FOR INTERCONNECTION NETWORK." International Journal of Advanced Research in Computer Science 14, no. 03 (2023): 56–59. http://dx.doi.org/10.26483/ijarcs.v14i3.7005.

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Parallel computing has become a crucial topic in the concern of computer science and also it is revealed to be critical when researching in high performance. The evolution of computer architectures towards an improved number of nodes, where parallelism could be the approach to option for speeding up an algorithm within the last few decades. The combination of processing units build a model of computation (circuits) has gained an essential place in the area of high performance computing (HPC) due to its configuration and considerable processing supremacy that is parallel, series, etc…. In this

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Tamrakar, Abha, and Rishabh Sharma. "Quantum Computing: A Comprehensive Review." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 3 (2019): 1634–42. http://dx.doi.org/10.61841/turcomat.v10i3.14623.

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Quantum computing has emerged as a revolutionary paradigm that promises to solve computational problems beyond the capabilities of classical computers. This comprehensive review paper explores the fundamentals, models, algorithms, technologies, challenges, and practical applications of quantum computing. The paper begins with an introduction to quantum computing, highlighting its defining features and significance. It then discusses the fundamentals of quantum computing, including qubits, quantum gates, quantum entanglement, and quantum parallelism. The paper also examines different quantum co

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Et.al, Ms P. Mahalakshmi. "Cross - Language based Multi-Document Summarization Model using Machine Learning Technique." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 331–35. http://dx.doi.org/10.17762/turcomat.v12i6.1393.

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Cross-Language Multi-document summarization (CLMDS) process produces a summary generated from multiple documents in which the summary language is different from the source document language. The CLMDS model allows the user to provide query in a particular language (e.g., Tamil) and generates a summary in the same language from different language source documents. The proposed model enables the user to provide a query in Tamil language, generate a summary from multiple English documents, and finally translate the summary into Tamil language. The proposed model makes use of naïve Bayes classifie

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Ahmed, Amjed, and Mohammad Hasan. "Exploring Quantum Computing: Potential Applications and Current Challenges in Algorithm Design." CyberSystem Journal 1, no. 1 (2024): 42–53. http://dx.doi.org/10.57238/4srf6b63.

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Quantum supremacy refers to the experimental demonstration that a quantum computing device performs a calculation that no classical computer can match in a reasonable time. Such machines would not be useful for solving problems of practical interest, but they would provide a proof of principle that larger and more useful machines could be built. The gap between classical and quantum computing is potentially enormous, and the apparent ease with which quantum states can be prepared, manipulated, and measured has led many to believe that quantum devices might one day outperform classical hardware

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Chabaud, Ulysse, Frédéric Grosshans, Elham Kashefi, and Damian Markham. "Efficient verification of Boson Sampling." Quantum 5 (November 15, 2021): 578. http://dx.doi.org/10.22331/q-2021-11-15-578.

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The demonstration of quantum speedup, also known as quantum computational supremacy, that is the ability of quantum computers to outperform dramatically their classical counterparts, is an important milestone in the field of quantum computing. While quantum speedup experiments are gradually escaping the regime of classical simulation, they still lack efficient verification protocols and rely on partial validation. Here we derive an efficient protocol for verifying with single-mode Gaussian measurements the output states of a large class of continuous-variable quantum circuits demonstrating qua

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ul Abdeen, Zain, and Mujeeb ur Rehman. "A numerical method for solving fractional differential equations." Engineering Computations 36, no. 2 (2019): 551–68. http://dx.doi.org/10.1108/ec-07-2018-0302.

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Purpose The purpose of this paper is to present a computational technique based on Newton–Cotes quadrature rule for solving fractional order differential equation. Design/methodology/approach The numerical method reduces initial value problem into a system of algebraic equations. The method presented here is also applicable to non-linear differential equations. To deal with non-linear equations, a recursive sequence of approximations is developed using quasi-linearization technique. Findings The method is tested on several benchmark problems from the literature. Comparison shows the supremacy

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Sarkar, Aritra, Zaid Al-Ars, and Koen Bertels. "Estimating Algorithmic Information Using Quantum Computing for Genomics Applications." Applied Sciences 11, no. 6 (2021): 2696. http://dx.doi.org/10.3390/app11062696.

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Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further conditioned to estimate the metrics. Specific cases are explored where the quantum implementation offe

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Dahiya, Amrita, and Brij B. Gupta. "An Economic Incentive-Based Risk Transfer Approach for Defending Against DDoS Attacks." International Journal of E-Services and Mobile Applications 12, no. 3 (2020): 60–84. http://dx.doi.org/10.4018/ijesma.2020070104.

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Volumetric DDoS attacks are continued to be an immense threat to e-commerce and other online businesses from the last decade as attackers have much resources to amplify scale and frequency day by day. Despite significant efforts by research community and security professionals, we are lacking a robust solution against DDoS attacks. Generally, the attacker's investment in sending large amount of traffic to paralyze a system is negligible as compared to the loss they caused to an organization. Therefore, in this article, a risk transfer approach, a combination of techno-economic aspects is propo

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Pathni, Rajiv Kumar. "Artificial Intelligence and the Myth of Objectivity." Journal of Healthcare Management Standards 3, no. 1 (2023): 1–14. http://dx.doi.org/10.4018/jhms.329234.

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Artificial intelligence (AI) is being rapidly integrated into healthcare with a naïve belief in the objectivity of AI and a complacent trust in the omniscience of computational knowledge. While AI has the potential to transform healthcare, there are significant ethical and safety concerns. The pace of AI development and the race for AI supremacy is leading to a rapid, and largely unregulated, proliferation of AI applications. It is important to understand that AI technologies bring new and accelerated risks and need meaningful human control and oversight. However, standards and regulation in t

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Gray, Johnnie, and Stefanos Kourtis. "Hyper-optimized tensor network contraction." Quantum 5 (March 15, 2021): 410. http://dx.doi.org/10.22331/q-2021-03-15-410.

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Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a vari

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Ivancova, Olga, Vladimir Korenkov, Olga Tyatyushkina, Sergey Ulyanov, and Toshio Fukuda. "Quantum supremacy in end-to-end intelligent IT. Pt. I:Quantum software engineering–quantum gate level applied models simulators." System Analysis in Science and Education, no. 1 (2020) (2020): 52–84. http://dx.doi.org/10.37005/2071-9612-2020-1-52-84.

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Principles and methodologies of quantum algorithmic gates design for master course and PhD students in computer science, control engineering and intelligent robotics described. The possibilities of quantum algorithmic gates simulation on classical computers discussed. Applications of quantum gate of nanotechnology in intelligent quantum control introduced. Anew approach to a circuit implementation design of quantum algorithm gates for fast quantum massive parallel computing presented. The main attention focused on the development of design method of fast quantum algorithm operators as superpos

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Ivancova, Olga, Vladimir Korenkov, Olga Tyatyushkina, Sergey Ulyanov, and Toshio Fukuda. "Quantum supremacy in end-to-end intelligent IT. Pt. 2: State -of -art of quantum SW/HW computational gate-model toolkit." System Analysis in Science and Education, no. 1 (2020) (2020): 85–125. http://dx.doi.org/10.37005/2071-9612-2020-1-85-125.

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Several paradigms of quantum computing are considered. Quantum computer simulators are de-scribed. Models of learning quantum systems from experiments are considered. Quantum speed-up limitation in two-level systems (qubit) is discussed. The approaches to the formation of a quantum variational intrinsic solver are considered.

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Oji, Majority. "Conspiracy theories, misinformation, disinformation and the coronavirus: A burgeoning of post-truth in the social media." Journal of African Media Studies 14, no. 3 (2022): 439–53. http://dx.doi.org/10.1386/jams_00087_1.

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This article examines the coronavirus pandemic against a burgeoning culture of post-truth in social media. The theoretical narrative reviews the social media cum post-truth epoch and identifies network fluidity of the medium, absence of gatekeepers, tempering of human thinking with machine thinking, and supremacy of alternative facts over truth as basic ingredients that oil-free reign of manipulative and propulsive forces of coronavirus’ conspiracy theories, forged news, tricky data, and disinformation. The article asserts that as the bubbles of post-truth swear up in the online media, conned

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Mokhinur Haydar kizi Raupova. "Quantum computing: The race to build tomorrow's supercomputers." International Journal of Science and Research Archive 14, no. 2 (2025): 1781–84. https://doi.org/10.30574/ijsra.2025.14.2.0570.

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Quantum computing stands at the frontier of computational revolution, promising to transform our ability to solve problems that remain intractable for today's most powerful classical computers. This article explores the fundamental principles of quantum computing, where quantum mechanical phenomena such as superposition and entanglement enable unprecedented parallel processing capabilities. We examine why quantum computers have become a focal point of global research and development, with potential applications ranging from drug discovery and climate modeling to breaking current encryption sta

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Faustin, Umuhire Mucyo, and Beiji Zou. "An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM)." Computation 10, no. 7 (2022): 104. http://dx.doi.org/10.3390/computation10070104.

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The objective of the present study is to improve the genetic algorithm (GA) supremacy in selecting the most suitable and relevant features within a highly dimensional dataset. This results in cost reduction and improving classification performance. During text classification, employing terms such as features using vector space representation can result in a high dimensionality of future space. This condition presents some issues, including high computation cost in data analysis and deteriorating classification accuracy performance. Several computational feature selection techniques can be appl

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Mahmood, Muhammad Arif, Anita Ioana Visan, Carmen Ristoscu, and Ion N. Mihailescu. "Artificial Neural Network Algorithms for 3D Printing." Materials 14, no. 1 (2020): 163. http://dx.doi.org/10.3390/ma14010163.

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Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating t

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Egri-Nagy, Attila, and Antti Törmänen. "The Game Is Not over Yet—Go in the Post-AlphaGo Era." Philosophies 5, no. 4 (2020): 37. http://dx.doi.org/10.3390/philosophies5040037.

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The game of Go was the last great challenge for artificial intelligence in abstract board games. AlphaGo was the first system to reach supremacy, and subsequent implementations further improved the state of the art. As in chess, the fall of the human world champion did not lead to the end of the game. Now, we have renewed interest in the game due to new questions that emerged in this development. How far are we from perfect play? Can humans catch up? How compressible is Go knowledge? What is the computational complexity of a perfect player? How much energy is really needed to play the game opt

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Et. al., Rojasree V. "Research Intuitions of Asymmetric Crypto System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5024–33. http://dx.doi.org/10.17762/turcomat.v12i3.2016.

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The fast moving world full of data exchange and communication technology, with all sensitive information of an individual virtually available anywhere and anytime, make the Internet world more critical in security aspects. The areas of risks are attended and assured to be safe by means of some sought of crypto mechanisms. The strength and vulnerability of the crypto mechanism defines the durability of the system. The encryption on the communication channel can implement either public or private key algorithms based on the area of applications. The public key cryptography is specifically design

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Augustin, NYEMBO MPAMPI, and MBAYO LUKASU Shadrack. "AI's First World War: A Silent Technological War with Global Strategic Stakes." International Journal of Mathematics and Computer Research 13, no. 05 (2025): 5267–75. https://doi.org/10.5281/zenodo.15562421.

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Artificial intelligence (AI) has become a major strategic issue, giving rise to a First Technological World War where major powers compete for computational and algorithmic supremacy. This competition is based on the mastery of advanced hardware infrastructures (GPUs, TPUs, ASICs) and increasingly powerful learning models, notably <em>Transformers and LLMs </em>. However, this rise in power is accompanied by new threats in cybersecurity , where AI is both an offensive weapon and a defensive shield. From adversarial attacks to autonomous malware, vulnerabilities in AI systems are beco

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Amin, Ruhul, Chowdhury Rafeed Rahman, Sajid Ahmed, et al. "iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters." Bioinformatics 36, no. 19 (2020): 4869–75. http://dx.doi.org/10.1093/bioinformatics/btaa609.

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Abstract Motivation Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra- and interclass variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge. Results We present iPromoter-BnCNN for identification and accurate classification of six types of promoters—σ24

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Yousif, Zahid, Intesab Hussain, Soufiene Djahel, and Yassine Hadjadj-Aoul. "A Novel Energy-Efficient Clustering Algorithm for More Sustainable Wireless Sensor Networks Enabled Smart Cities Applications." Journal of Sensor and Actuator Networks 10, no. 3 (2021): 50. http://dx.doi.org/10.3390/jsan10030050.

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Wireless Sensor Networks (WSNs) is a major sensing technology that has revolutionized the way information is collected, processed, and used in many smart cities’ applications that rely on sensing technologies for event detection and monitoring. Despite the multiple benefits that such technology offers, the quick depletion of sensors’ battery power represents a major concern, mainly due to the extensive computational tasks and communication operations performed by individual sensors. Indeed, the cost of replacing batteries can be prohibitively expensive, especially when sensors are deployed in

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Xia, Chenxi. "Comparative Analysis of ARIMA and LSTM Models for Agricultural Product Price Forecasting." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1032–40. http://dx.doi.org/10.54097/8q6nx369.

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The fluctuations in vegetable prices can have an impact on the economy. Machine learning can identify price trend changes. This study investigates the performance of ARIMA and LSTM models in predicting price trends for agricultural products, focusing on greens and lotus roots. The objective was to ascertain the superior model in terms of accurately reflecting market oscillations—a critical aspect for stakeholders in the agricultural sector. The investigation contrasted the ARIMA model's adeptness at detecting linear tendencies against the LSTM's capacity to decode intricate nonlinear dynamics.

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Alshiha, Abeer A. Mohamad, Mohammed W. Al-Neama, and Abdalrahman R. Qubaa. "Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods." International Journal of Electrical and Electronics Research 11, no. 4 (2023): 1013–21. http://dx.doi.org/10.37391/ijeer.110419.

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This paper introduces a pioneering Hybrid Parallel Multi-linear Face Recognition algorithm that capitalizes on multi-linear methodologies, such as Multi-linear Principal Component Analysis (MPCA), Linear Discriminant Analysis (LDA), and Histogram of Oriented Gradients (HOG), to attain exceptional recognition performance. The Hybrid Feature Selection (HFS) algorithm is meticulously crafted to augment the classification performance on the CK+ and FERET datasets by amalgamating the strengths of feature extraction techniques and feature selection methods. HFS seamlessly incorporates Principal Comp

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Schaeffer, Jonathan. "The 1970 United States computer chess championship: The start of the longest-running experiment in computer science history." ICGA Journal 42, no. 2-3 (2020): 72–85. http://dx.doi.org/10.3233/icg-200149.

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On August 31, 1970, an experiment began that continues to this day. The first chess tournament for computers was held as part of the Association for Computing Machinery’s (ACM’s) National Conference. The interest generated was tremendous, leading to ACM sponsoring an annual event until 1994. Chess competitions continue to this day, allowing for 50 years of data on the growth of artificial intelligence capabilities in this domain. During this period, program ratings have soared from roughly 1400 in 1970 to over 3500 today. The 1970 event was the first continuous competition in computer science

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Koh, Dax E. "Further extensions of Clifford circuits and their classical simulation complexities." Quantum Information and Computation 17, no. 3&4 (2017): 262–82. http://dx.doi.org/10.26421/qic17.3-4-5.

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Extended Clifford circuits straddle the boundary between classical and quantum computational power. Whether such circuits are efficiently classically simulable seems to depend delicately on the ingredients of the circuits. While some combinations of ingredients lead to efficiently classically simulable circuits, other combinations, which might just be slightly different, lead to circuits which are likely not. We extend the results of Jozsa and Van den Nest [Quant. Info. Comput. 14, 633 (2014)] by studying two further extensions of Clifford circuits. First, we consider how the classical simulat

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Gomaa Haroun, A. H., and Yin-Ya Li. "Ant Lion Optimized Fractional Order Fuzzy Pre-Compensated Intelligent Pid Controller for Frequency Stabilization of Interconnected Multi-Area Power Systems." Applied System Innovation 2, no. 2 (2019): 17. http://dx.doi.org/10.3390/asi2020017.

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Load frequency control (LFC) is considered to be the most important strategy in interconnected multi-area power systems for satisfactory operation and distribution. In order to transfer reliable power with acceptable quality, an LFC mechanism requires highly efficacy and intelligent techniques. In this paper, a novel hybrid fractional order fuzzy pre-compensated intelligent proportional-integral-derivative (PID) (FOFP-iPID) controller is proposed for the LFC of a realistic interconnected two-area power system. The proposed FOFP-iPID controller is incorporated into the power system as a seconda

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Tang, Guoyang, Xueyi Zhao, Yanyun Fu, and Xiaolin Ning. "Sampling clustering based on multi-view attribute structural relations." PLOS ONE 19, no. 5 (2024): e0297989. http://dx.doi.org/10.1371/journal.pone.0297989.

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In light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes. Notably, the practical significance of multi-view graph clustering is heightened due to the presence of diverse relationships within real-world graph data. Nonetheless, prevailing graph clustering techniques, predominantly grounded in deep learning neural networks, face challenges in effectively handling multi-view graph data. These challenges include the incapability

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Bhukya, Hanumanthu, and Sadanandam Manchala. "Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework." Journal of Intelligent Systems 31, no. 1 (2022): 1002–13. http://dx.doi.org/10.1515/jisys-2022-0066.

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Abstract Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, this article pr

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Kumar, Manish, Sudhansu Kumar Mishra, Sumit Kumar Choubey, Sanjay Shankar Tripathy, Dilip Kumar Choubey, and Dinesh Das. "Cat Swarm Optimization based Functional Link Multilayer Perceptron for Suppression of Gaussian and Impulse Noise from Computed Tomography Images." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 4 (2020): 329–39. http://dx.doi.org/10.2174/1573405614666180903115336.

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Background: The Gaussian and impulse noises corrupt the Computed Tomography (CT) images either individually or collectively, and the conventional fixed filters do not have the potential to suppress these noise. Objectives: These spurious noises affect the inherent features of CT image awkwardly. Hence, to handle such a situation adaptive Cat Swarm Optimization based Functional Link Multilayer Perceptron (CSO-FLMLP) has been proposed in this paper to get rid of unwanted noise from the CT images. Methods: Here, the nature-inspired CSO technique which is an optimization algorithm has been employe

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Amezquita, Herbert, Cindy P. Guzman, and Hugo Morais. "Forecasting Electric Vehicles’ Charging Behavior at Charging Stations: A Data Science-Based Approach." Energies 17, no. 14 (2024): 3396. http://dx.doi.org/10.3390/en17143396.

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The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs’ plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-pro

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Iqbal, Muhammad Ahmad, Muhammad Salman Fakhar, Noor Ul Ain, et al. "A New Fast Deterministic Economic Dispatch Method and Statistical Performance Evaluation for the Cascaded Short-Term Hydrothermal Scheduling Problem." Sustainability 15, no. 2 (2023): 1644. http://dx.doi.org/10.3390/su15021644.

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The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization problem that has been solved by conventional and metaheuristic algorithms in the past. As the CSTHTS problem falls under the category of applied operational research, therefore, the work is still on-going to find new algorithms and variants of the existing algorithms that would better approximate the optimal global solution in a shorter computational time. This article proposes a novel deterministic thermal economic dispatch method embedded with the improve

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Mahmood, Tahir, Zeeshan Ali, Kifayat Ullah, et al. "Complex pythagorean fuzzy aggregation operators based on confidence levels and their applications." Mathematical Biosciences and Engineering 19, no. 1 (2021): 1078–107. http://dx.doi.org/10.3934/mbe.2022050.

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<abstract> <p>The most important influence of this assessment is to analyze some new operational laws based on confidential levels (CLs) for complex Pythagorean fuzzy (CPF) settings. Moreover, to demonstrate the closeness between finite numbers of alternatives, the conception of confidence CPF weighted averaging (CCPFWA), confidence CPF ordered weighted averaging (CCPFOWA), confidence CPF weighted geometric (CCPFWG), and confidence CPF ordered weighted geometric (CCPFOWG) operators are invented. Several significant features of the invented works are also diagnosed. Moreover, to inv

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Mohamed, Nuzaiha, Reem Lafi Almutairi, Sayda Abdelrahim, et al. "Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning." Cancers 16, no. 1 (2023): 181. http://dx.doi.org/10.3390/cancers16010181.

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Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivati

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