Algorithms

14 pages, 1554 KiB

Open AccessReview

Review Quantum Circuit Synthesis for Grover’s Algorithm Oracle

by Miguel A. Naranjo and Luis A. Fletscher

Algorithms 2024, 17(9), 382; https://doi.org/10.3390/a17090382 - 28 Aug 2024

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The search for information in a system has been a continuous problem for a computer. This has resulted in the construction of a set of classical algorithms that can search for a set of data. This is why search systems can be divided [...] Read more.

The search for information in a system has been a continuous problem for a computer. This has resulted in the construction of a set of classical algorithms that can search for a set of data. This is why search systems can be divided into the type of information being searched, the number of solutions to find, and even the terms used for searching. With the emergence of quantum computing, new algorithms have been generated for this type of process. An example is the Grover algorithm, which performs theoretically better than traditional algorithms. This is why there has been research on optimizing it, applying it to new fields, and making it more accessible to industry users. Even if the algorithm is a promising alternative, one of the disadvantages of Grover’s algorithm is the use of an oracle function that must be generated for every set of search data. This review describes three sets of methodologies for generating quantum circuits that can be applied to constructing this oracle quantum circuit. Full article

(This article belongs to the Special Issue Quantum and Classical Artificial Intelligence)

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18 pages, 2372 KiB

Open AccessArticle

Multithreading-Based Algorithm for High-Performance Tchebichef Polynomials with Higher Orders

by Ahlam Hanoon Al-sudani, Basheera M. Mahmmod, Firas A. Sabir, Sadiq H. Abdulhussain, Muntadher Alsabah and Wameedh Nazar Flayyih

Algorithms 2024, 17(9), 381; https://doi.org/10.3390/a17090381 - 27 Aug 2024

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Abstract

Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique properties of the TPs and their remarkable performance. Nowadays, the demand for [...] Read more.

Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique properties of the TPs and their remarkable performance. Nowadays, the demand for high-quality images (2D signals) is increasing and is expected to continue growing. The processing of these signals requires the generation of accurate and fast polynomials. The existing algorithms generate the TPs sequentially, and this is considered as computationally costly for high-order and larger-sized polynomials. To this end, we present a new efficient solution to overcome the limitation of sequential algorithms. The presented algorithm uses the parallel processing paradigm to leverage the computation cost. This is performed by utilizing the multicore and multithreading features of a CPU. The implementation of multithreaded algorithms for computing TP coefficients segments the computations into sub-tasks. These sub-tasks are executed concurrently on several threads across the available cores. The performance of the multithreaded algorithm is evaluated on various TP sizes, which demonstrates a significant improvement in computation time. Furthermore, a selection for the appropriate number of threads for the proposed algorithm is introduced. The results reveal that the proposed algorithm enhances the computation performance to provide a quick, steady, and accurate computation of the TP coefficients, making it a practical solution for different applications. Full article

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15 pages, 1289 KiB

Open AccessArticle

An Improved Negotiation-Based Approach for Collecting and Sorting Operations in Waste Management and Recycling

by Massimiliano Caramia and Giuseppe Stecca

Algorithms 2024, 17(9), 380; https://doi.org/10.3390/a17090380 - 27 Aug 2024

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Abstract

This paper addresses the problem of optimal planning for collection, sorting, and recycling operations. The problem arises in industrial waste management, where distinct actors manage the collection and the sorting operations. In a weekly or monthly plan horizon, they usually interact to find [...] Read more.

This paper addresses the problem of optimal planning for collection, sorting, and recycling operations. The problem arises in industrial waste management, where distinct actors manage the collection and the sorting operations. In a weekly or monthly plan horizon, they usually interact to find a suitable schedule for servicing customers but with a not well-defined scheme. We proposal an improved negotiation-based approach using an auction mechanism for optimizing these operations. Two interdependent models are presented: one for waste collection by a logistics operator and the other for sorting operations at a recycling plant. These models are formulated as mixed-integer linear programs where costs associated with sorting and collection are to be minimized, respectively. We describe the negotiation-based approach involving an auction where the logistics operator bids for collection time slots, and the recycling plant selects the optimal bid based on the integration of sorting and collection costs. This approach aims to achieve an optimization of the entire waste management process. Computational experiments are presented. Full article

(This article belongs to the Special Issue 2024 and 2025 Selected Papers from Algorithms Editorial Board Members)

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23 pages, 1094 KiB

Open AccessArticle

Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation

by Sandip Shingade, Rajdeep Niyogi and Mayuri Pichare

Algorithms 2024, 17(9), 379; https://doi.org/10.3390/a17090379 - 26 Aug 2024

Viewed by 191

Abstract

Collaboration in a network is crucial for effective team formation. This paper addresses challenges in collaboration networks by identifying the skills required for effective team formation. The communication cost is low when agents with the same skills are connected. Our main objective is [...] Read more.

Collaboration in a network is crucial for effective team formation. This paper addresses challenges in collaboration networks by identifying the skills required for effective team formation. The communication cost is low when agents with the same skills are connected. Our main objective is to minimize team communication costs by selecting agents with the required skills. However, finding an optimal team is a computationally hard problem. This study introduces a novel hybrid approach called I-PSO-Jaya (improved PSO-Jaya, which combines PSO (Particle Swarm Optimization) and the Jaya algorithm with the Modified Swap Operator to form efficient teams. A potential application scenario of the algorithm is to build a team of engineers for an IT project. The implementation results show that our approach gives an improvement of 73% in the Academia dataset and 92% in the ACM dataset compared to existing methods. Full article

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26 pages, 3378 KiB

Open AccessArticle

Parallel PSO for Efficient Neural Network Training Using GPGPU and Apache Spark in Edge Computing Sets

by Manuel I. Capel, Alberto Salguero-Hidalgo and Juan A. Holgado-Terriza

Algorithms 2024, 17(9), 378; https://doi.org/10.3390/a17090378 - 26 Aug 2024

Viewed by 397

Abstract

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the [...] Read more.

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO. Full article

(This article belongs to the Collection Parallel and Distributed Computing: Algorithms and Applications)

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14 pages, 400 KiB

Open AccessArticle

Star Bicolouring of Bipartite Graphs

by Daya Gaur, Shahadat Hossain and Rishi Ranjan Singh

Algorithms 2024, 17(9), 377; https://doi.org/10.3390/a17090377 - 24 Aug 2024

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Abstract

We give an integer linear program formulation for the star bicolouring of bipartite graphs. We develop a column generation method to solve the linear programming relaxation to obtain a lower bound for the minimum number of colours needed. We determine the star bicolouring [...] Read more.

We give an integer linear program formulation for the star bicolouring of bipartite graphs. We develop a column generation method to solve the linear programming relaxation to obtain a lower bound for the minimum number of colours needed. We determine the star bicolouring using the iterative rounding method. We give computational results on arrowhead matrices, sparse random matrices, complete bipartite graphs, and matrices from the Harwell–Boeing collection. The findings demonstrate that the proposed method effectively establishes lower and upper bounds for the minimum number of colours needed for a star bicolouring of bipartite graphs, particularly for sparse bipartite graphs. Full article

(This article belongs to the Collection Feature Papers in Combinatorial Optimization, Graph, and Network Algorithms)

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36 pages, 2715 KiB

Open AccessArticle

Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases

by Virginia Sandulescu, Marilena Ianculescu, Liudmila Valeanu and Adriana Alexandru

Algorithms 2024, 17(9), 376; https://doi.org/10.3390/a17090376 - 23 Aug 2024

Viewed by 307

Abstract

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these [...] Read more.

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases. Full article

(This article belongs to the Special Issue Artificial Intelligence-based Algorithms with Potential Applications in Healthcare and Prediction of Disease Evolution)

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19 pages, 5132 KiB

Open AccessArticle

Synthetic Face Discrimination via Learned Image Compression

by Sofia Iliopoulou, Panagiotis Tsinganos, Dimitris Ampeliotis and Athanassios Skodras

Algorithms 2024, 17(9), 375; https://doi.org/10.3390/a17090375 - 23 Aug 2024

Viewed by 253

Abstract

The emergence of deep learning has sparked notable strides in the quality of synthetic media. Yet, as photorealism reaches new heights, the line between generated and authentic images blurs, raising concerns about the dissemination of counterfeit or manipulated content online. Consequently, there is [...] Read more.

The emergence of deep learning has sparked notable strides in the quality of synthetic media. Yet, as photorealism reaches new heights, the line between generated and authentic images blurs, raising concerns about the dissemination of counterfeit or manipulated content online. Consequently, there is a pressing need to develop automated tools capable of effectively distinguishing synthetic images, especially those portraying faces, which is one of the most commonly encountered issues. In this work, we propose a novel approach to synthetic face discrimination, leveraging deep learning-based image compression and predominantly utilizing the quality metrics of an image to determine its authenticity. Full article

(This article belongs to the Special Issue Algorithms for Image Processing and Machine Vision)

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Algorithms, Volume 17, Issue 9 (September 2024) – 8 articles

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