Algorithms, Volume 18, Issue 2

This paper introduces an improved version of the Federated Random High Local Performance (Fed-RHLP) algorithm, specifically aimed at addressing the difficulties posed by Non-IID (Non-Independent and Identically Distributed) data within the context of...

The Internet of Things (IoT) is developing quickly, which has led to the development of new opportunities in many different fields. As the number of IoT devices continues to expand, particularly in transportation and healthcare, the need for efficien...

Martensite start (Ms) temperature is a critical parameter in the production of parts and structural steels and plays a vital role in heat treatment processes to achieve desired properties. However, it is often challenging to estimate accurately throu...

In this work, the Bin Packing combinatorial optimization problem is studied from the practical side. The focus is on the Falkenauer T benchmark class, which is a collection of 80 problem instances that are considered hard to handle algorithmically. C...

Renewable energy sources must be scheduled to manage power flow and load demand. Photovoltaic power generation is usually connected to power distribution networks and is not designed to add significant amounts of production in the event of increased...

Combinatorial optimization on temporal graphs is critical for summarizing dynamic networks in various fields, including transportation, social networks, and biology. Among these problems, the Minimum Timeline Cover (MinTCover) problem, aimed at ident...

In this paper, we consider the minimal vertex cover and minimal dominating sets with capacity and/or connectivity constraint enumeration problems. We develop polynomial-delay enumeration algorithms for these problems on bounded-degree graphs. For the...

This paper addresses the challenge of optimizing microgrid sizing to enhance reliability and efficiency in electrical energy supply. A comprehensive framework that integrates multiyear optimization with uncertainty considerations is presented to faci...

Tourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize thei...

This study introduces a novel portfolio optimization approach that combines Bacterial Foraging Optimization (BFO) with risk management techniques and Sharpe ratio analysis. BFO, a nature-inspired algorithm, is employed to construct diversified portfo...

This paper presents a novel approach to audio classification leveraging the latent representation generated by Meta’s EnCodec neural audio codec. We hypothesize that the compressed latent space representation captures essential audio features m...

Cloud manufacturing represents a pioneering service paradigm that provides flexible, personalized manufacturing services to customers via the Internet. Service composition plays a crucial role in cloud manufacturing, which focuses on integrating disp...

In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. This paper examines the effectiveness of deep reinforcement learning (DRL) within interacti...

Traditionally, classifying species has required taxonomic experts to carefully examine unique physical characteristics, a time-intensive and complex process. Machine learning offers a promising alternative by utilizing computational power to detect s...

In this paper, a new algorithm for the training of Locally Recurrent Neural Networks (LRNNs) is presented, which aims to reduce computational complexity and at the same time guarantee the stability of the network during the training. The main feature...

This study addresses the challenge of detecting crimes against individuals in public security applications, particularly where the availability of quality data is limited, and existing models exhibit a lack of generalization to real-world scenarios....

The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain...

Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a crit...

In this paper, a semi-active and fully active suspension system using a PID controller were designed and tuned in MATLAB/Simulink to achieve simultaneous optimisation of comfort and road holding ability. This was performed in order to quantify and ob...

Cloud computing, a superset of heterogeneous distributed computing, allows sharing of geographically dispersed resources across multiple organizations on a rental basis using virtualization as per demand. In cloud computing, workflow allocation to ac...

Pediatric pneumonia remains a significant global health issue, particularly in low- and middle-income countries, where it contributes substantially to mortality in children under five. This study introduces a deep learning model for pediatric pneumon...

This paper presents algorithms to estimate the signal-to-noise ratio (SNR) in the time domain and frequency domain that employ a modified Constant Amplitude Zero Autocorrelation (CAZAC) synchronization preamble, denoted as CAZAC-TD and CAZAC-FD SNR e...

Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster and more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), have shown s...

Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photop...

Remote monitoring of a patient’s vital activities has become increasingly important in dealing with various medical applications. In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG)...

Accurately estimating house values is a critical challenge for real-estate stakeholders, including homeowners, buyers, sellers, agents, and policymakers. This study introduces a novel approach to this problem using Kolmogorov–Arnold networks (K...

Aiming at the uncertainty in cargo demand in the transportation process, the multimodal transportation path optimization problem is studied from the perspective of a low-carbon economy, and the robust optimization modeling method is introduced. First...

The thermal vibration of thick Terfenol-D control law on functionally graded material (FGM) plates/cylindrical shells in nonlinear unsteady supersonic flow with third-order shear deformation theory (TSDT) is investigated by using the generalized diff...

Brain tumors profoundly affect human health owing to their intricacy and the difficulties associated with early identification and treatment. Precise diagnosis is essential for effective intervention; nevertheless, the resemblance among tumor forms o...

To address the need for accurate classification of electrocardiogram (ECG) signals, we employ an interpretable KAN to classify arrhythmia diseases. Experimental evaluation of the MIT-BIH and PTB datasets demonstrates the significant superiority of th...

Estimating the Probability Density Function (PDF) of observed data is crucial as a problem in its own right, and also for diverse engineering applications. This paper utilizes two powerful mathematical tools, the concept of moments and the relatively...

This paper presents a new method of batch-to-batch optimization control for a fed-batch fermentation process. A recursively updated extreme learning machine (ELM) neural network model is used to model a fed-batch fermentation process. ELM models have...

Objective (1): To develop and validate a machine learning (ML) model using radiomic features (RFs) extracted from [18F]FDG PET-CT to predict abdominal aortic aneurysm (AAA) growth rate. Methods (2): This retrospective study included 98 internal and 5...

The interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues. However, the growing number of different s...

Many sciences exploit algorithms in a large variety of applications. In agronomy, large amounts of agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. In this particular field, the number of scien...

One of the most actively researched areas in the field of process mining is process discovery, which aims to construct a schema that aligns with existing event trace sequences. Current standard industrial workflow schema induction methods impose cert...

Pneumonia is a deadly disease affecting millions worldwide, caused by microorganisms and environmental factors. It leads to lung fluid build-up, making breathing difficult, and is a leading cause of death. Early detection and treatment are crucial fo...

The aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by...

Energy scheduling for hybrid unmanned aerial vehicles (UAVs) is of critical importance to their safe and stable operation. However, traditional approaches, predominantly rule-based, often lack the dynamic adaptability and stability necessary to addre...

For some classes of control plants (e.g., large time delay or inverse response), the PID controllers may offer unsatisfactory results; on the other hand, a Model Predictive Control (MPC) algorithm based on a linear model may offer insufficient contro...

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and...

The brain acts as the body’s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions c...

Machine learning (ML) techniques are increasingly used to diagnose faults in aerospace applications, but diagnosing multiple faults in aircraft fuel systems (AFSs) remains challenging due to complex component interactions. This paper evaluates the ac...

Broadcasting is a fundamental information dissemination problem in a connected network where one node, referred to as the originator, must distribute a message to all other nodes through a series of calls along the network’s links. Once informe...

Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a cl...

In this study, we analyze the performance of the machine learning operators in Apache Spark MLlib for K-Means, Random Forest Regression, and Word2Vec. We used a multi-node Spark cluster along with collected detailed execution metrics computed from th...

Members of a profession frequently show similar personality characteristics. In this research, we leverage recent advances in NLP to compute personal values using a moral values framework, distinguishing between four different personas that assist in...

In recent years, panoptic segmentation has garnered increasing attention from researchers aiming to better understand scenes in images. Although many excellent studies have been proposed, they share some common unresolved issues. Firstly, panoptic se...

Tensor networks are a very powerful data structure tool originating from simulations of quantum systems. In recent years, they have seen increased use in machine learning, mostly in trainings with gradient-based techniques, due to their flexibility a...

As security threats become more complex, the need for effective intrusion detection systems (IDSs) has grown. Traditional machine learning methods are limited by the need for extensive feature engineering and data preprocessing. To overcome this, we...