Algorithms  

Mojtaba Nayyeri, Modjtaba Rouhani, Hadi Sadoghi Yazdi, Marko M. Mäkelä, Alaleh Maskooki and Yury Nikulin

One of the main disadvantages of the traditional mean square error (MSE)-based constructive networks is their poor performance in the presence of non-Gaussian noises. In this paper, we propose a new incremental constructive network based on the correntrop... see more

Tushar Ganguli and Edwin K. P. Chong

We present a novel technique for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. Our technique is based on the number of times each neuron is activated during model ... see more

Wenny Hojas-Mazo, Francisco Maciá-Pérez, José Vicente Berná Martínez, Mailyn Moreno-Espino, Iren Lorenzo Fonseca and Juan Pavón

Analysing message streams in a dynamic environment is challenging. Various methods and metrics are used to evaluate message classification solutions, but often fail to realistically simulate the actual environment. As a result, the evaluation can produce ... see more

Mattia Neroni, Massimo Bertolini and Angel A. Juan

In automated storage and retrieval systems (AS/RSs), the utilization of intelligent algorithms can reduce the makespan required to complete a series of input/output operations. This paper introduces a simulation optimization algorithm designed to minimize... see more

Yuzhu Zhang and Hao Xu

This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present con... see more

Ioannis G. Tsoulos and V. N. Stavrou

In the current research, we consider the solution of dispersion relations addressed to solid state physics by using artificial neural networks (ANNs). Most specifically, in a double semiconductor heterostructure, we theoretically investigate the dispersio... see more

Károly Héberger

Background: The development and application of machine learning (ML) methods have become so fast that almost nobody can follow their developments in every detail. It is no wonder that numerous errors and inconsistencies in their usage have also spread wit... see more

Deyuan Zhong, Liangda Fang and Quanlong Guan

Encoding a dictionary into another representation means that all the words can be stored in the dictionary in a more efficient way. In this way, we can complete common operations in dictionaries, such as (1) searching for a word in the dictionary, (2) add... see more

Ken Jom Ho, Ender Özcan and Peer-Olaf Siebers

Solving multiple objective optimization problems can be computationally intensive even when experiments can be performed with the help of a simulation model. There are many methodologies that can achieve good tradeoffs between solution quality and resourc... see more

Stanislav Kirpichenko, Lev Utkin, Andrei Konstantinov and Vladimir Muliukha

A method for estimating the conditional average treatment effect under the condition of censored time-to-event data, called BENK (the Beran Estimator with Neural Kernels), is proposed. The main idea behind the method is to apply the Beran estimator for es... see more

Jiao Su, Yi An, Jialin Wu and Kai Zhang

Pedestrian detection has always been a difficult and hot spot in computer vision research. At the same time, pedestrian detection technology plays an important role in many applications, such as intelligent transportation and security monitoring. In compl... see more

Romain Amyot, Noriyuki Kodera and Holger Flechsig

Simulation of atomic force microscopy (AFM) computationally emulates experimental scanning of a biomolecular structure to produce topographic images that can be correlated with measured images. Its application to the enormous amount of available high-reso... see more

Jiaming Li, Ning Xie and Tingting Zhao

In recent years, with the rapid advancements in Natural Language Processing (NLP) technologies, large models have become widespread. Traditional reinforcement learning algorithms have also started experimenting with language models to optimize training. H... see more

Zheng Li, Xinkai Chen, Jiaqing Fu, Ning Xie and Tingting Zhao

With the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies. Multi-agent deep reinforcement learning shines b... see more

Alessio Cellupica, Marco Cirelli, Giovanni Saggio, Emanuele Gruppioni and Pier Paolo Valentini

In recent years, the boost in the development of hardware and software resources for building virtual reality environments has fuelled the development of tools to support training in different disciplines. The purpose of this work is to discuss a complete... see more

Mohammad Shokouhifar, Mohamad Hasanvand, Elaheh Moharamkhani and Frank Werner

Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, thereb... see more

Azal Ahmad Khan, Salman Hussain and Rohitash Chandra

Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In ... see more

Mahbuba Begum, Sumaita Binte Shorif, Mohammad Shorif Uddin, Jannatul Ferdush, Tony Jan, Alistair Barros and Md Whaiduzzaman

Digital multimedia elements such as text, image, audio, and video can be easily manipulated because of the rapid rise of multimedia technology, making data protection a prime concern. Hence, copyright protection, content authentication, and integrity veri... see more

Sardar Anisul Haque, Mohammad Tanvir Parvez and Shahadat Hossain

Matrix–matrix multiplication is of singular importance in linear algebra operations with a multitude of applications in scientific and engineering computing. Data structures for storing matrix elements are designed to minimize overhead information as well... see more

Ivan S. Maksymov

Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design... see more

Pornrawee Tatit, Kiki Adhinugraha and David Taniar

Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are... see more

Yiming Fan and Meng Wang

Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approa... see more

Virgilijus Sakalauskas and Dalia Kriksciuniene

The growing popularity of e-commerce has prompted researchers to take a greater interest in deeper understanding online shopping behavior, consumer interest patterns, and the effectiveness of advertising campaigns. This paper presents a fresh approach for... see more

Amr A. Abd El-Mageed, Ayoub Al-Hamadi, Samy Bakheet and Asmaa H. Abd El-Rahiem

It is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current–voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect para... see more

Costas Panagiotakis

In this paper, we present a general version of polygonal fitting problem called Unconstrained Polygonal Fitting (UPF). Our goal is to represent a given 2D shape S with an N-vertex polygonal curve P with a known number of vertices, so that the Intersection... see more

Marco Scutari

Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl’s causality, and determi... see more

Dthenifer Cordeiro Santana, Gustavo de Faria Theodoro, Ricardo Gava, João Lucas Gouveia de Oliveira, Larissa Pereira Ribeiro Teodoro, Izabela Cristina de Oliveira, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior, Job Teixeira de Oliveira and Paulo Eduardo Teodoro

Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing... see more

Danilo Pau, Andrea Pisani and Antonio Candelieri

In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger p... see more

?tefan Ionescu, Camelia Delcea, Nora Chiri?a and Ionu? Nica

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particul... see more

Jocelyn Sabatier and Christophe Farges

This paper proposes algorithms to model fractional (dynamical) behaviors using non-singular rational kernels whose interest is first demonstrated on a pure power law function. Two algorithms are then proposed to find a non-singular rational kernel that al... see more

Ana Corceiro, Nuno Pereira, Khadijeh Alibabaei and Pedro D. Gaspar

The global population’s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networ... see more

Alireza Rezvanian, S. Mehdi Vahidipour and Ali Mohammad Saghiri

Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA)... see more

David S. Pellicer and Emilio Larrodé

This paper shows the development of a numerical analysis model, which enables the calculation of the cargo transport capacity of a vehicle that circulates through a vacuum tube at high speed, whose effectiveness in transport is analyzed. The simulated tra... see more

Yuto Kamiwaki and Shinji Fukuda

This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. The input variables for the SPAD value prediction using Random Forests, XGBoost, and LightGBM were RGB values, HSL va... see more

António Pedro Branco, Cátia Vaz and Alexandre P. Francisco

There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks or cancerous cells in tumor progression trees. These tools rel... see more

Depeng Gao, Shuai Wang, Yuwei Yang, Haifei Zhang, Hao Chen, Xiangxiang Mei, Shuxi Chen and Jianlin Qiu

Servo motors play an important role in automation equipment and have been used in several manufacturing fields. However, the commonly used control methods need their parameters to be set manually, which is rather difficult, and this means that these metho... see more

Firas Alghanim, Ibrahim Al-Hurani, Hazem Qattous, Abdullah Al-Refai, Osamah Batiha, Abedalrhman Alkhateeb and Salama Ikki

Identifying menopause-related breast cancer biomarkers is crucial for enhancing diagnosis, prognosis, and personalized treatment at that stage of the patient’s life. In this paper, we present a comprehensive framework for extracting multiomics biomarkers ... see more

Mauro Dell’Amico, Jafar Jamal and Roberto Montemanni

The minimum-cost arborescence problem is a well-studied problem. Polynomial-time algorithms for solving it exist. Recently, a new variation of the problem called the Precedence-Constrained Minimum-Cost Arborescence Problem with Waiting Times was presented... see more

Vincenzo Manca

A symbolic analysis of Archimedes’s periodical number system is developed, from which a natural link emerges with the modern positional number systems with zero. After the publication of Fibonacci’s Liber Abaci, the decimal Indo-Arabic positional system w... see more

Fu-Shiung Hsieh

One of the most significant financial benefits of a shared mobility mode such as ridesharing is cost savings. For this reason, a lot of studies focus on the maximization of cost savings in shared mobility systems. Cost savings provide an incentive for rid... see more

François Legrand, Richard Macwan, Alain Lalande, Lisa Métairie and Thomas Decourselle

Automated Cardiac Magnetic Resonance segmentation serves as a crucial tool for the evaluation of cardiac function, facilitating faster clinical assessments that prove advantageous for both practitioners and patients alike. Recent studies have predominantl... see more

Fabi Prezja, Leevi Annala, Sampsa Kiiskinen and Timo Ojala

Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets... see more

Konstantin Gaipov, Daniil Tausnev, Sergey Khodenkov, Natalya Shepeta, Dmitry Malyshev, Aleksey Popov and Lev Kazakovtsev

Rapid growth in the volume of transmitted information has lead to the emergence of new wireless networking technologies with variable heterogeneous topologies. With limited radio frequency resources, optimal routing problems arise, both at the network des... see more

Nosa Aikodon, Sandra Ortega-Martorell and Ivan Olier

Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU pati... see more

Ioannis G. Tsoulos

Kai Gutenschwager, Markus Rabe and Jorge Chicaiza-Vaca

Fast growing e-commerce has a significant impact both on CEP providers and public entities. While service providers have the first priority on factors such as costs and reliable service, both are increasingly focused on environmental effects, in the inter... see more

Dena Kadhim Muhsen, Ahmed T. Sadiq and Firas Abdulrazzaq Raheem

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is e... see more

Antonio Angrisano, Giovanni Cappello, Salvatore Gaglione and Ciro Gioia

Velocity estimation has a key role in several applications; for instance, velocity estimation in navigation or in mobile mapping systems and GNSSs is currently a common way to achieve reliable and accurate velocity. Two approaches are mainly used to obtai... see more

Jih-Jeng Huang and Chin-Yi Chen

The Analytic Hierarchy Process (AHP) has been a widely used multi-criteria decision-making (MCDM) method since the 1980s because of its simplicity and rationality. However, the conventional AHP assumes criteria independence, which is not always accurate i... see more