Article
Computer Science, Information Systems
Surendra Kumar Nanda, Suneeta Mohanty, Prasant Kumar Pattnaik, Mangal Sain
Summary: Reversible cellular automaton provides enhanced throughput in cryptographic applications and is highly parallel. Optimized throughput algorithms perform better in high-performance systems with multiple CPU or GPU cores. Using different random numbers as seeds can make plaintext blocks immune to other blocks during cryptanalysis.
Article
Mathematics
George Cosmin Stanica, Petre Anghelescu
Summary: This study presents the theory and application of cellular automata (CA) for a stream cipher-based encryption principle. Certain fundamental transformations are developed using CA theory for decentralized computation to model different system behaviors. The changes in state transitions rely on simple evolution rules that can easily be translated into functions using logic operators. A class of linear hybrid cellular automata (LHCA), based on rules 90 and 150, is used to implement these functions. The suggested algorithm is based on symmetric key systems theory and utilizes the properties provided by LHCA evolution to convert plain text into cipher text and vice versa, starting from the same initial state and performing the same number of steps for each operation.
Article
Computer Science, Information Systems
Lama Sleem, Raphael Couturier
Summary: This paper proposes a new ultra-lightweight cryptographic algorithm based on the NSA-designed Speck algorithm, named Speck-R, which uses a dynamic substitution layer to reduce the number of rounds from 26 to 7, thus decreasing the execution time significantly while maintaining a high level of security. Extensive tests on different IoT devices show that Speck-R outperforms Speck in terms of execution times, proving its suitability for lightweight devices.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Alberto Dennunzio, Enrico Formenti, Darij Grinberg, Luciano Margara
Summary: This paper provides decidable characterizations for additive cellular automata over a finite abelian group, including injectivity, surjectivity, equicontinuity, sensitivity to initial conditions, topological transitivity, and ergodicity, which are important for designing applications based on additive CA. The authors also discuss how these results can be utilized in cryptographic applications and propose modifications to existing schemes to enhance security and increase the difficulty of attacks.
INFORMATION SCIENCES
(2021)
Article
Telecommunications
Ayan Banerjee, Anirban Kundu
Summary: Authors propose a hardware-based approach for reliable encryption in wireless network using Cellular Automata (CA), which involves distinct layers of encryption in transmitter and receiver modules for enhancing data security. The proposed model includes processes such as capturing environmental noise, transmitting encrypted signals, analyzing received signals, and calculating protection ratio. Key features of this method include the selection of hardware components, measurement of time complexity, and code break complexity.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Erick Hernandez-Diaz, Hector Perez-Meana, Victor Silva-Garcia, Rolando Flores-Carapia
Summary: This research introduces a new symmetric encryption system based on elliptical curve and chaos, which enhances security through improved encryption levels and increased diffusion with the use of strong curve solution points and random number strings. The system also includes a substitution box with a non-linearity of 100 to strengthen the cryptosystem against various attacks. The proposed cryptosystem is tested against different analyses to verify its robustness and the quality of the obtained cipher text.
Article
Computer Science, Information Systems
Lanhang Li, Yuling Luo, Senhui Qiu, Xue Ouyang, Lvchen Cao, Shunbin Tang
Summary: A new image encryption algorithm is proposed in this study, using chaotic maps and Cellular Automata to increase the sensitivity to plain-images and enhance security.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Mechanical
Manu Shrivastava, Satyabrata Roy, Krishna Kumar, Chirag Vinodkumar Pandey, Jyoti Grover
Summary: This paper introduces a color image cipher based on cellular automaton for IoT applications, achieving faster execution speeds, lower energy consumption, and a bigger key space. The cipher scheme has been tested and shown to perform well against cyberattacks.
NONLINEAR DYNAMICS
(2021)
Article
Computer Science, Information Systems
Erendira Corona-Bermudez, Juan Carlos Chimal-Eguia, German Tellez-Castillo
Summary: This paper proposes a security framework based on cellular automata, consisting of entity authentication, data encryption, and decryption. Authentication is achieved using a zero-knowledge protocol, where the shared secret is transformed into a more complex key by a two-dimensional cellular automaton. The sensitivity of cellular automata to initial conditions adds another layer of security to the system.
Article
Computer Science, Information Systems
Ashish Kumar, N. S. Raghava
Summary: In this paper, a lightweight cryptosystem is designed based on lookup table operations, reducing computational overhead and resource requirement. By combining one-dimensional elementary cellular automaton with Henon chaotic map, the designed cryptosystem demonstrates unprecedented results in cryptography.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Krishna Kumar, Satyabrata Roy, Umashankar Rawat, Astitv Shandilya
Summary: This paper proposes a novel lightweight image encryption technique that combines Second-Order Cellular Automata (SOCA) and a chaotic map. The proposed scheme achieves a high encryption speed and low computational complexity, making it suitable for real-time applications and resource-constrained devices. Extensive experimental results demonstrate its effectiveness and robustness against various attacks.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Telecommunications
Ritesh Kumar, Pritam Khan, Sudhir Kumar
Summary: In this article, we propose a low complexity hybrid cellular automata algorithm to secure healthcare data and enable remote monitoring of patients using IoT network. The experimental results demonstrate that our algorithm outperforms the state-of-the-art methods in terms of runtime complexity.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Physics, Multidisciplinary
Lianet Contreras Rodriguez, Evaristo Jose Madarro-Capo, Carlos Miguel Legon-Perez, Omar Rojas, Guillermo Sosa-Gomez
Summary: By comparing different estimators, the study experimentally determines the most suitable estimator for estimating the entropy of bytes and bits in short samples.
Article
Physics, Applied
Zhixin Liu, Qiaoling Xie, Yongfu Zha, Yumin Dong
Summary: This scheme utilizes the properties of quantum physics to achieve cloud database encryption, while solving the issues of large computation and insufficient resources at the client side, and ensuring the security of information.
JOURNAL OF APPLIED PHYSICS
(2022)
Article
Computer Science, Information Systems
Satyabrata Roy, Manu Shrivastava, Umashankar Rawat, Chirag Vinodkumar Pandey, Sanjeet Kumar Nayak
Summary: The paper discusses the issue of image encryption in IoT applications in sensitive sectors, proposing a new secure and efficient image encryption method based on Cellular Automata. Experimental results and performance analysis confirm the effectiveness and advantages of the proposed method.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2021)
Article
Chemistry, Analytical
Valquiria C. Rodrigues, Juliana C. Soares, Andrey C. Soares, Daniel C. Braz, Matias Eliseo Melendez, Lucas C. Ribas, Leonardo F. S. Scabini, Odemir M. Bruno, Andre Lopes Carvalho, Rui Manuel Reis, Rafaela C. Sanfelice, Osvaldo N. Oliveira
Summary: Developing simple and effective detection methods for various infections and diseases, including prostate cancer, is crucial. In this study, genosensors with different detection principles for a prostate cancer specific DNA sequence (PCA3) were developed. The electrochemical impedance spectroscopy showed the highest sensitivity with a detection limit of 83 pM, indicating its potential for early diagnosis. Further image analysis developments are needed for machine learning algorithms to improve the accuracy of distinguishing between different concentrations of PCA3.
Article
Computer Science, Information Systems
Rayner H. M. Condori, Odemir M. Bruno
Summary: The study examined the effects of global pooling measurements on extracting texture information, finding that the RANKGP-CNN and RauxGP-CNN methods are effective in extracting high-quality texture information and performing well on different texture problems.
INFORMATION SCIENCES
(2021)
Article
Biochemistry & Molecular Biology
Jeaneth Machicao, Francesco Craighero, Davide Maspero, Fabrizio Angaroni, Chiara Damiani, Alex Graudenzi, Marco Antoniotti, Odemir M. Bruno
Summary: This study investigates the utilization of the topological properties of sample-specific metabolic networks for cancer classification, demonstrating that useful information can be extracted from a relatively limited number of features. Support Vector Machines achieve the best classification accuracy, suggesting the effectiveness of using these topological properties for cancer classification.
Article
Engineering, Mechanical
Jeaneth Machicao, Odemir M. Bruno, Murilo S. Baptista
Summary: This paper introduces a method for generating PRNs with low correlation using chaotic maps, enhancing the security of the cryptosystem with minimal computational cost. By applying transformations to chaotic trajectories, the entropy and Lyapunov exponents are significantly increased, leading to a fast, light, and reliable chaos-based cryptosystem.
NONLINEAR DYNAMICS
(2021)
Article
Physics, Multidisciplinary
Leonardo F. S. Scabini, Lucas C. Ribas, Mariane B. Neiva, Altamir G. B. Junior, Alex J. F. Farfan, Odemir M. Bruno
Summary: The study utilizes a multi-layer complex network model to analyze the COVID-19 epidemic in Brazil, finding that current isolation levels may lead to a healthcare system crisis. If all activities return to normal, there could be a significant increase in deaths, highlighting the need to increase isolation to ensure healthcare system capacity.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Jeaneth Machicao, Quynh Quang Ngo, Vladimir Molchanov, Lars Linsen, Odemir Bruno
Summary: In this paper, a novel visual approach is proposed to analyze the randomness of different PRNGs and allow for ranking comparison regarding the quality of PRNGs. The interactive analysis of time series ensembles generated using various PRNG methods leads to an unsupervised classification, providing insights into the impact of PRNG parameters on randomness rankings. New findings are reported using this approach in a study of state-of-the-art PRNGs and chaos-based PRNG families.
INFORMATION SCIENCES
(2021)
Article
Mathematics, Interdisciplinary Applications
Joao Valle, Jeaneth Machicao, Odemir M. Bruno
Summary: This paper proposes the deep-zoom analysis of the composition of the logistic map and the tent map, and finds that the pseudo-random qualities of the composition map improve as the parameter k increases. The application of deep-zoom to the composition of chaotic maps is suitable for better randomization for PRNG purposes as well as for cryptographic systems.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Biochemical Research Methods
Nubia Rosa da Silva, Victor Deklerck, Jan M. Baetens, Jan Van den Bulcke, Maaike De Ridder, Melissa Rousseau, Odemir Martinez Bruno, Hans Beeckman, Joris Van Acker, Bernard De Baets, Jan Verwaeren
Summary: This study introduces a new dataset of microscopic images of the three main anatomical sections of 77 Congolese wood species, and develops a multi-view image classification method. The method outperforms single-view methods in accuracy and demonstrates that naive accuracy estimates can lead to a dramatic over-prediction of accuracy.
Article
Astronomy & Astrophysics
J. Machicao, A. Ben Abbes, L. Meneguzzi, P. L. P. Correa, A. Specht, R. David, G. Subsol, D. Vellenich, R. Devillers, S. Stall, N. Mouquet, M. Chaumont, L. Berti-Equille, D. Mouillot
Summary: The challenges of reproducibility and replicability in computer science experiments, particularly those involving deep learning techniques, have attracted attention in recent years. This article evaluates the reproducibility of three deep learning experiments analyzing visual indicators from satellite and street imagery, and proposes a checklist to improve reproducibility based on FAIR principles.
EARTH AND SPACE SCIENCE
(2022)
Article
Green & Sustainable Science & Technology
Rosa Virginia Encinas Quille, Felipe Valencia de Almeida, Joshua Borycz, Pedro Luiz Pizzigatti Correa, Lucia Vilela Leite Filgueiras, Jeaneth Machicao, Gustavo Matheus de Almeida, Edson Toshimi Midorikawa, Vanessa Rafaela de Souza Demuner, John Alexander Ramirez Bedoya, Bruna Vajgel
Summary: Recent studies emphasize the importance of decision making in Business Process Management (BPM) and incorporating sustainability in business for service innovation. Robotic Process Automation (RPA) is proposed as a solution to improve BPM and sustainability practices through digital transformation. This paper presents a model using the Performance Analysis Method to select indicators for determining the profitability of implementing RPA in selected business processes. The model predicts potential cost savings by automating parts of a process through data collection and discrete event simulations.
Proceedings Paper
Computer Science, Artificial Intelligence
Osvaldo Gogliano Sobrinho, Liedi Ledi Mariani Bernucci, Pedro Luiz Pizzigatti Correa, Rosangela dos Santos Motta, Jeaneth Machicao, Angelo Samuel Junqueira, Robson Correia da Costa, Wellingthon Dias de Queiroz, Thales Cesar Giriboni de Mello e Silva, Pedro Lopes Ferraz, Luciano Cassaro, Luciano Oliveira
Summary: This paper describes an ongoing study that uses data collected by an instrumented ore car on Vitoria-Minas Railway in Brazil, operated by Vale. The research applies big data analysis methods to analyze railway geometry issues and establish severity indexes for maintenance intervention.
2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
(2021)
Article
Chemistry, Multidisciplinary
Juliana Coatrini Soares, Andrey Coatrini Soares, Valquiria Cruz Rodrigues, Pedro Ramon Almeida Oiticica, Paulo Augusto Raymundo-Pereira, Jose Luiz Bott-Neto, Lorenzo A. Buscaglia, Lucas Daniel Chiba de Castro, Lucas C. Ribas, Leonardo Scabini, Lais C. Brazaca, Daniel S. Correa, Luiz Henrique C. Mattoso, Maria Cristina Ferreira de Oliveira, Andre Carlos Ponce Leon Ferreira de Carvalho, Emanuel Carrilho, Odemir M. Bruno, Matias Eliseo Melendez, Osvaldo N. Oliveira Jr
Summary: This study presents genosensors for detecting an ssDNA sequence from the SARS-CoV-2 genome using four detection principles and data processing techniques such as information visualization and machine learning. Genosensors were fabricated on different electrodes for electrical and electrochemical measurements, and on Au nanoparticles for optical measurements, showing high sensitivity and selectivity in detecting complementary ssDNA sequences. The results suggest promising applications of these genosensors for point-of-care detection of SARS-CoV-2 genetic material in biological fluids.
MATERIALS CHEMISTRY FRONTIERS
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
