Article
Computer Science, Artificial Intelligence
Michal Choras, Konstantinos Demestichas, Agata Gielczyk, Alvaro Herrero, Pawel Ksieniewicz, Konstantina Remoundou, Daniel Urda, Michal Wozniak
Summary: Fake news has become a significant problem affecting societies and individuals, posing a major challenge for using advanced Machine Learning methods to combat it. This paper reviews the current state of knowledge on applying intelligent tools in the fight against disinformation, including historical perspectives, the role of fake news in information warfare, proposed solutions, and the application of intelligent systems in detecting misinformation sources. The main goal of this work is to analyze existing knowledge, propose solutions, and identify challenges and gaps to guide future research efforts.
APPLIED SOFT COMPUTING
(2021)
Article
Psychology, Multidisciplinary
Francois t'Serstevens, Giulia Piccillo, Alexander Grigoriev
Summary: Despite the availability of abundant knowledge and information, fake news continues to spread more widely and deeply. Research findings show that the perceived veracity significantly predicts the likelihood of user reaction, with fake news being more likely to be shared than other types of news.
FRONTIERS IN PSYCHOLOGY
(2022)
Review
Chemistry, Multidisciplinary
Tanvir Ahmad, Eyner Arturo Aliaga Lazarte, Seyedali Mirjalili
Summary: This paper highlights the importance of integrating research on COVID-19 and fake news, with a focus on analyzing studies related to misinformation about the virus on social media. It emphasizes the need to address complementary issues and proposes the use of artificial intelligence to tackle the fake news problem.
APPLIED SCIENCES-BASEL
(2022)
Review
Computer Science, Information Systems
Miguel A. Alonso, David Vilares, Carlos Gomez-Rodriguez, Jesus Vilares
Summary: Fake news has been on the rise in recent years, posing a serious threat to social cohesion and trust in leaders. Automatic systems for fake news detection have become increasingly important due to the unfeasibility of manual verification, with sentiment analysis playing a key role in this process.
Article
Computer Science, Information Systems
Jing Jing, Hongchen Wu, Jie Sun, Xiaochang Fang, Huaxiang Zhang
Summary: This study proposes a progressive fusion network (MPFN) for multimodal fake news detection, which captures the representational information of each modality at different levels and establishes connections between modalities through feature fusion. The method achieves significant improvement in identifying fake news.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Abdelghani Dahou, Ahmed A. Ewees, Fatma A. Hashim, Mohammed A. A. Al-qaness, Dina Ahmed Orabi, Eman M. Soliman, Elsayed M. Tag-eldin, Ahmad O. Aseeri, Mohamed Abd Elaziz
Summary: The rapid proliferation of false information and news on social media platforms is a concerning issue. To address this, an innovative disinformation detection framework that leverages multi-task learning and meta-heuristic techniques is proposed. By extracting contextual features from Arabic social media posts and utilizing an advanced feature selection model, the framework achieves remarkable results.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Deepjyoti Choudhury, Tapodhir Acharjee
Summary: This study presents a genetic algorithm-based approach for fake news detection, aiming to address the quick spreading and widespread dissemination of fake news. By conducting a comparative analysis on different datasets using SVM, Naive Bayes, Random Forest, and Logistic Regression classifiers, it is found that SVM performs the best in terms of accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Theory & Methods
Wubetu Barud Demilie, Ayodeji Olalekan Salau
Summary: With the rise of social media platforms, detecting and tracking hate speech has become a major concern. Combating hate speech and fake news is crucial, yet challenging due to the difficulty in exposing false claims. The use of automatic fact or claim verification has attracted interest, but further research is needed. This study aims to analyze the optimal approaches for this problem and the relationship between approaches, dataset type, size, and accuracy.
JOURNAL OF BIG DATA
(2022)
Article
Physics, Multidisciplinary
Yuwei Chuai, Jichang Zhao
Summary: Fake news is more contagious online and this is positively associated with the anger it carries. Anger leads to more incentivized audiences and makes fake news more contagious.
FRONTIERS IN PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Jawaher Alghamdi, Yuqing Lin, Suhuai Luo
Summary: The COVID-19 pandemic has led to the spread of fake news, posing public health risks. This paper investigates the effectiveness of various machine learning algorithms and pre-trained transformer-based models in detecting COVID-19 fake news. Experimental results show that incorporating BiGRU on top of the CT-BERT model achieves outstanding performance, with a state-of-the-art F1 score of 98%. These findings have significant implications for combating COVID-19 misinformation and highlight the potential of advanced machine learning models in fake news detection.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ala Mughaid, Shadi Al-Zu'bi, Ahmed Al Arjan, Rula AL-Amrat, Rathaa Alajmi, Raed Abu Zitar, Laith Abualigah
Summary: This paper explores the technique of automatically detecting fake news and proposes a method that uses the world rank of news websites as the main factor of news accuracy and compares current news with fake news to determine their accuracy. Experimental results show that the proposed method performs well in defining news accuracy.
Article
Computer Science, Artificial Intelligence
Haixiao Chi, Beishui Liao
Summary: This study proposes a Quantitative Argumentation-based Automated eXplainable Decision-making System (QA-AXDS) to detect and explain fake news on social media. The system is fully data-driven, acquiring knowledge automatically and interacting with users to provide explanations and understanding of the reasoning process.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Psychology, Multidisciplinary
Daniel Jeffrey Sude, Gil Sharon, Shira Dvir-Gvirsman
Summary: Two studies conducted in 2020 and 2022 examined how people assess the authenticity of politically consistent news. The results showed that the perceived accuracy of news content was influenced by the attribution of the news outlet and the individual's partisan identity.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Lianwei Wu, Yuan Rao, Cong Zhang, Yongqiang Zhao, Ambreen Nazir
Summary: Existing data-driven methods for fake news detection capture credibility-indicative representations from relevant articles, but they have drawbacks: limited datasets due to the difficulty of collecting fake news, and difficulty in identifying credibility of unverified news lacking conflicting voices. This paper proposes a Category-controlled Encoder-Decoder model (CED) to generate examples with category-differentiated features from different news categories and enhance fake news detection. The experimental results demonstrate the superiority of CED in capturing general and wide-ranging differences between true and fake news.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Noman Islam, Asadullah Shaikh, Asma Qaiser, Yousef Asiri, Sultan Almakdi, Adel Sulaiman, Verdah Moazzam, Syeda Aiman Babar
Summary: This paper proposes a novel solution for detecting the authenticity of news through natural language processing techniques, consisting of three steps and utilizing various machine learning techniques, with the support vector machine algorithm achieving higher accuracy compared to other classifiers.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Software Engineering
Elif Varol Altay, Bilal Alatas
Summary: A new chaos-enhanced representation scheme based on chaos numbers is proposed for evolutionary optimization methods. This method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds accurate and comprehensible rules automatically. The performance of this method is promising with respect to different metrics based on real quantitative data sets.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Mathematics, Interdisciplinary Applications
Harun Bingol, Bilal Alatas
Summary: With the development of technology, access methods to information have changed, with internet blogs, news sites, and social media replacing traditional tools like TV and newspapers. Cheaper and faster access, as well as internet availability, are the main factors driving this change. However, information spread on the internet can lack accuracy and be driven by various motives. Detecting deceptive information in textual data is crucial, and this paper proposes a new approach using optimization methods like OIO, GWO, and CBOIOs, which are adapted for the deception detection problem. Experimental results show that CBOIOs are more effective than other machine learning algorithms.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Artificial Intelligence
Hande Yuksel Bayram, Harun Bingol, Bilal Alatas
Summary: This study proposes a tomato leaf disease classification model based on deep learning methods, which utilizes pre-trained convolutional neural network architectures to extract feature maps and employs optimized feature maps for intelligent classification. Experimental results show an average accuracy rate of 99.50% for the proposed model.
TRAITEMENT DU SIGNAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Erdal Ozbay, Feyza Altunbey Ozbay
Summary: This study proposes a precision medical image hashing method that addresses the issue of medical image retrieval by combining MRI images with feature fusion. Experimental results showed that the proposed method can effectively identify tumor regions and generate more accurate hash codes using three loss functions in feature fusion. It has been demonstrated that our method can effectively increase the accuracy of medical image retrieval and potentially be applied to computer-aided diagnosis systems(CADs).
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Biology
Sinem Akyol, Muhammed Yildirim, Bilal Alatas
Summary: Quality sleep is crucial for daily life, and sleep disorders can be diagnosed using computer-aided systems. A study utilized 700 sound data samples with three different feature extraction methods and optimized the feature maps using improved metaheuristic algorithms and machine learning methods, achieving a high accuracy rate.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Erdal Ozbay, Feyza Altunbey Ozbay
Summary: Cervical cancer is a common and deadly disease in women, and Pap-smear tests are preferred by doctors for early diagnosis. This study implemented an algorithm for retrieval of cervical cancer images using hash coding with a Convolutional Neural Network (CNN), and proposed a sensitive deep hashing method combining interpretable mask generation and rotation invariance for cervical cancer detection.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Hande Yuksel Bayram, Harun Bingol, Bilal Alatas
Summary: Brain tumors are dangerous and can be fatal, occurring in people of all ages. Early detection is crucial for treatment planning and survival. In this study, a hybrid deep model combining Convolutional Neural Network and Support Vector Machine was proposed for accurately classifying brain tumors based on MRI images. The proposed model achieved a high accuracy of 93.2%.
TRAITEMENT DU SIGNAL
(2023)
Article
Construction & Building Technology
Muhammed Ulucan, Gungor Yildirim, Bilal Alatas, Kursat Esat Alyamac
Summary: This study aims to develop a new AI model for predicting mix design and early-age compressive strength of recycled aggregate concrete. The model uses a metaheuristic mechanism to extract interpretable rules from experimental data. The proposed model is tested against other machine learning algorithms and rule-based methods, showing promising results in terms of accuracy and explainability.
STRUCTURAL CONCRETE
(2023)
Article
Engineering, Multidisciplinary
Feyza Altunbey Ozbay
Summary: Metaheuristic optimization algorithms, such as Seahorse Optimization (SHO), aim to efficiently explore search spaces by mimicking behaviors of sea horses. This study introduces Chaotic SHO (CSHO) which employs chaotic maps to improve its performance. CSHO is evaluated using benchmark functions and compared with other metaheuristic algorithms, showing promising results.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2023)
Article
Green & Sustainable Science & Technology
Shiyuan Gan, Xuejing Yang, Bilal Alatas
Summary: This study aims to address the challenges of continuity and intelligent intervention in English language teaching. By using an autoencoder for interest recognition and comprehensive assessment in online teaching, the research demonstrates high accuracy in identifying student interests and achieves a low error rate compared to teacher grades.
Article
Engineering, Multidisciplinary
Sinem Akyol, Mehmet Das, Bilal Alatas
Summary: A hybrid-optimization-based artificial intelligence classification method is applied for the first time to produce explainable models of compressor energy consumption in a vapor compression refrigeration system. This innovative method determines the energy consumption values of a refrigerant gas based on operating parameters, and allows for automatic identification of the operating conditions with the lowest energy consumption.
Article
Computer Science, Information Systems
G. Sandhya Rani, Sarada Jayan, Bilal Alatas
Summary: This paper studies the application of five chaotic maps in global optimization and proposes a global optimization method, Hybrid Chaotic Pattern Search Algorithm (HCPSA), for multivariable unconstrained optimization problems. Comparative results with other algorithms demonstrate the effectiveness of the proposed algorithm in higher dimensional non-linear functions. Additionally, the paper showcases the use of HCPSA in financial prediction and compares it with other algorithms, showing superior accuracy.
Article
Engineering, Multidisciplinary
Erdal Ozbay, Feyza Altunbey Ozbay, Farhad Soleimanian Gharehchopogh
Summary: This study proposes an improved ResNet50 convolutional neural network model combined with particle swarm optimization to detect acute lymphoblastic leukemia (ALL) and its subtypes. By augmenting the training dataset through color threshold-based segmentation, an accuracy of 99.65% is achieved.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Feyza Altunbey Ozbay, Erdal Ozbay
Summary: In this study, a feature selection method based on metaheuristic optimization algorithms is proposed for gender detection from voice data. The performances of different optimization algorithms for feature selection are compared, and it is found that this method can improve the success rate of classification algorithms.
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
(2023)
Article
Mathematics, Applied
Lamiaa M. El Bakrawy, Mehmet Akif Cifci, Samina Kausar, Sadiq Hussain, Md Akhtarul Islam, Bilal Alatas, Abeer S. Desuky
Summary: This study proposes a modified antlion optimization (MALO) algorithm to improve the primary antlion optimization algorithm (ALO) for the task of instance reduction. The results show that the MALO algorithm outperforms the basic ALO algorithm and other comparative algorithms in terms of convergence rate and performance measures like Accuracy, Balanced Accuracy (BACC), Geometric mean (G-mean), and Area Under the Curve (AUC). The MALO algorithm offers a potential solution to the problem of local optima stagnation and slow convergence speed.
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
