Article
Computer Science, Artificial Intelligence
Ganeshsree Selvachandran, Shio Gai Quek, Luong Thi Hong Lan, Le Hoang Son, Nguyen Long Giang, Weiping Ding, Mohamed Abdel-Basset, Victor Hugo C. de Albuquerque
Summary: This article introduces the Mamdani complex fuzzy inference system (Mamdani CFIS) as a more efficient method for handling time-series data and time-periodic phenomena by implementing complex numbers, which offers greater flexibility in dealing with unexpected nonlinear fluctuations. The proposed CFIS was successfully demonstrated in six commonly available datasets, showcasing its computational efficiency compared to traditional FIS and ANCFIS.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Construction & Building Technology
Atakan Mangir
Summary: This study presents a rule-based fuzzy inference model to determine seismic design parameters, which is suitable for situations where both input and output variables have numerical and linguistic uncertainties in seismic problems.
Article
Chemistry, Multidisciplinary
Martin Tabakov, Adrian Chlopowiec, Adam Chlopowiec, Adam Dlubak
Summary: In this research, a classification procedure based on rule induction and fuzzy reasoning is introduced to handle uncertainty in real data. The method involves defining a fuzzy information system, inducing fuzzy rules, and transforming them into interval type-2 fuzzy rules. Fuzzification is optimized with respect to uncertainty, and the proposed method is evaluated using the F-score measure on benchmark data.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Jie Gao, Zeshui Xu, Zhilei Liang, Yunshu Mao
Summary: This article studies how to integrate large-scale continuous hesitant fuzzy information more efficiently and proposes continuous hesitant fuzzy information integration models based on hesitant fuzzy calculus. The internal connections between the proposed models and hesitant fuzzy weighted operator are explored, and their effectiveness and rationality are demonstrated through application examples.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Martin Tabakov, Adrian B. Chlopowiec, Adam R. Chlopowiec
Summary: The main purpose of this research was to introduce a classification method that combines a rule induction procedure with the Takagi-Sugeno inference model. The research goal was to verify if the Mamdani fuzzy inference used in our previous research could be replaced with the first-order Takagi-Sugeno inference system. The introduced rule induction assumes an optimization procedure concerning the footprint of uncertainty of the considered type-2 fuzzy sets.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Pasquale D'Alterio, Jonathan Garibaldi, Christian Wagner
Summary: Data obtained from the real world is often uncertain due to measurement inaccuracies, variability in opinions, and human errors. Fuzzy sets have been used to capture this uncertainty and build automatic reasoning systems. This article proposes a novel approach that combines uncertain data modeled through parametric fuzzy sets using constrained interval type-2 fuzzy sets.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
A. S. Albahri, Shahad Sabbar Joudar, Rula A. Hamid, Idrees A. Zahid, M. E. Alqaysi, O. S. Albahri, A. H. Alamoodi, Gang Kou, Iman Mohamad Sharaf
Summary: This study proposes a new framework for explainable artificial intelligence in the context of multimodal triage for autism spectrum disorders. The framework consists of five phases, including data acquisition and diagnosis, triage methodology design, data balancing, development of artificial intelligence models, and model interpretation. Four new algorithms are presented in the developed framework.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Thierry Denaeux
Summary: This paper presents the theory of epistemic random fuzzy sets, which is a general theory of uncertainty that includes possibility theory and the Dempster-Shafer theory. The authors extend the theory by introducing Gaussian random fuzzy numbers to represent various beliefs encountered in applications. They also study the properties of one-to-one transformations and mixtures of random fuzzy variables, and demonstrate the practical applications of these models through two case studies.
FUZZY SETS AND SYSTEMS
(2023)
Article
Automation & Control Systems
Shuker Mahmood Khalil, Moataz Sajid Sharqi
Summary: This study introduces new cosine trigonometric operational laws (CTOLs) that combine the characteristics of cosine functions and the relevance of Pythagorean fuzzy sets (PFS). Based on this, cosine trigonometric Pythagorean fuzzy (CTPF) aggregation operators are created for a decision-making algorithm in multi-attribute decision-making situations. An example of internet finance soft power evaluation (IFSPE) is used to demonstrate the effectiveness of the method, and sensitivity and comparison analyses are conducted to evaluate its stability and validity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Qian Jiang, Xin Jin, Xiaohui Cui, Shaowen Yao, Keqin Li, Wei Zhou
Summary: Medical image fusion combines multiple features of human tissue from different source images, which is beneficial for clinical diagnosis. This study introduces a similarity measure of fuzzy set theory to abstract and measure fuzzy features in medical image fusion, and proposes a lightweight medical image fusion technique based on this new measure. Experimental results show that the similarity measure of fuzzy set theory achieves excellent performance in medical image fusion.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Francesco Abbondati, Salvatore Antonio Biancardo, Rosa Veropalumbo, Xinqiang Chen, Gianluca Dell 'Acqua
Summary: The study presents a method of modelling runway friction decay using an adaptive neuro-fuzzy inference system (ANFIS). By tuning the membership function parameters of a fuzzy inference system (FIS) using an optimization algorithm, the ANFIS is trained to learn from the given input/output data set and accurately predict the current or future friction of runways. This model provides an effective and efficient forecasting tool for airport managers in making maintenance decisions, without the need for on-site measurements or complex calculations.
Article
Biology
Vartika Bisht, Animesh Acharjee, Georgios Gkoutos
Summary: Microbiome data analysis faces challenges such as collinearity, sparsity, and effect size, with the need for interpretable models integrating data and background knowledge. A multimodal framework has been developed to address these challenges and provide candidate taxa/OTUs for future validation experiments.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Nitin Naik, Paul Jenkins, Nick Savage, Longzhi Yang
Summary: A honeypot is a concealed security system that aims to reveal attackers' information through decoy techniques. Attackers may use fingerprinting attacks to uncover the identity of honeypots, posing a challenge to their purpose. Currently, there is no specific method available to detect and predict fingerprinting attacks in real-time.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Automation & Control Systems
Xiaozhuan Gao, Yong Deng
Summary: The paper introduces a method to generate Pythagorean fuzzy numbers by considering the negation of probability, known as Pythagorean fuzzy sets based on negation (NPFS). This method establishes a connection between probability and PFS, and is effective in both artificial decision-making and data-driven applications for multi-criterion decision-making (MCDM) and classification problems. Numerical examples and real-world data are used to verify the effectiveness of the proposed method in different applications.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Tahir Mahmood, Ubaid Ur Rehman, Zeeshan Ali
Summary: Complex fuzzy N-soft set (CFN-SS) is an important technique that combines N-soft sets (N-SSs) and complex fuzzy sets (CFSs) to manage uncertain information, exploring its fundamental laws and algebraic properties through examples and analysis.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Information Systems
Aniello Castiglione, Michele Nappi, Fabio Narducci, Chiara Pero
Summary: Collaborative tools are widely adopted due to the COVID-19 outbreak, but the HTTP protocol used in these tools is often exploited for tracking and monitoring users. A novel steganographic protocol is proposed to protect user information in collaborative environments.
COMPUTER COMMUNICATIONS
(2021)
Article
Automation & Control Systems
Aniello Castiglione, Michele Nappi, Stefano Ricciardi
Summary: The proposed method utilizes dynamic facial features for identity recognition and matches the extracted features to a reference database using a cloud-based deep feedforward network, improving robustness and trustworthiness of identification in IIoT scenarios.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Aniello Castiglione, Pandi Vijayakumar, Michele Nappi, Saima Sadiq, Muhammad Umer
Summary: It is widely acknowledged that early disclosure of COVID-19 can limit its spread significantly. The ADECO-CNN model offers a highly accurate and precise method for classifying infected and not infected patients, showing superiority over other pretrained CNN-based models. Extensive analysis demonstrates the ADECO-CNN model's ability to classify CT images with exceptional accuracy, sensitivity, precision, and specificity.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Lucia Cascone, Michele Nappi, Fabio Narducci, Ignazio Passero
Summary: The article discusses the development of VPepper, a virtual replica of the Pepper robot, and its interaction with smart objects in a smart home environment. Through the use of digital twin, machine learning procedures can be seamlessly transferred between the virtual replica and the physical robot. The practical application shows promising opportunities for simulation accuracy and machine learning instruments in real settings.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Lucia Cascone, Michele Nappi, Fabio Narducci, Chiara Pero
Summary: This study focuses on the analysis of touch keystroke dynamics of smartphone users for demographic classification and continuous authentication. The results show that effective demographic analysis can be achieved using traditional lightweight machine learning algorithms.
PATTERN RECOGNITION LETTERS
(2022)
Article
Chemistry, Analytical
Muhammad Umer, Saima Sadiq, Hanen Karamti, Walid Karamti, Rizwan Majeed, Michele Nappi
Summary: This study proposes a smart healthcare framework using IoT and cloud technologies to improve the survival prediction of heart failure patients without manual feature engineering. The framework monitors patients in real-time and provides timely and effective healthcare services. Experimental results show that the CNN model outperforms other deep learning and machine learning models in accuracy.
Editorial Material
Computer Science, Information Systems
Shaohua Wan, Michele Nappi, Chen Chen, Stefano Berretti
Summary: This paper focuses on the emerging Internet of Medical Things. With the development of smart sensorial devices, AI, ML, DL, and other technologies, smart health plays a vital role in improving the accuracy and reliability of mobile sensory devices in the healthcare industry.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Engineering, Civil
Luca Anzalone, Paola Barra, Silvio Barra, Aniello Castiglione, Michele Nappi
Summary: This work combines Curriculum Learning with Deep Reinforcement Learning to learn a competitive driving policy without prior domain knowledge in the CARLA autonomous driving simulator. The approach divides the reinforcement learning phase into multiple stages of increasing difficulty, guiding the agent towards an increasingly better driving policy. The agent architecture includes various neural networks and novel value decomposition scheme and gradient size normalization function. Quantitative and qualitative results of the learned driving policy are presented.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Tanmay Kumar Behera, Sambit Bakshi, Michele Nappi, Pankaj Kumar Sa
Summary: This article proposes a superpixel-aided multiscale CNN architecture to address the misclassification issue in complex urban aerial images. The proposed model outperforms the state-of-the-art methods on two UAV-based image datasets.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Editorial Material
Automation & Control Systems
Zhiwei Gao, Aniello Castiglione, Michele Nappi
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Songtao Ding, Hongyu Wang, Hu Lu, Michele Nappi, Shaohua Wan
Summary: In this paper, a two-path gland segmentation algorithm of colon pathological image based on local semantic guidance is proposed. The improved candidate region search algorithm is employed to generate sub-datasets sensitive to specific features. The semantic feature-guided model is used to extract local adenocarcinoma features and enhance the network's learning ability to gland morphological features.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Information Systems
Lucia Cimmino, Michele Nappi, Fabio Narducci, Chiara Pero
Summary: This study explores the impact of wearing masks on face recognition and proposes a robust recognition approach for mobile devices by analyzing the spatio-temporal features of the periocular region. Machine learning techniques are used to classify and analyze the periocular region, and the experimental results show promising performance.
Proceedings Paper
Computer Science, Artificial Intelligence
Andrea Abate, Lucia Cimmino, Michele Nappi, Fabio Narducci
Summary: In this study, a dual-input Neural Network architecture for periocular recognition is proposed. Experimental results show promising performance for almost all experimental configurations with a worst-case accuracy of 90% and a best-case accuracy of 97%.
IMAGE ANALYSIS AND PROCESSING, ICIAP 2022, PT I
(2022)
Article
Computer Science, Artificial Intelligence
Carmen Bisogni, Michele Nappi, Chiara Pero, Stefano Ricciardi
Summary: FASHE, based on partitioned iterated function systems (PIFS), represents a method for head pose estimation by handling auto-similarities within domain blocks. Results show that this method performs well on multiple datasets and approaches the levels of the best performing methods.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Information Systems
Anam Yousaf, Muhammad Umer, Saima Sadiq, Saleem Ullah, Seyedali Mirjalili, Vaibhav Rupapara, Michele Nappi
Summary: With the proliferation of user-generated content on social media, opinion mining has become a challenging task, requiring sentiment analysis to analyze public opinion using machine learning models. In emotion recognition, the proposed voting classifier (LR-SGD) combined with TF-IDF method achieved the most optimal results.
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
