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Student’s performance prediction based on an improved multi-view hypergraph neural network J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-03-15 Xuefen Lin, Yada Guo
Predicting students’ academic performance is a crucial area of research in Educational Data Mining. Efficient performance prediction can significantly improve instructional effectiveness, facilitat...
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Satellite fault tolerant attitude control based on expert guided exploration of reinforcement learning agent J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-03-09 Hicham Henna, Houari Toubakh, Mohamed Redouane Kafi, Ömer Gürsoy, Moamar Sayed-Mouchaweh, Mohamed Djemai
This research provides a method that accelerates learning and avoids local minima to improve the policy gradient algorithm’s learning process. Reinforcement learning has the advantage of not requir...
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Generalized correlation coefficients of intuitionistic multiplicative sets and their applications to pattern recognition and clustering analysis J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-03-04 Ali Köseoğlu
Intuitionistic multiplicative preference relations (IMPRs) and intuitionistic multiplicative sets (IMSs) play a significant role in real-life problems that contain unsymmetrical and nonuniform info...
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Artificial intelligence: reflecting on the past and looking towards the next paradigm shift J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-02-28 Petar Radanliev
Artificial intelligence (AI) has undergone major advances over the past decades, propelled by key innovations in machine learning and the availability of big data and computing power. This paper su...
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Intelligent system for solid waste classification using combination of image processing and machine learning models J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-02-28 Hani Abu-Qdais, Nawras Shatnawi, Esra’a AL-Alamie
Solid waste is a major issue in all cities around the world. Classification and segregation of solid waste prior to reuse, recycle or recover is an important step towards sustainable waste manageme...
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Sewage water management and healthcare monitoring in IoT using Optimized deep residual network J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-02-23 Dipali Shende, Yogesh S. Angal
The Internet of Things (IoT) is termed as the interconnection of different smart objects with respect to devices. In this research, two different application scenarios are considered to show the ef...
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LBO-MPAM: Ladybug Beetle Optimization-based multilayer perceptron attention module for segmenting the skin lesion and automatic localization J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-01-21 Sellam V, Kannan Natrajan, Senthil Pandi S, Sathish Kumar K
In recent years, skin cancer has been the most dangerous disease noticed among people worldwide. Skin cancer should be identified earlier to reduce the rate of mortality. Employing dermoscopic imag...
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Gastric cancer classification in saliva data samples using Levy search updated rainfall hybrid deep dual-stage BILSTM J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-01-12 M. Kalimuthu, M. Ramya, S. Sreethar, N. Nandhagopal
An innovative approach is needed for the early identification of GC (Gastric cancer) to improve the prediction of GC patients. This work presents a GC prediction system to identify GC depending on ...
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A hybrid sequential forward channel selection method for enhancing EEG-Based emotion recognition J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-01-11 Shyam Marjit, Parag Jyoti Das, Upasana Talukdar, Shyamanta M Hazarika
In recent times, EEG-based emotion recognition has gained significant attention in affective computing. One of the major challenges in designing an efficient EEG-based emotion-recognition framework...
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Non-invasive anaemia detection based on palm pallor video using tree-structured 3D CNN and vision transformer models J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-01-08 Abhishek Kesarwani, Sunanda Das, Dakshina Ranjan Kisku, Mamata Dalui
Anaemia is a common disease that affects billions of people worldwide and is caused due to low blood haemoglobin level. According to WHO statistics, anaemia is the most prevalent in developing and ...
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Fuzzy logic in association rule mining: limited effectiveness analysis J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2024-01-05 Vugar E. Mirzakhanov
This paper presents a comparative effectiveness analysis of fuzzy and non-fuzzy association rule mining (ARM). The corresponding motivation is the lack of relevant papers devoted to the effectivene...
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Alternating Transfer Functions to Prevent Overfitting in Non-Linear Regression with Neural Networks J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-10-31 Philipp Seitz, Jan Schmitt
In nonlinear regression with machine learning methods, neural networks (NNs) are ideally suited due to their universal approximation property, which states that arbitrary nonlinear functions can th...
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The future of artificial intelligence and digital development: a study of trust in social robot capabilities J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-09-28 Chuntao Jiang, Xin Guan, Junfan Zhu, Zeyu Wang, Fanbao Xie, Weijia Wang
This paper aims to study people’s trust in the capabilities of social robots in the context of digital transformation. Firstly, the current application status of social robots is studied. Then, the...
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Novel hybrid soft set theories focusing on decision-makers by considering the factors affecting the parameters J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-09-21 O. Dalkılıç, N. Demirtaş
In this paper, the parameterisation tool of soft set theory is focused and factor sets are defined for all factors that can affect each parameter. Thus, more ideal results are aimed by determining ...
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Municipal Solid Waste Prediction using Tree Hierarchical Deep Convolutional Neural Network Optimized with Balancing Composite Motion Optimization Algorithm J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-09-20 T. Senthil Prakash, Annalakshmi M, Siva Prasad Patnayakuni, S. Shibu
Efficacious forecasting of a solid waste supervision system depends on the prediction accuracy of solid waste generation. Several existing methods on municipal solid waste prediction were suggested...
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Detection of Epilepsy patients using coot optimization based feed forward multilayer neural network J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-09-16 Neeraj Nagwanshi, Anjali Potnis
ABSTRACT A familiar nervous system disorder characterised by seizures is called as Epilepsy. It is indeed hard to control the suitable type as an outcome of insufficient EEG information. In order to overcome these issues, a Multilayer Neural Network (MLNN)-based classifier is proposed to recognise if the patients are affected by epileptic disease or not. EEG signal is a contribution, and the input
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Optimal control strategy for COVID-19 developed using an AI-based learning method J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-09-16 V. Kakulapati, A. Jayanthiladevi
ABSTRACT The corona virus pandemic has affected millions of people’s work and communication. Millions face a health crisis from SARS-CoV-2, the virus that causes most COVID-19 symptoms. The aim of the proposed research is to contribute towards AI (Artificial Intelligence) by developing a mathematical model for SEIR and SIR through CNN on images of affected people and to analyse the dataset of medical
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Correction J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-08-23
Published in Journal of Experimental & Theoretical Artificial Intelligence (Ahead of Print, 2023)
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Towards face anti-spoofing J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-08-22 Muhammad Ibrahim Syed, Amina Asif, Mohsin Shahzad, Uzair Khan, Sumair Khan, Zahid Mahmood
ABSTRACT Face anti-spoofing is an important part of the face recognition algorithm that aims to prevent face presentation attacks. To facilitate face anti-spoofing research, this paper introduces a novel method in which a simple model provides enhanced performance on the RGB modality images of the CASIA-SURF dataset. Initially, a simple model based on ResNet-50 architecture is proposed that uses three
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Block pruning residual networks using Multi-Armed Bandits J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-08-17 Mohamed Akrem Benatia, Yacine Amara, Said Yacine Boulahia, Abdelouahab Hocini
ABSTRACT Recently, deep neural networks have been adopted in many application domains showing impressive performances that sometimes even exceed those of humans. However, the considerable size of deep learning models and their computational cost limits their use in mobile and embedded systems. Pruning is an important technique that has been used in the literature to compress a trained model by removing
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An efficient banana plant leaf disease classification using optimal ensemble deep transfer network J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-08-10 N. Bharathi Raja, P. Selvi Rajendran
ABSTRACT Plants are a major source of food all around the world, and they are mainly affected by diseases caused by pathogens, insects, and parasitic plants. If the diseases are identified in the earlier stage, then it will be easy to apply pesticides and prevent the disease from further propagation. To recognise these diseases at earlier stages automatically, different researchers have established
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Deep learning algorithms for solving differential equations: a survey J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-08-07 Harender Kumar, Neha Yadav
ABSTRACT Differential equations (DEs) are widely employed in the mathematical modelling of a wide range of scientific and engineering problems. The analytical solution of these DEs is typically unknown for a variety of practical problems of relevance. Several numerical methods have been developed over time to find the solution to such DEs and numerous new approaches are still being proposed daily.
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Improved Cascade Chaotic Invasive Weed Optimization Algorithm (ICCIWO), application to controller tuning and optimization J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-08-01 Mohsen Esmaeili Ranjbar, Mahdi Yaghoobi, Gelareh Veisi
ABSTRACT An Improved Cascade Chaotic Invasive Weed Optimization Algorithm (ICCIWO) is proposed, employing cascade chaotic maps in the structure of a metaheuristic optimisation algorithm. The main aim of this modification is to provide a new algorithm for tuning the parameters of controllers. Conventionally, invasive weed optimisation algorithms (IWO) have been utilised in the literature to solve optimisation
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K–L divergence-based distance measure for Pythagorean fuzzy sets with various applications J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-07-29 Naveen Kumar, Anjali Patel, Juthika Mahanta
ABSTRACT The Pythagorean fuzzy sets have achieved remarkable success in curbing uncertainty in real-world problems. Distance measures are widely used to handle uncertainty and discriminate between two objects in a Pythagorean fuzzy environment. The literature suggests that many of the existing distance functions need to meet the circumstances of metric conditions. In addition to that, the calculation
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Human pose completion in partial body camera shots J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-07-28 Ruben Tous, Jordi Nin, Laura Igual
ABSTRACT Many actual images contain partial body camera shots, in which a significant part of the body is not visible. This issue is especially prevalent in film images, where less than 10% are full-body shots. Most 2D human pose estimation methods return incomplete poses when applied to partial body images. This lack of completeness becomes a problem in some situations, for example, when the 2D pose
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Similarity measures of neutrosophic fuzzy soft set and its application to decision making J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-06-29 Orhan Dalkiliç, Naime Demirtaş
ABSTRACT Complex data analysis should be done in the most accurate way in order to express the uncertain environments in the most ideal way. In addition, mathematical models that can best express the membership degrees that represent the objects are preferred for these data analyses. Taking these situations into account, this study proposes the concept of single-valued neutrosophic fuzzy soft set,
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Constructing condensed memories in functorial time J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-06-24 Shanna Dobson, Chris Fields
ABSTRACT If episodic memory is constructive, experienced time is also a construct. We develop an event-based formalism that replaces the traditional objective, agent-independent notion of time with a constructive, agent-dependent notion of time. We show how to make this agent-dependent time entropic and hence well-defined. We use sheaf-theoretic techniques to render agent-dependent time functorial
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Catalysing assistive solutions by deploying light-weight deep learning model on edge devices J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-06-22 Kanak Manjari, Madhushi Verma, Gaurav Singal, Vinay Chamola
ABSTRACT Nowadays, real-time object detection, which is a crucial task, is being performed through image processing and deep learning techniques. As there are several high-performance computing edge devices available, selecting the best-fit device for a particular problem is a tough task and keeping in mind the cost, performance, and weight of the device in mind. One faces several challenges while
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Optimisation algorithm in health care: review on the State-of-the-Art models J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-06-09 Priyanka Shivaprasad More, Baljit Singh Saini, Rakesh Kumar Sharma
ABSTRACT Humans are affected by some diseases due to ageing, which raises the necessity of effective healthcare operation schemes. Such techniques are necessary to provide efficiently and cost-effectively service to patients at the proper time. The huge knowledge required to process the HC application is obtained from the developed HC technologies. Research has recently shown that artificial intelligence
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Community Based Influencer Node Identification using Hybrid Optimisation Algorithm in Social Networks J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-06-07 S. Devi, M. Rajalakshmi
ABSTRACT In social networking, influence maximisation (IM) refers to identifying influential people who will maximise the adoption of information or products. Hence, in the proposed work, the system offers a novel hybrid optimisation approach for identifying the top influential nodes based on community structure, which improves the diffusion of influence in social networks. In this, the improved wild
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Integration of local position-POS awareness and global dense connection for ABSA J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-05-27 Wei Shi, Jing Zhang
ABSTRACT Aspect-based sentiment analysis (ABSA) is a key problem in text analysis. However, previous work ignores the fact that the joint effects of local and global features affect the classification accuracy. Therefore, an ABSA model based on local position-part of speech (POS) awareness and global dense connection (LPP-GDC) is proposed to fully grasp the information from both local and global features
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TransPose Re-ID: transformers for pose invariant person Re-identification J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-05-22 Nazia Perwaiz, Muhammad Shahzad, Muhammad Moazam Fraz
ABSTRACT Person re-identification (Re-ID) is a computer vision task that involves recognizing and tracking individuals across multiple non-overlapping cameras or over time within the same camera view. It is particularly important in surveillance systems, where it can help in identifying potential threats or tracking suspects. Convolutional neural networks (CNNs) have been used to extract invariant
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An optimal detection of fake news from Twitter data using dual-stage deep capsule autoencoder J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-04-18 Parimala Kanaga Devan K, Anandha Mala G S
The proposed work introduces a dual-stage deep capsule autoencoder (DSDC-AE) model for fake news detection on Twitter data. Initially, the input Twitter data are pre-processed using tokenisation, s...
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Anti-periodicity on inertial Cohen-Grossberg neural networks involving distributed delays J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-04-10 Xianhui Zhang, Le Li, Changchun Bao, Jian Zhang
ABSTRACT This work investigates a class of inertial Cohen-Grossberg neural networks incorporating distributed delays using a direct mathematical analysis approach. First, by exploiting Lyapunov functional method, one can verify that the solutions of the addressed system are exponentially attractive to each other. Second, with the help of differential inequality techniques, the existence and global
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Determining the most accurate machine learning algorithms for medical diagnosis using the monk’ problems database and statistical measurements J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-04-02 Emre Avuçlu
ABSTRACT Computer-aided diagnosis process in the field of health, especially cancer diagnosis, is of vital importance. Computer-aided diagnosis helps specialist physicians to make the most accurate diagnosis. According to research studies, it has been stated that the number of wrong or late diagnosis increases with each passing year and ultimately causes the death of people living in many parts of
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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-17 Sara Tabagchi Milan, Nima Jafari Navimipour, Hamed Lohi Bavil, Senay Yalcin
ABSTRACT Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic
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Gated Attention Based Deep Learning Modelfor Analysing the Influence of Social Media on Education J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-16 B. PraveenKumar, A.V. kalpana, S. Nalini
ABSTRACT The major aim of education organisation is to ensure better education to students and reducing the failure percentage of poor students. Early prediction of student’s performance is a complex process to enhance the academic performance. However, it is complex to analyse huge data manually. Therefore, an automated model is essential for mining the educational performance of the students. Educational
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Retraction J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-16
Published in Journal of Experimental & Theoretical Artificial Intelligence (Ahead of Print, 2023)
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New Cellular Learning Automata as a framework for online link prediction problem J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-11 Mozhdeh Khaksar Manshad, Mohammad Reza Meybodi, Afshin Salajegheh
ABSTRACT One of the main areas of research in Social Network Analysis (SNA) is Link Prediction (LP). The LP problem is useful in understanding the evolution mechanism of social networks, as well as in different applications such as recommendation systems, bioinformatics and marketing. In LP algorithms, prior network information is used to predict future connections in social networks. In this paper
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Ensemble feature selection using q-rung orthopair hesitant fuzzy multi criteria decision making extended to VIKOR J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-03 Kavitha S., Satheeshkumar J., Janani K., Amudha T., Rakkiyappan R.
ABSTRACT This paper investigates ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) process. A novel algorithm is proposed for the study of ensemble feature selection and it is called as q-rung orthopair hesitant fuzzy MCDM extended to the Visekriterijumska optimizacija Ikompromisno Resenje (VIKOR) (q-ROHFS VIKOR). This is the first time in the
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Intelligent hybrid hand gesture recognition system using deep recurrent neural network with chaos game optimisation J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-02 Jogi John, Shrinivas Deshpande
ABSTRACT Hand gesture recognition is considered an essential task in various human-computer interaction (HCI) applications. Therefore, developing a robust system for hand gesture recognition is a challenge. This work proposed a new hybrid approach named hybrid deep recurrent neural network (RNN) incorporated with a chaos game optimisation (CGO) algorithm for efficiently recognising hand gestures. The
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Blockchain-based cryptographic approach for privacy enabled data integrity model for IoT healthcare J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-01 Mohan Kumar Chandol, M Kameswara Rao
ABSTRACT The development of Internet of Things (IoT) technologies allowed the rapid generation of massive amounts of data by people. Although moving data to a server is a useful solution for storage, the owner of the data loses control, which results in security lapses. An efficient method for cloud-based data models is data integrity. In order to protect data privacy in IoT healthcare, this paper
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An LDOP approach for face identification under unconstrained scenarios J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-01 Rinku Datta Rakshit, Ajita Rattani, Dakshina Ranjan Kisku
ABSTRACT In unconstrained environments, it encounters a number of challenges when considering handcrafted features for face recognition, including changes in pose, illumination and facial expression and plastic surgery variations, look-alike faces and selfie images. As majority of the published works on local descriptors are based on the relationship between the centre pixel and neighbourhood pixels
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A computational model for assessing experts’ trustworthiness J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-03-01 G. Primiero, D. Ceolin, F. Doneda
ABSTRACT The algorithmic detection of disinformation online is currently based on two strategies: on the one hand, research focuses on automated fact-checking; on the other hand, models are being developed to assess the trustworthiness of information sources, including both empirical and theoretical research on credibility and content quality. For debates among experts, in particular, it might be hard
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Knowledge structure construction and skill reduction methods based on multi-scale context J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-02-28 Yinfeng Zhou, Jinjin Li, Hailong Yang, Qingyuan Xu, Yueli Zhou
ABSTRACT The conjunctive model of skill map reflects a way to solve the items in a knowledge domain. Currently, a skill map has been transformed into a formal context to construct a knowledge structure. In fact, the conjunctive model of skill map can be regarded as a special case of skill function. In this paper, we consider a way to solve the items in a knowledge domain as a scale. As a result, a
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Ant colony optimisation and fuzzy system for prediction of computational data of fluid flow in a bubble column reactor J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-02-28 Amirali Zandiyeh, Iman Behroyan, Mohammad Mahdi Noori, Meisam Babanezhad
ABSTRACT Computational data of a complex problem could be mapped and solved by artificial intelligence (AI) for postprocessing purposes and numerical time-saving. A fuzzy adaptive network as a hybrid AI system has already been used for CFD data machine learning. However, no investigation has been done for the ant colony optimisation (ACO) performance evaluation compared to the adaptive network. As
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A review of feature selection methods based on meta-heuristic algorithms J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-02-27 Zohre Sadeghian, Ebrahim Akbari, Hossein Nematzadeh, Homayun Motameni
ABSTRACT Feature selection is a real-world problem that finds a minimal feature subset from an original feature set. A good feature selection method, in addition to selecting the most relevant features with less redundancy, can also reduce computational costs and increase classification performance. One of the feature selection approaches is using meta-heuristic algorithms. This work provides a summary
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Neural networks with dimensionality reduction for efficient springback prediction in deep drawing of multi-material cylindrical cups J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-02-25 Chun Kit Jeffery Hou, Kamran Behdinan
ABSTRACT Deep drawing involves the use of a punch to plastically deform a workpiece to create sheet metal parts such as cups and channels. Upon release of the punch, the workpiece undergoes elastic recovery and results in a change in geometry known as the springback effect. The process is highly non-linear and involves many parameters, leading to large computation times to fully simulate the process
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A multi-attribute decision-making method for ternary hybrid decision matrices in view of (T, S)-fuzzy rough sets with fuzzy preference relations J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-02-23 Xiaofeng Liu
ABSTRACT As a significant part of modern decision science, multi-attribute decision-making (MADM) has attracted the interests of more and more researchers. In the process of decision analysis, decision-makers or experts usually give decision information in the forms of preference order, utility values or preference relation. Utility values, i.e. attribute values, generally include real-values, interval-values
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The future of endoscopy – what are the thoughts on artificial intelligence? J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-02-20 S. Nazarian, H.F Koo, E. Carrington, A. Darzi, N. Patel
ABSTRACT There is an emerging role of artificial intelligence (AI) in endoscopy with studies on early systems showing promising results. However, various limitations inhibit widespread use. The aim of this study was to ascertain the sentiments of endoscopists and understand the benefits and barriers towards adoption of AI systems into healthcare. An anonymous online 18-question survey was disseminated
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Why we need biased AI: How including cognitive biases can enhance AI systems J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-02-13 Thilo Hagendorff, Sarah Fabi
ABSTRACT This paper stresses the importance of biases in the field of artificial intelligence (AI). To foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the implementation of human cognitive biases in learning algorithms. We use insights from cognitive science and apply them to the AI field, combining theoretical considerations with
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A scaling up approach: a research agenda for medical imaging analysis with applications in deep learning J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-25 Yaw Afriyie, Benjamin A. Weyori, Alex A. Opoku
ABSTRACT Medical anomaly identification using machine learning is a significant subject that has received a lot of attention. Artificial neural networks’ successor, deep learning, is a well-developed technology with strong computational capabilities. Its popularity has increased in recent years due to the availability of rapid data storage and hardware parallelism. Numerous, sizeable medical imaging
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Periodic dynamic on a class of neutral-type Rayleigh equations incorporating D operators J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-22 Yanli Xu
ABSTRACT In this study, a class of neutral-type Rayleigh equations incorporating D operators and T-periodic time-varying coefficients are introduced. By utilising differential inequality techniques, some new sufficient criteria are derived to guarantee the existence and global exponential stability of T-periodic solutions for the addressed equations. Through numerical experiments, the validity of the
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Almost periodic stability on a delay Nicholson’s blowflies equation J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-22 Le Li, Xiaodan Ding, Weiping Fan
ABSTRACT By applying some novel inequality techniques, the properties of almost periodic function and the fluctuation lemma, this manuscript establishes the criteria for the existence and globally exponential stability of positive almost periodic solutions of a non-autonomous delayed Nicholson’s blowflies model under weaker conditions, which refine and complement some existing literature. In particular
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Retracted Article: Artificial intelligence for the identification of healthy fruits and vegetables using MMDL-ABO J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-22 P. Subhashini, John De Britto C, K. Upendra Babu, G. Sumathy
Published in Journal of Experimental & Theoretical Artificial Intelligence (Ahead of Print, 2023)
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A Novel Multispectral Maritime Target classification based on ThermalGAN (RGB-to-Thermal Image Translation) J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-19 Bouchenafa Mohamed El Mahdi, Nemra Abdelkrim, Amamra Abdenour, Irki Zohir, Boubertakh Wassim, Demim Fethi
ABSTRACT Convolutional Neural Networks (CNN) for ship classification in multi-spectral images (RGB, IR, etc.) is proposed in this paper. Recent developments in deep learning have significantly advanced the field of ship recognition. However, since maritime light intensity is frequently disturbed, multispectral imaging is considered a more robust substitute for RGB imaging. The proposed architectures
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Overlapping word removal is all you need: revisiting data imbalance in hope speech detection J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-18 Hariharan RamakrishnaIyer LekshmiAmmal, Manikandan Ravikiran, Gayathri Nisha, Navyasree Balamuralidhar, Adithya Madhusoodanan, Anand Kumar Madasamy, Bharathi Raja Chakravarthi
ABSTRACT Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-switched Tamil and Malayalam. In this paper, we present deep learning techniques
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Computer-aided COVID-19 diagnosis: a possibility? J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-16 Aamir Wali, Shahroze Ali, Asma Naseer, Saira Karim, Zareen Alamgir
ABSTRACT Coronavirus Disease 2019 (COVID-19) is extremely contagious with a very high mortality rate. Effective and early diagnosis of COVID-19 is therefore crucial when treating patients and limiting its spread. The currently available methods for reliably identifying COVID are time-consuming. Infected people display various symptoms, some of which can be manifested by radiographic imaging such as
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Decision support system (DSS) for traffic prediction and building a dynamic internet community using Netnography technology in the city of Amman J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-14 Nancy Shaar, Mohammad Alshraideh, Lara Shboul, Iyad AlDajani
ABSTRACT Recently, the increasing rapid number of cars on the roads and the great demand for traffic prediction, because of the increase in traffic congestion, has posed a great challenge to governments’ economic development and social stability of most countries in the world. While many challenges are facing governments to solve traffic congestion and reduce car accidents and pollution, the objective
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Multi-objective optimisation model and hybrid optimization algorithm for Electric Vehicle Charge Scheduling J. Exp. Theor. Artif. Intell. (IF 2.2) Pub Date : 2023-01-13 Durga Mahato, Vikas Kumar Aharwal, Apurba Sinha
ABSTRACT Electric vehicles (EV) are moderately defeating more roads and replacing pollutants of classical vehicles. Due to rising EV, it is imperative to provide charging stations. However, unscheduled EVs are left because of the unavailability of adequate energy or charging slots. This paper develops an optimisation aware technique for charge scheduling in EV. The first step is the simulation of EV