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Understanding the Limits of Explainable Ethical AI Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-01-09 Clayton Peterson, Jan Broersen
Artificially intelligent systems are nowadays presented as systems that should, among other things, be explainable and ethical. In parallel, both in the popular culture and within the scientific literature, there is a tendency to anthropomorphize Artificial Intelligence (AI) and reify intelligent systems as persons. From the perspective of machine ethics and ethical AI, this has resulted in the belief
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Learning from Highly Imbalanced Big Data with Label Noise Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Justin M. Johnson, Robert K. L. Kennedy, Taghi M. Khoshgoftaar
This study explores the effects of class label noise on detecting fraud within three highly imbalanced healthcare fraud data sets containing millions of claims and minority class sizes as small as 0.1%. For each data set, 29 noise distributions are simulated by varying the level of class noise and the distribution of noise between the fraudulent and non-fraudulent classes. Four popular machine learning
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Incorporating Normalized L1 Penalty and Eigenvalue Constraint for Causal Structure Learning Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Yunfeng Wang, Yuelong Zhu, Tingting Hang, Jiamin Lu, Jun Feng
Inferring causal relationships is key to data science. Learning causal structures in the form of directed acyclic graphs (DAGs) has been widely adopted for uncovering causal relationships, nonetheless, it is a challenging task owing to its exponential search space. A recent approach formulates the structure learning problem as a continuous constrained optimization task that aims to learn causal relation
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Ensemble Learning Based Gene Regulatory Network Inference Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Sergio Peignier, Baptiste Sorin, Federica Calevro
In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results
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Differential Evolution Algorithm with Dual Information Guidance Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Xinyu Zhou, Yanlin Wu, Hu Peng, Shuixiu Wu, Mingwen Wang
As an effective tool to solve continuous optimization problems, differential evolution (DE) algorithm has been widely used in numerous fields. To enhance the performance, in recent years, many DE variants have been developed based on the idea of multiple strategies. However, there still exists an issue for them that the strategy selection method relies on the historical search experience. The experience
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Neural Adversarial Attacks with Random Noises Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Hatem Hajri, Manon Césaire, Lucas Schott, Sylvain Lamprier, Patrick Gallinari
In this paper, we present an approach which relies on the use of random noises to generate adversarial examples of deep neural network classifiers. We argue that existing deterministic attacks, which perform by sequentially applying maximal perturbations on selected components of the input, fail at reaching accurate adversarial examples on real-world large scale datasets. By exploiting a simple Taylor
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Prediction Quality Meta Regression and Error Meta Classification for Segmented Lidar Point Clouds Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk
We present a post-processing tool for semantic segmentation of Lidar point clouds, called LidarMetaSeg, which estimates the prediction quality segmentwise and classifies prediction errors. For this purpose, we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures
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Improving Peer Assessment by Incorporating Grading Behaviors: Models and Practices Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Jia Xu, Jing Liu, Panyuan Yang, Pin Lv
Peer assessment, which requires students to evaluate their peers’ submissions, has become the paradigm for solving the grading challenge of large-scale open-ended assignments teachers face in MOOCs. Since peer grades may be biased and unreliable, a group of probabilistic graph models are proposed to improve the estimation of true scores for assignments based on peer grades, by explicitly modeling the
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QHAN: Quantum-inspired Hierarchical Attention Mechanism Network for Question Answering Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Peng Guo, Panpan Wang
The approach to question answering is challenging because it usually requires finding useful information from within and between question and answer sentences for sentence semantic matching. The key information mined from existing question and answer sentences, as a supplement to semantic information, maybe helpful for this task. However, capturing the intra-sentence and inter-sentence semantic interactions
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Keyboard Layout Optimization and Adaptation Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Keren Nivasch, Amos Azaria
Since the keyboard is the most common method for text input on computers today, the design of the keyboard layout is very significant. Despite the fact that the QWERTY keyboard layout was designed more than 100 years ago, it is still the predominant layout in use today. There have been several attempts to design better layouts, both manually and automatically. In this paper we improve on previous works
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An Adaptive Learning Environment for Programming Based on Fuzzy Logic and Machine Learning Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-08-16 Konstantina Chrysafiadi, Maria Virvou, George A. Tsihrintzis, Ioannis Hatzilygeroudis
In this paper, we present an Intelligent Tutoring System (ITS), for use in teaching the logic of computer programming and the programming language ‘C’. The aim of the ITS is to adapt the delivered learning material and the lesson sequence to the knowledge level and learning needs of each individual student. The adaptation of the presented ITS is based on fuzzy logic and a machine learning technique
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Enhancing Dynamic Multi-objective Optimization Using Opposition-based Learning and Simulated Annealing Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Kiran Ilyas, Irfan Younas
There are many dynamic real-life optimization problems in which objectives increase or decrease over time, which usually leads to variations in the dimensions of a Pareto front. Dynamic multi-objective optimization (DyMO) approaches aim to keep track of the updated Pareto front to tackle the changes which are caused by the dynamic environment. However, the current DyMO approaches do not handle dynamic
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Deep Learning with Game Theory Assisted Vertical Handover Optimization in a Heterogeneous Network Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Safak Kayikci, Nazeer Unnisa, Anupam Das, S. K. Rajesh Kanna, Mantripragada Yaswanth Bhanu Murthy, N. S. Ninu Preetha, G. Brammya
Problem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding
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RetU-Net: An Enhanced U-Net Architecture for Retinal Lesion Segmentation Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Sumod Sundar, S Sumathy
Diabetic retinopathy is a predominant vision-threatening disease affecting working-aged people specifically. Timely diagnosis through early detection and prevention helps to reduce the risk of severe vision loss. Computer-aided diagnosis in retinal image analysis through Machine Learning techniques will help medical professionals perform their analysis better. Automated image processing through Convolutional
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DSM-IDM-YOLO: Depth-wise Separable Module and Inception Depth-wise Module Based YOLO for Pedestrian Detection Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Sweta Panigrahi, U. S. N. Raju
Pedestrian detection is one of the most challenging research areas in computer vision. Compared to traditional hand-crafted methods, convolutional neural networks (CNNs) have superior detection results. The single-stage detection networks, particularly the state-of-the-art You Only Look Once (YOLO) network, have attained a satisfactory performance without compromising the computation speed in object
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Fatigue Detection System in Construction Site Using Extension Based Equilibrium with Capsule Autoencoder Network Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Ashish Sharma, Gaurav Sethi
Fatigue detection of workers is an important factor in construction site monitoring. Nowadays, worker exhaustion on construction sites causes tiredness and drowsiness. The prediction of mental exhaustion is critical because the job has increased over the years. Accurate fatigue detection is important for analyzing the stress level of work on construction sites. However, recording worker activities
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The Dead-reckoning Navigation Guidance Law Based on Neural Network Collaborative Forecasting Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Guochuan Yu, Tao Zhao, Bicong Ren
For predicting missile’s interception point, the current guidance law based on neural networks avoids to model the strong nonlinear motion of a missile and simultaneously improve the anti-jamming ability of the guidance law. Although the advantages of solving the predicted intercept point problem based on neural networks are obvious, the difficulty in obtaining the target missile information still
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A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Maliki Moustapha, Murat Tasyurek, Celal Ozturk
Computer Vision (CV) has become an essential field in Artificial Intelligence applications. Object detection and recognition (ODR) is one of the fundamental tasks of computer vision implementations. However, developing an efficient ODR model is still a significant problem. The model’s execution time and speed are the most critical features during the inference or detection and recognition process,
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Pinakas: A Methodology for Deep Analysis of Tables in Technical Documents Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 Michail S. Alexiou, Nikolaos G. Bourbakis
The holistic understanding of the information contained in technical documents depends on the understanding of the document’s individual modalities. These modalities are tables, graphics, diagrams, formulas, etc. and each of them is a standalone research topic that requires a different way of processing and understanding. These modalities, processed and combined with the document text, can introduce
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Effective Segmentation and Brain Tumor Classification Using Sparse Bayesian ELM in MRI Images Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-06-30 V. V. S. Sasank, S. Venkateswarlu
Classification of tumors from MRI plays very important role for diagnosing various diseases. But, it consumes an enormous amount of time for classification. Due to the similar structure of anomalous and typical tissues in the brain, it is difficult to complete the detection process successfully. Many researchers have developed new methods for detection and classification of tumors. But most of them
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Breast Masses Segmentation: A Framework of Skip Dilated Semantic Network and Machine Learning Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-05-22 Saliha Zahoor, Umar Shoaib, Ikram Ullah Lali
Many medical specialists used Computer Aided Diagnostic (CAD) systems as a second opinion to detect breast masses. The poor visualization of mass images makes it difficult to identify precisely. To segment the lesions from the mammograms is a difficult task due to different shapes, sizes, and locations of the masses. The motivation of this study is to develop a method that can segment breast mass lesions
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Multimodal Biometrics Authentication in Healthcare Using Improved Convolution Deep Learning Model Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-05-22 S Balaji, U Rahamathunnisa
In healthcare applications, biometric authentication is crucial in managing patient credential details. The limited usage of biometric traits causes personal details theft, treatment hacking, and payment hijacking. Multimodal biometrics should incorporate into the healthcare system to maintain security and privacy in healthcare applications. Previous methods ensure authentication and security consume
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An Approach for Gesture Recognition Based on a Lightweight Convolutional Neural Network Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-05-22 M. Ravinder, Kiran Malik, M. Hassaballah, Usman Tariq, Kashif Javed, Mohamed Ghoneimy
Gesture recognition, which plays an important role to understand meaningful movements of human bodies, is one of the most effective approaches for humans to interact. Sign language is a fundamental and innate means of communication for hearing-impaired individuals. Though significant progress has been made, the state-of-the-art gesture recognition methods yield week performance for conditions with
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Classification of Visually Evoked Potential EEG Using Hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron Classifier Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-05-22 Ravichander Janapati, Vishwas Dalal, Usha Desai, Rakesh Sengupta, Shrirang A. Kulkarni, D. Jude Hemanth
Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained importance because of lesser training time, higher precision
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Efficient Multimodal Biometric Recognition for Secure Authentication Based on Deep Learning Approach Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-05-22 Vani Rajasekar, Muzafer Saracevic, Mahmoud Hassaballah, Darjan Karabasevic, Dragisa Stanujkic, Mahir Zajmovic, Usman Tariq, Premalatha Jayapaul
Biometric identification technology has become increasingly common in our daily lives as the requirement for information protection and control legislation has grown around the world. The unimodal biometric systems use only biometric traits to authenticate the user which is trustworthy but it possesses various limitations such as susceptibility to attacks, noise occurring in a dataset, non-universality
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Fast Violence Recognition in Video Surveillance by Integrating Object Detection and Conv-LSTM Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-05-22 Nikita Jain, Vedika Gupta, Usman Tariq, D. Jude Hemanth
Video surveillance involves petabytes of data storage requiring expensive hardware, which might also be time-inefficient. The aim of this article is, therefore, to develop an intelligent system capable of analyzing long sequences of videos captured from CCTV, helping to mitigate catastrophe and mitigate the violent threats faced by citizens every day, economically and efficiently. Existing models have
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Neural Network-based Tool for Survivability Assessment of K-variant Systems Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-05-24 Berk Bekiroglu, Bogdan Korel
The K-variant is a multi-variant architecture to enhance the security of the time-bounded mission and safety-critical systems. Variants in the K-variant architecture are generated by controlled source program transformations. Previous experimental studies showed that the K-variant architecture might improve the security of systems against memory exploitation attacks. In order to estimate the survivability
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Glaucoma Detection in Retinal Fundus Images Based on Deep Transfer Learning and Fuzzy Aggregation Operators Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Mohammed Yousef Salem Ali, Mohammad Jabreel, Aida Valls, Marc Baget, Mohamed Abdel-Nasser
The early diagnosis of the glaucoma disease in the eye is crucial to avoid vision loss. This paper proposes an efficient computer-aided detection (CAD) system for diagnosing glaucoma based on fundus images, deep transfer learning and fuzzy aggregation operators. Specifically, the proposed CAD system includes three stages: (1) Detection of the region of interest of the optic disc using an efficient
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An Integrated Framework with Deep Learning for Segmentation and Classification of Cancer Disease Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Hemanta Kumar Bhuyan, Vinayakumar Ravi
This paper addresses radiologists’ specific diagnosis of cancer disease effectively using integrated framework of deep learning model. Although several existing diagnosis systems have been adopted by a physician, in few cases, it is not so practical to see the infected area from images in the normal eye. Thus, a fully integrated diagnosis framework for disease detection is proposed to find out the
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Ultrasound Image Segmentation and Classification of Benign and Malignant Thyroid Nodules on the Basis of Deep Learning Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Min Yang, Austin Lin Yee, Jiafeng Yu
This study aimed to investigate the effect of an image denoising algorithm based on weighted low-rank matrix restoration on thyroid nodule ultrasound images. A total of 1000 original ultrasound image data sets of thyroid nodules were selected as the study samples. The nodule segmentation data set of thyroid ultrasound region of interest (ROI) images was drawn and acquired. By introducing multiscale
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An Effective Depression Diagnostic System Using Speech Signal Analysis Through Deep Learning Methods Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Aman Verma, Pooja Jain, Tapan Kumar
According to the World Health Organization (WHO), depression is one of the largest contributors to the burden of mental and psychological diseases with more than 300 million people being affected; however a huge portion of this does not receive effective diagnosis. Traditional techniques to diagnose depression were based on clinical interviews. These techniques had several limitations based on duration
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autoFPR: An Efficient Automatic Approach for Facial Paralysis Recognition Using Facial Features Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Sridhar Reddy Gogu, Shailesh R. Sathe
Facial paralysis (FP) is the most common illness. Nerve damage can cause the affected muscles of the face to lose control. Most FP diagnosis systems heavily depend on skilled clinicians and lack automatic quantitative assessment. This paper introduces a novel automatic facial paralysis recognition (autoFPR) approach, a four-stage machine learning solution, for classifying FP and healthy subjects. Our
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Blockchain and Artificial Intelligence-based Solutions for Healthcare Management: Liver Disease Detection as a Case Study Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Zahraa Tarek, Mohamed Elhoseny, Ibrahim M. El-Hasnony
The issue of privacy in clinical data restricts data sharing among various organizations because of legal and ethical concerns. Every medical organization (hospital, research center, testing lab, etc.) needs to protect personal and medical data privacy and confidentiality while also sharing data with efficient and accurate learning models for various diseases. This paper addresses a method that combines
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Selecting the Best Health Care Systems: An Approach Based on Opinion Mining and Simplified Neutrosophic Sets Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Measuring what hospital offers the best services is very difficult, for that reason, the opinions from previous patients have become an essential tool for the new possible clients to decide which services they must select. Many online platforms deal with opinions to analyze their services/products, primarily, by means of aspect-based sentiment analysis techniques. These techniques are mainly based
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Multimodal Depression Detection: Using Fusion Strategies with Smart Phone Usage and Audio-visual Behavior Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Ravi Prasad Thati, Abhishek Singh Dhadwal, Praveen Kumar, P Sainaba
The problem of detecting depression is multi-faceted because of variability in depressive symptoms caused by individual differences. The variations can be seen in historical information (like decreased physical activity etc.) and also in verbal/non-verbal behaviors (like lower pitch, downward eye gaze etc.). The primary goal of this research is to develop a novel classification system for diagnosing
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Prediction of Heart Disease Using a Hybrid XGBoost-GA Algorithm with Principal Component Analysis: A Real Case Study Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Tuncay Ozcan, Ebru Pekel Ozmen
Cardiovascular diseases are one of the most common causes of death in the world. At this point, early diagnosis of heart diseases is critically important. The aim of this study is to predict the heart disease using feature selection, classification and optimization algorithms. Firstly, principal component analysis (PCA) is used to create the feature selection model and to determine the effective attributes
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Vision and Audio-based Methods for First Impression Recognition Using Machine Learning Algorithms: A Review Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 Sumiya Mushtaq, Neerendra Kumar, Yashwant Singh, Pradeep Kumar Singh
Personality is a psychological construct that embodies the unique characteristics of an individual. Automatic personality computing enables the assessment of personality elements with the help of machines. Over the last few decades, a lot of researchers have focussed on computing aspects of personality, emotions, and behavior with the help of machine learning. Efficient personality recognition using
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Preprocessing and Artificial Intelligence for Increasing Explainability in Mental Health Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-04-05 X. Angerri, Karina Gibert
This paper shows the added value of using the existing specific domain knowledge to generate new derivated variables to complement a target dataset and the benefits of including these new variables into further data analysis methods. The main contribution of the paper is to propose a methodology to generate these new variables as a part of preprocessing, under a double approach: creating 2nd generation
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An Information Extraction and Thorough Understanding Method for Test-question Graph of Junior High School Physical Mechanical Motion Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Gang Zhao, Jie Chu, Shufan Jiang, Hui He
Intelligent problem-solving technology is a typical application of artificial intelligence in the educational field. The purpose of intelligent problem-solving is to enable machine to solve problems like human beings and help people to find useful and accurate information in the test-questions. Correct understanding of test-questions is one of the key techniques of intelligent problem-solving. The
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Learning Student Intents and Named Entities in the Education Domain Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Oanh Thi Tran, Thang Van Nguyen, Tu Anh Nguyen, Ngo Xuan Bach
Detecting intents and extracting necessary contextual information (aka named entities) in input utterances are two fundamental tasks in understanding what the users say in chatbot systems. While most work in this field has been dedicated to high-resource languages in popular domains like business and home automation, little research has been done for low-resource languages, especially in a less popular
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Top-k Learned Clauses for Modern SAT Solvers Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Jerry Lonlac, Engelbert Mephu Nguifo
Clause Learning is one of the most important components of a conflict driven clause learning (CDCL) SAT solver that is effective on industrial SAT instances. Since the number of learned clauses is proved to be exponential in the worst case, it is necessary to identify the most relevant clauses to maintain and delete the irrelevant ones. As reported in the literature, several learned clauses deletion
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Entropy Weighted and Kernalized Power K-Means Clustering Based Lesion Segmentation and Optimized Deep Learning for Diabetic Retinopathy Detection Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 J. Granty Regina Elwin, K. Suresh Kumar, J. P. Ananth, R. Ramesh Kumar
Diabetic retinopathy (DR), a common complication of diabetes, is one of the leading causes of visual loss in a growing population. Thus, it is essential to identify DR at an early stage in order to minimize the problem of vision loss. As a result, the Henry Gas SailFish Optimizer (HGSO) algorithm and a successful lesion segmentation method based on Entropy Weighted and Kernalized Power K-Means Clustering
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Prediction of Incident Solar Radiation Using a Hybrid Kernel Based Extreme Learning Machine Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Preeti, Rajni Bala, Ram Pal Singh
Forecasting solar radiation for a given region is an emerging field of study. It will help to identify the places for installing large-scale photovoltaic-systems, designing energy-efficient buildings and energy estimation. The different machine learning kernel-based approaches for prediction problems uses either a local or global kernel. These models can provide either strong training capability or
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Automated Generation of Discrete Event Simulation Models for the Economic Assessment of Interventions for Rare Diseases Using the RaDiOS Ontology Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 David Prieto-González, Iván Castilla-Rodríguez, Evelio José González-González, María de la Luz Couce-Pico
Collection and synthesis of evidence is a key task in the development of the simulation models required for health technology assessment (HTA). The implementation of some of these models, such as discrete event simulation (DES) models, presents technical difficulties and requires higher technical skills. This work presents a method to extract the knowledge stored in an ontology, Rare Disease Ontology
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On the Inverse Frequent Itemset Mining Problem for Condensed Representations of Itemsets Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Petros N. Tamvakis, Evangelos Sakkopoulos, Vassilios S. Verykios
Inverse frequent itemset mining can be successfully modelled as an instance of the Probabilistic Satisfiability problem. Given a transaction database we can perform a frequent itemset mining algorithm, like the Apriori algorithm, to obtain useful itemset collections such as frequent or closed itemsets. We then use these itemset collections as frequency constraints in order to reconstruct the original
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Unexpectedness as a Measure of Semantic Learning When Training Transformer Models Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Ricardo A. Calix, Leili Javadpour
Many problems in NLP such as language translation and sentiment analysis have shown a lot of improvement in recent years. As simpler language problems are solved or better understood, the focus shifts to more complex problems such as semantic analysis and understanding. Unfortunately, a lot of studies in the literature suffer from a too much specificity problem. The algorithms and datasets are too
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Novel Sparse Feature Regression Method for Traffic Forecasting Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Athanasios I. Salamanis, George A. Gravvanis, Sotiris B. Kotsiantis, Michael N. Vrahatis
Traffic forecasting is an integral part of modern intelligent transportation systems. Although many techniques have been proposed in the literature to address the problem, most of them focus almost exclusively on forecasting accuracy and ignore other important aspects of the problem. In the paper at hand, a new method for both accurate and fast large-scale traffic forecasting, named “sparse feature
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A RERNN-SGO Technique for Improved Quasi-Z-Source Cascaded Multilevel Inverter Topology for Interfacing Three Phase Grid-Tie Photovoltaic System Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 M. Bhavani, P. S. Manoharan
In this manuscript, a proficient control strategy-based improved Quasi-Z-Source Cascaded Multilevel Inverter (QZS-CMI) topology for interfacing photovoltaic (PV) system is proposed. The control strategy is joint execution of both recalling-enhanced recurrent neural network (RERNN) and Shell Game Optimization (SGO), therefore it is called RERNN-SGO technique. The major intention of proposed system determined
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Double-inputs Illumination Pattern Recognizing Model with Automatic Shadow Detection Network in a Single Face Image Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Jiaqi Liu, Jizheng Yi, Aibin Chen
Illumination pattern recognition of face image has always been a hot research topic in the field of human-computer interaction, and has been widely used in lighting recovery, virtual scene construction and other multimedia fields. Most of the traditional methods achieve this task by analyzing the illumination components from the image texture and structure information, which is often considered to
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Dual Stream Conditional Generative Adversarial Network Fusion for Video Abnormal Behavior Detection Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Mengyao Zhao, Zhengping Hu, Shufang Li, Zhe Sun
Deep learning has been successfully applied to video anomaly detection. However, the way that deep network learn spatio-temporal features autonomously will ignore the specificity of different pattern features. Therefore, this paper focuses on how to efficiently learn deep appearance feature, introduces the idea of learning appearance information by predicting future frame, and proposes dual stream
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Reservoir Computing for Solving Ordinary Differential Equations Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-28 Marios Mattheakis, Hayden Joy, Pavlos Protopapas
There is a wave of interest in using physics-informed neural networks for solving differential equations. Most of the existing methods are based on feed-forward networks, while recurrent neural networks solvers have not been extensively explored. We introduce a reservoir computing (RC) architecture, an echo-state recurrent neural network capable of discovering approximate solutions that satisfy ordinary
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Data Expansion Approach with Attention Mechanism for Learning with Noisy Labels Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2023-02-14 Yuichiro Nomura, Takio Kurita
In recent years, the development of deep learning has contributed to various areas of machine learning. However, deep learning requires a huge amount of data to train the model, and data collection techniques such as web crawling can easily generate incorrect labels. If a training dataset has noisy labels, the generalization performance of deep learning significantly decreases. Some recent works have
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Online Feature Selection Using Sparse Gradient Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 N Nasrin Banu, Radha Senthil Kumar
Feature Selection (FS) is an important preprocessing step in data analytics. It is used to select a subset of the original feature set such that the selected subset does not affect the classification performance significantly. Its objective is to remove irrelevant and redundant features from the original dataset. FS can be done either in offline mode or in online mode. The basic assumption in the former
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Covid-19 Versus Lung Cancer: Analyzing Chest CT Images Using Deep Ensemble Neural Network Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 KC Santosh, Sourodip Ghosh
With a high rise in deaths caused due to novel coronavirus (nCoV), immunocompromised persons are at high risk. Lung cancer is no exception. Classifying lung cancer patients and Covid-19 is the primary aim of the paper. For this, we propose a deep ensemble neural network (VGG16, DenseNet121, ResNet50 and custom CNN) to detect Covid-19 and lung cancer using chest CT images. We validate our model using
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Improving Rice Disease Diagnosis Using Ensemble Transfer Learning Techniques Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 Mayuri Sharma, Chandan Jyoti Kumar
Early diagnosis of the disease in crop opens the door to potential care and treatment, which in turn improves the yield. Automated detection of rice plant disease from plant images is an emerging research field that is gaining prominence due to the rising interest in it from machine learning researchers. Convolution neural network-based learning techniques are widely used by the research community
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Micro-network Based Convolutional Neural Network with Integration of Multilayer Feature Fusion Strategy for Human Activity Recognition Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 Arati Kushwaha, Manish Khare, Ashish Khare
Convolutional neural networks (CNN) have shown remarkable performance in enormous computer vision applications over the years, and many works have been done for human activity recognition (HAR) using CNN. However, most of deep learning-based architectures require large training data and plenty of computational resources. Therefore, we proposed a simple and efficient deep learning model for human activity
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A New Indirect Adaptive Neural Control for Nonlinear Systems: A Real Validation on a Chemical Process Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 Rabab Hamza, Yassin Farhat, Ali Zribi
In the present work, an indirect adaptive neural control method for nonlinear systems having unknown dynamics is proposed. The proposed control architecture is composed by a neural emulator (NE) and a neural controller (NC) where a new decoupled variable learning rates (VLRs) combined with Taylor development (TD) are used to train the NE and the NC. The developed VLRs mixed with the TD (TDVLRs) ensure
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Underwater Target Tracking in Radar Images Using Exponential Competitive Swarm-based Deep Neuro Fuzzy Network Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 D. Thiruselvan, J. P. Ananth
The tracking of underwater targets represents a basic unit in underwater surveillance for offering effective search and rescue operations. However, the tracking of the target is a major constraint in finding the underwater target. This paper devises a model for underwater target tracking considering radar signals. Radar signals are subjected to the image reconstruction process to make them suitable
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Financial Time Series Forecasting: A Combinatorial Forecasting Model Based on STOA Optimizing VMD Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 Tianxiang Yao, Xichun Liu
The complexity and frequent fluctuations of economic data pose a significant challenge to forecasting studies. In order to predict financial data more accurately, we build a fusion model for predicting financial data based on the idea of decomposition-recombination by combining the Sooty Tern Optimization Algorithm (STOA), Variational Mode Decomposition (VMD), Support Vector Machine (SVM) and Back
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Predicting Integer Overflow Errors via Supervised Learning Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2022-12-28 Yu Luo, Weifeng Xu, Dianxiang Xu
An integer overflow error occurs when an integer operation in computer software evaluates a value out of the integer range. It can lead to a fatal system failure. The existing approaches to detecting integer overflow errors rely on data/control-flow analysis of the code or execution of the code with test cases. This paper presents a supervised learning approach to predicting whether each method in