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Intelligent Program Correction and Evaluation System Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 Isha Ganguli, Rajat Subhra Bhowmick, Shivam Biswas, Jaya Sil
The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder’s lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct
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IoT Based Wireless Communication System for Smart Irrigation and Rice Leaf Disease Prediction Using ResNeXt-50 Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 S. Sangeetha, N. Indumathi, Reena Grover, Rakshit Singh, Renu Mavi
Agriculture not only plays a vital role in human survival but also contributes to the nation’s greater economic development. With the use of technologies like IoT, WSNs, remote sensing, camera surveillance, and many more, precision agriculture is the newest buzzword in the field of technology. Its primary goal is to lessen the labour of farmers while increasing the output of farms. Many machine learning
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Named Entity Recognition of Tunisian Arabic Using the Bi-LSTM-CRF Model Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 Asma Mekki, Inès Zribi, Mariem Ellouze, Lamia Hadrich Belguith
Named Entity Recognition (NER) is an NLP field that deals with recognizing and classifying entities in written text. Most Arabic NER research studies discuss the Arabic NER challenge for the Modern Standard Arabic (MSA) language. However, the presence of dialectal Arabic textual resources in social media, blogs, TV shows, etc. is increasingly progressive. Therefore, the treatment of named entities
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Graph CNN-ResNet-CSOA Transfer Learning Architype for an Enhanced Skin Cancer Detection and Classification Scheme in Medical Image Processing Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 G. N. Balaji, S. A. Sahaaya Arul Mary, Nagesh Mantravadi, Francis H. Shajin
Skin cancer is a perilous kind of cancer caused by damaged DNA and it leads to death. This damaged DNA causes uncontrolled proliferation of cells. Even though, the image analysis of lesions is highly difficult due to light reflections from skin surface, fluctuations at color lighting, variety of lesions’ forms and sizes in skin cancer. Because of these issues, automatic recognition of skin cancer accurateness
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Classifying Hindi News Using Various Machine Learning and Deep Learning Techniques Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 Anusha Chhabra, Monika Arora, Arpit Sharma, Harsh Singh, Saurabh Verma, Rachna Jain, Biswaranjan Acharya, Vassilis C. Gerogiannis, Dimitrios Tzimos, Andreas Kanavos
Text classification involves organizing textual information into predefined classes, a task which is particularly useful in domains like sentiment analysis, spam detection, and content labeling. In India, where a massive amount of information is generated daily through newspapers and social media, Hindi is one of the most widely used and spoken languages. However, there is limited research on Hindi
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Intrusion Detection Using Bat Optimization Algorithm and DenseNet for IoT and Cloud Based Systems Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 H. Kanakadurga Bella, S. Vasundra
In Internet of Things (IoT) and cloud systems, Intrusion Detection (ID) is very vital for protecting the security infrastructures. ID techniques are extensively used to detect and track malicious threats in cloud and IoT systems. In the IoT based ID, the conventional techniques work based on the manual traffic feature values that increase the complexity of the networks and achieve a limited detection
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Traffic Congestion Prediction Using Feature Series LSTM Neural Network and a New Congestion Index Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 Manoj Kumar, Kranti Kumar
Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield’s model were used for the derivation
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MSA-UNet: A Multiscale Lightweight U-Net Lung CT Image Segmentation Algorithm Under Attention Mechanism Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 Chuantao Wang, Shuo Shao, Jiajun Yin, Xiumin Wang, Baoxia Li
Automatic and precise segmentation of lung images can assist doctors in locating and diagnosing lung lesions. However, current traditional lung CT image lesion segmentation algorithms suffer from the problem of low segmentation accuracy, while deep learning-based segmentation algorithms struggle to strike a better balance between lightweight and high accuracy. In response to this issue, a multi-scale
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Abax: Extracting Mathematical Formulas from Chart Images Using Spatial Pixel Information Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-03-30 Michail S. Alexiou, Nikolaos G. Bourbakis
Current state-of-the-art techniques in 2D chart analysis primarily emphasize the recognition of textual information as a means of comprehending and summarizing chart contents. However, the effective analysis and understanding of information embedded in chart images depends on accurate reverse-engineering of the behavior of depicted variables. In this paper, we propose a methodology, named Abax, as
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Intelligent Optimization Algorithm for Support Vector Machine: Research and Analysis of Prediction Ability Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Lian Lian
Support vector machine is a very classical and popular model for data prediction. Traditional support vector machines use grid search to determine its parameters. In order to improve the accuracy of prediction, more and more frameworks are proposed. Among them, the combination of support vector machine and intelligent optimization algorithm is the most commonly used solution at present. The optimization
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Accommodation Recommendation on Shared Platforms Considering Bidirectional Selection and Review Mechanisms Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Yuanyuan Lin, Chao Huang, Xi Zhang, Xin Li, Wei Yao
In recent years, shared accommodation platforms have developed rapidly and become increasingly popular. They commonly adopt unique bidirectional selection and review mechanisms. However, most existing accommodation recommendation strategies overlook the impact of these mechanisms on the recommendation performance. To address this gap, we propose a two-stage recommendation method as Shared Accommodation
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A Hybrid Attention-based Deep Model for Lung Cancer Subtype Classification from Multimodality Images Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Chinnu Jacob, Gopakumar C. Menon
Lung cancer is a deadly type of malignancy that poses a significant threat to human health. Accurately identifying the subtypes of lung cancer is critical for effective treatment. However, conventional methods for determining subtypes, such as histological examination, are invasive and time-consuming. In order to overcome this problem, a non-invasive approach for predicting lung cancer subtypes using
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Learning Polarity Embedding Attention for Aspect-based Sentiment Analysis Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Ramesh Wadawadagi, Sanjeevakumar M. Hatture, Veerappa Pagi
The primary goal of Sentiment Analysis (SA) is to recognize the emotions present in natural language text. Generally, in opinion content, emotions are often driven by several aspects of their interests. Any SA task that groups data into various aspects and identifies sentiments is referred to as Aspect-Based Sentiment Analysis (ABSA). Recent advances in Deep Learning (DL) have brought revolutionary
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An Intelligent Traffic Analysis and Prediction System Using Deep Learning Technique Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 S. Sasikala, R. Neelaveni, P. Sweety Jose
Accurate identification of vehicles and estimating density in traffic surveillance systems is a challenging task, particularly in scenarios with closely spaced lanes. Single Shot MultiBox Detector (SSD) is introduced in vehicle detection and classification due to its speed and accuracy. It utilizes a transfer learning technique that enables them to utilize features from pretrained Convolutional Neural
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Doctors Versus YOLO: Comparison Between YOLO Algorithm, Orthopedic and Traumatology Resident Doctors and General Practitioners on Detection of Proximal Femoral Fractures on X-ray Images with Multi Methods Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Muhammed Taha Zeren, Seher Arslankaya, Yusuf Altuntaş, Necmi Cam, Yasin Kırelli, Mustafa Hacı Özdemir
In the 1950s, the concept of artificial intelligence emerged, suggesting that machines could possess the ability to think and learn. In the 21st century, with advancements in GPUs and CPUs, deep learning has become an integral part of human life. Proximal femoral fractures are known to be one of the leading causes of mortality and injuries among the elderly population. This study aims to detect proximal
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Denoising Convolutional Autoencoder Based Approach for Disordered Speech Recognition Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 S. Chandrakala, Veni S Vishnika
Efficient assistive speech technology is essential for persons with cognitive disorders to improve their standard of life. Various kinds of cognitive disorders affect the speech articulation. Disordered Speech Recognition (DSR) can be used for rehabilitation and gain much importance as the disordered speakers population keeps increasing in recent years. The speech utterance is commonly represented
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Diagnosing Attention Deficit Hyperactivity Disorder Using Machine Learning Methods on Serious Game-generated Data Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Eleftherios Kouloumpris, Aristotelis Lazaridis, Anestis Fachantidis, Ioannis Vlahavas
Attention Deficit Hyperactivity Disorder (ADHD) is a frequent learning disorder affecting about 5%–8% of the student population globally. Currently, the traditional methods for ADHD diagnosis are not fully specified, due to difficulties in identifying the particular factors that cause this disorder. In this paper, we present a novel system for diagnosing ADHD, which does not need special equipment. Instead
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A Novel Hybrid Optimization-based Backstepping Fractional Order Sliding Mode Proportional-integral-derivative Controller for Nonlinear Biological System Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Wakchaure Vrushali Balasaheb, Chaskar Uttam
Nowadays, the role of the control system in the bioengineering field plays a major part due to its significant improvement and demand. Usually, if there are any changes in the parameter of a biological system, then it tends to cause crucial diseases in humans. Previously, various control algorithms have been developed; however, the proper control process failed due to the high error rate, slow convergence
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Text Document Clustering Using Modified Particle Swarm Optimization with k-means Model Int. J. Artif. Intell. Tools (IF 1.1) Pub Date : 2024-02-23 Ratnam Dodda, A. Suresh Babu
In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering
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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