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Enhancing scene-text visual question answering with relational reasoning, attention and dynamic vocabulary integration Comput. Intell. (IF 2.8) Pub Date : 2024-02-20 Mayank Agrawal, Anand Singh Jalal, Himanshu Sharma
Visual question answering (VQA) is a challenging task in computer vision. Recently, there has been a growing interest in text-based VQA tasks, emphasizing the important role of textual information for better understanding of images. Effectively utilizing text information within the image is crucial for achieving success in this task. However, existing approaches often overlook the contextual information
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Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain Comput. Intell. (IF 2.8) Pub Date : 2024-02-13 Turki Ali Alghamdi, Nadeem Javaid
In this paper, a hybrid blockchain-based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains
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Artificial intelligence and Internet of Things-enabled decision support system for the prediction of bacterial stalk root disease in maize crop Comput. Intell. (IF 2.8) Pub Date : 2024-02-13 Shaha Al-Otaibi, Rahim Khan, Jehad Ali, Aftab Ahmed
Although the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)-enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI-enabled IoTs
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Correction to Capacitated single-allocation hub location model for a flood relief distribution network Comput. Intell. (IF 2.8) Pub Date : 2024-01-23
Sangsawang O, Chanta S. Capacitated single-allocation hub location model for a flood relief distribution network. Computational Intelligence. 2020;36:1320–1347. The errors are in Section 3.2 Model formulation, Equations (1), (2), (4), and (7). These errors are critical, especially in the objective model (1). It appeared that the index was mixed with the decision variables, so it made the whole Equation
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XAI-driven model for crop recommender system for use in precision agriculture Comput. Intell. (IF 2.8) Pub Date : 2024-01-14 Parvathaneni Naga Srinivasu, Muhammad Fazal Ijaz, Marcin Woźniak
Agriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively
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Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement Comput. Intell. (IF 2.8) Pub Date : 2024-01-02 Yongho Kim, Jiha Kim, Cheolwoo You, Hyunhee Park
Indoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into
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Event assigning based on hierarchical features and enhanced association for Chinese mayor's hotline Comput. Intell. (IF 2.8) Pub Date : 2024-01-04 Gang Chen, Xiaomin Cheng, Jianpeng Chen, Xiangrong She, JiaQi Qin, Jian Chen
Nowadays, manual event assignment for Chinese mayor's hotline is still a problem of low efficiency. In this paper, we propose a computer-aided event assignment method based on hierarchical features and enhanced association. First, hierarchical features of hotline events are extracted to obtain event encoding vectors. Second, the fine-tuned RoBERTa2RoBERTa model is used to encode the “sanding” responsibility
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HyperED: A hierarchy-aware network based on hyperbolic geometry for event detection Comput. Intell. (IF 2.8) Pub Date : 2024-01-04 Meng Zhang, Zhiwen Xie, Jin Liu, Xiao Liu, Xiao Yu, Bo Huang
Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well-organized with a hierarchical structure in real-world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical
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A mechanism for network resource allocation and task offloading in mobile edge computing and network engineering Comput. Intell. (IF 2.8) Pub Date : 2024-01-03 Zhixu Shu, Kewang Zhang
At present, most of the resource allocation methods in mobile edge computing allocate computing resources according to the time order in which task requests are calculated and unloaded, without considering the priority of tasks in practical applications. According to the computing requirements in such cases, a priority task-oriented resource allocation method is proposed. According to the average processing
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Joint optimization of UAV position and user grouping for UAV-assisted hybrid NOMA systems Comput. Intell. (IF 2.8) Pub Date : 2024-01-02 Yuan Sun, Zhicheng Dong, Liuqing Yang, Donghong Cai, Weixi Zhou, Yanxia Zhou
This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non-orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non-convexity, calling for an effective solution. To address this
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Artificial intelligence control for trust-based detection of attackers in 5G social networks Comput. Intell. (IF 2.8) Pub Date : 2023-12-25 Davinder Kaur, Suleyman Uslu, Mimoza Durresi, Arjan Durresi
This study introduces a comprehensive framework designed for detecting and mitigating fake and potentially threatening user communities within 5G social networks. Leveraging geo-location data, community trust dynamics, and AI-driven community detection algorithms, this framework aims to pinpoint users posing potential harm. Including an artificial control model facilitates the selection of suitable
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Enhancing visual question answering with a two-way co-attention mechanism and integrated multimodal features Comput. Intell. (IF 2.8) Pub Date : 2023-12-21 Mayank Agrawal, Anand Singh Jalal, Himanshu Sharma
In Visual question answering (VQA), a natural language answer is generated for a given image and a question related to that image. There is a significant growth in the VQA task by applying an efficient attention mechanism. However, current VQA models use region features or object features that are not adequate to improve the accuracy of generated answers. To deal with this issue, we have used a Two-way
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Intelligent IoT framework with GAN-synthesized images for enhanced defect detection in manufacturing Comput. Intell. (IF 2.8) Pub Date : 2023-12-18 Somrawee Aramkul, Prompong Sugunnasil
The manufacturing industry is always exploring techniques to optimize processes, increase product quality, and more accurately identify defects. The technique of deep learning is the strategy that will be used to handle the issues presented. However, the challenge of using AI in this domain is the small and imbalanced dataset for training affected by the severe shortage of defective data. Moreover
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Feature redundancy removal for text classification using correlated feature subsets Comput. Intell. (IF 2.8) Pub Date : 2023-12-21 Lazhar Farek, Amira Benaidja
The curse of high dimensionality in text classification is a worrisome problem that requires efficient and optimal feature selection (FS) methods to improve classification accuracy and reduce learning time. Existing filter-based FS methods evaluate features independently of other related ones, which can then lead to selecting a large number of redundant features, especially in high-dimensional datasets
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Tripartite-structure transformer for hyperspectral image classification Comput. Intell. (IF 2.8) Pub Date : 2023-12-21 Liuwei Wan, Meili Zhou, Shengqin Jiang, Zongwen Bai, Haokui Zhang
Hyperspectral images contain rich spatial and spectral information, which provides a strong basis for distinguishing different land-cover objects. Therefore, hyperspectral image (HSI) classification has been a hot research topic. With the advent of deep learning, convolutional neural networks (CNNs) have become a popular method for hyperspectral image classification. However, convolutional neural network
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Sentiment analysis on Hindi tweets during COVID-19 pandemic Comput. Intell. (IF 2.8) Pub Date : 2023-12-20 Anita Saroj, Akash Thakur, Sukomal Pal
A gap among the people has been created due to a lack of social interactions. The physical void has led to an increase in online interaction among users on social media platforms. Sentiment analysis of such interactions can help us analyze the general public psychology during the pandemic. However, the lack of data in non-English and low-resource languages like ‘Hindi’ makes it difficult to study it
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A hybrid approach for portfolio construction: Combing two-stage ensemble forecasting model with portfolio optimization Comput. Intell. (IF 2.8) Pub Date : 2023-12-15 Wei Chen, Zinuo Liu, Lifen Jia
Combining the stock prediction with portfolio optimization can improve the performance of the portfolio construction. In this article, we propose a novel portfolio construction approach by utilizing a two-stage ensemble model to forecast stock prices and combining the forecasting results with the portfolio optimization. To be specific, there are two phases in the approach: stock prediction and portfolio
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Cache-aided multiuser UAV-MEC networks for smart grid networks: A DDPG approach Comput. Intell. (IF 2.8) Pub Date : 2023-12-12 Chun Yang, Zhe Wang, Binyu Xie
Mobile edge computing (MEC) is an important research topic in the field of wireless communication and mobile computing, as it can effectively decrease the latency and energy consumption due to the trade-off between the communication and computing, where some intensive computing tasks can be offloaded to computational access points (CAPs), especially when the wireless transmission channel is in good
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Improved secure PCA and LDA algorithms for intelligent computing in IoT-to-cloud setting Comput. Intell. (IF 2.8) Pub Date : 2023-12-04 Liu Jiasen, Wang Xu An, Li Guofeng, Yu Dan, Zhang Jindan
The rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT-to-cloud setting has been fully applied in various industry sectors, such as sensor-cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of
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A multi-modal fusion YoLo network for traffic detection Comput. Intell. (IF 2.8) Pub Date : 2023-11-29 Xinwang Zheng, Wenjie Zheng, Chujie Xu
Traffic detection (including lane detection and traffic sign detection) is one of the key technologies to realize driving assistance system and auto drive system. However, most of the existing detection methods are designed based on single-modal visible light data, when there are dramatic changes in lighting in the scene (such as insufficient lighting in night), it is difficult for these methods to
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Few-shot learning for word-level scene text script identification Comput. Intell. (IF 2.8) Pub Date : 2023-11-21 Veronica Naosekpam, Nilkanta Sahu
Script identification of text in scene images has attracted massive attention recently. However, the existing techniques primarily emphasize on scripts where data are available abundantly, such as English, European, or East Asian. Although these methods are robust in dealing with high-resource data, how these techniques will work on low-resource scripts has yet to be discovered. For example, in India
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Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews Comput. Intell. (IF 2.8) Pub Date : 2023-11-14 Asma Ameur, Sana Hamdi, Sadok Ben Yahia
In many fields, like aspect category detection (ACD) in aspect-based sentiment analysis, it is necessary to label each instance with more than one label at the same time. This study tackles the multilabel classification problem in the ACD task for the Arabic language. For this purpose, we used Arabic hotel reviews from the SemEval-2016 dataset, comprising 13,113 annotated tuples provided for training
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ResNLS: An improved model for stock price forecasting Comput. Intell. (IF 2.8) Pub Date : 2023-11-12 Yuanzhe Jia, Ali Anaissi, Basem Suleiman
Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices. The
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Cache-aided UAV-assisted relaying networks: Performance analysis and system optimization Comput. Intell. (IF 2.8) Pub Date : 2023-11-06 Zhe Wang, Chun Yang, Binyu Xie
The utilization of distributed multi-agent unmanned aerial vehicles (UAVs) for computing tasks in remote areas has gained significant traction in recent years due to their adaptability and capability to access hard-to-reach regions that are inaccessible to ground-based methods. However, establishing wireless communication between UAVs and ground-based data sources in remote areas presents considerable
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A joint hierarchical cross-attention graph convolutional network for multi-modal facial expression recognition Comput. Intell. (IF 2.8) Pub Date : 2023-10-25 Chujie Xu, Yong Du, Jingzi Wang, Wenjie Zheng, Tiejun Li, Zhansheng Yuan
Emotional recognition in conversations (ERC) is increasingly being applied in various IoT devices. Deep learning-based multimodal ERC has achieved great success by leveraging diverse and complementary modalities. Although most existing methods try to adopt attention mechanisms to fuse different information, these methods ignore the complementarity between modalities. To this end, the joint cross-attention
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Short-term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence Comput. Intell. (IF 2.8) Pub Date : 2023-10-24 Xiaoyang Lu, Yandang Chen, Qibin Li, Pingping Yu
Photovoltaic (PV) power generation is widely utilized to satisfy the increasing energy demand due to its cleanness and inexhaustibility. Accurate PV power forecasting can improve the penetration of PV power in the grid. However, it is pretty challenging to predict PV power in short-term under precious future meteorological information absence conditions. To address this problem, this study proposes
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A progressive mesh simplification algorithm based on neural implicit representation Comput. Intell. (IF 2.8) Pub Date : 2023-10-12 Yihua Chen
Progressive mesh simplification (PM) algorithm aims to generate simplified mesh at any resolution for the input high-precision mesh, and only needs to be optimized or fitted once. Most of the existing PM algorithms are obtained based on heuristic mesh simplification algorithms, which leads to redundant storage space and poor practice-ability of the algorithm. In this article, a progressive mesh simplification
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A semantically enhanced text retrieval framework with abstractive summarization Comput. Intell. (IF 2.8) Pub Date : 2023-09-28 Min Pan, Teng Li, Yu Liu, Quanli Pei, Ellen Anne Huang, Jimmy X. Huang
Recently, large pretrained language models (PLMs) have led a revolution in the information retrieval community. In most PLMs-based retrieval frameworks, the ranking performance broadly depends on the model structure and the semantic complexity of the input text. Sequence-to-sequence generative models for question answering or text generation have proven to be competitive, so we wonder whether these
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A localization method of manipulator towards achieving more precision control Comput. Intell. (IF 2.8) Pub Date : 2023-10-02 Hongwei Gao, Hongyang Zhang, Yueqiu Jiang, Jian Sun, Jiahui Yu
The monocular vision system is a crucial branch of machine vision research widely used in multiple industries as a research hotspot in the field of vision. Although the monocular vision system is of simple structure and cost-effectiveness, its positioning accuracy is insufficient in some industries. This article researched the robot arm positioning method via monocular vision. First, we built a vision
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Parallel accelerated computing architecture for dim target tracking on-board Comput. Intell. (IF 2.8) Pub Date : 2023-09-24 Jiyang Yu, Dan Huang, Wenjie Li, Xianjie Wang, Xiaolong Shi
The real-time tracking process of dim targets in space is mainly achieved through the correlation and prediction of dots after the detection and calculation process. The on-board calculation of the tracking needs to be completed in milliseconds, and it needs to reach the microsecond level at high frame rates. For real-time tracking of dim targets in space, it is necessary to achieve universal tracking
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Using LSTM neural networks for cross-lingual phonetic speech segmentation with an iterative correction procedure Comput. Intell. (IF 2.8) Pub Date : 2023-09-19 Zdeněk Hanzlíček, Jindřich Matoušek, Jakub Vít
This article describes experiments on speech segmentation using long short-term memory recurrent neural networks. The main part of the paper deals with multi-lingual and cross-lingual segmentation, that is, it is performed on a language different from the one on which the model was trained. The experimental data involves large Czech, English, German, and Russian speech corpora designated for speech
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Retina disease prediction using modified convolutional neural network based on Inception-ResNet model with support vector machine classifier Comput. Intell. (IF 2.8) Pub Date : 2023-09-10 Arushi Jain, Vishal Bhatnagar, Annavarapu Chandra Sekhara Rao, Manju Khari
Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic
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A novel feature ranking algorithm for text classification: Brilliant probabilistic feature selector (BPFS) Comput. Intell. (IF 2.8) Pub Date : 2023-08-18 Bekir Parlak
Text classification (TC) is a very crucial task in this century of high-volume text datasets. Feature selection (FS) is one of the most important stages in TC studies. In the literature, numerous feature selection methods are recommended for TC. In the TC domain, filter-based FS methods are commonly utilized to select a more informative feature subsets. Each method uses a scoring system that is based
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Classification analysis of burnout people's brain images using ontology-based speculative sense model Comput. Intell. (IF 2.8) Pub Date : 2023-08-06 Chandrakirishnan Balakrishnan Sivaparthipan, Priyan Malarvizhi Kumar, Thota Chandu, BalaAnand Muthu, Mohammed Hasan Ali, Boris Tomaš
Burnout is a state of exhaustion that results from prolonged, excessive workplace stress. This can be examined with the biological explications of burnout and physical consequences and classified against prolonged vigorous activities. The research aims to classify burnout people's brain images against prolonged emotional activities using ontology analysis of treatment and prevention and intermediate
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Computation of persistent homology on streaming data using topological data summaries Comput. Intell. (IF 2.8) Pub Date : 2023-07-30 Anindya Moitra, Nicholas O. Malott, Philip A. Wilsey
Persistent homology is a computationally intensive and yet extremely powerful tool for Topological Data Analysis. Applying the tool on potentially infinite sequence of data objects is a challenging task. For this reason, persistent homology and data stream mining have long been two important but disjoint areas of data science. The first computational model, that was recently introduced to bridge the
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An attention-based deep learning model for credibility assessment of online health information Comput. Intell. (IF 2.8) Pub Date : 2023-07-24 Swarup Padhy, Santosh Singh Rathore
With the surge of searching and reading online health-based articles, maintaining the quality and credibility of online health-based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate
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An intelligent hierarchical residual attention learning-based conjoined twin neural network for Alzheimer's stage detection and prediction Comput. Intell. (IF 2.8) Pub Date : 2023-07-17 Venkatesh Gauri Shankar, Dilip Singh Sisodia, Preeti Chandrakar
Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning-based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain-MRI) are used to detect
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Online short text clustering using infinite extensions of discrete mixture models Comput. Intell. (IF 2.8) Pub Date : 2023-07-10 Samar Hannachi, Fatma Najar, Hafsa Ennajari, Nizar Bouguila
Short text clustering is one of the fundamental tasks in natural language processing. Different from traditional documents, short texts are ambiguous and sparse due to their short form and the lack of recurrence in word usage from one text to another, making it very challenging to apply conventional machine learning algorithms directly. In this article, we propose two novel approaches for short texts
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Automated skin lesion detection and classification using fused deep convolutional neural network on dermoscopic images Comput. Intell. (IF 2.8) Pub Date : 2023-07-04 Rayappa Priyanka Pramila, Radhakrishnan Subhashini
Skin cancer becomes a deadly disease that affect people of all ages globally. The availability of various types of benign and malignant melanoma makes the skin lesion diagnostic process difficult. Since the visual inspection of skin cancer is costlier and lengthy process, it is needed to design automatic diagnosis model to classify skin lesions accurately and promptly. Computer-aided diagnosis models
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Policy generation network for zero-shot policy learning Comput. Intell. (IF 2.8) Pub Date : 2023-07-04 Yiming Qian, Fengyi Zhang, Zhiyong Liu
Lifelong reinforcement learning is able to continually accumulate shared knowledge by estimating the inter-task relationships based on training data for the learned tasks in order to accelerate learning for new tasks by knowledge reuse. The existing methods employ a linear model to represent the inter-task relationships by incorporating task features in order to accomplish a new task without any learning
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Convolutional neural networks combined with feature selection for radio-frequency fingerprinting Comput. Intell. (IF 2.8) Pub Date : 2023-07-03 Gianmarco Baldini, Irene Amerini, Franc Dimc, Fausto Bonavitacola
Radio-frequency fingerprinting is a technique for the authentication and identification of wireless devices using their intrinsic physical features and an analysis of the digitized signal collected during transmission. The technique is based on the fact that the unique physical features of the devices generate discriminating features in the transmitted signal, which can then be analyzed using signal-processing
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Smart analysis of anxiety people and their activities using heterogeneous quasiperiodic process Comput. Intell. (IF 2.8) Pub Date : 2023-07-03 Ludi Zhao, Xuting Guo, Guanpeng Song
The increase in anxiety levels worldwide can be described as a serious global health threat. Around 500 million people suffer from mental disorders and are suffering from depression, and other mental-oriented disabilities. The new technological paradigms such as the Internet of Things (IoT) were employed for detecting, and treating these disorders, which are being proposed, developed, and provide new
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Development of acoustic source localization with adaptive neural network using distance mating-based red deer algorithm Comput. Intell. (IF 2.8) Pub Date : 2023-06-29 E. Bharat Babu, D. Hari Krishna, S. Munavvar Hussain, Santhosh Kumar Veeramalla
Multichannel, audio processing approaches are widely examined in human–computer interaction, autonomous robots, audio surveillance, and teleconferencing systems. The numerous applications are linked to the speech technology and acoustic analysis area. Much attention is received to the active speakers and spatial localization of acoustic sources on the acoustic sensor arrays. Baseline approaches provide
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SVM-ABC based cancer microarray (gene expression) hybrid method for data classification Comput. Intell. (IF 2.8) Pub Date : 2023-06-27 Punam Gulande, R N Awale
Microarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction-based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC-support vector machine (SVM) method is used to classify the lung cancer datasets and compared them
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Automatic detection of microaneurysms using a novel segmentation algorithm based on deep learning techniques Comput. Intell. (IF 2.8) Pub Date : 2023-06-13 T. Monisha Birlin, C. Divya, J. John Livingston
Microaneurysms is the first stage of diabetic retinopathy (DR) and it plays a vital role in the computerized diagnosis. However, it is difficult to automatically detect microaneurysms in fundus images due to the complicated background and various illumination reasons. The motivation behind this, is the number of increases in diabetic patients is very large when compared with the number of ophthalmologists
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An explainable deep learning model for prediction of early-stage chronic kidney disease Comput. Intell. (IF 2.8) Pub Date : 2023-06-10 Vinothini Arumugham, Baghavathi Priya Sankaralingam, Uma Maheswari Jayachandran, Komanduri Venkata Sesha Sai Rama Krishna, Selvanayaki Sundarraj, Moulana Mohammed
Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end-stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep
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A dual-channel ensembled deep convolutional neural network for facial expression recognition in the wild Comput. Intell. (IF 2.8) Pub Date : 2023-06-06 Sumeet Saurav, Ravi Saini, Sanjay Singh
Facial expression recognition (FER) in the wild is an active and challenging field of research. A system for automatic FER finds use in a wide range of applications related to advanced human–computer interaction (HCI), human–robot interaction (HRI), human behavioral analysis, gaming and entertainment, etc. Since their inception, convolutional neural networks (CNNs) have attained state-of-the-art accuracy
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Residual neural network-assisted one-class classification algorithm for melanoma recognition with imbalanced data Comput. Intell. (IF 2.8) Pub Date : 2023-06-05 Lisu Yu, Yifei Wang, Liyu Zhou, Jinsheng Wu, Zhenghai Wang
Skin cancer, also known as melanoma, is a deadly form of skin cancer that can significantly improve survival rates when diagnosed at an early stage. It is usually diagnosed visually from dermoscopic images, and such visual assessment of skin cancer by the naked eye is a challenging and arduous task. Therefore, the detection of melanoma from dermoscopic images using trained artificial intelligence models
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A novel classification-based shilling attack detection approach for multi-criteria recommender systems Comput. Intell. (IF 2.8) Pub Date : 2023-05-08 Tugba Turkoglu Kaya, Emre Yalcin, Cihan Kaleli
Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive
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Distributed system anomaly detection using deep learning-based log analysis Comput. Intell. (IF 2.8) Pub Date : 2023-04-22 Pengfei Han, Huakang Li, Gang Xue, Chao Zhang
Anomaly detection is a key step in ensuring the security and reliability of large-scale distributed systems. Analyzing system logs through artificial intelligence methods can quickly detect anomalies and thus help maintenance personnel to maintain system security. Most of the current works only focus on the temporal or spatial features of distributed system logs, and they cannot sufficiently extract
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Extreme learning machine algorithm-based model for lung cancer classification from histopathological real-time images Comput. Intell. (IF 2.8) Pub Date : 2023-04-14 M. Grace John, S. Baskar
In the present scenario, developing an automatic and credible diagnostic system to analyze lung cancer type, stage, and level from computed tomography (C.T.) images is a very challenging task, even for experienced pathologists, due to the nonuniform illumination and artifacts. The nonuniform illumination and artifacts are the low-frequency changes in image intensity that arise from the sensor and the
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Discrete wavelet transform based branched deep hybrid network for environmental noise classification Comput. Intell. (IF 2.8) Pub Date : 2023-04-14 Syed Aamir Ali Shah, Abdul Bais, Abdulaziz Alashaikh, Eisa Alanazi
With ever growing urbanization, the environmental noise is becoming hazardous. Vehicular traffic, locomotives, heavy machinery in industry, and construction processes are the major sources of noise pollution. It has adverse effects on the health of humans as well as that of the wild life. World Health Organization (WHO) puts noise pollution as the second major cause of illness due to environmental
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ReSE-Net: Enhanced UNet architecture for lung segmentation in chest radiography images Comput. Intell. (IF 2.8) Pub Date : 2023-04-03 Tarun Agrawal, Prakash Choudhary
Automatic lung segmentation in the chest x-ray is important for computer aided diagnosis. It helps in the surgical planning and diagnosis of pulmonary diseases. Lung shape, size, overlapped area, and opacities make lung segmentation arduous. In this article, we have proposed a UNet-based model for lung segmentation. We have evaluated the model on difficult datasets that have chest radiographs of patients
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Improving robustness of industrial object detection by automatic generation of synthetic images from CAD models Comput. Intell. (IF 2.8) Pub Date : 2023-03-27 Igor Garcia Ballhausen Sampaio, José Viterbo, Joris Guerin
Object detection (OD) is used for visual quality control in factories. Images that compose training datasets are often collected directly from the production line and labeled with bounding boxes manually. Such data represent well the inference context but might lack diversity, implying a risk of overfitting. To address this issue, we propose a dataset construction method based on an automated pipeline
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The phenomenon of decision oscillation: A new consequence of pathology in game trees Comput. Intell. (IF 2.8) Pub Date : 2023-03-03 Mark Levene, Trevor Fenner
Random minimaxing studies the consequences of using a random number for scoring the leaf nodes of a full width game tree and then computing the best move using the standard minimax procedure. Experiments in Chess showed that the strength of play increases as the depth of the lookahead is increased. Previous research by the authors provided a partial explanation of why random minimaxing can strengthen
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Deep-CoV: An integrated deep learning model to detect COVID-19 using chest X-ray and CT images Comput. Intell. (IF 2.8) Pub Date : 2023-02-21 Sanjib Roy, Ayan Kumar Das
The COVID-19 virus has fatal effect on lung function and due to its rapidity the early detection is necessary at the moment. The radiographic images have already been used by the researchers for the early diagnosis of COVID-19. Though several existing research exhibited very good performance with either x-ray or computer tomography (CT) images, to the best of our knowledge no such work has reported
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Analyzing CT images for detecting lung cancer by applying the computational intelligence-based optimization techniques Comput. Intell. (IF 2.8) Pub Date : 2022-12-15 Mohamed Shakeel Pethuraj, Burhanuddin bin Mohd Aboobaider, Lizawati Binti Salahuddin
Lung cancer is the most critical disease because it affects both men and women. Most of the time, lung cancer leads to death due to less health care and medical attention. In addition, lung cancer is difficult to identify in earlier stages due to the low-level symptoms and risk factors. To overcome the complexity, effective techniques must predict lung cancer earlier. To attain the problem statement
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Multi-armed bandit heterogeneous ensemble learning for imbalanced data Comput. Intell. (IF 2.8) Pub Date : 2022-12-13 Qi Dai, Jian-wei Liu, Jiapeng Yang
One of the most widely used approaches to the class-imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training
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A lightweight deep learning with feature weighting for activity recognition Comput. Intell. (IF 2.8) Pub Date : 2022-12-12 Ayokunle Olalekan Ige, Mohd Halim Mohd Noor
With the development of deep learning, numerous models have been proposed for human activity recognition to achieve state-of-the-art recognition on wearable sensor data. Despite the improved accuracy achieved by previous deep learning models, activity recognition remains a challenge. This challenge is often attributed to the complexity of some specific activity patterns. Existing deep learning models