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The reconstruction of equivalent underlying model based on direct causality for multivariate time series PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-18 Liyang Xu, Dezheng Wang
This article presents a novel approach for reconstructing an equivalent underlying model and deriving a precise equivalent expression through the use of direct causality topology. Central to this methodology is the transfer entropy method, which is instrumental in revealing the causality topology. The polynomial fitting method is then applied to determine the coefficients and intrinsic order of the
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Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-18 Mahmudul Hasan, Md Abdus Sahid, Md Palash Uddin, Md Abu Marjan, Seifedine Kadry, Jungeun Kim
Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider
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Visual resource extraction and artistic communication model design based on improved CycleGAN algorithm PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-18
Through the application of computer vision and deep learning methodologies, real-time style transfer of images becomes achievable. This process involves the fusion of diverse artistic elements into a single image, resulting in the creation of innovative pieces of art. This article centers its focus on image style transfer within the realm of art education and introduces an ATT-CycleGAN model enriched
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Data aggregation algorithm for wireless sensor networks with different initial energy of nodes PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-15 Zhenpeng Liu, Jialiang Zhang, Yi Liu, Fan Feng, Yifan Liu
Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different
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Architecting an enterprise financial management model: leveraging multi-head attention mechanism-transformer for user information transformation PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-15 Wan Yu, Habib Hamam
Financial management assumes a pivotal role as a fundamental information system contributing to enterprise development. Nonetheless, prevalent methodologies frequently encounter challenges in proficiently overseeing diverse information streams inherent to financial management. This study introduces an innovative paradigm for enterprise financial management centered on the transformation of user information
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Blockchain based general data protection regulation compliant data breach detection system PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-15
Context Data breaches caused by insiders are on the rise, both in terms of frequency and financial impact on organizations. Insider threat originates from within the targeted organization and users with authorized access to an organization’s network, applications, or databases commit insider attacks. Motivation Insider attacks are difficult to detect because an attacker with administrator capabilities
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Sensor-based systems for the measurement of Functional Reach Test results: a systematic review PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-15 Luís Francisco, João Duarte, António Nunes Godinho, Eftim Zdravevski, Carlos Albuquerque, Ivan Miguel Pires, Paulo Jorge Coelho
The measurement of Functional Reach Test (FRT) is a widely used assessment tool in various fields, including physical therapy, rehabilitation, and geriatrics. This test evaluates a person’s balance, mobility, and functional ability to reach forward while maintaining stability. Recently, there has been a growing interest in utilizing sensor-based systems to objectively and accurately measure FRT results
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AutoSCAN: automatic detection of DBSCAN parameters and efficient clustering of data in overlapping density regions PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-14 Adil Abdu Bushra, Dongyeon Kim, Yejin Kan, Gangman Yi
The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various
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Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-14 Abdullah A. Asiri, Ahmad Shaf, Tariq Ali, Muhammad Aamir, Muhammad Irfan, Saeed Alqahtani
Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model
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An improved differential evolution algorithm for multi-modal multi-objective optimization PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-14
Multi-modal multi-objective problems (MMOPs) have gained much attention during the last decade. These problems have two or more global or local Pareto optimal sets (PSs), some of which map to the same Pareto front (PF). This article presents a new affinity propagation clustering (APC) method based on the Multi-modal multi-objective differential evolution (MMODE) algorithm, called MMODE_AP, for the
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SUTrans-NET: a hybrid transformer approach to skin lesion segmentation PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-13 Yaqin Li, Tonghe Tian, Jing Hu, Cao Yuan
Melanoma is a malignant skin tumor that threatens human life and health. Early detection is essential for effective treatment. However, the low contrast between melanoma lesions and normal skin and the irregularity in size and shape make skin lesions difficult to detect with the naked eye in the early stages, making the task of skin lesion segmentation challenging. Traditional encoder-decoder built
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Heart failure survival prediction using novel transfer learning based probabilistic features PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-12 Azam Mehmood Qadri, Muhammad Shadab Alam Hashmi, Ali Raza, Syed Ali Jafar Zaidi, Atiq ur Rehman
Heart failure is a complex cardiovascular condition characterized by the heart’s inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques
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Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-11 Shaha Al-Otaibi, Amjad Rehman, Muhammad Mujahid, Sarah Alotaibi, Tanzila Saba
Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult
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Predicting Chinese stock market using XGBoost multi-objective optimization with optimal weighting PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-08
The application of artificial intelligence (AI) technology in various fields has been a recent research hotspot. As a representative technology of AI, the specific application of machine learning models in the field of economics and finance undoubtedly holds significant research value. This article proposes Extreme Gradient Boosting Multi-Objective Optimization Model with Optimal Weights (OW-XGBoost)
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Designing defensive techniques to handle adversarial attack on deep learning based model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-08 Dhairya Vyas, Viral V. Kapadia
Adversarial attacks pose a significant challenge to deep neural networks used in image classification systems. Although deep learning has achieved impressive success in various tasks, it can easily be deceived by adversarial patches created by adding subtle yet deliberate distortions to natural images. These attacks are designed to remain hidden from both human and computer-based classifiers. Considering
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Electroencephalography (EEG) based epilepsy diagnosis via multiple feature space fusion using shared hidden space-driven multi-view learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-07 Xiujian Hu, Yicheng Xie, Hui Zhao, Guanglei Sheng, Khin Wee Lai, Yuanpeng Zhang
Epilepsy is a chronic, non-communicable disease caused by paroxysmal abnormal synchronized electrical activity of brain neurons, and is one of the most common neurological diseases worldwide. Electroencephalography (EEG) is currently a crucial tool for epilepsy diagnosis. With the development of artificial intelligence, multi-view learning-based EEG analysis has become an important method for automatic
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Design of smart citrus picking model based on Mask RCNN and adaptive threshold segmentation PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-04 Ziwei Guo, Yuanwu Shi, Ibrar Ahmad
Smart agriculture is steadily progressing towards automation and heightened efficacy. The rapid ascent of deep learning technology provides a robust foundation for this trajectory. Leveraging computer vision and the depths of deep learning techniques enables real-time monitoring and management within agriculture, facilitating swift detection of plant growth and autonomous assessment of ripeness. In
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AMCFCN: attentive multi-view contrastive fusion clustering net PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-05 Huarun Xiao, Zhiyong Hong, Liping Xiong, Zhiqiang Zeng
Advances in deep learning have propelled the evolution of multi-view clustering techniques, which strive to obtain a view-common representation from multi-view datasets. However, the contemporary multi-view clustering community confronts two prominent challenges. One is that view-specific representations lack guarantees to reduce noise introduction, and another is that the fusion process compromises
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Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29 Xinjing Qi, Huan Wang, Yubo Ji, Yuan Li, Xuguang Luo, Rongshan Nie, Xiaoyu Liang
As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have
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A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29
Locomotion mode recognition in humans is fundamental for flexible control in wearable-powered exoskeleton robots. This article proposes a hybrid model that combines a dense convolutional network (DenseNet) and long short-term memory (LSTM) with a channel attention mechanism (SENet) for locomotion mode recognition. DenseNet can automatically extract deep-level features from data, while LSTM effectively
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Advancing brain tumor detection: harnessing the Swin Transformer’s power for accurate classification and performance analysis PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29 Abdullah A. Asiri, Ahmad Shaf, Tariq Ali, Muhammad Ahmad Pasha, Aiza Khan, Muhammad Irfan, Saeed Alqahtani, Ahmad Alghamdi, Ali H. Alghamdi, Abdullah Fahad A. Alshamrani, Magbool Alelyani, Sultan Alamri
The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four
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CuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence? PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29 Antonio Ferrández, Rocío Lavigne-Cerván, Jesús Peral, Ignasi Navarro-Soria, Ángel Lloret, David Gil, Carmen Rocamora
In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of “tales with a message” is justified by their simplicity and easy understanding, thanks to their
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A proposed reconstruction method of a 3D animation scene based on a fuzzy long and short-term memory algorithm PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29
With the development of computer technology leading to a broad range of virtual technology implementations, the construction of virtual tasks has become highly demanded and has increased rapidly, especially in animation scenes. Constructing three-dimensional (3D) animation characters utilizing properties of actual characters could provide users with immersive experiences. However, a 3D face reconstruction
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A clustering effectiveness measurement model based on merging similar clusters PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29
This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters
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Eliminating bias: enhancing children’s book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29 Lijuan Shen, Liping Jiang
Managing user bias in large-scale user review data is a significant challenge in optimizing children’s book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children’s book recommendations
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Proj2Proj: self-supervised low-dose CT reconstruction PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29 Mehmet Ozan Unal, Metin Ertas, Isa Yildirim
In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning
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An integrative decision-making framework to guide policies on regulating ChatGPT usage PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29 Umar Ali Bukar, Md Shohel Sayeed, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Oluwatosin Ahmed Amodu
Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making
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Vulnerable JavaScript functions detection using stacking of convolutional neural networks PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-29 Abdullah Sheneamer
System security for web-based applications is paramount, and for the avoidance of possible cyberattacks it is important to detect vulnerable JavaScript functions. Developers and security analysts have long relied upon static analysis to investigate vulnerabilities and faults within programs. Static analysis tools are used for analyzing a program’s source code and identifying sections of code that need
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LFC-UNet: learned lossless medical image fast compression with U-Net PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Hengrui Liao, Yue Li
In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless compression. In recent years, learning-based lossless image compression methods, combining
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Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Soner Kiziloluk, Muhammed Yildirim, Harun Bingol, Bilal Alatas
It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial
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CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Pavani Cherukuru, Mumtaz Begum Mustafa
Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited
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Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Xixi Han, Renpeng Li, Boqin Wang, Zhibo Lin
The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient
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Multi-grained alignment method based on stable topics in cross-social networks PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Jing Lu, Qikai Gai
The user alignment of cross-social networks is divided into user and group alignments, respectively. Obtaining users’ full features is difficult due to social network privacy protection policies in user alignment mode. In contrast, the alignment accuracy is low due to the large number of edge users in the group alignment mode. To resolve this issue, First, stable topics are obtained from user-generated
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exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Tiancheng Yang, Ilia Sucholutsky, Kuang-Yu Jen, Matthias Schonlau
Background Pathology reports contain key information about the patient’s diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning
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A neural machine translation method based on split graph convolutional self-attention encoding PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Fei Wan, Ping Li
With the continuous advancement of deep learning technologies, neural machine translation (NMT) has emerged as a powerful tool for enhancing communication efficiency among the members of cross-language collaborative teams. Among the various available approaches, leveraging syntactic dependency relations to achieve enhanced translation performance has become a pivotal research direction. However, current
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Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Misbah Ali, Tehseen Mazhar, Amal Al-Rasheed, Tariq Shahzad, Yazeed Yasin Ghadi, Muhammad Amir Khan
Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1
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Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-28 Sudha Prathyusha Jakkaladiki, Filip Maly
Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers
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Algorithm design of a combinatorial mathematical model for computer random signals PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-27 Qinghua Yao, Benhua Qiu
To improve the processing effect of computer random signals, the manuscript employs the intelligent signal recognition algorithm to design a combinatorial mathematical model for computer random signals, and studies the parameter estimation of conventional frequency hopping signal (FHS) based on optimizing kernel function (KF). First, the mathematical form and graphical representation of the ambiguity
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A novel approach to recognition of Alzheimer’s and Parkinson’s diseases: random subspace ensemble classifier based on deep hybrid features with a super-resolution image PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-27
Background Artificial intelligence technologies have great potential in classifying neurodegenerative diseases such as Alzheimer’s and Parkinson’s. These technologies can aid in early diagnosis, enhance classification accuracy, and improve patient access to appropriate treatments. For this purpose, we focused on AI-based auto-diagnosis of Alzheimer’s disease, Parkinson’s disease, and healthy MRI images
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An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-27
Background and Objective One of the illnesses with most significant mortality and morbidity rates worldwide is lung cancer. From CT images, automatic lung tumor segmentation is significantly essential. However, segmentation has several difficulties, such as different sizes, variable shapes, and complex surrounding tissues. Therefore, a novel enhanced combined intelligent system is presented to predict
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Ensemble machine learning reveals key features for diabetes duration from electronic health records PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-26 Gabriel Cerono, Davide Chicco
Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often
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An approach to the dermatological classification of histopathological skin images using a hybridized CNN-DenseNet model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-26 Anubhav De, Nilamadhab Mishra, Hsien-Tsung Chang
This research addresses the challenge of automating skin disease diagnosis using dermatoscopic images. The primary issue lies in accurately classifying pigmented skin lesions, which traditionally rely on manual assessment by dermatologists and are prone to subjectivity and time consumption. By integrating a hybrid CNN-DenseNet model, this study aimed to overcome the complexities of differentiating
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RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-26 Aqsa Khalid, Ghulam Mustafa, Muhammad Rizwan Rashid Rana, Saeed M. Alshahrani, Mofadal Alymani
Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with
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Diffusion models in text generation: a survey PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-23 Qiuhua Yi, Xiangfan Chen, Chenwei Zhang, Zehai Zhou, Linan Zhu, Xiangjie Kong
Diffusion models are a kind of math-based model that were first applied to image generation. Recently, they have drawn wide interest in natural language generation (NLG), a sub-field of natural language processing (NLP), due to their capability to generate varied and high-quality text outputs. In this article, we conduct a comprehensive survey on the application of diffusion models in text generation
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Heterogeneous text graph for comprehensive multilingual sentiment analysis: capturing short- and long-distance semantics PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-23 El Mahdi Mercha, Houda Benbrahim, Mohammed Erradi
Multilingual sentiment analysis (MSA) involves the task of comprehending people’s opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a
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Automatic nutrient estimator: distributing nutrient solution in hydroponic plants based on plant growth PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-23 Tupili Sangeetha, Ezhumalai Periyathambi
Background The primary objective is to address the specific needs of plants at different growth stages by delivering precise nutrient concentrations tailored to their developmental requirements. Challenges such as uneven nutrient distribution, fluctuations in pH and electrical conductivity, and inadequate nutrient delivery pose potential hindrances to achieving optimal plant health and yield in hydroponic
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A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-23 Supriya Mahadevkar, Shruti Patil, Ketan Kotecha, Ajith Abraham
Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article’s novelty lies in the detailed discussion and implementation
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Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-22 Fanghong Li, Norliza Abdul Majid, Shuo Ding
This article aims to address the challenge of predicting the salaries of college graduates, a subject of significant practical value in the fields of human resources and career planning. Traditional prediction models often overlook diverse influencing factors and complex data distributions, limiting the accuracy and reliability of their predictions. Against this backdrop, we propose a novel prediction
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Identifying optical microscope images of CVD-grown two-dimensional MoS2 by convolutional neural networks and transfer learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-21 Cahit Perkgoz
Background In Complementary Metal-Oxide Semiconductor (CMOS) technology, scaling down has been a key strategy to improve chip performance and reduce power losses. However, challenges such as sub-threshold leakage and gate leakage, resulting from short-channel effects, contribute to an increase in distributed static power. Two-dimensional transition metal dichalcogenides (2D TMDs) emerge as potential
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Enhanced architecture and implementation of spectrum shaping codes PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-21 Bingrui Wang, Zhaopeng Xie, Xingang Zhang
Spectral shaping codes are modulation codes widely used in communication and data storage systems. This research enhances the algorithms employed in constructing spectral shaping codes for hardware implementation. We present a parallel scrambling calculation with a time complexity of O(1). Second, in the minimum accumulated signal power (MASP) module, the sine-cosine accumulation needs to be determined
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Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-21 Begüm Şener, Koray Acici, Emre Sümer
Early diagnosis is crucial in Alzheimer’s disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great
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A machine learning-based hybrid recommender framework for smart medical systems PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-20 Jianhua Wei, Honglin Yan, Xiaoli Shao, Lili Zhao, Lin Han, Peng Yan, Shengyu Wang
This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy
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A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-20 Zinnet Duygu Akşehir, Erdal Kılıç
Time series, including noise, non-linearity, and non-stationary properties, are frequently used in prediction problems. Due to these inherent characteristics of time series data, forecasting based on this data type is a highly challenging problem. In many studies within the literature, high-frequency components are commonly excluded from time series data. However, these high-frequency components can
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An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-20 Onur Karakaya, Zeynep Hilal Kilimci
Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended
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Structural-topic aware deep neural networks for information cascade prediction PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-19 Bangzhu Zhou, Xiaodong Feng, Hemin Feng
It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the
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Intelligent control strategy for industrial furnaces based on yield classification prediction using a gray relative correlation-convolutional neural network-multilayer perceptron (GCM) machine learning model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-19 Hua Guo, Shengxiang Deng, Jingbiao Yang
Industrial furnaces still play an important role in national economic growth. Owing to the complexity of the production process, the product yield fluctuates, and cannot be executed in real time, which has not kept pace with the development of the intelligent technologies in Industry 4.0. In this study, based on the deep learning theory and operational data collected from more than one year of actual
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Teaching computer architecture by designing and simulating processors from their bits and bytes PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-19 Mustafa Doğan, Kasım Öztoprak, Mehmet Reşit Tolun
Teaching computer architecture (Comp-Arch) courses in undergraduate curricula is becoming more of a challenge as most students prefer software-oriented courses. In some computer science/engineering departments, Comp-Arch courses are offered without the lab component due to resource constraints and differing pedagogical priorities. This article demonstrates how students working in teams are motivated
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Evaluating generative AI integration in Saudi Arabian education: a mixed-methods study PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-16 Abdullah Alammari
Incorporating generative artificial intelligence (GAI) in education has become crucial in contemporary educational environments. This research article thoroughly investigates the ramifications of implementing GAI in the higher education context of Saudi Arabia, employing a blend of quantitative and qualitative research approaches. Survey-based quantitative data reveals a noteworthy correlation between
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Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-16 Muhammad Shahid Iqbal Malik, Muhammad Zeeshan Younas, Mona Mamdouh Jamjoom, Dmitry I. Ignatov
Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure
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A low-cost wireless extension for object detection and data logging for educational robotics using the ESP-NOW protocol PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-02-16 Emma I. Capaldi
In recent years, inexpensive and easy to use robotics platforms have been incorporated into middle school, high school, and college educational curricula and competitions all over the world. Students have access to advanced microprocessors and sensor systems that engage, educate, and encourage their creativity. In this study, the capabilities of the widely available VEX Robotics System are extended