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Robust metabolic syndrome risk score based on triangular areal similarity PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-25 Hyunseok Shin, Simon Shim, Sejong Oh
One of the limitations of currently-used metabolic syndrome (MetS) risk calculations is that they often depend on sample characteristics. To address this, we introduced a novel sample-independent risk quantification method called ‘triangular areal similarity’ (TAS) that employs three-axis radar charts constructed from five MetS factors in order to assess the similarity between standard diagnostic thresholds
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Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-25 Syed Nisar Hussain Bukhari, E. Elshiekh, Mohamed Abbas
Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen
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GAT TransPruning: progressive channel pruning strategy combining graph attention network and transformer PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-23 Yu-Chen Lin, Chia-Hung Wang, Yu-Cheng Lin
Recently, large-scale artificial intelligence models with billions of parameters have achieved good results in experiments, but their practical deployment on edge computing platforms is often subject to many constraints because of their resource requirements. These models require powerful computing platforms with a high memory capacity to store and process the numerous parameters and activations, which
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Learning label smoothing for text classification PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-23 Han Ren, Yajie Zhao, Yong Zhang, Wei Sun
Training with soft labels instead of hard labels can effectively improve the robustness and generalization of deep learning models. Label smoothing often provides uniformly distributed soft labels during the training process, whereas it does not take the semantic difference of labels into account. This article introduces discrimination-aware label smoothing, an adaptive label smoothing approach that
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DenseHillNet: a lightweight CNN for accurate classification of natural images PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-22 Sheikh Muhammad Saqib, Muhammad Zubair Asghar, Muhammad Iqbal, Amal Al-Rasheed, Muhammad Amir Khan, Yazeed Ghadi, Tehseen Mazhar
The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural
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Modelling the effects of emotional engagement and peer interaction on the continuous intention to use asynchronous e-learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-22 Sultan Hammad Alshammari, Mohammed Habib Alshammari
Even though asynchronous e-learning has become popular among universities, few studies have examined how students intend to continue using it for their learning. This study proposed a theoretical model that aims to examine the effects of external factors—emotional engagement and peer interaction—and two constructs of technology acceptance model (TAM) on students’ continuous intention to use asynchronous
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Sleep stage prediction using multimodal body network and circadian rhythm PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-22 Sahar Waqar, Muhammad Usman Ghani Khan
Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect
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Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-22 Madiha Anjum, Raazia Saher, Muhammad Noman Saeed
Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus
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Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-22 Nur Haninie Abd Wahab, Khairunnisa Hasikin, Khin Wee Lai, Kaijian Xia, Lulu Bei, Kai Huang, Xiang Wu
Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve
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Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-19 Shtwai Alsubai
Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and
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Semi or fully automatic tooth segmentation in CBCT images: a review PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-19 Qianhan Zheng, Yu Gao, Mengqi Zhou, Huimin Li, Jiaqi Lin, Weifang Zhang, Xuepeng Chen
Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research
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E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-19 Zhuen Guo, Mingqing Yang, Li Lin, Jisong Li, Shuyue Zhang, Qianbo He, Jiaqi Gao, Heling Meng, Xinran Chen, Yuehao Tao, Chen Yang
Emotional recognition is a pivotal research domain in computer and cognitive science. Recent advancements have led to various emotion recognition methods, leveraging data from diverse sources like speech, facial expressions, electroencephalogram (EEG), electrocardiogram, and eye tracking (ET). This article introduces a novel emotion recognition framework, primarily targeting the analysis of users’
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Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-19 Tanzim Hossain, F M Javed Mehedi Shamrat, Xujuan Zhou, Imran Mahmud, Md. Sakib Ali Mazumder, Sharmin Sharmin, Raj Gururajan
Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic
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Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-19 Nofe Alganmi, Arwa Bashanfar, Reem Alotaibi, Haneen Banjar, Sajjad Karim, Zeenat Mirza, Heba Abusamra, Manal Al-Attas, Shereen Turkistany, Adel Abuzenadah
Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding
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Design of load-aware resource allocation for heterogeneous fog computing systems PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-18 Syed Rizwan Hassan, Ateeq Ur Rehman, Naif Alsharabi, Salman Arain, Asim Quddus, Habib Hamam
The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the
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CNN-GMM approach to identifying data distribution shifts in forgeries caused by noise: a step towards resolving the deepfake problem PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-17 Roaa Mohamed Alnafea, Liyth Nissirat, Aida Al-Samawi
Recently, there have been notable advancements in video editing software. These advancements have allowed novices or those without access to advanced computer technology to generate videos that are visually indistinguishable to the human eye from real ones to the human observer. Therefore, the application of deepfake technology has the potential to expand the scope of identity theft, which poses a
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Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-17 Mohamed Zul Fadhli Khairuddin, Suresh Sankaranarayanan, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Rosidah Omar
Background This study introduced a novel approach for predicting occupational injury severity by leveraging deep learning-based text classification techniques to analyze unstructured narratives. Unlike conventional methods that rely on structured data, our approach recognizes the richness of information within injury narrative descriptions with the aim of extracting valuable insights for improved occupational
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A review of social background profiling of speakers from speech accents PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-16 Mohammad Ali Humayun, Junaid Shuja, Pg Emeroylariffion Abas
Social background profiling of speakers is heavily used in areas, such as, speech forensics, and tuning speech recognition for accuracy improvement. This article provides a survey of recent research in speaker background profiling in terms of accent classification and analyses the datasets, speech features, and classification models used for the classification tasks. The aim is to provide a comprehensive
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Research on the prediction of English topic richness in the context of multimedia data PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-16 Jie Jiao, Hanan Aljuaid
With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant
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The effect of hair removal and filtering on melanoma detection: a comparative deep learning study with AlexNet CNN PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-16 Angélica Quishpe-Usca, Stefany Cuenca-Dominguez, Araceli Arias-Viñansaca, Karen Bosmediano-Angos, Fernando Villalba-Meneses, Lenin Ramírez-Cando, Andrés Tirado-Espín, Carolina Cadena-Morejón, Diego Almeida-Galárraga, Cesar Guevara
Melanoma is the most aggressive and prevalent form of skin cancer globally, with a higher incidence in men and individuals with fair skin. Early detection of melanoma is essential for the successful treatment and prevention of metastasis. In this context, deep learning methods, distinguished by their ability to perform automated and detailed analysis, extracting melanoma-specific features, have emerged
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Improving prediction of maternal health risks using PCA features and TreeNet model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-15 Leila Jamel, Muhammad Umer, Oumaima Saidani, Bayan Alabduallah, Shtwai Alsubai, Farruh Ishmanov, Tai-hoon Kim, Imran Ashraf
Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother’s health is susceptible to several complications and risks, and timely detection
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A detailed study of resampling algorithms for cyberattack classification in engineering applications PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-15 Óscar Mogollón Gutiérrez, José Carlos Sancho Núñez, Mar Ávila, Andrés Caro
The evolution of engineering applications is highly relevant in the context of protecting industrial systems. As industries are increasingly interconnected, the need for robust cybersecurity measures becomes paramount. Engineering informatics not only provides tools for knowledge representation and extraction but also affords a comprehensive spectrum of developing sophisticated cybersecurity solutions
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Text-image semantic relevance identification for aspect-based multimodal sentiment analysis PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-12 Tianzhi Zhang, Gang Zhou, Jicang Lu, Zhibo Li, Hao Wu, Shuo Liu
Aspect-based multimodal sentiment analysis (ABMSA) is an emerging task in the research of multimodal sentiment analysis, which aims to identify the sentiment of each aspect mentioned in multimodal sample. Although recent research on ABMSA has achieved some success, most existing models only adopt attention mechanism to interact aspect with text and image respectively and obtain sentiment output through
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Combined spatial and frequency dual stream network for face forgery detection PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-11 Hui Zhao, Xin Li, Bingxin Xu, Hongzhe Liu
With the development of generative model, the cost of facial manipulation and forgery is becoming lower and lower. Fraudulent data has brought numerous hidden threats in politics, privacy, and cybersecurity. Although many methods of face forgery detection focus on the learning of high frequency forgery traces and achieve promising performance, these methods usually learn features in spatial and frequency
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Real-time task parameter selection method of accounting system based on multi-objective optimization and genetic algorithm PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-11 Rongjie Qin, Muhammad Shahbaz
The progress of the digital economy has promoted the enterprise accounting system. To accelerate the update and evolution of accounting systems, we propose a parameter selection method based on multi-objective optimization and genetic algorithm. Firstly, this article proposes an accounting feature extraction method based on multimodal information embedding. The dual-branch structure and feature pyramid
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A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-11 Hai-Ying Zheng, Yang Li, Nan Wang, Yang Xiang, Jin-Hang Liu, Liu-Deng Zhang, Lan Huang, Zhong-Yi Wang
Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob.
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A lightweight framework for cyber risk management in Western Balkan higher education institutions PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-10 Krenar Kepuska, Milo Tomasevic
Higher education institutions (HEIs) have a significant presence in cyberspace. Data breaches in academic institutions are becoming prevalent. Online platforms in HEIs are a new learning mode, particularly in the post-COVID era. Recent studies on information security indicate a substantial increase in cybersecurity attacks in HEIs, because of their decentralized e-learning structure and diversity of
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Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-10 Zirong Wang, Zhengyu Han, Shahzadi Tayyaba
The utilization of drone technology thrives in diverse domains, including aviation, military operations, and logistics. The pervasive adoption of this technology aims to enhance efficiency while mitigating hazards and expenditures. In complex contexts, the governing parameters of uncrewed aerial vehicles (UAV) require real-time adjustments for flight safety and efficacy. To improve the attitude estimation
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Adaptive robust structure exploration for complex systems based on model configuration and fusion PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-08 Yingfei Qu, Wanbing Liu, Junhao Wen, Ming Li
Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates
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The impact of advance organizers in virtual classrooms on the development of integrated science process skills PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-05 Abdellah Ibrahim Mohammed Elfeky, Ali Hassan Najmi, Marwa Yasien Helmy Elbyaly
Unlike virtual classrooms that have received extensive research attention in both academic and practical contexts because of their ability to improve students’ outcomes, the use of advance organizers are still in need for more research to prove their efficacy in fulfilling expected learning outcomes in these virtual classrooms. Hence, the present study aims to identify the impact of using such organizers
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Multi-classification of disease induced in plant leaf using chronological Flamingo search optimization with transfer learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-05 Malathi Chilakalapudi, Sheela Jayachandran
Agriculture is imperative research in visual detection through computers. Here, the disease in plants can distress the quality and cultivation of farming. Earlier detection of disease lessens economic losses and provides better crop yield. Detection of disease from crops manually is an expensive and time-consuming task. A new scheme is devised for accomplishing multi-classification of disease using
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Demand prediction for urban air mobility using deep learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-05 Faheem Ahmed, Muhammad Ali Memon, Khairan Rajab, Hani Alshahrani, Mohamed Elmagzoub Abdalla, Adel Rajab, Raymond Houe, Asadullah Shaikh
Urban air mobility, also known as UAM, is currently being researched in a variety of metropolitan regions throughout the world as a potential new mode of transport for travelling shorter distances inside a territory. In this article, we investigate whether or not the market can back the necessary financial commitments to deploy UAM. A challenge in defining and addressing a critical phase of such guidance
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Adaptive quality of service for packet loss reduction using OpenFlow meters PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-04 Krishneel Deo, Kaylash Chaudhary, Mansour Assaf
Quality of Service (QoS) is a mechanism used in computer networks to prioritize, classify, and treat packets differently based on certain criteria. This helps the switching devices to schedule and reorder packets if there is congestion in the network. Edge routers experience high traffic congestion as a result of traffic aggregation from the internal network devices. A router can have multiple QoS
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Customized deep learning based Turkish automatic speech recognition system supported by language model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-03 Yasin Görmez
Background In today’s world, numerous applications integral to various facets of daily life include automatic speech recognition methods. Thus, the development of a successful automatic speech recognition system can significantly augment the convenience of people’s daily routines. While many automatic speech recognition systems have been established for widely spoken languages like English, there has
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LEET: stock market forecast with long-term emotional change enhanced temporal model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-02 Honglin Liao, Jiacheng Huang, Yong Tang
The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices
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A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-04-02
The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification
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Password authenticated key exchange-based on Kyber for mobile devices PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29 Kübra Seyhan, Sedat Akleylek, Ahmet Faruk Dursun
In this article, a password-authenticated key exchange (PAKE) version of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) public-key encryption and key-establishment standard is constructed. We mainly focused on how the PAKE version of PQC standard Kyber with mobile compatibility can be obtained by using simple structured password components. In the design process
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Short-term wind power forecasting through stacked and bi directional LSTM techniques PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29 Mehmood Ali Khan, Iftikhar Ahmed Khan, Sajid Shah, Mohammed EL-Affendi, Waqas Jadoon
Background Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of
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A survey on multi-lingual offensive language detection PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29
The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term “Offensive Language” encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single
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Self-distillation framework for document-level relation extraction in low-resource environments PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29
The objective of document-level relation extraction is to retrieve the relations existing between entities within a document. Currently, deep learning methods have demonstrated superior performance in document-level relation extraction tasks. However, to enhance the model’s performance, various methods directly introduce additional modules into the backbone model, which often increases the number of
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Fast and exact fixed-radius neighbor search based on sorting PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29 Xinye Chen, Stefan Güttel
Fixed-radius near neighbor search is a fundamental data operation that retrieves all data points within a user-specified distance to a query point. There are efficient algorithms that can provide fast approximate query responses, but they often have a very compute-intensive indexing phase and require careful parameter tuning. Therefore, exact brute force and tree-based search methods are still widely
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Deep ocular tumor classification model using cuckoo search algorithm and Caputo fractional gradient descent PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29
While digital ocular fundus images are commonly used for diagnosing ocular tumors, interpreting these images poses challenges due to their complexity and the subtle features specific to tumors. Automated detection of ocular tumors is crucial for timely diagnosis and effective treatment. This study investigates a robust deep learning system designed for classifying ocular tumors. The article introduces
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An intelligent diabetes classification and perception framework based on ensemble and deep learning method PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29 Qazi Waqas Khan, Khalid Iqbal, Rashid Ahmad, Atif Rizwan, Anam Nawaz Khan, DoHyeun Kim
Sugar in the blood can harm individuals and their vital organs, potentially leading to blindness, renal illness, as well as kidney and heart diseases. Globally, diabetic patients face an average annual mortality rate of 38%. This study employs Chi-square, mutual information, and sequential feature selection (SFS) to choose features for training multiple classifiers. These classifiers include an artificial
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Towards a GDPR-compliant cloud architecture with data privacy controlled through sticky policies PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29 M. Emilia Cambronero, Miguel A. Martínez, Luis Llana, Ricardo J. Rodríguez, Alejandro Russo
Data privacy is one of the biggest challenges facing system architects at the system design stage. Especially when certain laws, such as the General Data Protection Regulation (GDPR), have to be complied with by cloud environments. In this article, we want to help cloud providers comply with the GDPR by proposing a GDPR-compliant cloud architecture. To do this, we use model-driven engineering techniques
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Emotion detection from handwriting and drawing samples using an attention-based transformer model PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29 Zohaib Ahmad Khan, Yuanqing Xia, Khursheed Aurangzeb, Fiza Khaliq, Mahmood Alam, Javed Ali Khan, Muhammad Shahid Anwar
Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting
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Using deep learning-based artificial intelligence electronic images in improving middle school teachers’ literacy PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-29
With the rapid development of societal information, electronic educational resources have become an indispensable component of modern education. In response to the increasingly formidable challenges faced by secondary school teachers, this study endeavors to analyze and explore the application of artificial intelligence (AI) methods to enhance their cognitive literacy. Initially, this discourse delves
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A structure-preserving linearly homomorphic signature scheme with designated combiner PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-28 Xuan Zhou, Yuan Tian, Weidong Zhong, Tanping Zhou, Xiaoyuan Yang
Linearly homomorphic signature (LHS) allows the acquisition of a new legal signature using the homomorphic operation of the original signatures. However, the public composability of LHS also prevents it from being used in some scenarios where the combiner needs to be designated. The LZZ22 scheme designates a combiner and preserves the signature structure by having the signer and the designated combiner
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Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-28 Kurnia Muludi, Revita Setianingsih, Ridho Sholehurrohman, Akmal Junaidi
The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine
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Trajectory classification to support effective and efficient field-road classification PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-28 Ying Chen, Kaiming Kuang, Caicong Wu
Field-road classification, which automatically identifies in-field activities and out-of-field activities in global navigation satellite system (GNSS) recordings, is an important step for the performance evaluation of agricultural machinery. Although several field-road classification methods based only on GNSS recordings have been proposed, there is a trade-off between time consumption and accuracy
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Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-28
Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural
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Multiangle perception-oriented environmental facility design method based on joint fuzzy decision-making and transfer learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-28 Siconghui Yao
In modern society, the demand for environmental facilities is increasing, and how to effectively design and plan environmental facilities has become an urgent issue. However, traditional design methods often consider only certain requirements and perspectives, resulting in design results deviating from the expectations of actual users. In this study, first, perceptual fuzzy decision-making and design
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Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-27
This study investigates the effectiveness of various deep learning and classical machine learning techniques in identifying instances of cyberbullying. The study compares the performance of five classical machine learning algorithms and three deep learning models. The data undergoes pre-processing, including text cleaning, tokenization, stemming, and stop word removal. The experiment uses accuracy
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Structural health monitoring of aircraft through prediction of delamination using machine learning PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-27 Rajeswari D, Osamah Ibrahim Khalaf, Srinivasan R, Pushpalatha M, Habib Hamam
Background Structural health monitoring (SHM) is a regular procedure of monitoring and recognizing changes in the material and geometric qualities of aircraft structures, bridges, buildings, and so on. The structural health of an airplane is more important in aerospace manufacturing and design. Inadequate structural health monitoring causes catastrophic breakdowns, and the resulting damage is costly
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YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-27 Chengkai Yang, Xiaoyun Sun, Jian Wang, Haiyan Lv, Ping Dong, Lei Xi, Lei Shi
Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid
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Advertisement design in dynamic interactive scenarios using DeepFM and long short-term memory (LSTM) PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-27 Lingling Zeng, Muhammad Asif
This article addresses the evolving landscape of data advertising within network-based new media, seeking to mitigate the accuracy limitations prevalent in traditional film and television advertising evaluations. To overcome these challenges, a novel data-driven nonlinear dynamic neural network planning approach is proposed. Its primary objective is to augment the real-time evaluation precision and
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Deep learning-based recognition system for pashto handwritten text: benchmark on PHTI PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-27
This article introduces a recognition system for handwritten text in the Pashto language, representing the first attempt to establish a baseline system using the Pashto Handwritten Text Imagebase (PHTI) dataset. Initially, the PHTI dataset underwent pre-processed to eliminate unwanted characters, subsequently, the dataset was divided into training 70%, validation 15%, and test sets 15%. The proposed
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What are developers talking about information security? A large-scale study using semantic analysis of Q&A posts PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-26
Background Digitalization and rapid technological improvement in the present day bring numerous benefits, but they also raise the complexity and diversity of cyber security risks, putting critical information security issues on the agenda. Growing issues and worries about information security endanger not only the security of individuals and organizations but also global social and economic stability
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Special issue on software citation, indexing, and discoverability PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-26 Daniel S. Katz, Neil P. Chue Hong
Software plays a fundamental role in research as a tool, an output, or even as an object of study. This special issue on software citation, indexing, and discoverability brings together five papers examining different aspects of how the use of software is recorded and made available to others. It describes new work on datasets that enable large-scale analysis of the evolution of software usage and
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Blockchain-enabled infrastructural security solution for serverless consortium fog and edge computing PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-26 Abdullah Ayub Khan, Asif Ali Laghari, Abdullah M. Baqasah, Roobaea Alroobaea, Ahmad Almadhor, Gabriel Avelino Sampedro, Natalia Kryvinska
The robust development of the blockchain distributed ledger, the Internet of Things (IoT), and fog computing-enabled connected devices and nodes has changed our lifestyle nowadays. Due to this, the increased rate of device sales and utilization increases the demand for edge computing technology with collaborative procedures. However, there is a well-established paradigm designed to optimize various
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A machine learning based framework for IoT devices identification using web traffic PeerJ Comput. Sci. (IF 3.8) Pub Date : 2024-03-26 Sajjad Hussain, Waqar Aslam, Arif Mehmood, Gyu Sang Choi, Imran Ashraf
Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase