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摘要: Wear is one of the major causes that affect the performance and reliability of tribo-systems. To mitigate its adverse effects, it is necessary to monitor the wear progress so that preventive maintenance can be timely scheduled. An online visual ferrograph (OLVF) apparatus is used to obtain online measurements of wear particle quantities, and monitor the wearing of a four-ball tribometer under different lubrication conditions, and several popular deep learning algorithms are evaluated for their e关键词: deep learning;cross-sectional time series;wear prediction;four-ball test;online visual ferrograph;data-driven engineering
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摘要: Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant conc关键词: PM;2.5; concentration forecast;Traffic emissions;Deep learning;Attention mechanism;New energy vehicles
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摘要: Plant diseases often reduce crop yield and product quality; therefore, plant disease diagnosis plays a vital role in farmers’ management decisions. Visual crop inspections by humans are time-consuming and challenging tasks and, practically, can only be performed in small areas at a given time, especially since many diseases have similar symptoms. An intelligent machine vision monitoring system for automatic inspection can be a great help for farmers in this regard. Although many algorithms have 关键词: Artificial intelligence;Deep learning;Disease classification;Grape diseases;Machine vision
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摘要: Several factors associated with disease diagnosis in plants using deep learning techniques must be considered to develop a robust system for accurate disease management. A considerable number of studies have investigated the potential of deep learning techniques for precision agriculture in the last decade. However, despite the range of applications, several gaps within plant disease research are yet to be addressed to support disease management on farms. Thus, there is a need to establish a kno关键词: Plant diseases;Deep learning;Precision agriculture;Generalization;Review;Survey
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摘要: Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power 关键词: Deep learning; approximate multiplier; LSTM; jellyfish
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G. Ignisha Rajathi;R. Ramesh Kumar;D. Ravikumar;T. Joel;Seifedine Kadry;45;Chang-Won Jeong;Yunyoung Nam;7;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 2, 2023, PP 1793-1806
摘要: Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process b关键词: Internet of medical things; healthcare; brain tumor; disease classification; deep learning; metaheuristics
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摘要: A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network’s generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation me关键词: Super-resolution; deep learning; ERA5 dataset; GAN networks
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Ashit Kumar Dutta;Manal Al Faraj;Yasser Albagory;Mohammad zeid M Alzamil;Abdul Rahaman Wahab Sait;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 2, 2023, PP 1219-1231
摘要: A cyber physical energy system (CPES) involves a combination of processing, network, and physical processes. The smart grid plays a vital role in the CPES model where information technology (IT) can be related to the physical system. At the same time, the machine learning (ML) models find useful for the smart grids integrated into the CPES for effective decision making. Also, the smart grids using ML and deep learning (DL) models are anticipated to lessen the requirement of placing many power pl关键词: Stability prediction; smart grid; cyber physical energy systems; deep learning; data analytics; moth swarm algorithm
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摘要: Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system tha关键词: Data mining; deep learning; crop production; tweak chick swarm optimization algorithm; discrete deep belief network with VGG Net classifier
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摘要: Thyroid disease is a medical condition caused due to the excess release of thyroid hormone. It is released by the thyroid gland which is in front of the neck just below the larynx. Medical pictures such as X-rays and CT scans can, however, be used to diagnose it. In this proposed model, Deep Learning technology is used to detect thyroid diseases. A Convolution Neural Network (CNN) based modified ResNet architecture is employed to detect five different types of thyroid diseases namely 1. Hypothyr关键词: Thyroid; deep learning; convolution neural network; modified ResNet; dual optimizer
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E. Dhiravidachelvi;M.Suresh Kumar;L. D. Vijay Anand;D. Pritima;Seifedine Kadry;Byeong-Gwon Kang;Yunyoung Nam;7;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 2, 2023, PP 961-977
摘要: Human Activity Recognition (HAR) has been made simple in recent years, thanks to recent advancements made in Artificial Intelligence (AI) techniques. These techniques are applied in several areas like security, surveillance, healthcare, human-robot interaction, and entertainment. Since wearable sensor-based HAR system includes in-built sensors, human activities can be categorized based on sensor values. Further, it can also be employed in other applications such as gait diagnosis, observation of关键词: Artificial intelligence; human activity recognition; deep learning; deep belief network; hyperparameter tuning; healthcare
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摘要: Weed is a plant that grows along with nearly all field crops, including rice, wheat, cotton, millets and sugar cane, affecting crop yield and quality. Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity. To address this issue, an efficient weed classification model is proposed with the Deep Convolutional Neural Network (CNN) that implements automatic feature extraction and performs complex featur关键词: Deep learning; convolutional neural network; weed classification; transfer learning; particle swarm optimization; evolutionary computing
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摘要: Human Action Recognition (HAR) and pose estimation from videos have gained significant attention among research communities due to its application in several areas namely intelligent surveillance, human robot interaction, robot vision, etc. Though considerable improvements have been made in recent days, design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle, occlusion, background, mo关键词: Human activity recognition; pose estimation; key point extraction; classification; deep learning; RMSProp
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摘要: Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security a关键词: IoT; IDS; deep learning; machine learning; CNN; LSTM
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摘要: Medical image classification becomes a vital part of the design of computer aided diagnosis (CAD) models. The conventional CAD models are majorly dependent upon the shapes, colors, and/or textures that are problem oriented and exhibited complementary in medical images. The recently developed deep learning (DL) approaches pave an efficient method of constructing dedicated models for classification problems. But the maximum resolution of medical images and small datasets, DL models are facing the 关键词: Medical image classification; spiking neural networks; computer aided diagnosis; medical imaging; parameter optimization; deep learning
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摘要: The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical tec关键词: Copy move detection; image forgery; deep learning; machine learning; parameter tuning; forensics
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摘要: Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D c关键词: 3D-CNN; deep learning; driver drowsiness detection; LSTM; spatiotemporal features
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摘要: Big Data and artificial intelligence are used to transform businesses. Social networking sites have given a new dimension to online data. Social media platforms help gather massive amounts of data to reach a wide variety of customers using influence maximization technique for innovative ideas, products and services. This paper aims to develop a deep learning method that can identify the influential users in a network. This method combines the various aspects of a user into a single graph. In a s关键词: Deep learning; influence maximization; graph embedding; deepwalk
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摘要: In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm 关键词: Big data analytics; healthcare; deep learning; image classification; biomedical imaging; machine learning
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摘要: Due to the rising occurrence of skin cancer and inadequate clinical expertise, it is needed to design Artificial Intelligence (AI) based tools to diagnose skin cancer at an earlier stage. Since massive skin lesion datasets have existed in the literature, the AI-based Deep Learning (DL) models find useful to differentiate benign and malignant skin lesions using dermoscopic images. This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet (ARGS-OEN) technique for skin 关键词: Computer aided diagnosis; deep learning; image segmentation; skin lesion diagnosis; dermoscopic images; medical image processing
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Md. Saiful Islam;Shuvo Jyoti Das;Md. Riajul Alam Khan;Sifat Momen;Nabeel Mohammed;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 1, 2023, PP 519-534
摘要: COVID-19 has created a panic all around the globe. It is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), originated from Wuhan in December 2019 and spread quickly all over the world. The healthcare sector of the world is facing great challenges tackling COVID cases. One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases. In this article, we propose a deep Convolutional Neural Network (CNN关键词: COVID-19; convolutional neural network; deep learning; DenseNet201; model performance
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摘要: Recently, data science techniques utilize artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to take an influence and grow their businesses. For SMEs, owing to the inexistence of consistent data and other features, evaluating credit risks is difficult and costly. On the other hand, it becomes necessary to design efficient models for predicting business failures or financial crises of SMEs. Various data classification approaches for financial crisi关键词: Small medium-sized enterprises; deep learning; FCP; financial sector; prediction; metaheuristics; sailfish optimization
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摘要: Medical image assessment is based on segmentation at its fundamental stage. Deep neural networks have been more popular for segmentation work in recent years. However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable. For this reason, a novel optimized deep learning approach is proposed for medical image segmentation. Optimization plays an关键词: Deep learning; optimization; segmentation; medical images; tumors; classification
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摘要: Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, le关键词: Deep learning; dermoscopic images; intelligent models; machine learning; skin lesion
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摘要: This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The s关键词: Cybersecurity; deep learning; machine learning; user behavior analytics
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摘要: Smoking is a major cause of cancer, heart disease and other afflictions that lead to early mortality. An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives. Smoking activities often accompany other activities such as drinking or eating. Consequently, smoking activity recognition can be a challenging topic in human activity recognition (HAR). A deep learning framework for smoking activity re关键词: Smoking activity recognition; deep residual network; smartwatch sensors; deep learning
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摘要: Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users’ demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for 关键词: Recommender systems; context-aware; multi-criteria; deep learning; deep neural network
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摘要: The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. The proposed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Integrated sensor system (ISS) combining an array of关键词: Forest fire; deep learning; intelligent models; metaheuristics; integrated sensor system; hyperparameter tuning
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摘要: As the performance of single-channel speech separation systems has improved, there has been a shift in the research community towards tackling more challenging conditions that are more representative of many real-world applications, including the addition of noise and reverberation. The need for ground truth in training state-of-the-art separation systems leads to a requirement of training on artificial mixtures, where single-speaker recordings are summed digitally. However, this leads to two se关键词: Speech separation;Noisy speech;Deep learning
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摘要: In modern agriculture, visual recognition systems based on deep learning are arising to allow autonomous machines to execute field operations in crops. However, for obtaining high performances, these methods need high amounts of data, which are usually scarce in agriculture. The main reason is that building an agricultural dataset covering exhaustively a specific problem is challenging, as visual characteristics of the symptoms may change, and there is a high dependency on environmental factors,关键词: Open-data;Dataset;Precision agriculture;Deep learning;Computer vision;Robotics;Artificial intelligence
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