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摘要: It is the key to control bio-derived dissolved organic matters (DOM) in order to reduce the effluent concentration of wastewater treatment, especially for waste leachate with high organic contaminants. In the present study, the anaerobic degradation of aerobically stabilized DOM was investigated with DOM substrate isolated through electrodialysis. The degradation of bio-derived DOM was confirmed by reduction of 15% of total organic carbon in 100 days. We characterized the molecular behavior of b关键词: Soluble microbial products;Data mining;Machine learning;Solid waste;Orbitrap
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摘要: Acoustic technologies provide a non-invasive method to generate information about the health, welfare, and environmental impact of livestock. This study demonstrated that rear leg attached acoustic sensors can be used to differentiate between seven different acoustic classes, with six based on cow behaviours (Grazing, Breathing, Walking, Lying Down, Dung, Vocalization, Other) that were obtained from more than 150 cows under grazing conditions. The overall accuracy of the ensemble classification 关键词: Cow;Acoustic;Machine learning;Neural network;Behaviour;Livestock
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摘要: The amount of moisture in the air is represented by relative humidity (RH); an ideal level of humidity in the interior environment is between 40% and 60% at temperatures between 18° and 20° Celsius. When the RH falls below this level, the environment becomes dry, which can cause skin dryness, irritation, and discomfort at low temperatures. When the humidity level rises above 60%, a wet atmosphere develops, which encourages the growth of mold and mites. Asthma and allergy symptoms may occur as a 关键词: Machine learning; indoor air quality; humidity; carbon dioxide; relative humidity
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Journal
Ashit Kumar Dutta;Yasser Albagory;Manal Al Faraj;Yasir A. M. Eltahir;Abdul Rahaman Wahab Sait;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 2, 2023, PP 1517-1529
摘要: The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-ma关键词: Biomedical signals; EEG; sleep stage classification; machine learning; autoencoder; softmax; parameter tuning
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Journal
摘要: Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually关键词: Federated learning; machine learning; edge computing; resource management
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摘要: Brain tumors are potentially fatal presence of cancer cells over a human brain, and they need to be segmented for accurate and reliable planning of diagnosis. Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived. Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities. The scanner used 关键词: Glioma detection; segmentation; smaller tumour; growth; machine learning; feature analysis
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Journal
Polin Rahman;Ahmed Rifat;MD. IftehadAmjad Chy;Mohammad Monirujjaman Khan;Mehedi Masud;Sultan Aljahdali;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 1, 2023, PP 757-775
摘要: Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine关键词: Heart failure prediction; data visualization; machine learning; k-nearest neighbors; support vector machine; decision tree; random forest; logistic regression; xgboost and catboost; artificial neural network
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Journal
Nasser Ali Aljarallah;12;Ashit Kumar Dutta;3;Majed Alsanea;Abdul Rahaman Wahab Sait;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 2, 2023, PP 1853-1868
摘要: A learning management system (LMS) is a software or web based application, commonly utilized for planning, designing, and assessing a particular learning procedure. Generally, the LMS offers a method of creating and delivering content to the instructor, monitoring students’ involvement, and validating their outcomes. Since mental health issues become common among studies in higher education globally, it is needed to properly determine it to improve mental stability. This article develops a new s关键词: Learning management system; mental health assessment; intelligent models; machine learning; feature selection; performance assessment
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Journal
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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Journal
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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Journal
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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Journal
R. Gopi;R. Sheeba;K. Anguraj;T. Chelladurai;Haya Mesfer Alshahrani;Nadhem Nemri;6;Tarek Lamoudan;
COMPUTER SYSTEMS SCIENCE AND ENGINEERINGVolume 44, Issue 2, 2023, PP 1567-1582
摘要: Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with o关键词: Intrusion detection system; artificial intelligence; machine learning; industry 4.0; internet of things
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Journal
摘要: Renewable energy production plays a major role in satisfying electricity demand. Wind power conversion is one of the most popular renewable energy sources compared to other sources. Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator (PMSG) and the Doubly Fed Induction Generator (DFIG). The maximum power tracking algorithm is a crucial controller, a wind energy conversion system for generating maximum power in different wind speed condition关键词: Doubly fed induction generator; machine learning; convertors; generators; activation function
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Journal
摘要: Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem, especially in developing countries. There have been researches on human sepsis, vaccine response, and immunity. Also, machine learning methodologies were used for predicting infant mortality based on certain features like age, birth weight, gestational weeks, and Appearance, Pulse, Grimace, Activity and Respiration (APGAR) score. Sepsis, which is considered the most determining condition tow关键词: APGAR; sepsis; explainable AI; machine learning
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Journal
摘要: The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is 关键词: Load forecasting; distribution network; machine learning; renewable energy
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Journal
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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Journal
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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Journal
摘要: Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed. Although machine learning techniques have been frequently implemented in this area, the existing studies disregard to the natural order between the target attribute values of the historical sensor data. Thus, these methods cause losing the inherent order of the data that positively affects the prediction performances.关键词: Machine learning; multi-dimensional classification; ordinal classification; predictive maintenance
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Book Chapter
摘要: Health care is undergoing a technological transformation leveraging discoveries in machine learning, genomics, and data visualization. These forces are working to reshape the face of medicine and how providers work. The semiconductor revolution predicted by Gordon Moore in the early 1960s has propelled society into an era of high-speed communication, predictive analytics, and ubiquitous data. Medicine, while lagging behind other industries, faces enormous change as it attempts to assimilate thes关键词: Cloud computing;data visualization;decision support;machine learning;NoSQL;population health;relational database
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Book Chapter
摘要: Artificial intelligence (AI) and Big Data represent the big revolution of our time. AI embodies the ability of a computer to learn and master a skill that is traditionally human, whereas Big Data constitutes an incredibly rich source of information, with the potential to provide unprecedented insights in countless fields. Within the medical world, neurosurgery is particularly well suited to exploit the benefits provided by this revolution, in consideration of the great quantity and variety of da关键词: artificial intelligence;augmented surgery;big data;digital medicine;machine learning;neurosurgery;robotics
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Book Chapter
摘要: Spinal disorders, and in particular adult spinal deformity (ASD), are complex and heterogeneous diseases that vary dramatically in their presentation and impact on patients’ lives. Surgical correction has been proven to improve patient quality of life (QOL) in the vast majority of cases, but there remains a relatively high complication risk associated with complex procedures such as surgical correction of ASD. In order to better prognosticate patient outcomes, robust predictive models are of par关键词: artificial intelligence;machine learning;predictive model;spinal deformity;spine
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Journal
Liu Hao-Xuan;Yan Hai-Le;Jia Nan;Tang Shuai;Cong Daoyong;Yang Bo;Li Zongbin;Zhang Yudong;Esling Claude;Zhao Xiang;Zuo Liang;
Journal of Materials Science & TechnologyVolume 131, Issue , 2022, PP 1-13
摘要: Brittleness is a critical issue hindering the potential application of the X2YZ-type full Heusler alloys in several fields of state-of-the-art technologies. To realize optimization of brittleness or design a ductile Heuser alloy, it is greatly urgent to identify the key materials factors deciding brittleness and establish an empirical rule to effectively evaluate ductility. For this purpose, by using a machine learning and human analysis cooperation approach, the brittleness of the X2YZ-type Heu关键词: Heusler alloy;Machine learning;Ductility;Empirical formula;Pugh's ratio ;k
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Journal
摘要: Nitrogen (N) excreted in the urine from cattle is the primary source of N leaching loss from grazed pasture systems. The objective of this study was to use non-invasive accelerometer sensors to detect the time and duration of urination and defecation events from grazing cattle. Two accelerometer sensors were attached to cows to detect the back arching of cows during urination and defecation events. Trials were conducted under outdoor grazing conditions in autumn and summer with a total of 160 ur关键词: Cow;Machine learning;Audio recordings;Technology;Defecation;Frequency
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Journal
摘要: Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will i关键词: R&D;AI;GCNN;RNN;LSTM;Artificial intelligence;Design-make-test;Machine learning;Molecule design;Recurrent neural networks
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Journal
摘要: There is a documented shortage of reliable counting systems for the entrance of beehives. Movement at the entrance of a hive is a measure of hive health and abnormalities, in addition to an indicator of predators. To that end, two camera systems have been designed to provide a comparative analysis for a thermal camera system. The first, a visible spectrum camera, competed directly with the thermal camera. Machine learning is used to address the narrower field of view of the thermal camera, in ad关键词: Thermal camera;Optical camera;Machine learning;Insect tracking;Apis mellifera
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Journal
摘要: Objective(#br)Patient-physician communication affects cancer patients' satisfaction, health outcomes, and reimbursement for physician services. Our objective is to use machine learning to comprehensively examine the association between patient satisfaction and physician factors in clinical consultations about cancer prognosis and pain.(#br)Methods(#br)We used data from audio-recorded, transcribed communications between physicians and standardized patients (SPs). We analyzed the data using logist关键词: Patient-physician communication;Patient satisfaction;Machine learning;Cancer prognosis;Pain management
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Journal
关键词: Machine learning;Deep learning;Predictions;Uncertainty quantification
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Journal
Kayode Saheed Yakub;Idris Abiodun Aremu;Misra Sanjay;Kristiansen Holone Monica;Colomo-Palacios Ricardo;
Alexandria Engineering JournalVolume 61, Issue 12, 2022, PP 9395-9409
摘要: The Internet of Things (IoT) refers to the collection of all those devices that could connect to the Internet to collect and share data. The introduction of varied devices continues to grow tremendously, posing new privacy and security risks—the proliferation of Internet connections and the advent of new technologies such as the IoT. Various and sophisticated intrusions are driving the IoT paradigm into computer networks. Companies are increasing their investment in research to improve the detec关键词: Intrusion Detection System;Machine Learning;Internet of Things;Min-max Normalization;UNSWNB-15;Principal Component Analysis;Cat boost;XgBoost
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Journal
摘要: Today, electricity is in high demand in a variety of places, including hospitals, industry, households, transportation, and communication, among others. Renewable energy is a revolutionary type of energy that is increasingly being used to replace electricity demand because it has been regenerated and reused several times. Renewable energy is an intermediate and unpredictable natural resource, so it is difficult for many research studies to estimate its rate. To address this problem, this study u关键词: Machine Learning;Energy Consumption;Renewable Power Source;Multilayer Perception;CatBoost algorithm
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Journal
Baashar Yahia;Hamed Yaman;Alkawsi Gamal;Fernando Capretz Luiz;Alhussian Hitham;Alwadain Ayed;Al-amri Redhwan;
Alexandria Engineering JournalVolume 61, Issue 12, 2022, PP 9867-9878
摘要: Institutions of higher learning are currently facing the challenging task of attracting new students who can effectively meet their diverse academic demands. With these demands come the need for those institutions to develop strategies that can enhance students' learning experiences at various educational levels. Predicting the academic success at an early stage would allow academic institutions to develop specific enrolment guidelines while avoiding poor performance. The main purpose of this st关键词: Student performance;Prediction;Machine learning;Postgraduate level
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