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Articles 1 - 30 of 91
Full-Text Articles in Physical Sciences and Mathematics
An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei
An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei
Coal Geology & Exploration
Water hazard on the coal seam proof induced by high-intensity coal mining are increasingly prominent in the Inner Mongolia-Shaanxi border region. The effective, accurate water-source discrimination of the water inrushes is the key to water hazard prevention. This study investigated three typical mines in the Inner Mongolia-Shaanxi border region. To this end, principal component analysis (PCA) was employed to extract principal components from 80 groups of groundwater samples. Then, with inorganic indicators K++Na+, Ca2+, Mg2+, Cl−, SO4 2−, HCO3 − and TDS and organic indicators UV254 …
Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu
Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu
Coal Geology & Exploration
In order to solve the problems such as the difficulty in early warning and prediction of kicks and lost circulation accidents during emergency rescue drilling of mine, a machine learning-based early for warning and prediction model of drilling process was established. Firstly, the accident characterization parameters of the drilling parameters in the early stage of kicks and lost circulation accidents were analyzed. Secondly, the accident characterization parameters were cleaned and processed. On this basis, XGBoost and early warning model was used to carry out the early diagnosis and identification of kicks and lost circulation accidents. Then, the PSO-LSTM accident development …
Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection With Machine Learning, Rachel Meyer
Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection With Machine Learning, Rachel Meyer
ELAIA
Asteroid detection is a common field in astronomy for planetary defense, requiring observations from survey telescopes to detect and classify different objects. The amount of data collected each night is continually increasing as new and better-designed telescopes begin collecting information each year. This amount of data is quickly becoming unmanageable, and researchers are looking for ways to better process this data. The most feasible current solution is to implement computer algorithms to automatically detect these sources and then use machine learning to create a more efficient and accurate method of classification. Implementation of such methods has previously focused on larger …
Predicting Open-Pit Mine Production Using Machine Learning Techniques, Faustin Nartey Kumah, Alex Kwasi Saim, Millicent Nkrumah Oppong, Clement Kweku Arthur
Predicting Open-Pit Mine Production Using Machine Learning Techniques, Faustin Nartey Kumah, Alex Kwasi Saim, Millicent Nkrumah Oppong, Clement Kweku Arthur
Journal of Sustainable Mining
In mining, where production is affected by several factors, including equipment availability, it is necessary to develop reliable models to accurately predict mine production to improve operational efficiency. Hence, in this study, four (4) machine learning algorithms – namely: artificial neural network (ANN), random forest (RF), gradient boosting regression (GBR) and decision tree (DT)) – were implemented to predict mine production. Multiple Linear Regression (MLR) analysis was used as a baseline study for comparison purposes. In that regard, one hundred and twenty-six (126) datasets from an open-pit gold mine were used. The developed models were evaluated and compared using the …
Machine Learning Based Bioinformatics Analysis Of Intron Usage Alterations And Metabolic Regulation In Adipose Browning, Hamza Umut Karakurt, Pinar Pi̇r
Machine Learning Based Bioinformatics Analysis Of Intron Usage Alterations And Metabolic Regulation In Adipose Browning, Hamza Umut Karakurt, Pinar Pi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Adipose tissue is the major energy depot of the body and is considered an endocrine organ. Adipose tissue involves many different cell types, first and foremost, the adipocytes. White adipose cells that store fat and brown adipocytes that take part in lipid oxidation and heat generation are the most common cell types in adipose tissue. Even though brown adipocytes which have a high number of mitochondria and high fat-burning capacity are rare in adults, they are abundant in newborns and rodents. White adipocytes can gain a temporal brown-like character with a process called browning, which can be induced with cold …
Learning-Based Ant Colony Optimization Algorithm For Solving A Kind Of Complex 2-Echelon Vehicle Routing Problem, Xue Chen, Rong Hu, Hui Wang, Zuocheng Li, Bin Qian, Yixu Li
Learning-Based Ant Colony Optimization Algorithm For Solving A Kind Of Complex 2-Echelon Vehicle Routing Problem, Xue Chen, Rong Hu, Hui Wang, Zuocheng Li, Bin Qian, Yixu Li
Journal of System Simulation
Abstract: Aiming at green 2-echelon vehicle routing problem with simultaneous pick-up and delivery, a learning-based ant colony optimization algorithm combined with clustering decomposition is proposed. The objective function to be minimized is total transportation cost wherein carbon emission cost is specially considered. Associated with the mutual coupling features of the 2-echelon vehicle routing problem, we propose a distance-based clustering method to decompose the original problem into a set of sub-problems. Then, a learning-based ant colony optimization algorithm is presented to find the solutions of the sub-problems based on which the solution of the original problem can be obtained. In the …
Fuzzycsampling: A Hybrid Fuzzy C-Means Clustering Sampling Strategy For Imbalanced Datasets, Abdullah Maraş, Çi̇ğdem Erol
Fuzzycsampling: A Hybrid Fuzzy C-Means Clustering Sampling Strategy For Imbalanced Datasets, Abdullah Maraş, Çi̇ğdem Erol
Turkish Journal of Electrical Engineering and Computer Sciences
Classification model with imbalanced datasets is recently one of the most researched areas in machine learning applications since they induce to the emergence of low-performing machine learning models. The imbalanced datasets occur if target variables have an uneven number of examples in a dataset. The most prevalent solutions to imbalanced datasets can be categorized as data preprocessing, ensemble techniques, and cost-sensitive learning. In this article, we propose a new hybrid approach for binary classification, named FuzzyCSampling, which aims to increase model performance by ensembling fuzzy c-means clustering and data sampling solutions. This article compares the proposed approaches' results not only …
Rate-Of-Penetration (Rop) Prediction Model Based On Formation Characteristics Of Extremely Thick Plastic Mudstone In South China Sea, Zeng Xiaolong, Li Qian, Wei Hongchao, Chen Jiahao, Zhu Haiyan
Rate-Of-Penetration (Rop) Prediction Model Based On Formation Characteristics Of Extremely Thick Plastic Mudstone In South China Sea, Zeng Xiaolong, Li Qian, Wei Hongchao, Chen Jiahao, Zhu Haiyan
Coal Geology & Exploration
In terms of petroleum and gas resources, South China Sea is the important energy replacement area in China. However, most of the reservoirs are buried deep, and the strong plasticity of the formation under high confining pressure and the complex geological environment seriously affect the drilling efficiency. It is also very difficult to accurately predict the ROP. Hence, a set of intelligent ROP prediction model was established for the extremely thick mudstone formation with unique viscoelastic and strong plastic characteristics in South China Sea. The model took the actual data of 10 wells in an area of South China Sea …
A New Physics-Informed Method For The Fracability Evaluation Of Shale Oil Reservoirs, Li Yuwei, Li Zijian, Shao Lifei, Tian Fuchun, Tang Jizhou
A New Physics-Informed Method For The Fracability Evaluation Of Shale Oil Reservoirs, Li Yuwei, Li Zijian, Shao Lifei, Tian Fuchun, Tang Jizhou
Coal Geology & Exploration
The accurate evaluation of reservoir fracability is an essential prerequisite for the fracturing design and post-fracturing productivity evaluation of reservoirs. Rock mechanical parameters have been applied to the fracability evaluation of shales presently, exhibiting great field application performance. Accordingly, it is crucial to obtain accurate rock mechanical parameters. This study developed a physics-informed neural network (PINN) model. Driven by data and physical information, the PINN model can accurately predict rock mechanical parameters using only a small amount of data. To verify its performance, the PINN model was compared with the artificial neural network, random forest, and XGBoost models. The comparison …
Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson
Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson
Turkish Journal of Electrical Engineering and Computer Sciences
Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …
Wearable Sensor-Based Walkability Assessment At Ferry Terminal Using Machine Learning: A Case Study Of Mokpo, Korea, Jungyeon Choi, Hwayoung Kim
Wearable Sensor-Based Walkability Assessment At Ferry Terminal Using Machine Learning: A Case Study Of Mokpo, Korea, Jungyeon Choi, Hwayoung Kim
Journal of Marine Science and Technology
Walkability assessments are becoming more popular, as walking offers numerous health, environmental, and economic benefits to communities. However, previous studies on ferry terminal walkability assessment have been inadequate. This study aimed to develop a wearable sensor system to automatically assess walkability at ferry terminals without conducting surveys. We applied seven machine learning (ML) classifiers to detect different walking environments, including flat ground (FG), downhill slope (DS), uphill slope (US), and uneven surface (UE). The ML models were evaluated across different combinations of classes: 2-class (FG vs. UE), 3-class (U) (FG vs. US vs. UE), 3-class (D) (FG vs. DS vs. …
Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer
Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer
Turkish Journal of Electrical Engineering and Computer Sciences
Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
Turkish Journal of Electrical Engineering and Computer Sciences
Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …
Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma
Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma
Turkish Journal of Electrical Engineering and Computer Sciences
Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Applied Mathematics & Information Sciences
In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …
An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani
An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani
Journal of Digital Forensics, Security and Law
Since the past few years, the complexity and heterogeneity of digital crimes has increased exponentially, which has made the digital evidence & digital forensics paramount for both criminal investigation and civil litigation cases. Some of the routine digital forensic analysis tasks are cumbersome and can increase the number of pending cases especially when there is a shortage of domain experts. While the work is not very complex, the sheer scale can be taxing. With the current scenarios and future predictions, crimes are only going to become more complex and the precedent of collecting and examining digital evidence is only going …
A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç
A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç
Turkish Journal of Electrical Engineering and Computer Sciences
The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …
An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇
An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇
Turkish Journal of Electrical Engineering and Computer Sciences
Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …
Quantum Computing And Its Applications In Healthcare, Vu Giang
Quantum Computing And Its Applications In Healthcare, Vu Giang
OUR Journal: ODU Undergraduate Research Journal
This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.
Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu
Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu
Turkish Journal of Electrical Engineering and Computer Sciences
The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test …
A Survey And Evaluation Of Android-Based Malware Evasion Techniques And Detection Frameworks, Parvez Faruki, Rhati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun, Rajendra Pamula
A Survey And Evaluation Of Android-Based Malware Evasion Techniques And Detection Frameworks, Parvez Faruki, Rhati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun, Rajendra Pamula
School of Computer Science & Engineering Faculty Publications
Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion …
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
Journal of Aviation/Aerospace Education & Research
This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …
Prediction Method And Application Of Gas Emission From Mining Workface Based On Stl-Eemd-Ga-Svr, Lin Haifei, Liu Shihao, Zhou Jie, Xu Peiyun, Shuang Haiqing
Prediction Method And Application Of Gas Emission From Mining Workface Based On Stl-Eemd-Ga-Svr, Lin Haifei, Liu Shihao, Zhou Jie, Xu Peiyun, Shuang Haiqing
Coal Geology & Exploration
Accurate prediction of gas emission can provide important basis for mine ventilation and the prevention and measures of gas disasters. In order to improve the prediction accuracy of gas emission in the mining workface, the monitoring data of gas emission were decomposed into the trend term, periodic term and irregular fluctuation term by the Seasonal-Trend decomposition procedure based on Loess (STL) based on the monitoring data of gas emission from the mining workface of Huangling Mine in Shaanxi. Besides, the irregular fluctuation term was further broken down into the Intrinsic Mode Functions (IMFs) components with different characteristics and the residual …
Application Of Artificial Intelligence To Lithium-Ion Battery Research And Development, Zhen-Wei Zhu, Jing-Yi Qiu, Li Wang, Gao-Ping Cao, Xiang-Ming He, Jing Wang, Hao Zhang
Application Of Artificial Intelligence To Lithium-Ion Battery Research And Development, Zhen-Wei Zhu, Jing-Yi Qiu, Li Wang, Gao-Ping Cao, Xiang-Ming He, Jing Wang, Hao Zhang
Journal of Electrochemistry
Lithium-ion batteries (LIBs) have become one of the best solutions to the energy storage issue in modern society. However, the battery materials and device development are both complex, and involve multivariable problems. Traditional trial-and-error approach, which relies on researchers to conduct experiments, has encountered bottlenecks in the improvement of the battery performance. Artificial intelligence (AI) is the most potential technology to deal with this issue due to its powerful high-speed and capabilities of processing massive data. In particular, the capability of machine learning (ML) algorithms in assessing multidimensional data variables and discovering patterns in the sets are expected to assist …
Predicting The Stability Of Open Stopes Using Machine Learning, Alicja Szmigiel, Derek B. Apel
Predicting The Stability Of Open Stopes Using Machine Learning, Alicja Szmigiel, Derek B. Apel
Journal of Sustainable Mining
The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database …
Comparison Of Ml Algorithms To Distinguish Between Human Or Human-Like Targets Using The Hog Features Of Range-Time And Range-Doppler Images In Through-The-Wall Applications, Yunus Emre Acar, İsmai̇l Saritaş, Ercan Yaldiz
Comparison Of Ml Algorithms To Distinguish Between Human Or Human-Like Targets Using The Hog Features Of Range-Time And Range-Doppler Images In Through-The-Wall Applications, Yunus Emre Acar, İsmai̇l Saritaş, Ercan Yaldiz
Turkish Journal of Electrical Engineering and Computer Sciences
When detecting the human targets behind walls, false detections occur for many systematic and environmental reasons. Identifying and eliminating these false detections is of great importance for many applications. This study investigates the potential of machine learning (ML) algorithms to distinguish between the human and human-like targets behind walls. For this purpose, a stepped-frequency continuous-wave (SFCW) radar has been set up. Experiments have been carried out with real human targets and moving plates imitating a regular breath of a healthy human. Unlike conventional methods, human and human-like returns are classified using range-Doppler images containing range and Doppler information. Then, the …
Learning To Play An Imperfect Information Card Game Using Reinforcement Learning, Buğra Kaan Demi̇rdöver, Ömer Baykal, Ferdanur Alpaslan
Learning To Play An Imperfect Information Card Game Using Reinforcement Learning, Buğra Kaan Demi̇rdöver, Ömer Baykal, Ferdanur Alpaslan
Turkish Journal of Electrical Engineering and Computer Sciences
Artificial intelligence and machine learning are widely popular in many areas. One of the most popular ones is gaming. Games are perfect testbeds for machine learning and artificial intelligence with various scenarios and types. This study aims to develop a self-learning intelligent agent to play the Hearts game. Hearts is one of the most popular trick-taking card games around the world. It is an imperfect information card game. In addition to having a huge state space, Hearts offers many extra challenges due to its nature. In order to ease the development process, the agent developed in the scope of this …
Development Of A Hybrid System Based On Abc Algorithm For Selection Of Appropriate Parameters For Disease Diagnosis From Ecg Signals, Ersi̇n Ersoy, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Development Of A Hybrid System Based On Abc Algorithm For Selection Of Appropriate Parameters For Disease Diagnosis From Ecg Signals, Ersi̇n Ersoy, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Turkish Journal of Electrical Engineering and Computer Sciences
The number of people who die due to cardiovascular diseases is quite high. In our study, ECG (electrocar-diogram) signals were divided into segments and waves based on temporal boundaries. Signal similarity methods such as convolution, correlation, covariance, signal peak to noise ratio (PNRS), structural similarity index (SSIM), one of the basic statistical parameters, arithmetic mean and entropy were applied to each of these sections. In addition, a square error-based new approach was applied and the difference of the signs from the mean sign was taken and used as a feature vector. The obtained feature vectors are used in the artificial …
A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz
A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz
Turkish Journal of Electrical Engineering and Computer Sciences
Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is …
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Northeast Journal of Complex Systems (NEJCS)
In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …