• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.2
• /
• pp.504-519
• /
• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• Medical Lasers
• /
• v.8 no.1
• /
• pp.35-38
• /
• 2019

Thread-lifting is a minimally invasive procedure that shows good results and fewer complications as compared with those results and complications of standard surgery. Many procedures and techniques have been developed to create a younger appearance of facial/neck skin for aging people, and the demand for an improved aesthetic appearance is increasing. Since the incidence of side effects is much less than that of non-absorbable threads, which can lead to complications such as foreign body reactions, polydioxanone (PDO) threads are predominantly used for face lift procedures. A 66-year-old woman presented to our clinic with inflamed palpable masses. She had undergone a face lift with absorbable threads in our clinic 5 months previously. Excisional biopsy was performed with the patient under local anesthesia. During the operation, any threads were not detected and there was both fibrotic scar tissue and granulomatous tissue. For effectively promoting healing and managing the scars, treatment with LDM^®-MED was performed on the day after surgery. The treatment was performed according to the author's protocol. Although foreign body granuloma as a complication after using non-absorbable thread types have been previously reported, it is relatively rare to find this type of complication after using absorbable thread. In this report, we present a case in which a 66-year-old female with foreign body granuloma after undergoing a face lift using absorbable threads was treated with the application of dual-frequency ultrasound, which promoted wound healing.

• International Journal of Computer Science & Network Security
• /
• v.23 no.7
• /
• pp.39-48
• /
• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Geomechanics and Engineering
• /
• v.31 no.3
• /
• pp.265-279
• /
• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• Asia pacific journal of information systems
• /
• v.30 no.1
• /
• pp.31-52
• /
• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• Journal of Platform Technology
• /
• v.11 no.5
• /
• pp.110-125
• /
• 2023

In this paper, we utilize time-series aircraft location data measured based on 2019 domestic airports to analyze Go-Around and UOC_D situations during the approach phase of domestic airports. Various clustering-based machine learning techniques are applied to determine the most appropriate analysis method for domestic aviation data through experimentation. The ADS-B sensor is solely employed to measure aircraft positions. We designed a model using clustering algorithms such as K-Means, GMM, and DBSCAN to classify abnormal situations. Among them, the RF model showed the best performance overseas, but through experiments, it was confirmed that the GMM showed the highest classification performance for domestic aviation data by reflecting the aspects specialized in domestic terrain.

• Geomechanics and Engineering
• /
• v.39 no.1
• /
• pp.27-41
• /
• 2024

Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (α), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.

• Journal of ILASS-Korea
• /
• v.29 no.3
• /
• pp.147-154
• /
• 2024

This study explores the rate of injection (ROI) and injection quantities of a solenoid-type high-pressure injector under varying conditions by integrating experimental methods with machine learning (ML) techniques. Experimental data for fuel injection were obtained using a Zeuch-based HDA Moehwald injection rate measurement system, which served as the foundation for developing a machine learning model. An artificial neural network (ANN) was employed to predict the ROI, ensuring accurate representation of injection behaviors and patterns. The present study examines the impact of ambient conditions, including chamber temperature, chamber pressure, and injection pressure, on the transient profiles of the ROI, quasi-steady ROI, and injection duration. Results indicate that increasing the injection pressure significantly increases ROI, with chamber pressure affecting its initial rising peak. However, the chamber temperature effect on ROI is minimal. The trained ANN model, incorporating three input conditions, accurately reflected experimental measurements and demonstrated expected trends and patterns. This model facilitates the prediction of various ROI profiles without the need for additional experiments, significantly reducing the cost and time required for developing injection control systems in next-generation aero-engine combustors.

• Maxillofacial Plastic and Reconstructive Surgery
• /
• v.28 no.3
• /
• pp.202-212
• /
• 2006

It is well-known that paclitaxel($Taxol^{(R)}$), which is extracted from the pacific and English yew, has been used as a chemotherapeutic agent for ovarian carcinoma and advanced breast carcinoma and Cyclosporin A, which is highly lipophilic cyclic peptide and isolated from a fungus, has been also used as an useful immunosuppressive drug after transplantation and is associated with cellular apoptosis. Since 1953, in which James Watson, Rosalind Franklin and Francis Crick discovered the double helical structure of DNA, a few kinds of techniques for identifying gene expression have been developed. In postgenomic period, many of researchers have used the DNA microarray which is high throughput screening technique to screen large numbers of gene expression simultaneously. In this study, we searched and screened the gene expression in the oral squamous cell carcinoma cell lines treated with $Taxol^{(R)}$, cyclosporin or cyclosporin combined with $Taxol^{(R)}$ using cDNA microarray. The results were as following; 1. It was useful that the appropriate concentration of Cyclosporin A and $Taxol^{(R)}$ used in oral squamous cell carcinoma cell line was under 1${\mu}g/ml$ and 3${\mu}g/ml$. 2. In the experimental group in which $Taxol^{(R)}$ and $Taxol^{(R)}$ + Cyclosporin A were used, the cell growth was extremely decreased. 3. In the group in which Cyclosporin A was used, the MTT assay was rarely decreased which means the activity of succinyl dehydrogenase is remained in mitochondria but in the group in which the mixture of Cyclosporin A and $Taxol^{(R)}$ were used, the MTT assay was extremely decreased. 4. In the each group in which Cyclosporin A(3 ${\mu}g/ml$) and $Taxol^{(R)}$(1 ${\mu}g/ml$) were used, the cell arrest was appeared in $G_2/M$ phase and in the group in which $Taxol^{(R)}$(3 ${\mu}g/ml$) was used, the cell arrest was appeared in both S phase and $G_2/M$ phase. 5. In the oral squamous cell carcinoma cell line treated with $Taxol^{(R)}$, several genes including ANGPTL4, RALBP1 and TXNRD1, associated with apoptosis, SUI1, MAC30, RRAGA and CTGF, related with cell growth, HUS1 and DUSP5, related with cell cycle and proliferation, ATF4 and CEBPG, associated with transcription factor, BTG1 and VEGF, associated with angiogenesis, FDPS, FCER1G, GPA33 and EPHA4 associated with signal transduction and receptor activity and AKR1C2 and UGTA10 related with carcinogenesis were detected in increased levels. The genes that showed increaced expression in the oral squamous cell carcinoma cell line treated with Cyclosporin A were CYR61, SERPINB2, SSR3 and UPA3A which are known as genes associated with cell growth, carcinogenesis, receptor activity and transcription factor. The genes expressed in the HN22 cell line treated with cyclosporin combined with $taxol^{(R)}$ were ALCAM and GTSE1 associated with cancer invasiveness and cell cycle regulation.

• Korean Journal of Animal Reproduction
• /
• v.20 no.4
• /
• pp.371-378
• /
• 1997

Human lactoferrin (hLF) was expressed in the mammary gland of transgenic mice. Expresion of hLF was achieved by palcing its cDNA under the control of bovine $\beta$-casein gene. To improve the hLF expression level, two artificial introns were introduced into the expression vector. One intron is a hybrid-splice consisting of bovine $\beta$ casein intron 1 and rabbit $\beta$-casem intron II. The other intron is a DNA fragment spanning intron 8 of bovine $\beta$ casein gene. Trans sgenic mice were developed which expressed hLF in their milk. Twenty lines of transgenic mice were produced. hLF was present in the milk at concentrations of 1 ~ 200 ${\mu}\textrm{g}$ / ml. hLF RNA was only detected in the mammary gland of transgenic mice. The expressed RNA was cor r rectly spliced at the exon /intron junctions. To generate transgenic cows secreting active hLF in their milk, we transferred the DNA-injected bovine embryos to recipient heifers by surgical a and non-surgical methods out of 68 embryos transferred to 51 recipients by surgical or non-surgical method, 7 calves were normally born. Effect of embryo quality of DNA-injected blastocysts on pregnancy rate after transfer was investig a ated. Higher pregnancy rate of (38.9%) DNA-injected embryos was shown in excellent embryos. Pregnancy rates in the groups of good a and fair embryos were 15.4 and 14.3%, respectively. Effect of culture period of DNA-injected b bovine embryos on pregnancy rate after transfer was investigated. When Day-6 blastocysts of cuI ture were transferred, there was no pregnancy. Pregnancy rates of Day-7 and -8 blastocysts were 28.6 and 33.3%, respectively. There was no difference on pregnancy rate between Day-7 a and -8 bovine blastocysts after DNA injection. Thus, we established the techniques for transfer a and culture of DNA-injected bovine embryos. In a addition, factors affecting the pregnancy rate of DNA-injected embryos after transfer were investigated .