Search results for: diagnostic accuracy

Authors: Janpen Srijan, Nahathai Tanmang, Thanit Purathanang, Anun Dowchern, Saksit Summart, Seangduan Kampimpa

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This paper presents the resistance reader program based on image processing by using MATLAB. The proposed program is divided into six parts; the first part is the web camera; the second part is a watt selection before shooting the resistor; the third part is a part of finding the position of the color on the mid-point of resistor; the fourth part is a part of identifying color code of the resistor; the fifth part is a part of taking the number of values for each color for resistance calculation and the last part is a part of displaying result of resistance value. The experimental result of the resistance reader program based on image processing was able to display the resistance value of resistor. The accuracy of proposed program is 85 percent for 1 watt resistor. It has 15 percent of reading error because a problem with the color code of some resistor was too bright.

Keywords: resistance reader program, image processing, resistor, MATLAB

Procedia PDF Downloads 386

Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

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In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

Procedia PDF Downloads 365

Authors: Yang Zhou, Kangfeng Zheng, Wei Ni, Ren Ping Liu

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Software-defined networking (SDN) provides a solution for scalable network framework with decoupled control and data plane. However, this architecture also induces a particular distributed denial-of-service (DDoS) attack that can affect or even overwhelm the SDN network. DDoS attack detection problem has to date been mostly researched as entropy comparison problem. However, this problem lacks the utilization of SDN, and the results are not accurate. In this paper, we propose a DDoS attack detection method, which interprets DDoS detection as a signature matching problem and is formulated as Earth Mover’s Distance (EMD) model. Considering the feasibility and accuracy, we further propose to define the cost function of EMD to be a generalized Kullback-Leibler divergence. Simulation results show that our proposed method can detect DDoS attacks by comparing EMD values with the ones computed in the case without attacks. Moreover, our method can significantly increase the true positive rate of detection.

Keywords: DDoS detection, EMD, relative entropy, SDN

Procedia PDF Downloads 336

Authors: Teewin Plangsrinont, Wasawat Nakkiew

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In this study, computational fluid dynamics (CFD) was utilized to simulate and predict the path of water from water soot blower through an ambient flow field in 300-megawatt tangentially burned pulverized coal boiler that utilizes a water soot blower as a cleaning device. To predict the position of the impact of water on the opposite side of the water soot blower under identical conditions, the nozzle size and water flow rate were fixed in this investigation. The simulation findings demonstrated a high degree of accuracy in predicting the direction of water flow to the boiler's water wall tube, which was validated by comparison to experimental data. Results show maximum deviation value of the water jet trajectory is 10.2 percent.

Keywords: computational fluid dynamics, tangentially fired boiler, thermal power plant, water soot blower

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Authors: Lingmao Xu, Hui Zhou

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Ge film is widely used in infrared optical systems. Because of the special requirements of space application, it is usually used in low temperature. The refractive index of Ge film is always changed with the temperature which has a great effect on the manufacture of high precision infrared optical film. Specimens of Ge single film were deposited at ZnSe substrates by EB-PVD method. During temperature range 80K ~ 300K, the transmittance of Ge single film within 2 ~ 15 μm were measured every 20K by PerkinElmer FTIR cryogenic testing system. By the full spectrum inversion method fitting, the relationship between refractive index and wavelength within 2 ~ 12μm at different temperatures was received. It can be seen the relationship consistent with the formula Cauchy, which can be fitted. Then the relationship between refractive index of the Ge film and temperature/wavelength was obtained by fitting method based on formula Cauchy. Finally, the designed value obtained by the formula and the measured spectrum were compared to verify the accuracy of the formula.

Keywords: infrared optical film, low temperature, thermal refractive coefficient, Ge film

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Authors: Min L. Stewart, Patrick Johnston

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Predictive coding has been proposed as a general explanatory framework for understanding the neural mechanisms of perception. As such, an underweighting of perceptual priors has been hypothesised to underpin a range of differences in inferential and sensory processing in autism spectrum disorders. However, empirical evidence to support this has not been well established. The present study uses an electroencephalography paradigm involving changes of facial identity and person category (actors etc.) to explore how levels of autistic traits (AT) affect predictive coding at multiple stages in the visual processing hierarchy. The study uses a rapid serial presentation of faces, with hierarchically structured sequences involving both periodic and aperiodic repetitions of different stimulus attributes (i.e., person identity and person category) in order to induce contextual expectations relating to these attributes. It investigates two main predictions: (1) significantly larger and late neural responses to change of expected visual sequences in high-relative to low-AT, and (2) significantly reduced neural responses to violations of contextually induced expectation in high- relative to low-AT. Preliminary frequency analysis data comparing high and low-AT show greater and later event-related-potentials (ERPs) in occipitotemporal areas and prefrontal areas in high-AT than in low-AT for periodic changes of facial identity and person category but smaller ERPs over the same areas in response to aperiodic changes of identity and category. The research advances our understanding of how abnormalities in predictive coding might underpin aberrant perceptual experience in autism spectrum. This is the first stage of a research project that will inform clinical practitioners in developing better diagnostic tests and interventions for people with autism.

Keywords: hierarchical visual processing, face processing, perceptual hierarchy, prediction error, predictive coding

Procedia PDF Downloads 109

Authors: Prasanna Haddela

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Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm

Procedia PDF Downloads 112

Authors: Nasser A. Al-Azri

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The effectiveness of passive cooling techniques is assessed based on bioclimatic charts that require the typical meteorological year (TMY) for a specified location for their development. However, TMYs are not always available; mainly due to the scarcity of records of solar radiation which is an essential component used in developing common TMYs intended for general uses. Since solar radiation is not required in the development of the bioclimatic chart, this work suggests developing TMYs based solely on the relevant parameters. This approach improves the accuracy of the developed TMY since only the relevant parameters are considered and it also makes the development of the TMY more accessible since solar radiation data are not used. The presented paper will also discuss the development of the TMY from the raw data available at the NOAA-NCDC archive of world weather data and the construction of the bioclimatic charts for some randomly selected locations around the world.

Keywords: bioclimatic charts, passive cooling, TMY, weather data

Procedia PDF Downloads 239

Authors: Mengke Zhan, Cheng-Gang Xie, Jian-Jun Shu

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A venturi flowmeter is a common device used in multiphase flow rate measurement in the upstream oil and gas industry. Having a robust computational model for multiphase flow in a venturi is desirable for understanding the gas-liquid and fluid-pipe interactions and predicting pressure and phase distributions under various flow conditions. A steady Eulerian-Eulerian framework is used to simulate upward gas-liquid flow in a vertical venturi. The simulation results are compared with experimental measurements of venturi differential pressure and chord-averaged gas holdup in the venturi throat section. The choice of turbulence model is nontrivial in the multiphase flow modelling in a venturi. The performance cross-comparison of the k-ϵ model, Reynolds stress model (RSM) and shear-stress transport (SST) k-ω turbulence model is made in the study. In terms of accuracy and computational cost, the SST k-ω turbulence model is observed to be the most efficient.

Keywords: computational fluid dynamics (CFD), gas-liquid flow, turbulence modelling, venturi

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Authors: Khalil Khan, Ikram Syed, Muhammad Ehsan Mazhar, Iran Uddin, Nasir Ahmad

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Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method (FPL) which divides a given image into its constitutes parts is proposed in this paper. A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. The testing phase is performed with two semantic segmentation methods, i.e., pixel and super-pixel based segmentation. In pixel-based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixel only – as a result, the same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68 % and 93.45% respectively.

Keywords: face labeling, semantic segmentation, classification, face segmentation

Procedia PDF Downloads 253

Authors: Saghar Heidari

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In this paper, we study the valuation problem of European continuous-installment options under Markov-modulated models with a partial differential equation approach. Due to the opportunity for continuing or stopping to pay installments, the valuation problem under regime-switching models can be formulated as coupled partial differential equations (CPDE) with free boundary features. To value the installment options, we express the truncated CPDE as a linear complementarity problem (LCP), then a finite element method is proposed to solve the resulted variational inequality. Under some appropriate assumptions, we establish the stability of the method and illustrate some numerical results to examine the rate of convergence and accuracy of the proposed method for the pricing problem under the regime-switching model.

Keywords: continuous-installment option, European option, regime-switching model, finite element method

Procedia PDF Downloads 134

Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: B. S. Harish, M. B. Revanasiddappa

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Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

Procedia PDF Downloads 457

Authors: J. J. Mathew

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Pulmonary tuberculosis is highly prevalent in the Indian subcontinent. Most cases of pulmonary tuberculosis are diagnosed with sputum examinations and the vast majority of these are undertaken by the government run establishments. However, mycobacterial cultures are not routinely done, unless drug resistance is detected based on clinical response. Modern diagnostic tests like bronchoscopy and Genexpert are not routinely employed in the government institutions for the diagnosis of pulmonary tuberculosis, but have been accepted widely by good private institutions. The utility of these investigations in the private sector is not yet well recognized. This retrospective study aims to assess the usefulness of bronchoscopy and Genexpert in the diagnosis of pulmonary tuberculosis in quaternary care private hospital in India. 30 patients with respiratory symptoms raising the possibility of tuberculosis based on clinical and radiological features, but without any significant sputum production, were subject to bronchoscopy and BAL samples taken for microbiological studies, including Genexpert. 6 out of the 30 patients were found to be Genexpert positive and none of them showed Rifampicin resistance. All the 6 cases had upper zone predominant disease. One of the 6 cases of tuberculosis had another co-existent bacterial infection according to the routine culture studies. 6 other cases were proven to be due to other bacterial infections alone, 2 had a malignant diagnosis and the remaining cases were thought to be non-infective pathologies. The Genexpert results were made available within 48 hours in the 6 positive cases. All of them were commenced on standard anti-tuberculous regimen with excellent clinical response. The other infective cases were also managed successfully based on the drug susceptibilities. The study has shown the usefulness of these investigations as early intervention enabled diagnosis facilitating treatment and prevention of any clinical deterioration. The study lends support to early bronchoscopy and Genexpert testing in suspected cases of pulmonary tuberculosis without significant sputum production, in a high prevalence country which normally relies on sputum examination for the diagnosis of pulmonary tuberculosis.

Keywords: pulmonary, tuberculosis, bronchoscopy, genexpert

Procedia PDF Downloads 244

Authors: Tanapon Keatsamarn, Chuchart Pintavirooj

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Foot pressure measurement provides necessary information for diagnosis diseases, foot insole design, disorder prevention and other application. In this paper, dynamic foot pressure measurement is presented for pressure measuring with high resolution and accuracy. The dynamic foot pressure measurement system consists of hardware and software system. The hardware system uses a transparent acrylic plate and uses steel as the base. The glossy white paper is placed on the top of the transparent acrylic plate and covering with a black acrylic on the system to block external light. Lighting from LED strip entering around the transparent acrylic plate. The optical sensors, the digital cameras, are underneath the acrylic plate facing upwards. They have connected with software system to process and record foot pressure video in avi file. Visual Studio 2017 is used for software system using OpenCV library.

Keywords: foot, foot pressure, image processing, optical sensors

Procedia PDF Downloads 245

Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: S. Thongchai

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Result of the study on knowledge management systems in businesses was shown that the most of these businesses provide internet accessibility for their employees in order to study new knowledge via internet, corporate website, electronic mail, and electronic learning system. These business organizations use information technology application for knowledge management because of convenience, time saving, ease of use, accuracy of information and knowledge usefulness. The result indicated prominent improvements for corporate knowledge management systems as the following; 1) administrations must support corporate knowledge management system 2) the goal of corporate knowledge management must be clear 3) corporate culture should facilitate the exchange and sharing of knowledge within the organization 4) cooperation of personnel of all levels must be obtained 5) information technology infrastructure must be provided 6) they must develop the system regularly and constantly.

Keywords: business organizations, information technology application, knowledge management systems, prominent improvements

Procedia PDF Downloads 384

Authors: Chirawat Wattanapanich, Hong Wei

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This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

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Authors: Pouya Kaafi, Gholamreza Ghodrati Amiri

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Abnormal loads due to natural events, implementation errors and some other issues can lead to occurrence of progressive collapse in structures. Most of the past researches consist of 2- Dimensional (2D) models of steel frames without consideration of the floor system effects, which reduces the accuracy of the modeling. While employing a 3-Dimensional (3D) model and modeling the concrete slab system for the floors have a crucial role in the progressive collapse evaluation. In this research, a 3D finite element model of a 5-story steel building is modeled by the ABAQUS software once with modeling the slabs, and the next time without considering them. Then, the progressive collapse potential is evaluated. The results of the analyses indicate that the lack of the consideration of the slabs during the analyses, can lead to inaccuracy in assessing the progressive failure potential of the structure.

Keywords: abnormal loads, composite floor system, intermediate steel moment resisting frame system, progressive collapse

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Authors: A. Nachour, L. Ouzizi, Y. Aoura

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Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.

Keywords: edge detection, medical MRImages, multi-agent systems, vector field convolution

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Authors: Miljan B. Petrović, Dušan B. Petrović, Goran S. Nikolić

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This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful.

Keywords: noise, signal-to-noise ratio, stochastic signals, variance estimation

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Authors: Amar Ranjan, Julieana Durai, Pranay Tanwar

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Background &Introduction: Ovarian cancer is one of the most common malignant tumor in the female. 70% of the cases of ovarian cancer are diagnosed at an advanced stage. The five-year survival rate associated with ovarian cancer is less than 30%. The early diagnosis of ovarian cancer becomes a key factor in improving the survival rate of patients. Presently, CAl25 (carbohydrate antigen125) is used for the diagnosis and therapeutic monitoring of ovarian cancer, but its sensitivity and specificity is not ideal. The introduction of HE4, human epididymis protein 4 has attracted much attention. HE4 has a sensitivity and specificity of 72.9% and 95% for differentiating between benign and malignant adnexal masses, which is better than CA125 detection.  Methods: Serum HE4 and CA -125 were estimated using the chemiluminescence method. Our cases were 40 epithelial ovarian cancer, 9 benign ovarian tumor, 29 benign gynaecological diseases and 13 healthy individuals. This group include healthy woman those who have undergoing family planning and menopause-related medical consultations and they are negative for ovarian mass. Optimal cut off values for HE4 and CA125 were 55.89pmol/L and 40.25U/L respectively (determined by statistical analysis). Results: The level of HE4 was raised in all ovarian cancer patients (n=40) whereas CA125 levels were normal in 6/40 ovarian cancer patients, which were the cases of OC confirmed by histopathology. There is a significant decrease in the level of HE4 with comparison to CA125 in benign ovarian tumor cases. Both the levels of HE4 and CA125 were raised in the nonovarian cancer group, which includes cancer of endometrium and cervix. In the healthy group, HE4 was normal in all patients except in one case of the rudimentary horn, and the reason for this raised HE4 level is due to the incomplete development of uterus whereas CA125 was raised in 3 cases. Conclusions: Findings showed that the serum level of HE4 is an important indicator in the diagnosis of ovarian cancer, and it also distinguishes between benign and malignant pelvic masses. However, a combination of HE4 and CA125 panel will be extremely valuable in improving the diagnostic efficiency of ovarian cancer. These findings of our study need to be validated in the larger cohort of patients.

Keywords: human epididymis protein 4, ovarian cancer, diagnosis, benign lesions

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Authors: Fatemeh Zeinali Sehrig

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Background: miRNAs play an important role in the post-transcriptional regulation of genes, including genes involved in DNA methylation (DNMTs), and are also important regulators of oncogenic pathways. The study of microRNAs and DNMTs in breast cancer allows the development of targeted treatments and early detection of this cancer. Methods and Materials: Clinical Patients and Samples: Institutional guidelines, including ethical approval and informed consent, were followed by the Ethics Committee (Ethics code: IR.IAU.TABRIZ.REC.1401.063) of Tabriz Azad University, Tabriz, Iran. In this study, tissues of 100 patients with breast cancer and tissues of 100 healthy women were collected from Noor Nejat Hospital in Tabriz. The basic characteristics of the patients with breast cancer included: 1)Tumor grade(Grade 3 = 5%, Grade 2 = 87.5%, Grade 1 = 7.5%), 2)Lymph node(Yes = 87.5%, No = 12.5%), 3)Family cancer history(Yes = 47.5%, No = 41.3%, Unknown = 11.2%), 4) Abortion history(Yes = 36.2%).In silico methods (data gathering, process, and build networks): Gene Expression Omnibus (GEO), a high-throughput genomic database, was queried for miRNAs expression profiles in breast cancer. For Experimental protocol Tissue Processing, Total RNA isolation, complementary DNA(cDNA) synthesis, and quantitative real time PCR (QRT-PCR) analysis were performed. Results: In the present study, we found significant (p.value<0.05) changes in the expression level of miRNAs and DNMTs in patients with breast cancer. In bioinformatics studies, the GEO microarray data set, similar to qPCR results, showed a decreased expression of miRNAs and increased expression of DNMTs in breast cancer. Conclusion: According to the results of the present study, which showed a decrease in the expression of miRNAs and DNMTs in breast cancer, it can be said that these genes can be used as important diagnostic and therapeutic biomarkers in breast cancer.

Keywords: gene expression omnibus, microarray dataset, breast cancer, miRNA, DNMT (DNA methyltransferases)

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Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

Procedia PDF Downloads 229

Authors: Samart Rungjarean, Benchalak Muangmeesri, Dechrit Maneetham

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Monks' and Novices' chimes may have some restrictions, such as during the rain when a structure or location chimes or at a certain period. Alternately, certain temple bells may be found atop a tall, difficult-to-reach bell tower. As a result, the concept of designing a brass bell for use with a mobile phone over great distances was proposed. The Internet of Things (IoT) system will be used to regulate the bell by testing each of the three beatings with a wooden head. A stone-beating head and a steel beater. The sound resonates nicely, with the distance and rhythm of the hit contributing to this. An ESP8266 microcontroller is used by the control system to manage its operations and will communicate with the pneumatic system to convey a signal. Additionally, a mobile phone will be used to operate the entire system. In order to precisely direct and regulate the rhythm, There is a resonance of roughly 50 dB for this test, and the operating distance can be adjusted. Timing and accuracy were both good.

Keywords: automatic ring-bell, microcontroller, ring-bell, iot

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Authors: Shreeyam, Ranjan Kumar Sah, Shivangi

Abstract:

Recognizing and solving handwritten mathematical expressions can be a challenging task, particularly when certain characters are segmented and classified. This project proposes a solution that uses Convolutional Neural Network (CNN) and image processing techniques to accurately solve various types of equations, including arithmetic, quadratic, and trigonometric equations, as well as logical operations like logical AND, OR, NOT, NAND, XOR, and NOR. The proposed solution also provides a graphical solution, allowing users to visualize equations and their solutions. In addition to equation solving, the platform, called CNNCalc, offers a comprehensive learning experience for students. It provides educational content, a quiz platform, and a coding platform for practicing programming skills in different languages like C, Python, and Java. This all-in-one solution makes the learning process engaging and enjoyable for students. The proposed methodology includes horizontal compact projection analysis and survey for segmentation and binarization, as well as connected component analysis and integrated connected component analysis for character classification. The compact projection algorithm compresses the horizontal projections to remove noise and obtain a clearer image, contributing to the accuracy of character segmentation. Experimental results demonstrate the effectiveness of the proposed solution in solving a wide range of mathematical equations. CNNCalc provides a powerful and user-friendly platform for solving equations, learning, and practicing programming skills. With its comprehensive features and accurate results, CNNCalc is poised to revolutionize the way students learn and solve mathematical equations. The platform utilizes a custom-designed Convolutional Neural Network (CNN) with image processing techniques to accurately recognize and classify symbols within handwritten equations. The compact projection algorithm effectively removes noise from horizontal projections, leading to clearer images and improved character segmentation. Experimental results demonstrate the accuracy and effectiveness of the proposed solution in solving a wide range of equations, including arithmetic, quadratic, trigonometric, and logical operations. CNNCalc features a user-friendly interface with a graphical representation of equations being solved, making it an interactive and engaging learning experience for users. The platform also includes tutorials, testing capabilities, and programming features in languages such as C, Python, and Java. Users can track their progress and work towards improving their skills. CNNCalc is poised to revolutionize the way students learn and solve mathematical equations with its comprehensive features and accurate results.

Keywords: AL, ML, hand written equation solver, maths, computer, CNNCalc, convolutional neural networks

Procedia PDF Downloads 121

Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

Abstract:

Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

Procedia PDF Downloads 152

Authors: V. Paployan, K. Madoyan, A. Melikyan, H. Minassian

Abstract:

Resonant two-photon ionization (TPI) is a valuable technique for the study of clusters due to its ultrahigh sensitivity. The comparison of the observed TPI spectra with results of calculations allows to deduce important information on the shape, rotational and vibrational temperatures of the clusters with high accuracy. In this communication we calculate the TPI cross-section for pump-probe scheme in Ag neutral cluster. The pump photon energy is chosen to be close to the surface plasmon (SP) energy of cluster in dielectric media. Since the interband transition energy in Ag exceeds the SP resonance energy, the main contribution into the TPI comes from the latter. The calculations are performed by separating the coordinates of electrons corresponding to the collective oscillations and the individual motion that allows to take into account the resonance contribution of excited SP oscillations. It is shown that the ionization cross section increases by two orders of magnitude if the energy of the pump photon matches the surface plasmon energy in the cluster.

Keywords: resonance enhancement, silver clusters, surface plasmon, two-photon ionization

Procedia PDF Downloads 426

Authors: A. Ibovi Mouondayi, S. Zaher, R. Assadi, K. Erraoui, S. Sboul, J. Daoudim, S. Bousselham, K. Nassar, S. Janani

Abstract:

Introduction: Neuropathic pain (NP) is chronic pain; it can be observed in a large number of clinical situations. This pain results from a lesion of the peripheral or central nervous system. It is a frequent reason for consultations in rheumatology. This pain being chronic, can become disabling for the patient, thereby altering his quality of life. Objective: The objective of this study was to evaluate the impact of neuropathic pain on the quality of life of patients followed-up for chronic neuropathic pain. Material and Method: This is a monocentric, cross-sectional, descriptive, retrospective study conducted in our department over a period of 19 months from October 2020 to April 2022. The missing parameters were collected during phone calls of the patients concerned. The diagnostic tool adopted was the DN4 questionnaire in the dialectal Arabic version. The impact of NP was assessed by the visual analog scale (VAS) on pain, sleep, and function. The impact of PN on mood was assessed by the hospital anxiety, and depression scale (HAD) score in the validated Arabic version. The exclusion criteria were patients followed up for depression and other psychiatric pathologies. Results: A total of 1528 patient data were collected; the average age of the patients was 57 years (standard deviation: 13 years) with extremes ranging from 17 years to 94 years, 91% were women and 9% men with a sex ratio man/woman equal to 0.10. 67% of our patients were married, and 63% of our patients were housewives. 43% of patients were followed-up for degenerative pathology. The NP was cervical radiculopathy in 26%, lumbosacral radiculopathy in 51%, and carpal tunnel syndrome in 20%. 23% of our patients had poor sleep quality, and 54% had average sleep quality. The pain was very intense in 5% of patients; 33% had severe pain, and 58% had moderate pain. The function was limited in 55% of patients. The average HAD score for anxiety and depression was 4.39 (standard deviation: 2.77) and 3.21 (standard deviation: 2.89), respectively. Conclusion: Our data clearly illustrate that neuropathic pain has a negative impact on the quality of sleep and function, as well as the mood of patients, thus influencing their quality of life.

Keywords: neuropathic pain, sleep, quality of life, chronic pain

Procedia PDF Downloads 128