Search results for: lung disease classification

Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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Authors: Cheng-Cao Sun, Shu-Jun Li, De-Jia Li

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Determinants of growth and metastasis in cancer remain of great interest to define. MicroRNAs (miRNAs) have frequently emerged as tumor metastatic regulator by acting on multiple signaling pathways. Here, we report the definition of miR-346 as an oncogenic microRNA that facilitates non-small cell lung cancer (NSCLC) cell growth and metastasis. XPC, an important DNA damage recognition factor in nucleotide excision repair was defined as a target for down-regulation by miR-346, functioning through direct interaction with the 3'-UTR of XPC mRNA. Blocking miR-346 by an antagomiR was sufficient to inhibit NSCLC cell growth and metastasis, an effect that could be phenol-copied by RNAi-mediated silencing of XPC. In vivo studies established that miR-346 overexpression was sufficient to promote tumor growth by A549 cells in xenografts mice, relative to control cells. Overall, our results defined miR-346 as an oncogenic miRNA in NSCLC, the levels of which contributed to tumor growth and invasive aggressiveness.

Keywords: microRNA-346, miR-346, XPC, non-small cell lung cancer, oncogenesis

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Authors: Gleb Fomin, Kairat Tabynov, Nurkeldy Turebekov, Dinara Turegeldiyeva, Rinat Islamov

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The level of major cytokines in the blood of patients with COVID-19 varies greatly depending on age, gender, duration and severity of infection, and comorbidity. There are two clinically significant cytokines, IL-6 and TNF-α, which increase in levels in patients with severe COVID-19. However, in a model of COVID-19 in hamsters, TNF-α levels are unchanged or reduced, while the expression of other cytokines reflects the profile of cytokines found in patients’ plasma. The aim of our study was to evaluate the relationship between the level of cytokines in the blood, lungs, and lung damage in the model of the Syrian hamster (Mesocricetus auratus) infected with the SARS-CoV-2 strain. The study used outbred female and male Syrian hamsters (n=36, 4 groups) weighing 80-110 g and 5 months old (protocol IACUC, #4, 09/22/2020). Animals were infected intranasally with the hCoV-19/Kazakhstan/KazNAU-NSCEDI-481/2020 strain and euthanized at 3 d.p.i. The level of cytokines IL-6, TNF-α, IFN-α, and IFN-γ was determined by ELISA MyBioSourse (USA) for hamsters. Lung samples were subjected to histological processing. The presence of pathological changes in histological preparations was assessed on a 3-point scale. The work was carried out in the ABSL-3 laboratory. The data were analyzed in GraphPad Prism 6.00 (GraphPad Software, La Jolla, California, USA). The work was supported by the MES RK grant (AP09259865). In the blood, the level of TNF-α increased in males (p=0.0012) and IFN-γ in males and females (p=0.0001). On the contrary, IFN-α production decreased (p=0.0006). Only TNF-α level increased in lung tissues (p=0.0011). Correlation analysis showed a negative relationship between the level of IL-6 in the blood and lung damage in males (r -0.71, p=0.0001) and females (r-0.57, p=0.025). On the contrary, in males, the level of IL-6 in the lungs and score is positively correlated (r 0.80, p=0.01). The level of IFN-γ in the blood (r -0.64, p=0.035) and lungs (r-0.72, p=0.017) in males has a negative correlation with lung damage. No links were found for TNF-α and IFN-α. The study showed a positive association between lung injury and tissue levels of IL-6 in male hamsters. It is known that in humans, high concentrations of IL-6 in the lungs are associated with suppression of cellular immunity and, as a result, with an increase in the severity of COVID-19. TNF-α and IFN-γ play a key role in the pathogenesis of COVID-19 in hamsters. However, the mechanisms of their activity require more detailed study. IFN-α plays a lesser role in direct lung injury in a Syrian hamster model. We have shown the significance of tissue IL-6 and IFN-γ as predictors of the severity of lung damage in COVID-19 in the Syrian hamster model. Changes in the level of cytokines in the blood may not always reflect pathological processes in the lungs with COVID-19.

Keywords: syrian hamster, COVID-19, cytokines, biological model

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Authors: W. Soufi, F. Boukli Hacene, S. Ghalem

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Alzheimer's disease (AD) is a neurodegenerative disease that leads to a progressive and permanent deterioration of nerve cells. This disease is progressively accompanied by an intellectual deterioration leading to psychological manifestations and behavioral disorders that lead to a loss of autonomy. It is the most frequent of degenerative dementia. Alzheimer's disease (AD), which affects a growing number of people, has become a major public health problem in a few years. In the context of the study of the mechanisms governing the evolution of AD disease, we have found that natural flavonoids are good acetylcholinesterase inhibitors that reduce the rate of ßA secretion in neurons. This work is to study the inhibition of acetylcholinesterase (AChE) which is an enzyme involved in Alzheimer's disease, by methods of molecular modeling. These results will probably help in the development of an effective therapeutic tool in the fight against the development of Alzheimer's disease. Our goal of the research is to study the inhibition of acetylcholinesterase (AChE) by molecular modeling methods.

Keywords: Alzheimer's disease, acetylcholinesterase, flavonoids, molecular modeling

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Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Pooja Rawat, Rakhi Tyagi

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Globally, almost 3 billion people rely on biomass (wood, charcoal, dung and crop residues) and coal as their primary source of domestic energy. Cooking and heating with solid fuels on open fire give rise to major pollutants. Women are primarily affected by these pollutants as they spend most of their time in the house. The WHO World Health Report 2002 estimates that indoor air pollution (IAP) is responsible for 2.7% of the loss of disability adjusted life years (DALYs) worldwide and 3.7% in high mortality developing countries. Indoor air pollution has the potential to not only impact health, but also impact the general economic well-being of the household. Exposure to high level of household pollution lead to acute and chronic respiratory conditions (e.g.: pneumonia, chronic obstructive pulmonary disease, lung cancer and cataract). There has been many strategies for reducing IAP like subsidize cleaner fuel technologies, for example use of kerosene rather than traditional biomass fuels. Another example is development, promotion of 'improved cooking stoves'. India, likely ranks second- distributing over 12 million improved stoves in the first seven years of a national program to develop. IAP should be reduced by understanding the welfare effects of reducing IAP within households and to understanding the most cost effective way to reduce it.

Keywords: open fire, indoor pollution, lung diseases, indoor air pollution

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Authors: Diana López-Soto, Soumaya Yacout, Francisco Ángel-Bello

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Although inventories are considered as stocks of money sitting on shelve, they are needed in order to secure a constant and continuous production. Therefore, companies need to have control over the amount of inventory in order to find the balance between excessive and shortage of inventory. The classification of items according to certain criteria such as the price, the usage rate and the lead time before arrival allows any company to concentrate its investment in inventory according to certain ranking or priority of items. This makes the decision making process for inventory management easier and more justifiable. The purpose of this paper is to present a new approach for the classification of new items based on the already existing criteria. This approach is called the Logical Analysis of Data (LAD). It is used in this paper to assist the process of ABC items classification based on multiple criteria. LAD is a data mining technique based on Boolean theory that is used for pattern recognition. This technique has been tested in medicine, industry, credit risk analysis, and engineering with remarkable results. An application on ABC inventory classification is presented for the first time, and the results are compared with those obtained when using the well-known AHP technique and the ANN technique. The results show that LAD presented very good classification accuracy.

Keywords: ABC multi-criteria inventory classification, inventory management, multi-class LAD model, multi-criteria classification

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Authors: Harshadkumar Rajgor, Jeff Butterworth

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Background: Barretts oesophagus increases the risk of developing oesophageal adenocarcinoma. Over the last 40 years, there has been a 6 fold increase in the incidence of oesophageal adenocarcinoma in the western world and the incidence rates are increasing at a greater rate than cancers of the colon, breast and lung. Endoscopic mucosal resection (EMR) is a relatively new technique being used by 2 centres in the greater midlands cancer network. EMR can be used for curative or staging purposes, for high-grade dysplasia’s and T1 tumours of the oesophagus. EMR is also suitable for those who are deemed high risk for oesophagectomy. EMR has a recurrence rate of 21% according to the Wiesbaden data. Method: A retrospective study of prospectively collected data was carried out involving 24 patients who had EMR for curative or staging purposes. Complications of residual or recurrent disease following EMR that required further treatment were investigated. Results: In 54% of cases residual or recurrent disease was suspected. 96% of patients were given clear and concise information regarding their diagnosis of high-grade dysplasia or T1 tumours. All 24 patients consulted the same specialist healthcare team. Conclusion: EMR is a safe and effective treatment for patients who have high-grade dysplasia and T1NO tumours. In 54% of cases residual or recurrent disease was suspected. Initially, only single resections were undertaken. Multiple resections are now being carried out to reduce the risk of recurrence. Complications from EMR remain low in this series and consisted of a single episode of post procedural bleeding.

Keywords: endoscopic mucosal resection, oesophageal dysplasia, T1 tumours, cancer network

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Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

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Authors: Yu-Chen Hsu, Kuang C. Lin

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The artificial lung called extracorporeal membrane oxygenation (ECMO) is an important medical machine that supports persons whose heart and lungs dysfunction. Previously, investigation of steady deoxygenated blood flows passing through hollow fibers for oxygen transport was carried out experimentally and computationally. The present study computationally analyzes the effect of biological pulsatile flow on the oxygen transport in blood. A 2-D model with a pulsatile flow condition is employed. The power law model is used to describe the non-Newtonian flow and the Hill equation is utilized to simulate the oxygen saturation of hemoglobin. The dimensionless parameters for the physical model include Reynolds numbers (Re), Womersley parameters (α), pulsation amplitudes (A), Sherwood number (Sh) and Schmidt number (Sc). The present model with steady-state flow conditions is well validated against previous experiment and simulations. It is observed that pulsating flow amplitudes significantly influence the velocity profile, pressure of oxygen (PO2), saturation of oxygen (SO2) and the oxygen mass transfer rates (m ̇_O2). In comparison between steady-state and pulsating flows, our findings suggest that the consideration of pulsating flow in the computational model is needed when Re is raised from 2 to 10 in a typical range for flow in artificial lung.

Keywords: artificial lung, oxygen transport, non-Newtonian flows, pulsating flows

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, classification, sentiment analysis, tweets

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: M. Karthigayan, M. Rizon, Sazali Yaacob, R. Nagarajan, M. Muthukumaran, Thinaharan Ramachandran, Sargunam Thirugnanam

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In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate.

Keywords: ellipse fitness function, genetic algorithm, emotion recognition, fuzzy clustering

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Authors: Min-Chien Su, Tzu-Hsuan Hsu, Guan-Xuan Wu, Shyh-Ming Kuo

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Lung cancer is one of the clinically challenging malignant diseases worldwide. For efficient therapeutics in cancer, combination therapy has developed to acquire a better outcome. PLK-1 was one of the major factors affecting cell mitosis in cancer cells, its inhibitor Bi6727 was proven effective in treating several different cancers namely oral cancer, colon cancer and lung cancer. Despite its low toxicity toward normal cells compared to traditional chemotherapy, it is still yet to be evaluated in detail. Livistona Chinensis (LC) is a Chinese herb that used as a traditional prescription to treat lung cancer. Due to the uncertainty of the efficacy of LC, we utilized a water extraction method to extract the Livistona Chinensis and then lyophilized into powder for further study. In this study we investigated the antiproliferation activities of Bi6727 and LC extracts (LCE) on A549 non-small lung cancer cells. The IC50 of Bi6727 and LCE on A549 are 60 nM and 0.8 mg/mL, respectively. The fluorescent staining images shown nucleolus damage in cells treated with Bi6727 and mitochondrial damage after treated with LCE. A549 cells treated with Bi6727 and LCE showed increased expression of Bax, Caspase-3 and Caspase-9 proteins from Western blot assay. LCE also inhibited A549 cells growth keeping cells at G2-M phase from cell cycle assay. Apoptosis assay results showed that LCE induced late apoptosis of A549 cells. JC-1 assay showed that the mitochondria damaged at the LCE concentration of 0.4 mg/mL. In our preliminary anti-proliferation test of combined LCE and Bi-6727 on A549 cells, we found a dramatically decrease in proliferation after treated with LCE first for 24-h and then Bi-6727 for extra 24-h. This was an important finding regarding synergistic anti-proliferation effect of these drugs, However, the usage, the application sequence of LCE and Bi-6727 on A549 cells and their related mechanisms still need to be evaluated. In summary, the drugs exerted anti-proliferation effect on A549 cells independently. We hopefully combine the usage of these two drugs will bring a different and potential outcome in treating lung cancer.

Keywords: anti-proliferation, A549, Livistona Chinensis fruit extracts, PLK-1 inhibitor

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: Amdouni Hamida, Gammoudi Mohamed Mohsen

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In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.

Keywords: association rules, rule-based classification, classification quality, validation

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Authors: Jun-Yong Lee, Hyoung-Gook Kim

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In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing

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Authors: Zakirul Islam, Yugo Kumokawa, Quoc Thinh Tran, Motoki Kubo

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Clubroot is a disease of cruciferous plant caused by soil born pathogen Plasmodiophora brassicae and can significantly limit the production through rapid spreading. The present study was designed to investigate the effect of cultivation practices (chemical and organic soils) on clubroot disease development in Brassica rapa. Disease index and root bacterial composition were investigated for both chemical and organic soils. The bacterial biomass and diversity in organic soil were higher than those in chemical soil. Disease severity was distinct for two different cultivation methods. The number of endophytic bacteria decreased in the infected root for both soils. The increased number of endophytic bacterial number led to reduce the proliferation of pathogen spore inside the root and thus reduced the disease severity in organic plants.

Keywords: clubroot disease, bacterial biomass, root infection, disease index, chemical cultivation, organic cultivation

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Authors: Fathia Ehmouda Zaid, Reim Abudelnbi

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Cardiovascular disease is a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). Patients and method: Cross-section study (68) patients diagnosed as systemic lupus erythematosus (SLE), who visited the outpatient clinic of rheumatology, these patients were interviewed with a structured questionnaire about their past and current clinically for presence of Cardiovascular disease in systemic lupus and use SLEDAI, specific tests [ECG –ECHO –CXRAY] the data are analyzed statistically by Pearson's correlation coefficient was calculated and statistical significance was defined as P< 0.05,during period (2013-2014). Objective: Estimation Cardiovascular disease manifestation of systemic lupus erythematosus, correlation with disease activity, morbidity, and mortality. Result: (68) Patients diagnosed as systemic lupus erythematosus' age range from (18-48 years), M=(13±29Y), Sex were female 66/68 (97.1%), male 2/68 (2.9%),duration of disease range[1-15year], M =[7±8y], we found Cardiovascular disease manifestation of systemic lupus erythematosus 32/68 (47.1%), correlation with disease activity use SLEDAI,(r= 476** p=0.000),Morbidity,(r= .554**; p=0.000) and mortality (r=.181; p=.139), Cardiovascular disease manifestations of systemic lupus erythematosus are pericarditis 8/68 (11.8%), pericardial effusion 6/68 (8.8%), myocarditis 4/68 (5.9 %), valvular lesions (endocarditis) 1/68 (1.5%), pulmonary hypertension (PAH) 12/68 (17.6%), coronary artery disease 1/68 (1.5%), none of patients have conduction abnormalities involvement. Correlation with disease activity use SLEDAI, pericarditis (r= .210, p=.086), pericardial effusion (r= 0.079, p=.520), myocarditis (r= 272*, p=.027), valvular lesions (endocarditis) (r= .112, p= .362), pulmonary hypertension (PAH) (r= .257*, p=.035) and coronary artery disease (r=.075, p=.544) correlation between cardiovascular disease manifestations of systemic lupus erythematosus and specific organ involvement we found Mucocutaneous (r=.091 p= .459), musculoskeletal (MSK) (r=.110 p=.373), Renal disease (r=.278*, p=.022), neurologic disease (r=.085, p=.489) and Hematologic disease (r=-.264*, p=.030). Conclusion: Cardiovascular manifestation is more frequent symptoms with systemic lupus erythematosus (SLE) is 47 % correlation with disease activity and morbidity but not with mortality. Recommendations: Focus research to evaluation and an adequate assessment of cardiovascular complications on the morbidity and mortality of the patients with SLE are still required.

Keywords: cardiovascular disease, systemic lupus erythematosus, disease activity, mortality

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: Berrin Şentürk

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In this study, due to the recurrence of bovine tuberculosis, in the same areas, the risk factors for the disease were determined and evaluated at the local level. This study was carried out in 32 farms where the disease was detected in the district and center of Samsun province in 2014. Predetermined risk factors, such as farm, environmental and economic risks, were investigated with the survey method. It was predetermined that risks in the three groups are similar to the risk variables of the disease on the global scale. These risk factors that increase the susceptibility of the infection must be understood by the herd owners. The risk-based contagious disease management system approach should be applied for bovine tuberculosis by farmers, animal health professionals and public and private sector decision makers.

Keywords: bovine tuberculosis, disease management, control, outbreak, risk analysis

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

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Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

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Authors: H. Parameshwar Naik, Shripad Kulkarni

Abstract:

Powdery mildew is a serious disease among the fungal in high humid areas with varied temperature conditions. In recent days disease becomes very severe due to uncertain weather conditions and unique character of the disease is, it produces white mycelia growth on upper and lower leaf surfaces and in severe conditions it leads to defoliation. Results of the experiment revealed that sowing of crop in the I fortnight (FN) of July recorded the minimum mean disease severity (7.96 %) followed by crop sown in II FN of July (13.19 %) as against the crop sown in II FN of August (41.44 %) and I FN of September (33.78 %) and the I fortnight of October (33.77 %). In the first date of sowing infection started at 45 DAS and progressed till 73 DAS and it was up to 14.66 Percent and in second date of sowing disease progressed up to 22.66 percent and in the third date of sowing, it was up to 59.35 percent. Afterward, the disease started earlier and progressed up to 66.15 percent and in sixth and seventh date of sowing disease progressed up to 43.15 percent and 59.85 percent respectively. Disease progress is very fast after 45 days after sowing and highest disease incidence was noticed at 73 DAS irrespective of dates of sowing. From the results of the present study, it is very clear that disease development will be very high if crop sown in between 1st fortnight of August and the 1st fortnight of September.

Keywords: cucumber, India, Karnataka, powdery mildew

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

Abstract:

Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Khadija Bouzaachane, El Mahdi El Guarmah

Abstract:

The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.

Keywords: classification, deep learning, reinforcement learning, satellite imagery

Procedia PDF Downloads 172