Book of Proceedings: 1st International Conference on Chemo and BioInformatics,最新文献_第4页

USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION 使用卷积神经网络确定x射线图像的方向

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.263bs

Sandi Baressi Baressi Šegota, S. Lysdahlgaard, S. Hess, R. Antulov

{"title":"USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION","authors":"Sandi Baressi Baressi Šegota, S. Lysdahlgaard, S. Hess, R. Antulov","doi":"10.46793/iccbi21.263bs","DOIUrl":"https://doi.org/10.46793/iccbi21.263bs","url":null,"abstract":"The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89167866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COMPUTATIONAL VS. IN VITRO APPROACH TO PREDICT AERODYNAMIC PERFORMANCE OF DRY POWDERS FOR INHALATION 计算与体外方法预测干粉吸入空气动力学性能

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.096i

Jelisaveta Ignjatović, T. Šušteršič, S. Cvijić, Aleksandar Bodić, Jelena Đuriš, N. Filipovic

{"title":"COMPUTATIONAL VS. IN VITRO APPROACH TO PREDICT AERODYNAMIC PERFORMANCE OF DRY POWDERS FOR INHALATION","authors":"Jelisaveta Ignjatović, T. Šušteršič, S. Cvijić, Aleksandar Bodić, Jelena Đuriš, N. Filipovic","doi":"10.46793/iccbi21.096i","DOIUrl":"https://doi.org/10.46793/iccbi21.096i","url":null,"abstract":"Computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM) appeared as an alternative approach to the commonly used in vitro methods for the assessment of dry powders for inhalation (DPI) aerodynamic properties. The aim of this study was to compare the parameters that describe DPI aerodynamic performance, obtained computationally by CFD-DPM and in vitro by next generation impactor (NGI). The analyzed parameters included: emitted fraction (EF), fine particle fraction (FPF), mass median aerodynamic diameter (MMAD) and geometric standard deviation (GSD). The results showed that CFD-DPM simulated EF values were generally comparable to the NGI obtained values, but there were some differences between the results obtained by these two methods. On the other hand, CFD-DPM predicted MMAD values were almost twice bigger than the NGI determined values, while the predicted GSD values were lower than NGI obtained values. In addition, CFD-DPM predicted values indicated larger differences between MMAD for different formulations in comparison to the NGI results. The largest difference between CFD-DPM and NGI results was observed for FPF values. Namely, CFD-DPM predicted FPF values were markedly lower than the NGI determined values for four of five tested formulations. Overall, although the designed CFD-DPM model and NGI measurements provided comparable data on the DPI EF values, the other relevant parameters obtained by these two approaches largely diverged indicating the need for further refinement of computational models to fully capture DPI aerodynamic performance.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76033728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED IMAGE ANALYSIS IN BIOINFORMATICS 基于人工智能的图像分析在生物信息学中的应用

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.047c

Z. Car, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Sandi Baressi Baressi Šegota

{"title":"APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED IMAGE ANALYSIS IN BIOINFORMATICS","authors":"Z. Car, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Sandi Baressi Baressi Šegota","doi":"10.46793/iccbi21.047c","DOIUrl":"https://doi.org/10.46793/iccbi21.047c","url":null,"abstract":"The collection of image data is an extremely common procedure in clinical practice today. Many of the diagnostic approaches generate such data – computed tomography (CT), X-ray radiography, magnetic resonance imaging (MRI), and others. This data collection process allows for the use of computer vision approaches to be applied with the goal of analysis and diagnostics. Artificial Intelligence (AI) based algorithms have repeatedly been shown to be the best performing computer vision algorithms, in many fields including medicine. AI-based – or more precisely machine learning (ML) based, algorithms have capabilities which allow them to learn the patterns contained in the data from the data itself. Among the best performing algorithms are artificial neural networks (ANNs), or more precisely convolutional neural networks (CNNs). Their pitfall is the need for the large amounts of data – but as it has been previously mentioned, the amount of data collected in today’s clinical practice is large and ever increasing. This allows for the development of Smart Diagnostic systems which are meant to serve as support systems to the health professionals. In this paper first, the standard practices and review of the field is given – with the focus on challenges and best practices. Then, multiple examples of the research applying AI-based algorithm analysis are given – including diagnostics of various cancer types (bladder and oral) as well as COVID-19 severity diagnostics and image quality determination.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76681356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DIVERSITY OF EARTHWORMS (CLITELLATA: OLIGOCHAETA) FROM SERBIAN SIDE OF ŠAR MOUNTAIN Šar山塞尔维亚一侧蚯蚓的多样性(cliitellata: oligochaeta)

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.198s

J. Sekulić, M. Stojanović, Tanja B. Trakić, S. Radosavljević, Filip Popović

{"title":"DIVERSITY OF EARTHWORMS (CLITELLATA: OLIGOCHAETA) FROM SERBIAN SIDE OF ŠAR MOUNTAIN","authors":"J. Sekulić, M. Stojanović, Tanja B. Trakić, S. Radosavljević, Filip Popović","doi":"10.46793/iccbi21.198s","DOIUrl":"https://doi.org/10.46793/iccbi21.198s","url":null,"abstract":"This paper presents the currently known records on the diversity of earthworm fauna on Šar Mountain. The Šar Mt. is located in the south part of Serbia and is a part of the Šar-Pindus Mountain system. The complete list of present taxa of the family Lumbricidae in the researches area was formed by reviewing data from old collections, relevant literary sources and by its own field research in the period from 2019 to 2020. The list comprises 24 taxa, belonging to 10 genera of the family Lumbricidae. The genera with the largest number of the registered taxa are Dendrobaena (7) and Aporrectodea (5), while the genera Bimastos, Eiseniella, Helodrilus, Octodrilus and Octolasion are represented by one taxon. With respect to the zoogeographical analysis, the majority of the recorded taxa belong to the group of peregrine species (12). The endemic species are represented by two taxa and belong to the genera Dendrobaena and Helodrilus. The Balkan endemic subspecies Helodrilus balcanicus plavensis (Karaman, 1972) and illyric Dendrobaena illyrica (Cognetti, 1906), were recorded on new sites from the Šar Mt., that represent the southernmost limits of the geographical range of these species at the moment.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73630154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

APPLICABILITY OF BRAY-LIEBHAFSKY REACTION FOR CHEMICAL COMPUTING bray-liebhafsky反应在化学计算中的适用性

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.431c

Ž. Čupić, Ana I vanović Šašić, S. Maćešić, S. Anic, Ljiljana Kolar- Anić

引用次数: 0

RADIOACTIVITY ASSESSMENT OF NATURAL RADIONUCLIDES AND 137CS IN COMMONLY CONSUMED FOODS 常见食物中天然放射性核素及铯- 137的放射性评估

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.145z

Milena Živković, D. Nikezic, T. Miladinović, J. Stajic, D. Krstić

引用次数: 0

SYNTHESIS AND CHARACTERIZATION OF PLATINUM(II/IV) COMPLEXES WITH 2-AMINO-5-METHYL-4-PHENYLTHIAZOLE 2-氨基-5-甲基-4-苯噻唑铂(ii / iv)配合物的合成与表征

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.339s

D. Stojković, V. Jevtić, Đ. Petrović, Sandra S. Jovičić Milić, N. Vukovic, Milena D. Vukić, B. Stojanovic

引用次数: 0

A META-HEURISTIC MULTI-OBJECTIVE APPROACH TO THE MODEL SELECTION OF CONVOLUTION NEURAL NETWORKS FOR URINARY BLADDER CANCER DIAGNOSIS 基于元启发式多目标方法的卷积神经网络膀胱癌诊断模型选择

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.243l

I. Lorencin, Klara Smolić, D. Markić, J. Španjol

{"title":"A META-HEURISTIC MULTI-OBJECTIVE APPROACH TO THE MODEL SELECTION OF CONVOLUTION NEURAL NETWORKS FOR URINARY BLADDER CANCER DIAGNOSIS","authors":"I. Lorencin, Klara Smolić, D. Markić, J. Španjol","doi":"10.46793/iccbi21.243l","DOIUrl":"https://doi.org/10.46793/iccbi21.243l","url":null,"abstract":"Bladder cancer is one of the most common malignancies of the urinary tract. It is characterized by high metastatic potential and a high recurrence rate, which significantly complicates diagnosis and treatment. In order to increase the accuracy of the diagnostic procedure, algorithms based on artificial intelligence are introduced. This paper presents the principle of selection of convolutional neural network (CNN) models based on a multi-objective approach that maximizes classification and generalization performance. Model selection is performed on two standard CNN architectures, AlexNet and VGG-16. Classification performances are measured by using ROC analysis and the resulting AUC value. On the other hand, generalization performances are evaluated by using a 5-fold cross-validation procedure. By using these two metrics, a multi-objective fitness function, used in meta-heuristic algorithms, is designed. The multi-objective search was performed using a Genetic algorithm (GA) and a Discrete Particle Swarm (D-PS) algorithm. From obtained results, it can be noticed that such an approach has resulted in CNN models that are defined with high classification and generalization performances. When a GA-based approach is used, fitness values up to 0.97 are achieved. On the other hand, by using the D-PS approach, fitness values up to 0.99 are achieved pointing towards the conclusion that such an approach has provided models with higher classification and generalization performances.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72817629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CHEMICO-BIOLOGICAL INTERACTION OF SELECTED TETRAHYDROPYRIMIDINES 选定四氢嘧啶的化学-生物相互作用

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.347m

Emilija Milović, Nenad Ž. Janković, Jelena Petronijevic, Nenad Joksimović

引用次数: 0

CONSUMPTION OF RAW WATER – THE HEALTH RISKS RELATED TO THE PRESENCE OF HEAVY METALS AND „ESCHERICHIA COLI“ 饮用原水——与重金属和"大肠杆菌"的存在有关的健康风险

Book of Proceedings: 1st International Conference on Chemo and BioInformatics, Pub Date : 2021-01-01 DOI: 10.46793/iccbi21.210g

M. Grujović, K. Mladenović, Z. Simić, Simona R. Đuretanović

{"title":"CONSUMPTION OF RAW WATER – THE HEALTH RISKS RELATED TO THE PRESENCE OF HEAVY METALS AND „ESCHERICHIA COLI“","authors":"M. Grujović, K. Mladenović, Z. Simić, Simona R. Đuretanović","doi":"10.46793/iccbi21.210g","DOIUrl":"https://doi.org/10.46793/iccbi21.210g","url":null,"abstract":"This research aimed to investigate the quality of drinking water from the rural area of village Pajsijević (Šumadija, central Serbia). The water is consumed as raw since it is not purified or chlorinated before consumption. The water was collected at three sampling points – in the spring of Kotlenik Mountain stream (W1 sample), in the local reservoir (W2 sample), and from the tap (W3 sample). Also, the sediment samples (soil and sand) were analyzed, too. The health risks related to the presence and concentration of some major and trace elements (Ca, Cr, Cu, Fe, Mg, Mn, Ni, Zn) and N, NO, NN3, NH4, P, P2O5, and PO4 were evaluated. Additionally, the presence and the number of total coliform bacteria and Escherichia coli (as an indicator of fecal contamination) were evaluated. The concentrations of analyzed major and trace elements in all water samples were below those at which toxic effects may occur. The exception was the concentrations of Fe (2.02 – 2012 mg/L), which were higher than is allowed. The origin of Fe in water is from sediment (soil and sand), which also showed high content of Fe (3006.0 mg/g and 2229.9 mg/g, respectively). The results of the Colorimetric test indicated the presence of coliform bacteria as well as the presence of E. coli in all water samples. Further research needs to include characterization of isolated coliform bacteria and serological investigation of E. coli strains in order to evaluate the risks of consumption related to waterborne illness.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78427102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1