Search results for: semantic clinical classification

Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

Procedia PDF Downloads 326

Authors: Yasemin Filiz-Kuruel, Emine Koseoglu

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This study aims to evaluate Haskoy district of Beyoglu town of Istanbul. Haskoy is located in Halic region, between Kasimpasa district and Kagithane district. After the conquest of Istanbul, Fatih Sultan Mehmet (the Conqueror) set up his tent here. Therefore, the area gets its name as Haskoy, 'imperial village' that means a village which is special for Sultan. Today, there are shipyard and ateliers in variable sizes in Haskoy. In this study, the legibility of Haskoy streets is investigated comparatively. As a research method, semantic differential scale is used. The photos of the streets, which contain specific criteria, are chosen. The questionnaire is directed to first and third grade architecture students. The spatial evaluation of Haskoy streets is done through the survey.

Keywords: Haskoy, legibility, semantic differential scale, urban streets

Procedia PDF Downloads 566

Authors: Nasar Aldian Ambark Shashoa

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Classification with a dimension reduction based on Bayesian approach is proposed in this paper . The first step is to generate a sample (parameter) of fault-free mode class and faulty mode class. The second, in order to obtain good classification performance, a selection of important features is done with the discrete karhunen-loeve expansion. Next, the Bayes test for minimum error is used to classify the classes. Finally, the results for simulated data demonstrate the capabilities of the proposed procedure.

Keywords: analytical redundancy, fault detection, feature extraction, Bayesian approach

Procedia PDF Downloads 527

Authors: Yunwei Zhang, Na Li, Yuhong Niu

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Bone tissue engineering scaffold was regarded as an important clinical technology of curing bony defect. The patent protection of bone tissue engineering scaffold had been paid more attention and strengthened all over the world. This study analyzed the future development trends of international technologies in the field of bone tissue engineering scaffold and its patent protection. This study used the methods of data classification and classification indexing to analyze 2718 patents retrieved in the patent database. Results showed that the patents coming from United States had a competitive advantage over other countiries in the field of bone tissue engineering scaffold. The number of patent applications by a single company in U.S. was a quarter of that of the world. However, the capability of R&D in China was obviously weaker than global level, patents mainly coming from universities and scientific research institutions. Moreover, it would be predicted that synthetic organic materials as new materials would be gradually replaced by composite materials. The patent technology protections of composite materials would be more strengthened in the future.

Keywords: bone tissue engineering, patent analysis, Scaffold material, patent protection

Procedia PDF Downloads 133

Authors: Yaser Miaji, Mohammed Aloryani

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The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

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Authors: Nerella Srinivas

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This study explores the significance of patent protection for artificial intelligence (AI) innovations in the pharmaceutical sector, emphasizing applications in drug discovery, personalized medicine, and clinical trial optimization. The challenges of patenting AI-driven inventions are outlined, focusing on the classification of algorithms as abstract ideas, meeting the non-obviousness standard, and issues around defining inventorship. The methodology includes examining case studies and existing patents, with an emphasis on how companies like Benevolent AI and Insilico Medicine have successfully secured patent rights. Findings demonstrate that a strategic approach to patent protection is essential, with particular attention to showcasing AI’s technical contributions to pharmaceutical advancements. Conclusively, the study underscores the critical role of understanding patent law and innovation strategies in leveraging intellectual property rights in the rapidly advancing field of AI-driven pharmaceuticals.

Keywords: artificial intelligence, pharmaceutical industry, patent protection, drug discovery, personalized medicine, clinical trials, intellectual property, non-obviousness

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Authors: Zhiyan Meng, Dan Liu, Jintao Meng

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Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters.

Keywords: sharpness-aware optimization, encrypted traffic classification, squeeze-and-excitation block, pretrained model

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Authors: G. Leena Giri, Archana Mathur, S. H. Manjula, K. R. Venugopal, L. M. Patnaik

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Keyword search in XML documents is based on the notion of lowest common ancestors in the labelled trees model of XML documents and has recently gained a lot of research interest in the database community. In this paper, we propose the Modified Active (MA) algorithm which is an improvement over the active clustering algorithm by taking into consideration the entity aspect of the nodes to find the level of the node pertaining to a particular keyword input by the user. A portion of the bibliography database is used to experimentally evaluate the modified active algorithm and results show that it performs better than the active algorithm. Our modification improves the response time of the system and thereby increases the efficiency of the system.

Keywords: keyword matching patterns, MA algorithm, semantic search, knowledge management

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Authors: Aneena Suresh, C. S. Sidharth

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Drug related problems (DRPs) are common in chronic kidney disease (CKD) patients and end stage patients undergoing hemodialysis. To treat the co-morbid conditions of the patients, more complex therapeutic regimen is required, and it leads to development of DRPs. So, this calls for frequent monitoring of the patients. Due to the busy work schedules, physicians are unable to deliver optimal care to these patients. Addition of a clinical pharmacist in the team will improve the standard of care offered to CKD patients by minimizing DRPs. In India, the role of clinical pharmacists in the improving the health outcomes in CKD patients is poorly recognized. Therefore, this study is conducted to put an insight on the role of clinical pharmacist in improving Drug Related Problems in patients with chronic kidney disease, thereby helping them to achieve desired therapeutic outcomes in the patients. A prospective interventional study was conducted for a year in a 620 bedded tertiary care hospital in India. Data was collected using an unstructured questionnaire, medication charts, etc. DRPs were categorized using Hepler and Strand classification. Relationships between the age, weight, GFR, average no of medication taken, average no of comorbidities, and average length of hospital days with the DRPs were identified using Mann Whitney U test. The study population primarily constituted of patients above the age of 50 years with a mean age of 59.91±13.59. Our study showed that 25% of the population presented with DRPs. On an average, CKD patients are prescribed at least 8 medications for the treatment in our study. This explains the high incidence of drug interactions in patients suffering from CKD (45.65%). The least common DRPs in our study were found to be sub therapeutic dose (2%) and adverse drug reactions (2%). Out of this, 60 % of the DRPs were addressed successfully. In our study, there is an association between the DRPs with the average number of medications prescribed, the average number of comorbidities, and the length of the hospital days with p value of 0.022, 0.004, and 0.000, respectively. In the current study, 86% of the proposed interventions were accepted, and 41 % were implemented by the physician, and only 14% were rejected. Hence, it is evident that clinical pharmacist interventions will contribute significantly to diminish the DRPs in CKD patients, thereby decreasing the economic burden of healthcare costs and improving patient’s quality of life.

Keywords: chronic kidney disease, clinical pharmacist, drug related problem, intervention

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Authors: Vera Lucia Teixeira da Silva, Sandra Regina Buttros Gattolin de Paula

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The present study focuses on academic literacy and addresses the impact of semantic-discursive resources on the constitution of genres that are produced in such context. The research considers the development of writing in the academic context in Portuguese. Researches that address academic literacy and the characteristics of the texts produced in this context are rare, mainly with focus on the development of writing, considering three variables: the constitution of the writer, the perception of the reader/interlocutor and the organization of the informational text flow. The research aims to map the semantic-discursive resources of the written register in texts of several genres and produced by students in the first semester of the undergraduate course in Letters. The hypothesis raised is that writing in the academic environment is not a recurrent literacy practice for these learners and can be explained by the ontogenetic and phylogenetic nature of language development. Qualitative in nature, the present research has as empirical data texts produced in a half-yearly course of Reading and Textual Production; these data result from the proposition of four different writing proposals, in a total of 600 texts. The corpus is analyzed based on semantic-discursive resources, seeking to contemplate relevant aspects of language (grammar, discourse and social context) that reveal the choices made in the reader/writer interrelationship and the organizational flow of the Text. Among the semantic-discursive resources, the analysis includes three resources, including (a) appraisal and negotiation to understand the attitudes negotiated (roles of the participants of the discourse and their relationship with the other); (b) ideation to explain the construction of the experience (activities performed and participants); and (c) periodicity to outline the flow of information in the organization of the text according to the genre it instantiates. The results indicate the organizational difficulties of the flow of the text information. Cartography contributes to the understanding of the way writers use language in an effort to present themselves, evaluate someone else’s work, and communicate with readers.

Keywords: academic writing, Portuguese mother tongue, semantic-discursive resources, academic context

Procedia PDF Downloads 126

Authors: Dechen Tshering Vogel, Johannes T. Heverhagen, Bernard Kiss, Spyridon Arampatzis

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In addition to computed tomography (CT), contrast enhanced ultrasound (CEUS), and magnetic resonance imaging (MRI) are being increasingly used for imaging of renal lesions. The aim of this prospective study was to compare the classification of complex cystic renal lesions using the Bosniak classification with CEUS and MRI to CT. Forty-eight patients with 65 cystic renal lesions were included in this study. All participants signed written informed consent. The agreement between the Bosniak classifications of complex renal lesions ( ≥ BII-F) on CEUS and MRI were compared to that of CT and were tested using Cohen’s Kappa. Sensitivity, specificity, positive and negative predictive values (PPV/NPV) and the accuracy of CEUS and MRI compared to CT in the detection of complex renal lesions were calculated. Twenty-nine (45%) out of 65 cystic renal lesions were classified as complex using CT. The agreement between CEUS and CT in the classification of complex cysts was fair (agreement 50.8%, Kappa 0.31), and was excellent between MRI and CT (agreement 93.9%, Kappa 0.88). Compared to CT, MRI had a sensitivity of 96.6%, specificity of 91.7%, a PPV of 54.7%, and an NPV of 54.7% with an accuracy of 63.1%. The corresponding values for CEUS were sensitivity 100.0%, specificity 33.3%, PPV 90.3%, and NPV 97.1% with an accuracy 93.8%. The classification of complex renal cysts based on MRI and CT scans correlated well, and MRI can be used instead of CT for this purpose. CEUS can exclude complex lesions, but due to higher sensitivity, cystic lesions tend to be upgraded. However, it is useful for initial imaging, for follow up of lesions and in those patients with contraindications to CT and MRI.

Keywords: Bosniak classification, computed tomography, contrast enhanced ultrasound, cystic renal lesions, magnetic resonance imaging

Procedia PDF Downloads 143

Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

Procedia PDF Downloads 105

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

Procedia PDF Downloads 59

Authors: Souhir Chelly, Chahida Harizi, Aicha Hechaichi, Sihem Aissaoui, Leila Ben Ayed, Maha Bergaoui, Mohamed Kouni Chahed

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Introduction: The International Classification of Primary Care (ICPC) is part of the morbidity classification system. It had 17 chapters, and each is coded by an alphanumeric code: the letter corresponds to the chapter, the number to a paragraph in the chapter. The objective of this study is to show the utility of this classification in the coding of the reasons for demand for care in Primary health care (PHC), its advantages and limits. Methods: This is a cross-sectional descriptive study conducted in 4 PHC in Ariana district. Data on the demand for care during 2 days in the same week were collected. The coding of the information was done according to the CISP. The data was entered and analyzed by the EPI Info 7 software. Results: A total of 523 demands for care were investigated. The patients who came for the consultation are predominantly female (62.72%). Most of the consultants are young with an average age of 35 ± 26 years. In the ICPC, there are 7 rubrics: 'infections' is the most common reason with 49.9%, 'other diagnoses' with 40.2%, 'symptoms and complaints' with 5.5%, 'trauma' with 2.1%, 'procedures' with 2.1% and 'neoplasm' with 0.3%. The main advantage of the ICPC is the fact of being a standardized tool. It is very suitable for classification of the reasons for demand for care in PHC according to their specificity, capacity to be used in a computerized medical file of the PHC. Its current limitations are related to the difficulty of classification of some reasons for demand for care. Conclusion: The ICPC has been developed to provide healthcare with a coding reference that takes into account their specificity. The CIM is in its 10th revision; it would gain from revision to revision to be more efficient to be generalized and used by the teams of PHC.

Keywords: international classification of primary care, medical file, primary health care, Tunisia

Procedia PDF Downloads 267

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 459

Authors: Tianyi Wang, Hieyong Jeong, An Guo, Yuko Ohno

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Healthcare robot for assisting sit-to-stand (STS) performance had aroused numerous research interests. To author’s best knowledge, knowledge about how evaluating healthcare robot is still unknown. Robot should be labeled as fault if users feel demanding during STS when they are assisted by robot. In this research, we aim to propose a method to evaluate sit-to-stand assist robot through center of pressure (CoP), then classify different STS performance. Experiments were executed five times with ten healthy subjects under four conditions: two self-performed STSs with chair heights of 62 cm and 43 cm, and two robot-assisted STSs with chair heights of 43 cm and robot end-effect speed of 2 s and 5 s. CoP was measured using a Wii Balance Board (WBB). Bayesian classification was utilized to classify STS performance. The results showed that faults occurred when decreased the chair height and slowed robot assist speed. Proposed method for fault classification showed high probability of classifying fault classes form others. It was concluded that faults for STS assist robot could be detected by inspecting center of pressure and be classified through proposed classification algorithm.

Keywords: center of pressure, fault classification, healthcare robot, sit-to-stand movement

Procedia PDF Downloads 197

Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

Procedia PDF Downloads 405

Authors: Jerzy Banski

Abstract:

The basic aim of this study is to discuss and assess different classifications and research approaches to small towns that take their social and economic functions into account, as well as relations with surrounding areas. The subject literature typically includes three types of approaches to the classification of small towns: 1) the structural, 2) the location-related, and 3) the mixed. The structural approach allows for the grouping of towns from the point of view of the social, cultural and economic functions they discharge. The location-related approach draws on the idea of there being a continuum between the center and the periphery. A mixed classification making simultaneous use of the different approaches to research brings the most information to bear in regard to categories of the urban locality. Bearing in mind the approaches to classification, it is possible to propose a synthetic method for classifying small towns that takes account of economic structure, location and the relationship between the towns and their surroundings. In the case of economic structure, the small centers may be divided into two basic groups – those featuring a multi-branch structure and those that are specialized economically. A second element of the classification reflects the locations of urban centers. Two basic types can be identified – the small town within the range of impact of a large agglomeration, or else the town outside such areas, which is to say located peripherally. The third component of the classification arises out of small towns’ relations with their surroundings. In consequence, it is possible to indicate 8 types of small-town: from local centers enjoying good accessibility and a multi-branch economic structure to peripheral supra-local centers characterised by a specialized economic structure.

Keywords: small towns, classification, functional structure, localization

Procedia PDF Downloads 182

Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: client classification, loan suitability, risk rating, CART analysis

Procedia PDF Downloads 338

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

Procedia PDF Downloads 61

Authors: Vishwa Joshi

Abstract:

The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

Procedia PDF Downloads 499

Authors: Yu-Hong Lin

Abstract:

Background: An adverse drug reaction (ADR) reported is an injury which caused by taking medicines. Sometimes the severity of ADR reported may be minor, but sometimes it could be a life-threatening situation. In order to provide healthcare professionals as a better reference in clinical practice, we do data collection and analysis from our hospital. Methods: This was a retrospective study of ADRs reported performed from 2014 to 2015 in our hospital in Taiwan. We collected assessment items of ADRs reported, which contain gender and age, occurring sources, Anatomical Therapeutic Chemical (ATC) classification of suspected drugs, types of adverse reactions, Naranjo score calculating by Naranjo Adverse Drug Reaction Probability Scale and so on. Results: The investigation included two hundred and seven ADRs reported. Most of ADRs reported were occurring in outpatient department (92%). The average age of ADRs reported was 65.3 years. Less than 65 years of age were in the majority in this study (54%). Majority of all ADRs reported were males (51%). According to ATC classification system, the major classification of suspected drugs was cardiovascular system (19%) and antiinfectives for systemic use (18%) respectively. Among the adverse reactions, Dermatologic Effects (35%) were the major type of ADRs. Also, the major Naranjo scores of all ADRs reported ranged from 1 to 4 points (91%), which represents a possible correlation between ADRs reported and suspected drugs. Conclusions: Definitely, ADRs reported is still an extremely important information for healthcare professionals. For that reason, we put all information of ADRs reported into our hospital's computer system, and it will improve the safety of medication use. By hospital's computer system, it can remind prescribers to think of information about patient's ADRs reported. No drugs are administered without risk. Therefore, all healthcare professionals should have a responsibility to their patients, who themselves are becoming more aware of problems associated with drug therapy.

Keywords: adverse drug reaction, Taiwan, healthcare professionals, safe use of medicines

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Authors: Bowen Ding, Yihao Kuang

Abstract:

In recent years, large-scale pre-trained language models have achieved state-of-the-art performance on a variety of natural language processing tasks. The word vectors produced by these language models can be viewed as dense encoded presentations of natural language that in text form. However, it is unknown how much linguistic information is encoded and how. In this paper, we construct several corresponding probing tasks for multiple linguistic information to clarify the encoding capabilities of different language models and performed a visual display. We firstly obtain word presentations in vector form from different language models, including BERT, ELMo, RoBERTa and GPT. Classifiers with a small scale of parameters and unsupervised tasks are then applied on these word vectors to discriminate their capability to encode corresponding linguistic information. The constructed probe tasks contain both semantic and syntactic aspects. The semantic aspect includes the ability of the model to understand semantic entities such as numbers, time, and characters, and the grammatical aspect includes the ability of the language model to understand grammatical structures such as dependency relationships and reference relationships. We also compare encoding capabilities of different layers in the same language model to infer how linguistic information is encoded in the model.

Keywords: language models, probing task, text presentation, linguistic information

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

Abstract:

In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Maria Monica D. Espinosa

Abstract:

This study aimed to identify and examine the characteristics of an effective leader in a Hospital institution from the perspectives of military nurse-managers. The researcher extracted the different facets of leadership from the stories of six nurse- managers from a military hospital. The stories which are in pre-reflective stage convey an unbiased perspective from which clinical leadership may be defined. Using Phenomenology as a method of Research, the lived experiences of the military nurse-managers served as empirical data which were reflected upon until the formulation of insights. The information from the co-researchers became gallows from which the characteristics of effective leadership in the clinical area were drawn. These insights were synthesized through layers of reflection that resulted to the knowledge about clinical leadership. The reflections are the following, (a) Clinical leaders develop their skills through experiences and hardwork; (b) Clinical leaders are devoted; (c) Clinical leaders are focused; (d) Clinical leaders are good in interpersonal relationship; (e) Clinical leaders are mentors; (f) Clinical leaders seek affirmation and recognition; and (g) Clinical leaders are responsible and dependable. The common themes that emerged from the nurse manager’s stories showed that clinical leadership maybe attained if leaders possessed the following traits, (a) The gift to establish a steadfast and firm management; (b) The proficiency to guide and encourage others towards the achievement of their goals and objectives; (c) The ability to instigate participative and collaborative work among his/her subordinates and (d) The aptitude and skill to address the institutional concerns in their unit. In the future, Clinical leaders should continually adapt an evaluation program on how they can relate socially with their subordinates, the result of which can be used as a basis in developing strategies on relationship enhancement. Moreover, they should empower the nurses by allowing them to voice out their opinions and concerns regarding assignments, role expectations, and workload issues to improve and strengthen the relationships among nurses. Lastly, they can incorporate a collaborative strategy to promote professional socialization attitudes of nurse managers who work with staff nurses to improve the quality of their proficiencies and enhance a positive clinical environment.

Keywords: clinical leadership, experiences, implications, military nurse - managers, phenomenology

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Authors: C. Lanzerstorfer

Abstract:

The off-gas from the basic oxygen furnace (BOF), where pig iron is converted into steel, is treated in the primary ventilation system. This system is in full operation only during oxygen-blowing when the BOF converter vessel is in a vertical position. When pig iron and scrap are charged into the BOF and when slag or steel are tapped, the vessel is tilted. The generated emissions during charging and tapping cannot be captured by the primary off-gas system. To capture these emissions, a secondary ventilation system is usually installed. The emissions are captured by a canopy hood installed just above the converter mouth in tilted position. The aim of this study was to investigate the dependence of Zn and other components on the particle size of BOF secondary ventilation dust. Because of the high temperature of the BOF process it can be expected that Zn will be enriched in the fine dust fractions. If Zn is enriched in the fine fractions, classification could be applied to split the dust into two size fractions with a different content of Zn. For this air classification experiments with dust from the secondary ventilation system of a BOF were performed. The results show that Zn and Pb are highly enriched in the finest dust fraction. For Cd, Cu and Sb the enrichment is less. In contrast, the non-volatile metals Al, Fe, Mn and Ti were depleted in the fine fractions. Thus, air classification could be considered for the treatment of dust from secondary BOF off-gas cleaning.

Keywords: air classification, converter dust, recycling, zinc

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

Abstract:

SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

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Authors: Hamza Ikhlaq, Stephen Franks

Abstract:

Background: Polycystic ovarian syndrome (PCOS) is one of the most common endocrine disorders affecting women of reproductive age. The aetiology behind PCOS is poorly understood but influencing ethnic, environmental, and genetic factors have been recognised. However, literature examining the impact of ethnicity is scarce. We hypothesised Body Mass Index (BMI) and ethnicity influence the clinical, metabolic, and biochemical presentations of PCOS, with an interaction between these factors. Methods: A database of 1081 women with PCOS and a control group of 72 women were analysed. BMIs were grouped using the World Health Organisation classification into normal weight, overweight and obese groups. Ethnicities were classified into European, South Asian, and Afro-Caribbean groups. Biochemical and clinical presentations were compared amongst these groups, and statistical analyses were performed to assess significance. Results: This study revealed ethnicity significantly influences biochemical and clinical presentations of PCOS. A greater proportion of South Asian women are impacted by menstrual cycle disturbances and hirsutism than European and Afro-Caribbean women. South Asian and Afro-Caribbean women show greater measures of insulin resistance and weight gain when compared to their European peers. Women with increased BMI are shown to have an increased prevalence of PCOS phenotypes alongside increased levels of insulin resistance and testosterone. Furthermore, significantly different relationships between the waist-hip ratio and measures of insulin and glucose control for Afro-Caribbean women were identified compared to other ethnic groups. Conclusions: The findings of this study show ethnicity significantly influence the phenotypic and biochemical presentations of PCOS, with an interaction between body habitus and ethnicity found. Furthermore, we provide further data on the influences of BMI on the manifestations of PCOS. Therefore, we highlight the need to consider these factors when reviewing diagnostic criteria and delivering clinical care for these groups.

Keywords: PCOS, ethnicity, BMI, clinical

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