Search results for: Support Vector Machines (SVMs)

Authors: Rhea Mathews

Abstract:

The purpose of the study is to investigate the effects of perceived social support and cultural self-efficacy on the acculturative stress of international postgraduate students in the United Kingdom. The study adopted Berry, Kim, Minde & Mok’s (1987) acculturative framework on acculturative stress and examined the relationship between the variables. The study hypothesized that perceived social support and cultural self-efficacy would predict lower levels of acculturative stress among students. Postgraduate students in the United Kingdom (N = 76) completed three surveys measuring the variables; Acculturative Stress Scale for International Students, Multidimensional Scale of Perceived Social Support, and Cultural Self-efficacy for Adolescents. To evaluate the role of the perceived social support and cultural self-efficacy in determining the acculturative stress level of international students, multiple linear regression was employed. Both independent variables exhibited a significant, negative relationship with acculturative stress (p < 0.001; p < 0.01). Results described that cultural self-efficacy and perceived social support significantly predicted acculturative stress (p < 0.01). Together, the variables accounted for 22% of the variance in acculturative stress scores (adjusted R² = 0.22), with cultural self-efficacy playing a larger role in predicting the dependent variable. Limitations and implications of the study are noted. The findings of the study are discussed in relation to enhancing international students’ acculturative experience when relocating to a new environment.

Keywords: acculturative stress, coping, cultural adjustment, cultural self-efficacy, international education, international students, migration, perceived social support

Procedia PDF Downloads 319

Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

Abstract:

In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

Procedia PDF Downloads 262

Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

Abstract:

Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

Procedia PDF Downloads 82

Authors: Subagyo Yotopranoto, Sri Wijayanti Sulistyawati, Sukmawati Basuki, Budi Armika, Yoes Prijatna Dachlan

Abstract:

Malaria is a parasitic infectious disease that still remains a health problem in the world, including Indonesia. There is an outbreak happen at West Nusa Tenggara in 2007. A tourist spot in West Nusa Tenggara called West Lombok is mesoendemic area for malaria. Tanjung is the highest malaria morbidity subdistrict in West Lombok. Thus, the research conducted for the presence of a new species of malaria vectors, that are suspected of one factors which caused high morbidity of malaria in this region. The study was conducted in coastal and highland areas. We collected and identified Anopheles larvae from their breeding places. We also collected and identified Anopheles adult mosquitoes with outdoor cow net, indoor and outdoor human bait. In coastal area (Tembobor village), we found Anopheles vagus larvae from rivers as its breeding places. In highland area (Dasan Tengah village), we found An. subpictus from pool, lagoon, and river as its breeding places. In coastal area, with outdoor human bait, we collected An. vagus and An. subpictus adult mosquitoes. With indoor human bait, we collected An. subpictus adult mosquitoes. Whereas with outdoor cow net, we collected An. subpictus and An. maculatus, the first was more dominant. Furthermore, An subpictus strong suspected as malaria vector in coastal area. Anopheles subpictus was an anthropozoophylic mosquitoes, because it was found at indoor and outdoor places.

Keywords: malaria, vector, Tanjung, West Nusa Tenggara

Procedia PDF Downloads 361

Authors: Kathryn Norgang, Sarah Howrath, Auni Idi Muhire, Pacifique Umubyeyi

Abstract:

Description : A group of nurses address the shortage of trained staff to respond to critical patients at Rwanda Military Hospital (RMH) by developing a training program and a resuscitation response team. Members of the group who received the training when it first launched are now trainer of trainers; all components of the training program are organized and delivered by RMH staff-the clinical mentor only provides adjunct support. This two day training is held quarterly at RMH; basic life support and exposure to interventions for advanced care are included in the test and skills sign off. Seventy staff members have received the training this year alone. An increased number of admission/transfer to ICU due to successful resuscitation attempts is noted. Lessons learned: -Number of staff trained 2012-2014 (to be verified). -Staff who train together practice with greater collaboration during actual resuscitation events. -Staff more likely to initiate BLS if peer support is present-more staff trained equals more support. -More access to Advanced Cardiac Life Support training is necessary now that the cadre of BLS trained staff is growing. Conclusions: Increased access to training, peer support, and collaborative practice are effective strategies to strengthening resuscitation capacity within a hospital.

Keywords: resuscitation, basic life support, capacity building, resuscitation response teams, nurse trainer of trainers

Procedia PDF Downloads 299

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

Abstract:

Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

Procedia PDF Downloads 137

Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

Abstract:

In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

Procedia PDF Downloads 341

Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

Procedia PDF Downloads 13

Authors: Amir Rashid

Abstract:

Cloud Computing frameworks empower organizations to cut expenses by outsourcing computation resources on-request. As of now, customers of Cloud service providers have no methods for confirming the privacy and ownership of their information and data. To address this issue we propose the platform of a trusted cloud computing program (TCCP). TCCP empowers Infrastructure as a Service (IaaS) suppliers, for example, Amazon EC2 to give a shout box execution condition that ensures secret execution of visitor virtual machines. Also, it permits clients to bear witness to the IaaS supplier and decide if the administration is secure before they dispatch their virtual machines. This paper proposes a Trusted Cloud Computing Platform (TCCP) for guaranteeing the privacy and trustworthiness of computed data that are outsourced to IaaS service providers. The TCCP gives the deliberation of a shut box execution condition for a client's VM, ensuring that no cloud supplier's authorized manager can examine or mess up with its data. Furthermore, before launching the VM, the TCCP permits a client to dependably and remotely acknowledge that the provider at backend is running a confided in TCCP. This capacity extends the verification of whole administration, and hence permits a client to confirm the data operation in secure mode.

Keywords: cloud security, IaaS, cloud data privacy and integrity, hybrid cloud

Procedia PDF Downloads 294

Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

Abstract:

A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

Procedia PDF Downloads 312

Authors: V. Rashtchi, H. Bizhani, F. R. Tatari

Abstract:

This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation.

Keywords: induction machine, loss minimization, magnetizing current, particle swarm optimization

Procedia PDF Downloads 629

Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

Abstract:

Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

Procedia PDF Downloads 653

Authors: Amine M. Benmehaia, Amine Oulmane

Abstract:

The supply response of agricultural commodities to changes in price incentives is an important issue for the success of any policy reform in the agricultural sector. This study aims to quantify the responsiveness of producers of some livestock commodities to price incentives in Algerian context. Time series analysis is used on annual data for a period of 52 years (1966-2018). Both co-integration and vector error correction model (VECM) are used through the Nerlove model of partial adjustment. The study attempts to determine the long-run and short-run relationships along with the magnitudes of disequilibria in the selected commodities. Results show that the short-run price elasticities are low in cow and sheep meat sectors (8.7 and 8% respectively), while their respective long-run elasticities are 16.5 and 10.5, whereas eggs and milk have very high short-run price elasticities (82 and 90% respectively) with long-run elasticities of 40 and 46 respectively. The error correction coefficient, reflecting the speed of adjustment towards the long-run equilibrium, is statistically significant and have the expected negative sign. Its estimates are 12.7 for cow meat, 33.5 for sheep meat, 46.7 for eggs and 8.4 for milk. It seems that cow meat and milk producers have a weak feedback of about 12.7% and 8.4% respectively of the previous year's disequilibrium from the long-run price elasticity, whereas sheep meat and eggs producers adjust to correct long run disequilibrium with a high speed of adjustment (33.5% and 46.7 % respectively). The implication of this is that much more in-depth research is needed to identify those factors that affect agricultural supply and to describe the effect of factors that shift supply in response to price incentives. This could provide valuable information for government in the use of appropriate policy measures.

Keywords: Algeria, cointegration, livestock, supply response, vector error correction model

Procedia PDF Downloads 133

Authors: Ali Mamizadeh, Ires Iskender, Saeid Aghaei

Abstract:

Energy is one of the important issues with high priority property in the world. Energy demand is rapidly increasing depending on the growing population and industry. The useable energy sources in the world will be insufficient to meet the need for energy. Therefore, the efficient and economical usage of energy sources is getting more importance. In a survey conducted among electric consuming machines, the electrical machines are consuming about 40% of the total electrical energy consumed by electrical devices and 96% of this consumption belongs to induction motors. Induction motors are the workhorses of industry and have very large application areas in industry and urban systems like water pumping and distribution systems, steel and paper industries and etc. Monitoring and the control of the motors have an important effect on the operating performance of the motor, driver selection and replacement strategy management of electrical machines. The sensorless monitoring system for monitoring and calculating efficiency of induction motors are studied in this study. The equivalent circuit of IEEE is used in the design of this study. The terminal current and voltage of induction motor are used in this motor to measure the efficiency of induction motor. The motor nameplate information and the measured current and voltage are used in this system to calculate accurately the losses of induction motor to calculate its input and output power. The efficiency of the induction motor is monitored online in the proposed method without disconnecting the motor from the driver and without adding any additional connection at the motor terminal box. The proposed monitoring system measure accurately the efficiency by including all losses without using torque meter and speed sensor. The monitoring system uses embedded architecture and does not need to connect to a computer to measure and log measured data. The conclusion regarding the efficiency, the accuracy and technical and economical benefits of the proposed method are presented. The experimental verification has been obtained on a 3 phase 1.1 kW, 2-pole induction motor. The proposed method can be used for optimal control of induction motors, efficiency monitoring and motor replacement strategy.

Keywords: induction motor, efficiency, power losses, monitoring, embedded design

Procedia PDF Downloads 341

Authors: Charity Ifunanya Osakwe, Victoria Ogochuchukwu Obi-Nwosu, Chima Kenneth Anachedo

Abstract:

The study investigates how automated teller machines (ATM) and mobile banking affect deposit money banks in the Nigerian economy. The study made use of time series data which were obtained from the Central Bank of Nigeria Statistical Bulletin from 2009 to 2021. The Central Bank of Nigeria (CBN) data on automated teller machine and mobile phones were used to proxy electronic banking while total deposit in banks proxied the performance of deposit money banks. The analysis for the study was done using ordinary least square econometric technique with the aid of economic view statistical package. The results show that the automated teller machine has a positive and significant effect on the total deposits of deposit money banks in Nigeria and that making use of deposits of deposit money banks in Nigeria. It was concluded in the study that e-banking has equally increased banking access to customers and also created room for banks to expand their operations to more customers. The study recommends that banks in Nigeria should prioritize the expansion and maintenance of ATM networks as well as continue to invest in and develop more mobile banking services.

Keywords: electronic, banking, automated teller machines, mobile, deposit

Procedia PDF Downloads 47

Authors: Aniket Alam, Sravya Gurram

Abstract:

This paper proposes a categorization of sport that is based on the system of rules that a sport must adhere to. We focus on these systems of rules to examine how a winner is produced in different sports. The rules of a sport dictate the game play and the direction it takes. We propose to break down the game play into events. At this junction, we observe two kinds of events that constitute the game play of a sport –ones that follow sequential logic and ones that do not. Our focus is pertained to sports that are comprised of sequential events. To examine these events further, to understand how a winner emerges, we take the help of finite-state automaton from the theory of computation (Automata theory). We showcase how sequential sports are eligible to be represented as finite state machines. We depict these finite state machines as state diagrams. We examine these state diagrams to observe how a team/player reaches the final states of the sport, with a special focus on one final state –the final state which determines the winner. This exercise has been carried out for the following sports: Hurdles, Track, Shot Put, Long Jump, Bowling, Badminton, Pacman and Weightlifting (Snatch). Based on our observations of how this final state of winning is achieved, we propose a categorization of sports.

Keywords: sport classification, sport modelling, ontology, automata theory

Procedia PDF Downloads 113

Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

Abstract:

In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

Procedia PDF Downloads 46

Authors: Godfrey Nsereko, Daniel Kadobera, Denis Okethwangu, Joyce Nguna, Alex Riolexus Ario

Abstract:

Background: Malaria is a leading cause of morbidity and mortality in Uganda. In April 2018, malaria cases surged in Nwoya District, northern Uganda, exceeding the action thresholds. We investigated to assess the outbreak’s magnitude, identify transmission risk factors, and recommend evidence-based control measures. Methods: We defined a malaria case as onset of fever in a resident of Nwoya District with a positive Rapid Diagnostic Test or microscopy for malaria P. falciparum from 1 February to 22 May 2018. We reviewed medical records in all health facilities of affected sub-counties to find cases. In a case-control study, we compared exposure risk factors between 107 case-persons and 107 asymptomatic controls matched by age and village. We conducted entomological assessment on vector-density and behavior. Results: We identified 3,879 case-persons (attack rate [AR]=6.5%) and 2 deaths (case-fatality rate=5.2/10,000). Females (AR=8.1%) were more affected than males (AR=4.7%). Of all age groups, the 5-18 year age group (AR=8.4%) was most affected. Heavy rain started on 4 March; a propagated outbreak began during the week of 2 April. In the case-control study, 55% (59/107) of case-patients and 18% (19/107) of controls had stagnant water around households for several days following rainfall (ORM-H=5.6, 95%CI=3.0-11); 25% (27/107) of case-patients and 51% (55/107) of controls wore long-sleeve cloths during evening hours (ORM-H=0.30, 95%CI=0.20-0.60); 29% (31/107) of case-patients and 15% (16/107) of controls did not sleep under a long-lasting insecticide-treated net (LLIN) (ORM-H=2.3, 95%CI=1.1-4.9); 37% (40/107) of case-patients and 52% (56/107) of controls had ≥1 LLIN per 2 household members (ORM-H=0.54, 95%CI=0.30-0.97). Entomological assessment indicated active breeding sites; Anopheles gambiae sensu lato species were the predominant vector. Conclusion: Increased vector breeding sites after heavy rainfall, together with inadequate malaria preventive measures caused this outbreak. We recommended increasing coverage for LLINs and larviciding breeding sites.

Keywords: malaria outbreak, Plasmodium falciparum, global health security, Uganda

Procedia PDF Downloads 222

Authors: A. Mohammadpour, Ebrahim Najafi Kajabad, Ghazale Ipakchi

Abstract:

Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms.

Keywords: intrusion detection systems, clustering, k-means, k-medoids, SV clustering, ensemble

Procedia PDF Downloads 213

Authors: Umit Candan, Kadir Dogan, Ozkan Akin

Abstract:

In this study, the use of a modified rotor 6/4 Switched Reluctance Motor (SRM) and a Brushless Direct Current Motor (BLDC) in pan-tilt systems is compared. Pan-tilt systems are critical mechanisms that enable the precise orientation of cameras and sensors, and their performance largely depends on the characteristics of the motors used. The aim of the study is to determine how the performance of the SRM can be improved through rotor modifications and how these improvements can compete with BLDC motors. Using Finite Element Method (FEM) analyses, the design characteristics and magnetic performance of the 6/4 Switched Reluctance Motor are examined in detail. The modified SRM is found to offer increased torque capacity and efficiency while standing out with its simple construction and robustness. FEM analysis results of SRM indicate that considering its cost-effectiveness and performance improvements achieved through modifications, the SRM is a strong alternative for certain pan-tilt applications. This study aims to provide engineers and researchers with a performance comparison of the modified rotor 6/4 SRM and BLDC motors in pan-tilt systems, helping them make more informed and effective motor selections.

Keywords: reluctance machines, switched reluctance machines, pan-tilt application, comparison, FEM analysis

Procedia PDF Downloads 48

Authors: Yusri Yusof, Chen Wong Keong

Abstract:

This paper presents the implementation details of a Relational Database Management System of a STEP-technology product model repository. It is able support the implementation of any EXPRESS language schema, although it has been primarily implemented to support mechanical product life cycle applications. This database support the input of STEP part 21 file format from CAD in geometrical and topological data format and support a range of queries for mechanical product life cycle applications. This proposed relational database management system uses entity-to-table method (R1) rather than type-to-table method (R4). The two mapping methods have their own strengths and drawbacks.

Keywords: RDBMS, CAD, ISO 10303, part-21 file

Procedia PDF Downloads 532

Authors: Xiuying Wang, Changliang Li, Bo Xu

Abstract:

A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

Procedia PDF Downloads 347

Authors: Alexandra Tucaliuc, Alexandra C. Blaga, Anca I. Galaction, Lenuta Kloetzer, Dan Cascaval

Abstract:

Ergosterol (ergosta-5,7,22-trien-3β-ol), also known as provitamin D2, is the precursor of vitamin D2 (ergocalciferol), because it is converted under UV radiation to this vitamin. The natural sources of ergosterol are mainly the yeasts (Saccharomyces sp., Candida sp.), but it can be also found in fungus (Claviceps sp.) or plants (orchids). In the yeasts cells, ergosterol is accumulated in membranes, especially in free form in the plasma membrane, but also as esters with fatty acids in membrane lipids. The chemical synthesis of ergosterol does not represent an efficient method for its production, in these circumstances, the most attractive alternative for producing ergosterol at larger-scale remains the aerobic fermentation using S. cerevisiae on glucose or by-products from agriculture of food industry as substrates, in batch or fed-batch operating systems. The aim of this work is to analyze comparatively the influence of aeration efficiency on ergosterol production by S. cerevisiae in batch and fed-batch fermentations, by considering different levels of mixing intensity, aeration rate, and n-dodecane concentration. The effects of the studied factors are quantitatively described by means of the mathematical correlations proposed for each of the two fermentation systems, valid both for the absence and presence of oxygen-vector inside the broth. The experiments were carried out in a laboratory stirred bioreactor, provided with computer-controlled and recorded parameters. n-Dodecane was used as oxygen-vector and the ergosterol content inside the yeasts cells has been considered at the fermentation moment related to the maximum concentration of ergosterol, 9 hrs for batch process and 20 hrs for fed-batch one. Ergosterol biosynthesis is strongly dependent on the dissolved oxygen concentration. The hydrocarbon concentration exhibits a significant influence on ergosterol production mainly by accelerating the oxygen transfer rate. Regardless of n-dodecane addition, by maintaining the glucose concentration at a constant level in the fed-batch process, the amount of ergosterol accumulated into the yeasts cells has been almost tripled. In the presence of hydrocarbon, the ergosterol concentration increased by over 50%. The value of oxygen-vector concentration corresponding to the maximum level of ergosterol depends mainly on biomass concentration, due to its negative influences on broth viscosity and interfacial phenomena of air bubbles blockage through the adsorption of hydrocarbon droplets–yeast cells associations. Therefore, for the batch process, the maximum ergosterol amount was reached for 5% vol. n-dodecane, while for the fed-batch process for 10% vol. hydrocarbon.

Keywords: bioreactors, ergosterol, fermentation, oxygen-vector

Procedia PDF Downloads 182

Authors: Khouloud Safa Azoud, Süleyman Baştürk

Abstract:

In the pursuit of cleaner and more sustainable urban environments, advanced technologies play a critical role in evolving sanitation systems. This paper presents two distinct advancements in automated cleaning machines designed to improve urban sanitation. The first advancement is a semi-automatic road surface cleaning machine that integrates human labor with solar energy to enhance environmental sustainability and adaptability, especially in regions with limited access to electricity. By reducing carbon emissions and increasing operational efficiency, this approach offers significant potential for urban sanitation enhancement. The second advancement is a multifunctional semi-automatic street cleaning machine equipped with a camera, Arduino programming, and GPS for an autonomous operation aimed at addressing cost barriers in developing countries. Prioritizing low energy consumption and cost-effectiveness, this machine provides versatile cleaning solutions adaptable to various environmental conditions. By integrating solar energy with autonomous operating systems and careful design, these developments represent substantial progress in sustainable urban sanitation, particularly in developing regions.

Keywords: automated cleaning machines, solar energy integration, operational efficiency, urban sanitation systems

Procedia PDF Downloads 21

Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

Abstract:

Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

Procedia PDF Downloads 63

Authors: Jiří Barta, Oldřich Svoboda, Jiří F. Urbánek

Abstract:

In this paper will be discussed two coin´s sides of crisis scenarios dynamics. On the one's side is negative role of subsidiary scenario branches in its compactness weakening by means unduly chaotic atomizing, having many interactive feedbacks cases, increasing a value of a complexity here. This negative role reflects the complexity of use cases, weakening leader compliancy, which brings something as a ´readiness for controlling capabilities provision´. Leader´s dissatisfaction has zero compliancy, but factual it is a ´crossbar´ (interface in fact) between planning and executing use cases. On the other side of this coin, an advantage of rich scenarios embranchment is possible to see in a support of response awareness, readiness, preparedness, adaptability, creativity and flexibility. Here rich scenarios embranchment contributes to the steadiness and resistance of scenario mission actors. These all will be presented in live power-points ´Blazons´, modelled via DYVELOP (Dynamic Vector Logistics of Processes) on the Conference.

Keywords: leadership, controlling, complexity, DYVELOP, scenarios

Procedia PDF Downloads 398

Authors: Zimasa Siyasanga Gysman

Abstract:

This paper assesses the history of electronic music and its performance to illustrate that machines and technology have largely influenced how humans perform electronic music. The history of electronic music mainly focuses on the composition and production of electronic music with little to no attention paid to its performance by the majority of scholars in this field. Therefore, establishing a history of performance involves investigating what compositions of electronic music called for in the production of electronic music performance. This investigation into seminal works in the history of electronic music, therefore, illustrates the aesthetics of electronic music performance and the aesthetics established in the very beginnings of electronic music performance demonstrate the aesthetics of electronic music which are still prevalent today. The key aesthetics are the repurposing of technology and the hybridisation of technology. Performers take familiar technology (technology that society has become accustomed to using in daily life), not necessarily related to music or performance and use it as an instrument in their performances, such as a rotary dial telephone. Likewise, since the beginnings of electronic music, producers have always experimented with the latest technologies available to them in their compositions and performances. The spirit of performers of electronic music, therefore, revolves around repurposing familiar technologies and using them in new ways, whilst similarly experimenting with new technologies in their performances. This process of hybridisation plays a key role in the production and performance of electronic music in the twentieth century. Through various interviews with performers of electronic music, it is shown that these aesthetics are driving performance practices in the twenty-first century.

Keywords: body, hybridisation, performance, sound

Procedia PDF Downloads 155

Authors: Matthew Baucum

Abstract:

With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

Procedia PDF Downloads 441

Authors: Luca Martignoni

Abstract:

The increasing usage of clinical decision support systems in healthcare and the demand for software that enables doctors to take informed decisions is changing everyday clinical practice. However, as technology advances not only are the benefits of technology growing, but so are the potential risks. A growing danger is the doctors’ over-reliance on the proposed decision of the clinical decision support system, leading towards deskilling and rash decisions by doctors. In that regard, identifying doctors' requirements for software and developing approaches to prevent technological over-reliance is of utmost importance. In this paper, we report the results of a design science research study, focusing on the requirements and design principles of ultrasound software. We conducted a total of 15 interviews with experts about poten-tial ultrasound software functions. Subsequently, we developed meta-requirements and design principles to design future clinical decision support systems efficiently and as free from the occur-rence of technological over-reliance as possible.

Keywords: clinical decision support systems, technological over-reliance, design principles, design science research

Procedia PDF Downloads 94

Authors: Azadeh Yaraghchi, Reza Bagherian Sararoodi, Niknaz Salehi Moghadam, Mohammad Hossein Mandegar, Adis Kraskian Mujembari, Omid Rezaei

Abstract:

Background: Quality of life is one of the most important consequences of disease in psychosomatic disorders. Many psychological factors are considered in predicting quality of life in patients with coronary artery bypass graft (CABG). The present study was aimed to determine the relationship between perceived social support and quality of life in patients with coronary artery bypass graft (CABG). Methods: The population included 82 patients who had undergone CABG from October 2014 to May 2015 in four different hospitals in Tehran. The patients were evaluated with Multi-dimension scale of perceived social support (MSPSS) and after three months follow up were evaluated by Short-Form quality of life questionnaire (SF-36). The obtained data were analyzed through Pearson correlation test and multiple variable regression models. Findings: A relationship between perceived social support and quality of life in patients with CABG was observed (r=0.374, p<0.01). The results showed that 22.4% of variation in quality of life is predicted by perceived social support components (p<0.01, R2 =0.224). Conclusion: Based on the results, perceived social support is one of the predictors of quality of life in patients with coronary artery bypass graft. Accordingly, these results can be useful in conceiving proactive policies, detecting high risk patients and planning for psychological interventions.

Keywords: coronary artery bypass graft, perceived social support, psychological factors, quality of life

Procedia PDF Downloads 363