Search results for: influential features
2561 Enabling Non-invasive Diagnosis of Thyroid Nodules with High Specificity and Sensitivity
Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.
Abstract:
Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine
Procedia PDF Downloads 512560 A Large-Strain Thermoviscoplastic Damage Model
Authors: João Paulo Pascon
Abstract:
A constitutive model accounting for large strains, thermoviscoplasticity, and ductile damage evolution is proposed in the present work. To this end, a fully Lagrangian framework is employed, considering plane stress conditions and multiplicative split of the deformation gradient. The full model includes Gurson’s void growth, nucleation and coalescence, plastic work heating, strain and strain-rate hardening, thermal softening, and heat conductivity. The contribution of the work is the combination of all the above-mentioned features within the finite-strain setting. The model is implemented in a computer code using triangular finite elements and nonlinear analysis. Two mechanical examples involving ductile damage and finite strain levels are analyzed: an inhomogeneous tension specimen and the necking problem. Results demonstrate the capabilities of the developed formulation regarding ductile fracture and large deformations.Keywords: ductile damage model, finite element method, large strains, thermoviscoplasticity
Procedia PDF Downloads 802559 Radical Web Text Classification Using a Composite-Based Approach
Authors: Kolade Olawande Owoeye, George R. S. Weir
Abstract:
The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.Keywords: extremist, web pages, classification, semantics, posit
Procedia PDF Downloads 1382558 Measuring How Brightness Mediates Auditory Salience
Authors: Baptiste Bouvier
Abstract:
While we are constantly flooded with stimuli in daily life, attention allows us to select the ones we specifically process and ignore the others. Some salient stimuli may sometimes pass this filter independently of our will, in a "bottom-up" way. The role of the acoustic properties of the timbre of a sound on its salience, i.e., its ability to capture the attention of a listener, is still not well understood. We implemented a paradigm called the "additional singleton paradigm", in which participants have to discriminate targets according to their duration. This task is perturbed (higher error rates and longer response times) by the presence of an irrelevant additional sound, of which we can manipulate a feature of our choice at equal loudness. This allows us to highlight the influence of the timbre features of a sound stimulus on its salience at equal loudness. We have shown that a stimulus that is brighter than the others but not louder leads to an attentional capture phenomenon in this framework. This work opens the door to the study of the influence of any timbre feature on salience.Keywords: attention, audition, bottom-up attention, psychoacoustics, salience, timbre
Procedia PDF Downloads 1642557 Secured Embedding of Patient’s Confidential Data in Electrocardiogram Using Chaotic Maps
Authors: Butta Singh
Abstract:
This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.Keywords: chaotic maps, ECG steganography, data embedding, electrocardiogram
Procedia PDF Downloads 1842556 Giant Filiform Polyposis in a Patient with Ulcerative Colitis Mimicking Colorectal Cancer
Authors: Godwin Dennison, Edwin Cooper, George Theobald, Richard Dalton
Abstract:
We report an unusual case of giant filiform polyposis in a patient with ulcerative colitis, causing a large stricture in the colon. A 62-year-old man was referred to the Bowel Cancer Screening Programme with a positive Faecal Immunochemical Test (FIT). He was known to have UC for 30 years. A CT scan showed a 9 cm stricture in the transverse colon suspicious of malignancy. A colonoscopy was attempted three times, and biopsies confirmed features of ulcerative colitis. A laparoscopic assisted transverse colectomy (Left hemicolectomy) was performed, and the histology revealed giant filiform polyposis. This should be considered in a UC patient presenting with signs of obstruction mimicking a carcinoma. Whilst it is a benign condition, because of the size of the lesion, it often causes obstruction, and surgery is indicated to relieve symptoms.Keywords: giant inflammatory polyposis, filiform polyposis, ulcerative colitis, inflammatory bowel disease
Procedia PDF Downloads 1092555 A Novel Method for Silence Removal in Sounds Produced by Percussive Instruments
Authors: B. Kishore Kumar, Rakesh Pogula, T. Kishore Kumar
Abstract:
The steepness of an audio signal which is produced by the musical instruments, specifically percussive instruments is the perception of how high tone or low tone which can be considered as a frequency closely related to the fundamental frequency. This paper presents a novel method for silence removal and segmentation of music signals produced by the percussive instruments and the performance of proposed method is studied with the help of MATLAB simulations. This method is based on two simple features, namely the signal energy and the spectral centroid. As long as the feature sequences are extracted, a simple thresholding criterion is applied in order to remove the silence areas in the sound signal. The simulations were carried on various instruments like drum, flute and guitar and results of the proposed method were analyzed.Keywords: percussive instruments, spectral energy, spectral centroid, silence removal
Procedia PDF Downloads 4012554 Structure Domains Tuning Magnetic Anisotropy and Motivating Novel Electric Behaviors in LaCoO₃ Films
Authors: Dechao Meng, Yongqi Dong, Qiyuan Feng, Zhangzhang Cui, Xiang Hu, Haoliang Huang, Genhao Liang, Huanhua Wang, Hua Zhou, Hawoong Hong, Jinghua Guo, Qingyou Lu, Xiaofang Zhai, Yalin Lu
Abstract:
Great efforts have been taken to reveal the intrinsic origins of emerging ferromagnetism (FM) in strained LaCoO₃ (LCO) films. However, some macro magnetic performances of LCO are still not well understood and even controversial, such as magnetic anisotropy. Determining and understanding magnetic anisotropy might help to find the true causes of FM in turn. Perpendicular magnetic anisotropy (PMA) was the first time to be directly observed in high-quality LCO films with different thickness. The in-plane (IP) and out of plane (OOP) remnant magnetic moment ratio of 30 unit cell (u.c.) films is as large as 20. The easy axis lays in the OOP direction with an IP/OOP coercive field ratio of 10. What's more, the PMA could be simply tuned by changing the thickness. With the thickness increases, the IP/OOP magnetic moment ratio remarkably decrease with magnetic easy axis changing from OOP to IP. Such a huge and tunable PMA performance exhibit strong potentials in fundamental researches or applications. What causes PMA is the first concern. More OOP orbitals occupation may be one of the micro reasons of PMA. A cluster-like magnetic domain pattern was found in 30 u.c. with no obvious color contrasts, similar to that of LaAlO₃/SrTiO₃ films. And the nanosize domains could not be totally switched even at a large OOP magnetic field of 23 T. It indicates strong IP characters or none OOP magnetism of some clusters. The IP magnetic domains might influence the magnetic performance and help to form PMA. Meanwhile some possible nonmagnetic clusters might be the reason why the measured moments of LCO films are smaller than the calculated values 2 μB/Co, one of the biggest confusions in LCO films.What tunes PMA seems much more interesting. Totally different magnetic domain patterns were found in 180 u.c. films with cluster magnetic domains surrounded by < 110 > cross-hatch lines. These lines were regarded as structure domain walls (DWs) determined by 3D reciprocal space mapping (RSM). Two groups of in-plane features with fourfold symmetry were observed near the film diffraction peaks in (002) 3D-RSM. One is along < 110 > directions with a larger intensity, which is well match the lines on the surfaces. The other is much weaker and along < 100 > directions, which is from the normal lattice titling of films deposited on cubic substrates. The < 110 > domain features obtained from (103) and (113) 3D-RSMs exhibit similar evolution of the DWs percentages and magnetic behavior. Structure domains and domain walls are believed to tune PMA performances by transform more IP magnetic moments to OOP. Last but not the least, thick films with lots of structure domains exhibit different electrical transport behaviors. A metal-to-insulator transition (MIT) and an angular dependent negative magnetic resistivity were observed near 150 K, higher than FM transition temperature but similar to that of spin-orbital coupling related 1/4 order diffraction peaks.Keywords: structure domain, magnetic anisotropy, magnetic domain, domain wall, 3D-RSM, strain
Procedia PDF Downloads 1482553 Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques
Authors: Soheila Sadeghi
Abstract:
In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes
Procedia PDF Downloads 282552 A Content Analysis of the Introduction to the Philosophy of Religion Literature Published in the West between 1950-2010 in Terms of Definition, Method and Subjects
Authors: Fatih Topaloğlu
Abstract:
Although philosophy is inherently a theoretical and intellectual activity, it should not be denied that environmental conditions influence the formation and shaping of philosophical thought. In this context, it should be noted that the Philosophy of Religion has been influential in the debates in the West, especially since the beginning of the 20th century, and that this influence has dimensions that cannot be limited to academic or intellectual fields. The issues and problems that fall within the field of interest of Philosophy of Religion are followed with interest by a significant proportion of society through popular publications. Philosophy of Religion has its share in many social, economic, cultural, scientific, political and ethical developments. Philosophy of Religion, in the most general sense, can be defined as a philosophical approach to religion or a philosophical way of thinking and discussing religion. Philosophy of Religion tries to explain the epistemological foundations of concepts such as belief and faith that shape religious life by revealing their meaning for the individual. Thus, Philosophy of Religion tries to evaluate the effect of beliefs on the individual's values, judgments and behaviours with a comprehensive and critical eye. The Philosophy of Religion, which tries to create new solutions and perspectives by applying the methods of philosophy to religious problems, tries to solve these problems not by referring to the holy book or religious teachings but by logical proofs obtained through the possibilities of reason and evidence filtered through the filter of criticism. Although there is no standard method for doing Philosophy of Religion, it can be said that an approach that can be expressed as thinking about religion in a rational, objective, and consistent way is generally accepted. The evaluations made within the scope of Philosophy of Religion have two stages. The first is the definition stage, and the second is the evaluation stage. In the first stage, the data of different scientific disciplines, especially other religious sciences, are utilized to define the issues objectively. In the second stage, philosophical evaluations are made based on this foundation. During these evaluations, the issue of how the relationship between religion and philosophy should be established is extremely sensitive. The main thesis of this paper is that the Philosophy of Religion, as a branch of philosophy, has been affected by the conditions caused by the historical experience through which it has passed and has differentiated its subjects and the methods it uses to realize its philosophical acts over time under the influence of these conditions. This study will attempt to evaluate the validity of this study based on the "Introduction to Philosophy of Religion" literature, which we assume reflects this differentiation. As a result of this examination will aim to reach some factual conclusions about the nature of both philosophical and religious thought, to determine the phases that the Philosophy of Religion as a discipline has gone through since the day it emerged, and to investigate the possibilities of a holistic view of the field.Keywords: content analysis, culture, history, philosophy of religion, method
Procedia PDF Downloads 502551 The Historical Framework of International Crime in International Criminal Law
Authors: Tahraoui Boualem
Abstract:
Researching the historical framework of international crime means examining the historical facts that have contributed to uncovering this serious crime affecting international interests, and the law by which the study of the subject of international crime is determined is international criminal law, which is a branch of public international law. In this context, the historical study of international crime means recognizing the existence of an international community governed by international law, which makes us acknowledge that ancient societies lacked such stable and recurring international relations. Therefore, an attempt to monitor international crime in those ancient societies is only to demonstrate a historical fact that those societies have known some features of this crime, and have contributed in one way or another to the development of international criminal law without defining its concept or legal nature. The international community has affirmed the principle of establishing peace, achieving security, and respecting human rights. As a basis for friendly relations between the people of the international community and in case of prejudice, such as the aggressors breaching the obligations imposed on them, whether in time of peace or war.Keywords: historical framework, of international crime, peace or war., international law
Procedia PDF Downloads 952550 Effect of Clinical Depression on Automatic Speaker Verification
Authors: Sheeraz Memon, Namunu C. Maddage, Margaret Lech, Nicholas Allen
Abstract:
The effect of a clinical environment on the accuracy of the speaker verification was tested. The speaker verification tests were performed within homogeneous environments containing clinically depressed speakers only, and non-depresses speakers only, as well as within mixed environments containing different mixtures of both climatically depressed and non-depressed speakers. The speaker verification framework included the MFCCs features and the GMM modeling and classification method. The speaker verification experiments within homogeneous environments showed 5.1% increase of the EER within the clinically depressed environment when compared to the non-depressed environment. It indicated that the clinical depression increases the intra-speaker variability and makes the speaker verification task more challenging. Experiments with mixed environments indicated that the increase of the percentage of the depressed individuals within a mixed environment increases the speaker verification equal error rates.Keywords: speaker verification, GMM, EM, clinical environment, clinical depression
Procedia PDF Downloads 3692549 Behaviour of Non-local Correlations and Quantum Information Theoretic Measures in Frustrated Molecular Wheels
Authors: Amit Tribedi
Abstract:
Genuine Quantumness present in Quantum Systems is the resource for implementing Quantum Information and Computation Protocols which can outperform the classical counterparts. These Quantumness measures encompass non-local ones known as quantum entanglement (QE) and quantum information theoretic (QIT) ones, e.g. Quantum Discord (QD). In this paper, some well-known measures of QE and QD in some wheel-like frustrated molecular magnetic systems have been studied. One of the systems has already been synthesized using coordination chemistry, and the other is hypothetical, where the dominant interaction is the spin-spin exchange interaction. Exact analytical methods and exact numerical diagonalization methods have been used. Some counter-intuitive non-trivial features, like non-monotonicity of quantum correlations with temperature, persistence of multipartite entanglement over bipartite ones etc. indicated by the behaviour of the correlations and the QIT measures have been found. The measures, being operational ones, can be used to realize the resource of Quantumness in experiments.Keywords: 0D Magnets, discord, entanglement, frustration
Procedia PDF Downloads 2202548 Integrating Natural Language Processing (NLP) and Machine Learning in Lung Cancer Diagnosis
Authors: Mehrnaz Mostafavi
Abstract:
The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans
Procedia PDF Downloads 802547 An Ensemble-based Method for Vehicle Color Recognition
Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi
Abstract:
The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network
Procedia PDF Downloads 752546 Psychosocial Processes and Strategies behind Islamic Deradicalisation: A Scoping Review
Authors: Carvalho M. Catia, Pinto R. Isabel, Azevedo F. Luis, Guerreiro, T. Alexandre, Barbosa R. Mariana, Pinto S. Marta
Abstract:
Due to the loss of territory, foreign terrorist fighters who have joined Islamic State are returning to their home countries. In order to counter this threat to international security, it is important to implement deradicalisation programmes, through strategies and processes that can reverse radicalisation. The objectives of this scoping review - which is underway - are to provide a comprehensive overview of the programmes being implemented, its main characteristics, the main motives and processes leading to deradicalisation, and to identify the key findings and the existing gaps in the literature. The methodology to be implemented in this scoping review follows the guidelines proposed by Arksey and O’Malley and by The Joanna Briggs Institute. The main results will be the development of a synthesis map of the deradicalisation programmes existing in the world, its main features, and recommendations to policy-makers and professionals.Keywords: deradicalisation strategies, psychosocial processes, radicalisation, terrorism
Procedia PDF Downloads 3852545 Challenges and Opportunities of Cloud-Based E-Learning Systems
Authors: Kashif Laeeq, Zubair A. Shaikh
Abstract:
The paradigm of education is drastically changing from conventional to e-learning model. Due to ease of learning with various other benefits, several educational institutions are adopting the e-learning models. Some institutions are still willing to transform their educational system on to e-learning, but due to limited resources, they are still compromising on the old traditional system. The cloud computing could be one of the best solutions to overcome this problem by providing hardware, software, and infrastructure resources with cost efficient manner. The adoption of cloud computing in education will bring revolution in this paradigm. This paper introduces various positive features of e-learning and presents a way how cloud computing technology can be provisioned e-learning model. This paper also investigates the numerous challenges and opportunities that would be observed in cloud computing adoption in e-learning domain. The concept and knowledge present in this paper may create a new direction of research in the domain of cloud-based e-learning.Keywords: cloud-based e-learning, e-learning, cloud computing application, smart learning
Procedia PDF Downloads 4042544 Automatic Classification for the Degree of Disc Narrowing from X-Ray Images Using CNN
Authors: Kwangmin Joo
Abstract:
Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique
Procedia PDF Downloads 1192543 The Role of the Board of Directors and Chief Executive Officers in Leading and Embedding Corporate Social Responsibility within Corporate Governance Regulations
Authors: Khalid Alshaikh
Abstract:
In recent years, leadership, Corporate Governance (CG) and Corporate Social Responsibility (CSR) have been under scrutiny in the Libyan society. Scholars and institutions have commenced investigating the possible resolutions they can arrange to alleviate the economic, social and environmental problems the war has produced. Thus far, these constructs requisite an in-depth reinvestigation, reconceptualization, and analysis to clearly reconstruct their rules and regulations. With the demise of Qaddafi’s regime, levels, degrees, and efforts to apply CG regulations have varied in public and private commercial banks. CSR is a new organizational culture that still designs its route within these financial institutions. Detaching itself from any notion of dictatorship and autocratic traits, leadership counts on transformational and transactional styles. Therefore, this paper investigates the extent to which the Board of Directors and Chief Executive Officers (CEOs) redefine these concepts and how they entrench CSR within the framework of CG. The research methodology used both public and private banks as a case study and qualitative research to interview ten Board of Directors (BoDs) and eleven Chief executive managers to explore how leadership, CG, and CSR are defined and how leadership integrates CSR into CG structures. The findings suggest that the CG framework in Libya still requires great efforts to be developed. Full CG code implementation appears daunting. Also, the CSR is still influenced by the power of religion. Nevertheless, the Islamic perspective is more consistent with the social contract concept of the CSR. The Libyan commercial banks do not solely focus on the economic side of maximizing profits, but also concentrate on its morality. The issue is that CSR activities are not enough to achieve good charity publicly and needs strategies to address major social issues. Moreover, leadership is more transformational and transactional and endeavors to make economic, social and environmental changes, but these changes are curtailed by tradition and traditional values dominating the Libyan social life where religious and tribal practices establish the relationship between leaders and their subordinates. Finally, the findings reveal that transformational and transactional leadership styles encourage the incorporation of CSR into the CG regulations. The boardroom and executive management have such a particular role in flagging up how embedded corporate Social responsibility is in organizational culture across the commercial banks, yet it is still important that the BoDs and CEOs need to do much more to embed corporate social responsibility through their core functions. They need to boost their standing to be more influential and make sure that the right discussions about CSR happen with the right stakeholders involved.Keywords: board of directors, chief executive officers, corporate governance, corporate social responsibility
Procedia PDF Downloads 1602542 Increase of Energy Efficiency by Means of Application of Active Bearings
Authors: Alexander Babin, Leonid Savin
Abstract:
In the present paper, increasing of energy efficiency of a thrust hybrid bearing with a central feeding chamber is considered. The mathematical model was developed to determine the pressure distribution and the reaction forces, based on the Reynolds equation and static characteristics’ equations. The boundary problem of pressure distribution calculation was solved using the method of finite differences. For various types of lubricants, geometry and operational characteristics, axial gaps can be determined, where the minimal friction coefficient is provided. The next part of the study considers the application of servovalves in order to maintain the desired position of the rotor. The report features the calculation results and the analysis of the influence of the operational and geometric parameters on the energy efficiency of mechatronic fluid-film bearings.Keywords: active bearings, energy efficiency, mathematical model, mechatronics, thrust multipad bearing
Procedia PDF Downloads 2782541 Identification of Breast Anomalies Based on Deep Convolutional Neural Networks and K-Nearest Neighbors
Authors: Ayyaz Hussain, Tariq Sadad
Abstract:
Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.Keywords: breast cancer, DCNN, KNN, mammography
Procedia PDF Downloads 1282540 Timing and Noise Data Mining Algorithm and Software Tool in Very Large Scale Integration (VLSI) Design
Authors: Qing K. Zhu
Abstract:
Very Large Scale Integration (VLSI) design becomes very complex due to the continuous integration of millions of gates in one chip based on Moore’s law. Designers have encountered numerous report files during design iterations using timing and noise analysis tools. This paper presented our work using data mining techniques combined with HTML tables to extract and represent critical timing/noise data. When we apply this data-mining tool in real applications, the running speed is important. The software employs table look-up techniques in the programming for the reasonable running speed based on performance testing results. We added several advanced features for the application in one industry chip design.Keywords: VLSI design, data mining, big data, HTML forms, web, VLSI, EDA, timing, noise
Procedia PDF Downloads 2492539 User Requirements Analysis for the Development of Assistive Navigation Mobile Apps for Blind and Visually Impaired People
Authors: Paraskevi Theodorou, Apostolos Meliones
Abstract:
In the context of the development process of two assistive navigation mobile apps for blind and visually impaired people (BVI) an extensive qualitative analysis of the requirements of potential users has been conducted. The analysis was based on interviews with BVIs and aimed to elicit not only their needs with respect to autonomous navigation but also their preferences on specific features of the apps under development. The elicited requirements were structured into four main categories, namely, requirements concerning the capabilities, functionality and usability of the apps, as well as compatibility requirements with respect to other apps and services. The main categories were then further divided into nine sub-categories. This classification, along with its content, aims to become a useful tool for the researcher or the developer who is involved in the development of digital services for BVI.Keywords: accessibility, assistive mobile apps, blind and visually impaired people, user requirements analysis
Procedia PDF Downloads 1182538 Cognition Technique for Developing a World Music
Authors: Haider Javed Uppal, Javed Yunas Uppal
Abstract:
In today's globalized world, it is necessary to develop a form of music that is able to evoke equal emotional responses among people from diverse cultural backgrounds. Indigenous cultures throughout history have developed their own music cognition, specifically in terms of the connections between music and mood. With the advancements in artificial intelligence technologies, it has become possible to analyze and categorize music features such as timbre, harmony, melody, and rhythm and relate them to the resulting mood effects experienced by listeners. This paper presents a model that utilizes a screenshot translator to convert music from different origins into waveforms, which are then analyzed using machine learning and information retrieval techniques. By connecting these waveforms with Thayer's matrix of moods, a mood classifier has been developed using fuzzy logic algorithms to determine the emotional impact of different types of music on listeners from various cultures.Keywords: cognition, world music, artificial intelligence, Thayer’s matrix
Procedia PDF Downloads 732537 Sensor and Sensor System Design, Selection and Data Fusion Using Non-Deterministic Multi-Attribute Tradespace Exploration
Authors: Matthew Yeager, Christopher Willy, John Bischoff
Abstract:
The conceptualization and design phases of a system lifecycle consume a significant amount of the lifecycle budget in the form of direct tasking and capital, as well as the implicit costs associated with unforeseeable design errors that are only realized during downstream phases. Ad hoc or iterative approaches to generating system requirements oftentimes fail to consider the full array of feasible systems or product designs for a variety of reasons, including, but not limited to: initial conceptualization that oftentimes incorporates a priori or legacy features; the inability to capture, communicate and accommodate stakeholder preferences; inadequate technical designs and/or feasibility studies; and locally-, but not globally-, optimized subsystems and components. These design pitfalls can beget unanticipated developmental or system alterations with added costs, risks and support activities, heightening the risk for suboptimal system performance, premature obsolescence or forgone development. Supported by rapid advances in learning algorithms and hardware technology, sensors and sensor systems have become commonplace in both commercial and industrial products. The evolving array of hardware components (i.e. sensors, CPUs, modular / auxiliary access, etc…) as well as recognition, data fusion and communication protocols have all become increasingly complex and critical for design engineers during both concpetualization and implementation. This work seeks to develop and utilize a non-deterministic approach for sensor system design within the multi-attribute tradespace exploration (MATE) paradigm, a technique that incorporates decision theory into model-based techniques in order to explore complex design environments and discover better system designs. Developed to address the inherent design constraints in complex aerospace systems, MATE techniques enable project engineers to examine all viable system designs, assess attribute utility and system performance, and better align with stakeholder requirements. Whereas such previous work has been focused on aerospace systems and conducted in a deterministic fashion, this study addresses a wider array of system design elements by incorporating both traditional tradespace elements (e.g. hardware components) as well as popular multi-sensor data fusion models and techniques. Furthermore, statistical performance features to this model-based MATE approach will enable non-deterministic techniques for various commercial systems that range in application, complexity and system behavior, demonstrating a significant utility within the realm of formal systems decision-making.Keywords: multi-attribute tradespace exploration, data fusion, sensors, systems engineering, system design
Procedia PDF Downloads 1742536 Trans-Gendered Female Characters: A Comparative Study of Two Female Characters in English and Persian Literature - Lady Macbeth and Gord Afarid
Authors: Seyedeh Azadeh Johari
Abstract:
For thousand years, the literature of the world has been mostly composed of men, and in all different forms of it, men have tried to propose their masculine desires, ideologies, and beliefs. What has been less written about or studied, however, was the role that female desire plays in the predominantly masculine society, and mostly the role of male desires was the key point in literature. Male writers have mostly shown their female characters either as stereotypes and void of dynamic characters, images of a meek person who bent to the will of her male superiors or as wicked or villains. The only exception was the kind of strong and courageous women who have mostly been masculinized by their authors, mostly male authors, as showing the valuable or important features of men, instead of women’s. These characters are transgendered by the author and have a gender identity or expression that differs from the sex to which they were assigned. This is the issue that is discussed in this project. We will refer to some examples of female characters who show masculine traits and characteristics.Keywords: comparative literature, female, masculinized, transgendered
Procedia PDF Downloads 1482535 Evaluation of Subsurface Drilling and Geo Mechanic Properties Based on Stratum Index Factor for Humanities Environment
Authors: Abdull Halim Abdul, Muhaimin Sulam
Abstract:
This paper is about a subsurface study of Taman Pudu Ulu, Cheras, Kuala Lumpur with emphasize of Geo mechanic properties based on stratum index factor in humanities environment. Subsurface drilling and seismic data were used to understand the subsurface condition of the study area such as the type and thickness of the strata. Borehole and soil samples were recovered Geo mechanic properties of the area by conducting number of experiments. Taman Pudu Ulu overlies the Kuala Lumpur Limestone formation that is known for its karstic features such as caves and cavities. Hence by knowing the Geo mechanic properties such as the normal strain and shear strain we can plan a safer and economics construction that is plan at the area in the future.Keywords: stratum, index factor, geo mechanic properties, humanities environment
Procedia PDF Downloads 4922534 Optimising GIS in Cushioning the Environmental Impact of Infrastructural Projects
Authors: Akerele Akintunde Hareef
Abstract:
GIS is an integrating tool for storing, retrieving, manipulating, and analyzing spatial data. It is a tool which defines an area with respect to features and other relevant thematic delineations. On the other hand, Environmental Impact Assessment in short is both positive and negative impact of an infrastructure on an environment. Impact of infrastructural projects on the environment is an aspect of development that barely get extensive portion of pre-project execution phase and when they do, the effects are most times not implemented to cushion the impact they have on human and the environment. In this research, infrastructural projects like road constructions, water reticulation projects, building constructions, bridge etc. have immense impact on the environment and the people that reside in location of construction. Hence, the need for this research tends to portray the relevance of Environmental Impact assessment in calculating the vulnerability of human and the environment to imbalance necessitated by this infrastructural development and how the use of GIS application can be optimally applied to annul or minimize the effect.Keywords: environmental impact assessment (EIA), geographic information system (GIS), infrastructural projects, environment
Procedia PDF Downloads 5412533 Patient-Specific Modeling Algorithm for Medical Data Based on AUC
Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper
Abstract:
Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions
Procedia PDF Downloads 4722532 High Temperature Oxidation Behavior of Aluminized Steel by Arc Spray and Cementation Techniques
Authors: Minoo Tavakoli, Alireza Kiani Rashid, Abbas Afrasiabi
Abstract:
An aluminum coating deposited on mild steel substrate by electric arc spray and diffused to the base steel material by diffusion treatment at 800 and 900°C for 1 and 3 hours in a static air. Alloy layers formed by diffusion at both temperatures were investigated, and their features were compared with those of pack cementation aluminized steel. High-temperature oxidation tests were carried out in air at 600 °C for 145 hours. Results indicated that the aluminide coatings obtained from this process have significantly improved the high-temperature oxidation resistance in both methods due to the Al2O3 scale formation. Furthermore, it showed that the isothermal oxidation resistance of arc spray technique is better than pack cementation method. This can be attributed to voids that formed at the intermetallic layer /Al layer interface which is increased in the pack cementation method.Keywords: electric arc spray, pack cementation, oxidation resistance, aluminized steel
Procedia PDF Downloads 462