Search results for: local stakeholders network

Authors: Manuela S. Tolentino, John G. Nepomuceno

Abstract:

The purpose of this investigation was to assess the implementation of inclusive education (IE) in 6 elementary and 5 secondary public schools in the City Schools Division of Dasmarinas. Participants in this study were 11 school heads, 73 teachers, 22 parents and 22 students (regular and with special needs) who were selected using purposive sampling. A 30-item questionnaire was used to gather data on the extent of the implementation of IE in the division while focus group discussion (FGD) was used to gather insights on what facilitate and hinder the implementation of the IE program. This study assessed the following variables: school culture and environment, inclusive education policy implementation, and curriculum design and practices. Data were analyzed using frequency count, mean and ranking. Results revealed that participants have similar assessment on the extent of the implementation of IE. School heads rated school culture and environment as highest in terms of implementation while teachers and pupils chose curriculum design and practices. On the other hand, parents felt that inclusive education policies are implemented best. School culture and environment are given high ratings. Participants perceived that the IE program in the division is making everyone feel welcome regardless of age, sex, social status, physical, mental and emotional state; students with or without disability are equally valued, and students help each. However, some aspects of the IE program implementation are given low ratings namely: partnership between staff, parents and caregivers, school’s effort to minimize discriminatory practice, and stakeholders sharing the philosophy of inclusion. As regards education policy implementation, indicators with the highest ranks were school’s effort to admit students from the locality especially students with special needs, and the implementation of the child protection policy and anti-bullying policy. The results of the FGD revealed that both school heads and teachers possessed the welcoming gesture to accommodate students with special needs. This can be linked to the increasing enrolment of SNE in the division. However, limitations of the teachers’ knowledge on handling learners, facilities and collaboration among stakeholders hinder the implementation of IE program. Based on the findings, inclusion program framework was developed for program enhancement. This will be the basis for the improvement of the program’s efficiency, the relationship between stakeholders, and formulation of solutions.

Keywords: inclusion, inclusive education, framework, special education

Procedia PDF Downloads 167

Authors: José Luis Carrillo-Medina, Roberto Latorre

Abstract:

Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

Procedia PDF Downloads 485

Authors: Chunye Gong, Ming Tie, Jie Liu, Weimin Bao, Xinbiao Gan, Shengguo Li, Bo Yang, Xuguang Chen, Tiaojie Xiao, Yang Sun

Abstract:

Large-scale CFD simulation relies on high-performance parallel computing, and the load balance is the key role which affects the parallel efficiency. This paper focuses on the load-balancing problem of parallel CFD simulation with structured mesh. A mathematical model for this load-balancing problem is presented. The genetic algorithm, fitness computing, two-level code are designed. Optimal selector, robust operator, and local optimization operator are designed. The properties of the presented genetic algorithm are discussed in-depth. The effects of optimal selector, robust operator, and local optimization operator are proved by experiments. The experimental results of different test sets, DLR-F4, and aircraft design applications show the presented load-balancing algorithm is robust, quickly converged, and is useful in real engineering problems.

Keywords: genetic algorithm, load-balancing algorithm, optimal variation, local optimization

Procedia PDF Downloads 177

Authors: K. Govender, M. Sinclair

Abstract:

This paper demonstrates a proof of concept whereby shorter trips and land use densification can be promoted through an alternative approach to planning and implementation of road infrastructure in the South African context. It briefly discusses how the development of the Compact City concept relies on a combination of promoting shorter trips and densification through a change in focus in road infrastructure provision. The methodology developed in this paper uses a traffic model to test the impact of synthesized deterrence functions on congestion locations in the road network through the assignment of traffic on the study network. The results from this study demonstrate that intelligent planning of road infrastructure can indeed promote reduced urban sprawl, increased residential density and mixed-use areas which are supported by an efficient public transport system; and reduced dependence on the freeway network with a fixed road infrastructure budget. The study has resonance for all cities where urban sprawl is seemingly unstoppable.

Keywords: compact cities, densification, road infrastructure planning, transportation modelling

Procedia PDF Downloads 171

Authors: Anastasios Zopiatis, Antonis L. Theocharous

Abstract:

In the midst of a fierce war for talent, the hospitality industry is seeking new and innovative ways to enrich its image as an employer of choice and not a necessity. Historically, the industry’s professions are portrayed as ‘unattractive’ due to their repetitious nature, long and unsocial working schedules, below average remunerations, and the mental and physical demands of the job. Aligning with the industry, hospitality and tourism scholars embarked on a journey to investigate pertinent topics with the aim of enhancing our conceptual understanding of the elements that influence employees at the hospitality world of work. Topics such as job involvement, commitment, job and career satisfaction, and turnover intentions became the focal points in a multitude of relevant empirical and conceptual investigations. Nevertheless, gaps or inconsistencies in existing theories, as a result of both the volatile complexity of the relationships governing human behavior in the hospitality workplace, and the academic community’s unopposed acceptance of theoretical frameworks mainly propounded in the United States and United Kingdom years ago, necessitate our continuous vigilance. Thus, in an effort to enhance and enrich the discourse, we set out to investigate the relationship between intrinsic and extrinsic job satisfaction traits and the individual’s career satisfaction, and subsequent intention to remain in the hospitality industry. Reflecting on existing literature, a quantitative survey was developed and administered, face-to-face, to 650 individuals working as full-time employees in 4- and 5- star hotel establishments in Cyprus, whereas a multivariate statistical analysis method, namely Structural Equation Modeling (SEM), was utilized to determine whether relationships existed between constructs as a means to either accept or reject the hypothesized theory. Findings, of interest to both industry stakeholders and academic scholars, suggest that the individual’s future intention to remain within the industry is primarily associated with extrinsic job traits. Our findings revealed that positive associations exist between extrinsic job traits, and both career satisfaction and future intention. In contrast, when investigating the relationship of intrinsic traits, a positive association was revealed only with career satisfaction. Apparently, the local industry’s environmental factors of seasonality, excessive turnover, overdependence on seasonal, and part-time migrant workers, prohibit industry stakeholders in effectively investing the time and resources in the development and professional growth of their employees. Consequently intrinsic job satisfaction factors such as advancement, growth, and achievement, take backstage to the more materialistic extrinsic factors. Findings from the subsequent mediation analysis support the notion that intrinsic traits can positively influence future intentions indirectly only through career satisfaction, whereas extrinsic traits can positively impact both career satisfaction and future intention both directly and indirectly.

Keywords: career satisfaction, Cyprus, hotel employees, structural equation modeling, SEM

Procedia PDF Downloads 276

Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

Abstract:

Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

Procedia PDF Downloads 362

Authors: Melissa Teo

Abstract:

It is increasingly important that project managers develop greater capabilities to better manage the social, cultural, political, environmental and economic impacts on proposed construction projects. These challenges are best resolved in consultation with communities rather than in conflict with them. This is particularly important on controversial projects which are projects that have obtained government sanctioned ‘development approval’ but not ‘community approval’. While a rich body of research and intellectual frameworks exist in the fields of urban geography and planning to understand and manage community concerns during the pre-development approval stages of new projects, current theoretical frameworks guiding community engagement in project management are inadequate. A new and innovative research agenda is needed to guide thinking about the role of local communities in the construction process and is an important research gap that needs to be filled. Within this context, this research aims to assess the effectiveness of strategies adopted by project teams to engage with local communities so as to capture lessons learnt to apply to future projects. This paper reports a research methodology which uses Arnstein’s model of participation to better understand how power differentials between the project team and local communities can influence the adoption of community engagement strategies. A case study approach is utilizing interviews and documentary analysis of a large-scale controversial construction project in Queensland, Australia is presented. The findings will result in a number of recommendations to guide community engagement practices on future projects.

Keywords: community engagement, construction, case study, project management

Procedia PDF Downloads 249

Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari

Abstract:

This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.

Keywords: artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix

Procedia PDF Downloads 138

Authors: Jingting Luo, Yong Wang, Ying Wang

Abstract:

Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

Procedia PDF Downloads 137

Authors: Ardavan Zamanpour, Somaieh Yassari

Abstract:

ZigBee protocol network was developed in industries and MIT laboratory in 1997. ZigBee is a wireless networking technology by alliance ZigBee which is designed to low board and low data rate applications. It is a Protocol which connects between electrical devises with very low energy and cost. The first version of IEEE 802.15.4 which was formed ZigBee was based on 2.4GHZ MHZ 912MHZ 868 frequency band. The name of system is often reminded random directions that bees (BEES) traversing during pollination of products. Such as alloy of the ways in which information packets are traversed within the mesh network. This paper aims to study the performance and effectiveness of this protocol in wireless networks.

Keywords: ZigBee, protocol, wireless, networks

Procedia PDF Downloads 362

Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

Abstract:

Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

Procedia PDF Downloads 91

Authors: Henni Mansour Abdelwaheb

Abstract:

This study discusses the design of a bounded position/force feedback controller developed to ensure position and force tracking for bilateral teleoperation arms operating with variable delay, and actuator saturation. Also, an adaptive robust Radial Basis Function (RBF) neural network is used to estimate the environment torque. The parameters of the environment torque are then sent from the slave site to the master site as a non-power signal to avoid passivity problems. Moreover, a nonlinear function is applied to each controller term as a smooth saturation function, providing a bounded control signal and preserving the system’s actuators. Lastly, the Lyapunov approach demonstrates the global stability of the controlled system, and numerical experiment results further confirm the validity of the presented strategy.

Keywords: teleoperation manipulators system, time-varying delay, actuator saturation, adaptive robust rbf neural network approximation, uncertainties

Procedia PDF Downloads 70

Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

Abstract:

The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

Procedia PDF Downloads 174

Authors: Toufik Bensana, Slimane Mekhilef, Kamel Tadjine

Abstract:

Vibration analysis has been frequently applied in the condition monitoring and fault diagnosis of rolling element bearings. Unfortunately, the vibration signals collected from a faulty bearing are generally non stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, condition monitoring, local mean decomposition, rolling element bearing, vibration analysis

Procedia PDF Downloads 390

Authors: Oliver Fuo

Abstract:

One of the central features of post-apartheid constitutional reform was the establishment of local government as a distinct sphere of government in the Constitution of the Republic of South Africa, 1996. Local government, constituted by about 279 wall-to-wall municipalities, have legislative and executive powers vested in democratically elected municipal councils to govern areas within their jurisdiction subject only to limits imposed by the Constitution. In addition, unlike the past where municipalities merely played a service delivery role, they are now mandated to realise an expanded developmental mandate – pursue social justice and sustainable development; contribute, together with national and provincial government, to the realisation of socio-economic rights entrenched in the Bill of Rights; and facilitate public participation in local governance. In order to finance their developmental programmes, municipalities receive equitable allocations from national government and have legal powers to generate additional finances by charging rates on property and imposing surcharges on services provided. In addition to its general obligation to foster public participation in local governance, the law requires municipalities to facilitate public participation in their budgeting processes. This requirement is generally consistent with recent trends in local government democratic reforms which call for inclusive budget planning and implementation whereby citizens, civil society and NGOs participate in the allocation of resources. This trend is best captured in the concept of participatory budgeting. This paper specifically analyses the legal and policy framework for participatory budgeting at the local government level in South Africa. Using Borbet South Africa (Pty) Ltd and Others v Nelson Mandela Bay Municipality 2014 (5) SA 256 (ECP) as an example, this paper argues that the legal framework for participatory budgeting creates an illusory right for citizens to participate in municipal budgeting processes. This challenge is further compounded by the barrenness of the jurisprudence of courts that interpret the obligation of municipalities in this regard. It is submitted that the wording of s 27(4) of the Municipal Finance Management Act (MFMA) 53 of 2003 - which expressly stipulates that non-compliance by a municipality with a provision relating to the budget process or a provision in any legislation relating to the approval of a budget-related policy, does not affect the validity of an annual or adjustments budget – is problematic as it seems to trivialise the obligation to facilitate public participation in budgeting processes. It is submitted that where this provision is abused by municipal officials, this could lead to the sidelining of the real interests of communities in local budgets. This research is based on a critical and integrated review of primary and secondary sources of law.

Keywords: courts and jurisprudence, local government law, participatory budgeting, South Africa

Procedia PDF Downloads 386

Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

Procedia PDF Downloads 341

Authors: Clara T. Fatoye, April Betts, Abayomi Odeyemi, Francis A. Fatoye, Isaac O. Odeyemi

Abstract:

Background: The post-launch stage is the last stage in the development of a pharmaceutical product. It is important to involve patients in the development of pharmaceutical products at the post-launch stage, as patients are the end-users of pharmaceutical products. It is expected that involving them might ensure an effective working relationship among the various stakeholders. However, involving patients in the development of pharmaceutical products comes with its problems. Hence, this study examined how to resolve problems experienced by involving patients in the developments of pharmaceutical products’ at post-launch consisting of Positioning of pharmaceutical products (POPP), detailing of pharmaceutical products (DOPP) and reimbursement and Formulary Submission (R&FS). Methods: A questionnaire was used for the present study. It was administered at the ISPOR Glasgow 2017 to 104 participants, all of which were professionals from Market access (MA) and health economics and outcomes research (HEOR) backgrounds. They were asked how the issues experienced by patients can be resolved. Participants responded under six domains as follows: communication, cost, effectiveness, external factors, Quality of life (QoL) and safety. Thematic analysis was carried out to identify strategies to resolve issues experienced by patients at the post-launch stage. Results: Three (3) factors cut across at POPP, DOPP, and R&FS that is (external factors, communication and QoL). The first resolution method was an external factor that is, the relationship with stakeholders and policymakers. Communication was also identified as a resolution method that can help to resolve problems experienced by patients at the post-launch stage. The third method was QoL as perceived by the patients based on professionals’ opinions. Other strategies that could be used to resolve problems experienced were the effectiveness of pharmaceutical products at the DOPP level and cost at R&FS. Conclusion: The study showed that focusing on external factors, communication, and patients’ QoL are methods for resolving issues experienced by involving patients at the post-launch stage of pharmaceutical product development. Hence, effective working relationships between patients, policymakers and stakeholders may help to resolve problems experienced at the post-launch stage. Healthcare policymakers are to be aware of these findings as they may help them to put appropriate strategies in place to enhance the involvement of patients in pharmaceutical product development at the post-launch stage, thereby improving the health outcomes of the patients.

Keywords: patients, pharmaceutical products, post-launch stage, quality of life, QoL

Procedia PDF Downloads 128

Authors: Hocini Yousra, Medjnoun Amal, Khiatine Mohamed, Bahar Ramdane

Abstract:

The development of the regions of southern Algeria require the construction of numerous road, rail, and airport infrastructures. However, this development is very expensive given the very severe climatic conditions, the difficulty of reusing local materials, and the unavailability of water on the project sites; these regions are characterized by an arid or semi-arid climate, which means that water sources are very limited. The climatic conditions and the scarcity of water make soil compaction work very difficult and excessively expensive. These constraints related to the supply of water for irrigation of these construction sites make it necessary to examine the solution of compaction with low water content. This work studies the possibility of improving the compaction with a low water content of the soils of southern Algeria and this by using natural or recycled ecological materials. Local soils are first subjected to a series of laboratory characterization tests, then mixed with varying amounts of natural additives. The new materials are, in turn, subjected to road tests.

Keywords: compaction, low water content, sand, natural materials

Procedia PDF Downloads 112

Authors: Sheela Tiwari, R. Naresh, R. Jha

Abstract:

The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

Procedia PDF Downloads 367

Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

Abstract:

We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

Procedia PDF Downloads 365

Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

Abstract:

The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

Procedia PDF Downloads 139

Authors: Ojin Kwon, Yong-Jin Yoon, Liu Xin, Zhang Hongbao

Abstract:

Wireless Body Area Network (WBAN) is a short-range wireless communication around human body for various applications such as wearable devices, entertainment, military, and especially medical devices. WBAN attracts the attention of continuous health monitoring system including diagnostic procedure, early detection of abnormal conditions, and prevention of emergency situations. Compared to cellular network, WBAN system is more difficult to control inter- and inner-cell interference due to the limited power, limited calculation capability, mobility of patient, and non-cooperation among WBANs. In this paper, we compare the performance of resource allocation scheme based on several Pseudo Orthogonal Codewords (POCs) to mitigate inter-WBAN interference. Previously, the POCs are widely exploited for a protocol sequence and optical orthogonal code. Each POCs have different properties of auto- and cross-correlation and spectral efficiency according to its construction of POCs. To identify different WBANs, several different pseudo orthogonal patterns based on POCs exploits for resource allocation of WBANs. By simulating these pseudo orthogonal resource allocations of WBANs on MATLAB, we obtain the performance of WBANs according to different POCs and can analyze and evaluate the suitability of POCs for the resource allocation in the WBANs system.

Keywords: wireless body area network, body sensor network, resource allocation without feedback, interference mitigation, pseudo orthogonal pattern

Procedia PDF Downloads 347

Authors: Kelly La Venture

Abstract:

Destination promotion has become increasingly important to economic development in the United States. Key stakeholders of the First City on the Mississippi recognizes the importance of the visitor economy and decided to invest in destination promotion of the city as ‘True North’. The purpose of the study was to analyze the economic impact and stakeholder perceptions of destination promotion of the First City on the Mississippi as ‘True North’ using the case study method. Using a semi-structured guide consisting of 8 questions and additional probing questions, investigators engaged in 30-90 minute interviews with 20 key stakeholders. Data captured was then subjected to thematic analysis and interpretation. Through a review of literature and interview analysis, data indicate(1) there is increasing value in the visitor economy of the First City on the Mississippi,(2) the visitor economy warrants investments in destination promotion,(3) economic development can be more effective through coordination of destination promotion of as ‘True North’ and,(4) destination promotion fuels development across the entire economic spectrum. As a result, the First City on the Mississippi should prioritize marketing efforts of as ‘True North’ and coordinate these efforts with economic development initiatives. The destination promotion of the First City on the Mississippi as ‘True North’ may raise the communities profile and open doors to increased tourism, business relocation, business expansion, and entrepreneurs ready to start up a new organization.

Keywords: case study method, destination promotion, economic development, leisure and business travel

Procedia PDF Downloads 291

Authors: Gaurav Kumar Sinha

Abstract:

The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

Procedia PDF Downloads 29

Authors: Fadhl Alakwaa

Abstract:

One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.

Keywords: gene regulatory network, biclustering, denoising, system biology

Procedia PDF Downloads 229

Authors: Amani Tahat, Serdal Kirmizialtin

Abstract:

Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

Procedia PDF Downloads 220

Authors: Kaushik Sathupadi, Sandesh Achar

Abstract:

Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

Procedia PDF Downloads 12

Authors: Florian Engel

Abstract:

Only with a radical change to an intrinsically motivated project team, through giving the employees the freedom for autonomy, mastery and purpose, it is then possible to develop excellent products. With these changes, combined with using a rapid application development approach, the group of users serves as an important indicator to test the market needs, rather than only as the stakeholders for requirements.

Keywords: intrinsic motivation, requirements elicitation, self-directed work, stakeholder participation

Procedia PDF Downloads 334

Authors: Agnes Siwi Purwaning Tyas, Dewi Cahya Ambarwati

Abstract:

This paper concerns the language learning that develops as a habit formation and a constructive process while exercising an oppressive power to construct the learners. As a locus of discussion, the investigation problematizes the transfer of English language to Indonesian students of junior high school through the use of English textbooks ‘Real Time: An Interactive English Course for Junior High School Students Year VII-IX’. English language has long performed as a global language and it is a demand upon the non-English native speakers to master the language if they desire to become internationally recognized individuals. Generally, English teachers teach the language in accordance with the nature of language learning in which they are trained and expected to teach the language within the culture of the target language. This provides a potential soft cultural penetration of a foreign ideology through language transmission. In the context of Indonesia, learning English as international language is considered dilemmatic. Most English textbooks in Indonesia incorporate cultural elements of the target language which in some extent may challenge the sensitivity towards local cultural values. On the other hand, local teachers demand more English textbooks for junior high school students which can facilitate cultural dissemination of both local and global values and promote learners’ cultural traits of both cultures to avoid misunderstanding and confusion. It also aims to support language learning as bidirectional process instead of instrument of oppression. However, sensitizing and localizing this foreign language is not sufficient to restrain its soft infiltration. In due course, domination persists making the English language as an authoritative language and positioning the locality as ‘the other’. Such critical premise has led to a discursive analysis referring to how the cultural elements of the target language are presented in the textbooks and whether the local characteristics of Indonesia are able to gradually reduce the degree of the foreign oppressive ideology. The three textbooks researched were written by non-Indonesian author edited by two Indonesia editors published by a local commercial publishing company, PT Erlangga. The analytical elaboration examines the cultural characteristics in the forms of names, terminologies, places, objects and imageries –not the linguistic aspect– of both cultural domains; English and Indonesia. Comparisons as well as categorizations were made to identify the cultural traits of each language and scrutinize the contextual analysis. In the analysis, 128 foreign elements and 27 local elements were found in textbook for grade VII, 132 foreign elements and 23 local elements were found in textbook for grade VIII, while 144 foreign elements and 35 local elements were found in grade IX textbook, demonstrating the unequal distribution of both cultures. Even though the ideal pedagogical approach of English learning moves to a different direction by the means of inserting local elements, the learners are continuously imposed to the culture of the target language and forced to internalize the concept of values under the influence of the target language which tend to marginalize their native culture.

Keywords: bidirectional process, English, local culture, oppression

Procedia PDF Downloads 262

Authors: Ashish Payal, C. S. Rai, B. V. R. Reddy

Abstract:

With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m² grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

Keywords: localization, wireless sensor networks, artificial neural network, radial basis function, multi-layer perceptron, backpropagation, RSSI, GPS

Procedia PDF Downloads 330