Search results for: data driven diagnosis

Authors: Syamsul Syafiq, Maslina Daud, Hafizah Hasan, Ahmad Zairi, Shazil Imri, Ezaini Akmar, Norbazilah Rahim

Abstract:

Over recent years, web development has changed significantly. Driven largely by the rise of trends like mobiles, the world of development is rapidly evolving. The rise of the Internet makes web applications crucial nowadays. The web application has been an interface for a company and one of the ways they present their portfolio to the client. On the other hand, the web has become part of the file management system which takes over the role of paper. Due to high demand in web applications, developers are required to develop a web application that are cost-effective, secure and well coded. A framework has been proposed to develop an application rather than using library style development. The framework is helping the developer in creating the structure of a web automatically. This paper will compare the advantages and disadvantages of web development using framework against library-style development. This comparison is based on a previous research paper focusing on two main indicators, which are the impact to management and impact to the developer.

Keywords: framework, library style development, web application development, traditional web, static web, dynamic web

Procedia PDF Downloads 218

Authors: Nchanji Yvonne Kiki, Mala William Armand, Nchanji Eileen Bogweh, Ramcilovik-Suominen Sabaheta, Kotilainen Juha

Abstract:

The Campo Ma'an National Park was created as part of an environmental and biodiversity compensation for the Chad-Cameroon Oil Pipeline Project, which was meant to help alleviate poverty and boost the livelihood of rural communities around the area. This paper examines different strategies and coping mechanisms employed by the indigenous people and local communities to deal with the national and internationally driven conservation policies and initiatives in the case of the Campo Ma'an National Park. While most literature on park management/co-management/nature conservation has focused on the negative implications for local peoples’ livelihoods, fewer studies have investigated the strategies of local people to respond to these policies and renegotiate their position in a way that enables them to continue their traditional livelihoods using the existing local knowledge systems. This study contributes to the current literature by zooming into not only the impacts of nature conservation policies but also the local individual and collective strategies and responses to such policies and initiatives. We employ a qualitative research approach using ethnomethodology and a convivial lens to analyze data collected from October to November 2018. We find that conservation policies have worsened some existing livelihoods on the one hand and constrained livelihood improvement of indigenous people and local communities (IPLC) on the other hand. Nonetheless, the IPLC has devised individual and collective coping mechanisms to deal with these conservation interventions and the negative effects they have caused. Upon exploring these mechanisms and their effectiveness, this study proposes a management approach to conservation centered on both people and nature, based on indigenous and local people's knowledge and practices, promoting nature for and by humans and strengthening both livelihood and conservation. We take inspiration from the convivial conservation approach and thinking by Bucher and Fletcher.

Keywords: conservation policies, national park management, indigenous and local people’s experiences, livelihoods, local knowledge, coping strategies, conviviality

Procedia PDF Downloads 176

Authors: Fernando Doral

Abstract:

Family businesses are a key phenomenon within the business landscape. Nevertheless, it involves two terms (“family” and “business”) which are nowadays rapidly evolving. Consequently, it isn't easy to diagnose if a family business will be a growing or decreasing phenomenon, which is the objective of this study. For that purpose, a sample of 50 Spanish-established companies from various sectors was taken. Different factors were identified for each enterprise, related to the profile of the founders, such as age, the number of sons and daughters, or support received from the family at the moment to start it up. That information was taken as an input for a clustering method to identify groups, which could help define the founders' profiles. That characterization was carried as a base to identify three factors whose evolution should be analyzed: family structures, business landscape and entrepreneurs' motivations. The analysis of the evolution of these three factors seems to indicate a negative tendency of family businesses. Therefore the consequent diagnosis of this study is to consider family businesses as a declining phenomenon.

Keywords: business diagnose, business trends, family business, family business founders

Procedia PDF Downloads 200

Authors: Aniedi A. Udo, Magnus U. Igboekwe, Rasaaq Bello, Francis D. Eyenaka, Michael C. Ohakwere-Eze

Abstract:

Groundwater vulnerability to pollution was assessed in Akwa Ibom State, Southern Nigeria, with the aim of locating areas with high potentials for resource contamination, especially due to anthropogenic influence. The electrical resistivity method was utilized in the collection of the initial field data. Additional data input, which included depth to static water level, drilled well log data, aquifer recharge data, percentage slope, as well as soil information, were sourced from secondary sources. The initial field data were interpreted both manually and with computer modeling to provide information on the geoelectric properties of the subsurface. Interpreted results together with the secondary data were used to develop the DRASTIC thematic maps. A vulnerability assessment was performed using the DRASTIC model in a GIS environment and areas with high vulnerability which needed immediate attention was clearly mapped out and presented using an aquifer vulnerability map. The model was subjected to validation and the rate of validity was 73% within the area of study.

Keywords: groundwater, vulnerability, DRASTIC model, pollution

Procedia PDF Downloads 202

Authors: Tonderai Muchenje, Xolani Mkhwanazi

Abstract:

Precision agriculture uses a number of technologies, devices, protocols, and computing paradigms to optimize agricultural processes. Big data, artificial intelligence, cloud computing, and edge computing are all used to handle the huge amounts of data generated by precision agriculture. However, precision agriculture is still emerging and has a low level of security features. Furthermore, future solutions will demand data availability and accuracy as key points to help farmers, and security is important to build robust and efficient systems. Since precision agriculture comprises a wide variety and quantity of resources, security addresses issues such as compatibility, constrained resources, and massive data. Moreover, conventional protection schemes used in the traditional internet may not be useful for agricultural systems, creating extra demands and opportunities. Therefore, this paper aims at reviewing state of the art of precision agriculture security, particularly in open field agriculture, discussing its architecture, describing security issues, and presenting the major challenges and future directions.

Keywords: precision agriculture, security, IoT, EIDE

Procedia PDF Downloads 84

Authors: Fatten F. Elkomy

Abstract:

The Syrian refugees are the largest refugee population since World War II. Mostly, children, these individuals were exposed to intense traumatic events in their homeland, throughout their journey, and during settlement in foreign lands. Art is a universal language to express feelings and tough human experiences. It is also a medium for healing and promoting creativity and resilience. Literature review was conducted to examine the use of art to facilitate psychiatric interviews, diagnosis, and therapy with traumatized children. Results show a severe impact of childhood trauma on the increased risk for abuse, neglect, and psychiatric disorders. Clinicians must recognize, evaluated and provide help for these children. In conclusion, drawings are used to tell a story, reflect deep emotions, and create a meaningful self-recognition and determination. Participants will understand art therapy using the expressive therapies continuum framework to evaluate drawings and to promote healing for refugee children.

Keywords: art therapy, children drawings, Syrian refugees, trauma in childhood

Procedia PDF Downloads 160

Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

Abstract:

The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

Procedia PDF Downloads 73

Authors: Madiha Tariq, Hena Rabbani

Abstract:

Recently, cavity-optomechanics becomes an extensive research field that has manipulated the mechanical effects of light for coupling of the optical field with other physical objects specifically with regards to dynamical localization. We investigate the dynamical localization (both in momentum and position space) for a vibrational mirror in a Fabry-Pérot cavity driven by a single mode optical field and a transverse probe field. The weak probe field phenomenon results in classical chaos in phase space and spatio temporal dynamics in position |ψ(x)²| and momentum space |ψ(p)²| versus time show quantum localization in both momentum and position space. Also, we discuss the parametric dependencies of dynamical localization for a designated set of parameters to be experimentally feasible. Our work opens an avenue to manipulate the other optical phenomena and applicability of proposed work can be prolonged to turn-able laser sources in the future.

Keywords: dynamical localization, cavity optomechanics, Hamiltonian chaos, probe field

Procedia PDF Downloads 144

Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

Abstract:

The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

Procedia PDF Downloads 69

Authors: Nebi Gedik

Abstract:

One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: wave atom transform, statistical features, multi-resolution representation, mammogram

Procedia PDF Downloads 214

Authors: Mahesh Hariharan, Rajan Balasubramaniam, Sharath Kumar Shetty, Shanthala Yadavalli, Mohammed Ahetasham, Sravya Devarapalli

Abstract:

Acute abdomen is one of the most common clinical conditions requiring a radiological investigation. Ultrasound is the primary modality of choice which can diagnose some of the common causes of acute abdomen. However, sometimes the underlying cause for the pain is far more complicated than expected to mandate a high degree of suspicion to suggest further investigation with contrast-enhanced computed tomography or magnetic resonance imaging. Here, we have compiled a comprehensive series of selected cases to highlight the conditions which can be easily overlooked unless carefully sought for. This also emphasizes the importance of multimodality approach to arrive at the final diagnosis with an increased overall diagnostic accuracy which in turn improves patient management and prognosis.

Keywords: acute abdomen, contrast-enhanced computed tomography scan, magnetic resonance imaging, plain radiographs, ultrasound

Procedia PDF Downloads 355

Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

Abstract:

Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

Procedia PDF Downloads 105

Authors: Manh-Dung Ho, Van-Giap Pham, Van-Doanh Ho, Quang-Thien Tran, Tuan-Anh Tran

Abstract:

The k0-factors and related nuclear data, i.e. the Q0-factors and effective resonance energies (Ēr) of the selected radionuclides which are used in the k0-based neutron activation analysis (k0-NAA), were critically reviewed to be integrated in the “k0-DALAT” software. The k0- and Q0-factors of some short-lived radionuclides: 46mSc, 110Ag, 116m2In, 165mDy, and 183mW, were experimentally determined at the Dalat research reactor. The other radionuclides selected are: 20F, 36S, 49Ca, 60mCo, 60Co, 75Se, 77mSe, 86mRb, 115Cd, 115mIn, 131Ba, 134mCs, 134Cs, 153Gd, 153Sm, 159Gd, 170Tm, 177mYb, 192Ir, 197mHg, 239U and 239Np. The reviewed data as compared with the literature data were biased within 5.6-7.3% in which the experimental re-determined factors were within 6.1 and 7.3%. The NIST standard reference materials: Oyster Tissue (1566b), Montana II Soil (2711a) and Coal Fly Ash (1633b) were used to validate the new reviewed data showing that the new data gave an improved k0-NAA using the “k0-DALAT” software with a factor of 4.5-6.8% for the investigated radionuclides.

Keywords: neutron activation analysis, k0-based method, k0 factor, Q0 factor, effective resonance energy

Procedia PDF Downloads 117

Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

Abstract:

Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

Procedia PDF Downloads 183

Authors: R. Shamsi, F. Sharifi

Abstract:

In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose a multi-objective DEA-R model because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduce the efficiency score), an efficient decision-making unit (DMU) is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other cases, only the ratio of stochastic data may be available (e.g., the ratio of stochastic inputs to stochastic outputs). Thus, we provide a multi-objective DEA model without explicit outputs and prove that the input-oriented MOP DEA-R model in the invariable return to scale case can be replaced by the MOP-DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA-R, multi-objective programming, stochastic data, data envelopment analysis

Procedia PDF Downloads 101

Authors: Subrata Sarkar

Abstract:

The aerospace industry is experiencing a profound product development transformation driven by the powerful integration of digitalization and 3D printing technologies. This paper delves into the significant impact of this convergence on aerospace innovation, specifically focusing on developing jet pumps for fuel systems. This case study is a compelling example of the immense potential of these technologies. In response to the industry's increasing demand for lighter, more efficient, and customized components, the combined capabilities of digitalization and 3D printing are reshaping how we envision, design, and manufacture critical aircraft parts, offering a distinct paradigm in aerospace engineering. Consider the development of a jet pump for a fuel system, a task that presents unique and complex challenges. Despite its seemingly simple design, the jet pump's development is hindered by many demanding operating conditions. The qualification process for these pumps involves many analyses and tests, leading to substantial delays and increased costs in fuel system development. However, by harnessing the power of automated simulations and integrating legacy design, manufacturing, and test data through digitalization, we can optimize the jet pump's design and performance, thereby revolutionizing product development. Furthermore, 3D printing's ability to create intricate structures using various materials, from lightweight polymers to high-strength alloys, holds the promise of highly efficient and durable jet pumps. The combined impact of digitalization and 3D printing extends beyond design, as it also reduces material waste and advances sustainability goals, aligning with the industry's increasing commitment to environmental responsibility. In conclusion, the convergence of digitalization and 3D printing is not just a technological advancement but a gateway to a new era in aerospace product development, particularly in the design of jet pumps. This revolution promises to redefine how we create aerospace components, making them safer, more efficient, and environmentally responsible. As we stand at the forefront of this technological revolution, aerospace companies must embrace these technologies as a choice and a strategic imperative for those striving to lead in innovation and sustainability in the 21st century.

Keywords: jet pump, digitalization, 3D printing, aircraft fuel system.

Procedia PDF Downloads 48

Authors: S. Junaid S. Qazi, Denice C. Bay, Raymond Chew, Raymond J. Turner

Abstract:

EmrE, a member of the small multidrug resistance protein family in bacteria is considered to be the archetypical member of its family. It confers host resistance to a wide variety of quaternary cation compounds (QCCs) driven by proton motive force. Generally, purification yield is a challenge in all membrane proteins because of the difficulties in their expression, isolation and solubilization. EmrE is extremely hydrophobic which make the purification yield challenging. We have purified EmrE protein using two different approaches: organic solvent membrane extraction and hexahistidine (his6) tagged Ni-affinity chromatographic methods. We have characterized changes present between ligand affinity of untagged and his6-tagged EmrE proteins in similar membrane mimetic environments using biophysical experimental techniques. Purified proteins were solubilized in a buffer containing n-dodecyl-β-D-maltopyranoside (DDM) and the conformations in the proteins were explored in the presence of four QCCs, methyl viologen (MV), ethidium bromide (EB), cetylpyridinium chloride (CTP) and tetraphenyl phosphonium (TPP). SDS-Tricine PAGE and dynamic light scattering (DLS) analysis revealed that the addition of QCCs did not induce higher multimeric forms of either proteins at all QCC:EmrE molar ratios examined under the solubilization conditions applied. QCC binding curves obtained from the Trp fluorescence quenching spectra, gave the values of dissociation constant (Kd) and maximum specific one-site binding (Bmax). Lower Bmax values to QCCs for his6-tagged EmrE shows that the binding sites remained unoccupied. This lower saturation suggests that the his6-tagged versions provide a conformation that prevents saturated binding. Our data demonstrate that tagging an integral membrane protein can significantly influence the protein.

Keywords: small multidrug resistance (SMR) protein, EmrE, integral membrane protein folding, quaternary ammonium compounds (QAC), quaternary cation compounds (QCC), nickel affinity chromatography, hexahistidine (His6) tag

Procedia PDF Downloads 372

Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

Abstract:

As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

Procedia PDF Downloads 47

Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

Abstract:

Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a simple user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud computing, data security, SAAS, PAAS, IAAS, Blowfish

Procedia PDF Downloads 351

Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

Abstract:

In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

Procedia PDF Downloads 463

Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

Abstract:

In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

Procedia PDF Downloads 334

Authors: Masood Varshosaz, Kamyar Hasanpour

Abstract:

In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

Procedia PDF Downloads 86

Authors: Probir Kumar Banerjee

Abstract:

Though the revised DeLone and McLean (DM) model of IS success is found to be effective at the individual level of analysis, there is lack of consensus in regard to its effectiveness at the organisational level. This research reviews the DM model in the light of business/IT alignment theory and supporting literature, and suggests its reconceptualization. Specifically, arguments are made for augmenting it with business process quality. Business process quality, it is argued, captures the effect of intent to use, use and user satisfaction interactions, thus eliminating the need to capture their interaction effects in explaining organisational IS success. It is also argued that ‘operational performance’ driven by systems and business process quality, and higher order measures of organisational performance tied to operational performance are appropriate measures of ‘net benefit’. Suggestions are made for reconceptualisation of the other constructs and an adapted model of organisational IS success is proposed.

Keywords: organisational IS success, business/IT alignment, systems quality, business process quality, operational performance, market performance

Procedia PDF Downloads 389

Authors: Emine Akar

Abstract:

The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

Procedia PDF Downloads 82

Authors: Anung Style Bukhori, Ani Dijah Rahajoe

Abstract:

Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

Procedia PDF Downloads 49

Authors: Yuliana Wahyu, Ambros Leonangung Edu

Abstract:

The learning praxis that is proposed by 2013 Curriculum (K-13) is no longer school-oriented as a supply-driven, but now a demand-driven provider. This vision is connected with Jokowi-Kalla Nawacita program to create a competitive nation in the global era. Competition is a social fact that must be faced. Therefore the curriculum will design a process to be the innovators and entrepreneurs.To get this goal, K-13 implements the character education. This aims at creating the innovators and entrepreneurs from an early age (primary school). One part of strengthening it is literacy formations (reading, numeracy, science, ICT, finance, and culture). Thus, science literacy is an integral part of character education. The above outputs are only formed through the innovative process through intra-curricular (blended learning), co-curriculer (hands-on learning) and extra-curricular (personalized learning). Unlike the curriculums before that child cram with the theories dominating the intellectual process, new breakthroughs make natural, social, and cultural phenomena as learning sources. For example, Science in primary schoolsplaceBiology as the platform. And Science places natural, social, and cultural phenomena as a learning field so that students can learn, discover, solve concrete problems, and the prospects of development and application in their everyday lives. Science education not only learns about facts collection or natural phenomena but also methods and scientific attitudes. In turn, Science will form the science literacy. Science literacy have critical, creative, logical, and initiative competences in responding to the issues of culture, science and technology. This is linked with science nature which includes hands-on and minds-on. To sustain the effectiveness of science learning, K-13 opens a new way of viewing a contextual learning model in which facts or natural phenomena are drawn closer to the child's learning environment to be studied and analyzed scientifically. Thus, the topic of elementary science discussion is the practical and contextual things that students encounter. This research is about to contextualize Science in primary schools at Manggarai, NTT, by placing local wisdom as a learning source and media to form the science literacy. Explicitly, this study discovers the concept of science and mathematics in Mbaru Niang. Mbaru Niang is a forgotten potentials of the centralistic-theoretical mainstream curriculum so far. In fact, the traditional Manggarai community stores and inherits much of the science-mathematical indigenous sciences. In the traditional house structures are full of science and mathematics knowledge. Every details have style, sound and mathematical symbols. Learning this, students are able to collaborate and synergize the content and learning resources in student learning activities. This is constructivist contextual learning that will be applied in meaningful learning. Meaningful learning allows students to learn by doing. Students then connect topics to the context, and science literacy is constructed from their factual experiences. The research location will be conducted in Manggarai through observation, interview, and literature study.

Keywords: indigenous science, Mbaru Niang, science literacy, science

Procedia PDF Downloads 204

Authors: Takahiro Nishigaki, Takashi Onoda

Abstract:

In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness.

Keywords: independent topic analysis, topic extraction, topic naming, web search engine

Procedia PDF Downloads 116

Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

Abstract:

Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

Procedia PDF Downloads 134

Authors: Saleem Z. Ramadan

Abstract:

In this paper, a model is proposed to determine the life distribution parameters of the useful life region for the PV system utilizing a combination of non-parametric and linear regression analysis for the failure data of these systems. Results showed that this method is dependable for analyzing failure time data for such reliable systems when the data is scarce.

Keywords: masking, bathtub model, reliability, non-parametric analysis, useful life

Procedia PDF Downloads 553

Authors: Seyong Jeong, Jinmo Park, Jinhyoun Park, Boram Kim, Kyoungsoo Ahn

Abstract:

Energy management in the maritime industry is being required by economics and in conformity with new legislative actions taken by the International Maritime Organization (IMO) and the European Union (EU). In response, the various performance monitoring methodologies and data collection practices have been examined by different stakeholders. While many assorted advancements in operation and technology are applicable, their adoption in the shipping industry stays small. This slow uptake can be considered due to many different barriers such as data analysis problems, misreported data, and feedback problems, etc. This study presents a conceptual design of an energy management system (EMS) and proposes the methodology to resolve the limitations (e.g., data normalization using naval architectural evaluation, management of misrepresented data, and feedback from shore to ship through management of performance analysis history). We expect this system to make even short-term charterers assess the ship performance properly and implement sustainable fleet control.

Keywords: data normalization, energy management system, naval architectural evaluation, ship performance analysis

Procedia PDF Downloads 443