Search results for: enhanced data encryption

Authors: Christopher Muhawe, Masooda Bashir

Abstract:

With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.

Keywords: data breach and misuse, economic harms, privacy harms, psychological harms

Procedia PDF Downloads 195

Authors: Alex Kiselyov, Suehyun Cho, Darrell Harrington; Florent Cros, Olin Palmer, John Caputo, Michael Kardosh, Eran Oren, William Loudon, Michael Shpigelmacher

Abstract:

Despite the most aggressive surgical and adjuvant therapeutic strategies, treatment of both pediatric and adult brainstem tumors remains problematic. Novel strategies, including targeted biologics, immunotherapy, and specialized delivery systems such as convection-enhanced delivery (CED), have been proposed. While some of these novel treatments are entering phase I trials, the field is still in need of treatment(s) that exhibits dramatically enhanced potency with optimal therapeutic ratio. Bionaut Labs has developed a modular microrobotic platform for performing localized delivery of diverse therapeutics in vivo. Our biocompatible particles (Bionauts™) are externally propelled and visualized in real-time. Bionauts™ are specifically designed to enhance the effect of radiation therapy via anatomically precise delivery of a radiosensitizing agent, as exemplified by temozolomide (TMZ) and Avastin™ to the brainstem gliomas of diverse origin. The treatment protocol is designed to furnish a better therapeutic outcome due to the localized (vs systemic) delivery of the drug to the neoplastic lesion(s) for use as a synergistic combination of radiation and radiosensitizing agent. In addition, the procedure is minimally invasive and is expected to be appropriate for both adult and pediatric patients. Current progress, including platform optimization, selection of the lead radiosensitizer as well as in vivo safety studies of the Bionauts™ in large animals, specifically the spine and the brain of porcine and ovine models, will be discussed.

Keywords: Bionaut, brainstem, glioma, local delivery, micro-robot, radiosensitizer

Procedia PDF Downloads 195

Authors: Xiaoqian Cheng, Jon Kleppe, Ole Torsæter

Abstract:

The properties of rocks have effects on efficiency of surfactant. One objective of this study is to analyze the effects of rock properties (permeability, porosity, initial water saturation) on surfactant spontaneous imbibition at laboratory scale. The other objective is to evaluate existing upscaling methods and establish a modified upscaling method. A core is put in a container that is full of surfactant solution. Assume there is no space between the bottom of the core and the container. The core is modelled as a cuboid matrix with a length of 3.5 cm, a width of 3.5 cm, and a height of 5 cm. The initial matrix, brine and oil properties are set as the properties of Ekofisk Field. The simulation results of matrix permeability show that the oil recovery rate has a strong positive linear relationship with matrix permeability. Higher oil recovery is obtained from the matrix with higher permeability. One existing upscaling method is verified by this model. The study on matrix porosity shows that the relationship between oil recovery rate and matrix porosity is a negative power function. However, the relationship between ultimate oil recovery and matrix porosity is a positive power function. The initial water saturation of matrix has negative linear relationships with ultimate oil recovery and enhanced oil recovery. However, the relationship between oil recovery and initial water saturation is more complicated with the imbibition time because of the transition of dominating force from capillary force to gravity force. Modified upscaling methods are established. The work here could be used as a reference for the surfactant application in fractured reservoirs. And the description of the relationships between properties of matrix and the oil recovery rate and ultimate oil recovery helps to improve upscaling methods.

Keywords: initial water saturation, permeability, porosity, surfactant EOR

Procedia PDF Downloads 162

Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

Abstract:

This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

Procedia PDF Downloads 44

Authors: A. Tahar Djebbar

Abstract:

Modern technologies have penetrated all spheres of human life, education being one of them. This paper focuses the attention on the integration of technology-based education in the Algerian classroom in teaching foreign languages. It sheds light on a specific area of ICT application: ICT in English learning and teaching. Some Algerian teachers or tutors of English face many challenges among which the lack of teaching materials which are indispensable for transmitting knowledge to learners. Thus, they find themselves compelled to use online e-books or download them in PDF form to support their lessons. Teachers even download such teaching materials like pictures, videos, audios, podcasts, and flash cards from the internet and store them in their Flash USBs to shape up the teaching-learning conditions. They use computers, data shows, and the internet so as to facilitate the teaching–learning process in the classroom. Hence, technology has become a must in the Algerian classroom especially in teaching English which has become a very important language in a national and an international level. This study aims at showing that Algerian tutors/teachers who take up the challenge of getting involved in the technology-enhanced language learning and teaching in the Algerian schools and universities face many obstacles.

Keywords: computer, communication, English, internet, learners, language acquisition, teaching, technology

Procedia PDF Downloads 644

Authors: Endang Jati Mat Sahid, Hussain Ali Bekhet

Abstract:

Robust economic growth, increasing population, and personal consumption are the main drivers for the rapid increase of energy demand in Malaysia. Increasing demand has compounded the issue of national energy security due to over-dependence on fossil fuel, depleting indigenous domestic conventional energy resources which in turns has increased the country’s energy import dependence. In order to improve its energy security, Malaysia has seriously embarked on a renewable energy journey. Many initiatives on renewable energy have been introduced in the past decade. These strategies have resulted in the exploding growth of renewable energy deployment in Malaysia. Therefore, this study investigated the impact of renewable energy deployment on energy security. Secondary data was used to calculate the energy security indicators. The study also compared the results of applying different energy security indicators namely availability, applicability, affordability and acceptability dimension of energy resources. The evaluation shows that Malaysia will experience slight improvement in availability and acceptability dimension of energy security. This study suggests that energy security level could be further enhanced by efficient utilization of energy, reducing carbon content of energy and facilitating low-carbon industries.

Keywords: energy policy, energy security, Malaysia, renewable energy

Procedia PDF Downloads 244

Authors: Chuping Lo

Abstract:

This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

Procedia PDF Downloads 68

Authors: Fuad M. Alkoot

Abstract:

DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

Procedia PDF Downloads 257

Authors: Thu Nhi Tran Caliste

Abstract:

X-ray Bragg diffraction imaging (“topography”)entered into practical use when Lang designed an “easy” technical setup to characterise the defects / distortions in the high perfection crystals produced for the microelectronics industry. The use of this technique extended to all kind of high quality crystals, and deposited layers, and a series of publications explained, starting from the dynamical theory of diffraction, the contrast of the images of the defects. A quantitative version of “monochromatic topography” known as“Rocking Curve Imaging” (RCI) was implemented, by using synchrotron light and taking advantage of the dramatic improvement of the 2D-detectors and computerised image processing. The rough data is constituted by a number (~300) of images recorded along the diffraction (“rocking”) curve. If the quality of the crystal is such that a one-to-onerelation between a pixel of the detector and a voxel within the crystal can be established (this approximation is very well fulfilled if the local mosaic spread of the voxel is < 1 mradian), a software we developped provides, from the each rocking curve recorded on each of the pixels of the detector, not only the “voxel” integrated intensity (the only data provided by the previous techniques) but also its “mosaic spread” (FWHM) and peak position. We will show, based on many examples, that this new data, never recorded before, open the field to a highly enhanced characterization of the crystal and deposited layers. These examples include the characterization of dislocations and twins occurring during silicon growth, various growth features in Al203, GaNand CdTe (where the diffraction displays the Borrmannanomalous absorption, which leads to a new type of images), and the characterisation of the defects within deposited layers, or their effect on the substrate. We could also observe (due to the very high sensitivity of the setup installed on BM05, which allows revealing these faint effects) that, when dealing with very perfect crystals, the Kato’s interference fringes predicted by dynamical theory are also associated with very small modifications of the local FWHM and peak position (of the order of the µradian). This rather unexpected (at least for us) result appears to be in keeping with preliminary dynamical theory calculations.

Keywords: rocking curve imaging, X-ray diffraction, defect, distortion

Procedia PDF Downloads 131

Authors: R Palson Kennedy, P.Kiran Sai

Abstract:

This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

Procedia PDF Downloads 135

Authors: Steffany Anne Poblador, Ruben Jr. Tagare

Abstract:

The Philippine Education System is now undergoing transition as a result of the implementation of Republic Act 10533, commonly referred to as the Enhanced Basic Education Act. Since its implementation in 2013, researchers have been examining the initial impact of this transition; however, investigations into the gains and drawbacks of the Philippine Senior High School Sports Track Program based on teachers’ assessment were scarcely adequate. As a result, this research used a Qualitative Phenomenology Research Design to elicit information on the gains and drawbacks faced by these instructors. Focus group discussions, in-depth interviews, and extensive field observation were conducted with participants from selected schools in Cotabato Province. During the triangulation of the data, five (5) significant themes for gains and six (6) concerns from the research participants emerged. The findings were then used to provide recommendations for a more effective implementation of the Sports Track Program in the Philippine Senior High School program.

Keywords: teachers’ gains and drawbacks, Philippine K to 12 problems, K to 12 transition years, favorable experiences, phenomenology

Procedia PDF Downloads 236

Authors: Ruwan Gunawardane

Abstract:

Second language teaching and learning is a complex process in which various factors are identified as having a negative impact on the competency in English among undergraduates of Sri Lanka. One such issue is the lack of intrinsic motivation among them to learn English despite the fact that they all know the importance of English. This study attempted to ascertain how the intrinsic motivation of undergraduates to learn English can be improved through reading out of curiosity. Humans are curious by nature, and cognitive psychology says that curiosity facilitates learning, memory, and motivation. The researcher carried out this study during the closure of universities due to the outbreak of the coronavirus through ‘Online Reading Café’, an online reading programme introduced by himself. He invited 1166 students of the Faculty of Science, University of Ruhuna, to read 50 articles taken from CNN and the BBC and posted at least two to three articles on the LMS of the faculty almost every day over a period of 23 days. The themes of the articles were based on the universe, exploration of planets, scientific experiments, evolution, etc., and the students were encouraged to collect as many words, phrases, and sentence structures as possible while reading and to form meaningful sentences using them. The data obtained through the students’ feedback was qualitatively analyzed. It was found that these undergraduates were interested in reading something out of curiosity, due to which intrinsic motivation is enhanced, and it facilitates competence in L2.

Keywords: English, competence, reading, curiosity

Procedia PDF Downloads 138

Authors: Gebreegziabher Hailu Gebrecherkos

Abstract:

This study examines the effectiveness of GeoGebra in developing the conceptual understanding of transformation geometry among Grade 11 students. Utilizing a quasi-experimental design, the research compares the learning outcomes of students who engaged with GeoGebra against those who received traditional instruction. Pre- and post-tests were administered to assess students' grasp of key transformation concepts, including translations, rotations, reflections, and dilations. Additionally, qualitative data were gathered through student interviews and classroom observations to explore their experiences and perceptions of using GeoGebra. Results indicate that students utilizing GeoGebra showed significantly greater improvement in their understanding of transformation geometry concepts. The interactive features of GeoGebra facilitated visualization and exploration, leading to enhanced engagement and deeper conceptual insights. The findings underscore the potential of GeoGebra as a powerful educational tool that not only fosters mathematical understanding but also accommodates diverse learning styles in the classroom. This study contributes valuable insights for educators seeking to improve the teaching and learning of transformation geometry in secondary education.

Keywords: calculus, conceptual understanding, GeoGebra, transformation geometry

Procedia PDF Downloads 20

Authors: Rajinikanth Siddalingam, Poonguzhali Subramanian

Abstract:

The present study aims to prepare and evaluate the self-nano emulsifying drug delivery (SNEDDS) system to enhance the dissolution rate of a poorly soluble drug dutasteride. The formulation was prepared using capryol PGMC, Cremophor EL, and polyethylene glycol (PEG) 400 as oil, surfactant and co-surfactant, respectively. The pseudo-ternary phase diagrams with presence and absence of drug were plotted to find out the nano emulsification range and also to evaluate the effect of dutasteride on the emulsification behavior of the phases. Prepared SNEDDS formulations were evaluated for its particle size distribution, nano emulsifying properties, robustness to dilution, self-emulsification time, turbidity measurement, drug content and in-vitro dissolution. The optimized formulations are further evaluated for heating cooling cycle, centrifugation studies, freeze-thaw cycling, particle size distribution and zeta potential were carried out to confirm the stability of the formed SNEDDS formulations. The particle size, zeta potential and polydispersity index of the optimized formulation found to be 35.45 nm, -15.45 and 0.19, respectively. The in vitro results are revealed that the prepared formulation enhanced the dissolution rate of dutasteride significantly as compared with pure drug. The in vivo studies in was conducted using rats and the results are revealed that SNEDDS formulation has enhanced the bioavailability of dutasteride drug significantly as compared with raw drug. Based the results, it was concluded that the dutasteride-loaded SNEDDS shows potential to enhance the dissolution of dutasteride, thus improving the bioavailability and therapeutic effects.

Keywords: self-emulsifying drug delivery system, dutasteride, enhancement of bioavailability, dissolution enhancement

Procedia PDF Downloads 266

Authors: I Putu Indra Kusuma

Abstract:

The rapid development of Information and Communication Technology (ICT) tools has improved the implementation of flipped classrooms in English Language Teaching (ELT), especially in speaking course. Flipped classrooms have therefore evolved from the oldest version, which uses recorded videos to the newest one (3.0 version), which combines various materials and enables out-of-class interaction and learning engagement. However, how the latest version of flipped classrooms affects students’ speaking performance and influences students’ learning engagement remains unclear. This study therefore sought (1) to examine the effect of flipped classrooms 3.0 towards students’ speaking performance and (2) to explore the students’ learning engagement during the implementation of flipped classrooms in the speaking course. This study then employed explanatory sequential mixed-method design. This study conducted a quasi-experimental study by recruiting 164 twelfth grade students of a public senior high school in Indonesia as the sample. They were distributed into experimental (80 students) and control (84 students) groups. The experimental group was treated by implementing flipped classrooms with various use of ICT tools such as Schoology, Youtube, websites, and Flipgrid for eight weeks. Meanwhile, the control group implemented a conventional method. Furthermore, there were two variables examined in this study, such as the implementation of flipped classrooms 3.0 as the independent variable and students’ speaking performance as the dependent variable. The data of these two variables were then collected through administering a speaking test to both groups. The data from this experimental study were analyzed by using independent t-test analysis. Also, five students were invited to participate in semi-structured interviews to explore their learning engagement during the implementation of flipped classrooms. The findings revealed that there was a significant difference in students’ speaking performance between experimental where t (df = 162) = 5.810, p < 0.001, d = 0.91 in which experimental group performed better in speaking than the control group. Also, the results of interviews showed that the students had positive learning engagement during the implementation of flipped classrooms 3.0, especially on out-of-class interactions and face-to-face meetings. Some relevant implications to ELT, especially in speaking courses, are also drawn from the data findings. From the findings, it can be concluded that flipped classrooms 3.0 has a significant effect on students’ speaking performance and it promotes students’ learning engagement. Therefore, flipped classrooms 3.0 should be embraced as the newest version of flipped classrooms that promotes interaction outside the classrooms and learning engagement.

Keywords: Flipped Classrooms 3.0, learning engagement, teaching speaking with technology, technology-enhanced language learning

Procedia PDF Downloads 132

Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

Abstract:

Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

Procedia PDF Downloads 135

Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

Abstract:

Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: agent communication, introspective agent, isolation of agent, policy enforcement system

Procedia PDF Downloads 297

Authors: Priya Patel, Paresh Patel, Mihir Raval

Abstract:

Silibinin, a flavanone as an antimicrotubular agent used in the treatment of cancer, was encapsulated in nanoparticles (NPs) of poly (lactide-co-glycolide) (PLGA) polymer using the spray-drying technique. The effects of various experimental parameters were optimized by box-behnken experimental design. Production yield, encapsulation efficiency and dissolution study along with characterization by scanning electron microscopy, DSC, FTIR followed by bioavailability study. Particle size and zeta potential were evaluated by using zetatrac particle size analyzer. Experimental design it was evaluated that inlet temperature and polymer concentration influence on the drug release. Feed flow rate impact on particle size. Results showed that spray drying technique yield 149 nm indicate nanosize range. The small size of the nanoparticle resulted in an enhanced cellular entry and greater bioavailability. Entrapment efficiency was found between 89.35% and 98.36%. Zeta potential shows good stability index of nanoparticle formulation. The in vitro release studies indicated the silibinin loaded PLGA nanoparticles provide controlled drug release over a period of 32 h. Pharmacokinetic studies demonstrated that after oral administration of silibinin-loaded PLGA nanoparticles to rats at a dose of 10 mg/kg, relative bioavailability was enhanced about 8.85-fold, compared to silibinin suspension as control hence, this investigation demonstrated the potential of the experimental design in understanding the effect of the formulation variables on the quality of silibinin loaded PLGA nanoparticles. These results describe an effective strategy of silibinin loaded PLGA nanoparticles and might provide a promising approach against the cancer.

Keywords: silibinin, cancer, nanoparticles, PLGA, bioavailability

Procedia PDF Downloads 427

Authors: Farnoosh Faal

Abstract:

The trend among smart cities is to incorporate technological advancements to better manage and protect their cultural heritage sites. This study investigates how smart city solutions can improve the cultural and historical significance of urban heritage sites and assesses present practices and potential for the future. The paper delves into the literature to examine how smart city technologies can be utilized to increase knowledge and respect for cultural heritage, as well as promote sustainable tourism and economic growth. The article reviews various instances of smart city initiatives across different regions of the world, pinpointing innovative tactics and best practices in improving the cultural and historical worth of urban heritage sites. Additionally, it analyzes the difficulties and limitations associated with implementing these solutions, including community involvement, privacy concerns, and data management issues. The conclusions drawn from this paper propose that smart city solutions offer a substantial opportunity to augment the cultural and historical value of urban heritage sites. By effectively integrating technology into heritage management, there can be greater comprehension and admiration for cultural heritage, enhanced visitor experience, and support for sustainable tourism. However, to fully exploit the potential of smart city solutions in this context, it is crucial to prioritize community engagement and participation, as well as ensure that data management practices are transparent, responsible, and respectful of privacy. In summary, this paper offers guidance and advice to policymakers, urban planners, and heritage management professionals who want to increase the cultural and historical significance of urban heritage sites through the application of smart city solutions. It emphasizes the significance of creating comprehensive and cooperative strategies, as well as ensuring that efforts to preserve heritage are sustainable, fair, and efficient.

Keywords: smart city, Urban heritage, sustainable tourism, heritage preservation

Procedia PDF Downloads 92

Authors: Celestina Omoso Isiramen

Abstract:

The influence of witchcraft belief on disease causation, cure and public health system in Nigeria cannot be underrated. This paper investigated the nexus between witchcraft phenomenon and health-seeking behaviour of HIV sufferers in Edo state, Nigeria. Survey methodology was adopted and stratified random sampling technique was employed in the selection of 600 sample group spread into 200 HIV sufferers, 200 spiritual healers and 200 bio-medics from the three Senatorial districts of the state. Data were collected through the use of structured questionnaire and in-dept interview and analyzed using simple percentage and frequency. Major findings were: belief in witchcraft significantly influenced the people’s perception of HIV causation and wellness and this impacted adversely on public health-care. Poverty, ignorance and dearth of retroviral drugs enhanced the people’s recourse to spiritual healers. Collaboration between spiritual healing techniques and biomedicine was recommended as panacea for curbing HIV/AIDS related morbidity and mortality. It concluded that socio-economic problems must be addressed while the importance of integrating the values of spiritual healing into biomedicine cannot be overstressed.

Keywords: biomedicine, health care, HIV/AIDS, spirituality, witchcraft

Procedia PDF Downloads 139

Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim

Abstract:

It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

Procedia PDF Downloads 437

Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

Procedia PDF Downloads 93

Authors: Grant Bifolchi

Abstract:

As global environmental problems continue to have highly adverse effects, finding long-term, sustainable solutions to combat ecological distress are of growing paramount concern. Ghost Gear—also known as abandoned, lost or otherwise discarded fishing gear (ALDFG) and derelict fishing gear (DFG)—represents one of the greatest threats to the world’s oceans, posing a significant hazard to human health, livelihoods, and global food security. In fact, according to the UN Food and Agriculture Organization (FAO), abandoned, lost and discarded fishing gear represents approximately 10% of marine debris by volume. Around the world, many governments, governmental and non-profit organizations are doing their best to manage the reporting and retrieval of nets, lines, ropes, traps, floats and more from their respective bodies of water. However, these organizations’ ability to effectively manage files and documents about the environmental problem further complicates matters. In Ghost Gear monitoring and management, organizations face additional complexities. Whether it’s data ingest, industry regulations and standards, garnering actionable insights into the location, security, and management of data, or the application of enforcement due to disparate data—all of these factors are placing massive strains on organizations struggling to save the planet from the dangers of Ghost Gear. In this 90-minute educational session, globally recognized Ghost Gear technology expert Grant Bifolchi CET, BBA, Bcom, will provide real-world insight into how governments currently manage Ghost Gear and the technology that can accelerate success in combatting ALDFG and DFG. In this session, attendees will learn how to: • Identify specific technologies to solve the ingest and management of Ghost Gear data categories, including type, geo-location, size, ownership, regional assignment, collection and disposal. • Provide enhanced access to authorities, fisheries, independent fishing vessels, individuals, etc., while securely controlling confidential and privileged data to globally recognized standards. • Create and maintain processing accuracy to effectively track ALDFG/DFG reporting progress—including acknowledging receipt of the report and sharing it with all pertinent stakeholders to ensure approvals are secured. • Enable and utilize Business Intelligence (BI) and Analytics to store and analyze data to optimize organizational performance, maintain anytime-visibility of report status, user accountability, scheduling, management, and foster governmental transparency. • Maintain Compliance Reporting through highly defined, detailed and automated reports—enabling all stakeholders to share critical insights with internal colleagues, regulatory agencies, and national and international partners.

Keywords: ghost gear, ALDFG, DFG, abandoned, lost or otherwise discarded fishing gear, data, technology

Procedia PDF Downloads 96

Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

Abstract:

Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

Procedia PDF Downloads 157

Authors: Paula Simões, Isabel Natário

Abstract:

This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

Procedia PDF Downloads 593

Authors: Mohamad R. Moshtagh, Ahmad Bagheri

Abstract:

Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

Procedia PDF Downloads 80

Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

Abstract:

This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

Procedia PDF Downloads 354

Authors: Zineb Touati Hamad, Mohamed Ridda Laouar, Issam Bendib

Abstract:

With the massive development of technology today, the Arabic language has gained a prominent position among the languages most used for writing articles, expressing opinions, and also for citing in many websites, defying its growing sensitivity in terms of structure, language skills, diacritics, writing methods, etc. In the context of the spread of the Arabic language, the Holy Quran represents the most prevalent Arabic text today in many applications and websites for citation purposes or for the reading and learning rituals. The Quranic verses / surahs are published quickly and without cost, which may cause great concern to ensure the safety of the content from tampering and alteration. To protect the content of texts from distortion, it is necessary to refer to the original database and conduct a comparison process to extract the percentage of distortion. The disadvantage of this method is that it takes time, in addition to the lack of any guarantee on the integrity of the database itself as it belongs to one central party. Blockchain technology today represents the best way to maintain immutable content. Blockchain is a distributed database that stores information in blocks linked to each other through encryption, where the modification of each block can be easily known. To exploit these advantages, we seek in this paper to justify the use of this technique in preserving the integrity of Arabic texts sensitive to change by building a decentralized framework to authenticate and verify the integrity of the digital Quranic verses/surahs spread on websites.

Keywords: arabic text, authentication, blockchain, integrity, quran, verification

Procedia PDF Downloads 164

Authors: C. B. Le, V. N. Pham

Abstract:

In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

Procedia PDF Downloads 189

Authors: Ziwei Wang, Andrea Lucotti, Luigi Brambilla, Matteo Tommasini, Chiara Bertarelli

Abstract:

Surface-enhanced Raman Spectroscopy (SERS) is a highly sensitive detection that provides abundant information on low concentration analytes from various researching areas. Based on localized surface plasmon resonance, metal nanostructures including gold, silver and copper have been investigated as SERS substrate during recent decades. There has been increasing more attention of exploring good performance, homogenous, repeatable SERS substrates. Here, we show that electrospinning, which is an inexpensive technique to fabricate large-scale, self-standing and repeatable membranes, can be effectively used for producing SERS substrates. Nanoparticles and nanorods are added to the feed electrospinning solution to collect functionalized polymer fibrous mats. We report stable electrospun membranes as SERS substrate using gold nanorods (AuNRs) and poly(vinyl alcohol). Particularly, a post-processing crosslinking step using glutaraldehyde under acetone environment was carried out to the electrospun membrane. It allows for using the membrane in any liquid environment, including water, which is of interest both for sensing of contaminant in wastewater, as well as for biosensing. This crosslinked AuNRs/PVA membrane has demonstrated excellent performance as SERS substrate for low concentration 10-6 M Rhodamine 6G (Rh6G) aqueous solution. This post-processing for fabricating SERS substrate is the first time reported and proved through Raman imaging of excellent stability and outstanding performance. Finally, SERS tests have been applied to several analytes, and the application of AuNRs/PVA membrane is broadened by removing the detected analyte by rinsing. Therefore, this crosslinked AuNRs/PVA membrane is re-usable.

Keywords: SERS spectroscopy, electrospinning, crosslinking, composite materials

Procedia PDF Downloads 140