Search results for: fake media detection
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 6293

Search results for: fake media detection

5753 Transformation and Integration: Iranian Women Migrants and the Use of Social Media in Australia

Authors: Azadeh Davachi

Abstract:

Although there is a growing interest in Iranian female migration and gender roles, little attention has been paid to how Iranian migrant women in Australia access and sustain social networks, both locally and spatially dispersed over time. Social network theories have much to offer an analysis of migrant’s social ties and interpersonal relationships. Thus, it is important to note that social media are not only new communication channels in a migration network but also that they actively transform the nature of these networks and thereby facilitate migration for migrants. Drawing on that, this article will focus on Iranian women migrants and the use of social media in migration in Australia. Based on the case of main social networks such as Facebook and Instagram; this paper will investigate that how women migrants use these networks to facilitate the process of migration and integration. In addition, with the use of social networks, they could promote their home business and as a result become more engaged economically in Australian society. This paper will focus on three main Iranian pages in Instagram and Facebook, they will contend that compared to men, women are more active in these social networks. Consequently, as this article will discuss with the use of these social media Iranian migrant women can become more engaged and overcome post migration hardships, thus, gender plays a key role in using social media in migrant communities. Based on these findings from these social media pages, this paper will conclude that social media are transforming migration networks and thereby lowering the threshold for migration. It also will be demonstrated that these networks boost Iranian women’s confidence and lead them to become more visible in Iranian migrant communities comparing to men.

Keywords: integration, gender, migration, women migrants

Procedia PDF Downloads 155
5752 Intrusion Detection Based on Graph Oriented Big Data Analytics

Authors: Ahlem Abid, Farah Jemili

Abstract:

Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

Procedia PDF Downloads 141
5751 Design an Intelligent Fire Detection System Based on Neural Network and Particle Swarm Optimization

Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

Abstract:

In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

Procedia PDF Downloads 377
5750 Integration of Social Media in Teaching and Learning Activities: A Case Study

Authors: A. Nagaletchimee Annamalai

Abstract:

The study investigated on how a small group of pre-service teachers and lecturers used social media to interact and collaborate to complete their tasks. The study is a qualitative case study that explored the lecturers’ reflections and pre-service teachers’ interviews. The lecturers were given the option to choose Facebook or any other social media as their teaching and learning platforms. However, certain guidelines based on were given to lecturers to conduct their teaching and learning activities. The findings revealed that although Facebook was a popular social networking site, it was not a preferred educational platform. Lecturers preferred to use WhatsApp, Canvas, and email. The focus group interview found positive and negative experiences of the pre-service teachers. The study suggested several pedagogical implications and importantly highlighted the need for changes in curriculum to ensure lecturers leverage the potential of technology in education.

Keywords: social media, interactions, collaboration, online learning environment

Procedia PDF Downloads 179
5749 Ministers of Parliament and Their Official Web Sites; New Media Tool of Political Communication

Authors: Wijayanada Rupasinghe, A. H. Dinithi Jayasekara

Abstract:

In a modern democracy, new media can be used by governments to involve citizens in decision-making, and by civil society to engage people in specific issues. However new media can also be used to broaden political participation by helping citizens to communicate with their representatives and with each other. Arguably this political communication is most important during election campaigns when political parties and candidates seek to mobilize citizens and persuade them to vote for a given party or candidate. The new media must be used by Parliaments, Parliamentarians, governments and political parties as they are highly effective tools to involve and inform citizens in public policymaking and in the formation of governments. But all these groups must develop strategies to deal with a wide array of both positive and negative effects of these rapidly growing media.New media has begun to take precedent over other communication outlets in part because of its heightened accessibility and usability. Using personal website can empower the public in a way that is far faster, cheaper and more pervasive than other forms of communication. They encourage pluralism, reach young people more than other media and encourage greater participation, accountability and transparency. This research discusses the impact politicians’ personal websites has over their overall electability and likability and explores the integration of website is an essential campaign tactic on both the local and national level. This research examined the impact of having personal website have over the way constituents view politicians. This research examined how politicians can use their website in the most effective fashion and incorporate these new media outlets as essential campaign tools and tactics. A mixed-method approach using content analysis. Content analysis selected thirty websites in sri Lankan politicians. Research revealed that politician’s new media usage significantly influenced and enriched the experience an individual has with the public figure.

Keywords: election campaign ministers, new media, parliament, politicians websites

Procedia PDF Downloads 361
5748 Embedded Electrochemistry with Miniaturized, Drone-Based, Potentiostat System for Remote Detection Chemical Warfare Agents

Authors: Amer Dawoud, Jesy Motchaalangaram, Arati Biswakarma, Wujan Mio, Karl Wallace

Abstract:

The development of an embedded miniaturized drone-based system for remote detection of Chemical Warfare Agents (CWA) is proposed. The paper focuses on the software/hardware system design of the electrochemical Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) signal processing for future deployment on drones. The paper summarizes the progress made towards hardware and electrochemical signal processing for signature detection of CWA. Also, the miniature potentiostat signal is validated by comparing it with the high-end lab potentiostat signal.

Keywords: drone-based, remote detection chemical warfare agents, miniaturized, potentiostat

Procedia PDF Downloads 129
5747 Object Detection in Digital Images under Non-Standardized Conditions Using Illumination and Shadow Filtering

Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

Abstract:

In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

Procedia PDF Downloads 211
5746 The Influence of Negative Online Word of Mouth on Consumer's Online Purchasing Intention in Sri Lanka through Virtual Snowball Sampling Method: A Special Reference from Northern Province

Authors: Sutharsini Jesuthasan, N. Umakanth

Abstract:

Presently the impact of electronic word of mouth on consumer’s purchasing intentions very popular one for a long time period. Even though now this E-WOM got a new evolution through social media. Before this new concept, general people were able to speak with any people on the internet. But likely social media enable people to talk with colleagues, friends and other people on the internet. Meanwhile, this new path way of E-WOM might be more powerful in terms of confusing purchase intention. And negative side of E-WOM very important in this competitive era. So, this study elaborates the negative E-WOM within the context of social media such as face book. And especially this study identifies the influence of negative E-WOM in social media on consumer’s purchase intention. Virtual snowball sampling method was used by researcher to identify the hidden population. Finally, spss 20.0 also used for data analysis purpose. And conclusion and recommendations are given based on the findings. And this research also will support to both parties such as researcher and participants.

Keywords: word of mouth, social media, purchase intention, electronic word of mouth

Procedia PDF Downloads 138
5745 Bidirectional Long Short-Term Memory-Based Signal Detection for Orthogonal Frequency Division Multiplexing With All Index Modulation

Authors: Mahmut Yildirim

Abstract:

This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

Procedia PDF Downloads 63
5744 Tank Barrel Surface Damage Detection Algorithm

Authors: Tomáš Dyk, Stanislav Procházka, Martin Drahanský

Abstract:

The article proposes a new algorithm for detecting damaged areas of the tank barrel based on the image of the inner surface of the tank barrel. Damage position is calculated using image processing techniques such as edge detection, discrete wavelet transformation and image segmentation for accurate contour detection. The algorithm can detect surface damage in smoothbore and even in rifled tank barrels. The algorithm also calculates the volume of the detected damage from the depth map generated, for example, from the distance measurement unit. The proposed method was tested on data obtained by a tank barrel scanning device, which generates both surface image data and depth map. The article also discusses tank barrel scanning devices and how damaged surface impacts material resistance.

Keywords: barrel, barrel diagnostic, image processing, surface damage detection, tank

Procedia PDF Downloads 135
5743 Flicker Detection with Motion Tolerance for Embedded Camera

Authors: Jianrong Wu, Xuan Fu, Akihiro Higashi, Zhiming Tan

Abstract:

CMOS image sensors with a rolling shutter are used broadly in the digital cameras embedded in mobile devices. The rolling shutter suffers the flicker artifacts from the fluorescent lamp, and it could be observed easily. In this paper, the characteristics of illumination flicker in motion case were analyzed, and two efficient detection methods based on matching fragment selection were proposed. According to the experimental results, our methods could achieve as high as 100% accuracy in static scene, and at least 97% in motion scene.

Keywords: illumination flicker, embedded camera, rolling shutter, detection

Procedia PDF Downloads 415
5742 Spanish Language Violence Corpus: An Analysis of Offensive Language in Twitter

Authors: Beatriz Botella-Gil, Patricio Martínez-Barco, Lea Canales

Abstract:

The Internet and ICT are an integral element of and omnipresent in our daily lives. Technologies have changed the way we see the world and relate to it. The number of companies in the ICT sector is increasing every year, and there has also been an increase in the work that occurs online, from sending e-mails to the way companies promote themselves. In social life, ICT’s have gained momentum. Social networks are useful for keeping in contact with family or friends that live far away. This change in how we manage our relationships using electronic devices and social media has been experienced differently depending on the age of the person. According to currently available data, people are increasingly connected to social media and other forms of online communication. Therefore, it is no surprise that violent content has also made its way to digital media. One of the important reasons for this is the anonymity provided by social media, which causes a sense of impunity in the victim. Moreover, it is not uncommon to find derogatory comments, attacking a person’s physical appearance, hobbies, or beliefs. This is why it is necessary to develop artificial intelligence tools that allow us to keep track of violent comments that relate to violent events so that this type of violent online behavior can be deterred. The objective of our research is to create a guide for detecting and recording violent messages. Our annotation guide begins with a study on the problem of violent messages. First, we consider the characteristics that a message should contain for it to be categorized as violent. Second, the possibility of establishing different levels of aggressiveness. To download the corpus, we chose the social network Twitter for its ease of obtaining free messages. We chose two recent, highly visible violent cases that occurred in Spain. Both of them experienced a high degree of social media coverage and user comments. Our corpus has a total of 633 messages, manually tagged, according to the characteristics we considered important, such as, for example, the verbs used, the presence of exclamations or insults, and the presence of negations. We consider it necessary to create wordlists that are present in violent messages as indicators of violence, such as lists of negative verbs, insults, negative phrases. As a final step, we will use automatic learning systems to check the data obtained and the effectiveness of our guide.

Keywords: human language technologies, language modelling, offensive language detection, violent online content

Procedia PDF Downloads 122
5741 Fault Location Detection in Active Distribution System

Authors: R. Rezaeipour, A. R. Mehrabi

Abstract:

Recent increase of the DGs and microgrids in distribution systems, disturbs the tradition structure of the system. Coordination between protection devices in such a system becomes the concern of the network operators. This paper presents a new method for fault location detection in the active distribution networks, independent of the fault type or its resistance. The method uses synchronized voltage and current measurements at the interconnection of DG units and is able to adapt to changes in the topology of the system. The method has been tested on a 38-bus distribution system, with very encouraging results.

Keywords: fault location detection, active distribution system, micro grids, network operators

Procedia PDF Downloads 777
5740 Research on ARQ Transmission Technique in Mars Detection Telecommunications System

Authors: Zhongfei Cai, Hui He, Changsheng Li

Abstract:

This paper studied in the automatic repeat request (ARQ) transmission technique in Mars detection telecommunications system. An ARQ method applied to proximity-1 space link protocol was proposed by this paper. In order to ensure the efficiency of data reliable transmission, this ARQ method combined these different ARQ maneuvers characteristics. Considering the Mars detection communication environments, this paper analyzed the characteristics of the saturation throughput rate, packet dropping probability, average delay and energy efficiency with different ARQ algorithms. Combined thus results with the theories of ARQ transmission technique, an ARQ transmission project in Mars detection telecommunications system was established. The simulation results showed that this algorithm had excellent saturation throughput rate and energy efficiency with low complexity.

Keywords: ARQ, mars, CCSDS, proximity-1, deepspace

Procedia PDF Downloads 332
5739 Winery Owners’ Perceptions of Social Media in Promoting Wine Tourism: Case Study of Langhe, Italy

Authors: Magali Canovi, Francesca Pucciarelli

Abstract:

Over the past decade Langhe has developed as a wine tourism destination and has become increasingly popular on an international basis. Wine tourism has been recognized as an important business driver for wineries in Langhe and wine owners have taken advantage of this opportunity through developing a variety of tourism-related activities at their wineries, notably winery visits, wine tastings, cellar-door sales, B&Bs and/or restaurants. In order to promote these tourism-related activities and attract an increasing number of wine tourists, wineries have started to engage in social media. While tourism scholars are now well aware of the benefits social media provides to both travellers and service providers, the existing literature on social media from supplier’s perspective remains limited. Accordingly, this paper aims to fill this gap through providing new insights into how service providers, that is winery owners, exploit social media to promote tourism online. The paper explores the importance and the role of social media as part of wineries’ marketing strategies to promote wine tourism online. The focus lies on understanding, which motives drive winery owners to activate and implement social media activities in promoting wine tourism. A case study approach is adopted, using the North Italian wine region of Langhe in Piedmont. Empirical evidence is provided by a sample of 28 winery owners. An interpretivist approach to research is adopted in order to extend current understandings of social media within the context of wine tourism. In line with the interpretivist perspective, this paper uses discourse analysis (DA) as a methodological approach for analyzing and interpreting winery owners’ accounts. Three key findings emerge from this research. First, there is a general understanding among winery owners what social media represents an opportunity in promoting wine tourism – if not even a must have. Second, the majority of interviewed winery owners are currently applying to some extent social media to promote wine tourism online as well as to interact and engage with tourists directly. Lastly, a varying degree of usage of social media amongst wineries is identified, with some wineries not recognizing social media as a crucial tool in marketing communication strategies. On the other extent, some commonalities in strategies and platforms chosen can be detected by these wineries that actively participate in social media. In conclusion, the main contribution of this paper is that it extends current understandings of social media in the wine tourism context by offering valuable insights into how service providers perceive and engage in social media.

Keywords: langhe, promotion, social media, wine tourism

Procedia PDF Downloads 178
5738 3D Object Detection for Autonomous Driving: A Comprehensive Review

Authors: Ahmed Soliman Nagiub, Mahmoud Fayez, Heba Khaled, Said Ghoniemy

Abstract:

Accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles.

Keywords: computer vision, 3D object detection, autonomous vehicles, deep learning

Procedia PDF Downloads 59
5737 Role of Social Media for Institutional Branding: Ethics of Communication Review

Authors: Iva Ariani, Mohammad Alvi Pratama

Abstract:

Currently, the world of communication experiences a rapid development. There are many ways of communication utilized in line with the development of science which creates many technologies that encourage a rapid development of communication system. However, despite giving convenience for the society, the development of communication system is not accompanied by the development of applicable values and regulations. Therefore, it raises many issues regarding false information or hoax which can influence the society’s mindset. This research aims to know the role of social media towards the reputation of an institution using a communication ethics study. It is a qualitative research using interview, observation, and literature study for collecting data. Then, the data will be analyzed using philosophical methods which are hermeneutic and deduction methods. This research is expected to show the role of social media in developing an institutional reputation in ethical review.

Keywords: social media, ethics, communication, reputation

Procedia PDF Downloads 201
5736 Carbon-Based Electrochemical Detection of Pharmaceuticals from Water

Authors: M. Ardelean, F. Manea, A. Pop, J. Schoonman

Abstract:

The presence of pharmaceuticals in the environment and especially in water has gained increasing attention. They are included in emerging class of pollutants, and for most of them, legal limits have not been set-up due to their impact on human health and ecosystem was not determined and/or there is not the advanced analytical method for their quantification. In this context, the development of various advanced analytical methods for the quantification of pharmaceuticals in water is required. The electrochemical methods are known to exhibit the great potential for high-performance analytical methods but their performance is in direct relation to the electrode material and the operating techniques. In this study, two types of carbon-based electrodes materials, i.e., boron-doped diamond (BDD) and carbon nanofiber (CNF)-epoxy composite electrodes have been investigated through voltammetric techniques for the detection of naproxen in water. The comparative electrochemical behavior of naproxen (NPX) on both BDD and CNF electrodes was studied by cyclic voltammetry, and the well-defined peak corresponding to NPX oxidation was found for each electrode. NPX oxidation occurred on BDD electrode at the potential value of about +1.4 V/SCE (saturated calomel electrode) and at about +1.2 V/SCE for CNF electrode. The sensitivities for NPX detection were similar for both carbon-based electrode and thus, CNF electrode exhibited superiority in relation to the detection potential. Differential-pulsed voltammetry (DPV) and square-wave voltammetry (SWV) techniques were exploited to improve the electroanalytical performance for the NPX detection, and the best results related to the sensitivity of 9.959 µA·µM-1 were achieved using DPV. In addition, the simultaneous detection of NPX and fluoxetine -a very common antidepressive drug, also present in water, was studied using CNF electrode and very good results were obtained. The detection potential values that allowed a good separation of the detection signals together with the good sensitivities were appropriate for the simultaneous detection of both tested pharmaceuticals. These results reclaim CNF electrode as a valuable tool for the individual/simultaneous detection of pharmaceuticals in water.

Keywords: boron-doped diamond electrode, carbon nanofiber-epoxy composite electrode, emerging pollutans, pharmaceuticals

Procedia PDF Downloads 277
5735 Compliance to Compassion: How COVID-19 Changed the Way Educators Used Social Media to Collaborate with Families

Authors: Eloise Thomson

Abstract:

The COVID-19 global pandemic challenged our normative conceptualization of teaching across all age levels, requiring the transition to remote instruction, in some instances, literally overnight. Included in the rapidly changing education environment was the delivery of early childhood education. In Victoria, Australia, the capital city, Melbourne, became known as the most locked down city in the world. This presentation examines the ways educators used social media to collaborate with families before the COVID-19 pandemic and during the lockdown phase through the use of a Third Space conceptual framework and case study methodology. As a first step, the paper examines how social media may offer new opportunities for collaborative practice between educators and families. Second, the data is outlined and discussed with respect to collaborative practice and quality. Finally, a postscript then allows for insight into how educators’ practice of using social media to collaborate with families has been impacted by the COVID-19 global pandemic. Finally, the implications of the ways in which educators are using social media to collaborate with families are discussed. The use of social media in early-childhood education has the potential to provide a valuable platform for educators to connect with families and students. However, the use of social media by educators uncovered a dialogue of ‘quality’ and appeared to be dominated by evidence around compliance and attaining quality in a very specific, and perhaps narrow, way. The findings suggest a culture of compliance that is dominated by outcomes, standards and assessments and that this has changed the dynamics by which educators engage with families. Furthermore, findings highlighted the disparity between educators' and families' understanding of the intent of the collaborations themselves. This research was significant as it exposed the ways in which educators are engaging with social media, resulting in a discussion on the intent of collaborations, the questioning of imposed quality, and the notion that quality is measurable and exists in only one form.

Keywords: collaboration, compliance, early childhood, third space, pedagogy of caring, social media

Procedia PDF Downloads 63
5734 Electrochemical Detection of Polycyclic Aromatic Hydrocarbons in Urban Air by Exfoliated Graphite Based Electrode

Authors: A. Sacko, H. Nyoni, T. A. M. Msagati, B. Ntsendwana

Abstract:

Carbon based materials to target environmental pollutants have become increasingly recognized in science. Electrochemical methods using carbon based materials are notable methods for high sensitive detection of organic pollutants in air. It is therefore in this light that exfoliated graphite electrode was fabricated for electrochemical analysis of PAHs in urban atmospheric air. The electrochemical properties of the graphite electrode were studied using CV and EIS in the presence of acetate buffer supporting electrolyte with 2 Mm ferricyanide as a redox probe. The graphite electrode showed enhanced current response which confirms facile kinetics and enhanced sensitivity. However, the peak to peak (DE) separation increased as a function of scan rate. The EIS showed a high charger transfer resistance. The detection phenanthrene on the exfoliated graphite was studied in the presence of acetate buffer solution at PH 3.5 using DPV. The oxidation peak of phenanthrene was observed at 0.4 V. Under optimized conditions (supporting electrolyte, pH, deposition time, etc.). The detection limit observed was at 5x 10⁻⁸ M. Thus the results demonstrate with further optimization and modification lower concentration detection can be achieved.

Keywords: electrochemical detection, exfoliated graphite, PAHs (polycyclic aromatic hydrocarbons), urban air

Procedia PDF Downloads 200
5733 Application of Learning Media Based Augmented Reality on Molecular Geometry Concept

Authors: F. S. Irwansyah, I. Farida, Y. Maulana

Abstract:

Studying chemistry requires the ability to understand three levels of understanding in the form of macroscopic, submicroscopic and symbolic, but the lack of emphasis on the submicroscopic level leads to the understanding of chemical concepts becoming incomplete, due to the limitations of the tools capable of providing visualization of submicroscopic concepts. The purpose of this study describes the stages of making augmented reality learning media on the concept of molecular geometry and analyze the feasibility test result of augmented reality learning media on the concept of molecular geometry. This research uses Research and Development (R & D) method which produces a product of AR learning media on molecular geometry concept and test the effectiveness of the product. Research stages include concept analysis and learning indicators, design development, validation, feasibility, and limited testing. The stages of validation and limited trial are aimed to get feedback in the form of assessment, suggestion and improvement on learning aspect, material substance aspect, visual communication aspect and software engineering aspects and media feasibility in terms of media creation purpose to be used in learning. The results of the overall feasibility test obtained r-calculation 0,7-0,9 with the interpretation of high feasibility value, whereas the result of limited trial got the percentage of eligibility with the average value equal to 70,83-92,5%. This percentage indicates that AR's learning media product on the concept of molecular geometry, deserves to be used as a learning resource.

Keywords: android, augmented reality, chemical learning, geometry

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5732 Facile Synthesis of CuO Nanosheets on Cu Foil for H2O2 Detection

Authors: Yu-Kuei Hsu, Yan-Gu Lin

Abstract:

A facile and simple fabrication of copper(II) oxide (CuO) nanosheet on copper foil as nanoelectrode for H2O2 sensing application was proposed in this study. The spontaneous formation of CuO nanosheets by immersing the copper foil into 0.1 M NaOH aqueous solution for 48 hrs was carried out at room temperature. The sheet-like morphology with several ten nanometers in thickness and ~500 nm in width was observed by SEM. Those nanosheets were confirmed the monoclinic-phase CuO by the structural analysis of XRD and Raman spectra. The directly grown CuO nanosheets film is mechanically stable and offers an excellent electrochemical sensing platform. The CuO nanosheets electrode shows excellent electrocatalytic response to H2O2 with significantly lower overpotentials for its oxidation and reduction and also exhibits a fast response and high sensitivity for the amperometric detection of H2O2. The novel spontaneously grown CuO nanosheets electrode is readily applicable to other analytes and has great potential applications in the electrochemical detection.

Keywords: CuO, nanosheets, H2O2 detection, Cu foil

Procedia PDF Downloads 286
5731 Sentiment Analysis of Consumers’ Perceptions on Social Media about the Main Mobile Providers in Jamaica

Authors: Sherrene Bogle, Verlia Bogle, Tyrone Anderson

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In recent years, organizations have become increasingly interested in the possibility of analyzing social media as a means of gaining meaningful feedback about their products and services. The aspect based sentiment analysis approach is used to predict the sentiment for Twitter datasets for Digicel and Lime, the main mobile companies in Jamaica, using supervised learning classification techniques. The results indicate an average of 82.2 percent accuracy in classifying tweets when comparing three separate classification algorithms against the purported baseline of 70 percent and an average root mean squared error of 0.31. These results indicate that the analysis of sentiment on social media in order to gain customer feedback can be a viable solution for mobile companies looking to improve business performance.

Keywords: machine learning, sentiment analysis, social media, supervised learning

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5730 New Derivatives 7-(diethylamino)quinolin-2-(1H)-one Based Chalcone Colorimetric Probes for Detection of Bisulfite Anion in Cationic Micellar Media

Authors: Guillermo E. Quintero, Edwin G. Perez, Oriel Sanchez, Christian Espinosa-Bustos, Denis Fuentealba, Margarita E. Aliaga

Abstract:

Bisulfite ion (HSO3-) has been used as a preservative in food, drinks, and medication. However, it is well-known that HSO3- can cause health problems like asthma and allergic reactions in people. Due to the above, the development of analytical methods for detecting this ion has gained great interest. In line with the above, the current use of colorimetric and/or fluorescent probes as a detection technique has acquired great relevance due to their high sensitivity and accuracy. In this context, 2-quinolinone derivatives have been found to possess promising activity as antiviral agents, sensitizers in solar cells, antifungals, antioxidants, and sensors. In particular, 7-(diethylamino)-2-quinolinone derivatives have attracted attention in recent years since their suitable photophysical properties become promising fluorescent probes. In Addition, there is evidence that photophysical properties and reactivity can be affected by the study medium, such as micellar media. Based on the above background, 7-(diethylamino)-2-quinolinone derivatives based chalcone will be able to be incorporated into a cationic micellar environment (Cetyltrimethylammonium bromide, CTAB). Furthermore, the supramolecular control induced by the micellar environment will increase the reactivity of these derivatives towards nucleophilic analytes such as HSO3- (Michael-type addition reaction), leading to the generation of new colorimetric and/or fluorescent probes. In the present study, two derivatives of 7-(diethylamino)-2-quinolinone based chalcone DQD1-2 were synthesized according to the method reported by the literature. These derivatives were structurally characterized by 1H, 13C NMR, and HRMS-ESI. In addition, UV-VIS and fluorescence studies determined absorption bands near 450 nm, emission bands near 600 nm, fluorescence quantum yields near 0.01, and fluorescence lifetimes of 5 ps. In line with the foregoing, these photophysical properties aforementioned were improved in the presence of a cationic micellar medium using CTAB thanks to the formation of adducts presenting association constants of the order of 2,5x105 M-1, increasing the quantum yields to 0.12 and the fluorescence lifetimes corresponding to two lifetimes near to 120 and 400 ps for DQD1 and DQD2. Besides, thanks to the presence of the micellar medium, the reactivity of these derivatives with nucleophilic analytes, such as HSO3-, was increased. This was achieved through kinetic studies, which demonstrated an increase in the bimolecular rate constants in the presence of a micellar medium. Finally, probe DQD1 was chosen as the best sensor since it was assessed to detect HSO3- with excellent results.

Keywords: bisulfite detection, cationic micelle, colorimetric probes, quinolinone derivatives

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5729 Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks

Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

Abstract:

Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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5728 Polarisation in Latin America: Examining the Role of Social Media in Ideological Positioning Based on 2018 Census Data

Authors: Sarah Ledoux

Abstract:

This paper analyses the quantitative effects of political content consumption in social media platforms on self-reported ideological preference across the Latin American region. Initially praising the democratic potential of the internet and its social networking websites, digital politics scholars have transitioned their discourse to warning against the undemocratic side-effects it cultivates, such as hate speech, filter bubbles, and ideological polarisation. Holding technology solely responsible for political trends worldwide is an oversimplification of the factors influencing social change. Nonetheless, widespread use of social media in new democracies raises questions on the reproduction of recent trends that have been observed in the US and Western Europe. Through the analysis of ordered logistic regressions on data from the 2018 AmericasBarometer survey, this study examines the extent to which the relationship between the consumption of political content on social media is related to ideological polarisation in Latin America. The findings indicate that there is a close link between consumption of political information on social media, specifically on Facebook and WhatsApp, and ideological positioning on the extremes of the political left- and right-wings. This relation holds when controlling for individual-level demographic and attitudinal factors, as well as country-level effects. These results demonstrate with empirical evidence that viewing political content on social media has a significant positive effect on the likelihood that citizens position themselves on the extreme ends of the left-right ideological spectrum and implies that political polarisation is a phenomenon that accompanies politically driven social media use.

Keywords: Latin America, polarisation, political consumption, political ideology, social media, survey

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5727 High Level Synthesis of Canny Edge Detection Algorithm on Zynq Platform

Authors: Hanaa M. Abdelgawad, Mona Safar, Ayman M. Wahba

Abstract:

Real-time image and video processing is a demand in many computer vision applications, e.g. video surveillance, traffic management and medical imaging. The processing of those video applications requires high computational power. Therefore, the optimal solution is the collaboration of CPU and hardware accelerators. In this paper, a Canny edge detection hardware accelerator is proposed. Canny edge detection is one of the common blocks in the pre-processing phase of image and video processing pipeline. Our presented approach targets offloading the Canny edge detection algorithm from processing system (PS) to programmable logic (PL) taking the advantage of High Level Synthesis (HLS) tool flow to accelerate the implementation on Zynq platform. The resulting implementation enables up to a 100x performance improvement through hardware acceleration. The CPU utilization drops down and the frame rate jumps to 60 fps of 1080p full HD input video stream.

Keywords: high level synthesis, canny edge detection, hardware accelerators, computer vision

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5726 Keypoint Detection Method Based on Multi-Scale Feature Fusion of Attention Mechanism

Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

Abstract:

Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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5725 A Brief Study on the Mental Health vs. Mental Disorders in China, Suicide and the Entertainment Media

Authors: Patricia Portugal Marques de Carvalho Lourenço

Abstract:

Mental Health, mental illnesses, and suicide are old topics made young. While broadly addressed on a global scale to various extents and degrees, mental health, mental disorders, and suicide remain to a large extent a, taboo in a number of societies such as the Chinese. The country’s report on mental health was scrutinized for an in-depth understanding of the prevalence of mental disorders domestically, emphasizing depression, which is more accentuated in rural settings than urban, affecting a significant number of students, retired individuals and that unemployed country-wise. Depression in China is linked to anxiety in younger years, both decreasing as the population grows in age. Mental health, mental disorders and suicide remain for the most part, “forgotten”, despite statistically significant and the media’s yet small efforts in educating the population about the terms i.e. through online/television dramas that approach the topics, trying to demystify them. Whereas crucial to openly address mental health, mental disorders, and suicide, the issues remain an ongoing challenge in China, where series draw light into a reality the media and the population do not broadly converse about. The media in general and the entertainment media, in particular, have a vital role in helping China acknowledge mental health, mental disorders and suicide, albeit having a long way to go in assisting the Chinese population in dealing with the health of their inner minds.

Keywords: mental health, mental disorders, suicide, media, China, Chinese entertainment

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5724 Dependency on Social Media and Psychological Well-Being among Young Adults: Case Study of University Students in Pakistan

Authors: Ghazala Yasmeen, Zahid Yousaf

Abstract:

Frequent social media use has significantly changed people's life and communication styles during the last two decades. Social media use has multiple dimensions, and there are nuanced relationships between it and how it affects different societal subgroups. With the increased popularity and rapid growth of social networking sites, people are experiencing potential social media addiction, which causes severe mental health problems. How social media is dramatically influencing the lives and mental health of its users, and particularly of the students, creating psychological issues, e.g., isolation, depression, and anxiety, will be the primary objective of this study. This research will address the problems confronted by many students who are regular social media users and can undergo mental distress. This study aims to explore how social media use can lead to isolation, depression, and anxiety. This research will also investigate the effects of cyber-bullying on social, emotional, and psychological wellbeing. For this purpose, the researcher will use the survey technique as a method of inquiry. Ryff's theory of Psychological wellbeing will be used as a theoretical framework to explore the association between social media addiction and psychological effects among users. For data collection, the researcher will use the quantitative research method through a survey questionnaire from three universities in Pakistan from the public and private sectors. This study will imply a two-stage random sampling technique. At first, the researcher will select 20% of students from universities. In the second stage, 20% of students using different social networking sites will be chosen, and draw a representative sample from these will be. The intended study will use questionnaires comprising two portions. The first section will consist of social media engagement by the students, following impacts on their mental health and reported attitude towards psychological wellbeing. This study will spotlight the considerations of parents, educationists, and policymakers to take measures against the devastating effects of cyber-crimes on young adults.

Keywords: anxiety, depression, isolation, social media, wellbeing

Procedia PDF Downloads 72