Search results for: corona graph

Authors: Bansal Ashwani

Abstract:

Cartographic depiction and visualization of wetland changes is an important tool to map spatial-temporal information about the wetland dynamics effectively and to comprehend the response of these water bodies in maintaining the groundwater and surrounding ecosystem. This is true for the states of North Western India, i.e., J&K, Himachal, Punjab, and Haryana that are bestowed upon with several natural wetlands in the flood plains or on the courses of its rivers. Thus, the present study documents, analyses and reconstructs the lost wetlands, which existed in the flood plains of the major river basins of these states, i.e., Chenab, Jhelum, Satluj, Beas, Ravi, and Ghagar, in the beginning of the 20th century. To achieve the objective, the study has used multi-temporal datasets since the 1960s using high to medium resolution satellite datasets, e.g., Corona (1960s/70s), Landsat (1990s-2017) and Sentinel (2017). The Sentinel (2017) satellite image has been used for making the wetland inventory owing to its comparatively higher spatial resolution with multi-spectral bands. In addition, historical records, repeated photographs, historical maps, field observations including geomorphological evidence were also used. The water index techniques, i.e., band rationing, normalized difference water index (NDWI), modified NDWI (MNDWI) have been compared and used to map the wetlands. The wetland types found in the north-western states have been categorized under 19 classes suggested by Space Application Centre, India. These enable the researcher to provide with the wetlands inventory and a series of cartographic representation that includes overlaying multiple temporal wetlands extent vectors. A preliminary result shows the general state of wetland shrinkage since the 1960s with varying area shrinkage rate from one wetland to another. In addition, it is observed that majority of wetlands have not been documented so far and even do not have names. Moreover, the purpose is to emphasize their elimination in addition to establishing a baseline dataset that can be a tool for wetland planning and management. Finally, the applicability of cartographic depiction and visualization, historical map sources, repeated photographs and remote sensing data for reconstruction of long term wetlands fluctuations, especially in the northern part of India, will be addressed.

Keywords: cartographic depiction and visualization, wetland changes, NDWI/MDWI, geomorphological evidence and remote sensing

Procedia PDF Downloads 263

Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

Abstract:

Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

Procedia PDF Downloads 127

Authors: Andrey Khalov

Abstract:

The rapid proliferation of unstructured data in IT infrastructure management demands innovative approaches for extracting actionable knowledge. This paper presents a framework for ontology-based knowledge extraction that combines relational graph neural networks (R-GNN) with large language models (LLMs). The proposed method leverages the DOLCE framework as the foundational ontology, extending it with concepts from ITSMO for domain-specific applications in IT service management and outsourcing. A key component of this research is the use of transformer-based models, such as DeBERTa-v3-large, for automatic entity and relationship extraction from unstructured texts. Furthermore, the paper explores how transfer learning techniques can be applied to fine-tune large language models (LLaMA) for using to generate synthetic datasets to improve precision in BERT-based entity recognition and ontology alignment. The resulting IT Ontology (ITO) serves as a comprehensive knowledge base that integrates domain-specific insights from ITIL processes, enabling more efficient decision-making. Experimental results demonstrate significant improvements in knowledge extraction and relationship mapping, offering a cutting-edge solution for enhancing cognitive computing in IT service environments.

Keywords: ontology expansion, synthetic dataset, transformer fine-tuning, concept extraction, DOLCE, BERT, taxonomy, LLM, NER

Procedia PDF Downloads 14

Authors: Mairaj Ahmad, L. Paglia, F. Marra, V. Genova, G. Pulci

Abstract:

This study employed Rene N4 and FSX 414 superalloys, which are used in numerous turbine engine components due of their high strength, outstanding fatigue, creep, thermal, and corrosion-resistant properties. An in-depth examination of corrosion mechanisms with vapor present at high temperature is necessary given the industrial trend toward introducing increasing amounts of hydrogen into combustion chambers in order to boost power generation and minimize pollution in contrast to conventional fuels. These superalloys were oxidized in recent tests for 500, 1000, 2000, 3000 and 4000 hours at 982±5°C temperatures with a steady airflow at a flow rate of 10L/min and 1.5 bar pressure. These superalloys were also examined for wet corrosion for 500, 1000, 2000, 3000, and 4000 hours in a combination of air and water vapor flowing at a 10L/min rate. Weight gain, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive x-ray spectroscopy (EDS) were used to assess the oxidation and heat corrosion resistance capabilities of these alloys before and after 500, 1000, and 2000 hours. The oxidation/corrosion processes that accompany the formation of these oxide scales are shown in the graph of mass gain vs time. In both dry and wet oxidation, oxides like Al2O3, TiO2, NiCo2O4, Ni3Al, Ni3Ti, Cr2O3, MnCr2O4, CoCr2O4, and certain volatile compounds notably CrO2(OH)2, Cr(OH)3, Fe(OH)2, and Si(OH)4 are formed.

Keywords: hot corrosion, oxidation, turbine materials, high temperature corrosion, super alloys

Procedia PDF Downloads 85

Authors: Agil Gemilang Ramadhan, Novian Triandanu

Abstract:

Since 1929, the first appearance in sea level, Anak Krakatau volcano growth relatively quickly. During the 80 years up to 2010 has reached the height of 320 meter above sea level. The possibility of catastrophic explosive eruption could happen again if the chemical composition of rocks from the eruption changed from alkaline magma into acid magma. Until now Anak Krakatau volcanic activity is still quite active as evidenced by the frequency of eruptions that produced ash sized pyroclastic deposits - bomb. Purpose of this study was to identify changes in the percentage of rock geochemistry any results eruption of Anak Krakatau volcano to see consistency change the percentage content of silica in the magma that affect the type of volcanic eruptions. Results from this study will be produced in the form of a diagram the data changes the chemical composition of rocks of Anak Krakatau volcano. Changes in the composition of any silica eruption are illustrated in a graph. If the increase in the percentage of silica is happening consistently and it is assumed to increase in the time scale of a few percent, then to achieve silica content of 68 % (acid composition) that will produce an explosive eruption will know the approximate time. All aspects of the factors driving the increased threat of danger to the public should be taken into account. Catastrophic eruption katatropis mitigation can be planned early so that when these disasters happen later, casualties can be minimized.

Keywords: Krakatau volcano, rock geochemistry, catastrophic eruption, mitigation

Procedia PDF Downloads 281

Authors: Shao Qi

Abstract:

The article uses the CiteSpace knowledge graph analysis tool to sort and visualize the journal literature on urban plants and habitats in the Web of Science and China National Knowledge Infrastructure databases. Based on a comprehensive interpretation of the visualization results of various data sources and the description of the intrinsic relationship between high-frequency keywords using knowledge mapping, the research hotspots, processes and evolution trends in this field are analyzed. Relevant case studies are also conducted for the hotspot contents to explore the means of landscape intervention and synthesize the understanding of research theories. The results show that (1) from 1999 to 2022, the research direction of urban plants and habitats gradually changed from focusing on plant and animal extinction and biological invasion to the field of human urban habitat creation, ecological restoration, and ecosystem services. (2) The results of keyword emergence and keyword growth trend analysis show that habitat creation research has shown a rapid and stable growth trend since 2017, and ecological restoration has gained long-term sustained attention since 2004. The hotspots of future research on urban plants and habitats in China may focus on habitat creation and ecological restoration.

Keywords: research trends, visual analysis, habitat creation, ecological restoration

Procedia PDF Downloads 61

Authors: Shaleni Bajpai, Nancy Stephan

Abstract:

Various designs of women’s kurtis with different styles, motifs and prints were available in market but none of the kurtis was found in army print. Mostly army prints are used for men’s wear like jackets, trousers, caps, bags. The main colours available in military prints were beige, parrot green, red, dark blue, light blue, orange, bottle green, pink and the original military green colour. As the original camouflage is banned in civil wears so the different variety and colours were used in this study to popularize army prints in women’s wear. The aim of this project was to construct different styles of women kurti’s with various colours of different military prints. Mood board, inspiration and colour board was prepared to design the kurtis. The fabric used for construction was army printed poplin and crepe. The designing and construction of kurti’s were divided into two categories such as - casual and party wear. Casual wear had simple silhouette like a-line, high-low and waist coat style whereas party wear included princess line, panelled and bandhani style. Structured questionnaire was prepared to assess the acceptance of newly designed kurtis with respect to colour combination, overall appearance and cost. Purposively sampling method was adopted for selection of respondents. Opinion was taken from 100 women of various age groups. The result and analysis was presented through graph and percentage. Kurtis in army print of both the categories were appreciated by the respondents.

Keywords: army, kurti, casual wear, party wear

Procedia PDF Downloads 302

Authors: Simone Leonardi, Luca Ardito

Abstract:

Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

Procedia PDF Downloads 183

Authors: Angela U. Makolo, Temitayo A. Olagunju

Abstract:

The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained using high throughput experimental procedures. However, these high throughput methods are known to produce very high rates of false positive and negative interactions. In order to construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed. A weighted interaction graph of the Saccharomyces cerevisiae (Baker’s Yeast) organism using the proteins as the nodes and interactions between them as edges was constructed. The weights were obtained using Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model. A pathway was formalized as a simple path in the interaction network from a starting protein and an ending protein of interest. We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. cerevisiae.

Keywords: Bayesian networks, protein interaction networks, Saccharomyces cerevisiae, signalling pathways

Procedia PDF Downloads 542

Authors: Steven G. Sclan

Abstract:

Background: Since December 2019, the world has been afflicted by the spread of the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), which is responsible for the condition referred to as Covid-19. The illness has had a cataclysmic impact on the political, social, economic, and overall well-being of the population of the entire globe. While Covid-19 has had a substantial universal fatality impact, it may have an even greater effect on the socioeconomic, medical well-being, and healthcare planning for remaining societies. Significance: As these numbers illustrate, many more persons survive the infection than die from it, and many of those patients have noted ongoing, persistent symptoms after successfully enduring the acute phase of the illness. Recognition and understanding of these symptoms are crucial for developing and arranging efficacious models of care for all patients (whether or not having been hospitalized) surviving acute covid illness and plagued by post-acute symptoms. Furthermore, regarding Covid infection in children (< 18 y/o), although it may be that Covid “+” children are not major vectors of infective transmission, it now appears that many more children than initially thought are carrying the virus without accompanying obvious symptomatic expression. It seems reasonable to wonder whether viral effects occur in children – those children who are Covid “+” and now asymptomatic – and if, over time, they might also experience similar symptoms. An even more significant question is whether Covid “+” asymptomatic children might manifest increased multiple health problems as they grow – i.e., developmental complications (e.g., physical/medical, metabolic, neurobehavioral, etc.) – in comparison to children who had been consistently Covid “ - ” during the pandemic. Topics Addressed and Theoretical Importance: This review is important because of the description of both quantitative and qualitative methods for clinical and biomedical research. Topics reviewed will consider the importance of well-designed, comprehensive (i.e., quantitative and qualitative methods) longitudinal studies of Post Covid-19 symptoms in both adults and children. Also reviewed will be general characteristics of longitudinal studies and a presentation of a model for a proposed study. Also discussed will be the benefit of longitudinal studies for the development of efficacious interventions and for the establishment of cogent, practical, and efficacious community healthcare service planning for post-acute covid patients. Conclusion: Results of multi-dimensional, longitudinal studies will have important theoretical implications. These studies will help to improve our understanding of the pathophysiology of long COVID and will aid in the identification of potential targets for treatment. Such studies can also provide valuable insights into the long-term impact of COVID-19 on public health and socioeconomics.

Keywords: COVID-19, post-COVID-19, long COVID, longitudinal research, quantitative research, qualitative research

Procedia PDF Downloads 59

Authors: Mina Samizadeh

Abstract:

In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems.

Keywords: keyword extraction, n-gram extraction, text summarization, topic clustering, semantic analysis

Procedia PDF Downloads 70

Authors: Xinyuan Zou, Qihui Tang, Shujian Wang, Yulin Huang, Jie Gui, Xiangping Liu, Gang Liu, Yanqiang Tao

Abstract:

Childhood trauma can have a long-lasting influence on individuals and contribute to mental disorders, including depression and anxiety. The current study aimed to explore the symptomatic and developmental patterns of depression, anxiety, and stress among adolescents who have suffered from childhood trauma. A total of 3,598 college students (female = 1,617 (44.94%), Mean Age = 19.68, SD Age = 1.35) in China completed the Childhood Trauma Questionnaire (CTQ) and the Depression, Anxiety, and Stress Scales (DASS-21), and 2,337 participants met the selection standard based on the cut-off scores of the CTQ. The symptomatic network and directed acyclic graph (DAG) network approaches were used. The results revealed that males reported experiencing significantly more physical abuse, physical neglect, emotional neglect, and sexual abuse compared to females. However, females scored significantly higher than males on all items of DASS-21, except for “Worthless”. No significant difference between the two genders was observed in the network structure and global strength. Meanwhile, among all participants, “Down-hearted” and “Agitated” appeared to be the most interconnected symptoms, the bridge symptoms in the symptom network, as well as the most vital symptoms in the DAG network. Apart from that, “No-relax” also served as the most prominent symptom in the DAG network. The results suggested that intervention targeted at assisting adolescents in developing more adaptive coping strategies with stress and regulating emotion could benefit the alleviation of comorbid depression, anxiety, and stress.

Keywords: symptom network, childhood trauma, depression, anxiety, stress

Procedia PDF Downloads 59

Authors: Arul Aram, Vishnu Priya, Monicka Karunanithi

Abstract:

In India, in particular, festivals are the occasions of celebrations. They create beautiful moments to cherish. Mostly, people pay a visit to their native places to celebrate with their loved ones. So are wedding celebrations. The Covid-19 pandemic came upon us unexpectedly, and to fight it, the festivals and weddings are celebrated unusually. Crowded places are deserted. Mass gatherings are avoided, changes and alterations are made in our rituals and celebrations. The warmth usually people have at their heart during any festival and wedding has disappeared. Some aspects of the celebrations become virtual/digital rather than real -- for instance, digital greetings/invitations, digital conduct of ceremonies by priests, YouTube worship, online/digital cash gifts, and digital audience for weddings. Each festival has different rituals which are followed with the divine nature in every family, but the pandemic warranted some compromises on the traditions. Likewise, a marriage is a beautiful bond between two families where a lot of traditional customs are followed. The wedding ceremonies are colorful and celebrations may extend for several days. People in India spend financial resources to prepare and celebrate weddings. The bride's and the groom's homes are fully decorated with colors, balloons and other decorations. The wedding rituals and celebrations vary by religion, region, preference and the resources of the groom, bride and their families. They can range from one day to multiple-days events. But the Covid-19 pandemic situation changes the mindset of people over ceremonies. This lockdown has affected those weddings and industries that support them and make the people postpone or at times advance without fanfare their 'big day.' People now adopt the protocols, guidelines and safety measures to reduce the risk and minimize the fear during celebrations. The study shall look into: how the pandemic shattered the expectations of people celebrating; problems faced economically by people/service providers who are benefited by the celebrations; and identify the alterations made in the rituals or the practices of our culture for the safety of families. The study shall employ questionnaires, interviews and visual ethnography to collect data. The study found that during a complete lockdown, people have not bought new clothes, sweets, or snacks, as they generally do before a pandemic. Almost all of them kept their celebrations low-key, and some did not celebrate at all. Digital media played a role in keeping the celebration alive, as people used it to wish their friends and families virtually. During partial unlock, the situation was under control, and people began to go out and see a few family and friends. They went shopping and bought new clothes and needs, but they did it while following safety precautions. There is also an equal percentage of people who shopped online. Although people continue to remain disappointed, they were less stressed up as life was returning to normal.

Keywords: covid-19, digital, festivals, India, wedding

Procedia PDF Downloads 186

Authors: Richard Molyneux, Christopher Parrott, Kirill Horoshenkov

Abstract:

This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and stigmergy, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network.

Keywords: autonomous inspection, buried pipes, stigmergy, swarm intelligence, vehicle routing problem

Procedia PDF Downloads 166

Authors: Ali Essam El Shazly

Abstract:

The slum survey of 'Sequina' area in Alexandria details the building rooms of twenty-building samples according to the integral measure of space syntax. The essence of room organization sets the most integrative 'visitor' domain between the 'inhabitant' wings of less integrated 'parent' than the 'children' structure with visual ring of 'balcony' space. Despite the collective real relative asymmetry of 'pheno-type' aggregation, the relative asymmetry of individual layouts reveals 'geno-type' structure of spatial diversity. The multifunction of rooms optimizes the integral structure of graph and visibility merge, which contrasts with the deep tailing structure of distinctive social domains. The most integrative layout inverts the geno-type into freed rooms of shallow 'inhabitant' domain against the off-centered 'visitor' space, while the most segregated layout further restricts the pheno-type through isolated 'visitor' from 'inhabitant' domains across the 'staircase' public domain. The catalyst 'kitchen & living' spaces demonstrate multi-structural dimensions among the various social domains. The former ranges from most exposed central integrity to the most hidden 'motherhood' territories. The latter, however, mostly integrates at centrality or at the further ringy 'childern' domain. The study concludes social structure of spatial integrity for redevelopment, which is determined through the micro-level survey of rooms with integral dimensions.

Keywords: Alexandria, Sequina slum, spatial integration, space syntax

Procedia PDF Downloads 438

Authors: Anahit Hakobjanyan, Zdenka Navratilova, Gabriela Strakova, Martin Petrek

Abstract:

Aging has a suppressive influence on human immune cells. Apoptosis may play important role in age-dependent immunosuppression and lymphopenia. Prevention of apoptosis may be promoted by BCL2-dependent and BCL2-independent manner. BCL2 is an antiapoptotic factor that has an antioxidative role by locating the glutathione at mitochondria and repressing oxidative stress. STAT3 may suppress apoptosis in BCL2-independent manner and promote cell survival blocking cytochrome-c release and reducing ROS production. The aim of our study was to estimate the influence of aging on BCL2-dependent and BCL2-independent prevention of apoptosis via measurement of BCL2 and STAT3 mRNAs expressions. The study was done on Armenian population (2 groups: 37 healthy young (mean age±SE; min/max age, male/female: 37.6±1.1; 20/54, 15/22), 28 healthy aged (66.7±1.5; 57/85, 12/16)). mRNA expression in peripheral blood leukocytes (PBL) was determined by RT-PCR using PSMB2 as the reference gene. Statistical analysis was done with Graph-Pad Prism 5; P < 0.05 considered as significant. The expression of BCL2 mRNA was lower in aged group (0.199) compared with young ones (0.643)(p < 0.01). Decrease expression was also recorded for female and male subgroups (p < 0.01). The expression level of STAT3 mRNA was increased (young, 0.228; aged, 0.428) (p < 0.05) during aging (in the whole age group and male/female subgroups). Decreased level of BCL2 mRNA may indicate about the suppression of BCL2-dependent prevention of apoptosis during aging in peripheral blood leukocytes. At the same time increased the level of STAT3 may suggest about activation of BCL2-independent prevention of apoptosis during aging.

Keywords: BCL2, STAT3, aging, apoptosis

Procedia PDF Downloads 326

Authors: Shanshan Zhu, Mohammad Nasim

Abstract:

Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

Procedia PDF Downloads 41

Authors: Sawitree Wongkittirungrueang

Abstract:

The objectives of this research were: 1) to study the effect of social stories on social interaction of students with autism. The sample was Pratomsuksa level 5 student with autism, Khon Kaen University Demonstration School, who was diagnosed by the Physician as High Functioning Autism since he was able to read, write, calculate and was studying in inclusive classroom. However, he still had disability in social interaction to participate in social activity group and communication. He could not learn how to develop friendship or create relationship. He had inappropriate behavior in social context. He did not understand complex social situations. In addition, he did seemed not know time and place. He was not able to understand feeling of oneself as well as the others. Consequently, he could not express his emotion appropriately. He did not understand or express his non-verbal language for communicating with friends. He lacked of common interest or emotion with nearby persons. He greeted inappropriately or was not interested in greeting. In addition, he did not have eye contact. He used inadequate language etc. He was elected by Purposive Sampling. His parents were willing to allow them to participate in this study. The research instruments were the lesson plan of social stories, and the picture book of social stories. The instruments used for data collection, were the social interaction evaluation of autistic students. This research was Quasi Experimental Research as One Group Pre-test, Post-test Design. For the Pre-test, the experiment was conducted by social stories. Then, the Post-test was implemented. The statistic used for data analysis, included the Mean, and Standard Deviation. The research findings were shown by Graph. The findings revealed hat the autistic students taught by social stories indicated better social interaction after being taught by social stories.

Keywords: social story, autism spectrum disorder (ASD), autism, social interaction

Procedia PDF Downloads 246

Authors: Hounyèmè Romuald, Mama Daouda, Argillier Christine

Abstract:

The present study focused on the use of benthic macrofauna to define the reference state of an anthropized lagoon (Nokoué-Benin) from the responses of relevant metrics to proxies. The approach used is a combination of a joint species distribution model and Bayesian networks. The joint species distribution model was used to select the relevant metrics and generate posterior probabilities that were then converted into posterior response probabilities for each of the quality classes (pressure levels), which will constitute the conditional probability tables allowing the establishment of the probabilistic graph representing the different causal relationships between metrics and pressure proxies. For the definition of the reference thresholds, the predicted responses for low-pressure levels were read via probability density diagrams. Observations collected during high and low water periods spanning 03 consecutive years (2004-2006), sampling 33 macroinvertebrate taxa present at all seasons and sampling points, and measurements of 14 environmental parameters were used as application data. The study demonstrated reliable inferences, selection of 07 relevant metrics and definition of quality thresholds for each environmental parameter. The relevance of the metrics as well as the reference thresholds for ecological assessment despite the small sample size, suggests the potential for wider applicability of the approach for aquatic ecosystem monitoring and assessment programs in developing countries generally characterized by a lack of monitoring data.

Keywords: pressure proxies, bayesian inference, bioindicators, acadjas, functional traits

Procedia PDF Downloads 83

Authors: Marziye Hadian, Alireza Jabbari

Abstract:

Background: The World Health Organization has identified COVID-19 as a public health emergency and is urging governments to stop the virus transmission by adopting appropriate policies. In this regard, authorities have taken different approaches to cut the chain or controlling the spread of the disease. Now, the questions we are facing include what these approaches are? What tools should be used to implement each preventive protocol? In addition, what is the impact of each approach? Objective: The aim of this study was to determine the approaches to biological epidemics and related prevention tools with an emphasis on COVID-19 disease. Data sources: Databases including ISI web of science, PubMed, Scopus, Science Direct, Ovid, and ProQuest were employed for data extraction. Furthermore, authentic sources such as the WHO website, the published reports of relevant countries, as well as the Worldometer website were evaluated for gray studies. The time-frame of the study was from 1 December 2019 to 30 May 2020. Methods: The present study was a systematic study of publications related to the prevention strategies for the COVID-19 disease. The study was carried out based on the PRISMA guidelines and CASP for articles and AACODS for grey literature. Results: The study findings showed that in order to confront the COVID-19 epidemic, in general, there are three approaches of "mitigation", "active control" and "suppression" and four strategies of "quarantine", "isolation", "social distance" and "lockdown" in both individual and social dimensions to deal with epidemics. Selection and implementation of each approach requires specific strategies and has different effects when it comes to controlling and inhibiting the disease. Key finding: One possible approach to control the disease is to change individual behavior and lifestyle. In addition to prevention strategies, use of masks, observance of personal hygiene principles such as regular hand washing and non-contact of contaminated hands with the face, as well as an observance of public health principles such as sneezing and coughing etiquettes, safe extermination of personal protective equipment, must be strictly observed. Have not been included in the category of prevention tools. However, it has a great impact on controlling the epidemic, especially the new coronavirus epidemic. Conclusion: Although the use of different approaches to control and inhibit biological epidemics depends on numerous variables, however, despite these requirements, global experience suggests that some of these approaches are ineffective. The use of previous experiences in the world, along with the current experiences of countries, can be very helpful in choosing the accurate approach for each country in accordance with the characteristics of that country and lead to the reduction of possible costs at the national and international levels.

Keywords: novel corona virus, COVID-19, approaches, prevention tools, prevention strategies

Procedia PDF Downloads 126

Authors: Lela Gadrani, Mariam Tsitsagi

Abstract:

Human is a part of the urban landscape and responsible for it. Urbanization of cities includes the longest phase; thus none of the environment ever undergoes such anthropogenic impact as the area of large cities. The post-Soviet period is very interesting in terms of scientific research. The changes that have occurred in the cities since the collapse of the Soviet Union have not yet been analyzed best to our knowledge. In this context, the aim of this paper is to analyze the changes in the land use of the three large cities of Georgia (Tbilisi, Kutaisi, Batumi). Tbilisi as a capital city, Batumi as a port city, and Kutaisi as a former industrial center. Data used during the research process are conventionally divided into satellite and supporting materials. For this purpose, the largest topographic maps (1:10 000) of all three cities were analyzed, Tbilisi General Plans (1896, 1924), Tbilisi and Kutaisi historical maps. The main emphasis was placed on the classification of Landsat images. In this case, we have classified the images LULC (LandUse / LandCover) of all three cities taken in 1987 and 2016 using the supervised and unsupervised methods. All the procedures were performed in the programs: Arc GIS 10.3.1 and ENVI 5.0. In each classification we have singled out the following classes: built-up area, water bodies, agricultural lands, green cover and bare soil, and calculated the areas occupied by them. In order to check the validity of the obtained results, additionally we used the higher resolution images of CORONA and Sentinel. Ultimately we identified the changes that took place in the land use in the post-Soviet period in the above cities. According to the results, a large wave of changes touched Tbilisi and Batumi, though in different periods. It turned out that in the case of Tbilisi, the area of developed territory has increased by 13.9% compared to the 1987 data, which is certainly happening at the expense of agricultural land and green cover, in particular, the area of agricultural lands has decreased by 4.97%; and the green cover by 5.67%. It should be noted that Batumi has obviously overtaken the country's capital in terms of development. With the unaided eye it is clear that in comparison with other regions of Georgia, everything is different in Batumi. In fact, Batumi is an unofficial summer capital of Georgia. Undoubtedly, Batumi’s development is very important both in economic and social terms. However, there is a danger that in the uneven conditions of urban development, we will eventually get a developed center - Batumi, and multiple underdeveloped peripheries around it. Analysis of the changes in the land use is of utmost importance not only for quantitative evaluation of the changes already implemented, but for future modeling and prognosis of urban development. Raster data containing the classes of land use is an integral part of the city's prognostic models.

Keywords: analysis, geo information system, remote sensing, LULC

Procedia PDF Downloads 451

Authors: Tulsi Ram Bhusal

Abstract:

Global climate change is known to have a huge impact on agriculture due to changing in rainfall pattern and elevated air temperature that lead to drought and/or flooding. This systematic study has focused on agriculture in Nepal. The study has shown that the trend of current climatic change is affecting rice production, while the farmers with technological access have tried to adapt to the changing conditions at their level. There is insufficient intervention from the government side in terms of policies and schemes. The lack of sufficient funds is one of the significant reasons in terms of governance. The climatic trends and the way it is affecting the annual riceyieldinNepal has been discussed in this study thoroughly. This study has reviewed published studies and ferred important points regarding the Nepal’s status on rice production. Mainly due to the increasing graph of average temperature and other physical conditions needed for the proper cultivation of ricearechanging due to which there is significant dropofannual rice production. Although from corners of the country, many farmers have attempted to adapt the methods of cultivation to the changing climatic conditions, lack of access to technologies, and fund allocation from the governmental level, it is difficult for the mtobringchanges in rice production by the crown without any institutional help. This systematic study effectively presents the magnitude of the impact on rice cultivation due to climatic changes inrecenttimesinNepal. This review aims to bring the current scenarioofNepal’sricefarming, and it impacts due to changing climate, which can subsequently contribute in devising plans for proper governance, formulating policies, and allocation of funds for the betterment.

Keywords: rice, climate change, rice production, nepal, agriculture

Procedia PDF Downloads 92

Authors: Songul Cinaroglu

Abstract:

Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

Procedia PDF Downloads 126

Authors: Somayeh Komeylian

Abstract:

The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

Procedia PDF Downloads 100

Authors: H. Khanfari, M. Johari Fard

Abstract:

Dealing with carbonate reservoirs can be mind-boggling for the reservoir engineers due to various digenetic processes that cause a variety of properties through the reservoir. A good estimation of the reservoir heterogeneity which is defined as the quality of variation in rock properties with location in a reservoir or formation, can better help modeling the reservoir and thus can offer better understanding of the behavior of that reservoir. Most of reservoirs are heterogeneous formations whose mineralogy, organic content, natural fractures, and other properties vary from place to place. Over years, reservoir engineers have tried to establish methods to describe the heterogeneity, because heterogeneity is important in modeling the reservoir flow and in well testing. Geological methods are used to describe the variations in the rock properties because of the similarities of environments in which different beds have deposited in. To illustrate the heterogeneity of a reservoir vertically, two methods are generally used in petroleum work: Dykstra-Parsons permeability variations (V) and Lorenz coefficient (L) that are reviewed briefly in this paper. The concept of Lorenz is based on statistics and has been used in petroleum from that point of view. In this paper, we correlated the statistical-based Lorenz method to a petroleum concept, i.e. Kozeny-Carman equation and derived the straight line plot of Lorenz graph for a homogeneous system. Finally, we applied the two methods on a heterogeneous field in South Iran and discussed each, separately, with numbers and figures. As expected, these methods show great departure from homogeneity. Therefore, for future investment, the reservoir needs to be treated carefully.

Keywords: carbonate reservoirs, heterogeneity, homogeneous system, Dykstra-Parsons permeability variations (V), Lorenz coefficient (L)

Procedia PDF Downloads 220

Authors: Meng Su

Abstract:

High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

Procedia PDF Downloads 107

Authors: Martin H¨ausl, Maximilian Auch, Johannes Forster, Peter Mandl, Alexander Schill

Abstract:

In order to survive on the market, companies must constantly develop improved and new products. These products are designed to serve the needs of their customers in the best possible way. The creation of new products is also called innovation and is primarily driven by a company’s internal research and development department. However, a new approach has been taking place for some years now, involving external knowledge in the innovation process. This approach is called open innovation and identifies customer knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its initial phase, the Ideation phase. For this purpose, the social media posts are semantically structured with the help of an ontology and the authors are evaluated using graph-theoretical metrics such as density. For the structuring and evaluation of relevant social media posts, we also use the findings of Natural Language Processing, e. g. Named Entity Recognition, specific dictionaries, Triple Tagger and Part-of-Speech-Tagger. The selection and evaluation of the tools used are discussed in this paper. Using our ontology and metrics to structure social media posts enables users to semantically search these posts for new product ideas and thus gain an improved insight into the external sources such as customer needs.

Keywords: idea ontology, innovation management, semantic search, open information extraction

Procedia PDF Downloads 188

Authors: Kanika S. Raheja, A. Pandey, Shaila Bahl, Pratik Kumar, S. P. Lochab

Abstract:

The subject of undertaking for this paper is the study of BaSO4 nanophosphor doped with Europium in which mainly the concentration of the rare earth impurity Eu (0.05, 0.1, 0.2, 0.5, and 1 mol %) has been varied. A comparative study of the thermoluminescence(TL) properties of the given nanophosphor has also been done using a well-known standard dosimetry material i.e. TLD-100.Firstly, a number of samples were prepared successfully by the chemical co-precipitation method. The whole lot was then compared to a well established standard material (TLD-100) for its TL sensitivity property. BaSO4:Eu ( 0.2 mol%) showed the highest sensitivity out of the lot. It was also found that when compared to the standard TLD-100, BaSo4:Eu (0.2mol%) showed surprisingly high sensitivity for a large range of doses. The TL response curve for all prepared samples has also been studied over a wide range of doses i.e 10Gy to 2kGy for gamma radiation. Almost all the samples of BaSO4:Eu showed a remarkable linearity for a broad range of doses, which is a characteristic feature of a fine TL dosimeter. The graph remained linear even beyond 1kGy for gamma radiation. Thus, the given nanophosphor has been successfully optimised for the concentration of the dopant material to achieve its highest TL sensitivity. Further, the comparative study with the standard material revealed that the current optimised sample shows an astonishingly better TL sensitivity and a phenomenal linear response curve for an incredibly wide range of doses for gamma radiation (Co-60) as compared to the standard TLD-100, which makes the current optimised BaSo4:Eu quite promising as an efficient gamma radiation dosimeter. Lastly, the present phosphor has been optimised for its annealing temperature to acquire the best results while also studying its fading and reusability properties.

Keywords: gamma radiation, nanoparticles, radiation dosimetry, thermoluminescence

Procedia PDF Downloads 430

Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

Abstract:

Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

Procedia PDF Downloads 81

Authors: Po-Ting Chen, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

Abstract:

Classical molecular dynamics (CMD) simulations are highly efficient for material simulations but have limited accuracy. In contrast, ab initio molecular dynamics (AIMD) provides high precision by solving the Kohn–Sham equations yet requires significant computational resources, restricting the size of systems and time scales that can be simulated. To address these challenges, we employed NequIP, a machine learning model based on an E(3)-equivariant graph neural network, to accelerate molecular dynamics simulations of a 1M LiPF6 in EC/EMC (v/v 3:7) for Li battery applications. AIMD calculations were initially conducted using the Vienna Ab initio Simulation Package (VASP) to generate highly accurate atomic positions, forces, and energies. This data was then used to train the NequIP model, which efficiently learns from the provided data. NequIP achieved AIMD-level accuracy with significantly less training data. After training, NequIP was integrated into the LAMMPS software to enable molecular dynamics simulations of larger systems over longer time scales. This method overcomes the computational limitations of AIMD while improving the accuracy limitations of CMD, providing an efficient and precise computational framework. This study showcases NequIP’s applicability to electrolyte systems, particularly for simulating the dynamics of LiPF6 ionic mixtures. The results demonstrate substantial improvements in both computational efficiency and simulation accuracy, highlighting the potential of machine learning models to enhance molecular dynamics simulations.

Keywords: lithium-ion batteries, electrolyte simulation, molecular dynamics, neural network

Procedia PDF Downloads 18