Search results for: recommendation

Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

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In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps

Procedia PDF Downloads 125

Authors: Chayaporn Kaensar

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Recently, Job Recommender Systems have gained much attention in industries since they solve the problem of information overload on the recruiting website. Therefore, we proposed Extended Personalized Job System that has the capability of providing the appropriate jobs for job seeker and recommending some suitable information for them using Data Mining Techniques and Dynamic User Profile. On the other hands, company can also interact to the system for publishing and updating job information. This system have emerged and supported various platforms such as web application and android mobile application. In this paper, User profiles, Implicit User Action, User Feedback, and Clustering Techniques in WEKA libraries have gained attention and implemented for this application. In additions, open source tools like Yii Web Application Framework, Bootstrap Front End Framework and Android Mobile Technology were also applied.

Keywords: recommendation, user profile, data mining, web and mobile technology

Procedia PDF Downloads 313

Authors: Noura Al-Ajmi, Mohammed A. Almulla

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With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit.

Keywords: headache diagnosis system, prescription recommender system, expert system, backward rule-based system

Procedia PDF Downloads 215

Authors: Patik Joslin Kenfack, Polyakov Vladimir Mikhailovich

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Machine Learning (ML) systems are trained using human generated data that could be biased by implicitly containing racist, sexist, or discriminating data. ML models learn those biases or even amplify them. Recent research in work on has begun to consider issues of fairness. The concept of fairness is extended to recommendation. A recommender system will be considered fair if it doesn’t under rank items of protected group (gender, race, demographic...). Several metrics for evaluating fairness concerns in recommendation systems have been proposed, which take pairs of items as ‘instances’ in fairness evaluation. It doesn’t take in account the fact that the fairness should be evaluated across a list of items. The paper explores a probabilistic approach that generalize pairwise metric by using a list k (listwise) of items as ‘instances’ in fairness evaluation, parametrized by k. We also explore new regularization method based on this metric to improve fairness ranking during model training.

Keywords: Fairness, Recommender System, Ranking, Listwise Approach

Procedia PDF Downloads 148

Authors: Felix Böck

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Nowadays, data about learners and their digital activities are collected, which could help educational institutions to better understand learning processes, improve them and be able to provide better learning assistance. In this research project, custom knowledge- and data-driven recommendation algorithms will be used to offer students in higher education integrated learning assistance. The pre-requisite for this is a learner model that is as comprehensive as possible, which should first be created and then kept up-to-date largely automatically for being able to individualize and personalize the learning experience. In order to create such a learner model, a roadmap is presented that describes the individual phases up to the creation and evaluation of the finished model. The methodological process for the research project is disclosed, and the research question of how learners can be supported in their learning with personalized, customized learning recommendations is explored.

Keywords: research agenda, user model, learner model, higher education, adaptive educational digital learning environments, personalized learning paths, recommendation system, adaptation, personalization

Procedia PDF Downloads 16

Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

Procedia PDF Downloads 68

Authors: C. Rana, S. K. Jain

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The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail.

Keywords: collaborative filtering, data mining, evolutionary, clustering, algorithm, recommender systems

Procedia PDF Downloads 413

Authors: Smail Boussaadi, Hassina Aliane

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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.

Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model

Procedia PDF Downloads 97

Authors: Supattra Kanchanopast

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The purpose of this study was to study the relationship between customer satisfaction and customer loyalty through social media of service business. This study collected data from 187 customers who have used social media of service business to buy product or service in Thailand. Statistics including frequency, percentage, standard deviation and Person’s Correlation test were used. The finding revealed that the majority of the respondents were female, 25-40 years old, graduated the bachelor degree, had monthly income 15,000-25,000 Baht and worked in private sectors. The mostly respondents have reserved the accommodation/homestay/hotel through Facebook about 3-4 times. The hypothesis testing disclosed that the satisfaction in customer invitation and data presentation perspective had a correlation with the level of customer loyalty: recommendation to others in terms of sharing. In addition, the satisfaction in customer relationship management perspective had a positive correlation with customer loyalty through social media of service business with respect to repeat purchase and recommendation to others at the 0.05 level of significance.

Keywords: customer satisfaction, customer loyalty, relationship, service business, social media

Procedia PDF Downloads 445

Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

Procedia PDF Downloads 134

Authors: Farideh Doostan, Sahar Mohseni Taklloo, Mohammad Nosrati

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Chronic diseases are non-communicable and largely preventable by lifestyle changes including healthy diet consumption. They are the most common cause of death in the world and projected to increase by 15% globally between 2010 and 2020.The hazardous effects of behavioral and dietary risk factors on chronic disease have been established in prospective cohort studies and randomized trials. Because of some changes occur in college students’ lifestyle, assessment of dietary risk factors is important in these populations. Objective: This research was the first study that conducted to evaluate dietary intakes of Kermanian college students with WHO/FAO nutritional objectives. Material and Methods: In this descriptive cross-sectional study, 229 healthy college students of health faculty in Kerman University of Medical Sciences that do not intake any medical drugs were recruited using multistage sampling in 2013.Usual dietary intake was collected using a valid Food Frequency Questionnaire (FFQ) and diet quality was calculated based on WHO nutrient goals. To analysis of data between two groups, independent sample t. test and man whitney were applied. Results: Two hundred and twenty-nine college students; 151 females (65.9%) and 78 males (34.1%), the mean age of 21.9 years were studied. The mean of the Body Mass Index (Kg/m2) and Waist Circumference (cm) in males were 22.34 ±3.52 and 80.76±11.16 and in females were 21.19±2.62 and 73.67±7.65 respectively. Mean of daily cholesterol intake in males was significantly more than females (305±101 VS 268±98; P=0.008) and more than WHO/FAO recommendation (less than 300 mg/day). The mean of daily sodium intake in men and women were 10.4±1 and 10.9±5.3 respectively. These amounts were more than WHO/FAO recommendation (less than 2g/day). In addition, women were consumed fruit and vegetables more than men (839±336 VS 638±281; p ‹ 0.001) and these amounts were more than WHO/FAO recommendation (more than 400g/day) in both groups. Other intake indices were in the range of WHO/FAO recommendations, So that Percent of calories intake from total fat, saturated fatty acids, polyunsaturated fatty acids and added sugar were in compliance with WHO/FAO recommendations. Conclusion: Cholesterol intake in men and sodium intake in all participants were more than WHO/FAO recommendation. These dietary components are the most important causes of cardiovascular disease (one of the main causes of death in our population). These results indicated that proper nutritional education and interventions are needed in this population.

Keywords: college students, food intake, WHO /FAO nutrient intake goals, Kerman

Procedia PDF Downloads 404

Authors: Hoyeon Park, Yunhwan Keon, Kyoung-Jae Kim

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Location Based Social Network services (LBSNs) is a new term that combines location based service and social network service (SNS). Unlike traditional SNS, LBSNs emphasizes empirical elements in the user's actual physical location. Point-of-Interest (POI) is the most important factor to implement LBSNs recommendation system. POI information is the most popular spot in the area. In this study, we would like to recommend POI to users in a specific area through recommendation system using collaborative filtering. The process is as follows: first, we will use different data sets based on Seoul and New York to find interesting results on human behavior. Secondly, based on the location-based activity information obtained from the personalized LBSNs, we have devised a new rating that defines the user's preference for the area. Finally, we have developed an automated rating algorithm from massive raw data using distributed systems to reduce advertising costs of LBSNs.

Keywords: location-based social network services, point-of-interest, recommender systems, business analytics

Procedia PDF Downloads 229

Authors: Naman Jain, Shaun Fernandes

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With the increasing prevalence of online healthcare services, selecting the most suitable doctor has become a complex task, requiring careful consideration of both public sentiment and personal preferences. This paper proposes a sentiment analysis-driven method that integrates public reviews with user-specific criteria and correlated attributes to recommend online doctors. By leveraging Natural Language Processing (NLP) techniques, public sentiment is extracted from online reviews, which is then combined with user-defined preferences such as specialty, years of experience, location, and consultation fees. Additionally, correlated attributes like education and certifications are incorporated to enhance the recommendation accuracy. Experimental results demonstrate that the proposed system significantly improves user satisfaction by providing personalized doctor recommendations that align with both public opinion and individual needs.

Keywords: sentiment analysis, online doctors, personal preferences, correlated attributes, recommendation system, healthcare, natural language processing

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Authors: Ma Cecilia Siva

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This study introduces a Bidirectional Encoder Representations from Transformers (BERT)-based machine learning model aimed at improving educational counseling by automating the process of recommending academic strands for students. The framework is designed to streamline and enhance the strand selection process by analyzing students' profiles and suggesting suitable academic paths based on their interests, strengths, and goals. Data was gathered from a sample of 200 grade 10 students, which included personal essays and survey responses relevant to strand alignment. After thorough preprocessing, the text data was tokenized, label-encoded, and input into a fine-tuned BERT model set up for multi-label classification. The model was optimized for balanced accuracy and computational efficiency, featuring a multi-category classification layer with sigmoid activation for independent strand predictions. Performance metrics showed an F1 score of 88%, indicating a well-balanced model with precision at 80% and recall at 100%, demonstrating its effectiveness in providing reliable recommendations while reducing irrelevant strand suggestions. To facilitate practical use, the final deployment phase created a recommendation framework that processes new student data through the trained model and generates personalized academic strand suggestions. This automated recommendation system presents a scalable solution for academic guidance, potentially enhancing student satisfaction and alignment with educational objectives. The study's findings indicate that expanding the data set, integrating additional features, and refining the model iteratively could improve the framework's accuracy and broaden its applicability in various educational contexts.

Keywords: tokenized, sigmoid activation, transformer, multi category classification

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Authors: Hassen Mohammed, Michelle Clarke, Helen Marshall

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Maternal immunization is an effective strategy to protect pregnant women and their offspring from vaccine-preventable diseases. Despite the recommendation of maternal influenza and more recently pertussis immunization in Australia, uptake of these vaccines has been suboptimal. Monitoring the impact of the current funded vaccine programs for pregnant women is limited. The study aimed to assess the impact of the funded program and determine factors associated with vaccine uptake in pregnant women. This observational prospective study was undertaken between November 2014 and July 2016 at the Women’s and Children’s Hospital in South Australia (WCH). Demographic details and vaccination history from South Australian pregnant women who attended the WCH were reviewed. A standardized self-reported survey was conducted in antenatal care with a follow up telephone interview at 8-10 weeks post-delivery. A midwife delivered immunization program for pregnant women in antenatal clinic commenced in April 2015. Of the 180 pregnant women who completed the survey questionnaire, 75.5% and 80.5 % received maternal influenza and pertussis vaccines respectively. First-time mothers had twice the odds of having received influenza vaccine during pregnancy than multiparous women (OR 2.4; CI 1.14 - 4.94; p= 0.021). The proportion of women who received pertussis vaccine during pregnancy, following the introduction of the midwife delivered pertussis vaccination program (140/155, 90.3%) was significantly higher compared with women who received maternal pertussis vaccination prior to the introduction of the program (5/22, 23.7%, p < 0.001). The odds of women receiving maternal pertussis vaccine following the implementation of the midwife delivered program were 31 times higher than women who delivered babies prior to the program (OR 31.7, CI 10.24- 98.27; p < 0.001). High uptake of influenza and pertussis vaccines during pregnancy can be attained with health care provider recommendation and inclusion of maternal immunization as part of standard antenatal care.

Keywords: influenza, maternal immunization, pertussis, provider recommendation

Procedia PDF Downloads 273

Authors: Arturo J. Anci Alméstar, Jose D. Fernandez Huapaya, David Mauricio

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Companies make erroneous decisions by not evaluating the inherent difficulties of entering electronic commerce without a prior review of current digital and alternate means of payment. For this reason, it is very important for businesses to have reliable, complete and integrated information on the means of current digital and alternate payments that allow decisions to be made about which of these to use. However, there is no such consolidated information or criteria that companies use to make decisions about the means of payment according to their needs. In this paper, we propose a decision tree model based on a taxonomy that presents us with a categorization of digital and alternative means of payment, as well as the visualization of the flow of information at a high level from the company to obtain a recommendation. This will allow the company to make the most appropriate decision about the implementation of the digital means of payment or alternative ideal for their needs, which allows a reduction in costs and complexity of the payment process. Likewise, the efficiency of the proposed model was evaluated through a satisfaction survey presented to company personnel, confirming the satisfactory quality level of the recommendations obtained by the model.

Keywords: digital payment medium, decision tree, decision making, digital payments taxonomy

Procedia PDF Downloads 179

Authors: Heba M. Wagih, Hoda M. O. Mokhtar

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Social Network has recently played a significant role in both scientific and social communities. The growing adoption of social network applications has been a relevant source of information nowadays. Due to its popularity, several research trends are emerged to service the huge volume of users including, Location-Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. LBSNs applications are among the highly demanded applications that do not focus only on analyzing the spatiotemporal positions in a given raw trajectory but also on understanding the semantics behind the dynamics of the moving object. LBSNs are possible means of predicting human mobility based on users social ties as well as their spatial preferences. LBSNs rely on the efficient representation of users’ trajectories. Hence, traditional raw trajectory information is no longer convenient. In our research, we focus on studying human behavior trajectory which is the major pillar in location recommendation systems. In this paper, we propose an ontology design patterns with their underlying description logics to efficiently annotate human behavior trajectories.

Keywords: human behavior trajectory, location-based social network, ontology, social network

Procedia PDF Downloads 452

Authors: Cheng Xu

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As the metaverse emerges as a dynamic virtual simulacrum of reality, its implications on user privacy have become a focal point of interest. While previous discussions have ventured into metaverse privacy dynamics, a glaring empirical gap persists, especially concerning the effects of personalization in the context of news recommendation services. This study stands at the forefront of addressing this void, meticulously examining how users' privacy concerns shift within the metaverse's personalization context. Through a pre-registered randomized controlled experiment, participants engaged in a personalization task across both the metaverse and traditional online platforms. Upon completion of this task, a comprehensive news recommendation service provider offers personalized news recommendations to the users. Our empirical findings reveal that the metaverse inherently amplifies privacy concerns compared to traditional settings. However, these concerns are notably mitigated when users have a say in shaping the algorithms that drive these recommendations. This pioneering research not only fills a significant knowledge gap but also offers crucial insights for metaverse developers and policymakers, emphasizing the nuanced role of user input in shaping algorithm-driven privacy perceptions.

Keywords: metaverse, privacy concerns, personalization, digital interaction, algorithmic recommendations

Procedia PDF Downloads 117

Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui

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Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors.

Keywords: big data, decision making, deep learning, recommender system

Procedia PDF Downloads 478

Authors: Thusitha Mabotuwana, Christopher S. Hall

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Radiologists routinely make follow-up imaging recommendations, usually based on established clinical practice guidelines, such as the Fleischner Society guidelines for managing lung nodules. In order to ensure optimal care, it is important to make guideline-compliant recommendations, and also for patients to follow-up on these imaging recommendations in a timely manner. However, determining such compliance rates after a specific finding has been observed usually requires many time-consuming manual steps. To address some of these limitations with current approaches, in this paper we discuss a methodology to automatically detect finding-specific follow-up recommendations from radiology reports and create a visualization for relevant subsequent exams showing the modality transitions. Nearly 5% of patients who had a lung related follow-up recommendation continued to have at least eight subsequent outpatient CT exams during a seven year period following the recommendation. Radiologist and section chiefs can use the proposed tool to better understand how a specific patient population is being managed, identify possible deviations from established guideline recommendations and have a patient-specific graphical representation of the imaging pathways for an abstract view of the overall treatment path thus far.

Keywords: follow-up recommendations, follow-up tracking, care pathways, imaging pathway visualization

Procedia PDF Downloads 134

Authors: Mostafa Gamal, Hoda K. Mohamed, Islam El-Maddah, Ali Hamdi

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In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs.

Keywords: graph databases, NLP, recommendation systems, similarity metrics

Procedia PDF Downloads 104

Authors: Kennedy Efosa Ehimwenma, Sujatha Krishnamoorthy, Safiya Al‑Sharji

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The probability computation of events is in the interval of [0, 1], which are values that are determined by the number of outcomes of events in a sample space S. The probability Pr(A) that an event A will never occur is 0. The probability Pr(B) that event B will certainly occur is 1. This makes both events A and B a certainty. Furthermore, the sum of probabilities Pr(E₁) + Pr(E₂) + … + Pr(Eₙ) of a finite set of events in a given sample space S equals 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. This paper first discusses Bayes, the complement of probability, and the difference of probability for occurrences of learning-events before applying them in the prediction of learning objects in student learning. Given the sum of 1; to make a recommendation for student learning, this paper proposes that the difference of argMaxPr(S) and the probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates i) the probability of skill-set events that have occurred that would lead to higher-level learning; ii) the probability of the events that have not occurred that requires subject-matter relearning; iii) accuracy of the decision tree in the prediction of student performance into class labels and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning.

Keywords: complement of probability, Bayes’ rule, prediction, pre-assessments, computational education, information theory

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Authors: Davit Khachatryan, Shuo Xiang

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Numerous published studies on the use of prazosin in the treatment of PTSD-related sleep disturbances in adult population have resulted in updates to the recommendation for prazosin for nightmares that showed its strength of evidence elevated from C to B in the US Department of Veterans Affairs clinical practice guideline. In addition, the American Academy of Sleep Medicine clinical practice guideline gave prazosin a level-A recommendation for the treatment of PTSD-associated nightmares. The aim of this review is to summarize the available literature for prazosin use for nightmares and other sleep disturbances in children and adolescents with PTSD. Method: A comprehensive search for studies on prazosin use for sleep disturbances in child and adolescent population with PTSD has been performed. We looked at MEDLINE, EMBASE, PsycINFO, CINAHL, AMED, Scopus, Web of Science, and Cochrane CENTRAL databases. Results: Compared to adult population with similar psychopathology, the available literature in child and adolescent population is scarce. Despite increased interest in prazosin in the management of PTSD, only six studies investigating this medication in children and adolescents have been published. Conclusion: A large randomized control trial on this topic is needed for more definite evidence on the efficacy and safety of prazosin in the treatment of nightmares in children and adolescents with PTSD.

Keywords: guidelines, prazosin, PTSD, sleep disturbance

Procedia PDF Downloads 387

Authors: Sri Widati, Ira Nurmala

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The Se’i Tradition was considered to contribute highly to the high maternal mortality rate in South Amanuban, East Nusa Tenggara. This tradition is still preserved due to the social support that has influenced the decision to carry out the Se’i to pregnant women and post-partum women. The purpose of this study is to analyze this social support towards the Se’i Tradition on pregnant women in East Nusa Tenggara. This research was an explorative study with in-depth interviews, observations, and focus group discussions (FGD) in collecting the data. This study showed that emotional support towards Se’i was commonly given by families, specifically by the mother-in laws. Instrumental support was shown by the husbands and the traditional midwives who helped delivered the babies. Informational support was found on the pregnant women and their mother-in laws. Appraisal support was given by all the neighbors and relatives of the pregnant women by telling how comfortable it was to go through this tradition which eventually affected those women to carry it out themselves. The Se’i Tradition is still carried out and mostly supported by the relatives of the pregnant women. The first recommendation of this study is to suggest people to only follow the suggestions from the local health staff to give birth in the local health centers and not to do the tradition anymore. The second recommendation is to urge the government to give support in the form of transportation facilities for pregnant women to reach the local health staff.

Keywords: the Se’i tradition, social support, pregnant women, maternal mortality, post-partum women

Procedia PDF Downloads 530

Authors: Fitri Nugraheni, Adwitya Bhaskara, N. Faried Hanafi

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Coastal is one area that can be a place to live. Various buildings can be built in the area around the beach. Many Indonesians use beaches as housing and work, but we know that coastal areas are identical to tsunami and wind. Costs incurred due to permanent damage caused by tsunamis and wind disasters in Indonesia can be minimized by replacing permanent buildings into semi-permanent buildings. Semi-permanent buildings can be realized by using cold-formed steel as a building. Thus, the purpose of this research is to provide efficient semi-permanent building recommendations for residents around the coast. The research is done by first designing the building model by using sketch-up software, then the validation phase is done in consultation with the expert consultant of cold form steel structure. Based on the results of the interview there are several revisions on several sides of the building by adding some bracing rods on the roof, walls and floor frame. The result of this research is recommendation of semi-permanent building model, where the nature of the building; easy to disassemble and install (knockdown), tsunami-friendly (continue the tsunami load), cost and time efficient (using cold-formed-steel and prefabricated GRC), zero waste, does not require many workers (less labor). The recommended building design concept also keeps the architecture side in mind thus it remains a comfortable occupancy for the residents.

Keywords: construction method, cold-formed steel, efficiency, semi-permanent building, tsunami

Procedia PDF Downloads 285

Authors: Zhaohan Xie, Yining Yu, Mingliang Chen

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As research has shown a convergent tendency for aversion to AI recommendation, it is imperative to find a way to promote AI usage and better harness the technology. In the context of e-commerce, this study has found evidence that people show less avoidance of algorithms when recommending search products compared to experience products. This is due to people’s different attribution of mind to AI versus humans, as suggested by mind perception theory. While people hold the belief that an algorithm owns sufficient capability to think and calculate, which makes it competent to evaluate search product attributes that can be obtained before actual use, they doubt its capability to sense and feel, which is essential for evaluating experience product attributes that must be assessed after experience in person. The result of the behavioral investigation (Study 1, N=112) validated that consumers show low purchase intention to experience products recommended by AI. Further consumer neuroscience study (Study 2, N=26) using Event-related potential (ERP) showed that consumers have a higher level of cognitive conflict when faced with AI recommended experience product as reflected by larger N2 component, while the effect disappears for search product. This research has implications for the effective employment of AI recommenders, and it extends the literature on e-commerce and marketing communication.

Keywords: algorithm recommendation, consumer behavior, e-commerce, event-related potential, experience product, search product

Procedia PDF Downloads 153

Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

Procedia PDF Downloads 80

Authors: Mohd Azraei Pangah Pa'at, Mohd Ruzlin Mohd Mokhtar, Norhidayu Rameli, Tashia Marie Anthony, Huzainie Shafi Abd Halim

Abstract:

Partial Discharge (PD) diagnostic mapping testing is one of the main diagnostic testing techniques that are widely used in the field or onsite testing for underground power cable in medium voltage level. The existence of PD activities is an early indication of insulation weakness hence early detection of PD activities can be determined and provides an initial prediction on the condition of the cable. To effectively manage the results of PD Mapping test, it is important to have acceptable criteria to facilitate prioritization of mitigation action. Tenaga Nasional Berhad (TNB) through Distribution Network (DN) division have developed PD severity model name Severity Index (SI) for offline PD mapping test since 2007 based on onsite test experience. However, this severity index recommendation action had never been revised since its establishment. At presence, PD measurements data have been extensively increased, hence the severity level indication and the effectiveness of the recommendation actions can be analyzed and verified again. Based on the new revision, the recommended action to be taken will be able to reflect the actual defect condition. Hence, will be accurately prioritizing preventive action plan and minimizing maintenance expenditure.

Keywords: partial discharge, severity index, diagnostic testing, medium voltage, power cable

Procedia PDF Downloads 186

Authors: Livy Febria Tedjamulia, Aas Nurasyiah, Ivana Josephin Purnama, Monika Diah Maharani Kusumastuti, Achmad Ridwan Ariyantoro

Abstract:

Papua's candy is one of Indonesia’s indigenous consumption consisting of areca nut (Areca catechu), forest betel fruit (Piper aduncum), and CaCO3. This research aims to determine the concentration of tannins in areca nut, alkaloids in areca nut, flavonoids in forest betel fruit; detect their interaction and CaCO3; also toform a standardize consumption recommendation. The research methodwas includingDPPH assay for papua’s candy mixture, which resulted in IC50 value. Data analysis used is mathematical linear regression for each experiment. The test result of alkaloid is a Rf value of 0.773, while concentration of tannin and flavonoidare 0.603 mgGAE/g and 125.402 gQE/g, respectively. The IC50 value shows number of 3.0403, showing high antioxidant capacity.Other antioxidant assays were being studied using literature review, namely trolox and oxygen radical absorbance capacity, to figure out interaction among the bioactive compounds. It turned out that the interaction detected is antagonistic, which means the compound that is joined already has a stable molecular structure so that could reduce free radicals by donating hydrogen atoms. The recommendation consumptions given are 4 areca nuts, 5 forest betels, and 1 gram of lime betel. Therefore, papua's candy has its potential to be developed into functional food.

Keywords: antioxidant, bioactive compounds interaction, free radical, papua’s candy

Procedia PDF Downloads 203

Authors: Adekunle O. Afolabi, Pekka Toivanen

Abstract:

The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.

Keywords: recommendation systems, Internet of Things, healthcare, homecare, real-time

Procedia PDF Downloads 154