Search results for: personalized recommendation

Authors: Liwen Hou

Abstract:

The booming mobile business has been penetrating the taxi industry worldwide with P2P (peer to peer) taxi services, as an emerging business model, transforming the industry. Parallel with other mobile businesses, member recommendations and online reviews are believed to be very effective with regard to acquiring new users for P2P taxi services. Based on an empirical dataset of the taxi industry in China, this study aims to reveal which factors influence users’ recommendations and review-writing behaviors. Differing from the existing literature, this paper takes the taxi driver’s perspective into consideration and hence selects a group of variables related to the drivers. We built two models to reflect the factors that influence the number of recommendations and reviews posted on the platform (i.e., the app). Our models show that all factors, except the driver’s score, significantly influence the recommendation behavior. Likewise, only one factor, passengers’ bad reviews, is insignificant in generating more drivers’ reviews. In the conclusion, we summarize the findings and limitations of the research.

Keywords: online recommendation, P2P taxi service, review-writing, word of mouth

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Authors: Fabio Fabris, Alex A. Freitas

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Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: algorithm recommendation, meta-learning, bioinformatics, hierarchical classification

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Authors: Ritesh Kumar, Rajendra Pamula

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In this paper, we present our contribution for book recommendation. In our experiment, we combine the results of Sequential Dependence Model (SDM) and exploitation of book information such as reviews, tags and ratings. This social information is assigned by users. For this, we used CLEF-2016 Social Book Search Track Suggestion task. Finally, our proposed method extensively evaluated on CLEF -2015 Social Book Search datasets, and has better performance (nDCG@10) compared to other state-of-the-art systems. Recently we got the good performance in CLEF-2016.

Keywords: sequential dependence model, social information, social book search, query expansion

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Authors: Vijay Kumar Kutala, Addepalli Pavani, M. Amresh Rao, Naushad Sm

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In this study, we evaluated the impact of CYP2C9*2 and CYP2C9*3 variants on binding and hydroxylation of warfarin. In silico data revealed that warfarin forms two hydrogen bonds with protein backbone i.e. I205 and S209, one hydrogen bond with protein side chain i.e. T301 and stacking interaction with F100 in CYP2C9*1. In CYP2C9*2 and CYP2C9*3 variants, two hydrogen bonds with protein backbone are disrupted. In double variant, all the hydrogen bonds are disrupted. The distances between C7 of S-warfarin and Fe-O in CYP2C9*1, CYP2C9*2, CYP2C9*3 and CYP2C9*2/*3 were 5.81A°, 7.02A°, 7.43° and 10.07°, respectively. The glide scores (Kcal/mol) were -7.698, -7.380, -6.821 and -6.986, respectively. Increase in warfarin/7-hydroxy warfarin ratio was observed with increase in variant alleles. To conclude, CYP2C9*2 and CYP2C9*3 variants result in disruption of hydrogen bonding interactions with warfarin and longer distance between C7 and Fe-O thus impairing warfarin 7-hydroxylation due to lower binding affinity of warfarin.

Keywords: warfarin, CYP2C9 polymorphism, personalized medicine, in Silico

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Authors: Jalal Maqbool, Gyu Myoung Lee

Abstract:

Using smart devices for context aware mobile learning is becoming increasingly popular. This has led to mobile learning technology becoming an indispensable part of today’s learning environment and platforms. However, some fundamental issues remain - namely, mobile learning still lacks the ability to truly understand human reaction and user behaviour. This is due to the fact that current mobile learning systems are passive and not aware of learners’ changing contextual situations. They rely on static information about mobile learners. In addition, current mobile learning platforms lack the capability to incorporate dynamic contextual situations into learners’ preferences. Thus, this thesis aims to address these issues highlighted by designing a context aware framework which is able to sense learner’s contextual situations, handle data dynamically, and which can use contextual information to suggest bespoke learning content according to a learner’s preferences. This is to be underpinned by a robust recommendation system, which has the capability to perform these functions, thus providing learners with a truly context-aware mobile learning experience, delivering learning contents using smart devices and adapting to learning preferences as and when it is required. In addition, part of designing an algorithm for the recommendation engine has to be based on learner and application needs, personal characteristics and circumstances, as well as being able to comprehend human cognitive processes which would enable the technology to interact effectively and deliver mobile learning content which is relevant, according to the learner’s contextual situations. The concept of this proposed project is to provide a new method of smart learning, based on a capable recommendation engine for providing an intuitive mobile learning model based on learner actions.

Keywords: aware, context, learning, mobile

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Authors: Dequn Teng, Tianshuo Yang, Mingrui Wang, Qiuyu Chen, Xiao Wang, Katie Atkinson

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Currently, many students are kind of at a loss in the university due to the complex environment within the campus, where every information within the campus is isolated with fewer interactions with each other. However, if the on-campus resources are gathered and combined with the artificial intelligence modelling techniques, there will be a bridge for not only students in understanding themselves, and the teachers will understand students in providing a much efficient approach in education. The objective of this paper is to provide a competency level analysis method, apparatus, electronic equipment, and storage medium. It uses a user’s target competency level analysis model from a plurality of predefined candidate competency level analysis models by obtaining a user’s promotion target parameters, promotion target parameters including at least one of the following parameters: target profession, target industry, and the target company, according to the promotion target parameters. According to the parameters, the model analyzes the user’s ability level, determines the user’s ability level, realizes the quantitative and personalized analysis of the user’s ability level, and helps the user to objectively position his ability level.

Keywords: artificial intelligence, model, university, education, recommendation system, evaluation, job hunting

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Authors: Loren Clarke, Katie Reed

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The integration of artificial intelligence (AI) in high school education assessment offers transformative potential, providing more personalized, timely, and accurate evaluations of student performance. However, the successful adoption of AI-driven assessment systems requires robust change management strategies to navigate the complexities and resistance that often accompany such technological shifts. This presentation explores effective methods for implementing AI in high school assessment, emphasizing the need for strategic planning and stakeholder engagement. Focusing on a case study of a Victorian high school, it will examine the practical steps taken to integrate AI into teaching and learning. This school has developed innovative processes to support academic integrity and foster authentic cogeneration with AI, ensuring that the technology is used ethically and effectively. By creating comprehensive professional development programs for teachers and maintaining transparent communication with students and parents, the school has successfully aligned AI technologies with their existing curricula and assessment frameworks. The session will highlight how AI has enhanced both formative and summative assessments, providing real-time feedback that supports differentiated instruction and fosters a more personalized learning experience. Participants will learn about best practices for managing the integration of AI in high school settings while maintaining a focus on equity and student-centered learning. This presentation aims to equip high school educators with the insights and tools needed to effectively manage the integration of AI in assessment, ultimately improving educational outcomes and preparing students for future success. Methodologies: The research is a case study of a Victorian high school to examine AI integration in assessments, focusing on practical implementation steps, ethical practices, and change management strategies to enhance personalized learning and assessment. Outcomes: This research explores AI integration in high school assessments, focusing on personalized evaluations, ethical use, and change management. A Victorian school case study highlights best practices to enhance assessments and improve student outcomes. Main Contributions: This research contributes by outlining effective AI integration in assessments, showcasing a Victorian school's implementation, and providing best practices for ethical use, change management, and enhancing personalized learning outcomes.

Keywords: artificial intelligence, assessment, curriculum design, teaching and learning, ai in education

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Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

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Authors: Kunyanuth Kularbphettong, Kunnika Tenprakhon, Pattarapan Roonrakwit

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In this paper, we present a recommendation library application on Android system. The objective of this system is to support and advice user to use library resources based on mobile application. We describe the design approaches and functional components of this system. The system was developed based on under association rules, Apriori algorithm. In this project, it was divided the result by the research purposes into 2 parts: developing the Mobile application for online library service and testing and evaluating the system. Questionnaires were used to measure user satisfaction with system usability by specialists and users. The results were satisfactory both specialists and users.

Keywords: online library, Apriori algorithm, Android application, black box

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Authors: Veronika Lesáková, Silvia Slezáková, František Štěpánek

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Additive manufacturing technologies producing drug dosage forms aimed at personalized medicine applications are promising strategies with several advantages over the conventional production methods. One of the emerging technologies is 3D printing which reduces manufacturing steps and thus allows a significant drop in expenses. A decrease in material consumption is also a highly impactful benefit as the tested drugs are frequently expensive substances. In addition, 3D printed dosage forms enable increased patient compliance and prevent misdosing as the dosage forms are carefully designed according to the patient’s needs. The incorporation of multiple drugs into a single dosage form further increases the degree of personalization. Our research focuses on the development of 3D printed tablets incorporating multiple drugs (candesartan, losartan) and thermoplastic polymers (e.g., KlucelTM HPC EF). The filaments, an essential feed material for 3D printing,wereproduced via hot-melt extrusion. Subsequently, the extruded filaments of various formulations were 3D printed into tablets using an FDM 3D printer. Then, we have assessed the influence of the internal structure of 3D printed tablets and formulation on dissolution behaviour by obtaining the dissolution profiles of drugs present in the 3D printed tablets. In conclusion, we have developed tablets containing multiple drugs providing modified release profiles. The 3D printing experiments demonstrate the high tunability of 3D printing as each tablet compartment is constructed with a different formulation. Overall, the results suggest that the 3D printing technology is a promising manufacturing approach to dual tablet preparation for personalized medicine.

Keywords: 3D printing, drug delivery, hot-melt extrusion, dissolution kinetics

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Authors: Kazuki Kamada, Haruka Yamashita

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In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method.

Keywords: taxi industry, decision making, recommendation system, embedding model

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Nerella Srinivas

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This study explores the significance of patent protection for artificial intelligence (AI) innovations in the pharmaceutical sector, emphasizing applications in drug discovery, personalized medicine, and clinical trial optimization. The challenges of patenting AI-driven inventions are outlined, focusing on the classification of algorithms as abstract ideas, meeting the non-obviousness standard, and issues around defining inventorship. The methodology includes examining case studies and existing patents, with an emphasis on how companies like Benevolent AI and Insilico Medicine have successfully secured patent rights. Findings demonstrate that a strategic approach to patent protection is essential, with particular attention to showcasing AI’s technical contributions to pharmaceutical advancements. Conclusively, the study underscores the critical role of understanding patent law and innovation strategies in leveraging intellectual property rights in the rapidly advancing field of AI-driven pharmaceuticals.

Keywords: artificial intelligence, pharmaceutical industry, patent protection, drug discovery, personalized medicine, clinical trials, intellectual property, non-obviousness

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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

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Authors: Hamsa T. Tayeb, Mohammad A. Arafah, Dana M. Bakheet, Duaa M. Khalaf, Agnieszka Tarnoska, Nduna Dzimiri

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Background: CYP2D6 is a member of the cytochrome P450 mixed-function oxidase system. The enzyme is responsible for the metabolism and elimination of approximately 25% of clinically used drugs, especially in breast cancer and psychiatric therapy. Different phenotypes have been described displaying alleles that lead to a complete loss of enzyme activity, reduced function (poor metabolizers – PM), hyperfunctionality (ultrarapid metabolizers–UM) and therefore drug intoxication or loss of drug effect. The prevalence of these variants may vary among different ethnic groups. Furthermore, the xTAG system has been developed to categorized all patients into different groups based on their CYP2D6 substrate metabolization. Aim of the study: To determine the prevalence of the different CYP2D6 variants in our population, and to evaluate their clinical relevance in personalized medicine. Methodology: We used the Luminex xMAP genotyping system to sequence 305 Saudi individuals visiting the Blood Bank of our Institution and determine which polymorphisms of CYP2D6 gene are prevalent in our region. Results: xTAG genotyping showed that 36.72% (112 out of 305 individuals) carried the CYP2D6_*2. Out of the 112 individuals with the *2 SNP, 6.23% had multiple copies of *2 SNP (19 individuals out of 305 individuals), resulting in an UM phenotype. About 33.44% carried the CYP2D6_*41, which leads to decreased activity of the CYP2D6 enzyme. 19.67% had the wild-type alleles and thus had normal enzyme function. Furthermore, 15.74% carried the CYP2D6_*4, which is the most common nonfunctional form of the CYP2D6 enzyme worldwide. 6.56% carried the CYP2D6_*17, resulting in decreased enzyme activity. Approximately 5.73% carried the CYP2D6_*10, consequently decreasing the enzyme activity, resulting in a PM phenotype. 2.30% carried the CYP2D6_*29, leading to decreased metabolic activity of the enzyme, and 2.30% carried the CYP2D6_*35, resulting in an UM phenotype, 1.64% had a whole-gene deletion CYP2D6_*5, thus resulting in the loss of CYP2D6 enzyme production, 0.66% carried the CYP2D6_*6 variant. One individual carried the CYP2D6_*3(B), producing an inactive form of the enzyme, which leads to decrease of enzyme activity, resulting in a PM phenotype. Finally, one individual carried the CYP2D6_*9, which decreases the enzyme activity. Conclusions: Our study demonstrates that different CYP2D6 variants are highly prevalent in ethnic Saudi Arabs. This finding sets a basis for informed genotyping for these variants in personalized medicine. The study also suggests that xTAG is an appropriate procedure for genotyping the CYP2D6 variants in personalized medicine.

Keywords: CYP2D6, hormonal breast cancer, pharmacogenetics, polymorphism, psychiatric treatment, Saudi population

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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

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Authors: E. Kempen, M. J. Labuschagne, M. P. Jama

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There is evidence that experiential learning is truly influential and favored by the millennial generation. However, little is known about the role and attributes an educator has to adopt during the experiential learning cycle, especially when applied in optometry education. This study aimed to identify the roles and attributes of an optometry educator during the different modes of the experiential learning cycle. Methods: A qualitative case study design was used. Data was collected using an open-ended questionnaire survey, following the application of nine different teaching-learning methods based on the experimental learning cycle. The total sample population of 68 undergraduate students from the Department of Optometry at the University of the Free State, South Africa were invited to participate. Focus group interviews (n=15) added additional data that contributed to the interpretation and confirmation of the data obtained from the questionnaire surveys. Results: The perceptions and experiences of the students identified a variety of roles and attributes as well as recommendations on the effective adoption of these roles and attributes. These roles and attributes included being knowledgeable, creating an interest, providing guidance, being approachable, building confidence, implementing ground rules, leading by example, and acting as a mediator. Conclusion: The findings suggest that the actions of an educator have the most substantial impact on students’ perception of a learning experience. Not only are the recommendations based on the views of a millennial generation, but the implementation of the personalized recommendations may also transform a learning environment. This may lead an optometry student to a deeper understanding of knowledge.

Keywords: experiences and perceptions, experiential learning, millennial generation, recommendation for optometry education

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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

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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

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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

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Authors: Corbel Camille, Le Cerf Flora, Capriz Françoise, Vaillant-Ciszewicz Anne-Julie, Breaud Jean, Guerin Olivier, Corveleyn Xavier

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Background: The medical environment can generate stressful and anxiety-provoking situations for patients, particularly during painful care procedures for the older population. These stressful environments have deleterious effects on the quality of care and can even put the patient at risk and set the care team up for failure. The search for a solution is, therefore, imperative. The development of new technologies, such as virtual reality (VR), seems to be an answer to this problem. Objectives: The objective of this study is to compare the effects of virtual reality on pain and anxiety when caring for older hospitalized people with the effects of usual care. More precisely, different individual factors (age, cognitive level, individual preferences, etc.) and different virtual reality universes (personalized or non-personalized) are studied to understand the role of these factors in reducing pain and anxiety during care procedures. The aim of this study is to improve the quality of life of patients and caregivers in their work environment. Method: This mono-centered, randomized, controlled study was conducted from September 2023 to September 2024 on 120 participants recruited from the geriatric departments of the Cimiez Hospital, Nice, France. Participants are randomized into three groups: a control group, a personalized VR group and a non-personalized VR group. Each participant is followed during a painful care session. Data are collected before, during and after the care, using measures of pain (Algoplus and numerical scale) and anxiety (Hospital anxiety scale and numerical scale). Physiological assessments with an oximeter are also performed to collect both heart and respiratory rate measurements. The implementation of the care will be assessed among healthcare providers to evaluate its effects on the difficulty and fatigue associated with the care. Additionally, a questionnaire (System Usability Scale) will be administered at the conclusion of the study to determine the willingness of healthcare providers to integrate VR into their daily care practices. Result: The preliminary results indicate significant effects on anxiety (p=.001) and pain (p=<.001) following the VR intervention during care, as compared to the control group. Conclusion: The preliminary results suggest that VRI appears to be a suitable and effective method for reducing anxiety and pain among older hospitalized individuals compared with standard care. Finally, the experiences of healthcare professionals involved will also be considered to assess the impact of these interventions on working conditions and patient support.

Keywords: anxiety, care, pain, older adults, virtual reality

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Authors: Linda P. M. Pluymen, Mariska M. G. Leeflang, I. Stegeman, Henock G. Yebyo, Anne E. M. Brabers, Patrick M. Bossuyt, E. Dekker, Anke J. Woudstra, Mirjam P. Fransen

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Weighing the benefits and harms of colorectal cancer screening can be difficult for individuals. An existing online decision aid was expanded with a benefit-harm analysis to help people make an informed decision about participating in colorectal cancer screening. In a randomized controlled trial, we investigated whether those in the intervention group who used the decision aid with benefit-harm analysis were more certain about their decision than those in the control group who used the decision aid without benefit-harm analysis. Participants were 623 (39% of those invited) men and women aged 45 until 75 years old. Analyses were performed in those 386 participants (62%) who reported to have completed the entire decision aid. No statistically significant differences were observed between intervention and control group in decisional conflict score (mean difference 2.4, 95% CI -0.9, 5.6), clarity of values (mean difference 1.0, 95% CI -4.4, 6.6), deliberation score (mean difference 0.5, 95% CI -0.6, 1.7), anxiety score (mean difference 0.0, 95% CI -0.3, 0.3) and risk perception score (mean difference 0.1, -0.1, 0.3). Adding a benefit-harm analysis to an online decision aid did not improve informed decision making about participating in colorectal cancer screening.

Keywords: benefit-harm analysis, decision aid, informed decision making, personalized decision making

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Authors: T. Rocha, P. Carvalho, S. Paredes, J. Henriques, A. Bianchi, V. Traver, A. Martinez

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The main goal of LINK project is to join competences in intelligent processing in order to create a research ecosystem to address two central scientific and technical challenges for personal health care (PHC) deployment: i) how to merge clinical evidence knowledge in computational decision support systems for PHC management and ii) how to provide achieve personalized services, i.e., solutions adapted to the specific user needs and characteristics. The final goal of one of the work packages (WP2), designated Sustainable Linking and Synergies for Excellence, is the definition, implementation and coordination of the necessary activities to create and to strengthen durable links between the LiNK partners. This work focuses on the strategy that has been followed to achieve the definition of the Research Tracks (RT), which will support a set of actions to be pursued along the LiNK project. These include common research activities, knowledge transfer among the researchers of the consortium, and PhD student and post-doc co-advisement. Moreover, the RTs will establish the basis for the definition of concepts and their evolution to project proposals.

Keywords: LiNK Twin European Project, personal health care, cardiovascular diseases, research tracks

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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

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Authors: E. Kempen, M. J. Labuschagne, M. P. Jama

Abstract:

There is evidence that any learning experience should happen in a safe learning environment as students then will interact, experiment, and construct new knowledge. However, little is known about the specific elements required to create a safe learning environment for the millennial generation, especially in optometry education. This study aimed to identify the specific elements that will contribute to a safe learning environment for the millennial generation of optometry students. Methods: An intrinsic qualitative case study was undertaken with undergraduate students from the Department of Optometry at the University of the Free State, South Africa. An open-ended questionnaire survey was completed after the application of nine different teaching-learning methods based on the experiential learning cycle. A total number of 307 questionnaires were analyzed. Two focus group interviews were also conducted to provide additional data to supplement the data and ensure the triangulation of data. Results: Important elements based on the opinions, feelings, and perceptions of student respondents were analyzed. Students feel safe in an environment with which they are familiar, and when they are familiar with each other, the educators, and the surroundings. Small-group learning also creates a safe and familiar environment. Both these elements create an environment where they feel safe to ask questions. Students value an environment where they are able to learn without influencing their marks or disadvantaging the patients. They enjoy learning from their peers, but also need personal contact with educators. Elements such as consistency and an achievable objective also were also analyzed. Conclusion: The findings suggest that to respond to the real need of this generation of students, insight must be gained in students’ perceptions to identify their needs and the learning environment to optimize learning pedagogies. With the implementation of these personalized elements, optometry students will be able to take responsibility and accountability for their learning.

Keywords: experiences and perceptions, safe learning environment, millennial generation, recommendation for optometry education

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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

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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

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Authors: Georges Bou Ghantous

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This literature review examines the transformative impact of Artificial Intelligence (AI) on higher education, highlighting both the potential benefits and challenges associated with its adoption. The review reveals that AI significantly enhances personalized learning by tailoring educational experiences to individual student needs, thereby boosting engagement and learning outcomes. Automated grading systems streamline assessment processes, allowing educators to focus on improving instructional quality and student interaction. AI's data-driven insights provide valuable analytics, helping educators identify trends in at-risk students and refine teaching strategies. Moreover, AI promotes enhanced instructional innovation through the adoption of advanced teaching methods and technologies, enriching the educational environment. Administrative efficiency is also improved as AI automates routine tasks, freeing up time for educators to engage in research and curriculum development. However, the review also addresses the challenges that accompany AI integration, such as data privacy concerns, algorithmic bias, dependency on technology, reduced human interaction, and ethical dilemmas. This balanced exploration underscores the need for careful consideration of both the advantages and potential hurdles in the implementation of AI in higher education.

Keywords: administrative efficiency, data-driven insights, data privacy, ethical dilemmas, higher education, personalized learning

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Authors: Abdul Basit Kiani

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The software for the digital library system is a cutting-edge solution designed to revolutionize the way libraries manage and provide access to their vast collections of digital content. This advanced software leverages the power of technology to offer a seamless and user-friendly experience for both library staff and patrons. By implementing this software, libraries can efficiently organize, store, and retrieve digital resources, including e-books, audiobooks, journals, articles, and multimedia content. Its intuitive interface allows library staff to effortlessly manage cataloging, metadata extraction, and content enrichment, ensuring accurate and comprehensive access to digital materials. For patrons, the software offers a personalized and immersive digital library experience. They can easily browse the digital catalog, search for specific items, and explore related content through intelligent recommendation algorithms. The software also facilitates seamless borrowing, lending, and preservation of digital items, enabling users to access their favorite resources anytime, anywhere, on multiple devices. With robust security features, the software ensures the protection of intellectual property rights and enforces access controls to safeguard sensitive content. Integration with external authentication systems and user management tools streamlines the library's administration processes, while advanced analytics provide valuable insights into patron behavior and content usage. Overall, this software for the digital library system empowers libraries to embrace the digital era, offering enhanced access, convenience, and discoverability of their vast collections. It paves the way for a more inclusive and engaging library experience, catering to the evolving needs of tech-savvy patrons.

Keywords: software development, empowering digital libraries, digital cataloging and access, management system

Procedia PDF Downloads 83

Authors: Dongliu Li

Abstract:

Objective: This study systematically evaluates the incidence and influencing factors of economic toxicity in patients with gynecological malignant tumors. Methods: Literature on economic toxicity of gynecological malignancies were comprehensively searched in Pubmed, The Cochrane Library, Web of Science, Embase, CINAHL, CNKI, Wanfang Database, Chinese Biomedical Literature database and VIP database. The search period is up to February 2024. Stata 17 software was used to conduct a single-group meta-analysis of the incidence of economic toxicity in gynecological malignant tumors, and descriptive analysis was used to analyze the influencing factors. Results: A total of 11 pieces of literature were included, including 6475 patients with gynecological malignant tumors. The results of the meta-analysis showed that the incidence of economic toxicity in gynecological malignant tumors was 40% (95%CI 31%—48%). The influencing factors of economic toxicity in patients with gynecological malignant tumors include social demographic factors, medical insurance-related factors and disease-related factors. Conclusion: The incidence of economic toxicity in patients with gynecological malignant tumors is high, and medical staff should conduct early screening of patients according to relevant influencing factors, personalized assessment of patients' economic status, early prevention work and personalized intervention measures.

Keywords: gynecological malignancy, economic toxicity, the incidence rate, influencing factors, systematic review

Procedia PDF Downloads 30