User's Geographical Location Research Articles

Multimodal urban mobility solutions for a smart campus using artificial neural networks for route determination and an algorithm for arrival time prediction

Artificial intelligence algorithms play a key role in solving multimodal urban mobility problems, this study outlines a scheme covering bus, boat, and pedestrian transport modes. This involves designing an Artificial Neural Network (ANN) model to classify the most suitable routes for passengers, together with an algorithm capable of estimating the arrival times of various transportation modes at designated stopping points or buildings within the smart campus. The information used to train the ANN is obtained from an Internet of Things (IoT) network and a database that includes the available routes within the campus (which is situated in the Brazilian Amazon), where there is a multimodal electric mobility service. In seeking to achieve the objectives, this ANN relied on the user's geographic location in the input layer, and route mapping data stored in the database in the output layer, as well as the backpropagation algorithm for computational processing and adjustments of synaptic weight. The algorithm for modal arrival time prediction used real-time geographic location data, together with the Haversine formula to calculate geographic distance, while the average speed was obtained from the IoT network. A range of different ANN parameters were included in the experiments. In the study case, the ANN results show an improvement of above 92% for determining the best routes and predicting arrival times. The findings show that ANNs can effectively find the best route within a real-world smart campus environment. Moreover, the results show the arrival time can be estimated by using real-time geographic location data.

Rumor Detection of Covid-19 Related Microblogs on Sina Weibo

Since the outbreak of COVID-19 in Wuhan in 2019, Sina Weibo has become a platform for rumors to spread rapidly with more than 200 million daily active users. Based on this, we focus on the epidemic-related rumors on Sina Weibo during the epidemic. By summarizing the features extracted by feature engineering in the research of rumor detection from 2011 to now, we find that the existing features have low utilization rate of the user's geographical location property. Although the public display of the user's IP geographical location in Sina Weibo since April 28, 2022 has greatly improved the authenticity of this property, its utilization is still limited to extracting the explicit feature of IP geographical location. Moreover, the existing research has not paid attention to the filtering mechanism of the platform when extracting features, and has taken advantage of this phenomenon.According to the shortcomings of the existing research, we put forward two new indicators based on the filtering mechanism of Sina Weibo, the available comments and the first-class available comments. The available comments represent comments in the comment area of each microblog that have not been filtered by the filtering mechanism. The first-class available comments represent available comments that directly reply to the original microblog. Based on the new indicator of first-class available comments and users’ IP geographical location property, we further put forward 10 new features. Finally, we train seven classifiers, including logistic regression, SVM based on linear kernel, SVM based on RBF kernel, Naive Bayes, random forest, DNN and CNN. By comparing the prediction effects of each classifier and the prediction effects of the classifier before and after adding new features, it is concluded that the random forest classifier performed best in this application scenario, and the new features can improve the accuracy of random forest classifier by 1.33%, the precision rate of positive cases by 2%, the precision rate of negative cases by 5%, the recall rate of positive cases unchanged and the recall rate of negative cases by 14%.

Healthcare Recommender System Based on Medical Specialties, Patient Profiles, and Geospatial Information

The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and communication technologies to bring sustainability to health services. This paper proposes a novel, methodological, and collaborative approach based on patient-centered technology, which consists of a recommender system architecture to assist the health service level according to medical specialties. The system provides recommendations according to the user profile of the citizens and a ranked list of medical facilities. Thus, we propose a health attention factor to semantically compute the similarity between medical specialties and offer medical centers with response capacity, health service type, and close user geographic location. Thus, considering the challenges described in the state-of-the-art, this approach tackles issues related to recommenders in mobile devices and the diversity of items in the healthcare domain, incorporating semantic and geospatial processing. The recommender system was tested in diverse districts of Mexico City, and the spatial visualization of the medical facilities filtering by the recommendations is displayed in a Web-GIS application.

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

Assistance Gaps in Physical Rehabilitation: Spatial Analysis of Physical Therapy Services and Users' Households in a Brazilian Capital.

The aim of this study was to analyze the spatial distribution of outpatient physical therapy rehabilitation services and associate it with the geographic location of users' households. The public physical therapy rehabilitation services and addresses of users' households in Campo Grande (MS) were georeferenced in 2017-2019. Five physical therapy clinics were identified to supply the demand of 31,774 scheduled users. Most services are in the most central regions of the city, while most users reside in peripheral areas. An assistance gap in Physical therapy care was identified, with a restricted supply of services to meet demands and a geographic barrier, since the services are far from the users' households. Physical rehabilitation must be a priority in the country's public health, especially nowadays, where demands for rehabilitation of a significant number of Brazilians who evolved with sequelae of COVID-19 are so present.

Vazios assistenciais na reabilitação física: análise espacial dos serviços de fisioterapia e dos domicílios dos usuários em uma capital brasileira

ABSTRACT: Objective: The aim of this study was to analyze the spatial distribution of outpatient physical therapy rehabilitation services and associate it with the geographic location of users’ households. Methods: The public physical therapy rehabilitation services and addresses of users’ households in Campo Grande (MS) were georeferenced in 2017-2019. Results: Five physical therapy clinics were identified to supply the demand of 31,774 scheduled users. Most services are in the most central regions of the city, while most users reside in peripheral areas. Conclusion: An assistance gap in Physical therapy care was identified, with a restricted supply of services to meet demands and a geographic barrier, since the services are far from the users’ households. Physical rehabilitation must be a priority in the country’s public health, especially nowadays, where demands for rehabilitation of a significant number of Brazilians who evolved with sequelae of COVID-19 are so present.

A new congestion cost allocation method in a deregulated power system using weighting of contractual preferences and geographical location of users

The restructuring of a power system from its monopolistic market to a deregulated system introduces important power flow considerations such congestion in the transmission system due to economic factors like competition in generation. Transmission congestion leads to additional cost for the transmission operator. This paper elaborates a method allocation of this cost amongst users based on their individual utilization of transmission assets. The congestion is relieved by influencing the choices of the consumers in the energy market. The paper proposes a cost allocation scheme based on power flow tracing method, by distinguishing the part of the cost related to the consumers’ market preferences, as opposed to the cost due to their geographical locations in the network before weighting them into a unique cost. This can bring about more transparency and efficiency with regard to payments made by producers and consumers, and also guides them towards choices that alleviate congestions in network.

Location-based and Time-aware Service Recommendation in Mobile Edge Computing

With the rapid development of Internet of Things, mobile edge computing which provides physical resources closer to end users has gained considerable popularity in academic and industrial field. As the number of edge server increases, accessing effective edge services fast is an urgent problem to be solved. In this paper, we mainly focus on the cold-start problem for service recommendation based on location of users and services. Address this conundrum, we propose a service recommendation method based on collaborative filtering (CF) and location, by comprehensively considering the characteristic of services at the edge, mobility and demands of users at different time periods. In detail, we synthesize the service characteristics of each dimension in different time slices through multidimensional weighting method at first. Then We further introduce the idea of Inverse CF Rec to the traditional CF and predict the lost quality of service (QoS) to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on time-aware collaborative filtering (MDITCF) outperforms Inverse CF Rec in terms of the accuracy of recommendation.

EFAWATEERcom As an Electronic Bill Payment Service: The Case of Jordan

Considering an increasing burden of paying bills through an ordinary process, whether in terms of cost, time, or effort. The Central Bank of Jordan took the initiative role in coordination and cooperation with the National Payments Council to create eFAWATEER.com service as an electronic portal for presentation and billing collection. It is challenging to get a comprehensive study on the acceptance of eFAWATEER.com service in some of working banks in Jordan. eFAWATEER.com has been adopted by large number of Jordanians from different gender, age, educational levels, marital status, geographical areas, and monthly income. However, the users' intention to continue adopting eFAWATEER.com service might be influenced by the socio-demographic characteristics of those users. This research aims to investigate the difference in the intention to use eFAWATEER.com among users based on their gender, age, educational level, marital status, geographic location, and monthly income. Data were collected from eFAWATEER.com service users (i.e., Public and private sectors), using a paper-based questionnaire. 1000 questionnaires were distributed to the targeted sample, 456 valid responses were obtained. In order to get more information regarding to eFAWATEER.com service, an interview with the CEO of MadfooatCom company was conducted. The findings showed that, there is a significant difference when it comes to the user’s intention to continue adopting eFAWATEER.com as an electronic bill payment service determined by user's geographical location and gender. This research provides a general guidelines for "Madfooatcom Company" and the "Central Bank of Jordan" to make investments in another e-channels and e-services using eFAWATEER.com portal to work for many consumers categories in Jordan based on their geographical location and gender.

Friendship prediction model based on factor graphs integrating geographical location

With the development of network services and location-based systems, many mobile applications begin to use users’ geographical location to provide better services. In terms of social networks, geographical location is actively shared by users. In some applications with recommendation services, before the geographical location recommendation is provided, the authors have to obtain user's permission. This kind of social network integrated with geographical location information is called location-based social networks (abbreviate for LBSNs). In the LBSN, each user has location information when he or she checked in hotels or feature spots. Based on this information, they can identify user's trajectory of movement behaviour and activity patterns. In general, if there is friendship between two users, their trajectories in reality are likely to be similar. In this study, according to user's geographical location information over a period of time, they explore whether there exists friendly relationship between two users based on trajectory similarity and the structure theory of graphs. In particular, they propose a new factor function and a factor graph model based on user's geographical location to predict the friendship between two users in the real LBSN.

A Review: Recommendation System of Indian Cuisine Recipes Using Content-Based Approach

The recommendation system has helped user’s to take quick decisions and thus gaining popularity in recent years. The Indian traditional cuisine has been extensively praised for its remarkable use of herbs and spices. The food of India is famous for its great variety of dishes. The style of cooking differs from one area to another. India is well known for its extensive diversity of fine food procurable in a wide range of eatery and hotel resorts, which recalls the unity in diversity. The proposed approach recommends Indian cuisine recipes using the content-based approach. It recommends items on the basis of the geographical areas from where the cuisines belong to and also based on the specific dish that the user likes. It also recommends items on the basis of user's geographical location.

Evaluating the Uptake and Acceptability of Standardized Postoperative Rehabilitation Guidelines Using an Online Knowledge Translation Approach.

Providing physical therapists with evidence-based and consensus-derived guidelines to manage postoperative shoulder patients is essential; these guidelines should be readily available and provide clinically applicable information. Knowledge translation (KT) initiatives that encourage interaction between clinicians and researchers, that have multifaceted components and use a variety of strategies, can significantly change practice. The objective of this study was to determine the uptake and acceptability of standardized postoperative shoulder guidelines with an accompanying online KT resource through evaluation of website analytics and a quantitative survey. A multi-pronged approach was used to assess uptake and acceptability of the guidelines and online KT resource. Website analytics of usage and geographical location of users was measured as were physical therapist survey responses. Website analytics revealed that 5406 individuals used the online resource between October 2012 and September 2013 with the average visit lasting 8 minutes; only 47% of users were within the guideline developers' surgical referral region. Physical therapists who used the new shoulder guidelines were very satisfied or satisfied (96%) with the guidelines, reporting they promoted patient-specific clinical decision-making extremely or very well (68%). They viewed the online KT resource positively, with 79% rating it as "very useful" or "quite useful." Physical therapists from regions beyond those expected to use the new shoulder guidelines were also aware of the website and also rated it as very useful. The survey sample was relatively small and did not directly assess patient outcomes. An online KT web resource developed in conjunction with standardized postoperative shoulder guidelines was perceived as useful based on website analytics and survey responses. Active KT strategies such as this can improve uptake and dissemination of best practice in physical therapy.

Mobile Money

Access to financial institutions is difficult in developing economies and especially for the poor. However, the widespread adoption of mobile phones has enabled the development of mobile money systems that deliver financial services through the mobile phone network. Despite the success of mobile money, there is a lack of quantitative studies that unveil which factors contribute to the adoption and sustained usage of such services. In this paper, we describe the results of a quantitative study that analyzes data from the world's leading mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone communications and M-Pesa transactions in an African country. Our contributions are threefold: (1) we analyze the customers' usage of M-Pesa and report large-scale patterns of behavior; (2) we present the results of applying machine learning models to predict mobile money adoption (AUC=0.691), and mobile money spending (AUC=0.619) using multiple data sources: mobile phone data, M-Pesa agent information, the number of M-Pesa friends in the user's social network, and the characterization of the user's geographic location; (3) we discuss the most predictive features in both models and draw key implications for the design of mobile money services in a developing country. We find that the most predictive features are related to mobile phone activity, to the presence of M-Pesa users in a customer's ego-network and to mobility. We believe that our work will contribute to the understanding of the factors playing a role in the adoption and sustained usage of mobile money services in developing economies.

Security and Privacy in Location-Based Services for Vehicular and Mobile Communications: An Overview, Challenges, and Countermeasures

Location-based services (LBSs) have gained popularity as a result of the advances in mobile and communication technologies. LBS provide users with relevant information based on their location. In spite of the desirable features provided by LBS, the geographic locations of users are not adequately protected. Location privacy is one of the major challenges in vehicular and mobile networks. In this paper, we analyze the security and privacy requirements for LBS in vehicular and mobile networks. Specifically, this paper covers privacy enhancing technologies and cryptographic approaches that provide location privacy in vehicular and mobile networks. The different approaches proposed in literature are compared and open research areas are identified.

METHODS OF ASSESSING THE EFFICIENCY OF INTERNET MARKETING COMMUNICATIONS

The purpose of the study is to systematize the methodological foundations for evaluating the effectiveness of marketing communications on the Internet. In the literature, there are often limited definitions of interactivity, which do not reveal the essence and do not allow fully realizing the marketing opportunities associated with its phenomena. Today, interactive video, gaming technologies, which are increasingly used in the creation of advertising and communication materials, provide unprecedented opportunities for users to create and flexibly change their own scenarios for the development of events, to guide the history of the video and its heroes, which, among other things, makes it possible to make the presentation object not only goods or services but also the practice of behaviour in different situations of their purchase and consumption. In Internet marketing communications, preconditions for estimating the time budget, general stay in the network, implementation of specific behavioural scenarios of the full-fledged functioning of the user as a representative of the target audience, including through various devices and due to multimedia character, are formed. Optimization of Internet communications of enterprises is carried out on the basis of cyclic interactive communication and timely adjustments of parameters of the received traffic from automated services such as Google analytics, Yandex Metrica, CoMagic, and others. An audience behaviour analysis becomes possible by the use of cookie data. This fragment of data stored on Internet users’ computers reveals the broad opportunities for enterprises to target their advertising, identifying the target audience, for example, by geographic location of users, tracking their interests, counting impressions and passing through banners. Due to the high level of inherent interactivity, simplification of activity realization, and its coordination in a virtual environment, with real results, the development of marketing communications on the Internet contributes to the transition from the integrated application of specialized tools and technologies to their genuine integration. These new opportunities, subject to their implementation, will update one more aspect – the achievement of the effectiveness of Internet communications in marketing. Attempts and methodical options for the formation of a holistic set of marketing communications, with their declared effectiveness, have become widespread. The author proposes a more adaptive system for evaluating the effectiveness of marketing communications, where a balanced system of indicators and stages of work are complemented by the classical algorithms that create the necessary preconditions for preventing or neutralizing the identified problems in the organization of Internet marketing communications. The effectiveness of the CPM model depends entirely on the derivative results of user behaviour (review of site materials, ordering and purchasing goods). Therefore, depending on the goals of the campaign, that is, expanding the target audience or promoting sales, the result may be more predictable. The success of online platforms is based on the consistent development of activities, which is harmonized by three benchmarks: differentiation, effectiveness, efficiency. If earlier, marketing monitoring of the audience and behaviour of Internet users was mainly based on the features of its persons or contingents, and the information context, today it needs to be integrated (contextual targeting, retargeting, and other tools).

Evaluating Prediction Accuracy, Developmental Challenges, and Issues of Recommender Systems

Modern ways of living have made the people to depend on internet services for everything. The mounting information from various sources like social media, implicit and explicit information, user's geographical location, and the internet of things had increased the need of a recommender system. From e-governance to e-shopping, a recommender system helps people in finding the needed item or information and also boosts sales in the market of those items. Though many studies elaborate about recommendation systems, challenges in developing the recommendation systems, prevailing issues of recommendation systems and discussions on prediction accuracy are not detailed in any of the earlier works. Therefore, in this article, in order to increase the accuracy of the recommender system, the developmental challenges and issues in constructing recommender systems and for evaluation metrics in prediction accuracy are identified and detailed.

Location-based E-Commerce Services: (Re-) Designing using the ISO9126 Standard

E-commerce services based on user geographic location have emerged as a particularly important segment of modern information services. In these user-intensive applications, quality of service is important and design methods are increasingly relying on software standards to achieve quality. In this paper, we propose an evaluation model for location based e-services that provide insights on how overall system quality can be strengthened via identifying the most important quality characteristics of specific user-system interactions facets. The model categorizes location based services into taxonomies of components / functions, which are further analyzed in interaction facets and significance levels. A further mapping to external qualitative sub-characteristics of the ISO9126 quality standard is used to formally decompose design quality into quality attributes. The view of software design through quality attributes is supported by a mathematical model, which calculates significance weights on service components, defined either by designers or by the end users. An experiment, where this method is used to assess functionality is presented.

Digital divide, biometeorological data infrastructures and human vulnerability definition.

The design and implementation of any climate-related health service, nowadays, imply avoiding the digital divide as it means having access and being able to use complex technological devices, massive meteorological data, user's geographic location and biophysical information. This article presents the co-creation, in detail, of a biometeorological data infrastructure, which is a complex platform formed by multiple components: a mainframe, a biometeorological model called Pronbiomet, a relational database management system, data procedures, communication protocols, different software packages, users, datasets and a mobile application. The system produces four daily world maps of the partial density of the atmospheric oxygen and collects user feedback on their health condition. The infrastructure is shown to be a useful tool to delineate individual vulnerability to meteorological changes as one key factor in the definition of any biometeorological risk. This technological approach to study weather-related health impacts is the initial seed for the definition of biometeorological profiles of persons, and for the future development of customized climate services for users in the near future.

Style software critiques your fashion choices

New software judges outfits from a photograph and offers tips to make them look even more chic. Raquel Urtasun, a computer scientist at the University of Toronto, Canada, developed the software with colleagues in Spain. To teach the software about fashion, Urtasun's team showed it thousands of pictures from Chictopia, a popular style Web site. The more positive votes left by other users, the more fashionable the software perceived the look to be. It also noted other information about the photo, such as the user's geographic location, the date they had posted it, the background of the picture, and written descriptions of the clothing.

Using of cloud technologies while studying of exact sciences in secondary school

Research objectives: to analyze the experience of cloud technologies in education, namely the usage of sites in the Google Apps environment to study mathematics and physics in secondary school. The research goal is to determine the efficiency of this approach by analyzing site traffic statistics over a long period and geographic location of users. Object of research: using of cloud technologies in education. Subject of research: the using of cloud technologies while studying of mathematics and physics in secondary school. Research methods used: theoretical – analysis of government programs, scientific and methodological literature; empirical – learning, supervision over educational process, the analysis of statistical data. The results of the research obtained allow to determine the quality and effectiveness of the materials posted on the website. Website demonstrates compliance of the materials with international standards of education. Constant increase in the number of visitors of website of teachers of mathematics and physics demonstrates the growth of level of teaching exact sciences. Conclusions about the relevance and efficiency of cloud sites methodical and scientific direction for secondary school pupils was done. Application of cloud technology allows you to expand qualitatively organized educational process at virtually unlimited audience.