Context-aware recommender systems in mobile environment: On the road of future research

2017 ◽  
Vol 72 ◽  
pp. 27-61 ◽  
Author(s):  
Imen Ben Sassi ◽  
Sehl Mellouli ◽  
Sadok Ben Yahia
Keyword(s):  
Recommender Systems ◽  
Future Research ◽  
Context Aware ◽  
Mobile Environment ◽  
The Road ◽  
On The Road

On the Road to Ephesus

2015 ◽  
pp. 102-125
Author(s):  
Suzanne Roff-Wexler
Keyword(s):  
Big Data ◽  
Future Research ◽  
Review Of Literature ◽  
Healthcare Organizations ◽  
Research Directions ◽  
The Road ◽  
Future Research Directions ◽  
On The Road ◽  
Definition Of

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.

On the Road to Ephesus

Big Data ◽  
2016 ◽  
pp. 2275-2299
Author(s):  
Suzanne Roff-Wexler
Keyword(s):  
Big Data ◽  
Future Research ◽  
Review Of Literature ◽  
Healthcare Organizations ◽  
Research Directions ◽  
The Road ◽  
Future Research Directions ◽  
On The Road ◽  
Definition Of

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.

Context-Aware Service Discovery in Ubiquitous Computing

2009 ◽  
pp. 1087-1095
Author(s):  
Huaqun Guo ◽  
Daqing Zhang ◽  
Lek-Heng Ngoh ◽  
Song Zheng ◽  
Wai-Choong Wong
Keyword(s):  
Ubiquitous Computing ◽  
Service Discovery ◽  
Large Scale ◽  
Technical Problem ◽  
Personal Digital Assistant ◽  
Context Aware ◽  
Wireless Devices ◽  
The Road ◽  
On The Road ◽  
Context Aware Service

The decreasing cost of networking technology and network-enabled devices is driving the large scale deployment of such networks and devices so as to offer many new and innovative services to users in ubiquitous computing. For example, when you carry your mobile laptop or personal digital assistant (PDA) around, or drive on the road, various services have been made available, ranging from finding a local printer to print a file, to instantaneously knowing about the traffic situation from traffic-cameras and other sensors along a highway. To achieve the above, every participating network- enabled end-device must solve an interesting technical problem, i.e., to locate a particular network service or device out of hundreds of thousands of accessible services and devices. Such service advertising and discovery is important as mobile devices and mobile wireless devices proliferate on networks. For this reason, a service discovery and advertising protocol is an important tool to help these devices find services on the network wherever they connect, and to let other network users know about the services they are offering. Context-aware service discovery, on the other hand, would help users to find services that are most appropriate based on fast-changing client conditions, such as location. For example, most laptops are statically configured to print to dedicated office printers. With the help of the context-awareness, a laptop could find the nearest accessible printer attached to the network that the laptop is currently plugged into.

Current and Future Research Activities in Driver Training

1977 ◽  
Vol 21 (6) ◽  
pp. 482-484
Author(s):  
Robert M. Nicholson ◽  
Michael F. Smith
Keyword(s):  
Future Research ◽  
Safety Education ◽  
Driver Education ◽  
Driver Training ◽  
The Road ◽  
Elderly Driver ◽  
Research Activities ◽  
Driver Performance ◽  
Pros And Cons ◽  
On The Road

Research programs involving high school driver education, motorcyclist safety education, problem driver retraining, elderly driver retraining, handicapped driver training, commercial vehicle driver training, and an energy efficient driver training program are summarized. Some of the pros and cons of driver education are presented and problems with establishing valid on-the-road driver performance tests are discussed.

scholarly journals Computer Games as Learning Tool towards Children Road Safety Education

2018 ◽  
Vol 7 (4.15) ◽  
pp. 230
Author(s):  
Masnida Hussin ◽  
Nor Hanis Mohd Fouzi
Keyword(s):  
Computer Games ◽  
Road Safety ◽  
Road Traffic ◽  
Primary School Children ◽  
Future Research ◽  
Large Sample Size ◽  
Learning Tool ◽  
The Road ◽  
Safety Awareness ◽  
On The Road

Road safety awareness is one of the many awareness programs that are often highlighted and discussed around the world. The road accident statistics are increased due to the lack of exposure and awareness among communities about traffic environments and rules. Children are one of the most vulnerable populations involved in traffic accidents. The children are unable to familiarize themselves with the surroundings, especially when crossing the road. This research attempts to improve road-safety awareness among children by using computer games as a learning tool. Specifically, it determines the progress of knowledge on the road rules and conditions after the children using the tool. The computer online game is suitable methods to use for teaching them on road safety due to interactive application always intimate the children. Besides the survey questions that related to road traffic rules, we also measures the attitude towards road safety in the participant (i.e., children and adult). Descriptive analysis in frequency, mean, and percentage are used to describe the respondent’s information. Statistical Package for Social Science (SPSS) is used to analyze the findings. The overall findings show that all respondents have positive feedback on online games as a road safety tool. Interestingly, the significant output shows on the different knowledge about road safety when the children are analyzed for before and after they played the games. The future research is suggested to study the other group of participant as the respondent in this work is limited to the primary school children. It can be improved by involving the large sample size and wider location.                                                                                                                                           

scholarly journals In-Season Nutrition Strategies and Recovery Modalities to Enhance Recovery for Basketball Players: A Narrative Review

2021 ◽  
Author(s):  
Jon K. Davis ◽  
Sara Y. Oikawa ◽  
Shona Halson ◽  
Jessica Stephens ◽  
Shane O’Riordan ◽  
...  
Keyword(s):  
Future Research ◽  
Time Variability ◽  
Night Time ◽  
Basketball Players ◽  
Compression Garments ◽  
Practical Applications ◽  
Wake Time ◽  
The Road ◽  
Multiple Challenges ◽  
On The Road

AbstractBasketball players face multiple challenges to in-season recovery. The purpose of this article is to review the literature on recovery modalities and nutritional strategies for basketball players and practical applications that can be incorporated throughout the season at various levels of competition. Sleep, protein, carbohydrate, and fluids should be the foundational components emphasized throughout the season for home and away games to promote recovery. Travel, whether by air or bus, poses nutritional and sleep challenges, therefore teams should be strategic about packing snacks and fluid options while on the road. Practitioners should also plan for meals at hotels and during air travel for their players. Basketball players should aim for a minimum of 8 h of sleep per night and be encouraged to get extra sleep during congested schedules since back-to back games, high workloads, and travel may negatively influence night-time sleep. Regular sleep monitoring, education, and feedback may aid in optimizing sleep in basketball players. In addition, incorporating consistent training times may be beneficial to reduce bed and wake time variability. Hydrotherapy, compression garments, and massage may also provide an effective recovery modality to incorporate post-competition. Future research, however, is warranted to understand the influence these modalities have on enhancing recovery in basketball players. Overall, a strategic well-rounded approach, encompassing both nutrition and recovery modality strategies, should be carefully considered and implemented with teams to support basketball players’ recovery for training and competition throughout the season.

A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Quang-Hung Le ◽  
Son-Lam Vu ◽  
Thi-Kim-Phuong Nguyen ◽  
Thi-Xinh Le
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Contextual Information ◽  
Information Filtering ◽  
Weather Conditions ◽  
Research Literature ◽  
Future Research ◽  
Context Aware ◽  
Good Tool ◽  
Advantages And Disadvantages

In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.

Advanced Recommender Systems

2019 ◽  
pp. 299-311 ◽  
Author(s):  
Young Park
Keyword(s):  
Recommender Systems ◽  
Future Research ◽  
Context Aware ◽  
Research Topics ◽  
Systematic Overview ◽  
Cross Domain ◽  
Group Recommender Systems ◽  
Group Recommender

This chapter presents a brief and systematic overview of four major advanced recommender systems: group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. These advanced recommendations are characterized and compared in a unifying model as extensions of basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed. Advanced recommender technologies will continue to advance.

scholarly journals A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

2021 ◽  
Vol 11 (17) ◽  
pp. 8210
Author(s):  
Chaeyoung Lee ◽  
Hyomin Kim ◽  
Sejong Oh ◽  
Illchul Doo
Keyword(s):  
Deep Learning ◽  
Traffic Safety ◽  
Research Direction ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Video Data ◽  
Future Research ◽  
The Road ◽  
Detection Model ◽  
On The Road

This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using FFmpeg for model production, car collision types are classified, and only the head-on collision types are processed by using the deep learning object-detection algorithm YOLO (You Only Look Once). Using the car accident detection model that we built and the provided road obstacle-detection model, we programmed, for when the model detects abnormalities on the road, warning notification and photos that captures the accidents or obstacles, which are then transferred to the application. The proposed service was verified through application notification simulations and virtual experiments using CCTVs in Daegu, Busan, and Gwangju. By providing services, the goal is to improve traffic safety and achieve the development of a self-driving vehicle sector. As a future research direction, it is suggested that an efficient CCTV control system be introduced for the transportation environment.

scholarly journals Automated Data-Driven Hint Generation in Intelligent Tutoring Systems for Code-Writing: On the Road of Future Research

2018 ◽  
Vol 13 (09) ◽  
pp. 174
Author(s):  
Hieu Trong Bui ◽  
Syed Malek F D Syed Mustapha
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Research Field ◽  
Data Driven ◽  
Future Research ◽  
Large Solution ◽  
Tutoring Systems ◽  
The Road ◽  
On The Road ◽  
New Research

Introductory programming is an essential part of the curriculum in any engineering discipline in universities. However, for many beginning students, it is very difficult to learn. In particular, these students often get stuck and frustrated when attempting to solve programming exercises. One way to assist beginning programmers to overcome difficulties in learning to program is to use intelligent tutoring systems (ITSs) for programming, which can provide students with personalized hints of students’ solving process in programming exercises. Currently, mostly these systems manually construct the domain models. They take much time to construct, especially for exercises with very large solution spaces. One of the major challenges associated with handling ITSs for programming comes from the diversity of possible code solutions that a student can write. The use of data-driven approaches to develop these ITSs is just starting to be explored in the field. Given that this is still a relatively new research field, many challenges are still remained unsolved. Our goal in this paper is to review and classify analysis techniques that are requested to generate data-driven hints in ITSs for programming. This work also aims equally to identify the possible future directions in this research field.

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