With the rapid development of information and communication technologies, people are increasingly referring to web information to assist in their travel planning and decision making. Research shows that people conduct collaborative information searches while planning their travel activities online. However, little is known in depth about tourists' online collaborative search. This study examines tourists' collaborative information search behavior in detail, including their search stages, online search strategies, and information flow breakdowns. The data for analysis included pre‐ and postsearch questionnaires, web search and chat logs, and postsearch interviews. A model of tourist collaborative information retrieval was developed. The model identified collaborative planning, collaborative information searching, sharing of information, and collaborative decision making as four stages of tourists' collaborative search. The results show that tourists collaborated by planning their search strategies, dividing search tasks into subtasks and allocating workload, using search queries and URL links recommended by teammates, and discussing search results together. Related personal knowledge and experiences appeared important in trip planning and collaborative information search. During the collaborative search, tourists also encountered various information flow breakdowns in different search stages. These were classified and their effects on collaborative information search were reported. Implications for system design in support of collaborative information retrieval in travel contexts are also discussed.

Human Activity Recognition (HAR) simply refers to the capacity of a machine to perceive human actions. HAR is a prominent application of advanced Machine Learning and Artificial Intelligence techniques that utilize computer vision to understand the semantic meanings of heterogeneous human actions. This paper describes a supervised learning method that can distinguish human actions based on data collected from practical human movements. The primary challenge while working with HAR is to overcome the difficulties that come with the cyclostationary nature of the activity signals. This study proposes a HAR classification model based on a two-channel Convolutional Neural Network (CNN) that makes use of the frequency and power features of the collected human action signals. The model was tested on the UCI HAR dataset, which resulted in a 95.25% classification accuracy. This approach will help to conduct further researches on the recognition of human activities based on their biomedical signals.

Purpose Collaborative information search (CIS) is a growing and significant research area. Query formulation and reformulation is an important search strategy in information search. However, limited research has investigated query behavior during CIS. The purpose of this paper is to characterize collaborative query reformulation (CQR) by exploring the sources of collaborative query (CQ) terms and the types and patterns of CQR in the context of tourism information search. Design/methodology/approach An empirical study was designed to investigate search query reformulation as tourists performed CIS on a devised interface. A total of 36 participants (in 18 pairs) took part in the study; data were documented in pre- and post-search questionnaires, search logs and chat logs. Findings The findings show that participants intermixed individual search and collaborative search during CIS. Participants constructed CQ terms mainly by selecting terms from individual search queries and discussion chat logs. Eight types of CQR were identified, with specialization (82 percent) accounting for the most used search tactics. At most times, participants were found to add terms to the previous query. Findings demonstrated 27 specific CQR patterns; in excess of two-third participants (69 percent) took only one move to reformulate CQ by adding terms, or replacing/using new words. Practical implications The results of this research can be used to inform the design of search systems supporting collaborative querying in CIS. Originality/value This study is highlighting an important research direction of CQ reformulation in collaborative search while previous studies of the topic are limited, comparing to the vast body of work on query reformulation in individual information search using regular search systems.

Purpose Collaborative information searching is common for people when planning their group trip. However, little research has explored how tourists collaborate during information search. Existing tourism Web portals or search engines rarely support tourists’ collaborative information search activities. Taking advantage of previous studies of collaborative tourism information search behavior, in the current paper the purpose of this paper is to propose the design of a collaborative search system collaborative tourism information search (ColTIS) to support online information search and travel planning. Design/methodology/approach ColTIS was evaluated and compared with Google Talk-embedded Tripadvisor.com through a user study involving 18 pairs of participants. The data included pre- and post-search questionnaires, web search logs and chat history. For quantitative measurement, statistical analysis was performed using SPSS; for log data and the qualitative feedback from participants, the content analysis was employed. Findings Results suggest that collaborative query formulation, division of search tasks, chatting and results sharing are important means to facilitate tourists’ collaborative search. ColTIS was found to outperform Tripadvisor significantly regarding the ease of use, collaborative support and system usefulness. Originality/value The innovation of the study lies in the development of an integrated real-time collaborative tourism information search system with unique features. These features include collaborative query reformulation, travel planner and automatic result and query sharing that assist multiple people search for holiday information together. For system designers and tourism practitioners, implications are provided.

Term weighting is a core idea behind any information retrieval technique which has crucial importance in document ranking. In graph based ranking algorithm, terms within a document are represented as a graph of that document. Term weights for information retrieval are estimated using term's co-occurrence as a measure of term dependency between them. The weight of vertex in the document graph is calculated based on both local and global information of that vertex. This paper introduces a method of information retrieval using random walk model considering positional values of a term in the document for computing its inverse document frequency and assigning trained weight to terms in the user provided query. Experiments on standard datasets have shown that our approach provides improvement in recall and precision of information retrieval system.

Diabetic retinopathy (DR) is the primary cause of vision loss among grown-up people around the world. In four out of five cases having diabetes for a prolonged period leads to DR. If detected early, more than 90% of the new DR occurrences can be prevented from turning into blindness through proper treatment. Despite having multiple treatment procedures available that are well-capable to deal with DR, the negligence and failure of early detection cost most of the DR patients their precious eyesight. The recent developments in the field of Digital Image Processing (DIP) and Machine Learning (ML) have paved the way to use machines in this regard. The contemporary technologies allow us to develop devices capable of automatically detecting the condition of a person's eyes based on their retinal images. However, in practice, several factors hinder the quality of the captured images and impede the detection outcome. In this study, a novel early blind detection method has been proposed based on the color information extracted from retinal images using an ensemble learning algorithm. The method has been tested on a set of retinal images collected from people living in the rural areas of South Asia, which resulted in a 91% classification accuracy.

Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

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