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Abstract: In the game of cricket, there are various kinds of dismissals that can be caused due to multiple reasons. Few of them include the bowler’s brilliance and the batsman’s mistake. Run-out being one kind of dismissal, that completely changes the fortune and momentum of teams. Most of the time, it becomes difficult for the on-field umpire to give a judgement on run-out with naked eyes. So, the decisions are transferred to the third umpire, who gives the final decision based on a time-consuming technique. Therefore, we are proposing an approach, in which dismissal prediction is made using object detection and support vector machine. As the dismissal prediction is made based on images from different angles from various cameras, we were successful in achieving an accuracy rate of 87%. Additionally, since it works on an automated process, it is much more time-efficient than the traditional system. Thus by using this approach, errors are minimized and machine learning capabilities are provided to decision making in the game of cricket. PubDate: 2022-11-22 DOI: 10.1007/s41133-022-00058-6
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Abstract: Food waste is a significant challenge, and our societal behaviours play a role in the amount of food items discarded. Thus, an effective method to inform consumers about high wastage patterns may help reduce the amount thrown away. This research investigates how Augmented Reality can be harnessed to enlighten consumers and work towards addressing high food waste patterns. Yet research on this topic is still very much in its infancy. To pursue this solution, food behaviour data are employed to provide an insight into how much is wasted from 9 catering industry locations in the Netherlands. An Augmented Reality application is developed, where models of food are projected onto real-world environments to provide scale on waste over a 7-day period. A quantitative evaluation of higher-education attendees demonstrated the approach has potential to incentivise reduction in waste. PubDate: 2022-07-01 DOI: 10.1007/s41133-022-00057-7
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Abstract: Our travel planner mobile application was designed to fit the traveling post to COVID-19 outbreak following the lean product process principle. We intend to develop a travel application that brings back the joy of traveling. The objective of this study is to design a travel planner application that satisfies the user’s needs. In the first stage of the lean product process, we first determine our target customer by interviewing eight millennials. Once the persona was clarified, we explored the problem space, identified underserved customer needs, and prioritized those needs. The features with high importance but were underserved are COVID-19 guideline information, place recommendation, route optimization, and price comparison, which we believe could offer excellent opportunities to create customer value. The value proposition and feature set were also identified before creating the first MVP prototype. The detailed navigation flow and interactive MVP prototype were created and then tested with users. As a result, in the last iteration of usability testing, the value rating was increased from 6 to 9, and the ease of use rating was increased from 6 to 8 compared to the first rounds. PubDate: 2022-02-21 DOI: 10.1007/s41133-022-00056-8
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Abstract: Theme entertainment businesses rely very much on providing their visitors with the most memorable experiences. To do so, top operators have put a considerable amount of effort and investment in their facilities and activities in their parks. However, after a customer interview, several pain points are still found such as long toilet queues, finding available parking lots, being unable to find the parked car, and no real-time thrill ride information/status. Similar with other domains in the tourism market, most of these problems are easy to solve with the help of having a smartphone application that delivers such information right to the customer’s hands. This study outlines the development process of a theme park application based on a design thinking approach. Our MVP prototypes have obtained good feedback from potential users in both qualitative and quantitative usability tests. PubDate: 2021-11-13 DOI: 10.1007/s41133-021-00054-2
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Abstract: At the end of December 2019, the pandemic COVID-19 disease emerged in Wuhan city of China. It spread rapidly and affects other parts of China. To control the risk of further spread of disease the authorities in Wuhan locked down the city on 23 January 2020. The COVID-19 cases were detected in several other countries within a few weeks, and it became a global threat. On 6th June 2020, 115,942 active COVID-19 cases exist in India. To break the chain of transmission of infectious disease, the educational institutions are closed. Due to the COVID-19 pandemic, all schools/colleges/universities have postponed or cancelled all campus events such as examinations, workshops, conferences, admissions and other activities. The main objective of this study is to know the impact of COVID-19 on education, to study the perception of students towards online education during COVID-19 crisis and to study the student perception towards COVID-19 disease. The data are collected by structured questionnaire from 100 students in Tirupati city. The study includes both primary and secondary data. The convenient sampling method is used to collect data. Results are summarized using SPSS software and conclusion drawn that most of the respondents are satisfied towards online education and most of the students want to continue the online education in future also. The main problem facing by the students is network connectivity. In Tirupati, 45% of the colleges are using Canvas Instructure for online classes. To build young minds in the present crisis, an effective and well-rounded educational practice is needed. It will help students to improve their skills that will drive their productivity, employability, health and well-being in the decades to come and ensure the overall progress of India. PubDate: 2021-11-09 DOI: 10.1007/s41133-021-00055-1
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Abstract: Over recent decades smart glasses have gained increased attention in both the research arena and recently also in the consumer market, even though there is not yet a clear definition of what exactly smart glasses entail and underexposed perspectives are not represented. This study used a rapid review to assess the current understanding of smart glasses with the aim of defining them. Searches were performed across six databases, followed-up by a content-based evaluation of title and abstract. A total set of 14 relevant publications was identified to help arrive at a definition and characteristics of smart glasses. As a result, it was observed in both the research literature and in the public domain that many different names are used for smart glasses, and that in some cases there is unclarity about what constitute smart glasses. Therefore, an adapted definition of smart glasses is developed based on the existing original rationale of ubiquitous computing and taking the current state-of-the-art knowledge into account. This article provides an overview of and suggestion for defining smart glasses from a social sciences’ perspective to better inform researchers, developers, designers and companies who are involved in the design, development, research of smart glasses. PubDate: 2021-10-31 DOI: 10.1007/s41133-021-00053-3
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Abstract: Blind people can benefit greatly from a system capable of localising and reading comprehension text embedded in natural scenes and providing useful information that boosts their self-esteem and autonomy in everyday situations. Regardless of the fact that existing optical character recognition programmes seem to be quick and effective, the majority of them are not able to correctly recognise text embedded in usual panorama images. The methodology described in this paper is to localise textual image regions and pre-process them using the naïve Bayesian algorithm. A weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. The stroke width transform, Gabor’s transform, and weighted naïve Bayesian classifier methodologies have been used in complicated degraded images to segment, feature extraction, and detect textual and non-textual elements. Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimisation is being used. The text in the image is transformed into an acoustic output after identification. The dataset IIIT5K is used for the development portion, and the performance of the suggested come up is evaluated using parameters such as accuracy, recall, precision, and F1-score. PubDate: 2021-10-29 DOI: 10.1007/s41133-021-00051-5
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Abstract: Tourism is the main source of economic growth and a creator of employment of various kinds. The Rajasthan state got the fifth rank in the top 10 tourist destinations among domestic tourism ranks. The main inflection of this paper is to investigate the influence of tourist satisfaction of international tourists on future behavioral intentions with special reference to the desert triangle of Rajasthan. The holiday satisfaction model was used to construct the survey method applied in this study. According to the findings, tourist holiday satisfaction is particularly high for attraction qualities in numerous tourist sites in Rajasthan's desert triangle. The HOLSAT model was used to calculate the average mean of experience and expectation in this study, and the results show that the attributes described as attractions, accessibility and activities components are those that foreign visitors value the most. International tourists, on the other hand, give the lowest scores to qualities classed under amenities and accommodation components, indicating that international tourism has a long way to go.Thus, the tourism sector will need to guide human resources to curate and deliver these experiences. PubDate: 2021-10-27 DOI: 10.1007/s41133-021-00052-4
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Abstract: The rapid outbreak of COVID-19 is a global health problem that has a significant effect on the educational systems. Therefore, students shifted to distance learning through the digital platform. Since COVID-19 has consequences on mental health, the present study examined the perceived stress level in school students in distance learning during the COVID-19 period. A cross-sectional study of a sample consisting of 385 school students evaluated the Perceived Stress Scale (PSS) and their concerns and emotions during the COVID-19 pandemic. The results showed that the mean value of the total PSS scores was 19.50 ± 9.28. About 48.6% of students had moderate stress, 28.8% had low stress, and 22.6% had high stress. Significant associations were found between the level of stress and demographic variables: age, gender, educational level, and place of residence. Female students were significantly higher than the male students in the mean score of perceived stress level (20.14 versus 18.13; p-value = 0.018). Females were more likely (53.4%) to have moderate stress than males (38.2%) (p = 0.019). The students aged 15–18 years were significantly higher than those aged 11–14 years in the mean score of perceived stress level (22.64 versus 17.24, respectively; p-value < 0.001). The students aged 15–18 years were also more likely (n = 536, 96.4%) to had high levels of stress than the younger students (p < 0.001). The results showed that secondary school students were significantly higher than primary school students in the mean score of perceived stress level (23.75 versus 16.76, respectively; p < 0.001). Secondary students were also more likely (43.0%) to have high stress than primary students (9.4%) (p < 0.001). Students who live in the Rafah governorate had a significantly lowest mean score of perceived stress level when compared to other counterparts (p-value = 0.005). Gender (β = − 0.282, P < 0.001), age (β = − 0.379, P = 0.004), place of residence (β = − 0.096, P = 0.034), and educational level (β = 0.845, P < 0.001) were predictors of perceived stress. The results revealed moderate to high levels of stress in school students in the Gaza Strip during schools closure due to the COVID-19 pandemic. The prevalence of psychological distress attributed to suddenly shifted from traditional learning to distance learning. The incorporation of stress management programs and online counselling will minimize stress and enhance the mental health of school students during distance learning. PubDate: 2021-10-23 DOI: 10.1007/s41133-021-00050-6
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Abstract: This article addresses a renowned issue of allocating periodic tasks to a network of heterogeneous processors in distributed computing systems (DCS) subject to timing constraints, tasks precedence, and arbitrary communication among them, in order to lessen the overall busy time whereas guaranteeing the tasks deadlines. A new hybrid optimization (NHO) technique is introduced, a fusion of k-mean clustering (KMC) and Branch-and-Bound (B&B) method for reducing overall normalized busy time (NSBT) of the system. This technique is stationed on B&B method in which each branch grants scheduling solution. K-mean clustering (KMC) technique has been utilized to reduce the complexity of B&B technique by pruning the branches those do not lead feasible solution. A specialized case of non-periodic tasks allocation issue is also studied in this work. The problem is intractable in nature. Finally, a demonstrative example and comparison with some computational experiences are presented. Experimental results reveal that proposed technique achieves better proficiency than other existing techniques in literature. This model is advisable for arbitrary number of processors and tasks. PubDate: 2021-07-16 DOI: 10.1007/s41133-021-00049-z
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Abstract: The nation has already had to contend with such a major public health issue when this COVID-19 disease was propagated in March 2020. The corona's propagation does have an effect on all areas of people's lives. The hostile effect on education systems throughout all locations is indeed a diverse expansion impact. A complete shutdown keeps adding fresh problems for students to learn and also for educators to effectively manage the class, likely to result in the conversion of such an offline education system into a virtual class. This study investigated and described various online learning platforms, as well as teaching resource distribution technology and modern technologies used to ensure that students can study. Overall, this relates to channels that can be used to create a confidential environment in order to prevent copying in online tests. This also outlines so many difficulties in managing online teaching methods, like learners' perceptions of e-learning being more traumatic and having a negative impact on general wellbeing and social relationships. So, a technique has been proposed to provide learners with a superior online classroom teaching experience, allowing the online classroom to be as good as, if not better than, a single online classroom. This study focused on daily teaching methods that employ online learning supported by a machine teaching approach to provide an individual with a relevant stress-free solution. PubDate: 2021-06-08 DOI: 10.1007/s41133-021-00048-0
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Abstract: The professional capacity of preschool teachers is a decisive factor for the quality of child care and early education for children. However, many preschool teachers teaching in disadvantaged areas have not yet responded to the increasing demands of society and educational innovation. Therefore, developing educators’ competencies in disadvantaged areas is an important task in education renovation. This article investigates the status of the professional competencies of preschool teachers in disadvantaged and mountainous areas to find out the advantages and disadvantages in implementing educational innovation. The main method is the quantitative research method entailing observation, questionnaire investigation and in-depth interviews with 250 early childhood educators and 30 administrators. Research results have revealed that the professional capacity of teachers in mountainous areas has many limitations, especially the ability to apply information technology and develop their own professional skills. Besides, research also pointed out a number of factors affecting the professional capacity development of preschool teachers. This research result is the foundation for improving professional competencies for preschool teachers in educational innovation. PubDate: 2021-06-04 DOI: 10.1007/s41133-021-00047-1
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Abstract: The variability that occurs in spontaneous network communication has brought about increased attention in the area of study that is centered on analytical approaches and models aimed at addressing the shorter timescales conceivable with dynamic functional networks. As the shifts in functional connectivity have been immense in the quantification of task performance in the cognitive domain so has the usefulness in the clinical setting been predicted. More so, the analysis of dynamic functional connections can be of considerable clinical relevance as had been observed in the studies of pathologies such as schizophrenia, Alzheimer's disease mild cognitive impairment. The evaluation of dynamic functional connectivity is however far from being perfect. Though functional magnetic resonance, imaging which has been vastly employed in evaluating neural communication in the human brain, does not appear to be efficient in measuring neuronal dynamics, and this could be down to the variability in sampling, physiological, noise, and head motion that usually accompany fMRI. This is where EEG, despite its limited spatial resolution, has found significance owing to the delivery of temporal resolution which is higher in measuring the time-varying relationships feasible in the rhythmic patterns of neural activity. In this paper, we shall aim at reviewing the strides that have been made in the efforts to develop an effective technique for quantifying the transitions in functional connectivity that take place over specific timescales. PubDate: 2021-04-12 DOI: 10.1007/s41133-021-00046-2
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Abstract: Team games with a ball provide a great workout and are social activities, making them a great way to spend free time. Unfortunately, it can be challenging to play when the participants' level is not more or less equal. We use digital augmentation technology to promote collaboration during the game and soften the focus that is usually put exclusively on the physical skill levels. Using computer game elements (attack points, defense points, life points) enables us to create a unique mixed reality playing experience that is approachable and enjoyable to various people who have different playing skills. Therefore, it works for promoting physical activity among such players. When designing a fast-paced game with a real ball, taking place in the physical world and the virtual world, special considerations on tracking the game progress, information display, and player experience have to be taken. We describe the development of the augmented dodgeball game, which includes the systems required to play the game and player status display systems. We also present a user study, which consists of questionnaires, interviews, and player-observations. We found that players had a positive experience playing the developed game; they liked the game concept and saw it as a fun activity to do with friends. The players were also communicating and collaborating more during the augmented game. This research contributes to providing design insights specific to designing for augmented reality team games with the ball. PubDate: 2021-04-07 DOI: 10.1007/s41133-021-00045-3
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Abstract: Coronavirus disease 2019 (COVID-19) is a public health emergency and is of international concern. Till now, there is no effective pharmaceutical treatment available for this disease. This paper presents a multilayer neural network algorithm to calculate the risk factor of getting COVID-19 to the individual based on the symptoms described by World Health Organization. The aim of this study is to provide an approximate risk factor of getting COVID-19 to an individual that helps for further treatment. PubDate: 2021-03-09 DOI: 10.1007/s41133-021-00044-4
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Abstract: Today, technology has advanced tremendously that it is now being incorporated into the education sector for academic enhancement. Certain technologies like Artificial Intelligence, Machine Learning, Blockchain, Big data, Internet of Things, Augmented Reality, Cloud computing, etcetera changed the conventional education system making it a better platform for the growth of students. In this paper, we dissect the importance of two blooming technologies, Blockchain and Machine Learning, in the education field. Blockchain technology, having data immutability as one of its advantages, has been used in miscellaneous fields for security aspects. It can be used to securely store the degree or other achievement certificates. Such information would be added by the college or university to the blockchain, which can be accessed or shared by the student through the online CV with employers. This approach is secure as there is no need to worry about changes to the institution or the loss of data. Also, Machine learning with its fully capable learning algorithms is the breakthrough technology for future perspectives because it can accurately predict the future based on experience; hence, the incorporation of this technology in the educational field helps the student to make a strategy with the help of various algorithms. By doing such things, better outcomes should be made from present conditions. When the benefits of blockchain are combined with Machine Learning algorithms, we can get certain predictions beforehand and we can securely store the actual results, which is the proposed idea of this study. In this study, the emphasis is made on the impacts created by recent technologies in the educational field and review of various systems proposed by blockchain and machine learning technology and assumption is made for combining two technologies for the betterment of the educational field. PubDate: 2021-01-16 DOI: 10.1007/s41133-020-00039-7
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Abstract: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neuropsychiatric disorders in adolescence and adult, but the origin of this disorder is still under research. The focus of this paper is on classification of resting state functional magnetic resonance imaging (rs-fMRI) of ADHD and healthy controls using deep learning techniques. ADHD-200 dataset includes resting state rs-fMRI images of ADHD, and typically developing controls and deep learning-based techniques such as 2-dimensional convolutional neural network (CNN) algorithm and hybrid 2-dimensional convolutional neural network–long short-term memory (2D CNN–LSTM) were applied on this dataset for the classification of ADHD from typically developing controls. The proposed hybrid system evaluated on the basis of parameters, viz. accuracy, specificity, sensitivity, F1-score, and AUC. In comparison with existing methods, the proposed method achieved significant improvement in analyzing and detection of parameters. By incorporating techniques of deep learning with rs-fMRI, the results built up an adequate and intelligent model to comparatively diagnose ADHD from healthy controls. PubDate: 2021-01-12 DOI: 10.1007/s41133-020-00042-y
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Abstract: The film industry has begun to increase the frame rate of movies in order to enhance viewer's perception of visual smoothness. This decision is causing controversy, and it is exacerbated by the development of high frame rate technology for television. To address this issue, we investigated if higher (60 frames per second or fps) versus conventional lower frame rates (24 fps) influence viewing behaviour and preference. Observers (N = 30) were eye-tracked while they viewed pairs of identical movie clips that differed only in their frame rate. Results showed that individuals looked more frequently at the videos they preferred; however, many could not discriminate between the high and low rate clips. However, those individuals who could reliably discriminate between the two frames rates preferred the lower 24 fps clips. Our results provide empirical support to those who argue that the viewing quality of films at higher frame rates is compromised on 2D displays. PubDate: 2021-01-12 DOI: 10.1007/s41133-020-00040-0
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Abstract: As the number of patients with impaired nerves is continuously increasing day by day around the globe, rehabilitation training by the physical therapist is more time-consuming and less effective. From last two decades, many robot-based upper limb rehabilitation devices have been developed for physical therapy of the human upper limb by employing state-of-the-art technologies. Hence, there is a need for a comprehensive systematic analysis to understand the basic principle and working of rehabilitation devices. The devices are primarily classified as exoskeleton- and end-effector-oriented robotic devices depending on the alignment of the upper limb joints. The objective of the review is to investigate the functionality developments of the robot-based upper limb rehabilitation devices. In this work, a systematic analysis is being carried out depending on the factors such as compatible designs, control aspects, training modes, actuation methods, and clinical developments of the devices. Furthermore, a tabular comparison is presented for the above factors considering different types of robotic devices and the status of the developments. Finally, the scope of improvement is discussed by minimizing the potential gaps between design and prototyping establishments. This review will help the therapists, researchers, and manufacturers to augment the safety and cost-effective concerns for patients with upper limb impairments. PubDate: 2021-01-12 DOI: 10.1007/s41133-020-00043-x
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Abstract: In this paper, a one-dimensional ECG signal is decomposed as symmetry tree structure at each level using discrete wavelet transforms which outcomes from a larger quantity of insignificant coefficients. They are measured as zero amplitude value and represented as sparse datasets that improve the compression rate, and Huffman coding helps to represent the signal with low bit rate data. These results compressed data codes of large ECG time-series datasets of the signal. Here, different wavelet filters are evaluated for compression based on sparse data from wavelet decomposition. The performance of an algorithm in terms of compression is 43.52% and 42.8% with a 99.9% correlation between original and recovered signals from compressed ECG data for the MIT-BIH arrhythmia and compression dataset, respectively. Further, heart rate variability (HRV) analysis with correlation of R-R intervals in between original and reconstructed ECG signals validates the reconstruction as well as sensitivity of compression technique toward data accuracy. PubDate: 2021-01-12 DOI: 10.1007/s41133-020-00041-z
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