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Hybrid project management has gained significant popularity in recent years. Combining multiple project management methodologies, hybrid project management incorporates advantages from other, more-established methodologies. While the term hybrid refers to any new methodology created from combining two or more existing methodologies, in practice, the hybrid approach generally unites Agile methods with the more-traditional Waterfall model. Hybrid project management follows the structure from traditional project management and flexibility from agile project management. Combining both traditional and agile techniques resolve many situations during project development and can achieve success. This article explains the hybrid project management approach and its advantages over Waterfall and Agile methods.

The intrinsic connection between play and technology is becoming increasingly significant in early years education. This is especially true for children who belongs to the Generation Alpha. Gen Alpha generation is greatly influenced by technology, and they seek personalized and interactive education delivered through technology. Game-based learning offers a new way of learning, which conforms to the habits and interests of Gen Alpha. This paper provides an overview of a custom developed educational game and its applicability in the teaching of natural science and also investigates the outcomes obtained through game-based learning, in primary school children.

Digital media nowadays create new ways to teach, learn and interact. Prospective teachers are using technology to transfer their knowledge in a more interesting and accessible way, while at the same time making students more engaged and motivated. AR, as a technology, has the potential to be an effective learning tool in formal and informal learning environments. This paper, present the development of the AR application for geometry teaching for primary school students. By augmenting the student’s vision we enhance their ability to visualize what they are trying to learn. With the developed AR application, they can learn to make the differences between 2D and 3D shapes and how they relate to one another. The designed application is built using a WebAR-based approach and marker-based detection technology.

With the increased aging population, and declined support from the families, societies will need new tools to ensure the well-being of the elderly. Many of them would prefer living at home, but they will need help and assistance from someone. Technological innovations in the field of robotic systems can be used to enable independent living, to prolong the life of the elderly in their familiar home environments, to maintain the social connections by reducing social isolation and to improve the quality of life in general. In this paper, we present the design and validation of a low-cost mobile robot system that can assist elderly and professional caregivers in everyday activities. The robot structure and its control objectives are described in detail. The developed assistive telepresence robot was tested in simulation and experimentally. On field experiments were conducted in real environment, with potential end users, which is a major advantage of this study. The results of the evaluation were very satisfactory and have shown that participants can operate the robot safely and efficiently. The participants were very satisfied with the performance and features of the robot.

Agriculture has faced multiple challenges throughout 20th and 21st century. Population growth means more mouths to feed, but, unfortunately, smaller rural labor force, which stimulated people to develop more efficient and sustainable production methods in order to adapt plants to different climate changes and make them more resistible to diseases. Accurate and rapid diagnosis of plant diseases has shown to play an important role in improving the quality and quantity of agriculture crops. In this article we are proposing an automated disease detection and classification service for grape leaves using traditional image processing and AI techniques using Amazon Sage Maker service. We are focusing our work on detecting three different diseases of grape leaves: black rot, black measles, and leaf blight disease. The results of our study are presented and discussed in the following.

Stress has become a significant cause for many diseases in the modern society, such as high blood pressure, atherosclerosis, heart disease, obesity, diabetes, insomnia etc. Moreover, Covid-19 pandemic negatively affects people’s mental health, increasing depression and anxiety. This raised the question of whether automatic stress detection and recognition systems can be developed and used in everyday life. In this review study, we will examine the recent works on stress recognition systems by reviewing the techniques and methods used. Only studies involving human participants were taken into consideration, as no such analysis has been made so far. By providing a comprehensive review of the state-of-the-art, we would like to encourage other researchers to take an active participation in the field of stress research as well as to explore the benefits and opportunities offered by stress recognition systems.

This paper presents the application of the MQTT protocol in the communication with telepresence robots. They are used for various purposes in education, medicine, nursing homes, etc. To ensure a good user experience, sending data from applications to telepresence robots needs to be fast and reliable. The MQTT protocol is open, fast and can be easily implemented. Its features make it ideal for data transmission in the communication with telepresence robots.

Nowadays there is an increasing demand of creating accurate 3D models of various object. Visualization of these models allows the user to get the realistic impression of the object. 3D visualization has many applications in the areas of architecture, engineering, medicine, tourism, education etc. This paper presents the creation of 3D objects using photogrammetry technique. During our research, we have generated 3D model of various small objects. However, the focus was on generating 3D model of a human body, using Meshroom software. The lessons learned, during the data acquisition, as well as 3D reconstruction are presented in this study.

Ambient Assisted Living environments use different sensors and actuators to enable their endusers to live in their preferred environments. Unlike smart homes, where a target audience is usually a family unit, standard Ambient Assisted Living end users are care receivers and care providers. This article describes an approach based on the fog computing paradigm to detect sleep apnea in an Ambient Assisted Living context unobtrusively. The edge nodes process and detect local activities of daily living events and have direct control of the local environment. The fog nodes are used to further process and transmit data. The cloud is used for more complex and anonymous data computation. This research shows that sensors, which are unobtrusive and do not interfere with users' daily routines, can be successfully used for pattern observation.

In contemporary education, especially in natural and technical sciences courses, it is important for students to gain practical experience through the use of laboratory exercises. Although theory classes were conducted online during the COVID-19 pandemic, educational institutions found it difficult to complete the laboratory experiments. With the increasing availability, sophistication and power of web-based tools and computer simulations, a growing number of virtual laboratories are developed and published. In this study we are proposing an architecture for a virtual laboratory designed and developed as a progressive web application (PWA). The concept of the PWA together with its advantages are also explained in this paper.

Stress is a major problem of the modern society, as it is the cause of many health problems and huge economic losses in companies. It is a growing issue and it has become an inescapable part of our daily lives. The early detection of stress will decrease the damage it costs and prevents it from being chronic. The scientific community is aware of this and much progress has been done in the last years towards the development of an automatic stress measuring system. In this review study, we will examine the recent works on stress detection looking over the measurements executed along different modalities. Only studies involving human participants were taken into account, as no such analysis has been made so far. The performed analysis gives us hints about future development of appropriate methods for automatic stress recognition systems.

The usage of telepresence robots can have a huge positive impact on education. They can provide remote access to the classrooms. This would be really helpful for students who are absent from school. It was especially noticeable during this period of the Covid-19 pandemic. This technology can complement the online teaching with displaying classroom boards, performing laboratory exercises that require better view and movement, and so on. Here, we show the usage of telepresence robots in education and cases when robots can be used for remote learning. The main goal of this paper is to provide a comprehensive review of studies related to the application of telepresence robots in education in the last 4 years.

The COVID-19 pandemic has a severe impact on education all over the world. The closures of educational institutions affected teaching and learning processes and had an impact on students’ motivation and engagement. In this context, AR/VR technology can provide assistance to students and support for educators. This paper presents a review of literature on augmented and virtual reality in educational settings in the last 5 years. In total 23 research studies were included in the study. Target group, field of education, reported advantages, limitations and concerns have been investigated and reported. The findings showed that students’ learning can benefit both technologies by increasing engagement, motivation, collaboration and learning achievement.

The COVID-19 pandemic has forced the universities to close face-to-face (f2f) education and move all activities online. Online learning has become essential for students to continue their education. But the shift from traditional f2f to online learning, has raised some questions about the quality and success of the learning process. This study developed an integrated model based on both the TAM and ISS model which is best suited for investigating the impact of online learning systems on students' satisfaction and student outcomes. The results showed that good system quality and information quality will motivate the students to use the online learning system more actively. On the other hand, the system should be ease of use, and students should perceive it as useful for fulfilling their tasks. Such system will provide high level of satisfaction for students, which in turn will lead to positive student outcomes.

In an effort to prevent the transmission of COVID-19, governments around the world have closed universities and schools. Educational institutions needed to respond quickly and adapt to the new situation. Many of them have applied an online teaching model, but there were also some that have applied the flipped classroom model. This study investigates the effects of online and flipped classroom model, in 7th grade students. Empirical research was conducted in three elementary schools in Republic of North Macedonia during corona pandemic. The study lasted for two months and involved 167 participants. The results showed that both methods have a positive impact on the students’ learning motivation, attitudes, engagement, interaction, collaboration and general satisfaction. These findings suggest that the aforementioned teaching methods can facilitate the delivery of theoretical knowledge not only during emergencies (such as Covid-19 pandemic), but also during normal educational process.

Introduction and Objectives. Sleep apnea is medical condition that affects about 4% of the population and may cause various medical complications such as fatigue, hearth problems and elevated blood pressure, diabetes type II, metabolic syndrome and others. Nowadays, there is a huge demand for technology solutions and new care models that will help in understanding patient’s needs and characteristics, facilitating treatment adherence and shared-decision making. Methods. This paper proposes a system and methodology based on fog computing paradigm to unobtrusively detect sleep apnea and to enable patients with sleep apnea and health care providers to be active participants and collaborate in chronic disease management. The methodology is based on findings that sleep apnea is accompanied by body or leg movement. Therefore, the proposed system uses non-invasive PIR and piezoelectric-based sensors placed under the mattress. Data processing and sleep apnea detection is performed by machine learning algorithms on the edge nodes. Anonymized data are also sent to the cloud for further evaluation and assessment by medical experts and are used for model improvement. Results. In order to evaluate the proposed system and methodology, an experiment for continuous monitoring of a single person over a period of 8 hours was conducted. Signals obtained from PIR and bed sensors were segmented and signal features were extracted. Depending on the window length 250 to 270 features in total were generated, and then reduced to 32 by discarding those with low importance or high data drift sensitivity. Four machine learning algorithms for sleep apnea detection were applied on the obtained feature set and the results were compared. The accuracy of the different classifiers based on different sliding window configurations was analyzed. It was found that, as windows length increases, the accuracy increases too. When using windows of 5 seconds the accuracy was 80%, when the window length was increased to 10 seconds, the accuracy raised to around 90%, and for 20 seconds windows, the accuracy further improved to above 95%. Conclusions. The use of novel technology, like unobtrusive sensors and fog computing, can improve the patient-centered care for patients with sleep apnea. The flexibility of the fog architecture enables better placement of computing and network resources. The fact that accuracy is increasing for larger window length is an important discovery. It can be used for design of a system that makes several predictions at the same time. In this proof the concept of the proposed system architecture we have conducted experiment with only 3 patients, which has to be increased.

Mobile phones, especially Android based ones, are an important part of human everyday lives. Today smartphones have incorporated sensors that can be used for recognition of human activities. In recent years, Human Activity Recognition (HAR) through smart phones became a well-known field of research. Activity recognition is used in applications in healthcare, smart environment, country security,and entertainment. In this paper we are giving a survey of applications and implemented systems for high level activity recognition using android smart phone sensors.

One of the most popular and reliable distance learning technologies is videoconferencing. Videoconferencing uses audio and video telecommunications to bring people at different sites together when travel is not an option. Videoconferencing is used to bridge the distance gap between various sites, in order to stimulate collaboration, support and enhance student or staff communication and to enable flexible quality learning and accessibility with minimal costs. The aim of this study is to evaluate the potentials of videoconferencing distance learning and to understand student perceptions and their satisfaction with this kind of education. Also, a comparation of students’ attitude between videoconferencing distance learning and traditional classroom environment learning is made. The results showed that this form of distance education can be accepted equally good as traditional methods of teaching.

Recent demographic trends in most countries show that the percentage of elderly is rapidly increasing. Ageing society is demanding technological solutions that will help to overcome these issues. Robotic assistants used in the elderly and the disabled care has emerged as a potential solution. This study presents a low-cost assistive telepresence robot system for facilitating the health care of the elderly, and improving the quality of life of the elderly and handicapped.

The term educational robot is usually associated with systems that can be classified as robots and that are used in the process of education with a final goal to learn with, form or about it. In the last decades,due to the technological and pedagogical advancements as well as low prices of the educational robots, their usage is dramatically increased. This is also reflected in the increasing number of articles that are published in the last decades. Various education robots have different features from architectural, structural and functional point of view. Therefore, the main goal of this paper is to present a literature review of the educational robots and their characteristics.

Stock prediction is attracting a lot of attention, mainly due the financial gains that may be obtained. However, this is very difficult task and it is very challenging problem that intrigues multiple scientific disciplines such as finance, computer sciences as well as engineering and mathematics. There are many approaches and theories regarding stock movement prediction. In the era of social network and big data, as well as huge speed of news spreading, probably the most interesting are those theories based on social media sentiment analysis. This approach is taking as input non quantifiable data such as financial news articles, social networks posts, or tweeter messages, about a company and predicting its future stock trend with news sentiment classification. Sentiment classification is performed using artificial intelligence algorithms. Main aim of this paper is to give a comprehensive review of current state of the art related to stock movement prediction based on social media sentiment analysis with an emphasis on a Twitter platform.