Proceedings of Seventh International Congress on Information and Communication Technology

The visually impaired people’s ultimate goal is to walk freely and comfortability indoors and outdoors. They fear to hit into steps, stones, or uneven floor. Wearable technologies whether image-based or sensors-based provide a solution. However, image-based technologies have issues of detecting an obstacle accurately with no delay. The sensors-based technologies have limitations of the data quality. Therefore, the sensors need to be fitted closer to the obstacles to capture the data, and they require filter to remove the noise data. This work presents a novel wearable, simple, low-cost, user-friendly device. It supports visually impaired to walk in different areas. The system provides accurate data to support visually impaired detecting the obstacles surround them, i.e., front, left, right, and back. It works in multiple environments. The shoes will help them to walk indoors and avoid obstacles on the floor. In outdoors, like pedestrian road, parks, or forests, it will detect pit holes and pumps. The proposed system consists of three parts. The first part, which is a low-cost Internet of things (IoT) system, attaches sensors to shoes to collect data about the context. The second part works like a filter to remove the noise data. Four anomaly machine learning algorithms are applied to choose the most accurate—K-NN, SVM, decision tree, and random forest. The third part is a risk level assessment using fuzzy rules. The results of comparing the anomaly algorithms accuracy show that the random forest is 0.99 with a std. dev ± 0.01. The fuzzy rules defined the three different ranges for the levels of risk.

The lack of maximizing the technology in today’s historical tourism is the main concern for the success in this area. Previous studies have proven the success rate of using the current innovation and trends in general tourism sector in which they help to increase the visibility of the tourist attractions and number of visitors in one tourist spot. Terengganu, one of the states in Malaysia, is one of the best tourist attractions in the country. It is well-known for its popular islands, namely Redang Island, Lang Tengah Island, Kapas Island and Tenggol Island. Yet, many local and international tourists are not aware of the high potential historical island called Bidong Island. Not many tourists go there. Many come due to the informal communication such as from mouth to mouth with little formal promotions and marketing activities. Bidong Island was used as Vietnamese Boat People (VBP) refugee camp facilities from 1978 to 1991. Currently, this lonely island has started to show her visibility. This is a good sign of the state’s domestic tourism activity that will contribute towards the Gross Domestic Profits in tourism sector. Therefore, the aim of the present study is to provide an overview of database development on the Bidong Island for the Vietnamese Boat People former campsite facilities. A field observation study is proposed for the actual study.

Conditional statements are an important part of procedural knowledge as they determine the decision points in the control flow. In order to bootstrap conversation bots and automation tools automatically from natural language procedure documents, it is important to be able to classify the conditional statements accurately and separate the condition and effect correctly. This paper aims at exploring three different techniques to classify and analyze conditional statements and discusses the advantages and drawbacks of each of them. This paper also aims at understanding the drawbacks of the three techniques and overcoming them by building models with better performance.

The robotics industry is rapidly evolving driven by better and cheaper computer chips and affordable 3D printing technologies. All these aspects are a catalyst that helps in building new concepts and prototypes at a lower cost, and easier and faster than ever before. This paper presents the entire process of building an articulated robotic arm with 6 degrees of freedom (DOF) and a gripper, all controlled from a designed Arduino command center. The project will go through the 3D designing process and the selection of different actuators. Then, an optimization of the hardware options for controlling the motors and the software to operate the robotic arm is presented. Finally, advantages and drawbacks of the proposed architecture are discussed.

An improved method for the sizing of a stand-alone photovoltaic system in Ngoundiane site is investigated in this paper. From the numerical model, proposed in Sadio et al. (2018), the optimal combination between PV and battery capacities is found out, by taking into account the annual monthly irradiation variation and the simultaneity coefficient. So, all average monthly values of solar irradiation are considered instead of one average value and the maximal value of load demand is used and determined from energy balance and consumers behavior. After generating all PV/batteries combinations for each value of solar irradiation, the best configuration is chosen by using the TLCC. It is shown that the PV and battery capacities decrease by 25.13 and 88.39%, respectively, when compared to the intuitive method. However, the obtained LPSP, assessed to 0.3, is considered as high and is due to the lack of complete data.

Requirements analysis and engineering is a vital phase in any software project’s lifecycle, and the success or failure during this phase mainly determines the entire project’s outcome. Recently the challenges incredibly increased in the software industry either in the technology, project management, or requirements engineering, drive-by leveraging the diversity of the tremendous tools and techniques that aim to avoid failure in requirements engineering and analysis. However, the tools and techniques will not always tackle the most significant challenge: validate, align, and confirm the needs and outcomes accordingly to both parties, the customer and the vendor, ensuring the confirmation and validation process is trustworthy. A unique process that ensures the requirements reliability is by introducing a framework that will authenticate the requirements analytics and engineering by Ethereum blockchain technology smart contract for the customers and vendor to guarantee substantial agreement on all requirements aspects through the project’s lifecycle.

We simulated the response of a surface with a circular relief on a semiconductor wafer constructed using self-affine transformations to the effect of an incident electromagnetic wave. The study assumed that the main mechanism leading to the reaction of the plate to incident radiation is electric polarization, which, for example, is the basis for the functioning of a number of electronic components, such as a MOS FET or a CCD. Since silicon belongs to polarizable materials, a spatial separation of charges occurs in a changing electric field in the volume of a silicon crystal in accordance with the law of change of field. If a silicon wafer is used, on one of the surfaces of which a certain relief is created, then the distribution of charges under the relief will be uneven in space in accordance with the pattern of this relief.

The article presents data on the effectiveness of computer analysis of cardiac activity using the method scatter mapping of ECG. Based on the assessment of the integral index of the myocardium and other parameters in different age groups, it is proved that the method allows early detection of signs of cardiovascular diseases.

Traffic disturbance in urban cities challenges the most advanced traffic signal control systems (TSCS). The challenge is mainly related to the capability of TSCS to ensure a quick detection and to suggest suitable decisions. Neural network has shown great potential in predicting traffic disturbance. In addition, smart clustering could be beneficial to ensure fast disturbance reaction while TSCS are providing control decisions. Moreover, the immune network approach has succeeded in controlling interrupted intersections. Motivated by these assumptions, we propose in this paper a disturbance mining approach based on the occurrence of traffic disturbances to ensure optimal signals control that minimizes traffic delay. Initially, the queue delay is calculated based on mutual information of different traffic scenarios. At that point, within the maximum traffic delay constraint, the feedforward neural network is considered to find the optimal traffic delay and maximize traffic fluidity. As a result, disturbances and related control decisions are clustered based on the calculated traffic delay. Our approach helped the immune network control system (INCS) by prompting it with faster reaction and lower traffic delay compared to its classical version.

As schools and universities being shut across the world during the COVID-19 pandemic, a timely call for research on effective self-online learning approaches is emerged. To identify gaps in the field, this paper analyzes sample of existing personalized self-learning platform in terms of features and limitations. The paper highlights key review studies, underlining the research trends, potentials, and challenges of the use of advanced ICT techniques, in personalized education over a decade. Aiming to identify challenges, opportunities, and new trends in this research area, focus group sessions were conducted with educational technology experts from Egypt, to discuss opportunities and challenges of ICT-based personalized self-learning tools in the Egyptian education context in the time of pandemic. The thematic analysis of the collected qualitative data suggested some generic aspects for future trends, such as: Intelligent Tutoring, Content Generation, User Control, Career Path Advising, and Integration with web2.0. However, results of this research uncover the importance of additional aspects, which seems specific for developing countries context, such as: ICT Literacy and Digital Equity. Other Cultural Consideration were reveals, such as Language and Collectivism considerations. The results of this study revealed a collection of best practices, potential challenges, current limitations, and future scope for the use of ICT in personalized self-learning platforms in a developing country context. These results might be useful for education platform developers, and education policy makers, for an effective self-learning experience in the time of crisis. These results would provide a baseline for future research in the domain.

Weather, pests and various other factors cause a lot of crop yield to decrease. Crop losses are more in countries which are tropical and, knowledge and investments in crop health management is very less (Sufola Das Chagas Silva Araujo, Meenakshi Sundaram Karuppaswamy. Comparative Analysis of K-Means, K-Nearest Neighbor Segmentation Techniques, IEEE (2016) [15]). Manual detection are taxing as our eyes have to perceive the indications of the disease based on shape and color. A model of Guntur-4 variety of chili plants was developed that can classify particular diseases. There has been made use of multiple models to train and detect such diseases to figure out which model is more accurate. Each model uses object detection techniques to recognize certain features on leaves and categorize them into different diseases. Different infected leaf images of whitefly, Yellowing, Curled, and Healthy were collected and tested on different models built to try and find which model is best suited for this particular data set. Time complexity, accuracy, and resource usage were computed to build the best automatic leaf image disease detection model.

Inspired by the famous Star Wars movie, we decide a moving and interactive robot which is similar to the BB8 character of the sequel. The proposed system is based on a low-cost set of components allowing to control the device wirelessly by means of a mobile app. The robot incorporates an mp2 module and a visual interactive system, and it could be used for human–robot interactive applications.

Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature, and its powerful results. In this paper, we study a number of reinforcement learning algorithms, ranging from asynchronous q-learning to deep reinforcement learning. We focus on the improvements they provide over standard reinforcement learning algorithms, as well as the impact of initial starting conditions on the performance of a reinforcement learning agent.

In this paper, a new bridgeless single-phase AC–DC power factor corrector is presented. The proposed scheme is based on the interleaved topology of buck–boost converter without a bridge rectifier at the input stage for AC–DC conversion. The required AC–DC conversion is done with the help of the interleaved topology of the buck–boost converter. The absence of rectifier bridge at the input side of the circuit means the reduced numbers of the diodes in the circuit which results in less conduction and power losses. The advantages of such topology includes less power losses in the circuit, low voltage stress on the switch, and improved efficiency of the overall system. Two circuits are used in this paper which are connected in parallel to each other, each circuit comprising the two interleaved boost converters for AC–DC conversion to reduce the power losses and to provide an alternative approach for efficiency improvement. This Power Factor Correction PFC converter will control the output voltage to provide a regulated DC voltage at the user end, and at the same time, it will draw a sinusoidal input current from the power supply source to maintain the power factor of the system. MATLAB is used as a software for results, and simulation results are performed to present the feasibility of the proposed technique.

Kubernetes is a container orchestration system, which is used in production-ready platform as a service such as OpenShift. To manage and provision storage for application, the container storage interface (CSI) exists in container orchestration systems. The article contains comparison analyze of bare-metal CSI implementations that shows advantages and disadvantages of existing CSI implementations, based on which bare-metal implementation is thought to be a most powerful one, but not free from a gap for improvement. So, there is a need to extend CSI for bare-metal storage provisioning to avoid virtualization and cloud overhead and minimize manual storage management operations. The paper considers bare-metal CSI extension for automation of local disk management as well including ephemeral volume support.

Structural realizations of the digital ion selective transducers, constructed on different rutotom principles are proposed: ADC of the successive approximation, time-pulse conversion and voltage to frequency conversion. Corresponding conversion equations are obtained, their static characteristics are constructed, measurements errors, emerging as a result of using one or another construction principles are investigated. As a result of the research, it was found that in order to improve the measurement accuracy, it is advisable to introduce an additional measuring temperature channel. To ensure high accuracy of ion activity measurement in the lower measurement range of 0.3 pX and to take into account the temperature deviation by 1 degree Celsius, it is necessary to construct a temperature measuring channel with a relative error of 0.05%.

The use of augmented reality (AR) is widely accepted as a feasible training, planning, and prototyping tool. Unlike virtual reality (VR), which implies a complete immersion in a virtual world, AR adds digital elements to a live view by using a headset or camera on a smartphone. The ability to project digital elements into the physical world, combined with the Federal Food and Drug Administration (FDA) approval to use the Microsoft HoloLens in surgical procedures, presents a unique opportunity to explore and develop novel neurosurgical and orthopedic surgery training applications of AR, specifically in spine surgery. The potential of AR in spine surgery training lies in its ability to project CT-generated 3D models of the simulated patient’s bony anatomy with overlaid pre-planned screw trajectories, thus allowing learners to practice with real-time guidance. As AR technologies become more mature, numerous research studies have identified AR's potential detriments to learning, including distraction and increased extraneous cognitive load. In this paper, we present our work on evaluating the effect of the presence of a Microsoft HoloLens 1 AR headset on extraneous cognitive load and on task performance during a simulated surgical procedure. A matched crossover trial design was used in which a combined group of 22 neurosurgery and orthopedic surgery residents, ranging in their training from the second postgraduate year (PGY-2) to chief resident (PGY-7 for neurosurgery and PGY-5 for orthopedic surgery, respectively. Participants were asked to place cervical lateral mass screws in a standardized, 3D-printed cervical spine with and without the Microsoft HoloLens 1 headset worn. Lateral mass screws were placed bilaterally at C4 to C6, with six cervical lateral mass screws placed by each participant in each trial, totaling 12 total screws placed. Overall time to drill six pilot holes, time for placement of each individual screw, pilot hole proximity to a predetermined entry point as defined by the Magerl method, and the presence of medial/lateral breaches were assessed and used as surrogate measures of mental taxation. The SURG-TLX questionnaire, a validated measure of extraneous cognitive load, was also used to compare cognitive strain of the task with and without the HoloLens 1.

The present paper proposes the use of platforms for modeling of communication networks, in particular modeling of IP networks. The study of IP networks, by using modeled IP networks, has many advantages, which are described in the work. The study of IP networks through modeled ones is shown in practice, through the GNS3 platform. Different capabilities of the proposed platform are presented when using modeled IP networks.

This research study explores this original phenomenon for proposing a new concept that will act as an overarching descriptor of innovation types: idea, object and behaviour. This proposed concept, relating to intangible innovation, will explain the sequence within one or many connected intangible activities that provide novelty to its end user relative to previous activities and practices. Using a design science research approach, the study comprises two goals: (a) identifying opportunities and issues to measure intangible inputs to the innovation and (b) proposing a framework for extending the existing innovation theories that to better capture intangible end-user innovation and its diffusion insights in their online environment across nations.

This paper addresses formalisation and computational modelling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end a second-order adaptive self-modelling network model is introduced and an example simulation for the case study is discussed.

Consider a finite field $$\mathbb {F}_q$$ F q having cardinality $$q=p^n$$ q = p n , for an odd prime p and a natural number n. In this chapter, we consider $$S=\mathbb {F}_q+v\mathbb {F}_q$$ S = F q + v F q , where $$v^2=1$$ v 2 = 1 and obtain conditions for double circulant (DC) codes over S to be LCD and self-dual. Using these conditions, we enumerate LCD and self-dual DC codes over S of length 2m. Further, we obtain some examples of DC codes by defining a Gray map $$\psi $$ ψ from the ring $$S^m$$ S m to $$\mathbb {F}_q^{2m}$$ F q 2 m . Finally, some bounds are presented on the relative distances of the families of LCD and self-dual DC codes.

The article presents the study of the relationship between metabolic and regulatory disorders which is one of the most promising areas of research, especially in patients with systemic disorders such as diabetes mellitus. Modern hardware and computer technologies make it possible, at a fairly subtle level, to assess the effect of infringement of systemic regulatory mechanisms on morphological changes at the level of the vascular bed. We come to the explanation of morphological changes in the body of patients from the organism level of regulation through the methods of mathematical analysis and computer modeling.

ICT is critical to the well-being of any modern enterprise and should therefore also be governed and managed appropriately. As such, it is imperative that corporate governance of ICT (CGICT) be implemented and supported by the Board. It is quite clear that the Board remains ultimately accountable for CGICT. Consequently, the Board should have peace of mind regarding its fiduciary duties on CGICT. In other words, the Board must be provided with assurance on the overall efforts of CGICT within the enterprise, in order to provide stakeholder value. Therefore, the focus of this paper is twofold in an attempt to address the relationship between assurance and CGICT. Firstly, this paper will identify important principles and criteria from an assurance point of view, which can ideally be used to assist assurance professionals with understanding the underlying factors of assurance and its relationship with CGICT. Secondly, the principles and criteria identified in this paper can be used to develop a framework for assurance on CGICT, so as to formalise a process for providing the Board with peace of mind on their efforts towards good CGICT.

COVID-19 pandemic gives a very significant impact on many aspects of life in a global scale. In Indonesia, as in many countries in the world, public activities involving many people are restricted and regulated to prevent the virus’ broader spread. Technology which still enables activities to be conducted individually and remotely is needed, because during the pandemic, they can only be done very limitedly, if not stopped. Therefore, people are utilizing telecommunications to assist activities during the COVID-19 pandemic. Several innovations involving telecommunication technology are applied, e.g., distance learning (e-learning), online business (e-commerce), and digital health services (e-health). However, there are many challenges in using telecommunication technology during a pandemic in developing countries, especially in Indonesia. This article examines (1) the role of telecommunication technology in education, economy, and health sectors, (2) the challenges, and (3) how telecommunication technology as the solution can be more effective in future. This article provides an outlook and in-depth overview of how the impact of recent technology, especially telecommunication, played a vital role in the three crucial sectors in mid-low countries like Indonesia during the COVID-19 outbreak.

Ever since Malaysian Government imposed movement control order (MCO) due to covid-19, there is a surge in online purchase. Covid-19 pandemic has affected shopping and purchasing behavior of consumers. It is in the view that shifting consumer habits are changing Malaysia’s future for retail. Such scenario brought forth by covid-19 pandemic to online purchase, its growth and future. Consumers’ buying behavior has been the interest in this research. This research intends to determine the factors of online purchase during the pandemic. It is to identify the common problems encountered by online consumers. It also intends to find out whether it is a temporary behavioral change of the consumer because of the benefits of online shopping during this covid-19 pandemic. Technology acceptance model and the theory of planned behavior used are to study the factors of online purchase and consumers’ behavior while adopting online purchase during covid-19 pandemic period and in the future. The subjects for this research are 150 volunteering adults, aged between 20 and 30 years old with at least one-time experience of online purchase during covid-19 pandemic. Google Form has been used, and snowballing is used to reach the subjects. This research has concluded that online purchase is resulting in a positive way. Online sales and promotion are the main reason for the surge of online purchase. Besides, consumers also want to avoid crowds during covid-19. More importantly is that most respondents will continue to purchase online even after the pandemic despite problems encountered in the process.

In this work, we study a new image annotation task named Extractive Tags Summarization (ETS). The goal is to extract important tags from the context lying in an image and its corresponding tags. We adjust some state-of-the-art deep learning models to utilize both visual and textual information. Our proposed solution consists of different widely used blocks like convolutional and self-attention layers, together with a novel idea of combining auxiliary loss functions and the gating mechanism to glue and elevate these fundamental components and form a unified architecture. Besides, we introduce a simple but effective data augmentation technique dedicated to alleviate the effect of outliers on the final results. Last but not least, we explore a self-supervised pre-training strategy to further boost the performance of the model by making use of the abundant amount of available unlabeled data. Our model shows the good results as 90% $$F_{1}$$ F 1 score on the public NUS-WIDE benchmark, and 50% $$F_{1}$$ F 1 score on a noisy large-scale real-world private dataset. Source code for reproducing the experiments is publicly available at: https://github.com/pixta-dev/labteam .

This paper presents a mobile outdoor augmented reality application that enables a realistic three-dimensional on-site visualization of planned wind turbines in their surroundings. For this purpose, the requirements for such an application are presented, which were obtained from the discussion with representatives of the relevant target groups. In addition, the essential aspects of the graphical user interface and the technical concept (architecture) as well as the implementation and evaluation of the application are presented.

The SAVE system is an incorporated solution whose main purpose is to support elder people in staying in their familiar surroundings for as long as possible, whilst still be safe and optimally cared for. From an architectural point of view, the SAVE system is designed on the model of microservices-based architecture, which exposes a Representational State Transfer (REST) communication interface, making use of the HTTP/S protocol. The user interfaces (the frontend) of the web applications are developed using the Angular framework and the data is retrieved using the REST APIs of the backend (built on the Spring Boot framework). The messages they exchange with each other are formalized using the JavaScript Object Notation (JSON) format. To ensure data persistence, a relational database management system (Oracle’s MySQL) was employed, providing a more efficient solution for organizing information in the system. We created a web application—SAVE Admin Centre—to manage data efficiently and securely within the SAVE system, which consists of several modules: Dashboard, RO Data, Data Kit, Kits, Devices, Device Types and Users. The data collection sub-system was design to be able to connect to different data sources through data adapters. The security of the SAVE system comes from the use of public key infrastructure (PKI), through HTTPS, whilst using standard protocols to ensure maximum interoperability.

Today the most widely used technology is GPS, which does not perform optimally in buildings because it does not have the necessary accuracy. For these cases Bluetooth technology is the most recommended to be used as it provides optimal performance. This paper is focused on comprehensively examining papers published between the years 2016–2020. In the present research, articles were collected from databases such as IEEE Xplore, Scopus, IOPScience, Ebsco and Dialnet systematizing 68 articles between the years 2016–2021.

Due to the pandemic that emerged at the end of 2019 (COVID-19), humanity is going through a great crisis and one of the most affected sectors is work due to the large amount of unemployment that it generated, thus resulting in a very low rate of employability. That is why the present study aims to find the determining factors to increase employability. The study conducted is a review of scientific literature, which collects 61 articles from the databases: IEEE Xplore, Scopus, Proquest, Ebsco, and other databases.

Over the Covid-19 pandemic, physical education has been shifted to online education internationally. Parents, students and teachers faced numerous obstacles from the sudden transition of face-to-face classes to online learning. In this research, three categories of respondents are being studied who are students, teachers and parents, to find out the acceptance and challenges of online learning among them. The research is conducted to determine how online classes have affected students, teachers and parents mentally and physically. Quantitative survey questionnaire has been used to collect all the data from each category. The finding indicates, people still prefer physical classes despites all the conveniences of online learning. The result also indicates the paramount of face-to-face communication in human communication, particularly education. Future research can look more in depth into how the demographic and courses students enrolled affect their acceptance and challenges toward online education, and, what are the improvements to better the interaction via online classes. The study is significant for government and the involved parties to endeavor more challenges in the unknown future.

Asking questions relates to the cognitive ability of language comprehension and context understanding. For that reason, question generation is a challenging topic in Natural Language Understanding. In this work, we propose a task called “question generation with masked target answer,” which emphasizes asking questions from text passages without providing a target answer. Compared to other related question generation tasks, our task demands rigorous language comprehension and closely resembles the question asking ability of humans. We then propose various sequence to sequence-based models leveraging additional information about the text, such as its part of speech and named entity recognition(NER) tags. Results show that the proposed models perform on par with other related question generation tasks, despite lacking the key answer phrase.

Service discovery combined with security usually plays a role in defining if an application shall be provided in EDGE/FOG or cloud. Most existing solutions focus on the ability of the infrastructure itself to distribute the clients, but it usually raises the question to identify a trustworthy server. We also want to explore the capabilities of mobile devices for service discovery, especially in terms of location awareness, which will aid us to identify the best suitable FOG/EDGE server. We shift the paradigm of searching an edge-instance to addressing an edge based on the location of the mobile device. To do so, we use DNS, and by using sub-domains, we address a location and an app and will use the identified instance for further user processing.

Agro-allied supply chain management (ASCM) presents unique issues ranging from dependence on climate, the engagement of many actors, to the bulk of the personnel’s lack of literacy, all of which need the use of communication and information technologies (IT). The purpose of the research is to present technologies centered on the Internet of things (IoT) and describe their applicability within the agro-allied industrial supply chain of a developing nation like Nigeria. The study recognized IoT-developed technologies in the framework of ASCM based on literature. In line with the study findings, the application of IoT in the food and agro-allied sector in Nigeria may help boost the growth of the agro-allied supply chain through significant reduction of waste as well as serving users’ needs in a long-term manner. In a developing country like Nigeria, IoT-based technology can integrate multiple ASCM tasks in an industrial setting.

In this paper, we have realized a system to maintain good work performance by conducting the following three experiments. (1) Created a machine learning model with EEG as the objective variable and motion information as the explanatory variable. After that, we established a method for estimating the level of concentration based on movement information. (2) When work performance deteriorates, we intentionally change the indoor environment to verify whether it is effective as a method to recover work performance. (3) While estimating work performance using the model created in No. 1, verify whether work performance recovered from the estimated value by using the changes in the indoor environment verified in No. 2.

Sign language is the native form of expression used by deaf people in the world. With the recognition techniques applied to sign language, a significant need for developing tools to facilitate the accessibility of information to the deaf public has arisen. Little work deals with recognizing Moroccan sign language (MoSL) for the Moroccan deaf community. In this paper, a deep learning architecture is presented to be used to recognize MoSL signs. The proposed system uses 3D convolution neural networks to describe effectively video sequences containing Moroccan signs. Experiments showed that the system is able reliably to recognize Moroccan word signs, with 99.60% of accuracy.

The aim of this research was to determine what is known about the domotic system and the quality of life in low-income households, through a systematic review between 2010 and 2021. The study made it possible to review important databases such as Scielo, Scopus, EBSCO, and ScienceDirect. The search combination used “domotic system”; “quality of life”; “domotics”; “technology”; “automation”. Finally, 30 empirical articles in English and Spanish were systematized. In the analyzed literature, it is evident that the lack of knowledge in the concept of home automation, added to the low investment of assets for research and generation of proprietary technology, has caused a slow inclusion of home automation in different places, which is alarming since it includes important issues such as comfort, security, and energy savings that are applied to homes, buildings, and shopping centers. It was possible to reach the conclusion that the use of home automation in homes would include a better living condition in low-income households, oriented to the reduction of duplicated expenses or the efficient use of resources.

Detecting termites in wood structures is complex, and the most available detection methods are potentially damaging to property. The goal of this study is to develop a proof-of-concept termite detection system for an indoor environment. Thermal imaging and microwave radar sensors are used to detect the presence of termites, while a mobile application is used to view the termites’ status using a heat map and a wave pattern. Testing is carried out based on the reliability and efficiency of the two methods for detecting termites. The results show that the thermal camera can detect hot and cold spots on the wooden surface up to 15 cm, while the microwave radar sensor can detect termite movement inside the wood up to 3 cm.

Target detection in infrared imagery is a particularly challenging problem due to the presence of terrain clutter. The TCRNet-2 CNN architecture was introduced to combat this issue and has been shown to perform better than conventional networks such as faster RCNN and YOLOv3. In this paper, we evaluate the performance of the Boosted 2-Stream TCRNet in detail (including robustness to range variations, performance under day and night conditions) and compare it with that of YOLOv5. A MWIR dataset released by DSIAC is used for training and testing the network. We also propose the MWIR target classifier that recognizes the 10 classes in the NVESD dataset and achieves an accuracy of 65.72% which is state-of-the-art to date.

Nowadays, obtaining alternative forms of electrical power is a topic of worldwide interest, since fact that dependence on non-renewable resources such as hydrocarbons, coal, and uranium is becoming more and more costly due to their depletion. An alternative solution to this problem is the generation of electric energy by means of the use of solar energy captured by solar panels and the use of multilevel converters for the conversion to AC, which is how it is normally used in the home and industry. In this paper, we start with the review of the state of the art of the cascade multilevel inverters (CHMLI) and the modulation techniques commonly used for these inverters. Subsequently, the single-phase CHMLI is designed and simulated using MATLAB/Simulink software. To improve the signal quality, Butterworth filters were used for both the input and output of the CHMLI, resulting in an almost pure sinusoid with a very low THD.

The significant value related to cultural heritage (CH) is inestimable, and it also represents a resource in economic terms due to tourism. Therefore, CH must be safeguarded to avoid losses that in many cases can no longer be recovered. Due to the variability of elements and conditions, each step in CH protection and conservation is represented by a wide range of variable factors. In this scenario, the ability to intervene seems limited, given the variety of factors to be monitored remotely. However, the deployment of current technology, which retains the ability to provide low-cost sensors, could significantly contribute. The purpose of this paper is to introduce an IoT-based methodology for cultural heritage preservation. Expert users have tested a system prototype to monitor and manage a portion of a building within the Archaeological Park of Pompeii, predict deterioration, and schedule conservation interventions and choose the combination of interventions. Preliminary results are promising.

Building information model (BIM) is primarily a 3D digital representation of a structure and features such as geometry, spatial relationships, and geographic information to support integrated design. BIM in recent years has evolved from simple 3D to interacting with virtual and augmented reality. Such interaction aims to improve work productivity, home comfort, and entertainment, common Internet of things (IoT) goals. The IoT represents a possible evolution of the use of the Internet in which objects are able to communicate data about themselves and with other devices autonomously. One of the main goals of IoT is to build a digital copy of the real world. Therefore, BIM and IoT can integrate through data acquired in a BIM model; this model can be helpful in predictive analysis as needed. This paper aims to describe a methodology that allows the visualization and representation of data from sensors within the BIM environment to support decisions, sometimes complex, requiring interdisciplinary expertise. The study focusses on a real case study: a scale prototype of a single-family house that includes several sensors capable of producing data that feed a database based on the predictive/decision-making phase developed through machine learning techniques. The proposed methodology integrates an IoT-based platform that allows communication between sensors and Dynamo software to access sensor data, automatically updating the information contained in the BIM model.

The paper is concerned with the cases where machine-based image recognition fails to succeed and becomes inferior to human visual cognition. We consider the computational experiments on the set of specific images and speculate on the nature of these images that is perceivable only by natural intelligence. We deduce that image recognition and computer vision both based on machine learning or even more sophisticated AI models are unable to represent features of human vision due to the lack of tight coupling with the respective physiology.

An electronic health record (EHR) typically contains sensitive medical records, personal information, doctors’ provided prescription, and other physical histories of a patient. This digital approach remodeled the health sector while increasing privacy concerns and possibility of security breaches. This paper proposes an EHR system based on blockchain, interplanetary file system (IPFS), and cryptographic functions and includes features like secure access control having accountability, transparency, immutability of data in a cost-efficient patient-centered architecture which is free from third-party interruption. Here, we divided data into two categories and three types of participants who are verified with digital certificates are granted permission by the patients and then they can access data. Finally, we build and investigate a simple implementation to analyze the cost of the system and propose some approaches to optimize it.

Vehicular ad-hoc networks are important components in intelligent transportation systems which provide a reliable means of communication. Given that a security breach in such networks can result in fatal situation, the security of these networks is as important as their reliability. However, implementing security is not always a straightforward task especially when there are different devices with different capabilities. Additionally, current public-key infrastructure makes use of public-key algorithms that are susceptible to quantum-computation attacks making them not usable if quantum computers become a practical reality. In this paper, we present a public-key infrastructure implementation which makes use of a post-quantum algorithm providing an extra layer of protection to it. We compare the results of our implementation to implement which make the use of classical algorithms and show that they are very comparable in terms of speed, power, and energy consumption.

This study answers the research question, “how do teachers perceive the readiness of information and communication technology (ICT) school policies in supporting the provision of remote m-learning as a strategy for mitigating learning disruptions caused by COVID-19?” To investigate this question, the study adopted and adapted the UNESCO policy guideline for m-learning as an evaluation framework. Data were collected through semi-structured interviews from five schools in South Africa, and ten teachers participated in the study. The sampled teachers perceived the ICT policies at the selected schools as outdated, not recognizing mobile devices as tools for learning, and silent on providing teachers with resources that facilitate m-learning. The results indicate that the ICT policies at the sampled schools were perceived as not supporting the UNESCO policy guideline for m-learning. Perceptions of teachers were investigated in this study because teachers are the custodians of m-learning at schools.

Excessive runoffs from various non-point source land uses and other point sources are rapidly contaminating the river water quality in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the significant freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land use practices. In this study, a model was developed for predicting one of the significant WQPs, fecal coliform (FC) from precipitation, temperature, forest land use factor (FLUF), agricultural land use factor (ALUF), and urban land use factor (ULUF) using random forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94), and precipitation and temperature are the primary influencing factors for FC.

Photovoltaic (PV) plants utilization for green solar energy is growing exponentially in demand as industries committed to move away from carbon energy sources such as coals, oil, or gas. However, for efficient green solar energy utilization, a precise prediction method is required to minimize design composition wastage. The measured output current determined by empirical method will be compared with the predicted current obtained from the proposed neural network (ANN) and random forest (RF) methods. The comparative analysis of the measured and the proposed models is evaluated by using the minimum root means square error (RMSE), mean absolute percentage error (MAPE), and mean bias error (MBE). The obtained results suggest the superiority of RF over the ANN with improvement performance metrics values of 173% for RMSE, 39% for MAPE, and 188% for MBE.

This article presents the results of a study of social media accounts of the heads of the regions of the Russian Federation for the period January-May 2021 and a survey of 54 government officials for the period August-November 2021. The study shows what communication tasks are addressed through social networks, how government officials perceive and assess them and what problems they face. The data assessment techniques derive from actor-network theory.

Voting is the most representative way to express individual decision making in a democratic society. The classic voting method requires a lot of confirmation procedures, and requires a lot of time and money. In order to solve these kinds of problems, there have been efforts to introduce e-voting with IT services advantages, such as reduced election costs and shorter tally hours, compared to the existing voting methods. Despite many advantages, e-voting is not widely used for various technical concerns, including the risk of data manipulation. There has been much effort recently to apply the blockchain for voting data integrity to e-voting as a way to reduce the risk of voting data security and integrity. Blockchain could guarantee the integrity of voting data, but it has problems to be directly applied to e-voting system. In this paper, by applying cryptographic algorithms to the blockchain, we propose a verifiable blockchain-based e-voting system that allows voters to verify their votes while separating voters and voting results. Proposed the verifiable blockchain-based e-voting system satisfies the requirements of voting, including completeness, soundness, privacy, un-reusability, eligibility, fairness, verifiability.

DevOps has an important role in supporting critical decisions in software and systems development in highly regulated industry. To determine best practice, I have reviewed industry papers, standards, and comments of implementers. A systematic review of key reports on DevOps was conducted. Surveys conducted by a number of organizations with over 277,000 respondents are considered here. Key questions posed are how is DevOps perceived by industry, what are the key benefits from an industrial perspective, and what practices contribute to success. Results of this analysis show that while commonly accepted commercial reports make interesting reading; they are not sufficient to base critical decision in highly regulated industry. Furthermore, standards are required to establish confidence in process and product. This paper provides insights and guidance for software and systems development with DevOps practices in a highly regulated environment. I present the need for consistent quality to be encapsulated through industry standards.

Disaster Management is a method for saving lives and property that has been built over many years at great expense and effort. Forecasting demand, determining needs, procuring, storing, and handling inventory and coordination, and distributing relief to reduce losses before, during, and after any disaster are all important aspects of disaster management. Since natural disasters strike without warning, disaster management requires well-organized and coordinated planning before, during, and after the event. The use of ICT-based co-production can significantly improve disaster management by effectively and conveniently performing different operations to include diverse stakeholders. This study focuses on citizens utilizing social media in Disaster Management anchored on the co-production theory, as the framework in analyzing the interaction of citizens with the government through social media platform. Co-production theory considers other stakeholders outside an organization by their input toward producing products or services. Using qualitative case study design, this research was able to identify the factors that lead citizens to use social media during disaster and crisis situations using reflexive thematic analysis technique, for locating, analyzing, and interpreting meaning patterns (themes) in qualitative data. Findings indicated that using social media it will strengthen the communication between citizens and government or with the constituents, the ability to generate situational awareness about the crisis and disaster situations. The factors that lead citizens to used social media are the “Socialization” and “Altruism” themes. Those themes come from the interview conducted and transcribed the audio recordings into text conversation, collecting all the common among codes and produces themes. How social media facilitate citizens in co-producing valuable information includes “Collective Action,” “Collaboration,” and “Venue for Engagement” themes. The researchers further urge that this research be expanded to include not only ordinary citizens who use social media, but also social media leaders and community organizations, as they play an important role in society during disasters. The government may have all the means and equipment needed to mitigate disaster risks, but citizens, particularly community leaders, have the knowledge and expertise that the government needs to act and respond in those unfortunate situations.

As a classic problem in artificial intelligence research, robot welding path planning has been extensively studied. Related scholars have also proposed many solutions, such as heuristic algorithm, neural network, genetic simulated annealing algorithm, improved genetic algorithm. But there are still deficiencies in welding torch posture, welding position, and robot motion stability. Because of the characteristics of the welding seam have a vital influence on the path planning of the welding robot, it is also the basis for ensuring the welding accuracy. From the perspective of graphic calculation, the graphical computing method of precise welds is analyzed, the point cloud graphics of the welded parts are used to calculate the overlap of primitives, and the accurate and rapid extraction of weld features are realized by changing the graphic representation of the welded parts model. According to the connection characteristics of the weldment, the characteristics of the weldment are collected, and a simple, fast, and more versatile method for extracting and calculating weld features is designed, and the weld features are discretized. Discrete weld feature points are used as the basis for path planning of the welding robot to carry out reasonable welding path planning, which reduces the manual teaching process and workload. Finally, a robot welding path planning method based on graphical computing is proposed, and corresponding simulation experiments are carried out.

The solution of the second kind weakly singular Volterra integral equations (VIEs) was solved applying an interpolation technique based on the Vandermonde matrix. We devised optimal rules for the node distributions of the two kernel variables, ensuring that the singularity in the kernel is eliminated completely. The unknown solution is interpolated based on the Vandermonde matrix through three matrices in total, one of which is a monomial matrix. Five matrices, two of which are monomial basis, are used to convert the interpolated singular kernel into a double-interpolated non-singular polynomial. A linear system is produced by substituting the interpolated unknown function into the integral equation. The unknown coefficients are obtained from the direct solution of this system, and then, the interpolated solution is obtained. Using the lowest degree of interpolation, the results for the two solved examples strongly converge to the exact ones. This demonstrates the method’s originality and accuracy.

Cloud-based communicable disease surveillance (CCDS) allows the infection control staff of Taiwanese hospitals to simplify many of their reporting procedures and improve patient safety more efficiently and cost-effectively. Despite its great potential, there are gaps in health information technology researchers’ understanding of how hospitals decide to adopt CCDS. Therefore, our study proposes a research model that incorporates technological, organizational, environmental, transaction cost, and sociological perspectives to determine the factors that influence hospitals’ intention to adopt CCDS. A series of surveys was used to empirically test the organizational adoption model through the chief information officers (CIOs) of Taiwanese hospitals. The 206 valid questionnaires were selected for data analysis. The results showed hospitals’ decision to adopt CCDS as based on relative advantages, hospital size, top management support, government policies, uncertainty, and trust. These findings provide valuable insights and implications for medical informatics practices to facilitate the likelihood of cloud technology adoption.

In this research, we design an open, easy-to-use robotics platform for education applications, focused on primary education. Our platform is statistically evaluated and is modular, expandable, and scalable in terms of supporting the development of new modules. Our proposed platform, in contrast to other commercial ones, is easy to use, cheap, and modular. Additionally, we present initial results regarding the evaluation of the usage of the proposed robotic structure under the technology acceptance model (TAM) in terms of easiness of usage. According to the results, the proposed open educational robotic platform shows a positive effect toward its usage by active teachers.

The study aims to determine whether landmarks and routes influence navigational efficiency. In this study, 79 subjects participated in the experiments, and we evaluated their cognitive loads based on the generated psychophysiological measures and performance features from the driving system. The virtual reality system recorded the participant’s heart rate, eye gaze, pupil size, as well as the driving performance metrics. The participants were presented with different landmarks (sufficient and insufficient landmarks) and routes (easy and difficult routes) to help them reach their respective destinations. An analytic strategy method was employed to measure neuro-cognitive load for user classifications. The participants were divided into two groups, each group having two sessions. Each session had either sufficient landmarks or insufficient landmarks. The results showed that insufficient landmarks and difficult routes elicited an increase in heart rate and pupil size, which caused the participants to commit more mistakes. It also showed that easy routes with sufficient landmarks achieved higher-navigation efficiency. These results would help improve the use of landmarks and the design of the driving routes. It could also be used to analyze traffic safety by utilizing the driver's cognition and performance.

The article is based on the idea that in the conditions of a digital communicative environment, there are some changes in the structure of the human-translator activity and their functions, some of which are transferred to machine translation. It allows us to talk about the transposition of the translator linguistic persona’s functions. The research aims at finding ways to integrate translation activities into a new communicative environment associated with digital platforms, with the development of new linguistic technologies in the process of transcultural communication. The methodological basis of the research is the concept of translation space based on a synergetic approach to translation, which assumes the harmonization of the source and target meanings, i.e., their coordination and proportionality. The result of the study is understanding the correlation between the subject-centric and text-centric functions of a human-translator developing their reflexive, empathic, and other cognitive abilities, which cannot be delegated to machine translation. Therefore, we observe the transposition process of the translator language persona’s functions when the translator retains all subject-centric functions, and part of the text-centric functions are performed by the translation software. At this time, the social and cultural responsibility of the translator increases since the translation is assigned by the human and performed for the human.

Enterprise Resource Planning (ERP) systems are a strategic component of most organisations’ information systems and have evolved to become intelligent ERPs. ERP migrations are often marked by huge costs which is a challenge for developing economies. It remains unclear what the determinants for ERP migrations by organisations are for developing economies in the era of digital transformation. The aim of this study was to identify the factors that influence the decisions of ERP customers in developing countries to adopt intelligent ERPs. The Technological-Organisational-Environmental (TOE) and the institutional theory frameworks were used as the foundation upon which these factors were studied. The study focused on adopters and non-adopters of SAP S/4 HANA within a South African context. Guided by the positivist paradigm, a sample of 95 South African based companies was selected where four employees were targeted per company to respond to an electronic survey. Data was collected using an online data collection tool called, QuestionPro. The data was analysed through the IBM SPSS data analysis tool. The findings revealed that ICT infrastructure, availability of cyber-security systems, mimetic forces, and normative pressures are the influencing factors for intelligent ERPs adoption. The contributions of this study are discussed in this paper.

Most of the solar cells that dominate the market are single-junction solar cells. These solar cells use mainly silicon, and some of them use relatively new materials like copper, indium, gallium, selenide (CIGS) and perovskite. These materials show a good performance, but they have a limitation of performance and are also costly or unstable. The strategy for improving the performance of single-junction solar cells in this paper is based on the use of promising material. The proposed single-junction solar cell uses germanium telluride (GeTe) as an absorber layer and zinc oxide (ZnO) as an electron transport layer. Germanium telluride has main advantages compared to many materials. It has a high electrical conductivity and a small energy gap, allowing it to absorb a larger portion of the input spectrum. The cell shows a 21.58% power conversion efficiency at room temperature using input spectrum air mass (AM 1.5). The effects of the doping profiles and thickness of the used materials are studied and improved to find the highest possible performance, and this improves short-circuit current density, open-circuit voltage and fill factor resulting in increasing the efficiency to be 33.18%.

In this work, we address the challenging problem of color-dependent and imbalanced datasets. For many use cases, the training of models based on such data will not generalize well enough and fail even on slight domain variations. This issue is usually addressed by artificially extending the data by manipulating input data or using synthetic data. In this context, we introduce a new augmentation method for extended color mapping from single-channel depth images that reduce color dependency and decrease the amount of annotated data needed for comparable model performance. We found that this method improves the generalization of models for depth-based hand detection on our dataset captured at a manual assembly workspace. Additionally, we validated our results on a publicly available dataset.

Retinopathy of prematurity is the leading cause of blindness in children around the world. This paper exhibited ten deep convolutional neural networks (DCNNs) models to detect ROP stages in fundus images using deep neural networks. A dataset of 3720 fundus images was collected from the private clinic Al-Amal eye centre, which consisted of 3 classes of ROP stages. A training dataset and a test dataset were created from the images. VGG16, ResNet50, ResNet101, ResNet152, SqueezNet1_0, SqueezNet1_1, DenseNet121, DenseNet169, AlexNet169, and Inception_v3 were trained to make differential diagnoses and then tested. The classification accuracies for the highest three DCNN (ResNet152, DenseNet169, Inception_v3) were 73.95, 77.14, and 99.50%, respectively.To conclude, after training with an extensive dataset, the Inception v3 DCNN model presented large potential in facilitating the diagnosis of ROP stages utilizing fundus images.

This chapter studies the structure-preserving iterative approach for reduction of index-2 periodic models descriptor systems using generalized inverses of periodic matrix pairs. This work fulfills two objectives. The first part of our research is concerned with the discrete-time generalized a system which reformulate from the discrete-time descriptor system by changing the structure of the system. Then the periodic matrix pairs are computed from the generalized inverse matrices, and the reformulated system is represented by a cyclic lifted structure. Smith method is exploited to find the iterative solutions of the associated Lyapunov equations of the cyclic lifted system. The original periodic system is contained in the solutions of the periodic Lyapunov equations. The periodic system is then reduced by using projectors computed from those periodic solutions. The above procedures are applied to reduce an artificial problem of the index-2 periodic structure. To verify the accuracy and performance of the algorithm, we have demonstrated the results obtained from numerical simulations.

Crop planning prevents inappropriate crop selection and rotation by farmers which can cause economic loss and biodiversity issues. Technology solution for crop planning is a digital innovation in conserving traditional crop varieties and achieving optimal yield. Solutions range from database-oriented crop decision support systems to big data analytics platforms. However, technological capability must match the situated stage of farmers’ digital fluency and skill as traits of their digital culture for decision-making. This research presents a work to explore two aspects of the smart village crop planning application model, which are the pragmatic use of the prediction model and rural community as-is digital culture. As a result, a smart village crop planning application model recommends three components, crop predictor, collaborative tool, and ask expert application, to enhance farmer’s crop planning decision-making culture.

There is a huge amount of textual information in digital form growing exponentially over the years. With this data explosion, retrieving relevant information through information retrieval (IR) systems has become crucial. Due to the growing popularity of various tools such as voice assistants and chatbots, that rely on human–machine interaction, it is important that such systems are able to answer any query directly, rather than leading the user to a set of references. Recently developed question answering (QA) systems aim at providing direct answers to the user’s queries, hence, becoming of great interest to a large community. In this work, we propose an end-to-end pipeline that performs this task by creating a knowledge graph from the corpus. It uses embeddings to predict the missing links in the knowledge graph and a unique answer selection module in order to reach the correct answer. We test the proposed methodology on the SciQ dataset and obtain an accuracy of 62%. We also test this methodology on a curated knowledge base, Aristo, and present a comparative study highlighting the aspects on which information extraction models can improve, thus opening possibilities for future work in this field.

Cultural tourist sites in search of an intercultural approach, participatory in the face of quality management processes and focused on the interest of the visitor with a cultural visit profile, through continuous improvement, ISO standards and indicators that contribute to organizational effectiveness that provide responses to the changes generated in cultural sites and allow the consolidation of the administration, operation and structure of cultural institutions, to which, the study analyzed the quality levels of the representative cultural centers of the rural parish of San Miguelito. The objective of this study is to analyze quality management in the satisfaction of tourists in visiting cultural tourist destinations. This research is correlational with the use of Pearson's correlation and simple descriptive, due to the detail of the phenomena influenced in visiting cultural tourist destinations, it is a determining factor in quality, through the perception of quality, through satisfaction it is possible to define different categories, in addition to taking the character of non-experimental design research, with a cross-sectional design because the level and state of the variables are analyzed at a single point in time; to verify the relational trend of the variables, using a validated data collection instrument focused on the adapted SERVPERF model. Furthermore, high positive correlation results were achieved between quality management in cultural sites, the results at the time of making the relationship through the t statistic, showed a correlation with a level of significance < 1%, with which the hypothesis was validated, it was supported with a level of 1% (0.610).

Generally, when starting a first undergraduate numerical methods course, the first method taught to calculate the root of a root of a nonlinear equation in a single variable is the bisection method, in which the initial interval is divided into two subintervals taking the midpoint of the segment as a reference, the subinterval containing the root is bisected again, and so on until the desired root is approximated. The question that naturally arises from students is why would the interval necessarily have to be bisected? What if instead of bisecting the initial interval, we divide it according to a given ratio? This chapter describes the interval method divided by a given reason to approximate the root of a nonlinear equation in a single variable as a generalization of the bisection method. Proposing a new method for teaching the calculation of roots of a nonlinear equation.

The pandemic has made digital transformation faster, and Indonesia is no exception, especially in education. Learning management system (LMS) is a learning media that is widely used in educational institutions. However, not all educational institutions have sufficient resources to build an LMS from scratch. “Angkasa LMS” Web is a Web that allows educational institutions to order a ready-to-use LMS easily. The target users of this service are pretty varied because they consist of education management and educational management foundations, especially in areas of Indonesia that have Internet access. To be used easily by these varied target users, this Website must have good usability in its user interface. For this reason, usability testing needs to be carried out in order to get feedback for improving the user interface design before the application is delivered to the public. However, the developer has obstacles related to the COVID-19 pandemic policy to carry out usability testing, which limits direct interaction with people. Moreover, the project time is quite narrow, and the schedule is quite tight for the developer team. Based on these limitations, this study conducted usability testing of the “Angkasa LMS” Web case study using the unmoderated remote usability testing method. The experimental results show that this method can be used to obtain insightful feedback from the participants, with additional treatment such as the use of convenience sampling, periodic reminders for participants, and the increasing number of participants exceeding the target.

With the rapid growth of the wireless network scale and the aggressive development of communication technology, the communication network connection is required to drift to digits in order to ameliorate the network efficiency. Digital twin (DT) is one of the most promising techniques, which promotes the digital transition of communication networks by establishing mappings between virtual models and physical objects. Nevertheless, due to the limitation and heterogeneity of equipment resources, it is a great challenge to provide efficient network resource allocation. To solve this problem, the authors propose a novel network paradigm based on digital twin to build the topology and model of the communication system. Then a distributed deep reinforcement learning (DRL) method is designed to dispose the problem of resource allocation in cellular networks, and an online–offline learning framework is proposed. Firstly, the offline training is carried out in the simulation environment, and the DRL algorithm is applied to train the deep neural network (DNN). Secondly, in the process of online learning, the real data are further utilized to fine-tune the DNN. Numerical results illustrate the superiority of the proposed method in terms of average system capacity. In the case of different user densities, the performance of the proposed algorithm has more advantages than that of benchmark algorithms and has better generalization ability.

This article presents a systematic literature review aiming to understand how the COVID-19 pandemic enforced reluctant entrepreneurial businesses to adopt technology into their business. A total of 32 academic literature articles published after 2004 in English were identified and analysed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles. This article focuses on five main discussion points, namely COVID-19 effect on business, cause for technology adoption, hesitancy towards technology adoption, reasons for technology adoption, the government’s role in technology adoption, and the outcome of forced technology adoption. COVID-19 is a relatively recent and developing topic; however, based on the outcome of the discussion, it was found that from a business continuation and survival perspective for entrepreneurial businesses, the COVID-19 pandemic has in many ways enforced the adoption of technology for reluctant entrepreneurial businesses. There is a need for further studies at a later stage to understand the effect the COVID-19 pandemic had on entrepreneurial businesses in conjunction with the adoption of technology. These studies should aid in understanding the effectiveness of technology adoption in response to business disruption.

Digital health technologies have for number years now been expected to reduce the skyrocketing health related costs as well as the care burden on traditional healthcare systems. However, their adoption and scaling have consistently been unsatisfactory and sometimes, outright disappointing. Scholars have offered several valuable, insightful, and pertinent contributions to address the above challenge. However, these contributions are in most cases atomistic, transitory, non-spatial, and dispersed. The above state of affairs has left practitioners in limbo as to where and when to apply which insights to what type of digital health intervention and in which context. In this article, a new holistic and integrated theoretical framework, specifically focusing on remote patients’ health management tools and systems (RPMTSs) used to engage patients and potential patients at distance or away from healthcare facilities is proposed and introduced to address the above existing fragmentation and gaps. The new framework demonstrates how a clear and holistic understanding of “adoption” and “scaling” processes in the context of a given type and nature of digital health intervention along with an adaptive complex, processual and systems thinking approach can help confront the complexity of the healthcare apparatus while at the same time responding to its constantly evolving, dynamic nature with “agents” who may sometimes act irrationally or behave in unpredictable ways. In the process, a new framework is added to the knowledge base to guide and support the adoption and scaling of RPMTSs.

Learning Management Systems are of utmost importance in our technological world for educating young minds in the courses they are passionate about. The recent pandemic has only shown us how utilizing an online platform for learning and integrating it within the classroom has become vital. Education is crucial because it provides us with the tools and knowledge to move through our careers and life. Moving forward from this arduous time, there will be a need for various learning tools that we can provide to students and use as educators. This project provides an understanding of learning management systems and provides an LMS web application. In developing this web platform, there were various steps involved, such as research, design, implementation, testing, and integration. With this application, students, teachers, and administration will create a dynamic and cohesive learning environment.

Rendering animations into 2D or 3D involves sequential proceeding of inputs. This causes operational bottleneck, resulting in expensive and lengthy inefficient processes. In this paper, an efficient Blender-based software for creating 3D animations from related sources is proposed. It introduces a horizontally scaled and concurrent rendering of multiple Blender-based projects. It runs in three modes that combine to accomplish its task: Master, Client, and Slave. A single Master instance exposes a web GUI to the user, maintains the master list of render job states, and controls Slave instances. Client instances are launched on demand to provide users with a GUI to submit render jobs to the Master instance. Slave instances run on rendering machines and will, in turn, launch subprocesses of Blender to render individual frames when instructed to by the Master instance. Test implementation of our solutions indicate improvements over vertical scaling (increasing the power of a single rendering machine) and greatly reduces the overall time taken to render a complex animation project.

Over the decades, a typical imaging test that has been used is an X-ray. It allows doctors to see into the body without an incision. As a result, an X-ray can aid in diagnosing, monitoring, and treating a variety of medical disorders by detecting diseases beforehand. Among the diseases, pneumonia got major heed because of its intensity. As the lungs are the most vulnerable part of the body when it comes to pneumonia, doctors rely on the chest X-ray to diagnose the disease. In this research, we have worked on the X-ray images to discern pneumonia using our custom CNN model and different types of transfer learning models and manifested a comparison of those methods in terms of their ability to detect the disease. Furthermore, we performed generative adversarial networks (GAN) with deep convolutional layers to generate and merge a new training dataset using existing image data. Then, we executed the models anew after acquiring a new artificial dataset. Before using GAN, we got accuracy of 94%, 94%, 73%, 73%, 96%, 97%, and 94% in Custom CNN, InceptionV3, ResNet50, EfficientNetB0, VGG16, DenseNet201, and Xception, respectively. However, we observed improved accuracy from all models applying GAN except for DenseNet201. Moreover, VGG16, DenseNet201, and custom CNN acquired the higher accuracy overall.

One of the major threats to the information and communications technology (ICT) supply chain is the introduction of counterfeit parts and components. Global efforts have been intensified to defend against counterfeiters and counterfeit products due to their detrimental impact on the economy, safety, and security. Among the extensive literature of papers, reviews, books, and articles, this review attempts to include a detailed selection of most significant research work done in the intersection of ICT, supply chains, and counterfeits to provide a reference source for researchers. Citation network and global citation scores have been used to extract and analyze papers and discuss them in different types of clusters (electronic, medical, food, and anti-counterfeiting technologies and approaches). Our review approaches the clustered papers by focusing on (1) their contribution in documenting and modeling the intrusion of counterfeit electronic parts in the ICT supply chain, (2) the proposed counterfeits’ detection and avoidance techniques in the ICT supply chain, and (3) the contribution of ICT in thwarting counterfeits in medical, pharmaceutical, and food supply chains. This review provides a better understanding of the global efforts to address counterfeits in the ICT supply chain, as well as the role of ICT in thwarting counterfeits in other supply chains, which can guide future research to minimize the impact of counterfeits on supply chains.

Banking credit risk analysis is a form of evaluation conducted by financial institutions to determine applicants’ ability to repay their debt obligation. Financial institutions, such as banks, set objectives to offer credit to creditworthy customers, after spending time trying to evaluate their repaying capacity. In this paper, we propose a credit risk analysis system based on an artificial neural network (ANN) to identify customers who will default. A feedforward propagation algorithm is used to train the model consisting of three layers. Data pre-processing is performed to clean the datasets and check for missing variables. The datasets were normalized using min–max normalization to get the correlation among the variables. The datasets are applied to the proposed model and logistic regression models, and the comparison shows the proposed model which has a better performance.

The high torque and power density of permanent magnet synchronous motor (PMSM) make it potentially eligible for playing the crucial role of transmitting energy from battery pack to wheel system of electric vehicle (EV) in electric powertrain (PT). This paper presents a sensorless field controlled EVPT test bench emulating the EV propulsion system. The complete EVPT model developed in MATLAB/Simulink is analyzed for different modes of operations. The PMSM is fed by a voltage source inverter (VSI). Speed regulation is performed via TWR-KV46F150 microcontroller in EVPT emulator. The simulation results and experimental results are presented and verified. This work helps to understand the real-time operational environment of EV and is beneficial in expansion of PT system control.

This study focused on resilience and examined the effects of individual differences such as self-regulation and learning style on learning. Resilience and self-regulation have a strong relation. Thus, in this study, the value of self-regulation was used the value of resilience instead. The instructional design method was applied to develop text, interactive, and video teaching materials. The teaching materials were designed to support Japanese driver’s license holders to recognize learning goals and to create opportunities for reflection when learning about automated driving level 3. If the teaching material development method is effective, it is assumed that the low resilience and self-regulation can be compensated. As a result, developing teaching materials that support goal setting and reflection was effective, and this study found that the influence of learning style differs depending on the media used.

In this research paper, a new automatic system is introduced to detect vehicle and recover the same at toll plaza. This aims to propose and implement a new security system based on RFID, ANPR, GSM, OTP, and car lift up technology. The system helps the police department to detect theft vehicle and to recover that vehicle and also to improve the vehicle theft recovery rate in India. According to the articles published in newspapers, vehicle theft detection and recovery are the cases which are least solving in India. After a vehicle is theft, there are so many situations where police department and toll plazas are not able to detect and recover the same. Currently, the ETC system helps to detect theft vehicle at toll gates, but there are some limitations with this system also. To solve these limitations, a new system is proposed in this research paper. This new automatic vehicle theft detection and recovery system (AVTDRS) based on ICT which will be worked at toll plazas. This paper also approaches currently working technologies like GSM, GPS, OTP, RFID, smart phone applications, QR codes, and fingerprint identification system. These all technologies are currently using for detection and recovery of vehicles in India. The prime motive of written paper is to observe boundaries of present working system and to develop an updated automatic system to solve these problems.

Antimicrobial drug resistance (AMR) in bacteria is a public health hazard and is growing alarmingly. There is a development of multidrug-resistant organisms due to the selective pressure exerted on organisms by drugs. Due to delay in antibiotic susceptibility testing results, artificial intelligence (AI) is employed to control the organism’s resistance against the last resort drugs and speeding up the AMR detection process. Therefore, machine learning (ML), a mathematical tool for AI, is used. For this study, 6 classification ML models were used to train and forecast the resistance of β-lactam drugs in Klebsiella pneumoniae and were carried out on orange tool. Out of the 6 ML classifier models, KNN and random forest outperformed the remaining 4 classifiers. The purpose of this research was to develop an AI-based model to classify strains based on specific features.