Future Internet, Volume 13, Issue 10 (October 2021) – 26 articles

The use of advanced computer technologies has dramatically changed marketing. Concepts such as smart, sustainable, and green marketing have emerged in the last 20 years. One of these new technologies is the Internet of Things (IoT), which has led to the development of the activities and performances of industries in various dimensions. For the various objects, such as people, processes, and data, involved in marketing activities, the Internet of Everything (IoE) as an evolved IoT is a possible future scenario. Some sectors pretend to be the first to implement this, and the more they rely on dynamic, unstable customer needs, the better a solution the IoE is for them. Therefore, this paper presents a clear vision of smart, sustainable marketing based on the IoE in one of the fast-moving consumer goods (FMCG) industries, the dairy industry. Key factors are identified to help readers understand this concept better. The expert interview makes it possible to draw a picture of the factors that have helped successfully implement the IoE in the dairy sector. Full article

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods. Full article

Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing. Full article

A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly challenging, as they elicit rigorous computational tasks over the communication network. For example, it was discovered that the time to execute a task in an unsuited resource consumes more cost and time in the cloud data centers. In this paper, a new cost- and time-efficient planning algorithm for scientific workflow scheduling has been proposed for heterogeneous systems in the cloud based upon the Predict Optimistic Time and Cost (POTC). The proposed algorithm computes the rank based not only on the completion time of the current task but also on the successor node in the critical path. Under a tight deadline, the running time of the workflow and the transfer cost are reduced by using this technique. The proposed approach is evaluated using true cases of data-exhaustive workflows compared with other algorithms from written works. The test result shows that our proposed method can remarkably decrease the cost and time of the experimented workflows while ensuring a better mapping of the task to the resource. In terms of makespan, speedup, and efficiency, the proposed algorithm surpasses the current existing algorithms—such as Endpoint communication contention-aware List Scheduling Heuristic (ELSH)), Predict Earliest Finish Time (PEFT), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Minimal Optimistic Processing Time (MOPT) and Predict Earlier Finish Time (PEFT)—while holding the same time complexity. Full article

Standalone Name Resolution (SNR) is an essential component of many Information-Centric Networking (ICN) infrastructures that maps and stores the mappings of IDs and locators. The delivery of data can be realized only when the name resolution process is completed correctly. It also makes the SNR become the key target of network attackers. In this paper, our research focuses on the more covert and complex Content Pollution Attack (CPA). By continuously sending invalid content to the network at a low speed, attackers will consume a lot of the resources and time of the SNR system, resulting in a serious increase in the resolution delay of normal users and further cache pollution in ICN. It is difficult to be quickly detected because the characteristics of attack are inconspicuous. To address the challenge, a register access control scheme for an SNR system based on a malicious user blacklist query is proposed. A neighbor voting algorithm is designed to discover possible attacks in the network quickly and build a blacklist of malicious users reasonably. Users on the blacklist will be restricted from accessing the ICN network during the registration phase with the resolution system. Incentives and punishments for network users are introduced to automate responses about the potential malicious behavior reports. Our scheme is more efficient as users do not have to wait for an additional system component to perform operations. In addition, our algorithm can better solve the collusion problem in the voting process when compared with the others. We experimentally evaluate our protocol to demonstrate that the probability of successful collusion attack can be reduced to less than 0.1 when the attacker ratio is 0.5. Full article

To optimize the data migration performance between different supercomputing centers in China, we present TCP-DQN, which is an intelligent TCP congestion control method based on DQN (Deep Q network). The TCP congestion control process is abstracted as a partially observed Markov decision process. In this process, an agent is constructed to interact with the network environment. The agent adjusts the size of the congestion window by observing the characteristics of the network state. The network environment feeds back the reward to the agent, and the agent tries to maximize the expected reward in an episode. We designed a weighted reward function to balance the throughput and delay. Compared with traditional Q-learning, DQN uses double-layer neural networks and experience replay to reduce the oscillation problem that may occur in gradient descent. We implemented the TCP-DQN method and compared it with mainstream congestion control algorithms such as cubic, Highspeed and NewReno. The results show that the throughput of TCP-DQN can reach more than 2 times of the comparison method while the latency is close to the three compared methods. Full article

Satellite–terrestrial integrated networks (STINs) are regarded as a promising solution to meeting the demands of global high-speed seamless network access in the future. Software-defined networking and network function virtualization (SDN/NFV) are two complementary technologies that can be used to ensure that the heterogeneous resources in STINs can be easily managed and deployed. Considering the dual mobility of satellites and ubiquitous users, along with the dynamic requirements of user requests and network resource states, it is challenging to maintain service continuity and high QoE performance in STINs. Thus, we investigate the service migration and reconfiguration scheme, which are of great significance to the guarantee of continuous service provisioning. Specifically, this paper proposes a dynamic service reconfiguration method that can support flexible service configurations on integrated networks, including LEO satellites and ground nodes. We first model the migration cost as an extra delay incurred by service migration and reconfiguration and then formulate the selection processes of the location and migration paths of virtual network functions (VNFs) as an integer linear programming (ILP) optimization problem. Then, we propose a fuzzy logic and quantum genetic algorithm (FQGA) to obtain an approximate optimal solution that can accelerate the solving process efficiently with the benefits of the high-performance computing capacity of QGA. The simulation results validate the effectiveness and improved performance of the scheme proposed in this paper. Full article

The paper proposes a data-driven strategy for predicting technical ticket reopening in the context of customer service for telecommunications companies providing 5G fiber optic networks. Namely, the main aim is to ensure that, between end user and service provider, the Service Level Agreement in terms of perceived Quality of Service is satisfied. The activity has been carried out within the framework of an extensive joint research initiative focused on Next Generation Networks between ELIS Innovation Hub and a major network service provider in Italy over the years 2018–2021. The authors make a detailed comparison among the performance of different approaches to classification—ranging from decision trees to Artificial Neural Networks and Support Vector Machines—and claim that a Bayesian network classifier is the most accurate at predicting whether a monitored ticket will be reopened or not. Moreover, the authors propose an approach to dimensionality reduction that proves to be successful at increasing the computational efficiency, namely by reducing the size of the relevant training dataset by two orders of magnitude with respect to the original dataset. Numerical simulations end the paper, proving that the proposed approach can be a very useful tool for service providers in order to identify the customers that are most at risk of reopening a ticket due to an unsolved technical issue. Full article

Real-time business practices require huge amounts of data directly from the production assets. This new thirst for accurate and timely data has forced the convergence of the traditionally business-focused information technology (IT) environment with the production-focused operational technology (OT). Recently, software-defined network (SDN) methodologies have benefitted OT networks with enhanced situational awareness, centralized configuration, deny-by-default forwarding rules, and increased performance. What makes SDNs so innovative is the separation between the control plane and the data plane, centralizing the command in the controllers. However, due to their young age, the use of SDNs in the industry context has not yet matured comprehensive SDN-based architectures for IT/OT networks, which are also resistant to security attacks such as denial-of-service ones, which may occur in SDN-based industrial IoT (IIoT) networks. One main motivation is that the lack of comprehensive SDN-based architectures for IT/OT networks making it difficult to effectively simulate, analyze, and identify proper detection and mitigation strategies for DoS attacks in IT/OT networks. No consolidated security solutions are available that provide DoS detection and mitigation strategies in IT/OT networks. Along this direction, this paper’s contributions are twofold. On the one hand, this paper proposes a convergent IT/OT SDN-based architecture applied in a real implementation of an IT/OT support infrastructure called SIRDAM4.0 within the context of the SBDIOI40 project. On the other hand, this paper proposes a qualitative analysis on how this architecture works under DoS attacks, focusing on what the specific problems and vulnerabilities are. In particular, we simulated several distributed denial-of-service (DDoS) attack scenarios within the context of the proposed architecture to show the minimum effort needed by the attacker to hack the network, and our obtained experimental results show how it is possible to compromise the network, thus considerably worsening the performance and, in general, the functioning of the network. Finally, we conclude our analysis with a brief description on the importance of employing machine learning approaches for attack detection and for mitigation techniques. Full article

Dynamic wireless charging (DWC) is a promising technology to charge Electric Vehicles (EV) using on-road charging segments (CS), also known as DWC pads. In order to ensure effective utilization of this on-the-road charging service, communication and coordination need to be established between the EVs and the different network entities, thereby forming an Internet of Electric Vehicles (IoEV). In an IoEV, EVs can utilize different V2X communication modes to enable charging scheduling, load management, and reliable authentication and billing services. Yet, designing an authentication scheme for dynamic EV charging presents significant challenges given the mobility of the EVs and the short contact time between the EVs and the charging segments. Accordingly, this work proposes a fast, secure and lightweight authentication scheme that allows only authentic EVs with valid credentials to charge their batteries while ensuring secure and fair payments. The presented scheme starts with a key pre-distribution phase between the charging service company (CSC) and the charging pad owner (PO), followed by a hash chain and digital signature-based registration and authentication phase between the EV and the CSC, before the EV reaches the beginning of the charging lane. These preliminary authentication phases allow the authentication between the EVs and the charging segments to be performed using simple hash key verification operations prior to charging activation, which reduces the computational cost of the EVs and the CS. Symmetric and asymmetric key cryptography are utilized to secure the communication between the different network entities. Analysis of the computational and transmission time requirements of the proposed authentication scheme shows that, for an EV traveling at 60 km/h to start charging at the beginning of the charging lane, the authentication process must be initiated at least $1.35$ m ahead of the starting point of the lane as it requires ≃81 ms to be completed. Full article

At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new users or newly installed Apps. There are some existing works related to mobile App start-up prediction using multi-user data, which require the integration of multi-party data. In this case, a typical solution is distributed learning of centralized computing. However, this solution can easily lead to the leakage of user privacy data. In this paper, we propose a mobile App start-up prediction method based on federated learning and attributed heterogeneous network embedding, which alleviates the cold start problem of new users or new Apps while guaranteeing users’ privacy. Full article

Education 4.0 demands a flexible combination of digital literacy, critical thinking, and problem-solving in educational settings linked to real-world scenarios. Haptic technology incorporates the sense of touch into a visual simulator to enrich the user’s sensory experience, thus supporting a meaningful learning process. After developing several visuo-haptic simulators, our team identified serious difficulties and important challenges to achieve successful learning environments within the framework of Education 4.0. This paper presents the VIS-HAPT methodology for developing realistic visuo-haptic scenarios to promote the learning of science and physics concepts for engineering students. This methodology consists of four stages that integrate different aspects and processes leading to meaningful learning experiences for students. The different processes that must be carried out through the different stages, the difficulties to overcome and recommendations on how to face them are all described herein. The results are encouraging since a significant decrease (of approximately 40%) in the development and implementation times was obtained as compared with previous efforts. The quality of the visuo-haptic environments was also enhanced. Student perceptions of the benefits of using visuo-haptic simulators to enhance their understanding of physics concepts also improved after using the proposed methodology. The incorporation of haptic technologies in higher education settings will certainly foster better student performance in subsequent real environments related to Industry 4.0. Full article

COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses. Full article

Due to the recent COVID-19 pandemic that has swept the globe, more people are working from home. People use synchronous applications to communicate remotely because they are not able to meet face-to-face. However, few research studies on the issues surrounding the virtual conference application, particularly those that include collaborative activities, have been conducted. The usability study recruited 16 participants (in four groups of four) to communicate synchronously while performing collaborative activities, such as drawing together on a shared screen. According to the findings of the usability study, users do not often use the collaborative tools provided by the current virtual conference application. This is due to low exposure and unfamiliarity with the use of collaborative tools. The findings also show that users frequently do not turn on the web camera due to several reasons, including privacy, connectivity issues, the environment, and background distraction. Turning on the web camera can also cause anxiety due to shyness in front of the camera. However, some participants prefer to turn on the web camera so that they can see each other’s reactions when performing collaborative activities. The article provides several guidelines to assist in the design of virtual conference applications, including a simple familiar intuitive interface to encourage the use of collaborative tools and also introduces the use of virtual avatars as a way to represent oneself during online meetings to allow affective sharing while respecting the privacy of its users. Full article

The development of smart cities is a clear growing trend all around the world. The convergence of different technological, social, political, economical, and ecological trends has allowed the concepts to rise up quickly in governmental agendas. In this paper, we analyze the situation of Russia regarding smart cities. Moscow, Saint Petersburg, and Kazan are considered at the “Smart City 3.0” stage of development, meaning that the citizens are participating in their advancement. Our reasons to focus on Russia are two-fold: (1) we know the situation well, as we live and work in a new city, Innopolis, founded in 2015 and meant to be a blueprint for smart cities; (2) large Russian cities are actively developing projects in this sphere and are highly regarded worldwide in these endeavors. It is therefore worth analyzing the context and the trends. By studying the scientific literature and categorizing the features of smart cities the world over, we found that large Russian cities are developing most of the components necessary in order to be called smart. Herein we also discuss areas of possible growth for Russian cities, such as green technologies and a smart environment. Full article

In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (POI) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, POI data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (TF-IDF), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection. Full article

Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a “proof of concept” system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a fast region-based convolutional neural network that detects horses in an image. The model is a convolutional neural network that predicts the emotions of those horses. These two elements were trained with multiple images of horses until they achieved high accuracy in their tasks. In total, 400 images of horses were collected and labeled to train both the detector and the model while 40 were used to test the system. Once the two components were validated, they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through the head, neck, ear, muzzle, and eye position. The system showed an accuracy of 80% on the validation set and 65% on the test set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock. Full article

Low-code is an emerging concept that transforms visual representations into functional software, allowing anyone to be a developer. However, building a low-code platform from scratch can be challenging concerning the scarce available literature about the topic. In this sense, this paper proposes an Open Low-Code Platform (OLP), a low-code solution that enables regular users to create applications. Furthermore, it presents low-code’s functional and nonfunctional requirements, as well as its similarities and its differences with the no-code concept. The experience obtained while developing OLP was translated into a pipeline that details how code was transformed from the visual representations into a fully fledged application. The paper demonstrates the solution’s viability and is especially useful for building a low-code platform from scratch or improving an existing one. Full article

The rapid advancement in artificial intelligence (AI) and wide deployment of Internet of Video Things (IoVT) enable situation awareness (SAW). The robustness and security of IoVT systems are essential for a sustainable urban environment. While blockchain technology has shown great potential in enabling trust-free and decentralized security mechanisms, directly embedding cryptocurrency oriented blockchain schemes into resource-constrained Internet of Video Things (IoVT) networks at the edge is not feasible. By leveraging Electrical Network Frequency (ENF) signals extracted from multimedia recordings as region-of-recording proofs, this paper proposes EconLedger, an ENF-based consensus mechanism that enables secure and lightweight distributed ledgers for small-scale IoVT edge networks. The proposed consensus mechanism relies on a novel Proof-of-ENF (PoENF) algorithm where a validator is qualified to generate a new block if and only if a proper ENF-containing multimedia signal proof is produced within the current round. The decentralized database (DDB) is adopted in order to guarantee efficiency and resilience of raw ENF proofs on the off-chain storage. A proof-of-concept prototype is developed and tested in a physical IoVT network environment. The experimental results validated the feasibility of the proposed EconLedger to provide a trust-free and partially decentralized security infrastructure for IoVT edge networks. Full article

Recently, with the great development of e-health, more and more countries have made certain achievements in the field of electronic medical treatment. The digitization of medical equipment and the structuralization of electronic medical records are the general trends. While bringing convenience to people, the explosive growth of medical data will further promote the value of mining medical data. Obviously, finding out how to safely store such a large amount of data is a problem that urgently needs to be solved. Additionally, the particularity of medical data makes it necessarily subject to great privacy protection needs. This reinforces the importance of designing a safe solution to ensure data privacy. Many existing schemes are based on single-server architecture, which have some natural defects (such as single-point faults). Although blockchain can help solve such problems, there are still some deficiencies in privacy protection. To solve these problems, this paper designs a medical data privacy protection system, which integrates blockchain, group signature, and asymmetric encryption to realize reliable medical data sharing between medical institutions and protect the data privacy of patients. This paper proves theoretically that it meets our security and privacy requirements, and proves its practicability through system implementation. Full article

The pandemic caused by COVID-19 led schools to continue their work by relying on digital technologies. Changes in this matter are observed within three dimensions in the theoretical and conceptual background: (a) the influence of ITC in education, (b) the macrosocial changes in the educational systems and public policy derived from the COVID-19 pandemic, and (c) the impact of the COVID-19 pandemic in higher education and its role for the future. The general objective of this research was to characterize the educational practices executed by the university community (students, professors, and managers) during the emerging remote classes derived from the pandemic at the Universidad Autonoma Metropolitana, Cuajimalpa Campus, a public educational institution in Mexico (through an explanation for each educational actor profile). As specific research objectives, this paper: (a) examines whether the professors and students had enough digital technology to continue with the classes, (b) defines the obstacles they had in the use of said digital technology, and (c) recognizes the existence of innovative educational practices and determines whether stated learning was achieved in educational programs. For this purpose, a mixed methodology was chosen, comprising the application of surveys to students and professors and semi-structured interviews with managers, professors, and students. It was found that there was innovation in the area of resources (material–economic dimension) and in the area of relationships (socio-political dimension), while the discursive dimension (cultural-discursive dimension) was negatively impacted. Based on the above, we conclude that the school culture of the UAM-C is solid and that it has the necessary technological resources to continue with the teaching–learning process. The educational practice was transformed, which resulted in advantages and disadvantages, but despite these situations, most students developed their learning. Full article

Web surveys are an integral part of the feedback of Internet services, a research tool for respondents, including in the field of health and psychology. Web technologies allow conducting research on large samples. For mental health, an important metric is reaction time in cognitive tests and in answering questions. The use of mobile devices such as smartphones and tablets has increased markedly in web surveys, so the impact of device types and operating systems needs to be investigated. This article proposes an architectural solution aimed at reducing the effect of device variability on the results of cognitive psychological experiments. An experiment was carried out to formulate the requirements for software and hardware. Three groups of 1000 respondents were considered, corresponding to three types of computers and operating systems: Mobile Device, Legacy PC, and Modern PC. The results obtained showed a slight bias in the estimates for each group. It is noticed that the error for a group of devices differs both upward and downward for various tasks in a psychological experiment. Thus, for cognitive tests, in which the reaction time is critical, an architectural solution was synthesized for conducting psychological research in a web browser. The proposed architectural solution considers the characteristics of the device used by participants to undergo research in the web platform and allows to restrict access from devices that do not meet the specified criteria. Full article

Social media platforms such as Facebook, Instagram, and Twitter are an inevitable part of our daily lives. These social media platforms are effective tools for disseminating news, photos, and other types of information. In addition to the positives of the convenience of these platforms, they are often used for propagating malicious data or information. This misinformation may misguide users and even have dangerous impact on society’s culture, economics, and healthcare. The propagation of this enormous amount of misinformation is difficult to counter. Hence, the spread of misinformation related to the COVID-19 pandemic, and its treatment and vaccination may lead to severe challenges for each country’s frontline workers. Therefore, it is essential to build an effective machine-learning (ML) misinformation-detection model for identifying the misinformation regarding COVID-19. In this paper, we propose three effective misinformation detection models. The proposed models are long short-term memory (LSTM) networks, which is a special type of RNN; a multichannel convolutional neural network (MC-CNN); and k-nearest neighbors (KNN). Simulations were conducted to evaluate the performance of the proposed models in terms of various evaluation metrics. The proposed models obtained superior results to those from the literature. Full article

The Cross-Industry Standard Process for Data Mining (CRISP-DM), despite being the most popular data mining process for more than two decades, is known to leave those organizations lacking operational data mining experience puzzled and unable to start their data mining projects. This is especially apparent in the first phase of Business Understanding, at the conclusion of which, the data mining goals of the project at hand should be specified, which arguably requires at least a conceptual understanding of the knowledge discovery process. We propose to bridge this knowledge gap from a Data Science perspective by applying Natural Language Processing techniques (NLP) to the organizations’ e-mail exchange repositories to extract explicitly stated business goals from the conversations, thus bootstrapping the Business Understanding phase of CRISP-DM. Our NLP-Automated Method for Business Understanding (NAMBU) generates a list of business goals which can subsequently be used for further specification of data mining goals. The validation of the results on the basis of comparison to the results of manual business goal extraction from the Enron corpus demonstrates the usefulness of our NAMBU method when applied to large datasets. Full article

Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model. Full article

Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA. Full article