Appl. Syst. Innov., Volume 6, Issue 5 (October 2023) – 23 articles

The field of health and medical sciences has witnessed a surge of published research exploring the applications of ChatGPT. However, there remains a dearth of knowledge regarding its specific potential and limitations within the domain of nutrition. Given the increasing prevalence of nutrition-related diseases, there is a critical need to prioritize the promotion of a comprehensive understanding of nutrition. This paper examines the potential utility of ChatGPT as a tool for improving nutrition knowledge. Specifically, it scrutinizes its characteristics in relation to personalized meal planning, dietary advice and guidance, food intake tracking, educational materials, and other commonly found features in nutrition applications. Additionally, it explores the potential of ChatGPT to support each stage of the Nutrition Care Process. Addressing the prevailing question of whether ChatGPT can replace healthcare professionals, this paper elucidates its substantial limitations within the context of nutrition practice and education. These limitations encompass factors such as incorrect responses, coordinated nutrition services, hands-on demonstration, physical examination, verbal and non-verbal cues, emotional and psychological aspects, real-time monitoring and feedback, wearable device integration, and ethical and privacy concerns have been highlighted. In summary, ChatGPT holds promise as a valuable tool for enhancing nutrition knowledge, but further research and development are needed to optimize its capabilities in this domain. Full article

Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers are using deep learning (ML) and deep learning (DL) with ECG data to detect different types of cardiac issues as well as other health problems such as respiration rate, sleep apnea, and blood pressure, etc. This study presents an extensive literature review based on research performed in the last few years where ML and DL have been applied with ECG data for many diagnoses. However, the review found that, in published work, the results showed promise. However, some significant limitations kept that technique from implementation in reality and being used for medical decisions; examples of such limitations are imbalanced and the absence of standardized dataset for evaluation, lack of interpretability of the model, inconsistency of performance while using a new dataset, security, and privacy of health data and lack of collaboration with physicians, etc. AI using ECG data accompanied by modern wearable biosensor technologies has the potential to allow for health monitoring and early diagnosis within reach of larger populations. However, researchers should focus on resolving the limitations. Full article

Gun violence and mass shootings kill and injure people, create psychological trauma, damage properties, and cause economic loss. The loss from gun violence can be reduced if we can detect the gunshot early and notify the police as soon as possible. In this project, a novel gunshot detector device is developed that automatically detects indoor gunshot sound and sends the gunshot location to the nearby police station in real time using the Internet. The users of the device and the emergency responders also receive smartphone notifications whenever the shooting happens. This will help the emergency responders to quickly arrive at the crime scene, thus the shooter can be caught, injured people can be taken to the hospital quickly, and lives can be saved. The gunshot detector is an electronic device that can be placed in schools, shopping malls, offices, etc. The device also records the gunshot sounds for post-crime scene analysis. A deep learning model, based on a convolutional neural network (CNN), is trained to classify the gunshot sound from other sounds with 98% accuracy. A prototype of the gunshot detector device, the central server for the emergency responder’s station, and smartphone apps have been developed and tested successfully. Full article

Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R²) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved. Full article

This paper presents multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023. First, the analysis shows that the average number of Tweets per month wherein individuals self-reported Long COVID on Twitter was considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings from sentiment analysis using VADER show that the percentages of Tweets with positive, negative, and neutral sentiments were 43.1%, 42.7%, and 14.2%, respectively. To add to this, most of the Tweets with a positive sentiment, as well as most of the Tweets with a negative sentiment, were not highly polarized. Third, the result of tokenization indicates that the tweeting patterns (in terms of the number of tokens used) were similar for the positive and negative Tweets. Analysis of these results also shows that there was no direct relationship between the number of tokens used and the intensity of the sentiment expressed in these Tweets. Finally, a granular analysis of the sentiments showed that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively. Full article

Visual impairment should not hinder an individual from achieving their aspirations, nor should it be a hindrance to their contributions to society. The age in which persons with disabilities were treated unfairly is long gone, and individuals with disabilities are productive members of society nowadays, especially when they receive the right education and are given the right tools to succeed. Thus, it is imperative to integrate the latest technologies into devices and software that could assist persons with disabilities. The Internet of Things (IoT), artificial intelligence (AI), and Deep Learning (ML)/deep learning (DL) are technologies that have gained momentum over the past decade and could be integrated to assist persons with disabilities—visually impaired individuals. In this paper, we propose an IoT-based system that can fit on the ring finger and can simulate the real-life experience of a visually impaired person. The system can learn and translate Arabic and English braille into audio using deep learning techniques enhanced with transfer learning. The system is developed to assist both visually impaired individuals and their family members in learning braille through the use of the ring-based device, which captures a braille image using an embedded camera, recognizes it, and translates it into audio. The recognition of the captured braille image is achieved through a transfer learning-based Convolutional Neural Network (CNN). Full article

This article addresses the need for early emergency detection and safety monitoring in public spaces using deep learning techniques. The problem of discerning relevant sound events in urban environments is identified, which is essential to respond quickly to possible incidents. To solve this, a method is proposed based on extracting acoustic features from captured audio signals and using a deep learning model trained with data collected both from the environment and from specialized libraries. The results show performance metrics such as precision, completeness, F1-score, and ROC-AUC curve and discuss detailed confusion matrices and false positive and negative analysis. Comparing this approach with related works highlights its effectiveness and potential in detecting sound events. The article identifies areas for future research, including incorporating real-world data and exploring more advanced neural architectures, and reaffirms the importance of deep learning in public safety. Full article

As a plan, Industry 4.0 encourages manufacturing companies to switch from conventional Product-Service Systems to Digital Product-Service Systems. Systems of goods, services, and digital technologies known as “Digital Product-Service Systems” are provided to improve consumer satisfaction and business success in the marketplace. Previous studies have looked into various elements of this area for industrial companies and academic institutions. Digital Product-Service Systems’ overall worth and expected course of growth are still ignored. The authors use bibliometric analysis to organize the body of prior knowledge in this discipline and, more significantly, to identify areas for further study in order to cover the literature deficit. The results of the most esteemed authors, nations, and sources in the subject were given by this study. The findings also show that terms like digitization, sustainability, and business have grown in popularity over the previous year. This study also offered insight into how Industry 5.0, a new manufacturing strategy, would include Digital Product-Service Systems. Finally, the findings of this research demonstrate three new service orientations, namely resilient, sustainable, and human-centric, in manufacturing firms. Full article

Industries are rapidly moving toward mitigating errors and manual interventions by automating their process. The same motivation is carried out in this research which targets to study a conveyor system installed in soda ash manufacturing plants. Our aim is to automate the determination of optimal parameters, which are chosen by identifying the flow rate of the materials available on the conveyor belt for maintaining the ratio between raw materials being carried. The ratio is essential to produce 40% pure carbon dioxide gas needed for soda ash production. A visual sensor mounted on the conveyor belt is used to estimate the flow rate of the raw materials. After selecting the region of interest, a segmentation algorithm is defined based on a voting-based technique to segment the most confident region. Moments and contour features are extracted and passed to machine learning algorithms to estimate the flow rate of different experiments. An in-depth analysis is completed on various techniques and convincing results are achieved on the final data split with the best parameters using the Bagging regressor. Each step of the process is made resilient enough to work in a challenging environment even if the belt is placed in an outdoor environment. The proposed solution caters to the current challenges and serves as a practical solution for estimating material flow without manual intervention. Full article

In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation. Full article

The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient Boost for the regression part of this study had the least Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of 12.93 and 18.28, respectively, in the case of the Boruta selection method. In contrast, RFI was found to be the superior classification method, yielding an accuracy rate of 78% in the classification part. This research emphasized the significance of employing appropriate feature engineering and selection methodologies to enhance the efficacy of machine learning algorithms. Using a diverse set of machine learning techniques, this study analyzed the OULA dataset, focusing on both feature engineering and selection. Our approach was to systematically compare the performance of different models, leading to insights about the most effective strategies for predicting student success. Full article

Retailers must have accurate sales forecasts to efficiently and effectively operate their businesses and remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can often better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision making for inventory management, purchasing, and other operational decisions. In this study, we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study’s findings, we used the M5 forecasting competition’s openly accessible and well-established dataset. The results obtained from DeepAR models trained on different combinations of features indicate that the inclusion of time-, event-, and ID-related features consistently enhances the forecast accuracy. The optimal performance is attained when all these covariates are employed together, leading to a 1.8% improvement in RMSSE and a 6.5% improvement in MASE compared to the baseline model without features. It is noteworthy that all DeepAR models, both with and without covariates, exhibit a significantly superior forecasting performance in comparison to the seasonal naïve benchmark. Full article

The promising research on flexible and tactile sensors requires conducting polymer materials and an accurate system for the transduction of pressure into electrical signals. In this paper, the intelligent sensitive mat, based on Velostat, which is a polymeric material impregnated with carbon black, is investigated. Various designs and geometries for home-made sensor mats have been proposed, and their electrical and mechanical properties, including reproducibility, have been studied through the tests performed. The mat pressure sensors have been interfaced with an Arduino microcontroller in order to monitor, read with high precision, and control the variation of the resistance under applied pressure. An approximation method was then developed based on a neural network algorithm to explore the relationship between different mat shapes, the pressure and stresses applied on the mat, the resistance of the conductive Velostat material, and the number of active sensing cells in order to control system input signal management. Full article

Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities. Full article

The issue of Automated Meter Management (AMM), an integral part of modern energy smart grid systems, has become a hot topic in recent years. With the current energy crisis, and given the new approaches to smart energy and its regulation, implemented at the level of the European Union, the gradual introduction of AMM as a standard for the regulation and management of the distribution system is an absolute necessity. Modern smart grids incorporate elements of smart regulation that rely heavily on the availability and quality of the data generated or used during AMM as part of the smart grid. In this paper, based on an analytical view of AMM as a whole and guided interviews with the sponsors of each service and owners of each dataset, criteria are proposed and a Business Impact Analysis (BIA) is implemented, the results of which are used to determine security measures for the safe and reliable running of the AMM system. This paper offers a unique view of the AMM system as an integral part of modern smart grid networks from a data-driven perspective that enables the subsequent implementation and fulfillment of security requirements by ISO/IEC 27001 and national security standards, as the AMM system is also a critical information system under the EU directive regarding the cybersecurity of network and information systems, which are subject to newly defined security requirements in the field of cybersecurity. Full article

Recent developments in electromagnetic launchers have created potential applications in transportation, space, and defense systems. However, the total efficiency of these launchers has yet to be fully realized and optimized. Therefore, this paper introduces a new design idea based on increasing the magnetic flux lines that facilitate high output velocity without adding any excess energy. This design facilitates obtaining a mathematical equation for the launcher inductance which is difficult to analytically represent. This modification raises the launcher efficiency to 36% higher than that of the ordinary launcher at low operating voltage. The proposed design has proven its superiority to traditional launchers, which are limited in their ability to accelerate microsatellites from the ground to low Earth orbit due to altitude and velocity constraints. Therefore, an aircraft is used as a flying launchpad to carry the launcher and bring it to the required height to launch. Meanwhile, it is demonstrated experimentally that magnetic dipoles in the projectile material allow the launcher coil’s magnetic field to accelerate the projectile. This system consists of the launcher coil that must be triggered with a high amplitude current from the high DC voltage capacitor bank. In addition, a microcontroller unit controls all processes, including the capacitor bank charging, triggering, and velocity measurement. Full article

In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems. Full article

Wearable devices, such as smartwatches integrating heart rate and activity sensors, have the potential to transform health monitoring by enabling continuous, near real-time data collection and analytics. In this paper, we present a novel modular architecture for collecting and end-to-end processing of high-resolution signals from wearable sensors. The system obtains minimally processed data directly from the smartwatch and further processes and analyzes the data stream without transmitting it to the device vendor cloud. The standalone operation is made possible by a software stack that provides data cleaning, extraction of physiological metrics, and standardization of the metrics to enable person-to-person and rest-to-activity comparisons. To illustrate the operation of the system, we present examples of datasets from volunteers wearing Garmin Fenix smartwatches for several weeks in free-living conditions. As collected, the datasets contain time series of each interbeat interval and the respiration rate, blood oxygen saturation, and step count every 1 min. From the high-resolution datasets, we extract heart rate variability metrics, which are a source of information about the heart’s response to external stressors. These biomarkers can be used for the early detection of a range of diseases and the assessment of physical and mental performance of the individual. The data collection and analytics system has the potential to broaden the use of smartwatches in continuous near to real-time monitoring of health and well-being. Full article

The healthcare industry has recently faced the issues of enhancing patient care, streamlining healthcare operations, and offering high-quality services at reasonable costs. These crucial issues include general healthcare administration, resource allocation, staffing, patient care priorities, and effective scheduling. Therefore, efficient staff scheduling, resource allocation, and patient assignments are required to address these challenges. To address these challenges, in this paper, we developed a mixed-integer linear programming (MILP) model employing the Gurobi optimization solver. The model includes staff assignments, patient assignments, resource allocations, and overtime hours to minimize healthcare expenditures and enhance patient care. We experimented with the robustness and flexibility of our model by implementing two distinct scenarios, each resulting in two unique optimal solutions. The first experimental procedure yielded an optimal solution with an objective value of 844.0, with an exact match between the best-bound score and the objective value, indicating a 0.0% solution gap. Similarly, the second one produced an optimal solution with an objective value of 539.0. The perfect match between this scenario’s best-bound score and objective value resulted in a 0.0% solution gap, further affirming the model’s reliability. The best-bound scores indicated no significant differences in these two procedures, demonstrating that the solutions were ideal within the allowed tolerances. Full article

Planning and managing the production process are key challenges faced by every manufacturing organization. The main contribution of this article lies in the analysis and design of a planning algorithm that takes into consideration the specifics of this environment. The proposed algorithm encompasses elements of batch production, including a just-in-time approach. The article focuses on scenarios within batch production. Managers of manufacturing and supply companies must ensure smooth fulfillment and uninterrupted production of the agreed-upon quantity of parts. However, this task presents complex challenges. The product portfolio requires meticulous sequencing of production batches, and subsequent parts need to be temporarily stored in their raw state for further processing. Moreover, product variability necessitates frequent adjustments to the production line, resulting in delays. Shortages in manpower additionally place demands on shift organization. The company’s primary objective is to increase production efficiency while simultaneously reducing inventory and minimizing non-standard shift work. The challenge was to reconcile seemingly conflicting company requirements and to concentrate on solutions with swift implementation and minimal costs. Ensuring seamless production operation can be addressed by expanding supporting technologies or by increasing production capacity, such as acquiring an additional production line. However, these options entail costs and do not align with the company’s expectation for immediate impact and cost savings. However, improving production efficiency can also be achieved by altering the approach to production planning, which is the central theme of this article. The key element is ensuring that the customer plan is adhered to while working with a fixed production logic and variable input factors that must account for various non-standard situations. Full article

According to the European Green Deal, excessive carbon emissions are the origin of global warming and must be drastically reduced. Given that the building sector is one of the major sources of carbon emissions, results imperative to limit these emissions, especially in a city context where the density of buildings is commonly higher and rapidly increasing. All stages of the life cycle of a building, including raw material harvesting, manufacturing of products, use phase of the building, end of life, all generate or reduce carbon. The manufacture of construction materials accounts for 11% of all energy and process-related emissions annually. Additionally, recent estimates indicate that over 80% of all product-related environmental impacts of a building are determined during the design phase of the building. These indicators reflect the urgent need to explore a low-carbon measure method for building design. This is here done using a linear regression Reverse Engineering model and percentage calculation. One of the hypotheses formulated relates Global Warming Potential (GWP) of −30.000 CO₂eq or lower (around −165 CO₂eq/m²) in the 25% of a block of houses, to carbon further reductions by 11%. This paper has identified barriers in terms of the databases needed to achieve this task. Full article

Change and innovation are increasingly exerting a significant influence on the daily activities of companies. To ensure optimal control, innovative solutions are employed that are encapsulated in the concept of change management. In the engineering change sector, the proposed approach involves developing solutions and making continuous adjustments to the manufacturing process to enhance productivity and to meet customer needs. Within the automotive industry, companies utilize innovations and process change management to continuously improve and strengthen their position in the market, such as KPI/KPRS and PCI. To achieve this, the present study gathers real digital data from the Romanian branches of two renowned automotive companies. The data regarding change requests include 215 registrations for the first company and 734 registrations for the second company. By employing complex statistical methods such as ANOVA, Student’s t-test, the Mann–Whitney test, and a regression model, the primary objective of this study is to model and to identify the best predictor of change request status. Additionally, this study aims to explore how this change process influences the economic performances of the companies and the performance indicators of change management in manufacturing processes. The findings indicate that, both in the organizations in general and within the automotive industry, when products experience high demand in the market, the number of change requests increases. This highlights the importance of internal optimization of the automation system. Moreover, the study results underscore the crucial role of an effective smart manufacturing and optimal change management system to uphold and to enhance the economic performance of automotive companies. Full article

The modern tourist industry is characterized by an abundance of applied multicriteria decision-making tasks. Several researchers have demonstrated that such tasks can be effectively resolved using aggregation operators based on fuzzy integrals and fuzzy measures. At the same time, the implementation of this mathematical tool is limited by weak intuitive understanding by the practicing specialists of the aggregation process as well as fuzzy measures in general. Some researchers have proposed different aggregation visualization methods, but these methods have several properties that block their wide implementation in decision-making practice. The purpose of this study is to develop a decision-making approach that will allow practitioners to have a clear intuitive vision of the aggregation process and fuzzy measures. This article proposes an approach to decision making in the tourist industry based on the synthesis of the aggregation operator that includes 3D visualization graphics in virtual reality. Firstly, some research devoted to decision-making methods in tourism was assessed along with “smart” tourism, aggregation operators and their visualization. Secondly, a 3D visualization in the form of a balance model was introduced. Thirdly, the method of aggregation-operator synthesis based on the 3D balance model and the 2-order Choquet integral was developed. Finally, an illustrational example of implementing such an approach for resolving the task of assessing and choosing a hotel was described. Full article