Software Applications Research Articles

The effect of cinnamic acid derivative on the activity of mitochondrial enzymes in brain tissue under conditions of experimental Parkinson's disease

The aim: to evaluate the effect of cinnamic acid derivative on changes in mitochondrial enzymes activity in rats brain tissue under conditions of experimental Parkinson's disease. Materials and methods. Parkinson's disease was modeled in male Wistar rats by direct injection of rotenone solution (5 mg/ml) into the striatum. The analyzed compound 4-hydroxy-3,5-di-tretbutyl cinnamic acid and the reference ethylmethylhydroxypyridine succinate were administered orally in equivalent doses (100 mg/kg) for 30 days from the moment of pathology modeling. Next, a brain supernatant was obtained by differential centrifugation, in which changes in the activity of enzymes: citrate synthase, succinate dehydrogenase, cytochrome c oxidase and aconitase were evaluated. The obtained results were processed statistically. During the analysis, the StatPlus 7.0 application software package was used. Results. During the study, it was found that the administration of 4-hydroxy-3,5-di-tretbutyl cinnamic acid to rats increased the activity of citrate synthase, succinate dehydrogenase, cytochrome c oxidase and aconitase relative to untreated animals by 109.7% (p0.05); 53.6% (p0.05); 65.0% (p0.05) and 63.1% (p0.05), respectively, whereas against the background of the use of the reference, the activity of these enzymes increased by 56.3% (p0.05); 57.7% (p0.05); 71.7% (p0.05) and 49.1% (p0.05), respectively. At the same time, the activity of citrate synthase in animals treated by 4-hydroxy-3,5-di-tretbutyl cinnamic acid was higher than that in rats treated by the reference by 34.2% (p0.05). Conclusions. The study showed that the course administration of 4-hydroxy-3,5-di-tretbutyl cinnamic acid to animals with experimental Parkinson's disease is accompanied by an increase in the activity of mitochondrial enzymes, which may reflect the significant effect of this compound on the processes of mitochondrial biogenesis, mitophagy and generation of mitochondrial reactive oxygen species.

Impact of Digital Innovations on Health Literacy Applied to Patients with Special Needs: A Systematic Review

MHealth strategies have been used in various health areas, and mobile apps have been used in the context of health self-management. They can be considered an adjuvant intervention in oral health literacy, mainly for people with special health needs. Thus, the aim of this study was to identify the improvement of oral health literacy in patients with special needs when using digital platforms. A systematic literature review, based on the Joanna Briggs Institute (JBI) guidelines, was the main research method employed in this study. A search was undertaken in PubMed/MEDLINE and Cochrane Central Register of Controlled Trials (CENTRAL) databases, according to the relevant Mesh descriptors, their synonyms, and free terms (Entry Terms). Studies published between the years 2012 and 2023 were included. Two researchers independently assessed the quality of the included studies by completing the Newcastle–Ottawa Quality Assessment Scale questionnaire. The analysis corpus comprised 5 articles among the 402 articles selected after applying the inclusion/exclusion criteria (k = 0.97). The evidence from the considered articles is consensual regarding the effectiveness of using new technologies and innovations in promoting oral health literacy in patients with special health needs. The interventions were based on using the Illustration Reinforcement Communication System, inspired by the Picture Exchange Communication System, Nintendo® Wii™ TV, virtual reality, smartphones, with software applications to read messages sent, Audio Tactile Performance technique, and Art package. One study had a low-quality assessment, and four had a high quality. The evidence from the articles included in this systematic review is consistent regarding the effectiveness of using new technologies and innovations in promoting oral health literacy in patients with special health needs.

Explore to Mantai, the Ancient Port-city and the International Trade-hub of Maritime Silk Road in Northern Coast of Sri Lanka

Abstract. Mantai Ancient Port City is located in Mannar, Northern Coast of Sri Lanka, archeological reserves declared by the Department of Archeology in Sri Lanka. In concern with historical international trades for almost 2000 years, Mantai had been an excellent port city as discovered by 1980s investigations. Today, this historically valued archeology reserves are being covered by various unauthorized constructions. The artifacts found in 1980s excavations figured out in further research directives to unveil the ancient cultural and social relations that had been experienced over two millennium around the globe. This research paper elaborates change detection of temporal aspects of unauthorized constructions within the respective geographical domain. Change detection researches are based on the historical maps, aerial photographs, satellite images and recent spatial data sources played over the application software. The investigation mission deployed in 1980 had unveiled evidences of civilization history and artifacts from many countries. However the archeological research and investigations were disrupted in 1983 with escalation of civil war in Northern Province. The Research elaborates visual facts and figures of the constructions erected after 1980 against their chronological measures. The research team further discusses about the tremendous variety of fields that Mantai opens to collaborate with the international research organizations.

Smart Phone Application Reminder for Patients with Fixed Orthodontic Appliance to Improve Oral Hygiene

INTRODUCTION: In light of the extensive use of mobile phones and their associated software applications (Apps), as well as the undisputed role of reminders and Apps in behavioral interventions, the goal of this study was to evaluate the usefulness of an application for smartphones (Brush DJ) to enhance oral hygiene compliance in individuals with fixed orthodontic therapy. MATERIALS AND METHODS: In this prospective study, 82 patients between the ages of 18 and 22 were divided into four groups, two of which (the experimental group and the control group) had just begun fixed orthodontic treatment (called New) and the other two (the experimental group and the control group) were have passed fifth months from the beginning of the treatment (called Old). Patients in the intervention group were asked to use the Brush DJ smartphone App after getting traditional oral hygiene training, compared to patients in the control group who got conventional oral hygiene instruction. At baseline (T0), 4 weeks, 8 weeks, and 12 weeks following the start of the study, the plaque index (PI) and marginal bleeding index (MBI) were measured. Only patients with the software completed a questionnaire to determine how frequently and how long they brushed their teeth each day. RESULTS: Both New and Old orthodontic cases in the intervention groups showed improvements in PI, whereas the parameter of bleeding showed no significant differences. During the follow-up period, application usage was associated with a higher brushing frequency and duration. CONCLUSION: Apps might significantly improve the oral hygiene compliance of orthodontic patients by acting as reminders and motivators.

Innovative mobile app solution for facial nerve rehabilitation: a usability analysis.

Facial palsy after vestibular schwannoma surgery is temporary in many cases but can significantly affect patients' quality of life. Physical training-initially guided and subsequently performed by the patient-is of paramount importance for recovery of facial nerve function. The introduction of medical application software (apps) might improve therapy by maintaining motivation for daily home-based training and surveilling patients' rehabilitation progress. We developed a mobile app, "FACEsemper", for home-based facial nerve rehabilitation. This app guides patients through a daily training program comprising six variable exercises, each performed in three repetitions. The app allows the user to customize the exercise intensity for different facial areas and includes a reminder function for daily training. Additional features include photo documentation, a calendar function, training report generation, and the possibility of direct communication with the attending physician. The app's usability was prospectively investigated with 27 subjects, including 8 physicians, 9 patients with facial palsy and 10 healthy subjects, over a two-week period. Usability was assessed using various self-rating questionnaires (i.e., mHealth App Usability Questionnaire, MAUQ; System Usability Scale, SUS; Visual Aesthetics of Apps Inventory, VisAAI) and scores were compared across the groups. The participants reported an average smartphone use of 12.19 years and completed a mean number of 290 ± 163 facial exercises during the study period. Patients used the app significantly more frequently than the other two groups (p = 0.017). The average total scores of the questionnaires were: MAUQ 5.67/7, SUS 89.6/100, VisAAI 5.88/7 and specific rating 6.13/7. In particular, the simplicity of use and craftsmanship of the app were rated very highly. Usability scores did not significantly differ between groups. A primary limitation identified was malfunction of the daily reminder feature in some Android versions. This usability study demonstrated a positive user experience and excellent usability of the FACEsemper app. However, some limitations and areas for improvement were identified. As a next step, the app should be evaluated in a large patient cohort with facial palsy to determine its potential medical benefits for facial rehabilitation compared to traditional training methods.

Методологическое обоснование содержания, структуры и программной реализации базы геопространственных знаний территориальных образований

The article deals with substantiating the contents, structure and software implementation of the geospatial knowledge base. The importance and relevance of forming its infrastructure and the corresponding informational base on the territory in modern geoinformatics and cartography are noted. The requirements for the contents, formal and logical representation of the mentioned base, its structure and connections of the elements at the logical level are given. The authors consider the software implementation of the processes of searching and issuing geospatial knowledge in a visualized form at the request of untrained users in the field of geoinformatics and cartography. The requisitions and information on the special original software, which allows observing the contents of the geospatial knowledge base, are shown. The effectiveness of visualizing the geospatial knowledge base through interactive configuration of software elements is substantiated, based on the metadata principle proposed by the authors, two formalisms and an algorithm for creating corresponding services for geospatial knowledge and other related data. These formalisms of the client’s parameters and server software applications are described, a variant of the geospatial knowledge base structure is given, and the requirements for the necessary software for the effective solution of the task are formulated. The authors` solution is considered in the form of a complex of multicomponent software, used at working with geodata and creating their own interactive maps by poorly trained non-professional users

Quiet Pandemic Amid COVID-19: A literature review on gamification for mental health

Background: COVID-19 affects individuals both physically and mentally. The key is to address emerging needs with a modest technique that helps boost the positive approach in individuals. Gamification offers attempts to engage users with the non-game approach, and its growing popularity in other fields, such as finance and retail, is evident. Objective: This research aims to study the available literature that analyzes and understands using the Gamification approach for mental health in the pandemic. Methods: The author performed a systematic literature review using the PRISMA technique among the selected articles published until Jul 2023, focusing on Gamification, mental health, and COVID in highly reputed record databases, which were peer-reviewed studies. Therefore, the studies using the standard international language of English were eligible for this Study. Results: The Study identified elevated articles exploring Gamification through mobile application software, web-based platforms, and other tools since COVID-19 limited in-person social contact for individual safety during peak time. Conclusion: There are limited studies of Gamification in mental health, and the existing studies suggest that Gamification supports the betterment of mental health. Future studies can explore the different areas of mental health with gamification-based applications or gamified approaches.

Evaluation of an Automated Decision Aid for the Further Referral of Emergency Room Patients—A Prospective Cohort Study.

Instruments for the initial evaluation of emergency room patients as an aid to their onward referral to ambulatory care structures are a matter of current interest. In this study, we assess the safety of the software application SmED-Kontakt+ for this purpose. SmED is an abbreviation for Strukuturierte Medizinische Ersteinschätzung Deutschland, "structured initial medical evaluation in Germany." In this prospective cohort study, we compared the recommendations of SmED-Kontakt+ concerning the time and place of further care with those of the treating physicians. The subjects were adult patients who were able to walk and had presented themselves to the emergency room. Whenever SmED-Kontakt+ assessed the situation less critically than the physicians, and in 5% of the remaining cases, the potential endangerment of patient safety was assessed by an expert panel (expected value <1%). In a total of 1840 cases, SmED-Kontakt+ agreed with the physicians' assessment in 353 (19%). The assessment of SmED-Kontakt+ was more critical in 1221 cases (66%) and less critical in 266 (15%; potential undertriage). There was potential endangerment in 49 cases (2.7%; 95% confidence interval [2.0; 3.5]). Potential endangerment was less common among patients in the more urgent recommendation categories [0.1; 2.6] and more common in the categories of a recommended outpatient physician visit or telephone consultation within or beyond 24 hours [2.4; 17.2]. SmED-Kontakt+ generally assessed these patients' needs for further care as more urgent than the treating physicians did. Nevertheless, the percentage of potentially endangered patients was higher than expected. We conclude that further care should be provided in timely fashion with an obligatory appointment. The sites of further ambulatory care should be immediately accessible and properly equipped. Patients should not be referred for further care at later times on the basis of an SmED-Kontakt+ assessment.

Component-based modeling of cascading failure propagation in directed dual-weight software networks

Software vulnerabilities often lead to cascading failures, resulting in service unavailability and potential breaches of user data. However, existing models for cascading failure propagation typically focus solely on the static design’s calling relationships, disregarding dynamic runtime propagation paths. Moreover, current network topology models primarily consider function calling frequency while overlooking critical factors like internal failure probability and component failure tolerance rates. Yet, these factors significantly influence the actual propagation of software cascading failures. In this study, we address these limitations by incorporating internal failure probabilities and calling frequencies as node and edge weights, respectively. This forms the basis of our component-based directed dual-weight software network cascading failure propagation model. This model encompasses the evaluation of cascading failure propagation through intra-component and inter-component propagation probabilities, alongside the constraint of component failure tolerance rates. Through extensive experiments conducted on six real-world software applications, our model has demonstrated its effectiveness in predicting software cascading failure propagation processes. This method deepens our understanding of software failures and structures, equipping software testers with the knowledge to make well-informed judgments regarding software quality concerns.

Applications for Semantic 3D Streetspace Models and Their Requirements—A Review and Look at the Road Ahead

In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models of buildings or the terrain so far, the increasing availability of data on roads and other transportation infrastructure opened up a range of emerging use cases in the field of semantic 3D streetspace models. While there are already a number of implemented examples, there is also a potential for new use cases not yet established in the field of 3D city modeling, which benefit from detailed representations of roads and their environment. To ensure clarity in our discussions, we introduce an unambiguous distinction between the terms `application domain’, `use case’, `functionality’ and `software application’. Based on these definitions, use cases are categorized according to their primary application domain and discussed with respect to relevant literature and required functionalities. Furthermore, requirements of functionalities towards semantic 3D streetspace models are determined and evaluated in detail with regard to geometric, semantic, topological, temporal and visual aspects. This article aims to give an overview on use cases in the context of semantic 3D streetspace models and to present requirements of respective functionalities, in order to provide insight for researchers, municipalities, companies, data providers, mapping agencies and other stakeholders interested in creating and using a digital twin of the streetspace.

You are not Alone: Confusion with Software Applications in African University Libraries

Building on Nardi and O’Day’s (1999) classic book on information ecologies exploring the complexities of using multiple software applications in settings including libraries, we note that even though the technologies have changed over the decades, the importance of the work of librarians in assisting patrons has not changed much. Keeping up with popular, constantly evolving software packages like EndNote, Koha, NewGenlib, Baobab eLibrary, Openbiblio, Evergreen, Greenstone, and D-Space to support digital collections in various formats remains a constant challenge. constant challenge. Some librarians feel daunted by learning and explaining new software in a constantly changing technological ecosystem, while others embrace the challenge. As well as using software for their work, librarians have the responsibility to help their patrons learn, adapt, and apply new software to meet their information needs more effectively and efficiently. Like other client-facing professionals, librarians must also be mindful of the ‘myths’ (Ehrlich &amp; Cash, 1994) that clients understand their problems and use online information if available. These software-related challenges have been documented in many countries (Asim et al., 2023, Adhikari, 2023), but our research identified a gap in the literature regarding the situation in Africa. The study implies the importance of education for librarians in adopting the right software and avoiding confusion. We conducted a review of LIS papers harvested from the LIS Ebscohost database about this topic in various countries of Africa between 2012 and 2022, identifying 75 papers as relevant according to our criteria. The poster presents the findings of the scoping review.

Exploring the role of online discussion forums in endorsing Egyptian undergraduates’ EFL learning

eLearning Management systems (eLMS) are software applications used in universities worldwide to implement specific educational goals. eLearning platforms have several features that support traditional Face to Face (FTF) teaching and provide valuable opportunities for independent, self-paced learning. One of these features is the Online Discussion Forums (ODFs). This research paper investigates the role of ODFs in endorsing English as a Foreign Language (EFL) learning by examining students' perceptions of its accessibility, peer/instructor interaction, assessment criteria, and discussion topics. The paper also investigates the influence of gender on students' perceptions of the role of ODFs. This is a non-experimental research design, and 108 Egyptian undergraduates completed a semi-structured questionnaire of five dimensions. Percentages of frequency were calculated and quantitatively analysed via the SPSS. Research findings revealed male and female students’ positive perceptions of using ODFs in their learning process. Results also revealed that students highly perceived choice of topics and assessment as crucial factors for successful learning via ODFs, followed by peer/instructor interaction and accessibility of the tool. Implications for further research include employing ODFs to improve project-based teaching in different disciplines.

Application of interactive software in classrooms: a case of GeoGebra in learning geometry in secondary schools in Uganda

The incorporation of subject-specific technologies in teaching and learning mathematics has been extensively investigated in different areas outside Uganda. The benefits are extensive, although the adoption of these technologies has been threatened by teachers needing help to apply them effectively for instruction. While this alone would aptly justify a comprehensive assessment of its applicability on the local scene, the post-COVID-19 era dynamics escalated its urgency and relevance. Thus, third-year pre-service mathematics teachers’ knowledge and aptitude to use GeoGebra in teaching and learning geometry were developed in this study. In addition, the consequence of using this software on students’ achievement in geometry was investigated using a quantitative research approach. Ninety-four third-year pre-service mathematics teachers completed a three-week intensive capacity-building workshop using GeoGebra. Their self-ratings on their technological pedagogical content knowledge (TPACK) before and after the training were measured using a self-administered questionnaire. A total of 406 students from four schools in Central Uganda participated in the quasi-experiment, with 199 and 207 students assigned to the experimental and control groups, respectively. Pre- and post-achievement tests were meted out to the students. While descriptive statistics showed a positive change in the teachers’ technological knowledge (TK), technological pedagogical knowledge (TPK), technological content knowledge (TCK), and TPACK, independent samples t-tests revealed a substantial difference in the post-test mean scores between the two groups since the experimental group students outshone those in the control group. These findings are consistent with global literature, and thus, schools in Uganda should adopt the use of GeoGebra through a continuous capacity-building approach, while considering the challenges and limitations associated with the software.

Development of a silicon carbide radiation detection system and experimentation of the system performance

Silicon carbide (SiC) detectors have excellent radiation detection capabilities for various radiation particles, including high energy resolution, fast response times, and good radiation resistance. A SiC radiation detection system was developed to measure the neutron fluence rate and the γ-ray dose rate in high intensity radiation fields. The system was composed of two SiC detectors, a temperature monitor, two preamplifiers for each SiC detector, a data acquisition unit with two signal channels, three pairs of communication devices, and an application software to analyze and visualize the measurement data. The two SiC detectors were fabricated based on two kinds of 4H-SiC diodes and used to respectively respond to neutrons and γ-rays. Repeated experiments showed that the two SiC detectors of the system can respond to α-particles, neutrons, and γ-rays. To verify the performance of the SiC detection system, including the response linearity of the neutron fluence rate, the measurement range of the γ-ray dose rate, and the radiation resistance of the SiC radiation detectors, the system was tested in multiple neutron and γ-ray fields. The tests results show the system can measure the neutron fluence rate from 5.64 × 10 2 cm−2 s−1 to 1.03 × 10 5 cm−2 s−1 with excellent linearity response, and the γ-ray dose rate from 0.005 Gy/h to 20 Gy/h. Furthermore, the SiC detectors demonstrated good radiation resistance. The neutron and γ-ray radiation field can still be measured stably by the system after exposure to neutron fluence of 1.07 × 10 14 cm−2 and γ-ray dose of 3.52 × 10 4 Gy. This work is the preliminary research to continue the exploration how to measure the n/γ hybrid fields accurately using SiC detectors considering the different energy of neutrons.

Teachers’ Utilization of Information and Communications Technology in Blended Learning Modality

The United Nations Educational, Scientific, and Cultural Organization (UNESCO) claims that when teachers are trained in ICT and possess a strong digital literacy, they can help students develop higher-order thinking skills, offer unique and innovative ways for them to communicate what they know, and better equip them to deal with the constant changes in technology that occur in both the workplace and society at large. Given this background, this study investigated the extent of teachers’ utilization of information and communications technology in blended learning modality in one of the schools in a fifth-class municipality in Central Philippines during the School Year 2022-2023. The study used a descriptive research design and a self-made questionnaire to gather baseline data from 34 public secondary school teachers. The result of the study showed that the extent of teachers’ utilization of ICT in blended learning modality in software applications and online platforms was high. Furthermore, there was no significant difference in the extent of teachers’ utilization of ICT in blended learning modality when respondents were grouped according to the variables above. Various recommendations were provided to address the lowest mean scores in both areas, like the BLOCKED TIME Program and the utilization of various online platforms like Group Chats and Google Meet, among others. Keywords: Information and communications technology, teachers’ utilization of ICT, blended learning modality

Research on Medical Device Software Based on Artificial Intelligence and Machine Learning Technologies

With the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, new opportunities for innovation and application in medical device software have emerged. This paper explores the current research on medical device software based on AI and ML, analyzing the practical applications and outcomes of these technologies in the medical field. First, the basic concepts of AI and ML and their significance in medical devices are introduced. Then, an in-depth analysis of the functional requirements of medical device software, user experience, and relevant regulations is provided. Next, the paper discusses the specific applications of AI and ML in data processing, model training, and software implementation, showcasing their practical effects through case studies. Finally, the paper summarizes the current technical challenges and future opportunities, hoping to provide insights for further development in the medical device industry.

Application of 3D-Slicer Software in the Treatment of Gliomas.

To explore the application of 3D-Slicer multimodal image fusion technology in the surgical treatment of gliomas. A retrospective analysis of clinical data from 48 cases of intracranial gliomas treated with 3D-Slicer multimodal image fusion technology from September 2021 to February 2023. Preoperative cranial CT, MRI, TOF-MRA, DTI, and MRV sequence examinations were completed. DICOM format medical imaging data were processed using the 3D-Slicer software. Multimodal fusion and 3-dimensional reconstruction of the tumor and surrounding structures were conducted to clarify the relationship between the tumor and surrounding structures. The surgery path was simulated through 3D virtual visualization images, enabling precise preoperative planning and assisting in surgical treatment. Out of 48 patients, complete tumor resection was achieved in 45 cases. One case encountered severe postoperative cerebral edema, no infections were reported, 1 patient had slightly reduced muscle strength after surgery compared with before, and no cases of severe neurological function damage or surgical death were recorded. 3D-Slicer multimodal image fusion technology can assist in detailed preoperative planning for the surgical treatment of intracranial glioma, reducing surgical complications, improving the rate of total tumor resection, and reducing postoperative complications.

Trimodal Watch-Type Wearable Health Monitoring Device

In the domain of healthcare, wearable health monitoring devices have emerged as essential tools for the advancement of patient health tracking. These devices facilitate the provision of real-time medical data to clinicians, enabling early diagnosis, timely intervention, and enhanced management of individual health. This study introduces an innovative trimodal wearable health monitoring device in the form of a wristwatch. The device integrates a breath analyzer for the assessment of gaseous phase biomarkers, a sweat analyzer for the evaluation of aqueous-phase biomarkers, and an infrared sensor for the measurement of body temperature in the optical phase. Engineered on a compact 3 cm × 3 cm printed circuit board, the device has been optimized for wearability, power efficiency, and seamless integration with both wired and wireless charging and communication systems. Furthermore, custom software applications, designed for both Windows and Android platforms, have been developed to facilitate intuitive data visualization and storage on personal computers and smartphones. Empirical results from real-time chemical testing substantiate the device’s efficacy and potential as an advanced solution for wearable health monitoring.

The “Cognitive” Architectural Design Process and Its Problem with Recent Artificial Intelligence Applications

The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations without fully understanding how spatial configurations influence human cognition and behavior. While recent AI applications in architecture, such as Computer-Aided Drafting (CAD), Building Information Modeling (BIM), and interactive web and VR presentations, show promising advancements, AI still struggles with complex architectural functions. AI lacks the creativity and imagination inherent in human cognition. It operates based on fixed programming, producing specific outcomes and requiring human oversight to apply insights from one dataset to another. The primary challenge in using AI for architectural design is ensuring minimal design flaws, as replicating human cognitive abilities with AI and various machine learning techniques remains difficult. This research paper aims to explore the relationship between cognitive science, artificial intelligence, and the architectural design process through four main objectives: First, to investigate the integration of AI with current architectural software applications. Second, to examine potential connections between AI and major architectural design trends. Third, to define two frameworks for the Cognitive Architectural Design Process to guide the development of AI systems in architectural design by analyzing key cognitive design theories. Finally, to create a proposed &amp;quot;Architectural Design Process-Cognitive Pilot Map&amp;quot; from an architectural perspective to aid AI programmers in developing architecture design software applications.

Leveraging Large Language Model ChatGPT for enhanced understanding of end-user emotions in social media feedbacks

For software evolution, user feedback has become a meaningful way to improve applications. Recent studies show a significant increase in analyzing end-user feedback from various social media platforms for software evolution. However, less attention has been given to the end-user feedback for low-rating software applications. Also, such approaches are developed mainly on the understanding of human annotators who might have subconsciously tried for a second guess, questioning the validity of the methods. For this purpose, we proposed an approach that analyzes end-user feedback for low-rating applications to identify the end-user opinion types associated with negative reviews (an issue or bug). Also, we utilized Generative Artificial Intelligence (AI), i.e., ChatGPT, as an annotator and negotiator when preparing a truth set for the deep learning (DL) classifiers to identify end-user emotion. For the proposed approach, we first used the ChatGPT Application Programming Interface (API) to identify negative end-user feedback by processing 71853 reviews collected from 45 apps in the Amazon store. Next, a novel grounded theory is developed by manually processing end-user negative feedback to identify frequently associated emotion types, including anger, confusion, disgust, distrust, disappointment, fear, frustration, and sadness. Next, two datasets were developed, one with human annotators using a content analysis approach and the other using ChatGPT API with the identified emotion types. Next, another round is conducted with ChatGPT to negotiate over the conflicts with the human-annotated dataset, resulting in a conflict-free emotion detection dataset. Finally, various DL classifiers, including LSTM, BILSTM, CNN, RNN, GRU, BiGRU and BiRNN, are employed to identify their efficacy in detecting end-users emotions by preprocessing the input data, applying feature engineering, balancing the data set, and then training and testing them using a cross-validation approach. We obtained an average accuracy of 94%, 94%, 93%, 92%, 91%, 91%, and 85%, with LSTM, BILSTM, RNN, CNN, GRU, BiGRU and BiRNN, respectively, showing improved results with the truth set curated with human and ChatGPT. Using ChatGPT as an annotator and negotiator can help automate and validate the annotation process, resulting in better DL performances.