ASSESSMENT OF LIBRARY USERS’ FEEDBACK USING MODIFIED MULTILAYER PERCEPTRON NEURAL NETWORKS

This paper investigates the estimation of R-values from transient time series using regression outputs of different neural network architectures, specifically a naive multilayer perceptron (MLP) and a physics-informed neural network (PINN). The PINN incorporates a bias correction for steady-state R-value calculations. The ground truth is derived from transient simulations of wall assemblies. A parameter study examines the prediction accuracy of various MLP architectures within the training process. Further evaluations assess the MLP performances on both validation and independent test datasets. The results indicate that neural networks can effectively enhance R-value estimation. However, in the proposed setup, PINNs did not yield significantly better predictions than conventional MLPs. Future research should explore alternative PINN architectures incorporating more temporal context of the transient input data. Additional studies should also investigate the applicability of more context-aware neural networks, such as convolutional or recurrent neural networks.

In recent years, the usage of virtual assistants to complete tasks like service scheduling and online shopping has increased in both popularity and need. An end user’s task goals are the main objectives of a task-oriented conversation agent, and those should be served effectively and successfully. Beside that, user satisfaction is one of the most important aspect that should be taken care of. Communication with multi-modal responses makes the conversation easier and more attractive. Responses through proper images can improve the quality of a task oriented conversation in terms of user satisfaction. Keeping these aspects in mind, we propose a framework which infuses multi-modality with an end-to-end persuasive task oriented dialogue generation module. Additionally, we create a personalised persuasive multi-modal dialogue (PPMD) corpus with slot, sentiment, and agent action annotation at turn level that contains multi-modal responses from both ends. The results and thorough analysis on this dataset show that the suggested multi-modal persuasive virtual assistant achieves better performance over traditional task-oriented frameworks in terms of user satisfaction.

Higher education institutions' wireless networks have different roles and network requirements, ranging from educational platforms and informative consultations. Currently, the inefficient use of network resources, poor wireless planning, and other factors, affect having a robust and stable network platform. Different authors have investigated the various strategies for the optimization of wireless infrastructures. Still, most of the cases studied aim to improve traditional performance variables without considering maximizing the level of user satisfaction, which represents a flaw that this research paper hopes to solve through SDWN and a predictive model. The authors will determine an appropriate methodology to estimate the user's level of satisfaction through an algorithm or predictive model based on nonlinear multiple regression supported on network performance variables, making a characterization of the project's environment analyzing the wireless conditions. The investigation phases will follow the life cycle guidelines defined by the Cisco PPDIOO methodology (Prepare, Plan, Design, Implement, Operate, Optimize). As a result, it is expected that the project will be the beginning of academic research that will help create strategies to optimize the WiFi network of any educational institution to maximize user satisfaction. In short, the optimization process provides the network with differentiating factors through a modular design with variable modification of parameters according to the users' requirements and needs.

This paper focuses on the various neural network techniques including Multilayer perceptron (MLP) neural network, classifier and self-organizing maps (SOMs). Various aspects of neural network techniques are mentioned in this paper along with the advantages and drawbacks. The neural network models are trained with measured values of the field strength at arbitrary points. The back propagation training algorithm is used for the learning process in MLP. The mechanism of supervised and unsupervised learning are also specified.

The research determined the usage and satisfaction levels with one of two point-of-care (PoC) resources among health care providers in a rural state. In this randomized controlled trial, twenty-eight health care providers in rural areas were stratified by occupation and region, then randomized into either the DynaMed or the AccessMedicine study arm. Study participants were physicians, physician assistants, and nurses. A pre- and post-study survey measured participants' attitudes toward different information resources and their information-seeking activities. Medical student investigators provided training and technical support for participants. Data analyses consisted of analysis of variance (ANOVA), paired t tests, and Cohen's d statistic to compare pre- and post-study effects sizes. Participants in both the DynaMed and the AccessMedicine arms of the study reported increased satisfaction with their respective PoC resource, as expected. Participants in both arms also reported that they saved time in finding needed information. At baseline, both arms reported too little information available, which increased to "about right amounts of information" at the completion of the study. DynaMed users reported a Cohen's d increase of +1.50 compared to AccessMedicine users' reported use of 0.82. DynaMed users reported d2 satisfaction increases of 9.48 versus AccessMedicine satisfaction increases of 0.59 using a Cohen's d. Participants in the DynaMed arm of the study used this clinically oriented PoC more heavily than the users of the textbook-based AccessMedicine. In terms of user satisfaction, DynaMed users reported higher levels of satisfaction than the users of AccessMedicine.

Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip

The Ministry of Education and Culture responded by issuing a policy of learningfrom home, through online learning, the current education system must be ready tomake the leap to transform online learning for all students. E-learning is a distancelearning process whose implementation is supported by computers and internettechnology, so that the learning process can be carried out at any time without ameeting between lecturers and students. From the explanation above, a study wasconducted that measured the Level of Satisfaction of E-learning Students at Indo GlobalMandiri University using the End User Computing Sastification (EUCS) method. Thisresearch will analyze the level of student satisfaction using the E-learning web whichhas an important role in higher education. Analysis of the level of satisfaction usingEUCS is expected to be a measuring tool for the success of a system The model of EndUser Computing Satisfication (EUSC) was developed by Doll & Torkzadeh. EUCS is amethod that can be used to measure the level of satisfaction of users of a system, bycomparing expectations and reality of a system. Evaluation using the EUCS model ismore focused on end user satisfaction with technological aspects. The dimensions inEnd User Computing Satisfaction (EUCS) according to Doll & Torkzadeh are five, namelycontent (Content), accuracy (Accuracy), form (Format).From the results of dataprocessing that has been done previously, it can be concluded in this study that theresults of quantitative analysis on the variable respondents who strongly agree (SS) are80.0%, who agree 20.0% so it can be concluded that user satisfaction with theperformance of e-learning students of Indo Global Mandiri University, Faculty ofInformatics Engineering is very satisfied.

Objectives: The nonlinearity of the stock market is widely accepted all over the world and to reveal such non-linearity the most effective technique has proved to be constructed through application of either data mining or neural network. Pharmaceutical sector is a rapidly growing in Bangladeshi stock market. The objective of this paper is to investigate whether the hybridization of data mining and neural network technique can be applied in predicting the stock price for Pharmaceutical sector of Dhaka Stock Exchange (DSE). Methods/Analysis: This study uses daily trade data for Pharmaceutical sector of DSE. We have analysed the behaviour of daily average price for Pharmaceutical sector of DSE. For this study, 6 top listed pharmaceutical companies have been selected to perform the analysis and selected time frame for the research is 15 years (2000-2015). The analysis is performed in two stages where first stage performs the K-means clustering of data mining method to discover the stock with most useful pattern and second stage applies the nonlinear autoregressive with Exogenous Input neural network method to predict the closing price for the selected stock. Findings: The prediction performance through the hybridization of data mining and neural network technique is evaluated and positive performance improvement of prediction is observed which is very encouraging for investors. The research also depicts that hybridization of data mining and neural network technique can be applied in determining the stock investment decision for Pharmaceutical sector of DSE though the impact of many different information has greater influence in determining the stock price. Novelty/Improvement: We intend to apply the data mining and optimized neural network in predicting stock market. We would like to work with the parameter and learning of the neural network to achieve better result. We will further investigate the effect of various factors viz. dollar price, gold price, FDI, bank interest rate etc. on stock price and index movement.

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

With the development of social economy and technological progress, the integration of higher education and industrial demand has become an important way to enhance the entrepreneurial ability of college students. Currently, universities are manually selecting industries and entrepreneurship majors that integrate industry and education. The application of neural networks has brought new opportunities for the integration of industry and education. We propose a method of integrating industry and education based on neural networks, which optimizes the allocation of educational resources through neural networks and improves the entrepreneurial abilities of college students. By utilizing the deep learning capabilities of neural networks, the corresponding entrepreneurial needs can be intelligently matched with educational resources to achieve precise and personalized educational guidance. The superiority of the algorithm proposed in this study was verified through experiments. The classification accuracy has been improved to 95.5%, with an F1 score of 94.2%, which is 4.6% higher than traditional methods such as multi-layer perceptron. The coverage rate of the recommendation system is 87%, and the novelty index is 0.76, both of which are better than existing models. The success rate of student entrepreneurship has increased from 82.1% to 98.9%, and user satisfaction has increased to 99.1%.

User satisfaction can impact reuse and foster user loyalty. If users feel dissatisfied, they may potentially switch to other competitors. This can impact the sustainability of the company in the future, particularly for Maxim, an online transportation company. Therefore, it is crucial for the company to understand user satisfaction levels to improve specific aspects and achieve desired user satisfaction. This research aims to determine the satisfaction levels of young generation with the Maxim app in Jakarta using the End User Computing Satisfaction (EUCS) approach. EUCS is used to measure the satisfaction of end users with the Maxim application system. An online questionnaire was distributed through Google Form, gathering data from 100 respondents who have used the Maxim app in Jakarta. The data was analyzed using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA). From the CSI calculation, it was found that the level of satisfaction of young generation users on the Maxim application in Jakarta obtained the “Satisfied” criteria. Furthermore, IPA results indicated that improving the effectiveness of the screen display and ensuring prompt driver allocation in the app were prioritized for Maxim. Easy menu navigation, user-friendly interface design, easy-to-use features, and displaying real-time information in the app were identified as aspects to maintain, as users rated these aspects as important and satisfied. Thus, this research is expected to assist the company in improving user satisfaction by identifying areas for improvement to increase the effectiveness and quality of user experience when using the Maxim app.

Research Question: What are the accounting benefits perceived by accountants and internal auditors that entail through Enterprise Resource Planning (ERP) systems? Is there an interaction of ERP accounting benefits with the accountants’ and internal auditors’ satisfaction level in using ERP systems? Motivation: Many organizations are implementing ERP systems extensively while sharing a common database and being real-time. Past scholars summon empirical studies on ERP's impact on accounting benefits and user satisfaction. Therefore, this study was conducted from accountants’ and internal auditors’ perspective as recent scholars shed light on the cruciality of these two groups' perceptions as key ERP users. Idea: This study examines the accounting benefits that entail through the adoption of ERP systems by Sri Lankan companies with regard to ERP user satisfaction from the perspective of accountants and internal auditors. Data: The data were collected from 38 accountants and 19 internal auditors across 24 companies that are implementing ERP systems for more than four years. Tools: A quantitative survey methodology is employed to draw empirical evidence. Findings: The findings confirm that organizational, operational (time), and IT accounting benefits derived from ERP systems significantly and positively influence ERP users' satisfaction. In contrast, operational accounting benefits (cost) received the lowest rating and did not significantly influence user satisfaction. Contribution: While these findings are valuable to accountants and internal auditors to better understand the ERP accounting benefits, ERP consultants and system providers also benefit from identifying ERP accounting features to uplift user satisfaction in developing countries.

In this paper an innovative Radio Resource Management (RRM) algorithm is proposed with the purpose of increasing High Speed Packet Access (HSPA) performances, in terms of system capacity and service quality, when the Multimedia Broadcast Multicast Services (MBMS) is supplied. The proposed RRM algorithm exploits channel quality indications to set up point-to-multipoint connections to subgroups of multicast users and to select the proper modulation and coding schemes on the downlink. The number of subgroups is determined through an optimization technique that also takes into account the user satisfaction. An exhaustive simulation campaign is conducted to compare the proposed algorithm with the most promising approaches in the literature. Comparisons aim to assess the capability of the proposed RRM algorithm to efficiently manage group oriented services by providing an increment in terms of user satisfaction.

PurposeSchools struggle with sustaining their educational innovations (Cohen and Mehta, 2017; Askell-Williams and Koh, 2020) and may benefit from concrete and practical guidance (Askell-Williams and Koh, 2020). A dialogue between staff within schools can be a way to promote self-evaluation regarding the innovation. Therefore, a self-evaluation tool for educators was developed: The Sustainability Meter (TSM). The purpose of the tool is to gain insight into the different perspectives and experiences of stakeholders within the school organization regarding the innovation, as a basis for improvement-directed actions to promote sustainable educational innovation.Design/methodology/approachIn this small-scale study, the authors explored conceptual and instrumental use of the Sustainability Meter in two phases, and also examined user satisfaction. In phase 1, the tool was used under the guidance of the researcher (first author), who supported the chairs in taking steps before the group dialogue took place, and who then guided the dialogue itself as a moderator. In phase 2, work with TSM was organized independently by the schools themselves, supported by the manual. Data were collected in the form of observations of the dialogue, group interviews and documents generated by the participants.FindingsIn terms of conceptual use, in general, participants gained better understanding of each other's perspectives and backgrounds with regard to the (sustainability of the) innovation. The dialogue also led to insights into challenges for growth toward sustainable innovation. For instrumental use, the results of the analysis were incorporated in a plan of action in the majority of the participants' schools. In terms of user satisfaction, participants in all groups perceived TSM as an enjoyable support for high-quality dialogue. This research provides some indications that the tool might lead to sustainable educational innovations.Originality/valueNext to developing an action plan based on the results of the school, the tool also appeared to help breaking up the process in smaller, clearer and more feasible improvement-directed actions. The results of this study further show that the authors could distinguish between three types of instrumental use. The improvement-directed actions in this research often were a combination of this three types: initial solutions, short-term and longer-term measures. This research provides some indications that the tool might lead to sustainable educational innovations.

The rise of mobile computing for Group Decision Support Systems: A comparative evaluation of mobile and desktop