https://reunir.unir.net/handle/123456789/13927 2024-11-08T12:41:07Z 2024-11-08T12:41:07Z González-Crespo, Rubén https://reunir.unir.net/handle/123456789/13947 2023-06-29T11:51:58Z

Editor's Note González-Crespo, Rubén The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI – provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances in Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present regular issue includes 13 articles. The first block of articles deals with problems related to images as diverse as the artificial generation of images or the optimization of their storage and transmission through compression techniques. The applications are very diverse, including the identification of forgeries, tumors or even misplaced face masks. Another block contains only one paper on speech recognition targeted on specific users suffering from dysarthria. Other block of two articles focuses on the education field problems of automation of teachers’ certification processes or prediction of students’ academic failure. Last block of articles covers services and products, commerce, marketing and user experience issues, as well as the ethical implications of AI. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T11:00:57Z No. of bitstreams: 1 ijimai7_7_0.pdf: 66853 bytes, checksum: 0aba5440ad126684b1d595a028e101c5 (MD5); Made available in DSpace on 2022-12-20T11:00:57Z (GMT). No. of bitstreams: 1 ijimai7_7_0.pdf: 66853 bytes, checksum: 0aba5440ad126684b1d595a028e101c5 (MD5)

Hussein Arif, Zainab Mahmoud, Moamin Hameed Abdulkareem, Karrar Kadry, Seifedine Abed Mohammed, Mazin Nasser Al-Mhiqani, Mohammed Al-Waisy, Alaa S. Nedoma, Jan https://reunir.unir.net/handle/123456789/13946 2022-12-20T10:24:11Z

Adaptive Deep Learning Detection Model for Multi-Foggy Images Hussein Arif, Zainab; Mahmoud, Moamin; Hameed Abdulkareem, Karrar; Kadry, Seifedine; Abed Mohammed, Mazin; Nasser Al-Mhiqani, Mohammed; Al-Waisy, Alaa S.; Nedoma, Jan The fog has different features and effects within every single environment. Detection whether there is fog in the image is considered a challenge and giving the type of fog has a substantial enlightening effect on image defogging. Foggy scenes have different types such as scenes based on fog density level and scenes based on fog type. Machine learning techniques have a significant contribution to the detection of foggy scenes. However, most of the existing detection models are based on traditional machine learning models, and only a few studies have adopted deep learning models. Furthermore, most of the existing machines learning detection models are based on fog density-level scenes. However, to the best of our knowledge, there is no such detection model based on multi-fog type scenes have presented yet. Therefore, the main goal of our study is to propose an adaptive deep learning model for the detection of multi-fog types of images. Moreover, due to the lack of a publicly available dataset for inhomogeneous, homogenous, dark, and sky foggy scenes, a dataset for multi-fog scenes is presented in this study (https://github.com/Karrar-H-Abdulkareem/Multi-Fog-Dataset). Experiments were conducted in three stages. First, the data collection phase is based on eight resources to obtain the multi-fog scene dataset. Second, a classification experiment is conducted based on the ResNet-50 deep learning model to obtain detection results. Third, evaluation phase where the performance of the ResNet-50 detection model has been compared against three different models. Experimental results show that the proposed model has presented a stable classification performance for different foggy images with a 96% score for each of Classification Accuracy Rate (CAR), Recall, Precision, F1-Score which has specific theoretical and practical significance. Our proposed model is suitable as a pre-processing step and might be considered in different real-time applications. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T10:24:11Z No. of bitstreams: 1 ijimai7_7_3.pdf: 602150 bytes, checksum: 528455ea70d04817764aeae63ad6fb9d (MD5); Made available in DSpace on 2022-12-20T10:24:11Z (GMT). No. of bitstreams: 1 ijimai7_7_3.pdf: 602150 bytes, checksum: 528455ea70d04817764aeae63ad6fb9d (MD5)

Hinderks, Andreas Winter, Dominique Domínguez Mayo, Francisco José Escalona, María José Thomaschewski, Jörg https://reunir.unir.net/handle/123456789/13945 2022-12-20T10:13:18Z

UX Poker: Estimating the Influence of User Stories on User Experience in Early Stage of Agile Development Hinderks, Andreas; Winter, Dominique; Domínguez Mayo, Francisco José; Escalona, María José; Thomaschewski, Jörg Agile methods are used more and more frequently to develop products by reducing development time. Requirements are typically written in user stories or epics. In this paper, a new method called UX Poker is presented. This is a method to estimate the impact of a user story on user experience before development. Thus, there is the opportunity that the product backlog can also be sorted according to the expected UX. To evaluate UX Poker, a case study was conducted with four agile teams. Besides, a workshop followed by a questionnaire was conducted with all four agile teams. The goal of being able to estimate the UX even before development was achieved. Using UX Poker to create another way to sort the product backlog can be considered achieved in this first evaluation. The results show that UX Poker can be implemented in a real- life application. Additionally, during the use of UX Poker, it was found that a shared understanding of UX began. The participants clarified in the team discussion about UX Poker what related to influence the user stories had on UX and what UX meant for their product. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T10:13:18Z No. of bitstreams: 1 ijimai7_7_11.pdf: 256171 bytes, checksum: 2c20e6e6daddf62c4d9ebf8cd2908e2a (MD5); Made available in DSpace on 2022-12-20T10:13:18Z (GMT). No. of bitstreams: 1 ijimai7_7_11.pdf: 256171 bytes, checksum: 2c20e6e6daddf62c4d9ebf8cd2908e2a (MD5)

López Rivero, Alfonso José Beato, M. Encarnación Muñoz Martínez, César Cortiñas Vázquez, Pedro Gonzalo https://reunir.unir.net/handle/123456789/13944 2022-12-20T10:04:49Z

Empirical Analysis of Ethical Principles Applied to Different AI Uses Cases López Rivero, Alfonso José; Beato, M. Encarnación; Muñoz Martínez, César; Cortiñas Vázquez, Pedro Gonzalo In this paper, we present an empirical study on the perception of the ethical challenges of artificial intelligence groups in the classification made by the European Union (EU). The study seeks to identify the ethical principles that cause the greatest concern among the population, analyzing these characteristics among different actors. The main study analyses the difference between Information and Communications Technology (ICT) professionals and the rest of the population. Along with this study, we conducted a gender study; in addition, we studied differences between university students, classified as future professionals who can work in Artificial Intelligence, and other university students. We believe that this work is a starting point for an informed debate in the scientific community and industry on the ethical implications of artificial intelligence based on the classification of ethical principles made by the EU, which can be extrapolated to any analysis carried out on the use of data to apply them in algorithms based on Artificial Intelligence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T10:04:49Z No. of bitstreams: 1 ijimai7_7_12.pdf: 395197 bytes, checksum: c1aa6db4da4fc00ea66c89e3f05f3920 (MD5); Made available in DSpace on 2022-12-20T10:04:49Z (GMT). No. of bitstreams: 1 ijimai7_7_12.pdf: 395197 bytes, checksum: c1aa6db4da4fc00ea66c89e3f05f3920 (MD5)

Regueras, Luisa M. Verdú, María J de Castro, Juan-Pablo https://reunir.unir.net/handle/123456789/13943 2023-06-26T08:57:08Z

A Rule-Based Expert System for Teachers’ Certification in the Use of Learning Management Systems Regueras, Luisa M.; Verdú, María J; de Castro, Juan-Pablo In recent years and accelerated by the arrival of the COVID-19 pandemic, Learning Management Systems (LMS) are increasingly used as a complement to university teaching. LMS provide an important number of resources and activities that teachers can freely select to complement their teaching, which means courses with different usage patterns difficult to characterize. This study proposes an expert system to automatically classify courses and certify teachers’ LMS competence from LMS logs. The proposed system uses clustering to stablish the classification scheme. From the output of this algorithm, it defines the rules used to classify courses. Data registered from a university virtual campus with 3,303 courses and two million interactive events have been used to obtain the classification scheme and rules. The system has been validated against a group of experts. Results show that it performs successfully. Therefore, it can be concluded that the system can automatically and satisfactorily evaluate and certify the teachers’ LMS competence evidenced in their courses. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T09:57:57Z No. of bitstreams: 1 ijimai7_7_8.pdf: 405410 bytes, checksum: 9fd1999cc93eae210c239882c736428c (MD5); Made available in DSpace on 2022-12-20T09:57:57Z (GMT). No. of bitstreams: 1 ijimai7_7_8.pdf: 405410 bytes, checksum: 9fd1999cc93eae210c239882c736428c (MD5)

Fraile-Narvaez, Marcelo Sagredo-Olivenza, Ismael McGowan, Nadia https://reunir.unir.net/handle/123456789/13936 2023-08-30T14:52:52Z

Painting Authorship and Forgery Detection Challenges with AI Image Generation Algorithms: Rembrandt and 17th Century Dutch Painters as a Case Study Fraile-Narvaez, Marcelo; Sagredo-Olivenza, Ismael; McGowan, Nadia Image authorship attribution presents many challenges and difficulties which have increased with the capabilities presented by synthetic image generation through different artificial intelligence algorithms available today. The hypothesis in this research considers the possibility of using artificial intelligence as a tool to detect forgeries through the usage of a deep learning algorithm. The proposed algorithm was trained using a dataset comprised of paintings by Rembrandt and other 17th century Dutch painters. Three experiments were performed with the proposed algorithm. The first was to build a classifier able to ascertain whether a painting belongs to the Rembrandt or non-Rembrandt category, depending on whether it was painted by this author or not. The second tests included other 17th century painters in four categories. Artworks could be classified as Rembrandt, Eeckhout, Leveck or other Dutch painters. The third experiment used paintings generated by Dall-e 2 and attempted to classify them using the prior categories. Experiments confirmed the hypothesis with best executions reaching accuracy rates of more than 90%. Future research with extended datasets and improved image resolution are suggested to improve the obtained results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T11:36:31Z No. of bitstreams: 1 ijimai7_7_1.pdf: 357350 bytes, checksum: 54def274ce6c750fdcc43ac62677a515 (MD5); Made available in DSpace on 2022-12-16T11:36:31Z (GMT). No. of bitstreams: 1 ijimai7_7_1.pdf: 357350 bytes, checksum: 54def274ce6c750fdcc43ac62677a515 (MD5)

Khemchandani, Maahi Amit Jadhav, Shivajirao Manikra Iyer, B. R. https://reunir.unir.net/handle/123456789/13935 2022-12-16T10:45:19Z

Brain Tumor Segmentation and Identification Using Particle Imperialist Deep Convolutional Neural Network in MRI Images Khemchandani, Maahi Amit; Jadhav, Shivajirao Manikra; Iyer, B. R. For the past few years, segmentation for medical applications using Magnetic Resonance (MR) images is concentrated. Segmentation of Brain tumors using MRIpaves an effective platform to plan the treatment and diagnosis of tumors. Thus, segmentation is necessary to be improved, for a novel framework. The Particle Imperialist Deep Convolutional Neural Network (PI-Deep CNN) suggested framework is intended to address the problems with segmenting and categorizing the brain tumor. Using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm, the input MRI brain image is segmented, and then features are extracted using the Scatter Local Neighborhood Structure (SLNS) descriptor. Combining the scattering transform and the Local Neighborhood Structure (LNS) descriptor yields the proposed descriptor. A suggested Particle Imperialist algorithm-trained Deep CNN is then used to achieve the tumor-level classification. Different levels of the tumor are classified by the classifier, including Normal without tumor, Abnormal, Malignant tumor, and Non-malignant tumor. The cell is identified as a tumor cell and is subjected to additional diagnostics, with the exception of the normal cells that are tumor-free. The proposed method obtained a maximum accuracy of 0.965 during the experimentation utilizing the BRATS database and performance measures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T10:45:19Z No. of bitstreams: 1 ijimai7_7_4.pdf: 1266936 bytes, checksum: 9ba9c6bbe3fef052c28a25d529aad2d6 (MD5); Made available in DSpace on 2022-12-16T10:45:19Z (GMT). No. of bitstreams: 1 ijimai7_7_4.pdf: 1266936 bytes, checksum: 9ba9c6bbe3fef052c28a25d529aad2d6 (MD5)

Moon, Chang Bae Lee, Jong Yeol Kim, Byeong Man https://reunir.unir.net/handle/123456789/13934 2022-12-16T10:00:55Z

A Fuzzy-Based Multimedia Content Retrieval Method Using Mood Tags and Their Synonyms in Social Networks Moon, Chang Bae; Lee, Jong Yeol; Kim, Byeong Man The preferences of Web information purchasers are rapidly evolving. Cost-effectiveness is now becoming less regarded than cost-satisfaction, which emphasizes the purchaser’s psychological satisfaction. One method to improve a user’s cost-satisfaction in multimedia content retrieval is to utilize the mood inherent in multimedia items. An example of applications using this method is SNS (Social Network Services), which is based on folksonomy, but its applications encounter problems due to synonyms. In order to solve the problem of synonyms in our previous study, the mood of multimedia content is represented with arousal and valence (AV) in Thayer’s two-dimensional model as its internal tag. Although some problems of synonyms could now be solved, the retrieval performance of the previous study was less than that of a keyword-based method. In this paper, a new method that can solve the synonym problem is proposed, while simultaneously maintaining the same performance as the keyword-based approach. In the proposed method, a mood of multimedia content is represented with a fuzzy set of 12 moods of the Thayer model. For the analysis, the proposed method is compared with two methods, one based on AV value and the other based on keyword. The analysis results demonstrate that the proposed method is superior to the two methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T10:00:55Z No. of bitstreams: 1 ijimai7_7_7.pdf: 768608 bytes, checksum: d517d301ec21de2de6abdb13ee3f430c (MD5); Made available in DSpace on 2022-12-16T10:00:55Z (GMT). No. of bitstreams: 1 ijimai7_7_7.pdf: 768608 bytes, checksum: d517d301ec21de2de6abdb13ee3f430c (MD5)

Sahu, Laxmi Priya Pradhan, Gayadhar Singh, Jyoti Prakash https://reunir.unir.net/handle/123456789/13933 2022-12-16T08:21:37Z

Modeling Sub-Band Information Through Discrete Wavelet Transform to Improve Intelligibility Assessment of Dysarthric Speech Sahu, Laxmi Priya; Pradhan, Gayadhar; Singh, Jyoti Prakash The speech signal within a sub-band varies at a fine level depending on the type, and level of dysarthria. The Mel-frequency filterbank used in the computation process of cepstral coefficients smoothed out this fine level information in the higher frequency regions due to the larger bandwidth of filters. To capture the sub-band information, in this paper, four-level discrete wavelet transform (DWT) decomposition is firstly performed to decompose the input speech signal into approximation and detail coefficients, respectively, at each level. For a particular input speech signal, five speech signals representing different sub-bands are then reconstructed using inverse DWT (IDWT). The log filterbank energies are computed by analyzing the short-term discrete Fourier transform magnitude spectra of each reconstructed speech using a 30-channel Mel-filterbank. For each analysis frame, the log filterbank energies obtained across all reconstructed speech signals are pooled together, and discrete cosine transform is performed to represent the cepstral feature, here termed as discrete wavelet transform reconstructed (DWTR)- Mel frequency cepstral coefficient (MFCC). The i-vector based dysarthric level assessment system developed on the universal access speech corpus shows that the proposed DTWRMFCC feature outperforms the conventional MFCC and several other cepstral features reported for a similar task. The usages of DWTR- MFCC improve the detection accuracy rate (DAR) of the dysarthric level assessment system in the text and the speaker-independent test case to 60.094 % from 56.646 % MFCC baseline. Further analysis of the confusion matrices shows that confusion among different dysarthric classes is quite different for MFCC and DWTR-MFCC features. Motivated by this observation, a two-stage classification approach employing discriminating power of both kinds of features is proposed to improve the overall performance of the developed dysarthric level assessment system. The two-stage classification scheme further improves the DAR to 65.813 % in the text and speaker- independent test case. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T08:21:37Z No. of bitstreams: 1 ijimai7_7_6.pdf: 355090 bytes, checksum: 2b3f70053a83aef40a9570661816538e (MD5); Made available in DSpace on 2022-12-16T08:21:37Z (GMT). No. of bitstreams: 1 ijimai7_7_6.pdf: 355090 bytes, checksum: 2b3f70053a83aef40a9570661816538e (MD5)

Lies, Jan https://reunir.unir.net/handle/123456789/13932 2022-12-16T08:07:54Z

Marketing Intelligence: Boom or Bust of Service Marketing? Lies, Jan Marketing intelligence fosters two major developments within digital service marketing. On the one hand, a boom of services seems to have evolved, accelerated by the opportunities of marketing intelligence. It has contributed to the optimization of customer experiences, e.g., supported by mobile, personalized, and customized marketing services. On the other hand, (digital) self-services are likely to pervert the term “service”. Lifecycle marketing, including annoying marketing communication in real-time, automated price adjustment and programmatic advertising based on artificial intelligence, affects the vision of fully standardized marketing automation. Additionally, there are incentives to pollute the digital information in order to manufacture opinions. Fake news is one popular example. This leads to the (open) question if marketing intelligence means service boom or bust of marketing. This contribution aims to elaborate the boom-and-bust aspects of marketing intelligence and suggests a trade-off. The method applied in this paper will be a descriptive and conceptual literature review, through which the paradigmatic thoughts will be juxtaposed from the perspective of service. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T08:07:54Z No. of bitstreams: 1 ijimai7_7_13.pdf: 169541 bytes, checksum: ff93eba5b42aa5bd7aeada5b596b4573 (MD5); Made available in DSpace on 2022-12-16T08:07:54Z (GMT). No. of bitstreams: 1 ijimai7_7_13.pdf: 169541 bytes, checksum: ff93eba5b42aa5bd7aeada5b596b4573 (MD5)

Rincón-Flores, Elvira G. López-Camacho, Eunice Mena, Juanjo Olmos, Omar https://reunir.unir.net/handle/123456789/13931 2022-12-16T07:52:48Z

Teaching through Learning Analytics: Predicting Student Learning Profiles in a Physics Course at a Higher Education Institution Rincón-Flores, Elvira G.; López-Camacho, Eunice; Mena, Juanjo; Olmos, Omar Learning Analytics (LA) is increasingly used in Education to set prediction models from artificial intelligence to determine learning profiles. This study aims to determine to what extent K-nearest neighbor and random forest algorithms could become a useful tool for improving the teaching-learning process and reducing academic failure in two Physics courses at the Technological Institute of Monterrey, México (n = 268). A quasi-experimental and mixed method approach was conducted. The main results showed significant differences between the first and second term evaluations in the two groups. One of the main findings of the study is that the predictions were not very accurate for each student in the first term evaluation. However, the predictions became more accurate as the algorithm was fed with larger datasets from the second term evaluation. This result indicates how predictive algorithms based on decision trees, can offer a close approximation to the academic performance that will occur in the class, and this information could be use along with the personal impressions coming from the teacher. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T07:52:47Z No. of bitstreams: 1 ijimai7_7_9.pdf: 977826 bytes, checksum: 622f301873713d52a8e6b1096e914ca3 (MD5); Made available in DSpace on 2022-12-16T07:52:47Z (GMT). No. of bitstreams: 1 ijimai7_7_9.pdf: 977826 bytes, checksum: 622f301873713d52a8e6b1096e914ca3 (MD5)

Kaur, Surinder Chaudhary, Gopal Dinesh Kumar, Javalkar Pillai, Manu S. Gupta, Yash Khari, Manju García-Díaz, Vicente Parra Fuente, Javier https://reunir.unir.net/handle/123456789/13930 2023-05-19T11:08:54Z

Optimizing Fast Fourier Transform (FFT) Image Compression using Intelligent Water Drop (IWD) Algorithm Kaur, Surinder; Chaudhary, Gopal; Dinesh Kumar, Javalkar; Pillai, Manu S.; Gupta, Yash; Khari, Manju; García-Díaz, Vicente; Parra Fuente, Javier Digital image compression is the technique in digital image processing where special attention is provided in decreasing the number of bits required to represent a digital image. A wide range of techniques have been developed over the years, and novel approaches continue to emerge. This paper proposes a new technique for optimizing image compression using Fast Fourier Transform (FFT) and Intelligent Water Drop (IWD) algorithm. IWD-based FFT Compression is a emerging ethodology, and we expect compression findings to be much better than the methods currently being applied in the domain. This work aims to enhance the degree of compression of the image while maintaining the features that contribute most. It optimizes the FFT threshold values using swarm-based optimization technique (IWD) and compares the results in terms of Structural Similarity Index Measure (SSIM). The criterion of structural similarity of image quality is based on the premise that the human visual system is highly adapted to obtain structural information from the scene, so a measure of structural similarity provides a reasonable estimate of the perceived image quality. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-14T13:34:35Z No. of bitstreams: 1 ijimai7_7_5.pdf: 1424355 bytes, checksum: 2c436692e61ddc6c505755cec07a1376 (MD5); Made available in DSpace on 2022-12-14T13:34:35Z (GMT). No. of bitstreams: 1 ijimai7_7_5.pdf: 1424355 bytes, checksum: 2c436692e61ddc6c505755cec07a1376 (MD5)

Bhaik, Anubha Singh, Vaishnavi Gandotra, Ekta Gupta, Deepak https://reunir.unir.net/handle/123456789/13929 2022-12-14T13:18:35Z

Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19 Bhaik, Anubha; Singh, Vaishnavi; Gandotra, Ekta; Gupta, Deepak Coronavirus disease 2019 has had a pressing impact on people all around the world. Ceasing the spread of this infectious disease is the urgent need of the hour. A vital method of protection against the virus is wearing masks in public areas. Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people. In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly. A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures. OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification. The method has been implemented on various input images and classification results have been obtained for the same. The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively. Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-14T13:18:35Z No. of bitstreams: 1 ijimai7_7_2.pdf: 700700 bytes, checksum: e8748c9b4bcea9a2e54cd3b267c2a1a6 (MD5); Made available in DSpace on 2022-12-14T13:18:35Z (GMT). No. of bitstreams: 1 ijimai7_7_2.pdf: 700700 bytes, checksum: e8748c9b4bcea9a2e54cd3b267c2a1a6 (MD5)

Majumder, Soumi Chowdhury, Soumalya Dey, Nilanjan Santosh, K. C. https://reunir.unir.net/handle/123456789/13928 2022-12-14T13:10:14Z

Balance Your Work-Life: Personal Interactive Web-Interface Majumder, Soumi; Chowdhury, Soumalya; Dey, Nilanjan; Santosh, K. C. The term work-life balance can be described as a path to manage stresses and burnouts in the workplace. In this Covid-19 pandemic, work-from-home practice includes both personal and professional spaces as employees, more often, stay digitally connected. As a result, personal life hardly can be separated, which will potentially create imbalanced life, which creates problems regarding physical and mental health of the employees. In such unprecedented situations, we are required to maintain and/or integrate balanced work-life. A balanced work-life gives employees a stress-free environment to work and improves employees' mental and physical health conditions and relationships. In this study, we focus on maintaining a proper work-life balance through a monitoring tool, the ‘Wheel of Life.’ Considering the drastic changes in work culture (due to Covid-19, for example), we introduce an interactive interface based on ‘Wheel of life’ concept. Our interface helps tune various important factors, such as business, creative, social, love and life purpose, and provides multiple recommendations. The purpose of the study is to assist web users to balance their work-life, improve psychological well-being and quality of life in this unforeseen situation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-14T13:10:14Z No. of bitstreams: 1 ijimai7_7_10.pdf: 671815 bytes, checksum: 0f10b3440a65c2a8d054c5a8ebe8c0bd (MD5); Made available in DSpace on 2022-12-14T13:10:14Z (GMT). No. of bitstreams: 1 ijimai7_7_10.pdf: 671815 bytes, checksum: 0f10b3440a65c2a8d054c5a8ebe8c0bd (MD5)