{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:03:10Z","timestamp":1781017390164,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>In an era of information overload, artificial intelligence plays a pivotal role in supporting everyday decision-making. This paper introduces EverydAI, a virtual AI-powered assistant designed to help users make informed decisions across various daily domains such as cooking, fashion, and fitness. By integrating advanced natural language processing, object detection, augmented reality, contextual understanding, digital 3D avatar models, web scraping, and image generation, EverydAI delivers personalized recommendations and insights tailored to individual needs. The proposed framework addresses challenges related to decision fatigue and information overload by combining real-time object detection and web scraping to enhance the relevance and reliability of its suggestions. EverydAI is evaluated through a two-phase survey, each one involving 30 participants with diverse demographic backgrounds. Results indicate that on average, 92.7% of users agreed or strongly agreed with statements reflecting the system\u2019s usefulness, ease of use, and overall performance, indicating a high level of acceptance and perceived effectiveness. Additionally, EverydAI received an average user satisfaction score of 4.53 out of 5, underscoring its effectiveness in supporting users\u2019 daily routines.<\/jats:p>","DOI":"10.3390\/systems13090753","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T13:01:13Z","timestamp":1756818073000},"page":"753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["EverydAI: Virtual Assistant for Decision-Making in Daily Contexts, Powered by Artificial Intelligence"],"prefix":"10.3390","volume":"13","author":[{"given":"Carlos E.","family":"Pardo B.","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0017-0976","authenticated-orcid":false,"given":"Oscar I.","family":"Iglesias R.","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maicol D.","family":"Le\u00f3n A.","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0918-9375","authenticated-orcid":false,"given":"Christian G.","family":"Quintero M.","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,31]]},"reference":[{"key":"ref_1","unstructured":"Patel, N.C., Jain, R., and Patel, N. (2021). Som Lalit Institue of Management Studies and Som Lalit Institute of Business Management Abundant Choices: Consumer\u2019s Dilemma."},{"key":"ref_2","first-page":"182","article-title":"Difficult Choices: Exploring Basic Reasons of Difficult Choices","volume":"3","author":"Xuan","year":"2021","journal-title":"J. Sociol. Ethnol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s40537-022-00592-5","article-title":"A Systematic Review and Research Perspective on Recommender Systems","volume":"9","author":"Roy","year":"2022","journal-title":"J. Big Data"},{"key":"ref_4","first-page":"1","article-title":"Adaptive Virtual Assistant Interaction through Real-Time Speech Emotion Analysis Using Hybrid Deep Learning Models and Contextual Awareness","volume":"1","author":"Zadeh","year":"2024","journal-title":"Int. J. Adv. Hum. Comput. Interact."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dong, X.L., Moon, S., Xu, Y.E., Malik, K., and Yu, Z. (2023, January 6\u201310). Towards Next-Generation Intelligent Assistants Leveraging LLM Techniques. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Long Beach, CA, USA.","DOI":"10.1145\/3580305.3599572"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100211","DOI":"10.1016\/j.hcc.2024.100211","article-title":"A Survey on Large Language Model (LLM) Security and Privacy: The Good, The Bad, and The Ugly","volume":"4","author":"Yao","year":"2024","journal-title":"High-Confid. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Naik, D., Naik, I., and Naik, N. (2024, January 3\u20134). Decoder-Only Transformers: The Brains Behind Generative AI, Large Language Models and Large Multimodal Models. Proceedings of the Contributions Presented at the International Conference on Computing, Communication, Cybersecurity and AI, London, UK.","DOI":"10.36227\/techrxiv.173198819.91727188\/v1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.54060\/a2zjournals.jmss.57","article-title":"Chatbot: Chatbot Assistant","volume":"4","author":"Singh","year":"2024","journal-title":"J. Manag. Serv. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pereira, R., Lima, C., Pinto, T., and Reis, A. (2023). Virtual Assistants in Industry 4.0: A Systematic Literature Review. Electronics, 12.","DOI":"10.3390\/electronics12194096"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s11280-024-01291-2","article-title":"A Survey on Large Language Models for Recommendation","volume":"27","author":"Wu","year":"2024","journal-title":"World Wide Web"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3702249","article-title":"Introduction to the Special Issue on Trustworthy Recommender Systems","volume":"3","author":"Schedl","year":"2025","journal-title":"ACM Trans. Recomm. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, A., Chen, Y., Sheng, L., Wang, X., and Chua, T.S. (2024, January 14\u201318). On Generative Agents in Recommendation. Proceedings of the SIGIR 2024\u2014Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, USA.","DOI":"10.1145\/3626772.3657844"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Altundas, S., and Karaarslan, E. (2023). Cross-Platform and Personalized Avatars in the Metaverse: Ready Player Me Case. Digital Twin Driven Intelligent Systems and Emerging Metaverse, Springer Nature.","DOI":"10.1007\/978-981-99-0252-1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"034001","DOI":"10.1117\/1.AP.5.3.034001","article-title":"Metasurface-Enabled Augmented Reality Display: A Review","volume":"5","author":"Liu","year":"2023","journal-title":"Adv. Photonics"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sirisuriya, S.C.M.D.S. (2023, January 25\u201326). Importance of Web Scraping as a Data Source for Machine Learning Algorithms\u2014Review. Proceedings of the 2023 IEEE 17th International Conference on Industrial and Information Systems, ICIIS 2023\u2014Proceedings, Peradeniya, Sri Lanka.","DOI":"10.1109\/ICIIS58898.2023.10253502"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vu, M.D., Wang, H., Li, Z., Chen, J., Zhao, S., Xing, Z., and Chen, C. (2024, January 13\u201316). GPTVoiceTasker: Advancing Multi-Step Mobile Task Efficiency Through Dynamic Interface Exploration and Learning. Proceedings of the UIST\u201924: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, Pittsburgh, PA, USA.","DOI":"10.1145\/3654777.3676356"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zheng, J., and Fischer, M. (2023). BIM-GPT: A Prompt-Based Virtual Assistant Framework for BIM Information Retrieval. arXiv.","DOI":"10.1016\/j.autcon.2023.105067"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"814","DOI":"10.22214\/ijraset.2023.49519","article-title":"AI-Based Virtual Assistant Using Python: A Systematic Review","volume":"11","author":"Manojkumar","year":"2023","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Anand, A., Subha, R., Rajan, S., Bharathi, N., and Srivastava, A.K. (2023, January 28\u201329). An Efficient, Precise and User Friendly AI Based Virtual Assistant. Proceedings of the 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023, Bangalore, India.","DOI":"10.1109\/IC-RVITM60032.2023.10435167"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Babu, G.J., Safrinfathima, S., Reddy, K.D., and Sen, S.K. (2024, January 18\u201319). Transforming Human-Machine Interaction: Generative AI Virtual Asst. Proceedings of the 2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024, Chikkaballapur, India.","DOI":"10.1109\/ICKECS61492.2024.10617388"},{"key":"ref_22","first-page":"28541","article-title":"LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day","volume":"36","author":"Li","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dhelia, A., Chordia, S., and Kanisha, B. (2024, January 3\u20135). YOLO-Based Food Damage Detection: An Automated Approach for Quality Control in Food Industry. Proceedings of the 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024\u2014Proceedings, Kirtipur, Nepal.","DOI":"10.1109\/I-SMAC61858.2024.10714664"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nitiavintokana, F., Rouf, M.A., Yu, X., Wu, H., Wang, A., and Iwahori, Y. (2024, January 17\u201319). Intelligent Food Identification System Using Improved YOLOv8. Proceedings of the ISCSET 2024\u201413th International Symposium on Computer Science and Educational Technology, Lauta\/Laubusch, Germany.","DOI":"10.1109\/ISCSET58624.2024.10807955"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiang, H., Zhang, Y., and Li, W. (2024, January 22\u201323). Construction of a Food Packaging Safety Closure Detection System Based on Improved Yolo Algorithm. Proceedings of the 2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024, Kalaburagi, India.","DOI":"10.1109\/ICIICS63763.2024.10860088"},{"key":"ref_26","unstructured":"Meenpal, T., Khan, M.S., and Sahu, M. (2024, January 24\u201328). Automatic Food Billing System of Indian Food Using YOLOv8 Model. Proceedings of the 2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024, Kamand, India."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Han, X., Zheng, D., and Wang, D. (2024, January 7\u20139). An Enhanced Clothing Detection Model for E-Commerce Applications. Proceedings of the Proceedings\u20142024 39th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2024, Dalian, China.","DOI":"10.1109\/YAC63405.2024.10598795"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jain, D., Thazhathu, E.M., Adiraju, I., Bhattacharya, J., and Singh, D. (2024, January 1\u20133). FashionAI: Image-Based Clothing Detection and Shopping Recommendation. Proceedings of the 2024 3rd International Conference for Innovation in Technology, INOCON 2024, Bangalore, India.","DOI":"10.1109\/INOCON60754.2024.10512219"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"34","DOI":"10.9734\/ajrcos\/2025\/v18i1546","article-title":"Comparative Reliability Analysis of Selenium and Playwright: Evaluating Automated Software Testing Tools","volume":"18","author":"Almabruk","year":"2025","journal-title":"Asian J. Res. Comput. Sci."},{"key":"ref_30","unstructured":"(2025, July 01). AI Model & API Providers Analysis|Artificial Analysis Intelligence, Performance & Price Analysis|Artificial Analysis. Available online: https:\/\/artificialanalysis.ai\/models\/o4-mini."},{"key":"ref_31","unstructured":"Yaman, E. (2025, March 01). FOOD INGREDIENTS DETECTION Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/yaman-e\/food-ingredients-detection-qfit7."},{"key":"ref_32","unstructured":"(2025, July 01). Python, Ingredients Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/python-wfiwe\/ingredients-mqhxf."},{"key":"ref_33","unstructured":"(2025, March 01). DelfinPE3 Clothes Detection V1 Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/delfinpe3\/clothes-detection-v1-loczc."},{"key":"ref_34","unstructured":"(2025, March 01). cnn with Fashion Detection cnn Fashion Detion in Ecommerce Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/cnn-with-fashion-detection\/cnn-fashion-detion-in-ecommerce."},{"key":"ref_35","unstructured":"(2025, March 01). Main Fashion Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/dataset-yn4f8\/main-fashion."},{"key":"ref_36","unstructured":"(2025, March 01). FitFuel All Gym Equipment Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/bruuj\/main-fashion-wmyfk."},{"key":"ref_37","unstructured":"(2025, March 01). RizzLabzz GymBro Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/rizzlabzz\/gymbro."},{"key":"ref_38","unstructured":"Pruzek, F.E.R.C. (2025, March 01). Fitness Equipment Recognition Dataset. In Roboflow Universe. Available online: https:\/\/universe.roboflow.com\/fitness-equipment-recognition-colin-pruzek\/fitness-equipment-recognition-wlluo."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"ooae100","DOI":"10.1093\/jamiaopen\/ooae100","article-title":"Perceptions and Attitudes toward Artificial Intelligence among Frontline Physicians and Physicians\u2019 Assistants in Kansas: A Cross-Sectional Survey","volume":"7","author":"Dean","year":"2024","journal-title":"JAMIA Open"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"348","DOI":"10.14245\/ns.2143080.540","article-title":"Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction","volume":"19","author":"Nam","year":"2022","journal-title":"Neurospine"},{"key":"ref_41","first-page":"174","article-title":"Artificial intelligence for emrloyee engagement and productivity","volume":"22","author":"Gusti","year":"2024","journal-title":"Probl. Perspect. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1177\/0004563221992088","article-title":"Best Practice in Statistics: Use the Welch t-Test When Testing the Difference between Two Groups","volume":"58","author":"West","year":"2021","journal-title":"Ann. Clin. Biochem."},{"key":"ref_43","unstructured":"Jan\u00e9, M.B., Xiao, Q., Yeung, S.K., Ben-Shachar, M.S., Caldwell, A.R., Cousineau, D., Dunleavy, D.J., Elsherif, M., Johnson, B.T., and Moreau, D. (2025, July 01). Guide to Effect Sizes and Confidence Intervals. Available online: https:\/\/matthewbjane.quarto.pub\/."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Miller, R.B. (1968, January 9\u201311). Response Time in Man-Computer Conversational Transactions. Proceedings of the AFIPS\u201968 Fall Joint Computer Conference, San Francisco, CA, USA.","DOI":"10.1145\/1476589.1476628"},{"key":"ref_45","unstructured":"Card, S.K., Robertson, G.G., and Mackinlay, J.D. (May, January 27). The information visualizer, an information workspace. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:36:33Z","timestamp":1760034993000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,31]]},"references-count":45,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["systems13090753"],"URL":"https:\/\/doi.org\/10.3390\/systems13090753","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,31]]}}}