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Journal articles on the topic 'Intelligent recommendation system'

Author: Grafiati

Published: 16 March 2023

Last updated: 30 July 2025

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1

Kathait, ShailendraSingh, Shubhrita Tiwari, and PiyushKumar Singh. "INTELLIGENT RECOMMENDATION SYSTEM." International Journal of Advanced Research 5, no. 2 (2017): 1649–56. http://dx.doi.org/10.21474/ijar01/3328.

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R, Rajasekar, Niranchana Radhakrishnan, Sridar K, et al. "Intelligent movie recommendation system." Salud, Ciencia y Tecnología - Serie de Conferencias 4 (March 12, 2025): 1438. https://doi.org/10.56294/sctconf20251438.

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A smart piece of technology, a movie recommendation system uses intricate algorithms to deliver users personalized movie recommendations, enhancing their viewing experience. By utilizing many data sources, including user ratings, movie metadata, and viewing history, the system creates comprehensive user profiles that encompass distinct interests and behaviors. Content-based filtering, which considers storyline keywords, character, and genre, suggests movies that are similar to the ones the buyer has already enjoyed. Collaborative filtering approaches enhance suggestions even further by identif

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Mishra, Ikshita, Ankita Sharma, and Tanuj Deria. "Intelligent Tourist Recommendation System." IJARCCE 6, no. 4 (2017): 384–91. http://dx.doi.org/10.17148/ijarcce.2017.6474.

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K, Abhay Sankar. "Intelligent Travel Recommendation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3122–29. http://dx.doi.org/10.22214/ijraset.2023.54131.

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Abstract: This survey paper presents a project focused on developing an online platform designed to provide users with efficient recommendations to enhance their travel experiences. The platform aims to assist users in selecting their preferred location of stay, exploring recommended places to visit, choosing appropriate modes of transportation, and identifying suitable dining options, all while considering the user's budget. The project incorporates various filtering algorithms to ensure accurate and tailored recommendations. Through this platform, users can create personalized itineraries al

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Shiva, Derangula. "Personalized Book Intelligent Recommendation System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49744.

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Abstract - As libraries undergo digital transformation, accompanied by advancements in information technology in academic libraries, it is evident that readers will want different and personalized services in addition to checking out books. Along with meeting the changing expectations of users, this research proposes an enhanced item-based collaborative-filtering recommendation algorithm that employs an average model representation to improve the accuracy measurements, as well as the use of Neural Collaborative Filtering (NCF) for modeling the user-item interaction by utilizing deep neural net

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Rtili, Mohammed Kamal, Ali Dahmani, and Mohamed Khaldi. "Recommendation System Based on the Learners' Tracks in an Intelligent Tutoring System." Journal of Advances in Computer Networks 2, no. 1 (2014): 40–43. http://dx.doi.org/10.7763/jacn.2014.v2.79.

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Sheshnarayan Pandey, Komal. "“TripMitra : Travel Recommendation System”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45112.

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Tourism is a rapidly growing industry, and travelers often seek personalized recommendations to explore destinations that align with their interests. TripMitra is a web-based travel recommendation system designed to provide intelligent and customized travel suggestions based on user preferences and interactions. The system incorporates machine learning algorithms to enhance the accuracy of recommendations and features an image-based search that allows users to upload a photo to find visually similar destinations.Developed using Flask (Python) for backend processing, OpenCV for image analysis,

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Yang, Fan. "A hybrid recommendation algorithm–based intelligent business recommendation system." Journal of Discrete Mathematical Sciences and Cryptography 21, no. 6 (2018): 1317–22. http://dx.doi.org/10.1080/09720529.2018.1526408.

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Naik, Pratiksha Ashok. "Intelligent Food Recommendation System Using Machine Learning." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 616–19. http://dx.doi.org/10.38124/ijisrt20aug414.

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The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same produc

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., Jay Borade. "INTELLIGENT AGENT FOR TOURISM RECOMMENDATION SYSTEM." International Journal of Research in Engineering and Technology 07, no. 04 (2018): 39–46. http://dx.doi.org/10.15623/ijret.2018.0704007.

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Nalawade, Viraj, Bhagyashree Kadam, Chetan Jadhav, Gaurav Pabale, and Pradeep Kokane. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering 5, no. 1 (2025): 1–6. https://doi.org/10.54105/ijae.a1525.05010525.

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Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system takes inputs such as historic

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Sohel, Shaik, Vanukuri Manideepa, Alla Sai Pavan, Danaboina Vamsi Krishna, and KRMC Sekhar. "EMUS: An Intelligent Music Recommendation System." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 751–55. https://doi.org/10.54660/.ijmrge.2025.6.2.751-755.

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Music plays a prominent role in various aspects of human life, culture, and society by influencing emotions, strengthening social bonds, preserving traditions, and shaping personal and collective identities. As AI emerges as a powerful tool to automate various tasks, music recommendation systems have become an integral part of this transformation. These systems automatically generate personalized music playlists for users based on their mood and listening behavior. By analyzing factors like facial expressions, voice tone, text input, and listening history, AI-driven music recommendation system

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Viraj, Nalawade. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering (IJAE) 5, no. 1 (2025): 1–6. https://doi.org/10.54105/ijae.A1525.05010525.

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<strong>Abstract: </strong>Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system ta

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Zhang, Xinyi. "Research on Intelligent Recommendation Algorithm Based on Deep Learning." International Journal of Computer Science and Information Technology 4, no. 3 (2024): 299–309. https://doi.org/10.62051/ijcsit.v4n3.31.

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With the wide application of intelligent recommendation system in e-commerce shopping, video websites and social media platforms, timeliness, accuracy, scalability and interpretability have gradually become important criteria to measure the excellence of a recommendation system.The most widely used recommendation system is collaborative filtering recommendation system. Its advantages include high accuracy, good real-time performance, and strong scalability, but there are still disadvantages, including cold start, data sparsity, and vulnerability. In the current flourishing of deep learning res

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Ramzan, Bushra, Imran Bajwa, Rafaqut Kazmi, and Shabana Ramzan. "An Intelligent Data Analytics based Model Driven Recommendation System." JUCS - Journal of Universal Computer Science 25, no. (10) (2019): 1353–72. https://doi.org/10.3217/jucs-025-10-1353.

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The recommendation systems are getting important due to their significance in decision making, social and economic impact on customers and getting detailed information relevant to a required product or a service. A challenge in getting true recommendations in terms of relevance is the heterogenous nature of data (likes, ratings, reviews, etc.) that a recommendation engine has to cope with. This paper presents an intelligent approach to handle heterogeneous and large-sized data of user reviews and generate true recommendations for the future customers. The proposed approach makes use of Apache

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P, Bhagya Sri, Sindhu Sri G, Jaya Sri K, Leela Poojitha V, and Sajida Sultana Sk. "Intelligent book recommendation system using ML techniques." ITM Web of Conferences 74 (2025): 03007. https://doi.org/10.1051/itmconf/20257403007.

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The current research focuses on a recommendation system based on Decision Tree, Naive Bayes, Ridge Classifier, and Random Forest, using a new hybrid method combining Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN). The Decision Tree model reaches a good trade-off for precision, recall, and F1 metrics, acting as a benchmark. On the other hand, the hybrid model greatly surpasses the remaining ones in such a way that precision is as high as 89.35%, recall is 59.01%, and F1 is up to 71.30%, thus reinforcing the notion that it finds user preferences for recommendations more effecti

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Kumar, Abhishek. "Ethical Movie Recommender System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49531.

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ABSTRACT This research paper presents CineScope, a smart movie recommendation system utilizing artificial intelligence and voice recognition. Built with Python, Streamlit , and integrated with the TMDB API, the system provides intelligent, personalized movie recommendations based on content similarity and user interaction. With support for voice-based search, mood-driven design, trending/upcoming movie displays, and trailer previews. CineScope delivers an immersive user experience aimed at enhancing movie discovery.

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Mao, Qingqing, Aihua Dong, Qingying Miao, and Lu Pan. "Intelligent Costume Recommendation System Based on Expert System." Journal of Shanghai Jiaotong University (Science) 23, no. 2 (2018): 227–34. http://dx.doi.org/10.1007/s12204-018-1933-x.

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Hirolikar, D. S., Ajinkya Satuse, Omkar Bhalerao, Pavan Pawar, and Hrithik Thorat. "Intelligent Movie Recommendation System Using AI and ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 611–22. http://dx.doi.org/10.22214/ijraset.2022.42255.

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Abstract: Recommender system are systems which provide you with a similar type of products or solutions and results, you are looking for. For example, if you go to a Clothing shop, you ask for a T-shirt with different designs or different colors, Then the shopkeeper recommends you with different colors. This recommending task for websites is done by recommending systems. A recommendation engine uses several algorithms to filter data and then recommends the most relevant items to consumers. A Movie Recommender system will recommend the most relevant and connected movie for the given category of

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Nabilah, Nisa, and Zanariah Zanariah. "Intelligence Book Recommendation System Using Collaborative Filtering." IC-ITECHS 5, no. 1 (2024): 332–38. https://doi.org/10.32664/ic-itechs.v5i1.1675.

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The rapid growth of online literary material has changed the way users discover books, revealing the limitations of traditional recommendation algorithms. This paper presents a review about an intelligent book recommendation system that uses collaborative filtering (CF) and artificial intelligence techniques to address major obstacles such as cold-start issues, data scarcity, and privacy concerns. The suggested method guarantees customized, accurate, and diversified recommendations by merging hybrid approaches such as CF with content-based filtering and matrix factorization. To measure perform

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Shao, Ruihua. "Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System." Advances in Multimedia 2022 (July 14, 2022): 1–13. http://dx.doi.org/10.1155/2022/7860718.

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In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in tra

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Ma, Chuanming, Shu Xu, and Mingming Zhang. "Design of Intelligent Advertising Recognition System Based on Intelligent Recommendation." Procedia Computer Science 247 (2024): 780–87. http://dx.doi.org/10.1016/j.procs.2024.10.094.

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Ben Zaken, Daniel, Kobi Gal, Guy Shani, Avi Segal, and Darlene Cavalier. "Intelligent Recommendations for Citizen Science." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 14693–701. http://dx.doi.org/10.1609/aaai.v35i17.17726.

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Citizen science refers to scientific research that is carried out by volunteers, often in collaboration with professional scientists. The spread of the internet has allowed volunteers to contribute to citizen science projects in dramatically new ways while creating scientific value and gaining pedagogical and social benefits. Given the sheer size of available projects, finding the right project, which best suits the user preferences and capabilities, has become a major challenge and is essential for keeping volunteers motivated and active contributors. We address this challenge by developing a

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Poorani, G., S. Mohammed Rishwan, R. Pavan Kumar, and M. Ragul. "Sustainable Intelligent Prediction and Price Recommendation System." Journal of Physics: Conference Series 1916, no. 1 (2021): 012086. http://dx.doi.org/10.1088/1742-6596/1916/1/012086.

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Lin, Qi. "Intelligent Recommendation System Based on Image Processing." Journal of Physics: Conference Series 1449 (January 2020): 012131. http://dx.doi.org/10.1088/1742-6596/1449/1/012131.

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Goretskii, I. A., and D. S. Lavrova. "Intelligent Recommendation System for Countering Network Attacks." Automatic Control and Computer Sciences 58, no. 8 (2024): 1386–91. https://doi.org/10.3103/s0146411624701050.

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Cui, Xiaoyue. "An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database." Mobile Information Systems 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/3334047.

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In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer’s attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and

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Song, Yongmei, and Xuelian Jiao. "A Real-Time Tourism Route Recommendation System Based on Multitime Scale Constraints." Mobile Information Systems 2023 (April 26, 2023): 1–10. http://dx.doi.org/10.1155/2023/4586047.

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In order to increase the capability of real-time intelligent recommendation of tourists’ information on cross-regional city-level tourist routes with epidemic normalization, a real-time intelligent recommendation algorithm for cross-regional city-level tourist routes with epidemic normalization based on multi-time scale constraints is proposed. Under the training of limited samples, the tourist correlation model of the epidemic normalization of cross-regional city-level tourist routes is created. In addition, two kernel functions i.e. the mixed and the global are assembled to excerpt the corre

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Pooja and Vishal Bhatnagar. "A Prospect on an Intelligent Recommender System." International Journal of Service Science, Management, Engineering, and Technology 12, no. 2 (2021): 25–43. http://dx.doi.org/10.4018/ijssmet.2021030102.

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User satisfaction is the principle component in the prosperity of a recommender system to provide an exact recommendation within a rational amount of time. The recommendation system is an intelligent system that analyzes the large quantity of online data to predict the patterns. In this paper, various recommendation techniques are described as a literature survey and their classifications are explained based upon the attributes and characteristics required for the recommendation process. The categorization of the recommendation system hinge on the analysis of the research papers and identifies

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Rani, Geeta, Vijaypal Singh Dhaka, Sonam, Upasana Pandey, and Pradeep Kumar Tiwari. "Intelligent and Adaptive Web Page Recommender System." International Journal of Web Services Research 18, no. 4 (2021): 27–50. http://dx.doi.org/10.4018/ijwsr.2021100102.

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In this manuscript, an intelligent and adaptive web page recommender system is proposed that provides personalized, global, and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: uniformity and recommendation strength. The system continuously tracks the

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Jiang, Ji, and Jian Gang Tang. "Research on Intelligent Knowledge Recommendation System for Police Applications." Applied Mechanics and Materials 530-531 (February 2014): 447–51. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.447.

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This paper proposed knowledge content tag recommendation algorithm in cloud computing Environment, and applied to police information knowledge. The algorithm analyzed user behavior history of operation and considered the similarity knowledge of the entries on the tag of police information, marked weight of tag in predicting when a user rating. On this basis, the police information implementations specific recommendations based on the specific user application knowledge. Meanwhile, combined the tag of system entry contents correlation with user correlation analysis, and solved the problems of s

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Chen, Qing Zhang, Yu Jie Pei, Yan Jin, and Li Yan Zhang. "Research on Intelligent Recommendation Method and its Application on Internet Bookstore." Advanced Materials Research 121-122 (June 2010): 447–52. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.447.

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As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is impl

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Zhang, Liwei, and Kaixuan Hou. "An Intelligent Manufacturing Models Recommendation Model Based on Knowledge Graph and Recommendation Algorithms." Journal of Physics: Conference Series 2665, no. 1 (2023): 012009. http://dx.doi.org/10.1088/1742-6596/2665/1/012009.

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Abstract With the concept of intelligent manufacturing, a variety of intelligent manufacturing models emerge. But companies don’t have enough knowledge to choose the suitable intelligent manufacturing model. To solve this problem, based on lean design principles, combined with expert knowledge and expertise, we propose an Intelligent Manufacturing Models Recommendation Model (referred to as IMMR Model). First, we categorize customer requirements into real need, customer preference and customer expectation to accurately identify customer requirements. Then, we use knowledge graph to build a pro

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Kingchang, Thanarat, Pinanta Chatwattana, and Panita Wannapiroon. "Intelligent Educational Recommendation Platform with AI Chatbots." International Education Studies 16, no. 5 (2023): 19. http://dx.doi.org/10.5539/ies.v16n5p19.

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The objectives of this research were as follows. 1) Analyze the intelligent educational recommendation platform with AI Chatbots. 2) Design the architecture of the intelligent educational recommendation platform with AI Chatbots. 3) Develop the architecture of the intelligent educational recommendation platform with AI Chatbots. 4) Study the appropriateness of developing the intelligent educational recommendation platform with AI Chatbots. The sample used in the research was seven experts in information system development from various institutions in higher education. The architecture of the i

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Xin, Xin, Tianlei Shi, and Mishal Sohail. "Knowledge-Based Intelligent Education Recommendation System with IoT Networks." Security and Communication Networks 2022 (March 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/4140774.

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The intelligent education recommendation system can recommend knowledge suitable for students' personal learning. However, the traditional recommendation algorithm has generality problems, which lead to poor knowledge recommendation effects. In order to improve the performance of the education recommendation system, based on the machine learning algorithm, this paper combines the knowledge graph algorithm to improve the recommendation algorithm and decomposes the matrix with a higher dimension into several matrices with relatively small dimensions through matrix transformation. Moreover, this

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A, Ramshad. "AI Fashion Recommendation System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42713.

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A recommender system suggests items based on user preferences, and deep learning enhances its accuracy by handling large datasets efficiently. As online shopping grows, retailers seek intelligent methods to recommend clothing tailored to user interests, boosting sales. We propose an AI-powered, content-based fashion recommendation system using deep neural networks. Unlike traditional methods requiring manual feature extraction, our system automatically identifies product features, including clothing categories, streamlining the process. It also integrates gender classification to refine recomm

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Luo, Youyi, and Ting Jiang. "Computer Hardware Resources E-commerce Platform Based on Intelligent Recommendation and High Concurrency Processing - "Beiji Technology"." IC-ITECHS 5, no. 1 (2024): 865–73. https://doi.org/10.32664/ic-itechs.v5i1.1651.

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With the rapid development of e-commerce and the diversification of the computer hardware market, consumers are facing more and more difficulties in choosing computer accessories, and traditional e-commerce platforms are difficult to meet the user's demand for accurate shopping and efficient recommendation. The purpose of this paper is to build an intelligent computer accessories shopping platform (Beiji Technology), through intelligent recommendation algorithms, to improve the user shopping experience. The platform adopts mainstream front-end technology, efficient and stable back-end architec

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Kim, Gui-Jung, Bong-Han Kim, and Jung-Soo Han. "Customizing Intelligent Recommendation System based on Compound Knowledge." Journal of the Korea Contents Association 10, no. 8 (2010): 26–31. http://dx.doi.org/10.5392/jkca.2010.10.8.026.

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Roshan, Shiva Hassanjani, Seyyed Javad Kazemitabar, and Ghorban Kheradmandian. "Intelligent Chemical Fertilizer Recommendation System for Rice Fields." Asian Journal of Applied Science and Technology 05, no. 03 (2021): 184–95. http://dx.doi.org/10.38177/ajast.2021.5318.

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Zheng, Fangxia. "Personalized Education Based on Hybrid Intelligent Recommendation System." Journal of Mathematics 2022 (January 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/1313711.

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Differentiated pedagogy is a flexible and organized adaptation of teaching and learning as it argues that students, even those of the same age, have differences in learning readiness, interests, learning style, experiences, and living circumstances. These differences are important in the determination of requirements of their learning and the way of effective learning. In addition, the foundation for effective learning is the sense of community within the classroom, the authentic learning opportunities of using educational equipment, and the connection of the lesson with the experiences and in

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Ou, Tsung-Yin, Guan-Yu Lin, Hsin-Pin Fu, Shih-Chia Wei, and Wen-Lung Tsai. "An Intelligent Recommendation System for Real Estate Commodity." Computer Systems Science and Engineering 42, no. 3 (2022): 881–97. http://dx.doi.org/10.32604/csse.2022.022637.

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Trappey, Amy J. C., Charles V. Trappey, Chun-Yi Wu, Chin Yuan Fan, and Yi-Liang Lin. "Intelligent patent recommendation system for innovative design collaboration." Journal of Network and Computer Applications 36, no. 6 (2013): 1441–50. http://dx.doi.org/10.1016/j.jnca.2013.02.035.

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Chang, Jui-Hung, Chin-Feng Lai, Ming-Shi Wang, and Tin-Yu Wu. "A cloud-based intelligent TV program recommendation system." Computers & Electrical Engineering 39, no. 7 (2013): 2379–99. http://dx.doi.org/10.1016/j.compeleceng.2013.04.025.

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Li, Hui, Haining Li, Shu Zhang, Zhaoman Zhong, and Jiang Cheng. "Intelligent learning system based on personalized recommendation technology." Neural Computing and Applications 31, no. 9 (2018): 4455–62. http://dx.doi.org/10.1007/s00521-018-3510-5.

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K, DanielRaj. "Intelligent Crop Recommendation System Using Machine Learning Approach." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5675–81. https://doi.org/10.22214/ijraset.2025.69677.

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The agricultural sector in India, despite its status as a leading producer globally, grapples with low farm productivity, resulting in diminished incomes for farmers. Addressing this challenge requires a strategic approach centered around increasing productivity, thereby enhancing farmer livelihoods. Crucially, farmers must be equipped with the knowledge of which crops are best suited to their specific plots of land to optimize yield potential. This entails consideration of various factors such as temperature, humidity, soil pH, rainfall patterns, and nutrient composition. However, many farmer

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Rahman, Rakhmadi, Alya Wulan Apriliyani, and Siti Nur Azizah Ibrahim. "Development of an Operating System Supporting Intelligent Predictions and Recommendations." ITEJ (Information Technology Engineering Journals) 9, no. 1 (2024): 1–14. http://dx.doi.org/10.24235/itej.v9i2.127.

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This Study discusses the development of an intelligent operating system feature that supports smart prediction and recommendations using artificial intelligence (AI) capabilities within the Linux operating system. The study aims to integrate AI-driven features into Linux to enhance user productivity and efficiency by providing relevant application recommendations based on user behavior patterns. The implementation involves data collection of application usage, training machine learning models for application recommendations, and integrating these features into the Linux environment. The projec

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Gao, Chenchen, and Kai Zhang. "Research on remote education intelligent recommendation system of computer network Ancient Literature Resources Database." MATEC Web of Conferences 365 (2022): 01055. http://dx.doi.org/10.1051/matecconf/202236501055.

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With the development of intelligent education and the development of distance education becoming more and more intelligent and three-dimensional, an intelligent recommendation system is constructed on the basis of learners'individual demands, will achieve learning requirements and recommended content of the precise match. The construction of ancient literature resource database by means of computer network and the timely push of intelligent recommendation system to students will promote the essential change of distance education on the basis of reconstructing the ecology of distance education.

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Li, Liuqing. "Cross-Border E-Commerce Intelligent Information Recommendation System Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (February 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/6602471.

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In order to improve the effect of cross-border e-commerce intelligent information recommendation, this paper applies deep learning to the intelligent information processing and intelligent recommendation of e-commerce and proposes an improved version of the topic model to solve the problem of feature extraction of the text of the recommendation system. In order to deal with translation problems, this paper proposes an end-to-end sequence-to-sequence learning method. In addition, this study uses the long tail theory to excavate the mass commodities in the niche and recommends these products to

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Li, Pengjiao, and Jun Yang. "PSO Algorithm-Based Design of Intelligent Education Personalization System." Computational Intelligence and Neuroscience 2022 (July 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/9617048.

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The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of

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Uhryn, D. I., Yu O. Ushenko, A. Ya Dovhun, and A. D. Kalancha. "Intelligent system for identifying the user's trust rating." Optoelectronic Information-Power Technologies 46, no. 2 (2023): 150–58. http://dx.doi.org/10.31649/1681-7893-2023-46-2-150-158.

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The article develops an intelligent system for identifying the user's trust rating, which allows viewing information about contacts that are more aimed at creating trust in the interlocutor or providing information that helps to identify the caller or the person with whom we are trying to contact. The k-nearest neighbours method was chosen to create the recommendation system. The main advantage of using the k-nearest neighbours method is the ability to take into account the unique trust rating of each phone number. It is important not only to find phone numbers with a similar rating, but also

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