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Informatica (Slovenia) 2025年第8期49卷 189-204页

作者： Remadnia, Ouahiba Maazouzi, Faiz Chefrour, Djalel Dept. of Computer Science University of Souk Ahras Algeria

We propose a hybrid e-book recommendation mechanism that leverages collaborative filtering and content-based recommendation paradigms to address inherent challenges in e-learning systems. For collaborative filtering, we present an innovative deep learning framework that utilizes embeddings to enhance accuracy and manage large datasets efficiently. This framework effectively addresses the cold start problem, thereby improving recommendation precision. In content-based recommendation, we introduce a regression-based technique to elevate system capabilities by incorporating content attributes. The integration of these techniques into our deep learning model creates a comprehensive and adaptable solution with scalability and effectiveness. Experiments on the Book Recommendation dataset demonstrate that our solution provides better suggestions and outperforms existing works in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), achieving values of 0.69 and 0.51, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Collaborative learning

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Differential Privacy-Based Data Collection for Improving Data Utility and Reducing Computational Overhead

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 102-108页

作者： Kim, Jong Wook Dept. of Computer Science Sangmyung University Korea Republic of

With the rise of real-time data collection through mobile devices such as smartphones, user-driven decision-making systems in various fields such as transportation and healthcare have advanced significantly. However, increasing concerns about the privacy of sensitive information have emerged, leading to the widespread use of differential privacy (DP) to mitigate these risks. Among DP-based data collection methods, local differential privacy (LDP) and distributed differential privacy (DDP) are prominent. While LDP provides low computational overhead, it compromises data utility, while DDP provides better utility at the cost of higher computational overhead. To address these limitations, this paper introduces a DP-compliant (θ,λ) data collection method. The proposed approach collects a fraction of θ of the total items and a fraction of λ of the total user data, achieving improved data utility compared to LDP while significantly reducing computational overhead compared to DDP, resulting in more efficient data collection. Experimental results on real-world datasets show that the proposed method not only improves data utility over LDP, but also effectively reduces computational overhead over DDP. Copyright © The Korean Institute of Electrical Engineers.

关键词： Data collection

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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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MOPO-HBT: A movie poster dataset for title extraction and recognition

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Multimedia Tools and Applications 2024年第18期83卷 54545-54568页

作者： Ghosh, Mridul Roy, Sayan Saha Banik, Bivan Mukherjee, Himadri Obaidullah, Sk Md Roy, Kaushik Dept. of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah India Dept. of Computer Science & Engineering Aliah University Kolkata India Dept. of Electronics & Electrical Communication Engineering IIT Kharagpur Kharagpur India TISA Lab Dept. of Computer Science West Bengal State University Barasat India

Real-world images often encompass embedded texts that adhere to disparate disciplines like business, education, and amusement, to name a few. Such images are graphically rich in terms of font attributes, color distribution, foreground-background similarity, and component organization. This aggravates the difficulty of recognizing texts from these images. Such characteristics are very prominent in the case of movie posters. One of the first pieces of information on movie posters is the title. Automatic recognition of movie titles from images can aid in efficient indexing as well as information conveyance. However, it is accompanied by other texts like the names of actors, producers, taglines, dates, etc. Though the organization of components is somewhat similar across different film industries like Tollywood (West Bengal), Bollywood (Mumbai), and Hollywood (Los Angeles), the graffiti patterns differ in multifarious instances. To address the problem of movie title understanding, we propose a dataset named MOvie POsters-Hollywood Bollywood Tollywood (MOPO-HBT) that encompasses movie posters from the aforementioned industries. The entire dataset is publicly available (http://***/11564) for research purposes. The baseline results of title identification and recognition were obtained with a CNN-based (Convolutional Neural Network) approach, wherein the titles were extracted using the M-EAST (Modified-Efficient and Accurate Scene Text) detector model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Character recognition

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Scalable and cohesive swarm control based on reinforcement learning

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Cognitive Robotics 2024年第1期4卷 88-103页

作者： Blais, Marc-Andrė Akhloufi, Moulay A. Dept. of Computer Science Université de Moncton MonctonNB Canada

Unmanned vehicles have seen a significant increase in a wide variety of fields such as for logistics, agriculture and other commercial applications. Controlling swarms of unmanned vehicles is a challenging task that requires complex autonomous control systems. Reinforcement learning has been proposed as a solution to this challenge. We propose an approach based on agent masking to enable a simple Deep Q-Network algorithm to scale on large swarms while training on relatively smaller swarms. We train our approach using multiple swarm sizes and learning rates and compare our results using metrics such as the number of collisions. We also compare the ability of our approach to scale on swarms ranging from five to 25 agents using metrics and visual analysis. Our proposed solution was able to guide a swarm of up to 100 agents to a target while keeping a good swarm cohesion and avoiding collision. © 2024

关键词： Intelligent systems

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AI in CS Education: Opportunities, Challenges, and Pitfalls to Avoid

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ACM Inroads 2024年第3期15卷 52-57页

作者： Vahid, Frank Dept. of Computer Science and Engineering University of California Riverside United States

Csinstructors realize education may be impacted by generative AI (artificial intelligence). This article describes (1) opportunities, like 24/7 help or auto-grading, (2) challenges, like increased cheating or student isolation, and (3) pitfalls to avoid, like inequitable access or false cheating accusations. The descriptions come from 15 CS educators and practitioners, from diverse institutions, who met at a 2023 NSF workshop on AI in CS education. The article aims to assist the CS community to take advantage of generative AI to improve courses, embark on new research, treat students equitably, and ultimately improve CS education for instructors and students. © 2024 Association for Computing Machinery. All rights reserved.

关键词： Students

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Artificial Intelligence and Machine Learning Algorithms to Analyse Integrated Nutrient Management Strategies for Optimal Yield and Quality of Oilseeds Crops for Sustainable Development 4

Artificial Intelligence and Machine Learning Algorithms to A...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Mithra, C. Dar, Showkat A. Preethi, A. Dept. of Computer Science and Engineering Chennai India Githam University Dept. of Computer Science and Engineeing India Dept. of Computer Science and Engineering Chidambaram India

ISBN: (纸本)9798331515720

The main objective of this research is to deduce the efficacy of integrated nutrient management (INM) technologies in production of oilseed crops for sustainable development. A great amount of experience is needed in selecting the most effective INM strategy. A new recommendation system to circumnavigate this issue is proposed. It lets farmers decide on the best INM strategy to maximize oilseed crop yield and quality. This system is built on the techniques of advanced machine Learning (ML) and aritifical Intelligence (AI). Oilseed crop date in Tamil Nadu from 1961 to 2019 was used to develop the proposed algorithm. The proposed algorithm for crop yield prediction (CYP) which includes a Soft Voting Ensemble Classifier with weights (SVECWW), a Soft Voting Ensemble Classifier without weights (SVECWOW) along with the SVM technique are compared and contrasted with existing algorithms and also proves that SVECWW outperforms other ML algorithms with an accuracy rate of 97.2%. Furthermore, the Stacked Generalization Ensemble model is employed and compared with another Deep Neural Network (DNN) for the INM crop recommendation system which offers a simple graphical user interface (GUI) for farmers to use and received an accuracy of 97.5%. This GUI enables farmers to access valuable information such as the optimal timing for cultivating oilseed crops, the appropriate types and quantities of organic manures, inorganic fertilizers, and bio-fertilizers required for successful oilseed crop production. The study shows, on its whole, how to create tailored recommendation systems for farmers using GUI models with artificial intelligence and machine learning algorithms. Implementing these systems is expected to significantly improve oilseed crop production and quality significantly, benefiting the whole agricultural sector for sustainable development. Artificial intelligence (AI) makes a recommendation system more accurate and adaptable by looking through complex datasets and patterns

关键词： Adversarial machine learning

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Enhancing Application Placement in Cloud-Fog Environment Based on Multicriteria Decision Making 3

Enhancing Application Placement in Cloud-Fog Environment Bas...

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3rd International Conference on Advanced Electrical Engineering, ICAEE 2024

作者： Arichi, Bochra Amraoui, Asma Dept. Computer science Tlemcen13000 Algeria Dept. Computer science Tlemcen13000 Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349351

Fog computing (FC) is a distributed infrastructure computing that extends cloud computing (CC) capabilities to the edge of the network, closer to where data is generated and consumed. This approach responds to the challenges caused by Internet of Things (IoT) applications, which generate massive amounts of data that are costly and time-consuming to send to the cloud for processing. By leveraging fog computing, the cloud's limitations were successfully addressed, such as unpredictable latency. However, this shift introduced a new challenge. One of these challenges is finding the best placement of applications on distributed fog devices. This paper outlines a study aimed at addressing this challenge by utilizing multi-criteria decision-making (MCDM) techniques, including the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This study aims to choose the most appropriate fog nodes for positioning and processing application modules. The simulation was carried out on the iFogSim simulator, and the findings indicate that the suggested technique provides significant improvements in placement time and latency in a fog computing environment. © 2024 IEEE.

关键词： Fog computing

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A Number of Conceptual Scalable Node-Organizing Multi-Tiered Blockchain Architectures for IoT 17

A Number of Conceptual Scalable Node-Organizing Multi-Tiered...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Elsaadany, Riham Bégin, Guy Computer Science Dept. Canada

ISBN: (纸本)9798350351637

The high volume and rapid pace of transactions generated by IoT devices pose challenges for current blockchain designs, which typically employ flat or two-tiered node organizations. These models often lack the scalability, throughput, and consensus efficiency required for the IoT environment. This study proposes a multi-tiered clustered design concept that enhances blockchain scalability and efficiency. We compare our approach with flat and two-tiered models to demonstrate its viability. Central to our design is a node lookup and message routing mechanism, coupled with a consensus-accelerating protocol. Transaction, block verification, and creation processes involve a logarithmically bounded number of nodes per tier, improving consensus time and scalability as tier levels increase. The design concept maintains a balance between consensus decentralization and node overhead capacity, ensuring scalability and security. This design concept can be implemented using various algorithmic approaches to produce multiple proposed architectures, all leveraging the consensus-accelerating protocol. Simulations performed on one of the architectures provide solid proof of concept, demonstrating improved performance over flat and two-tier models. The proposed mechanisms provide general-purpose scalability, load balancing, node lookup, and message routing solutions, and are application- and consensus-algorithm-agnostic. © 2024 IEEE.

关键词： Efficiency

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Application of computer Vision Approach for Automation and Classification of Fashion Store

Application of Computer Vision Approach for Automation and C...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adebayo, Aminat Abiodun, Moses Kazeem Awoniran, Ifeoluwa Temitayo Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Adeniyi, Jide Kehinde Rueben Aremu, Dayo Alhikman University Dept. of Computer Science Ilorin Nigeria Landmark University Dept. of Computer Science Omu-Aran Nigeria Bowen University Dept. of Computer Science Iwo Nigeria University of Ilorin Dept. of Computer Science Ilorin Nigeria

ISBN: (纸本)9798350358155

The fashion industry has long been interested in utilizing computer vision techniques to automate tasks such as recognizing different types of clothing items in images. This study proposed a novel convolutional neural network (CNN) architecture for the classification of fashion outfits into three categories: shoes, sunglasses, and trousers. The proposed CNN architecture is based on state-of-the-art deep learning techniques and is trained on a fairly large-scale dataset of fashion images. The effectiveness of the proposed CNN architecture is evaluated through extensive experiments and analysis. The result demonstrates that the proposed CNN architecture achieves a high accuracy rate of 0.99 on each diagonal value of the confusion matrix. This indicates that the proposed CNN is capable of accurately classifying each item type with high accuracy. Additionally, the study investigates the impact of various hyperparameters on the performance of the proposed CNN architecture and found that the model which uses a 7x7 filter size and 64 filter number yields higher accuracy compared to other filter combinations. The study demonstrates the potential of CNN in automating fashion item recognition, which can lead to improved efficiency and accuracy in the fashion industry. It can also form the basis for developing more advanced computer vision systems for the fashion industry. © 2024 IEEE.

关键词： Convolutional neural networks

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