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Online Behavior Balancing Model for Influence Maximization in Twitter

Recent Advances in computer science and Communications 2022年第8期15卷 1059-1073页

作者： Agarwal, Sakshi Mehta, Shikha Department of Computer Science & Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

Background: Social influence estimation is an important aspect of viral marketing. The majority of the influence estimation models for online social networks are either based on Independent Cascade (IC) or Linear Threshold (LT) models. These models are based on some hypothesis: (1) process of influence is irreversible;(2) classification of user’s status is binary, i.e., either influenced or non-influenced;(3) process of influence is either single person’s dominance or collective dominance but not both at the same time. However, these assumptions are not always valid in the real world, as human behavior is unpredictable. Objective: To develop a generalized model to handle the primary assumptions of the existing influence estimation models. Methods: This paper proposes a Behavior Balancing (BB) Model, which is a hybrid of IC and LT models and counters the underlying assumptions of the contemporary models. Results: The efficacy of the proposed model to deal with various scenarios is evaluated over six different twitter election integrity datasets. Results depict that BB model is able to handle the stochastic behavior of the user with up to 35% improved accuracy in influence estimation as compared to the contemporary counterparts. Conclusion: The BB model employs the activity or interaction information of the user over the social network platform in the estimation of diffusion and allows any user to alter their opinion at any time without compromising the accuracy of the predictions. © 2022 Bentham science Publishers.

关键词： Social networking (online)

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User Experience and Usability Challenges in E-Tourism: Bangladesh Perspective 23th

User Experience and Usability Challenges in E-Tourism: Ban...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Hossain, Md Zahid Zaman, Syed Rohit Islam, Muhammad Nazrul Department of Computer Science and Engineering Military Institute of Science and Technology Mirpur Cantonment Dhaka1216 Bangladesh

ISBN: (纸本)9783031647789

Usability plays a critical role in ensuring the efficient delivery of services on e-tourism websites, particularly for travelers in developing countries. This study’s objectives aim to assess the current usability status of e-tourism websites, focusing on prominent tourist destinations in a developing nation. Through a heuristic evaluation of three randomly selected mostly used e-tourism websites (Discovery Bangladesh, Beautiful Bangladesh, and Travel BD) in Bangladesh, this research has revealed 23 usability problems across the selected websites. These usability issues encompass aspects such as system status visibility, flexibility, efficiency of use, consistency, adherence to standards, alignment with real-world usage, and user control. Some usability problems are characterized by high severity ratings, with the majority falling within the 2.5 to 3.5 range on a 0 to 4 scale. Among the selected websites, Discovery Bangladesh website showed the maximum number of problems, while the problems found in Travel BD website showed the highest average severity. As such, the outcomes of this research highlight that addressing the revealed usability concerns is crucial to enhance user experiences, attract more tourists, promote sustainable tourism growth of such context. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Developing countries

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Optimizing Music Genre Classification: A Hybrid Approach with ACO and Ensemble Learning

Optimizing Music Genre Classification: A Hybrid Approach wit...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Reddy, Sai Kiran A. Likitha, V. Prasad, Venkata Durga P. Bharath, Naga V. Kumar, Venkatrama Phani S. Kishore, Venkata Krishna K. Department of Computer Science and Engineering Vignan’s Foundation For Science Technology and Research Guntur522213 India

ISBN: (纸本)9798350374933

The classification of music genres accurately is an ongoing challenge due to the diverse and complex nature of music. Current methods can struggle to identify genres correctly. This study introduces a combination of ensemble learning and Ant Colony Optimization (ACO) to better select features that are key to identifying music genres. Using a diversified collection of songs from Spotify, the research applies detailed data preparation before utilizing a Stacking Classifier framework, which brings together several base learners and an Extra Trees algorithm to improve prediction accuracy. This method achieved a 96.75% accuracy in classifying music genres, surpassing previous methods and advancing the field. The study also compares this new model with other innovative models, highlighting the effectiveness of using a mix of methods. These improvements have important implications for building better music recommendation systems that are both accurate and user-friendly, paving the way for future developments in automatic music analysis. © 2024 IEEE.

关键词： Federated learning

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Anticipating Graduate Program Admission Through Implementation of Deep Learning Models

Anticipating Graduate Program Admission Through Implementati...

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International Conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Shaik, Nazeer Singh, Jagendra Gupta, Ankur Hasan, Dler Salih Manikandan, N. Chandan, Radha Raman Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Virudhunagar India Varanasi India

ISBN: (纸本)9789819986606

Acceptance into a graduate program must be part of a student’s academic journey. Every year, a huge number of people apply to schools and universities, and the admissions process may be tough and time-consuming. Many factors are considered while evaluating a student's application, including academic achievement, test scores, LOR, and extracurricular activities. However, selecting the best choices can still be arbitrary and prone to mistakes. As a result, it is required to develop a more efficient and objective technique of evaluating an applicant's chances of admission to a graduate course based on their application materials. The purpose of this study is to develop a ML model that can predict a student’s prospects of acceptance into a graduate school. The model will be trained using a dataset containing different characteristics, such as GRE scores, GPA, and letters of recommendation. The dataset will be preprocessed to cope with missing values, outliers, and categorical data. A variety of ML methods, including LR, DT, and SVM, will be used to build the model. The algorithm’s efficacy will be measured using a variety of measures, including accuracy, precision, recall, and F1 score. The best-performing model will then be picked and used to evaluate the admissions outcomes of fresh applicants. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Recent Research on Reinforcement Learning for Open RAN 15

Recent Research on Reinforcement Learning for Open RAN

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Park, Heejae Seol, Kihyun Song, Seungyeop Lee, Yerin Park, Laihyuk Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9798350364637

Open Radio Access Network (O-RAN) has been proposed as a flexible, interoperable framework that enhances innovation for network vendors and operators. However, as O-RAN deployments expand, challenges such as dynamic resource management and real-time decision-making emerge. Recent studies have explored integrating Reinforcement Learning (RL) to address these issues. This paper analyzes current research trends in applying RL within the O-RAN framework to provide insights for future developments. © 2024 IEEE.

关键词： Radio access networks

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Image To Text Description Platform For Yakshagana Images By Detecting Crown Using CNN

Image To Text Description Platform For Yakshagana Images By ...

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2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

作者： Kiranraj, M. Murthy, Anantha Manish Prathwini, Prathwini Savitha, G. Harshitha, G.M. NMAM Institute of Technology Department of Master of Computer Applications Karnataka Nitte574110 India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India Manipal Academy of Higher Education Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798331527518

This research called "Yakshakatha - Image to Text Platform for Yakshagana Images."It employs deep learning to classify crowns in Yakshagana Kirita using CNN classification. Yakshagana is a traditional South Indian art form, well known for its intricate costumes and headgear, posing specific challenges in identifying and classifying crowns. The system aims to classify various Kirita styles and provide detailed descriptions of the respective Yakshagana characters, or Vesha, offering valuable cultural and symbolic insights. The model was trained on a self-labelled dataset of Yakshagana images and used convolutional and pooling layers to capture fine details in crown designs. It achieved an impressive accuracy of 99%, ensuring precise identification and classification. Real-time predictions for the input images are made, which have predetermined mappings and description content for recognized characters. The system employs Google Gemini AI, in order to enrich the text description of the image by integrating cultural background. The responsive interface is provided for users on the image upload, real-time result, and enriched visual feedback using annotated output. This project combines modern image recognition with rich cultural content to preserve and promote the traditional heritage of Yakshagana. It will help the users to understand and appreciate the vibrancy of this art form. © 2025 IEEE.

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Reversible data hiding in encrypted images with block-based bit-plane reallocation

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Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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A low-cost AI- Powered System for Early Detection of Diabetic Retinopathy and Ocular diseases in resource Limited Settings

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IEEE Access 2025年 13卷 97322-97336页

作者： Vohra, Harpreet Hasan, Mohammad Kamrul Abdullah, Siti Norul Huda Sheikh Islam, Shayla Rahman, Abdul Hadi Abd Bhojake, Shreya N. Bhobal, Dhruv Gandhi, Awal Garg, Muskan Dhasarathan, Chandramohan Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab Patiala India Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Kuala Lumpur43600 Malaysia UCSI University Institute of Computer Science and Digital Innovations Kuala Lumpur56000 Malaysia

Eye Health is a vital yet often neglected aspect of healthcare, especially in distant and deprived regions that are less accessible to specialized diagnostic tools and professionals. This paper introduces an intelligent diabetic retinopathy detection system to enhance affordability, accessibility, and early diagnosis. The system features a cost-effective imaging setup utilizing a 28D lens and a camera to capture high-quality fundus images. These images are analyzed using advanced Convolutional Neural Networks to determine the cases of people suffering from eye issues: Age-related vascular degeneration (AMD), Diabetic retinopathy (DR), and Glaucoma. A user-friendly web application hosted on AWS facilitates seamless image upload, automated diagnosis, and report generation. Achieving an accuracy of 96.5%, the system ensures reliable diagnostics, enabling timely medical intervention. By integrating AI-driven analysis and cloud-based accessibility, this solution aims to transform eye care in resource-limited settings. Performance evaluation against existing methods highlights its superiority, with an F1 score of 98.2 % for DR, 92.5% for Glaucoma, and 98.8% for AMD, outperforming many previous approaches that either focused on single–disease detection or relied on costly hardware. © 2013 IEEE.

关键词： Convolutional neural networks

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Advancing blood cell detection and classification: performance evaluation of modern deep learning models

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BMC Medical Informatics and Decision Making 2025年第1期25卷 1-22页

作者： Choudhary, Shilpa Kumar, Sandeep Siddhaarth, Pammi Sri Charitasri, Guntu Gulhane, Monali Rakesh, Nitin Mon, Feslin Anish Al-Rasheed, Amal Getahun, Masresha Soufiene, Ben Othman Department of CSE (AIML) Neil Gogte Institute of Technology Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal 506371 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering (AIML) Keshav Memorial Engineering College Hyderabad India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Faculty in Department of Information technology University of Technology and Applied Sciences Ibri Oman Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia Department of Computer Science and Information Technology College of Engineering and Technology Kebri Dehar University Kebri Dehar Ethiopia PRINCE Laboratory Research ISITcom Hammam Sousse University of Sousse Sousse Tunisia

The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification has a high clinical relevance in these patients because this would help to prevent false-negative diagnosis and to treat them in a timely and effective manner, thus reducing their clinical *** research aims to automate the process and eliminate manual efforts in blood cell counting. While our primary focus is on detection and classification, the output generated by our approach can be useful for disease prediction. This follows a two-step approach, where YOLO-based detection is first performed to locate blood cells, followed by classification using a hybrid CNN model to ensure accurate identification. We conducted a thorough and extensive comparison with other state-of-the-art models, including MobileNetV2, ShuffleNetV2, and DarkNet, for blood cell detection and classification. In terms of real-time performance, YOLOv10 outperforms other object detection models with better detection rates and classification accuracy. But MobileNetV2 and ShuffleNetV2 are more computationally efficient, which becomes more appropriate for resource-constrained environments. In contrast, DarkNet outperformed in terms of feature extraction performance, and the fine blood cell type classification. Additionally, an annotated blood cell data set was generated for this study. A diverse set of blood cell images with fine-grained annotations is contained in this dataset to make it useful for deep learning models training and evaluation. Because the present dataset will be an important resource for researchers and developers working on automatic blood cell detection and classification systems, we will make it publicly available under the open-access nature in order to accelerate the collaboration and progress in this field. © The Author(s) 2

关键词： Blood cell detection Classification DarkNet MobileNetV2 Precision medicine Real-time detection ShuffleNetV2 YOLOv10

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An Asynchronous Data Transmission Policy for Task Offloading in Edge-Computing Enabled Ultra-Dense IoT

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computers, Materials & Continua 2024年第12期81卷 4465-4483页

作者： Dayong Wang Kamalrulnizam Bin Abu Bakar Babangida Isyaku Liping Lei Department of Computer Science Faculty of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310JohorMalaysia Department of Computer Science Faculty of Information Communication TechnologySule Lamido UniversityKafin Hausa741103JigawaNigeria School of Political Science and Public Administration East China University of Political Science and LawShanghai201620China

In recent years,task offloading and its scheduling optimization have emerged as widely discussed and signif-icant *** multi-objective optimization problems inherent in this domain,particularly those related to resource allocation,have been extensively ***,existing studies predominantly focus on matching suitable computational resources for task offloading requests,often overlooking the optimization of the task data transmission *** inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network,resulting in increased service times due to elevated network transmission latencies and idle computational *** address this gap,we propose an Asynchronous Data Transmission Policy(ADTP)for optimizing data transmission for task offloading in edge-computing enabled ultra-dense *** dynamically generates data transmission scheduling strategies by jointly considering task offloading decisions and the fluctuating operational states of edge computing-enabled IoT *** contrast to existing methods,the Deep Deterministic Policy Gradient(DDPG)based task data transmission scheduling module works asynchronously with the Deep Q-Network(DQN)based Virtual Machine(VM)selection module in *** significantly reduces the computational space required for the scheduling *** continuous dynamic adjustment of data transmission bandwidth ensures timely delivery of task data and optimal utilization of network bandwidth *** reduces the task completion time and minimizes the failure rate caused by ***,the VM selection module only performs the next inference step when a new task arrives or when a task finishes its *** a result,the wastage of computational resources is further *** simulation results indicate that the proposed ADTP reduced average data transmission delay and service time by 7.11%and 8.09%,***,the tas

关键词： Bandwidth allocation edge computing internet of things task offloading reinforcement learning

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