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IAENG International Journal of computer science 2024年第7期51卷 754-764页

作者： Teng, Yue Yang, Kai Yue Teng is a postgraduate student at the School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today’s society, communication among people has become more frequent and extensive due to the rapid development of science, technology, and the Internet. This vast communication occurs in real life and virtual online worlds. However, with the overwhelming amount of information available, people often struggle with finding and accessing the information they need, leading to frustration. To address this challenge, we conducted a study to extract valuable information and insights from users’ social relationships. While existing models can help us understand and analyze users’ social relationships to some extent, there are still significant challenges in extracting valuable insights. Therefore, we conducted in-depth research to improve the GraphRec model and created a new social recommendation model called the CWYGNN model. The CWYGNN model is unique because it can identify deeper connections in users’ social interactions through in-depth learning of the social sequences between users. This is important for understanding users’ behavioral patterns, predicting their needs, and providing personalized services. To validate the effectiveness of the CWYGNN model, we conducted experiments and comparisons on several public datasets. The experimental results show that the CWYGNN model outperforms similar approaches in processing user social relationships and extracting valuable insights. This result provides a new way of thinking and methodology to help us better understand and serve users and ultimately enhance their overall experience. © (2024) International Association of Engineers. All rights reserved.

关键词： Recommender systems

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A Hybrid Harmony Search Algorithm based on Firefly Algorithm and Boltzmann Machine

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 241-251页

作者： Yang, J. Zhang, Xiaoxia Fu, S. Hu, Y.Y. Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Professor of School of computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China

Harmony Search Algorithm (HS) is a new meta-heuristic algorithm based on music creation process. The algorithm has been widely used in many fields because of its simple and flexible structure and fewer controlled parameters. However, HS is easy to be affected by the initial harmony memory and falls into local optimization. In addition, there are also problems, such as low searching accuracy and slow convergence speed of the optimal solution. Therefore, this paper proposes a hybrid harmony search algorithm based on firefly algorithm and boltzmann machine (HSFA-BM). First, in order to avoid the possibility of harmony search algorithm falling into local optimum, boltzmann machine (BM) mechanism is used to update the harmony memory in this paper. Second, in order to enhance the ability of the algorithm to exploit optimal solutions, this paper uses the movement mode of fireflies to generate new solutions. The improved algorithm enhances the ability of global optimization and search precision, and speeds up the convergence speed. In this paper, six classical test functions are used to test the effectiveness of the algorithm, and a very obvious optimization result is obtained. © 2022. All Rights Reserved.

关键词： Flexible structures

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CE-HigherHRNet: Enhancing Channel Information for Small Persons Bottom-Up Human Pose Estimation

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 260-269页

作者： Li, M.Y. Zhao, J. Master’s Student of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Accurately detecting the keypoints of small persons in an image using bottom-up multi-person pose estimation algorithms is exceptionally difficult owing to scale variation challenges. HigherHRNet initially solved the challenge of multi-player scale change pose estimation. However, because it uses repeated cross-scale fusion, owing to inherent defects in channel reduction, semantic information is lost. Furthermore, the aliasing effects produced by the miscellaneous feature maps formed after cross-scale fusion have a significant impact on the detection accuracy of small persons. In this paper, we propose a novel bottom-up human pose estimation algorithm based on HigherHRNet, called Channel-Enhanced HigherHRNet (CE-HigherHRNet). CE-HigherHRNet comprises three main components: a multiscale sub_pixel skip fusion module, a lightweight attention mechanism (with channel attention enhanced and spatial attention modules), and a high-resolution feature pyramid with an added Dupsampling module. The lightweight attention mechanism optimizes the feature map after each fusion. Deconvolution is replaced with Dupsampling, which strengthens the network’s scale awareness and makes it more sensitive to robust scale changes. The average precision (AP) of CE-HigherHRNet on the COCO test-dev dataset was 71.9% (an improvement of 1.4% compared with HigherHRNet). Furthermore, the average detection accuracy of small persons was 68.1% AP (an improvement of 1.5% AP). These results verify that the proposed CE-HigherHRNet is more robust in processing scale changes and has a stronger ability to handle crowded environments. Thus, it is more accurate in positioning small persons in images and human bodies in crowded environments. © 2022. All Rights Reserved.

关键词： Semantics

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StorySpinner: AI Based Technique to Narrate the Web Links 4

StorySpinner: AI Based Technique to Narrate the Web Links

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kumbar, Prajwal Sharma, Shamneesh Batra, Isha Malik, Arun Ashfaq, Farzeen School of Computer Science and Engineering Lovely Professional University Punjab India upGrad Campus upGrad Education Private Limited Delivery and Student Success Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia

ISBN: (纸本)9798350353266

The rapid advancement of LLM has facilitated a variety of large language model applications in natural language processing. This report details the development of StorySpinner, an application based on large language models and retrieval-augmented generation that allows yield from articles to the format of an interesting narrative. Moreover, the model addresses processing issues related to the specifics of diverse Internet re-sources, webpages, blogs, and even YouTube videos, by combining web scraping methods, text summarization models, and semantic paragraphs with the use of the extraction of material from extracted abstracts into LLM. The present research investigated the specifics of StorySpinner architecture, the used techniques of information extraction, summarization, and narrative generation, and possible applications of this technology such as educational purposes, accessibility issues, and content development. The paper also provided a discussion of ethical aspects, including bias prevention, copyright issues, and AI technology influence on society. StorySpinner was introduced as a method to fill the gap between web content and human understanding, offering a new way to access information as a story. © 2024 IEEE.

关键词： Semantics

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Identification and Classification of Rare Medicinal Plants 4

Identification and Classification of Rare Medicinal Plants

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Bora, Piyush Singh Sharma, Shamneesh Batra, Isha Malik, Arun Ashfaq, Farzeen School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Education Private Limited upGrad Campus Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia

ISBN: (纸本)9798350353266

The identification and conservation of rare medicinal plants are crucial, given their invaluable role in healthcare, pharmaceuticals, and biodiversity preservation. Numerous varieties face extinction according to the International Union for Conservation of Nature (IUCN), necessitating advanced image processing and computer vision techniques for accurate identification and documentation. This paper presents a novel deep learning approach leveraging convolutional neural networks (CNNs) and transfer learning to adapt the MobileNet V2 architecture for automated identification and classification of rare medicinal plants found in different areas, utilizing a curated dataset of 6,900 images across 80 distinct classes. The robust deep learning model extracts discriminative visual features from digital images, facilitating accurate taxonomic classification, and outperforming traditional techniques and shallow models. The proposed system empowers stakeholders with reliable accessible tools and plays a pivotal role in biodiversity conservation and sustainable utilization, ensuring nature's therapeutic bounty for generations. © 2024 IEEE.

关键词： Plant diseases

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Fakenews AI: Identifying and Flagging Misinformation through Artificial Intelligence 4

Fakenews AI: Identifying and Flagging Misinformation through...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, Aman Raj, Rohan Sharma, Ajay Kumar, Abhishek Sharma, Shamneesh Lovely Professional University School of Computer Science and Engineering Phagwara India UpGrad Education Private Limited Delivery and Student Success Karnataka Banglore India

ISBN: (纸本)9798350364729

Misinformation has become a pervasive problem in today's society. With the rise of social media and the ease of sharing information, false or misleading information can spread rapidly and have a significant impact on public opinion and decision-making. The challenge of identifying and flagging misinformation has become increasingly difficult, as conventional methods of fact-checking and verification struggle to keep up with the scale and speed of information dissemination. In this study, the author will explore the function of artificial intelligence (AI) in identifying and flagging misinformation, and the potential implications and future directions of the technology, looking at different types of models like Naive Bayes, LR, DT, RF, SVM, and LSTM. The author has tested how well they worked using measures like accuracy, precision, recall, and F1-score. The author has observed that the Decision Tree model is the best one overall. It had the highest accuracy and did the best job at classifying things compared to the other models. © 2024 IEEE.

关键词： Contrastive Learning

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Bi-Directional Conversion Algorithm of Database Based on Artificial Intelligence 2

Bi-Directional Conversion Algorithm of Database Based on Art...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Tao, Ning Geng, Lin Xu, Chong Shenyang Jianzhu University School Of Computer Science And Engineering Liaoning Shenyang China Shenyang Jianzhu University Department Of Student Affairs Liaoning Shenyang China

ISBN: (纸本)9798350381665

The traditional database conversion algorithm is difficult to implement, expensive and difficult to popularize. The lack of conversion algorithm directly affects the result of data integration. The core problem of the mutual transformation between XML(eXtensible Markup Language) and relational data is how to construct the mapping between relational database and XML document, that is, the mapping between objects and relationships. In this paper, the element tree is used to transform the probabilistic relational data model and the probabilistic XML data model in heterogeneous databases. First, the corresponding relationship between the element tree and the unit nodes and various data models is established. Then, on this basis, the bidirectional transformation of the data model is realized through the mapping relationship between the element tree and the data. When the transaction sending frequency is 400 transactions /s, the write throughput is 89TPS and the query throughput is 185TPS. The query and write performance of the database bidirectional conversion algorithm based on artificial intelligence is better. © 2024 IEEE.

关键词： Mapping

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Smart Fashion Recommendation System using Random Forest Algorithm

Smart Fashion Recommendation System using Random Forest Algo...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Patel, Shivangi Kaur, Arneet Nair, Theertha Sudheer Babu Sharma, Shamneesh Verma, Vikas Agnihotri, Nishant Lovely Professional University School of Computer Science and Engineering Punjab India UpGrad Education Private Limited Delivery and Student Success Karnataka Bangalore India

ISBN: (纸本)9798350378092

The fashion sector is expanding quickly in the digital age, and this means that more sophisticated recommendation systems that improve user experiences and expedite the online purchasing process are required. This work proposes a sophisticated and all-inclusive fashion recommendation system that uses machine learning techniques, specifically Random Forest, to enhance suggestion accuracy and efficiency. Users can choose their preferred fit and style using an interactive interface, which enables the system to offer clothes based on their individual profiles. Moreover, Context-based filtering involves personalizing recommendations to users based on a variety of contextual factors which may include user preferences, current fashion trends, occasion, style, body type and others. As a result of the dynamic recommendation process, the recommendations are constantly improved and grow more specific and well-rounded over time. The outcomes showcase the system's capacity to effectively combine various machine learning algorithms to evaluate a wide range of data sources, including user preferences, item features, and contextual information, to provide individualized recommendations. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards Optimized Physical Activity: A Fusion of Graph Convolutional Networks and XGBoost for Exercise Analysis

Towards Optimized Physical Activity: A Fusion of Graph Convo...

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Smart Systems for Electrical, Electronics, Communication and computer engineering (ICSSEECC), International Conference on

作者： Kishan V Rajesh M Dhanush B Rajiv Vincent School of Computer Science and Engineering Student Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Student Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This research explores the implementation of the GCN-XGBoost with attention mechanism within the detection and correction of exercises through computer vision, utilizing the 2-dimensional pose coordinates from the MMFit [ 1 ] dataset. The objective is to enhance the precision and reliability of squat performance analysis, leveraging the robust, scalable machine learning capabilities of XGBoost. By processing and analyzing two-dimensional skeletal data, the study aims to identify key postural deviations and provide corrective feedback in realtime, potentially reducing the risk of injury and improving overall exercise effectiveness. Initial results demonstrate the model's capability to discern between correct and incorrect exercise forms with high accuracy, indicating the feasibility of applying advanced machine learning techniques to personal fitness and rehabilitation contexts. This paper details the methodology, from data preprocessing to model training and evaluation, highlighting the contributions to the fields of sports science and machine learning in physical activity monitoring. We have used MMFit dataset for the model that we have trained. We use the 2D coordinates of 18 of the 33 joints in the human body as the main features to categorize various exercises and forecast the accuracy of the poses identified by the Google's AI framework MediaPipe [2]. In this paper, we first classify an individual's exercises (squats, lunges, bicep curls, situps, pushups, triceps extensions, dumbbell rows, jumping jacks, dumbbell shoulder press, lateral shoulder raises) and check whether the pose associated with an exercise is correct. The final model trained has a 98% final validation accuracy.

关键词： Training Presses Accuracy Lung Internet Performance analysis Reliability

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Classification of Usual and Atypical Ductal Hyperplasia using Deep Learning

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Procedia computer science 2025年 260卷 561-569页

作者： Puneet Pandit Shrinivas D Desai Gururaj N Bhadri Student School of Computer Science & Engineering KLE Technological University Hubli - 580031 Professor School of Computer Science & Engineering KLE Technological University Hubli – 580031 Professor Department of Mathematics KLE Technological University Hubli - 580031

Breast Cancer is the leading cause of death among women. In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. One such challenge includes the classification of usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). Prognosis and diagnosis of intraductal breast lesions pinpointing invasive and non-invasive cancer lesions is a monotonous task due to their similar structures in biopsy samples of breast. Expert’s differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of great importance in arriving at an optimal treatment plan. In this research work, we present AI based solution to classify the most confusing stages of breast cancer, that is Usual ductal hyperplasia (UDH), Atypical ductal hyperplasia (ADH) and Ductal carcinoma in situ (DCIS) in breast biopsy. Statistical analysis is carried out to justify the results. Data set having images of normal mammary glands, invasive ductal lesions, non-invasive ductal lesions which include Usual ductal hyperplasia (UDH), Atypical ductal hyperplasia (ADH), Ductal carcinoma in situ (DCIS) in breast biopsy specimens are employed in this study. Statistical analysis is performed to provide the statistical evidence of experiments. Analysis of Variance (ANOVA) as a feature selection method is used to select best features. The proposed method has performed quite significantly, where scores are statistically and medically acceptable. The highest accuracy recorded is 83.33%. When tested for robustness, the accuracy has dropped down to 65.25% allowing one to carry out further investigations.

关键词： Deep Learning Classification Intraductal lesion Invasive ductal carcinoma (IDC) Dimensionality curse Analysis of Variance (ANOVA)

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