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Hierarchical Community Detection and Network Analysis: A 2-Level Approach for Social Media Networks

Hierarchical Community Detection and Network Analysis: A 2-L...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Vishakha Shelke Priyan Parmar Darshit Adhiya Department of Computer Science and Engineering (ICB) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

As social networking site users and their relationships generate enormous amounts of data, these networks become complicated, requiring sophisticated research approaches to extract valuable information. This research presents an innovative 2-level hierarchical method for community discovery. Greedy Modularity and Label Propagation community detection algorithms applied in the first level to find communities. Next, node similarity is assessed using similarity metrics such Jaccard, Cosine, Katz, and Adamic-Adar. These nodes are grouped utilizing K-Nearest Neighbors (KNN) using common neighbors and vicinity to centroids once initial communities are identified, which refines the neighborhood framework to form a 2-tiered hierarchy. NetworkX visualizations for link prediction techniques used to highlight additional network topology insights. The proposed method improves on conventional community detection by identifying intricate hierarchical patterns, providing a more thorough comprehension of the dynamics of social media networks. The effectiveness of the presented hierarchical community detection methodology is assessed using quantitative indicators, such as F1 score, precision, and recall. The outcomes show the reliability of our approach in accurate community detection.

关键词： Social networking (online) Scalability Network analyzers Nearest neighbor methods Network architecture Gain measurement Real-time systems Reliability Detection algorithms Informatics

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Forecasting Sustainability: A Study of Demand Prediction in Circular Economics 1st

Forecasting Sustainability: A Study of Demand Prediction in ...

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1st international research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Harshavardhini, C. Sarayu, K. Roshan, C.R. Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai India SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9783031823824

The study explores the potential of AI enhanced demand prediction for recycled and upcycled products, aiming to bridge the gap between sustainable production and market adoption. We propose a study utilizing machine learning algorithms to analyze historical sales data, market trends, and consumer sentiment, thereby forecasting demand for eco-friendly products with unprecedented accuracy. Leveraging business analytics and social media influence data, we propose a novel framework to: (1) compare consumer preferences and purchase patterns for recycled and upcycled products against their traditional counterpart (2) identify key factors driving demand for sustainable products, (3) compare demand patterns between ARIMA and LSTM models, and (4) analyze the impact of social media influence on consumer preferences. By predicting future demand with enhanced accuracy, this framework aims to empower businesses to optimize production, pricing, and marketing strategies, ultimately enabling a more sustainable and circular economy. The study holds significant implications for both businesses and consumers seeking to minimize environmental impact while catering to evolving market trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Circular economy

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Large Language Models for Marketing research: A Survey and New Perspectives 58

Large Language Models for Marketing Research: A Survey and N...

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58th Hawaii international conference on System sciences, HICSS 2025

作者： Qian, Yang Chen, Shaoge Wu, Chenyang Yuan, Kun Du, Yanan Jiang, Yuanchun Liu, Yezheng School of Management Hefei University of Technology China School of Health Service Management Anhui Medical University China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei 230009 China

ISBN: (纸本)9780998133188

Large Language Models (LLMs) have brought unprecedented innovation opportunities to the marketing field. However, the practical applications of LLMs within the marketing landscape currently exhibit a fragmented and scattered nature. In this study, we aim to aggregate these scattered literature to create a holistic view of LLMs capabilities for marketing research. Specifically, we present an overview of LLMs using the evolution of LMs. Subsequently, we explore their application in the marketing domain across five distinct dimensions: data annotation, idea inspiration and content generation, substitution of human participants, user behavior learning and prediction, and evaluation of LLM feedback. Finally, we discuss the new trends and challenges for LLMs in marketing. This study enriches the theoretical foundations of integrating generative AI with marketing practices. © 2025 IEEE computer Society. All rights reserved.

关键词： Content generation Data annotations Large Language Models Marketing insight Marketing research

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Performance Analysis of Image Augmentation Driven by Language Based on Diffusion Models 2nd

Performance Analysis of Image Augmentation Driven by Languag...

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2nd international conference on Applied Intelligence, ICAI 2024

作者： Chen, Wei Zhou, Peng He, Lina Wang, Huan Cao, Bingyu School of Information Science and Engineering Xinjiang College of Science and Technology Xinjiang Korla841000 China

ISBN: (纸本)9789819619030

Images play a crucial role across diverse fields;however, traditional image enhancement methods possess certain limitations. In contrast, the Diffusion Model presents distinct advantages in image generation. IADL (Image Augmentation Driven by Language) utilizes large vision and language models to generate natural language descriptions and augment training data. The research focuses on the field of botany, especially the identification of precious flowers. Experiments were conducted on the 102 Category Flower Dataset, and the performance of IADL was compared with traditional image enhancement techniques. The results show that traditional techniques still perform better, but Img2Img outperforms Pix2Pix, highlighting the efficacy of feature extraction networks. The IADL method achieves notable results as a generative approach for image creation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Modeling languages

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D.R.E.A.M. App to Promote the Mental Health in Higher Education Students

D.R.E.A.M. App to Promote the Mental Health in Higher Educat...

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international conference on advanced research in Technologies, information, Innovation and Sustainability, ARTIIS 2024

作者： Vaz, Clara B. Galvão, Ana Pais, Clarisse Pinheiro, Marco Instituto Politécnico de Bragança SusTEC Bragança5300-253 Portugal LiveWell - Research Centre for Active Living and Wellbeing Bragança Portugal Instituto Politécnico de Bragança Bragança5300-253 Portugal ISCTE-IUL Lisbon Portugal

ISBN: (纸本)9783031834349

This paper presents the development process of the mobile App D.R.E.A.M., Design-thinking to Reach-out, Embrace and Acknowledge Mental health, which is a tool for self-assessment and self-care in promoting the mental health of higher education students. In Portugal, the program for promoting Mental Health in higher education advocates the development and use of digital tools, such as apps and/or social networks and platforms, aimed at promoting wellbeing and with the potential for use to be more accessible to higher education students. The objective of this app is to promote the mental health and wellbeing of higher education students. Design Thinking was used as the methodology for building the app, which was developed using a combination of low-code/no-code tools, Flutter/Dart coding, and Google's Firebase capabilities and database functionalities. In the first semester of the 2023/2024 academic year, 484 students downloaded the app, and 22 emails were received for psychological consultations. A dynamic update of the app is required, with modules on time management and study organization, structured physical activity programs, development of socio-entrepreneurial skills, and vocational area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Puthucode Ganesh Vignesh Nithin Kumar S Senthil Kumar T Jeyakumar G Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments.

关键词： Privacy Accuracy Laser radar Three-dimensional displays Sensors Indoor environment Convolutional neural networks Fall detection Older adults Monitoring

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advanced Face Detection Using SVM and Haar-like Features: A Comprehensive Approach in AI and Image Processing

Advanced Face Detection Using SVM and Haar-like Features: A ...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Devendra Somwanshi Archika Jain Kapil Joshi Vishnu Kant Salil Bharany Bura Vijay Kumar Department of EC Poornima College of Engineering Jaipur India Department of CSE Management & Gramothan (SKIT) Swami Keshvanand Institute of Technology Jaipur India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Face detection is a vibrant research field spanning artificial intelligence, image processing, video surveillance, human-machine interaction, identity authentication, machine learning, pattern recognition, and so on. While humans effortlessly recognize faces, computers face challenges due to facial variability. The complexity lies in factors like expressions, lighting changes, and head positions. Thus, solving the problem of face detection has an important academic value and provides important enlightenment. There are many aspects to face detection such as facial expression, head position, changing lighting conditions and many others. To deal with face detection problems various approaches are used such as template matching methods, knowledge-based methods, feature invariant methods, and appearance-based methods. The proposed approach for face detection utilizes Support Vector Machine (SVM) and Haar-like Features. It begins by generating an integral image from a binary image measuring 19 x 19 pixels. Following this, the Haar feature extraction method is employed to obtain Haar values from both face and non-face isommages. A set of 200 face and non-face images from MIT database have been used. Features have been reduced five times to make different features sets of images. These features are passed to Support Vector Machine as input parameters and processed to classify face and non-face datasets. Support Vector Machine is used to create, train and test the neural network. Features are extracted on MATLAB and further processed in SVM using NeuroSolutions tool. The results are evaluated based on two scenarios as varying number of images and features.

关键词： Support vector machines Training Face recognition Lighting Machine learning Feature extraction Magnetic heads Face detection MATLAB Faces

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CGP-Mamba: An Intrusion Detection Method Based on State Space Model 3rd

CGP-Mamba: An Intrusion Detection Method Based on State Spac...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： He, Jingheng Wang, Gang Liu, Naiwei Kong, Lingfei Inner Mongolia University of Technology Hohhot 010080 China

ISBN: (纸本)9789819645084

The access of high proportion of clean energy and high proportion of power electronic devices has significantly improved the level of digitization and interconnection of The Energy Internet, resulting in an increase in the attack surface of The Energy Internet. As an important national infrastructure, The Energy Internet has become an important target of high-level persistent threats. In response to advanced persistent threats, the sequence model is applied to the field of intrusion detection by virtue of its superiority in capturing long-distance dependencies. However, the sequence model will face the problem of loss of local information and semantic information, which makes it difficult to further improve the detection accuracy. In order to solve the above problems, this paper proposes an intrusion detection method based on state space model-CGP-Mamba. Firstly, this paper proposes a temporal feature extraction technology combining CNN and Mamba, which breaks through the limitation of sequence model on local feature extraction and improves the model's ability to capture local information. Secondly, a log semantic representation method based on BERT is proposed, which solves the problem of insufficient semantic understanding in complex scenes and improves the semantic representation ability of the model. Finally, the PagFM fusion mechanism is introduced to realize the deep fusion of semantic information and temporal information, which enhances the global recognition ability of the model. CGP-Mamba is compared with ATLAS and other methods on two log detection datasets, and the precision rate reaches 96.23%. The contribution of each model component is evaluated by ablation experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BERT Intrusion Detection Local information Mamba Semantic information

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Context-Aware Legal Summarization using Reinforcement Learning

Context-Aware Legal Summarization using Reinforcement Learni...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Jatin Kumar Verma Rohit Verma Bhaskar Verma Diwaker Arora Pushpendra Singh Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad UP India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Legal judgments are often lengthy and complex, making it difficult for legal professionals to efficiently extract relevant information such as legal principles, precedents, and case outcomes. While existing automatic summarization techniques have proven useful for general domains, they struggle to address the specific requirements of legal texts. This paper presents a novel context-aware summarization model fine-tuned using reinforcement learning (RL), designed to optimize summaries based on legal-specific criteria. By leveraging a custom reward function, our model enhances the extraction of essential components of judgments, such as legal principles and precedents, while maintaining clarity and coherence. We train and evaluate our model on a dataset of Indian Supreme Court judgments, demonstrating significant improvements in summary quality over baseline models. Our experiments show that RL fine-tuning leads to better alignment with legal requirements, as evidenced by both automatic metrics (ROUGE, BLEU) and domain-specific evaluations. This work offers a step forward in the application of AI to legal text processing, with potential implications for improving the efficiency of legal research and judgment analysis.

关键词： Measurement Accuracy Law Text summarization Reinforcement learning Coherence Transformers Standards Context modeling Text processing

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research on Temperature Adaptive Wavelength Assignment Based on Improved Ant Colony Algorithm in Photonic Network on Chip 2nd

Research on Temperature Adaptive Wavelength Assignment Based...

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2nd international conference on Applied Intelligence, ICAI 2024

作者： Zhu, Aijun Cao, Liyun Hu, Cong Xu, Chuanpei Gu, Zhanqi School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin541004 China

ISBN: (纸本)9789819619030

Although Photonic Network-on-Chip (PNoC) has many advantages, such as high bandwidth and low delay, there is crosstalk when optical signals are transmitted by wavelength division multiplexing (WDM) in PNoC and the system performance will be further affected when the temperature changes. The crosstalk of optical signals in PNoC by wavelength division multiplexing is analyzed, and the influence of temperature on crosstalk noise of wavelength assignment is discussed. A wavelength assignment method based on improved ant colony algorithm is proposed. After the communication path between IP cores is established, according to the communication complexity between IP cores and the temperature distribution of the network, the number and spacing of wavelengths that each pair of communication nodes can allocate are searched, and the wavelengths are allocated to the communication nodes according to the objective function of minimum crosstalk. The experimental results show that the proposed wavelength allocation strategy reduces the crosstalk noise energy consumption by 25% on average compared with the traditional wavelength allocation strategy under different mapping models and network temperature distributions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Crosstalk

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