Procedia Computer Science最新文献

Strategies for Improved Out-of-Distribution Detection in Drone vs. Bird Classification 无人机与鸟类分类中改进的非分布检测策略

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.341

Ami Pandat , Punna Rajasekhar , Gopika Vinod , Rohit Shukla

{"title":"Strategies for Improved Out-of-Distribution Detection in Drone vs. Bird Classification","authors":"Ami Pandat , Punna Rajasekhar , Gopika Vinod , Rohit Shukla","doi":"10.1016/j.procs.2025.03.341","DOIUrl":"10.1016/j.procs.2025.03.341","url":null,"abstract":"<div><div>The use of drones has expanded significantly across various applications over the past decade, leading to increased surveillance-related challenges. These challenges raised the necessity of developing Anti-Drone systems. One of the critical requirements for an effective Anti-Drone system is the ability to accurately distinguish drones from birds in the sky. While deep learning-based classification techniques have been employed for this task, they often suffer from high false positive rates. To address this challenge, Out-of-Distribution (OOD) detection is essential for enhancing the reliability and robustness of drone surveillance systems, particularly in differentiating drones from birds. This paper explores several techniques to improve OOD detection performance, focusing on Energy-Based Models (EBM) and Variational Autoencoders (VAE). We evaluate four loss functions within the EBM framework: Mean Squared Error (MSE) Loss, Mean Squared Error with OOD Penalty, Contrastive Loss, and Binary Cross-Entropy with Energy Regularization. Our results demonstrate that the Mean Squared Error with OOD Penalty function achieves the best performance, with an AUC of 0.9, providing clearer separation between in-distribution (drones) and out-of-distribution (birds) samples. However, the VAE approach did not yield significant results for the binary classification task. Future work could explore hybrid approaches to further enhance OOD detection in such applications.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"259 ","pages":"Pages 398-407"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformative Tech and Social Dynamics: Redefining Customer Engagement in Industry 5.0 变革技术和社会动态：重新定义工业5.0中的客户参与

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.325

Bhawana Kothari , Ambica Prakash Mani , V M Tripathi

{"title":"Transformative Tech and Social Dynamics: Redefining Customer Engagement in Industry 5.0","authors":"Bhawana Kothari , Ambica Prakash Mani , V M Tripathi","doi":"10.1016/j.procs.2025.03.325","DOIUrl":"10.1016/j.procs.2025.03.325","url":null,"abstract":"<div><div>In the era of Industry 5.0, this paper offers a thorough assessment of how transforming technologies have changed consumer involvement. We start by looking at the change from conventional, linear methods to client participation towards current, technologically driven techniques. The emphasis is on how digitization is drastically changing consumer expectations and the policies companies have to follow to quickly fit these developments. Examining the ability of modern technologies such as artificial intelligence (AI), machine learning, the Internet of Things (IoT), and blockchain to influence modern customer engagement methods takes up a good amount of this research. We look at how machine learning and artificial intelligence improve predictive analytics, therefore allowing companies to proactively satisfy consumer wants and customize experiences. We also explore the key part IoT plays in creating a consistent and flawless consumer experience at many points of contact. This talk also addresses the creative use of virtual assistants and chatbots, assessing their efficiency in providing real-time consumer help. These AI-driven solutions are evaluated for their capacity to provide a degree of customizing like that of human interactions, hence improving client connections. This paper explores several aspects of customer commitment in the computerized era using approaches, innovations, and best practices that enable companies to create further associations, enhance customer interactions, and propel supportable development in an era marked by mechanical disturbance.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"259 ","pages":"Pages 240-249"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Analysis Method of E-commerce Data Based on Various Machine Learning Algorithms 基于多种机器学习算法的电子商务数据智能分析方法

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.008

Bo Yang

{"title":"Intelligent Analysis Method of E-commerce Data Based on Various Machine Learning Algorithms","authors":"Bo Yang","doi":"10.1016/j.procs.2025.04.008","DOIUrl":"10.1016/j.procs.2025.04.008","url":null,"abstract":"<div><div>Under the current rapid development of the e-commerce industry, most e-commerce companies are pursuing to enhance the clicks of products and its conversion rate to buy. And there are many machine learning algorithms for the intelligent analysis of e-commerce data, among which, the most widely used is the recurrent neural network (RNN) and collaborative filtering algorithm. Based on the use of multiple machine learning algorithms, this paper compares the differences in the clicks of products and the purchase conversion rates between the RNN algorithm and the collaborative filtering algorithm. The RNN algorithm can make full use of the behavior sequence time dependence and context information and the collaborative filtering algorithm is based on the similarities between user and product. The evaluation results are as follows: the products clicked by the RNN algorithm are between 18,000 and 25,000, which is significantly higher than the products clicked by the collaborative filtering algorithm. In order to improve user purchase decisions and overall sales efficiency, e-commerce operators can select the RNN algorithm to fully understand the user’s interests and needs, and provide accurate personalized product recommendations.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"259 ","pages":"Pages 591-597"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced Video-Based Deep Learning Framework for Comprehensive Detection, Diagnosis, and Classification of Dermatological Conditions in Real-Time Datasets 先进的基于视频的深度学习框架，用于在实时数据集中全面检测、诊断和分类皮肤病

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.344

Syed Thouheed Ahmed , Amogh S Guthur , Pratyush Kumar Rai , Pranava Swaroop N

{"title":"Advanced Video-Based Deep Learning Framework for Comprehensive Detection, Diagnosis, and Classification of Dermatological Conditions in Real-Time Datasets","authors":"Syed Thouheed Ahmed , Amogh S Guthur , Pratyush Kumar Rai , Pranava Swaroop N","doi":"10.1016/j.procs.2025.03.344","DOIUrl":"10.1016/j.procs.2025.03.344","url":null,"abstract":"<div><div>The advanced acne detection model showcased in this project utilizes deep learning methods to accurately classify skin conditions, including blackheads, dark areas, blemishes, and creases. It employs a YOLOv5 format annotation scheme to analyze spatial and temporal information from video sequences, resulting in exceptional performance in detecting seven distinct classes. The model’s resilient performance indicates high accuracy, with a mean Average Precision (mAP) of about 0.85-0.9 at an IoU threshold of 0.5. It also demonstrates generalization and robustness with an mAP of 0.5-0.55 across IoU thresholds from 0.5 to 0.95, making it suitable for real-world dermatological assessments. The proposed method enables early detection and more effective treatment by monitoring skin conditions over time, significantly impacting dermatological image analysis. The goal is to improve patient outcomes and provide personalized skincare recommendations using deep learning techniques, benefiting clinicians and researchers in analyzing and categorizing skin conditions. Additionally, incorporating additional data sources like clinical images or medical histories can enhance the model’s diagnostic capabilities and accuracy. Expanding the dataset will enhance the model’s generalizability and robustness for new skin conditions</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"259 ","pages":"Pages 424-432"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Application of Neural Network Algorithm in Computer Mathematical Modeling 神经网络算法在计算机数学建模中的应用

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.012

Wenying Zhao , Xiaohong Li , Mingjie Shi

引用次数: 0

Review of Segmentation Techniques for Weed Detection in Agricultural Crops 农作物杂草检测的分割技术综述

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.307

Akanksha Bodhale , Seema Verma

引用次数: 0

Advanced AI-Powered Intrusion Detection Systems in Cybersecurity Protocols for Network Protection 网络安全协议中基于ai的入侵检测系统

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.315

Hari Mohan Rai , Aditya Pal , Rashidov Akbar Ergash o’g’li , Bobokhonov Akhmadkhon Kholmirzokhon Ugli , Yarmatov Sherzojon Shokirovich

{"title":"Advanced AI-Powered Intrusion Detection Systems in Cybersecurity Protocols for Network Protection","authors":"Hari Mohan Rai , Aditya Pal , Rashidov Akbar Ergash o’g’li , Bobokhonov Akhmadkhon Kholmirzokhon Ugli , Yarmatov Sherzojon Shokirovich","doi":"10.1016/j.procs.2025.03.315","DOIUrl":"10.1016/j.procs.2025.03.315","url":null,"abstract":"<div><div>Conventional rule-based network intrusion detection systems (NIDS) find it difficult to remain with the increasing complexity of cyber-attacks. To solve these issues, this study examines the development of NIDS as well as the transformative potential of artificial intelligence (AI). AI-enhanced NIDS can efficiently identify and respond to known and unknown threats in real-time by utilizing machine learning (ML) techniques. The system can differentiate between typical network behavior and abnormalities using both supervised and unsupervised learning techniques, as opposed to depending exclusively on pre-established rules. The accuracy and adaptability of the system are further improved by deep learning (DL) architectures like recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The paper explores the past developments of intrusion detection, comparing rule-based approaches to modern AI-driven systems. It discusses cutting-edge techniques like anomaly detection, ensemble methods, and hybrid models. While Recognizing issues such as adversarial attacks and interpretability, the article underlines the importance of AI-enhanced NIDS in protecting digital infrastructure. This study provides a complete overview, unique insights, and practical advice for cybersecurity experts looking to install and optimize AI-powered intrusion detection solutions.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"259 ","pages":"Pages 140-149"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fake News Detection in Indian Languages: A Case Study with Hindi Using CNN-LSTM 印度语言中的假新闻检测：以CNN-LSTM为例

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.316

Rajeev Kumar Gupta , Vaibhav Sharma , R.K. Pateriya , Vasudev Dehalwar , Punit Gupta

{"title":"Fake News Detection in Indian Languages: A Case Study with Hindi Using CNN-LSTM","authors":"Rajeev Kumar Gupta , Vaibhav Sharma , R.K. Pateriya , Vasudev Dehalwar , Punit Gupta","doi":"10.1016/j.procs.2025.03.316","DOIUrl":"10.1016/j.procs.2025.03.316","url":null,"abstract":"<div><div>The increasing spread of false information also known as fake news has had a negative impact on the society and the political system hence the need for detection tools. This research work presents a hybrid CNN-LSTM deep learning model for detecting fake news in Hindi, a language that lacks adequate dataset and resources. A new dataset of 6,724 Hindi news articles (2,704 fake and 4,020 real) was collected from the trusted sources which are members of International Fact Checking Network (IFCN). The model uses FastText pretrained embeddings, a Conv1D layer for local feature extraction and LSTM units for sequential feature extraction, and is able to achieve 97% accuracy on the proposed dataset and an F1 score of 89% on CONSTRAINT2021 dataset.</div><div>This paper also presents a new dataset for future research and the first work done towards developing a system for detecting fake news in Hindi language. In the future, the work will be continued by trying to apply this approach to other sparse Indian languages and by using transformer-based models to improve results.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"259 ","pages":"Pages 150-160"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategic Insights into Customer Diversity: Unraveling Purchase Patterns, Income Disparities, and Relationship Dynamics through K-means Clustering for Enhanced Engagement and Loyalty 顾客多样性的战略洞察：通过k均值聚类分析购买模式、收入差异和关系动态，以增强参与和忠诚度

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.301

Shraddha Sharma , Rupali Satsangi , Preeti Manani , Priti Sharma , Jyoti Gupta

{"title":"Strategic Insights into Customer Diversity: Unraveling Purchase Patterns, Income Disparities, and Relationship Dynamics through K-means Clustering for Enhanced Engagement and Loyalty","authors":"Shraddha Sharma , Rupali Satsangi , Preeti Manani , Priti Sharma , Jyoti Gupta","doi":"10.1016/j.procs.2025.03.301","DOIUrl":"10.1016/j.procs.2025.03.301","url":null,"abstract":"<div><div>This research paper explores the integral role of customer segmentation in modern marketing strategies, emphasizing its significance in understand-ing and catering to diverse customer needs. Acknowledging Exploratory Data Analysis (EDA) as a crucial preliminary step, the study investigates how EDA acts as a catalyst for effective segmentation algorithms by un-veiling hidden patterns in raw data. Through empirical evidence and case studies, the paper demonstrates the transformative impact of incorporat-ing EDA before deploying segmentation models. The results underscore the necessity of a robust EDA framework to extract actionable insights, enhancing the precision of targeted marketing efforts and aligning seg-mentation strategies with real-world customer dynamics. This research contributes valuable insights for practitioners and researchers seeking to optimize marketing strategies through a holistic approach that combines customer segmentation with exploratory data analysis.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"259 ","pages":"Pages 1-10"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Evaluation of Advanced Classification Models Combined with Feature Selection for Credit Risk Performance 结合特征选择的高级分类模型信用风险绩效评价

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.265

Sarakam Tribuvan , Sandeep Deepak Kamath , Sparsh Mishra , Usha M , Shreyas J , Gururaj H L , Dayananda P , Karthik S A

{"title":"Performance Evaluation of Advanced Classification Models Combined with Feature Selection for Credit Risk Performance","authors":"Sarakam Tribuvan , Sandeep Deepak Kamath , Sparsh Mishra , Usha M , Shreyas J , Gururaj H L , Dayananda P , Karthik S A","doi":"10.1016/j.procs.2025.04.265","DOIUrl":"10.1016/j.procs.2025.04.265","url":null,"abstract":"<div><div>In this study, we propose an advanced methodology utilizing machine learning models for predicting home equity credit risk on a real-world dataset. Traditional credit risk models often rely on outdated statistical methods that fail to capture complex, non-linear relationships in data, resulting in suboptimal accuracy and limited interpretability. Furthermore, existing models lack transparency, making it difficult for stakeholders to understand and act on the predictions. To address these issues, we employ state-of-the-art machine learning algorithms such as Decision Trees, AdaBoost, Support Vector Machine (SVM), Neural Networks, and Random Forest, along with feature selection techniques like Boruta and Principal Component Analysis (PCA) to enhance both accuracy and explainability. Our approach aims to provide improved credit risk assessment tools, offering better interpretability for loan companies, regulators, and applicants, while ensuring robust performance. The results demonstrate that our proposed models outperform traditional methods and offer actionable insights for stakeholders, enhancing decision-making processes.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"258 ","pages":"Pages 278-287"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0