Procedia Computer Science最新文献_第4页

Influence of process parameters and wall thicknesses on the properties of tool steel 1.2709 (X3NiCoMoTi18-9-5) processed by Selective Laser Melting 工艺参数和壁厚对选择性激光熔化加工1.2709 （X3NiCoMoTi18-9-5）工具钢性能的影响

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.147

Sara Halilovic , Norbert Wild , Marko Orsolic , Aziz Huskic

{"title":"Influence of process parameters and wall thicknesses on the properties of tool steel 1.2709 (X3NiCoMoTi18-9-5) processed by Selective Laser Melting","authors":"Sara Halilovic , Norbert Wild , Marko Orsolic , Aziz Huskic","doi":"10.1016/j.procs.2025.01.147","DOIUrl":"10.1016/j.procs.2025.01.147","url":null,"abstract":"<div><div>Selective Laser Melting (SLM) has gained significant importance as a manufacturing process for complex geometries and high-precision components. For tool steels such as 1.2709, which are valued for their high strength and hardness, optimizing the process parameters is essential to achieve the desired mechanical properties and minimize defects. The ability to control factors like wall thickness and energy density is essential for producing thin-walled components with high structural integrity and reliability. In this study, the influence of the selected process parameter and wall thickness on the fabrication of tool steel 1.2709 by SLM was investigated. The tool steel was processed using two different process parameters and eleven wall thicknesses ranging from 0.2 to 1.0 mm. The two process parameters V1 (87 J/mm<sup>3</sup>) and V2 (48 J/mm<sup>3</sup>) differ significantly in their energy density. The specimens were examined for their final wall thickness. Furthermore, the influence of the process parameter and the wall thickness on the mechanical properties, the defects and the microstructure were determined. The investigations revealed a fine dendritic microstructure in the as-built condition. Depending on the process parameter, the mechanical properties are weakened by the porosity observed in thinner wall thicknesses.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 853-862"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Circular Data Service Cards: A Card-Based Ideation Tool For Data Services Supporting The Twin Transition 循环数据服务卡：支持双转换的数据服务的基于卡片的构思工具

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.150

Gert Breitfuss , Lilia Yang , Viktoria Pammer-Schindler , Leonie Disch

{"title":"Circular Data Service Cards: A Card-Based Ideation Tool For Data Services Supporting The Twin Transition","authors":"Gert Breitfuss , Lilia Yang , Viktoria Pammer-Schindler , Leonie Disch","doi":"10.1016/j.procs.2025.01.150","DOIUrl":"10.1016/j.procs.2025.01.150","url":null,"abstract":"<div><div>The Twin Transition encompasses the progression of both digital and green transformations. The integration of data science, data analytics, and data services with Industry 4.0 principles significantly enhances the operational efficiency, decision-making, and sustainability of manufacturing systems. In the context of green transformation, Circular Economy (CE) business models aim to optimize resource use and reduce environmental impact. The development of data services and the application of artificial intelligence (AI) are essential for CE, as these technologies improve efficiency, traceability, and resource optimization. This paper presents the development process of the Circular Data Service Cards (DSC), an extension card set (20 newly designed cards, one new category) to the existing Data Service Cards (50 cards, grouped into 5 categories) to assist the co-creation process in developing data services that support the circular economy. The cards address the challenges of interdisciplinary collaboration (user-centered service design, data science and circular economy) and varying expertise levels essential for building a circular data-driven business. Alongside the developed sub-categories (new cards), the outcomes of this study provide a valuable enhancement of the existing DSC. Initial evaluation results indicate that the Circular DSC are perceived as both useful and user-friendly. This research contributes to the twin transition by providing an actionable tool to support the digital transformation and the development of circular data-driven services.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 882-891"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Sarcoma Based on Genomic Data Using Machine Learning Models 基于基因组数据的肉瘤分类使用机器学习模型

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.034

Pratham Gala , Yash Pandloskar , Shubham Godbole , Fayed Hakim , Pratik Kanani , Lakshmi Kurup

{"title":"Classification of Sarcoma Based on Genomic Data Using Machine Learning Models","authors":"Pratham Gala , Yash Pandloskar , Shubham Godbole , Fayed Hakim , Pratik Kanani , Lakshmi Kurup","doi":"10.1016/j.procs.2024.12.034","DOIUrl":"10.1016/j.procs.2024.12.034","url":null,"abstract":"<div><div>The proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. The previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. The model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. This innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. Thus, the findings from this research could change cancer diagnosis forever; they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 317-330"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Multi-Label Fuzzy Classification System using Topic Detection Data set 基于主题检测数据集的多标签模糊分类系统实现

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.003

R. Kanagaraj , N. Krishnaraj , J. Selvakumar , J. Ramprasath

{"title":"Implementation of Multi-Label Fuzzy Classification System using Topic Detection Data set","authors":"R. Kanagaraj , N. Krishnaraj , J. Selvakumar , J. Ramprasath","doi":"10.1016/j.procs.2024.12.003","DOIUrl":"10.1016/j.procs.2024.12.003","url":null,"abstract":"<div><div>Multiclass Classification can be implemented by using consequent approaches to translate the multiclass problem into binary class classification problems and fuzzy classification methods. This work proposes a predictive analysis of the multiclass fuzzy Classification integrated with time series historical data and topic detection. The fuzzy classification techniques can be successfully applied to Topic detection and sub-topic detection. Text databases’ manual topic detection method must be more feasible, uncontrollable and effective. Thus, initiating the huge amount of data implemented by manual methods is idealistic. Fuzzy historical data is more significant for data analysis in different models to make predictions. Innumerable fuzzy logic on time series methods has been implemented for data prediction. A Multiclass Fuzzy Time Series Classification Algorithm has been implemented to analyze and predict the topic detection database. The outcomes of the Fuzzy classification technique have been implemented for the need for an extensive pattern of topic detection. An enhanced Multiclass Fuzzy Time Series Classification Algorithm has been applied to achieve the efficient de-fuzzification operation of the topic detection data set. To illuminate the forecasting method, the historical data of multi-labeled has been used for the predictive model. The investigation result illustrates that the MHTSC algorithm generates mode fuzzy classification and irregular rules, efficiently reducing the error rate from multi-labeled data.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 15-24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A GAN-Enhanced Multimodal Diagnostic Framework Utilizing an Ensemble of BiLSTM, BiGRU, and RNN Models for Malaria and Dengue Detection 利用BiLSTM， BiGRU和RNN模型集成的gan增强多模态诊断框架用于疟疾和登革热检测

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.039

Rathnakar Achary, Chetan J Shelke, Alluru Lekhya

{"title":"A GAN-Enhanced Multimodal Diagnostic Framework Utilizing an Ensemble of BiLSTM, BiGRU, and RNN Models for Malaria and Dengue Detection","authors":"Rathnakar Achary, Chetan J Shelke, Alluru Lekhya","doi":"10.1016/j.procs.2024.12.039","DOIUrl":"10.1016/j.procs.2024.12.039","url":null,"abstract":"<div><div>Quick detection of Malaria and Dengue is crucial for doctors to start treatment and manage patients effectively. As patient conditions become more complex with overlapping symptoms, traditional diagnostic tools become inefficient, slow, and less accurate. Modernizing diagnostics with AI-powered systems is essential. Inaccurate or delayed diagnoses lead to transmission and sustained spread of these diseases. Improving diagnostic tools with accuracy, precision, recall, and speed enhances patient outcomes, reduces infection spread, and streamlines health sector operations. Despite advances, current diagnostic algorithms have weaknesses, especially in applying machine learning to diverse datasets at granular levels. Continuous effort is needed to improve accuracy and recall. This research proposes a GAN-Based Synthesized Multimodal Diagnostic System, combining BiLSTM, BiGRU, and RNN approaches. Utilizing GANs for data augmentation and recurrent networks, this framework shows innovative infectious disease detection. It improves diagnostic precision by 4.9%, accuracy by 3.5%, recall by 3.5%, and AUC by 4.5%, while reducing the gap between disease progression and detection by 8.3%. These outcomes can reduce triage time, misdiagnoses, and lead to faster, quality healthcare. The GAN-Enhanced Multimodal Diagnostic Framework shows promise for diagnosing Malaria, Dengue, and other infectious diseases.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 381-393"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stray Dog Detection System using YOLOv5 使用YOLOv5的流浪狗检测系统

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.041

Ashwini Bhosale , Pranav Shinde , Yash Firke , Shivprasad Patil , Pranav Mitake , Samruddhi Shinde

{"title":"Stray Dog Detection System using YOLOv5","authors":"Ashwini Bhosale , Pranav Shinde , Yash Firke , Shivprasad Patil , Pranav Mitake , Samruddhi Shinde","doi":"10.1016/j.procs.2025.01.041","DOIUrl":"10.1016/j.procs.2025.01.041","url":null,"abstract":"<div><div>Stray dogs present significant public health and safety risks, particularly in developing countries like India, where the stray dog population is the largest globally. This paper details the implementation of a Stray Dog Detection System using the YOLOv5 object detection model to automatically detect and track stray dogs in real time via CCTV feeds. YOLOv5’s high accuracy and real-time processing capabilities make it well-suited for detecting stray dogs in complex, crowded urban environments. The system leverages a YOLOv5 model trained on custom datasets tailored to local conditions, including specific dog breeds and deployment environments. It integrates an alert mechanism that triggers when stray dog populations surpass predefined thresholds, allowing timely interventions. Additionally, the system incorporates geographic mapping to provide data-driven insights for municipal authorities to manage stray populations effectively and ethically. Experimental results demonstrate an F1 score of 0.97, validating the system’s robustness for practical deployment. This paper discusses system architecture, implementation, and performance, highlighting its scalability and cost-effectiveness for humane stray dog population control.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 806-813"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influencer Ranking Framework Using TH-DCNN for influence maximization 使用TH-DCNN实现影响力最大化的影响者排名框架

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.018

Vishakha Shelke , Ashish Jadhav Dr.

{"title":"Influencer Ranking Framework Using TH-DCNN for influence maximization","authors":"Vishakha Shelke , Ashish Jadhav Dr.","doi":"10.1016/j.procs.2025.01.018","DOIUrl":"10.1016/j.procs.2025.01.018","url":null,"abstract":"<div><div>As the influencer gains more significance in social media marketing, companies raise their budgets for influencer campaigns. With business increasing day by day, finding efficient influencers is becoming the most prominent factor for success, but choosing the right influencer from these social media users is quite a challenge. This manuscript proposes a novel method to rank influencers by their effectiveness based on their posting behavior and social relations over time. Initially, the data from Twitter is collected from the Indian politics tweets and reactions dataset. This raw data undergoes preprocessing using various techniques including, tokenization, stemming, lemmatization, stop word removal, and data normalization using the Min-Max normalization approach to ensure the data is relevant and suitable format for analysis. Next, construct a heterogeneous network to represent the complex interactions between entities like users, tweets, hashtags, and mentions. Then Tree Hierarchical Deep Convolutional Neural Network (TH-DCNN) is applied to these networks to derive information representation for each influencer at each period. Finally, a Cosine similarity (CS) is used to learn from the network and predict the influencer rankings. The performance metrics such as accuracy, f1-score, mean average precision (MAP), Normalized Discounted Cumulative Gain (NDCG), Receiver Operating characteristic (ROC), Mean Reciprocal Rank (MRR), and Hit Rate are analyzed in experimental evaluations. The proposed method improved the accuracy compared with existing techniques.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 583-592"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Location Verification of Wireless Sensor Node using Integrated Trilateration in Outdoor WSN 户外WSN中基于集成三边测量的无线传感器节点位置验证

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.016

Arjun , Supreeth N M , Akhil K M , Sougandh Sunil

{"title":"Location Verification of Wireless Sensor Node using Integrated Trilateration in Outdoor WSN","authors":"Arjun , Supreeth N M , Akhil K M , Sougandh Sunil","doi":"10.1016/j.procs.2025.01.016","DOIUrl":"10.1016/j.procs.2025.01.016","url":null,"abstract":"<div><div>This paper presents a novel method for enhancing localization accuracy in Wireless Sensor Networks (WSNs) through an improved trilateration approach. Despite advancements in localization techniques, challenges remain in achieving reliable accuracy, particularly in complex environments. The proposed method enhances traditional trilateration by integrating angle of arrival (AoA) measurements, leading to better positioning of sensor nodes. In this study, the aim is to confirm whether the final coordinates of trilateration can be supported by adding residual analysis and AoA observations. Experiments were conducted to assess the localization system’s effectiveness. The results showed that residual validation outperformed AoA localization, particularly in noisy outdoor environments, providing more reliable distance estimates even under challenging conditions. This method enhances the trustworthiness of the localization system while also minimizing hardware needs and reducing computational complexity, making it a practical choice for resource-limited WSNs. The AoA verification method achieved average accuracies of 54% for x-coordinates and 55% for y-coordinates. In contrast, incorporating residual analysis improved these figures to 79% for x-coordinates and 80% for y-coordinates. These insights focus on the localization process and demonstrate the value of residual analysis in boosting system performance.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 567-575"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Binary SGO-GA for solving MAX-SAT problem 求解MAX-SAT问题的混合二进制SGO-GA

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.055

Rhiddhi Prasad Das , Anuruddha Paul , Junali Jasmine Jena , Bibhuti Bhusan Dash , Utpal Chandra De , Mahendra Kumar Gourisaria

{"title":"Hybrid Binary SGO-GA for solving MAX-SAT problem","authors":"Rhiddhi Prasad Das , Anuruddha Paul , Junali Jasmine Jena , Bibhuti Bhusan Dash , Utpal Chandra De , Mahendra Kumar Gourisaria","doi":"10.1016/j.procs.2025.01.055","DOIUrl":"10.1016/j.procs.2025.01.055","url":null,"abstract":"<div><div>The Maximum Satisfiability Problem (MAX-SAT) is a crucial NP-hard optimization problem with applications in artificial intelligence, circuit design, scheduling, and combinatorial optimization. In this work, we provide a unique hybrid strategy that blends Genetic Algorithms (GA) with Social Group Optimization (SGO) algorithm to effectively solve the MAX-SAT problem. The SGO algorithm, inspired by the social behavior of groups, excels in exploring diverse regions of the search space. w used a binary variant of SGO i.e. Binary-SGO which is defined specifically for binary search spaces, while GA leverages evolutionary principles to exploit local optima through selection, crossover, and mutation. By integrating the exploration capabilities of SGO with the exploitation strengths of GA, the hybrid approach strikes an optimal balance between global and local search. Extensive experimental evaluations conducted on standard MAX-SAT benchmarks demonstrate that our hybrid algorithm outperforms several existing state-of-the-art meta-heuristic algorithms. Hybrid BSGO-GA achieved the highest average fitness values, with an average accuracy of 99.7% in Experiment 1, 99.61% in Experiment 2, and 99.21% in Experiment 3 and achieved complete satisfiability in 55 out of 75 cases in Experiment 1, 42 out of 75 cases in Experiment 2, and 7 out of 75 cases in Experiment 3. This approach demonstrates the potential of hybrid metaheuristics in addressing complex optimization problems and offers a robust framework for tackling other NP-hard problems.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 944-953"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A neural network approach for collaborative cells: an innovative online rescheduling strategy for maximizing productivity 协作细胞的神经网络方法：一种创新的在线重调度策略，以最大限度地提高生产率

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.103

Irene Granata , Matthias Bues , Martina Calzavara , Maurizio Faccio , Benjamin Wingert

引用次数: 0