Procedia Computer Science最新文献_第2页

HerbSimNet: Deep Learning -Based Classification of Indian Medicinal Plants with High Inter-Class Similarities HerbSimNet：基于深度学习的高类间相似性印度药用植物分类

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.309

N. Shobha Rani , Bhavya K R , Pushpa B.R. , Ragavendra M. Devadas

{"title":"HerbSimNet: Deep Learning -Based Classification of Indian Medicinal Plants with High Inter-Class Similarities","authors":"N. Shobha Rani , Bhavya K R , Pushpa B.R. , Ragavendra M. Devadas","doi":"10.1016/j.procs.2025.04.309","DOIUrl":"10.1016/j.procs.2025.04.309","url":null,"abstract":"<div><div>Medicinal plant species recognition is important across diverse sectors such as Ayurveda, agriculture, environment conservation and botanical research. Specific groups of plants in Indian medicinal plant ecosystem exhibit significant inter-class similarities due to varying abundance and ecological factors. To address the challenges involved in the process of classifying these species in this work a deep learning model Herb-SimNet is proposed. The Herb-SimNet analyzes similarity of plant species over other plant species using vision based deep learning and machine learning techniques. The proposed model works based on the combination of wavelet features and convolutional features extracted using three sequential convolution layers to extract the prominent features that distinguish variations among the inter class similarity plant species. To perform experiments, a dataset is created by capturing medicinal plant leaf images using box model in plain background and uniform lighting. A smart phone captured twelve Indian medicinal plant species comprising of about 1400+ samples that belongs different plant species but similar morphological structure is collected. Baseline experiments are carried out between Herb-SimNet and other state-of-the-art deep learning models for classification based on the proposed dataset. The outcomes demonstrate that Herb_SimNet provides clear interpretation one plant variety with others and achieves superior accuracy in prediction than that of state-of-the-art approaches. Furthermore, the model demonstrates better generalization towards the other inter-class similarity groups considered for testing. In conclusion, the proposed dataset and Herb-SimNet plays a a crucial role in advancement of research concerning Indian medicinal plant species classification resulting into enhancement of AI-based technology for biodiversity conservation and ethnobotanical studies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"258 ","pages":"Pages 765-774"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931508","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

Optimal resource selection for Green Software Development using Machine Learning 利用机器学习进行绿色软件开发的最佳资源选择

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.298

Nisha Kumari , Tirthankar Gayen

引用次数: 0

Named Entity Recognition in Assamese Language using two separate models: BiLSTM and BERT 阿萨姆语命名实体识别使用两个独立的模型：BiLSTM和BERT

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.262

Plabita Baruah, Bandana Dutta, Shikhar Kumar Sarma, Kuwali Talukdar

{"title":"Named Entity Recognition in Assamese Language using two separate models: BiLSTM and BERT","authors":"Plabita Baruah, Bandana Dutta, Shikhar Kumar Sarma, Kuwali Talukdar","doi":"10.1016/j.procs.2025.04.262","DOIUrl":"10.1016/j.procs.2025.04.262","url":null,"abstract":"<div><div>Named Entity Recognition (NER) is a tool based on principles of Artificial Intelligence (AI) and Natural Language Processing (NLP) for automatically tagging Named Entities from unstructured text. In the realm of Natural Language Processing (NLP) applications, Named Entity Recognition (NER) holds significance as it involves the crucial task of identifying and categorizing proper nouns into classes such as person, location, organization, and miscellaneous. While considerable progress has been made in widely spoken languages like English and other European languages, resulting in higher accuracy rates, the task of NER in Indian languages prove to be challenging due to limited resources. This study explores the implementation of NER in Assamese using two separate approaches: BiLSTM and BERT. The proposed methodology achieves an accuracy of 31%in the BiLSTM model. While using BERT, which is a pretrained model, fine-tuned for Assamese, we achieved a precision of 81.5% and F1- score of 0.383. Our comparative analysis shows that both models are effective for NER in a resource-scarce language like Assamese, but BERT performs better overall in recognizing entities. This suggests that BERT could play a key role in improving NER techniques for underrepresented languages.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"258 ","pages":"Pages 242-251"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931654","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

Empirical Study on Efficiency of Different Language Modeling Techniques using Masking of Named Entities for Indic Languages 基于命名实体遮蔽的不同语言建模技术对印度语建模效率的实证研究

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.228

Sravan Kumar Reddy, Shailashree K Sheshadri, Krishna Likith Avatapalli, Deepa Gupta

{"title":"Empirical Study on Efficiency of Different Language Modeling Techniques using Masking of Named Entities for Indic Languages","authors":"Sravan Kumar Reddy, Shailashree K Sheshadri, Krishna Likith Avatapalli, Deepa Gupta","doi":"10.1016/j.procs.2025.04.228","DOIUrl":"10.1016/j.procs.2025.04.228","url":null,"abstract":"<div><div>Processing unstructured text in Natural Language Processing (NLP) poses significant challenges for Indic languages, which feature flexible word order, spelling variations, and complex sentence structures. Traditional models often struggle with these complexities, leading to issues such as out-of-vocabulary (OOV) words and increased perplexity. Neural Language Models (NLMs), particularly transformer-based models, address some of these challenges by employing word representations and self-attention mechanisms. However, OOV problems persist, especially with named entities, which are dynamic and vary across domains, making it difficult to create comprehensive lists of names for people, organizations, and locations. To address this, the Masked Entity-Based Language Model (ME-LM) has been introduced, focusing on masking named entities identified through Named Entity Recognition (NER) using pre-trained models like BERT-base-NER and IndicNER. Applied to Indic languages such as Hindi, Kannada, and Telugu for the first time, ME-LM has significantly reduced OOV occurrences by 18.60% to 94.70% and lowered perplexity. Since this is the first application of ME-LM to these languages, no standard benchmark exists for direct comparison, but the results show strong potential for improving named entity handling in these languages.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"258 ","pages":"Pages 146-159"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931690","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

Optimized Feature Engineering for Dentition based Cattle Age Estimation 基于牙列的牛龄估计的优化特征工程

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.334

D S Guru , Swaroop D , Anusha P , Keerthana N , Shivaprasad D L

{"title":"Optimized Feature Engineering for Dentition based Cattle Age Estimation","authors":"D S Guru , Swaroop D , Anusha P , Keerthana N , Shivaprasad D L","doi":"10.1016/j.procs.2025.04.334","DOIUrl":"10.1016/j.procs.2025.04.334","url":null,"abstract":"<div><div>Accurate age estimation of cattle is crucial for effective herd management, breeding, and health monitoring. In this novel study, a unique methodology for cattle age estimation is introduced using high-resolution images of the teeth and canal, captured at local farms and from cow breeders. This approach involves capturing these images, annotating them to distinguish between teeth and canal, and employing a tailored YOLO v9 deep learning model for detection and segmentation, achieving a mean Average Precision (mAP) of 98% with a confidence threshold of 0.5 to 0.95. The teeth and canal regions are prominent in age computation for experts. After segmenting these Regions of Interest (RoI), conventional feature descriptors were used to extract edge features from the segmented images such as Histogram of Oriented Gradients (HOG). Initial linear regression analysis of these features yielded a Root Mean Square Error (RMSE) close to 52. To enhance predictive performance, personalized feature engineering pipelines incorporating advanced feature engineering and selection techniques were developed. This refinement led to a substantial improvement, reducing RMSE to approximately 0.06 with an R² of 0.99 for HOG features. HOG was selected over Convolutional Neural Networks (CNNs) due to its computational efficiency and suitability for resource-constrained environments. HOG demonstrated strong performance with minimal computational requirements, making it well-suited for real-time applications on mobile devices. While CNNs offer potential for future enhancements, our current approach prioritizes practicality and performance for small-scale applications. Our research significantly advances machine-learning-based cattle age prediction, offering a reliable, scalable solution for agricultural practices and also paving the way for future research in this field.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"258 ","pages":"Pages 961-980"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931767","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

Meta-Heuristic Optimization Algorithms for Resource Allocation in 5G New Radio Networks 5G新型无线网络资源分配的元启发式优化算法

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.277

Jyoti , Amandeep Noliya , Dharmender Kumar

{"title":"Meta-Heuristic Optimization Algorithms for Resource Allocation in 5G New Radio Networks","authors":"Jyoti , Amandeep Noliya , Dharmender Kumar","doi":"10.1016/j.procs.2025.04.277","DOIUrl":"10.1016/j.procs.2025.04.277","url":null,"abstract":"<div><div>The objective of this research paper is to evaluate effectiveness of various resource allocation algorithms currently used in 5G new radio networks. Due to these complications, the network is experiencing operational difficulties. Incorporating the development trend of 5G into efficient resource management is not only imperative but also requires hardware requirements and improvements to the current network architecture. In order to effectively tackle issue of resource allocation (RA) in a 5G network, primary purpose is to present a proposed scheme for RA that employs learning-based as well as optimization resource allocation methodologies. To ensure effective management of network traffic and operations, resource allocation has emerged as a problematic issue due to the concomitant increase in cellular service demand and the constrained resources at our disposal to provide it. In order to attain the desired level of quality of service (QoS), one of the most critical issues that must be resolved is the reduction of interference activity within the network. This study investigates the subject of resource allocation and optimization and the inspiration for the hunting behavior of meta-heuristic algorithms. This paper evaluates the current 5G NR network resource allocation technique. We formulate the issue of resource allocation as a stochastic optimization problem. Furthermore, throughput and path loss, SNR, and SINR are considered when performing this optimization. The comparison study shows that COA performs best in SNR optimization and FMNS in SINR optimization in resource allocation. Lower standard deviations suggest stability in algorithms like KOA. For effective wireless communication system resource management, the best method relies on network criteria such signal quality and consistency.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"258 ","pages":"Pages 408-419"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931781","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

A Reinforcement Learning based Hybrid GR-DQN Model for Predicting Ichthyophthiriosis Disease in Aquaculture Through Water Quality Analysis 基于强化学习的混合GR-DQN模型在水产养殖鱼鳞病预测中的应用

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.274

Bhawna Kol , Khetavath Jairam Naik

{"title":"A Reinforcement Learning based Hybrid GR-DQN Model for Predicting Ichthyophthiriosis Disease in Aquaculture Through Water Quality Analysis","authors":"Bhawna Kol , Khetavath Jairam Naik","doi":"10.1016/j.procs.2025.04.274","DOIUrl":"10.1016/j.procs.2025.04.274","url":null,"abstract":"<div><div>Aquaculture is a fast-growing industry that provides nutritious food to a growing population and generates substantial revenue for countries. The high water quality is required to be maintained for aquatic animal’s survival and health; otherwise, it may cause many diseases like Furunculosis, Bacterial gill disease, and others. Traditionally available methods for water quality analysis are typically difficult to perform due to being time-consuming and lacking accuracy. In this study, a new approach has been developed using an optimal deep reinforcement learning technique, Hybrid Gated Recurrent Unit (GRU) network with Deep Q-Network (DQN), to analyze the state of the water quality of aquaculture by predicting Ichthyophthiriosis (white spot diseases) in an aquaculture environment. The GRU deep learning model with DQN helps in improving the prediction by approximating Q-values and produces a loss function to guide the learning process; rewards are provided due to correct predictions, thereby disease detection corrected accuracy was enhanced. The proposed hybrid GR-DQN model was implemented on the “Pondsdata” dataset and compared the results with the existing model M-DQN. The Hybrid GR-DQN achieved 94.69% accuracy in comparison to the existing model M-DQN’s 84.16% accuracy on the same dataset.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"258 ","pages":"Pages 374-385"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931853","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

A Comparison of Educational Perspectives on VDI 2221 and Axiomatic Design VDI 2221与公理化设计的教育视角比较

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.073

Patrick Kröpfl , Christian Landschützer , Hannes Hick , Wajih Haider Awan , Christopher A. Brown

{"title":"A Comparison of Educational Perspectives on VDI 2221 and Axiomatic Design","authors":"Patrick Kröpfl , Christian Landschützer , Hannes Hick , Wajih Haider Awan , Christopher A. Brown","doi":"10.1016/j.procs.2025.01.073","DOIUrl":"10.1016/j.procs.2025.01.073","url":null,"abstract":"<div><div>Engineering design methods play a crucial role in both academia and industry. These systematic approaches facilitate product and system development, allowing for innovative solutions and refinements. Specifically, this paper will compare two common engineering design methods Axiomatic Design (AD) and VDI 2221 in terms of their application in teaching and their transferability to industry, especially for small and medium-sized enterprises (SMEs). Firstly, a quantitative comparison of the two methods will be conducted. Comparative factors will include scope, accessibility, required prior knowledge, and the availability of tools for each method. Following this, insights from teaching experiences at the Technical University of Graz and Worcester Polytechnic Institute (WPI) will be discussed, focusing on the teachability of the methods. This will provide insights into the effectiveness and suitability of the methods for higher education. The transfer potential of the methods to SMEs will be derived from these. Finally, the findings and improvement potential will be summarized, and possibilities for the knowledge transfer of engineering design methods to SMEs will be formulated.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 94-103"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480244","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

Game-based learning for industrial maintenance: a Unity 3D educational game of compressed air system training 基于游戏的工业维护学习：压缩空气系统培训的Unity 3D教育游戏

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.140

Birkan Işık , Gülbahar Emir Işık , Miroslav Zilka

{"title":"Game-based learning for industrial maintenance: a Unity 3D educational game of compressed air system training","authors":"Birkan Işık , Gülbahar Emir Işık , Miroslav Zilka","doi":"10.1016/j.procs.2025.01.140","DOIUrl":"10.1016/j.procs.2025.01.140","url":null,"abstract":"<div><div>This research introduces an innovative approach to industrial maintenance training by developing an interactive game using an interactive game developed with the Unity 3D engine and extended reality technologies. The game simulates the compressed air system maintenance, aiming to improve technicians’ practical skills and safety awareness through immersive, realistic scenarios. Leveraging Unity 3D’s advanced graphical and physics capabilities, it creates an engaging environment where participants interact with dynamic modules, enhancing decision-making, problem-solving, and analytical thinking. Gameplay involves guiding participants through the compressed air systems maintenance process with realistic controls that respond dynamically to user inputs, thereby allowing technicians to refine technical skills with a strong emphasis on safety. Performance is evaluated based on safety compliance and technical accuracy, demonstrating the value of game-based learning in technical education. This study highlights the potential of game-based learning within Industry 5.0, promoting lifelong learning and preparing professionals for future industrial challenges.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 784-793"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480648","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

Towards AI-enhanced process planning: assessing machine tool capability based on part design 面向人工智能增强的工艺规划：基于零件设计的机床能力评估

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.122

Sepideh Abolghasem , Matthew Youssef , Faruk Abedrabbo , Amman Pandde

{"title":"Towards AI-enhanced process planning: assessing machine tool capability based on part design","authors":"Sepideh Abolghasem , Matthew Youssef , Faruk Abedrabbo , Amman Pandde","doi":"10.1016/j.procs.2025.01.122","DOIUrl":"10.1016/j.procs.2025.01.122","url":null,"abstract":"<div><div>The emergence of the fourth industrial revolution, or Industry 4.0, necessitates a more automated approach to manufacturing process planning. This process begins with evaluating machine tool capabilities to handle specific part geometries and microstructures. Once a match is established, the focus shifts to developing an efficient method for converting design elements into physical components. This work aims to create and validate a framework that assesses the manufacturability of design features based on the available machinery and materials. Specifically, it involves classifying manufacturing processes, such as turning and milling, for a given part design geometry. To achieve this, feature attributes like rotational symmetry and D2 distribution are calculated for a dataset used to train a decision tree. This model then suggests the appropriate manufacturing process for a given CAD model. The decision tree is validated with a separate dataset, showing reasonable accuracy. Ultimately, the goal is to enhance process planning, ensuring the seamless translation of designs into physical products, with a particular emphasis on geometry, microstructure, and cost.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 603-611"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480710","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