Procedia Computer Science最新文献_第3页

Deep Learning-Based Vehicle Speed Estimation in Bidirectional Traffic Lanes

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.024

Jen Aldwayne B. Delmo

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Floral-Inspired Artificial Magnetic Conductor for Versatile Dual-Band Wireless Communication

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.043

Vishakha Yadav , Abhijyoti Ghosh , Achinta Baidya , Zonunmawii , L. Lolit Kumar Singh , Sudipta Chattopadhyay

引用次数: 0

Intrusion Detection in Wireless Sensor Networks using Machine Learning

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.052

Hajar Fares , Amol D. Vibhute , Yassine Mouniane , Habiba Bouijij

{"title":"Intrusion Detection in Wireless Sensor Networks using Machine Learning","authors":"Hajar Fares , Amol D. Vibhute , Yassine Mouniane , Habiba Bouijij","doi":"10.1016/j.procs.2025.01.052","DOIUrl":"10.1016/j.procs.2025.01.052","url":null,"abstract":"<div><div>Attackers are continuously enhancing their techniques to attack confidential data. Thus, the classical security solutions based on cryptography and older detection systems must be revised, especially for a network dataset. Wireless sensor networks (WSN) consist of several smart devices distributed randomly in hostile areas with many restrictions, such as low operational time, short memory and minimal energy resources, and are susceptible to numerous attacks, such as Denial-of-Service (DoS). Recently, intrusion detection based on artificial intelligence models has provided an alternative and efficient solution with low computational resources. Therefore, the present study provides an innovative approach for detecting DoS attacks in the network using machine learning. Three machine learning methods, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and decision tree, were implemented with a well-known WSN-DS data specialized for DoS attacks. The performance of the models has been evaluated using standard metrics like accuracy, precision, F1-score and recall. The experimental results demonstrated the effectiveness of the KNN model after achieving the highest accuracy of 99.76% in DoS detection. Thus, the present study’s approach can be used in real-time intrusion detection.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 912-921"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376773","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

An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.054

J.G. Rajeswari , R. Kousalya Dr.

{"title":"An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET","authors":"J.G. Rajeswari , R. Kousalya Dr.","doi":"10.1016/j.procs.2025.01.054","DOIUrl":"10.1016/j.procs.2025.01.054","url":null,"abstract":"<div><div>The use of Flying Adhoc Networks (FANETs), also known as Unmanned Aerial Vehicles (UAVs), has increased in recent years. However, the fast movement of UAVs can lead to unreliable links and inefficient data transmission. To address this issue, the Intelligent-based Energy and Mobility-aware Clustering (IEMC) protocol has been developed, utilizing Battle Royale Optimization (BRO) for Cluster Head (CH) selection and a Deep Q-Learning (DQL)-based fast dynamic hello interval algorithm for path maintenance. Despite these advancements, FANETs still face challenges due to environmental obstacles affecting communication routes. To solve these issues, this article proposes an Intelligent-based Energy, Mobility, and Obstacle-aware Clustering (IEMOC) protocol for FANET routing. This protocol uses an intelligent Bezier route selection technique to deal with obstacles obstructing the paths of FANET nodes and a speed-based mobility prediction technique to reduce the impact of mobility during transmission. If link failure occurs due to an obstacle in the network, the IEMOC protocol selects an optimal alternative routing path via neighboring nodes based on its mobility awareness factor, link duration, network connectivity, and route availability, recovering the failed route without initiating the route discovery process. The effectiveness of the IEMOC protocol is validated through performance evaluations using the Network Simulator (NS)-2.35, and simulation results demonstrate that the IEMOC protocol outperforms conventional routing protocols in FANETs.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 934-943"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376775","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

Fake News Detection: Exploring the Efficiency of Soft and Hard Voting Ensemble

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.035

Arifur Rahman , Sakib Zaman , Shahriar Parvej , Pintu Chandra Shill , Md. Shahidul Salim , Dola Das

{"title":"Fake News Detection: Exploring the Efficiency of Soft and Hard Voting Ensemble","authors":"Arifur Rahman , Sakib Zaman , Shahriar Parvej , Pintu Chandra Shill , Md. Shahidul Salim , Dola Das","doi":"10.1016/j.procs.2025.01.035","DOIUrl":"10.1016/j.procs.2025.01.035","url":null,"abstract":"<div><div>Fake news dissemination is a critical problem, jeopardizing the reliability of information and eroding public confidence. Machine learning provides a solution by distinguishing between real and misleading news. In this study, we utilized various classifiers such as KNN, Logistic Regression, Decision Tree, LightGBM, XGBoost, Gradient Boosting, Random Forest, and Naive Bayes on an open-source dataset. Tf-idf (Term frequency-inverse document frequency) and Count vectorization were used for text preprocessing, and soft and hard voting ensembles were applied to the best three and five models to boost performance. Additionally, we employed BERT (Bidirectional Encoder Representations from Transformers) and CNN (Convolutional Neural Network) models with various optimizers (adam, rmsprop, SGD, adadelta, and adagrad) to further improve classification. The BERT model, optimized with adam, achieved the highest accuracy of 99.93%, with perfect precision (100%) and high recall (99.84%). Furthermore, using Count vectorization for feature extraction, the soft voting ensemble of the top five models (LGBM, XGB, RF, DT, LR) achieved peak performance among all ensemble models, achieving 99.87% accuracy, with precision, recall, f1-score, MAE, MSE, RMSE, RAE, and RRSE of 99.83%, 99.88%, 99.86%, 0.13%, 0.13%, 3.66%, 0.27%, and 7.80%, respectively. The exhaustive experimentation confirms the applicability and efficiency of the recommended models in identifying fake news.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 748-757"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376777","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

Multi-agent digital twin of broccoli: development and test evaluation

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.027

Petr Skobelev , Aleksey Tabachinskiy , Elena Simonova , Yulia Zhuravel , Anastasiya Galitskaya

{"title":"Multi-agent digital twin of broccoli: development and test evaluation","authors":"Petr Skobelev , Aleksey Tabachinskiy , Elena Simonova , Yulia Zhuravel , Anastasiya Galitskaya","doi":"10.1016/j.procs.2025.01.027","DOIUrl":"10.1016/j.procs.2025.01.027","url":null,"abstract":"<div><div>Crop production is a complex multi-domain field of knowledge dealing with living objects. Ongoing global climate change has been destroying this long-established sustainable knowledge system. Precise farming needs digital integration of several domain fields: technologies, machinery and equipment, knowledge about plant growth, predicting the impact of activities on crop yield online. Digital twin of plant, mirroring and predicting plant’s state and growth in real-time should be the central element of precision farming system. In the paper, a Smart Plant Digital Twin (SPDT) is proposed as a smart software system with a knowledge base and methods of reasoning. SPDT is developed for online management and simulation of plant behaviour in sync with development of the real plant. A multi-agent implementation of multi-level plant structure is discussed in the paper, which considers crop physiology and resource demand, describing internal processes inside a plant, and a method for calculating the crop parameters and duration of plant development stages based on expert knowledge. Ontological model of SDTP for crop cultivation domain reflects the production process of each field or greenhouse crop and allows the scale up of the number of simulated cultures, specifying the differences between the varieties and cultivars, and widening the effects of agricultural measures. The plant model is illustrated in broccoli growth process. The model was validated in real experiment compared to the growth of real broccoli crops, planted in the Taiwan region, and real data from the field sensors and agronomists from the farms acquired data from their sensors and worked with digital twins of their crops.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 674-683"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376849","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

Sustainable development of solar power through the investigation of Partial Shading effect of solar module in terms of experimental set up and MATLAB simulation

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.030

Mohiuddin Khan , Mahbub Ferdous , Suman Chowdhury

{"title":"Sustainable development of solar power through the investigation of Partial Shading effect of solar module in terms of experimental set up and MATLAB simulation","authors":"Mohiuddin Khan , Mahbub Ferdous , Suman Chowdhury","doi":"10.1016/j.procs.2025.01.030","DOIUrl":"10.1016/j.procs.2025.01.030","url":null,"abstract":"<div><div>The analysis on partial shading for a PV module is presented in this paper as a critical criterion for sustainable development in practical life. Nowadays it is an important challenge for the sustainable development in the energy sector where solar panel can take a vital role in this regard. A partial shading experiment on a PV module will be shown in this investigation, as well as comparisons of IV properties and PV panel efficiency with and without shade. The many causes of partial shade will be examined and there will be corrective ways to avoid partial shading. This project is developed into two stages namely, hardware and software implementation. For hardware implementation two 20W polycrystalline solar panel and different types of variable loads were used. In addition to this, the impact of partial shading of single and two different solar PV configurations are investigated: 1) Series 2) Parallel. The SIMULINK environment of MATLAB is utilized for simulation. Under partial shading conditions, the effect of two environmental elements, irradiance and temperature, will be seen in the simulated features. The bypass and blocking diodes are employed to increase the performance of PV arrays, and their influence can be observed in the simulation. From the practical observation it is seen that almost 86.2 % more power can be obtained from the solar module from 25 % shading to 0% shading on module.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 702-707"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376852","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

Integration of Coalescent Theory and Generative Adversarial Network (GAN) for Synthesizing High-Fidelity Textual Financial Data

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.019

Ashwini Dalvi , Shriya Pingulkar , Aryaman Tiwary , Diti Divekar , Irfan N A Siddavatam , Nilkamal More

{"title":"Integration of Coalescent Theory and Generative Adversarial Network (GAN) for Synthesizing High-Fidelity Textual Financial Data","authors":"Ashwini Dalvi , Shriya Pingulkar , Aryaman Tiwary , Diti Divekar , Irfan N A Siddavatam , Nilkamal More","doi":"10.1016/j.procs.2025.01.019","DOIUrl":"10.1016/j.procs.2025.01.019","url":null,"abstract":"<div><div>Financial data analysis faces significant challenges due to limitations in the quality, scope, and biases of existing datasets. This research work introduces a novel approach to creating synthetic financial datasets using coalescent theory, a principle from evolutionary biology, combined with deep learning methodologies to address constraints on scope, accessibility, and diversity in financial datasets. While methods such as the Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Networks (GANs) have shown some success in generating synthetic data, particularly in textual domains, they still face significant challenges in producing realistic and balanced textual data. The proposed method in this research improves the stability and quality of synthetic data generation by integrating coalescent theory with GANs, resulting in a more stable architecture that mitigates mode collapse and captures complex temporal dependencies and non-linear relationships in financial datasets. The generated data accurately mirrors the intricacies of real-world financial markets, enhancing the quality, diversity, and authenticity of synthetic data for robust predictive modelling. This research works details the integration of evolutionary algorithms with deep learning to create datasets that authentically represent financial contexts and are nearly indistinguishable from genuine data. By introducing this interdisciplinary approach, this research aims to enrich the toolkit for financial analysis and set a new standard in synthetic data generation.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 593-602"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376899","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

Evolutionary Computation in Early Detection and Classification of Plant Diseases from Aerial View of Agricultural lands

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.001

K. Sujatha , R.S. Ponmagal , Prameeladevi Chillakuru , U. Jayalatsumi , N. Janaki , N.P.G. Bhavani

{"title":"Evolutionary Computation in Early Detection and Classification of Plant Diseases from Aerial View of Agricultural lands","authors":"K. Sujatha , R.S. Ponmagal , Prameeladevi Chillakuru , U. Jayalatsumi , N. Janaki , N.P.G. Bhavani","doi":"10.1016/j.procs.2024.12.001","DOIUrl":"10.1016/j.procs.2024.12.001","url":null,"abstract":"<div><div>This research presents a new combined deep learning system for effective and reliable identification of plant diseases in complicated agricultural environments. One of the most difficult jobs in agriculture is identifying plant diseases early on. Early disease detection in plants is crucial for increasing agricultural yield. With the application of machine learning and deep learning techniques, this issue has been resolved. Large crop farms can now detect plant illnesses automatically, which is advantageous as it reduces the monitoring time. The suggested approach consists of multiple important stages. To begin with, image quality of the agricultural lands is improved through preprocessing techniques like noise reduction, gamma correction and white balancing. Data augmentation is incorporated to expand the dataset and improve the generalization capacity of the model. Efficient methods such as EfficientDet and Squeeze Net, as well as color and shape based features, are included in feature extraction. The most relevant features are selected by a Hybrid Optimization Algorithm (HOA), which integrates Mother Optimization Algorithm (MOA), Teaching learning-based optimization (TLBO) and Improved Wild Horse Optimization to detect the various plant diseases like Bacterial Blight, Tungro, Blast and Brown spot. At last, a deep learning detector, which may include Recurrent Convolutional Neural Networks (R-CNNs) and Recurrent Neural Network (RNN), identifies the location and type of objects. The use of hyper parameter tuning techniques is also implemented to avoid over fitting and improve the overall generalization. This comprehensive approach depicts encouraging results in overcoming challenges in plant disease detection.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376752","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

Adaptive Hybrid Genetic-Ant Colony Optimization for Dynamic Self-Healing and Network Performance Optimization in 5G/6G Networks

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.041

Aanchal Agrawal , A.K. Pal

{"title":"Adaptive Hybrid Genetic-Ant Colony Optimization for Dynamic Self-Healing and Network Performance Optimization in 5G/6G Networks","authors":"Aanchal Agrawal , A.K. Pal","doi":"10.1016/j.procs.2024.12.041","DOIUrl":"10.1016/j.procs.2024.12.041","url":null,"abstract":"<div><div>The rapid growth of 5G/6G networks requires resilient solutions to optimize network performance while ensuring adaptability against failures. This paper introduces a novel Adaptive Hybrid Genetic-Ant Colony Optimization (GA-ACO) framework, designed for dynamic self-healing and multi-objective performance optimization in next-generation mobile networks. The developed method combines the global optimization competencies of a Genetic Algorithm (GA) with the local rerouting performance of Ant Colony Optimization (ACO), developing a dynamic switching mechanism. When no faults are detected, GA optimizes critical objectives such as latency minimization, bandwidth utilization, and energy efficiency. After identifying network faults, such as base station failures, ACO quickly reroutes impacted devices to preserve fault tolerance and minimize downtime. Main network metrics, including latency, bandwidth utilization, energy efficiency, and fault tolerance, are optimized at the same time utilizing a weighted-sum fitness function. The model adjusts dynamically to changing network situations, making it perfectly appropriate for real-time applications in 5G/6G networks, such as smart cities and mission-critical communications. Simulation results show the efficiency of the GA-ACO hybrid, demonstrating improved network efficiency and rapid recovery during failures. This innovative adaptive approach guarantees a more effective, efficient, and sustainable mobile communication network, competent of facing the complex needs of future 5G/6G technologies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 404-413"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377143","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