2022 International Conference on Engineering and Emerging Technologies (ICEET)最新文献_第6页

Improved Fake News Detection Method based on Deep Learning and Comparative Analysis with other Machine Learning approaches 基于深度学习和与其他机器学习方法比较分析的改进假新闻检测方法

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007214

R. Khan, A. S. M. Shihavuddin, M. M. Syeed, Rakib Ul Haque, Mohammad Faisal Uddin

{"title":"Improved Fake News Detection Method based on Deep Learning and Comparative Analysis with other Machine Learning approaches","authors":"R. Khan, A. S. M. Shihavuddin, M. M. Syeed, Rakib Ul Haque, Mohammad Faisal Uddin","doi":"10.1109/ICEET56468.2022.10007214","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007214","url":null,"abstract":"Recently, researchers have massively worked on fake news identification. Most of them focus on the classification method. These methods have accuracy problems and fail to perform well on diverse datasets due to the lack of a generalized feature extraction method. This study aims to enhance the score of the fake news identification model with a generalized and robust feature extraction method to handle the above problems. This study uses a popular fake news dataset, which is available in the Kaggle. The proposed approach uses Stemming that helps to convert all the words into their corresponding root word. Then TF-IDF and BERT convert all the texts into a feature vector for machine learning (Logistic Regression, Naive Bayes, Support Vector Machine, Passive Aggressive, K-means, K-medoids, and K-nearest neighbor) and deep learning (BERT), respectively. Performance analysis shows that BERT with the stemming Natural Language Processing (NLP) technique outperforms all the previous methods and achieves an accuracy of 99.74%. The previous state-of-the-art method (fakeBERT) has shown an accuracy of 98.90%. The primary reason for this performance gain is the stemming, which transforms all words in a sentence to their root word, resulting in a generalized vector that aids the model performance. On the other hand, the support vector machine (linear kernel) and passive-aggressive classifier method with stemming TF-IDF vectorizer also outperforms all the aforementioned approaches with the accuracy of 99.11% and 98.99%.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124448692","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

Music Classification Using Fourier Transform and Support Vector Machines 基于傅里叶变换和支持向量机的音乐分类

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007421

Davis Moswedi, Ritesh Ajoodha

引用次数: 0

Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems 移动不变支持向量机在自动目标识别系统中的图像分类

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007129

Ehimwenma Omoregbee, M. Ndoye, J. Khan

引用次数: 0

Comparative Analysis of Testing Black Hole Attack and Rushing Attack on VANET (Vehicular Ad-Hoc Network) with AOMDV Routing Protocol 基于AOMDV路由协议的VANET(车载自组织网络)黑洞攻击和快速攻击测试对比分析

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007272

S. Masruroh, Yusran Syuja Farghani, Aries Kusdaryono, Andrew Fiade, Rizka Amalia Putri, Luigi Ajeng Pratiwi

{"title":"Comparative Analysis of Testing Black Hole Attack and Rushing Attack on VANET (Vehicular Ad-Hoc Network) with AOMDV Routing Protocol","authors":"S. Masruroh, Yusran Syuja Farghani, Aries Kusdaryono, Andrew Fiade, Rizka Amalia Putri, Luigi Ajeng Pratiwi","doi":"10.1109/ICEET56468.2022.10007272","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007272","url":null,"abstract":"Increased transportation and communication allow communication between vehicles to occur. Wireless technology such as VANET (Vehicular Ad-Hoc Network) is a network that can communicate vehicles to vehicles (V2V) technology and make safety and comfort when driving. VANET networks are still vulnerable to malicious nodes which can damage data communication on the VANET network. Offensive attacks against VANET networks are rushing attacks and black hole attacks. The routing protocol used in this study is AOMDV. This study uses a simulation method and using supporting applications such as OpenStreet Map, SUMO, NS2, NAM, and AWK to do simulations. This study uses the Quality of Service (QoS) parameters such as throughput, packet loss, packet delivery ratio, and end-to-end delay. The number of simulation nodes is as many as 30 nodes, with the condition of 10 black hole nodes and 10 rushing nodes in a simulation it is different from the attacker node that appears at the beginning of the appearance of the node. In comparison testing of a black hole and rushing attacks, the results show that the throughput value decreases, packet loss increases, the packet delivery ratio decreases and the end-to-end delay results increase.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123181979","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

Robust Stabilization for a Class of Descriptor Systems under Input Delays: The Attractive Invariant Ellipsoid Approach 一类输入时滞广义系统的鲁棒镇定:吸引不变椭球法

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007193

R. J. Toro, V. Azhmyakov, M. A. Corrales Astorga, C. Salcido

{"title":"Robust Stabilization for a Class of Descriptor Systems under Input Delays: The Attractive Invariant Ellipsoid Approach","authors":"R. J. Toro, V. Azhmyakov, M. A. Corrales Astorga, C. Salcido","doi":"10.1109/ICEET56468.2022.10007193","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007193","url":null,"abstract":"The paper deals with the robust control design for descriptor (implicit) linear systems governed by a semi-explicit Differential Algebraic Equation (DAE) under two conditions: the unknown time-varying input delay and the unbounded exogenous perturbation presence. In order to provide numeric stabilizing conditions of the descriptor system solution, a linear feedback control and a Luenberguer-like observer are designed by the application of the conventional Attractive Invariant Ellipsoid Method (AIEM). The AIEM ensures both practical stability and robustness properties of the descriptor system solution. Practical stability is associated with the implicit system solution convergence into a specific geometrical region delimited by an Ellipsoid. Robustness properties are described in the sense of the exogenous perturbation rejection. The AIEM is an effective and well-known approach to ensure stability and robustness of dynamical systems governed by ordinary differential equations (ODE) but not for implicit systems governed by DAE. The main contribution on this work is the use of AIEM under unknown time-varying input delay presence. From the principles of AIEM for time-delay systems we propose an adequate linear feedback control and a Lyapunov-Krasovskii functional associated to a convergence zone in form of an ellipsoid. Minimization of the attractive ellipsoid is numerically described by the solution of a Bilinear Matrix Inequiality (BMI). Finally, an academic example supports the theoretical results.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131277744","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

Experimentally Fault detection scheme on shading and cracks in PV modules 光伏组件遮阳和裂纹故障检测方法实验研究

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007097

X. P. Yokwana, T. Mosetlhe, A. Yusuff

引用次数: 0

The Effective Zone Routing Protocol Design Using Deep Recurrent Neural Network for The Next Generation Wireless Network 基于深度递归神经网络的下一代无线网络有效区域路由协议设计

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007329

Fadli Sirait, M. F. Md Din, M. T. Jusoh, K. Dimyati

{"title":"The Effective Zone Routing Protocol Design Using Deep Recurrent Neural Network for The Next Generation Wireless Network","authors":"Fadli Sirait, M. F. Md Din, M. T. Jusoh, K. Dimyati","doi":"10.1109/ICEET56468.2022.10007329","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007329","url":null,"abstract":"This study proposes the usage of LSTM-RNN to allow ZRP to adjust the value of zone radius to the environment by sizing each node’s routing zone based on network performance input metrics such as Routing Overhead, Energy Consumption, Throughput, and User Usage. Those input metrics were used as a dataset, and split into 500 as data training, and 100 as data testing to get the zone radius value as an output value in the simulation. The proposed algorithm was tested in two scenarios: a static node environment and a mobility node environment using MATLAB as a simulator. The bandwidth capacity used in this study is 300 Mbps, which meets the requirement of next-generation wireless networks (5G and beyond). Furthermore, the proposed algorithm’s (LSTM-RNN ZRP) results are compared to conventional ZRP in both scenarios. The range of zone radius for mobile node environments is wider than for static node environments, with a range of 2-6 for LSTM-RNN ZRP and 2-7 for conventional ZRP. Meanwhile, the range for mobile node environments is 1-7 for both LSTM-RNN ZRP and conventional ZRP. According to the relationship between input metrics and zone radius determination, the proposed algorithm is more effective when used in a static node environment. However, both algorithms are acceptable for application in a static and mobile node environment.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131942741","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

ICEET 2022 Cover Page icee2022封面页

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/iceet56468.2022.10007422

引用次数: 0

Automated Brain Tumor Classification System using Convolutional Neural Networks from MRI Images 基于卷积神经网络的MRI图像自动脑肿瘤分类系统

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007116

Diponkor Bala, Mohammad Anwarul Islam, Mohammad Iqbal Hossain, Mohammed Mynuddin, Mohammad Alamgir Hossain, Md. Shamim Hossain

{"title":"Automated Brain Tumor Classification System using Convolutional Neural Networks from MRI Images","authors":"Diponkor Bala, Mohammad Anwarul Islam, Mohammad Iqbal Hossain, Mohammed Mynuddin, Mohammad Alamgir Hossain, Md. Shamim Hossain","doi":"10.1109/ICEET56468.2022.10007116","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007116","url":null,"abstract":"Recent advances in machine learning have employed deep learning to do several tasks. Deep learning has been used in the health sector to solve complex problems that require human intelligence. Without timely medical attention, the prognosis for patients with brain tumors is dismal. Radiologists are responsible for classifying tumors in radiographic images, which is a complex and time-consuming process that relies solely on their expertise. Modern radiology diagnosis, such as magnetic resonance (MR) scans, is largely subjective, putting patients at risk of damage. Use of Artificial Intelligence (AI) technology in order to avoid making mistakes when diagnosing is important to success. An automated approach for classifying different brain tumor classes in patients using magnetic resonance imaging (MRI) was suggested in this research, which focused on merging deep learning and radionics. We performed our work on three unique datasets with several classes. The proposed technique makes use of a convolutional neural network (CNN) as our deep learning model with the K-fold cross-validation concept in order to perform both binary and multiclass classification on our magnetic resonance imaging (MR) data. We took advantage of the power of CNN architecture in medical imaging. The model was trained and tested on random folded images from the dataset and was able to get an accuracy rate of 100%, 99.86%, and 100% in the corresponding dataset respectively, those are utterly remarkable, to put it mildly.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129560146","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: 4

Predicting Covid-19 Cases for 12 Countries using Long Short-Term Memory 利用长短期记忆预测12个国家的Covid-19病例

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10006845

P. Ramesh, J. Jothi

{"title":"Predicting Covid-19 Cases for 12 Countries using Long Short-Term Memory","authors":"P. Ramesh, J. Jothi","doi":"10.1109/ICEET56468.2022.10006845","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10006845","url":null,"abstract":"A novel virus named coronavirus or ‘COVID-19’ by the World Health organization (WHO) has spread around the entire world placing mankind in a situation that no one had predicted. The rise of the number of infected and death cases around the world is alarming and has caused hysteria among mankind. Considering the adversity of the COVID-19, some immediate plan to monitor the number of cases in the future needs to be maneuvered. In this paper, we aim to implement a method to envision the number of COVID-19 cases for the future. We achieve the result by using a deep learning algorithm known as Long Short-Term Memory (LSTM) over the real-time dataset provided by WHO for predicting the number of COVID-19 cases in twelve countries. The countries considered in this study are United States of America, China, United Arab Emirates, India, Brazil, France, Germany, Spain, Republic of Korea, Italy, Singapore, and Argentina. The contribution of this paper is to provide each country with their own model that can help predict their respective future COVID-19 cases. With these predictions, each country can then come up with solutions to reduce the number of infected cases in their respective nation. The proposed LSTM model was evaluated using metrics such as Correlation Coefficient and R2 Error. The results show that the model was giving high R2 score (≥ 0.7) and high correlation coefficient (≥ 0.7) between the test and train datasets. In the cases where R2 score (< 0.7) and correlation coefficient (< 0.7) were low, the train and test values of the datasets were similar making the predictions accurate.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134595157","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