International Journal of Emerging Technology and Advanced Engineering最新文献_第3页

Detection of Hate Posts and Tweets in the Social Network Society 社交网络社会中仇恨帖子和推文的检测

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-05-13 DOI: 10.46338/ijetae0523_02

Maryam Alhawity, Nourah Alessa, Amal Majdua, S. Alshehri, M. Aborokbah, Mohammed Alotaibi

{"title":"Detection of Hate Posts and Tweets in the Social Network Society","authors":"Maryam Alhawity, Nourah Alessa, Amal Majdua, S. Alshehri, M. Aborokbah, Mohammed Alotaibi","doi":"10.46338/ijetae0523_02","DOIUrl":"https://doi.org/10.46338/ijetae0523_02","url":null,"abstract":"Hate speech is a prevalent issue on social media platforms that has recently been a cause for concern due to its detrimental effects on individuals and society. The development of effective hate speech detection procedures and algorithms is crucial to address this issue. However, the existing natural language processing (NLP) algorithms and machine learning models face several challenges in accurately identifying and categorizing hate speech. These challenges include the ambiguity and variability of language use, the lack of standardized definitions and guidelines for hate speech, and the rapid evolution of new and creative forms of hate speech. In this paper, we propose a technique that leverages classic machine learning and deep learning methods to locate and categorize hate speech in social media. Our approach involves the use of Support Vector Machines (SVM) and Long ShortTerm Memory (LSTM) networks for classification. We evaluate the performance of our model on a hate speech dataset and compare it with a deep learning-based model. Our results show that the SVM model outperforms the deep learning-based model in accuracy and efficiency. Our approach offers a promising solution to the challenges posed by hate speech detection on social media and contributes towards building a safer and more welcoming online community. Keywords—Hate speech, Social Networks, NLP, LSTM, Transformers","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126566114","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

An Empirical Analysis of Credit Risk Based on NonPerforming Assets of Selected Banking Sectors of India: Data Validation by Using Machine Learning Algorithms 基于印度银行业不良资产的信用风险实证分析:机器学习算法的数据验证

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-05-13 DOI: 10.46338/ijetae0523_05

Ankur Joshi, N. V. Rao

{"title":"An Empirical Analysis of Credit Risk Based on NonPerforming Assets of Selected Banking Sectors of India: Data Validation by Using Machine Learning Algorithms","authors":"Ankur Joshi, N. V. Rao","doi":"10.46338/ijetae0523_05","DOIUrl":"https://doi.org/10.46338/ijetae0523_05","url":null,"abstract":"It was an attempt to study the impact of nonperforming assets (NPAs) in the selected public and private banking sectors from 2008 to 2019. An empirical study wasvalidatedby using machine learning (ML) algorithm models (Python via Jupiter Notebook, version 3.6) to know the credit risk. It was predicted the overall credit risk as per cut off GNPA>6, and GNPA>7through two types of models such as Regression and Classification models. In empirical findings, highly significant (p<0.001) change between studied banks as well as yearly data and GNPA & NNPA was recorded. Moreover,highly significant (p<0.001) differences were noted for the banking performance based on GNPA and NNPA and other macroeconomic variables viz. Unsecured/Tot Advances, GDP, CPI, Total Profit/ Total Advances, TL, GDP-1, Total Advances, RR, STA, Total Earnings/Total Advances, PSL, CPI-1.ForML study, the Naïve Bayes Classification was predicted to know how the Gross NPA is getting effected by different variables and obtained an accuracy of about 86% and the Support Vector Classification was obtained an accuracy of about 97% and about 100% for the Random Forest classifications, which seems like more realistic models.It may be varied with other independent variables like credit risk parameters and macroeconomic variables, etc. It is suggested in future to study with these cut off values for the determination of credit risk of these banking sectors. Keywords –Indian banking sectors; Machine learning algorithms; Non-performing assets; Empirical study","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132826507","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

Design of Circular Microstrip Patch Antenna-Array for 5G Wi-Fi Network 5G Wi-Fi网络环形微带贴片天线阵列设计

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_07

Rovin Tiwari, Raghavendra Sharma, Rahul Dubey

引用次数: 0

Software Defined Networking Based Detection of DDoS Attacks 基于软件定义组网的DDoS攻击检测

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_11

Manisha Singh, Vandana Rathore, V. Patel, Kaushik Mishra, R.N Singh, R. Bhardwaj

{"title":"Software Defined Networking Based Detection of DDoS Attacks","authors":"Manisha Singh, Vandana Rathore, V. Patel, Kaushik Mishra, R.N Singh, R. Bhardwaj","doi":"10.46338/ijetae0423_11","DOIUrl":"https://doi.org/10.46338/ijetae0423_11","url":null,"abstract":"Software-defined networking (SDN) separates network management from data-traffic routes. More businesses are adopting it because of its flexibility, adaptability, and ability to improve traffic movement. SDN, or security-by-design, might be a desirable alternative for securing networks. While there have been many advancements in SDN technology, it is still susceptible to DDoS assaults. The growing frequency and scope of DDoS attacks pose a threat to network security, despite the availability of various methods for detecting and countering such attacks. There are two main methods to spot a distributed denial of service (DDoS) attack: signature recognition and abnormality detection. When personal characteristics such as fingerprints or iris scans are used to verify identity. Anomaly-based detection, which relies on network behavior, employs machine learning methods. We present a strategy for SDNs to identify DDoS assaults in this article. In the proposed architecture, DDoS attacks are detected using the Advanced Support Vector Machine (ASVM) technique. When compared to the SVM method, ASVM has the benefit of significantly less testing and training time. To evaluate the effectiveness of the suggested system, we use the Hierarchical Task Analysis (HTA) method of measuring human error.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124271223","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 Comparative Performance Evaluation of GRP and MDORA Protocols for VANET VANET中GRP和MDORA协议的性能比较评价

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_09

Dania Mohammed, M. Mansor, Goh Chin Hock

引用次数: 0

Analysis on the Current Consumption of Applying the Huffman Codification for Data Transmission using the ESP-NOW Protocol 利用ESP-NOW协议进行数据传输时霍夫曼编码的电流消耗分析

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_02

Mateos Luis, Arenas Julio, Palomino Joel

{"title":"Analysis on the Current Consumption of Applying the Huffman Codification for Data Transmission using the ESP-NOW Protocol","authors":"Mateos Luis, Arenas Julio, Palomino Joel","doi":"10.46338/ijetae0423_02","DOIUrl":"https://doi.org/10.46338/ijetae0423_02","url":null,"abstract":"IoT devices have restrictions regarding their energy consumption. Their hardware allows them to consume less energy to work for long periods of time with a small battery. Research works that are aimed at decreasing energy consumption, focus on the device hardware components, while other research focus on transmission algorithms to reduce the number of transmissions. In this context compression algorithms play an important role in reducing the amount of data to transmit. The Huffman codification is very easy to understand and has many source codes available. Therefore, this research focuses on quantifying the amount of energy saved after applying the Huffman codification. The tests were performed using the ESP32 which has an integrated Wi-Fi radio; and has the ESP-NOW protocol to transmit data between two devices without too much hardware setup. The results show that as more data is compressed the device energy consumption tends to reduce by 2 mA after continuous transmissions. The final energy consumption value is similar to the device current consumption while processing data.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116701224","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 Audio Encryption Approach uses a Unimodular Matrix and a Logistic Function 音频加密方法使用了一个单模矩阵和一个逻辑函数

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_08

S. Arifin, K. Tan, Audrey Tabitha Ariani, Sri Rosdiana, Mohammad Nasir Abdullah

引用次数: 0

Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks 基于卷积神经网络迁移学习的皮肤病分类

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_01

Jessica S. Velasco, Jomer V. Catipon, Edmund G. Monilar, Villamor M. Amon, Glenn C. Virrey, L. K. Tolentino

{"title":"Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks","authors":"Jessica S. Velasco, Jomer V. Catipon, Edmund G. Monilar, Villamor M. Amon, Glenn C. Virrey, L. K. Tolentino","doi":"10.46338/ijetae0423_01","DOIUrl":"https://doi.org/10.46338/ijetae0423_01","url":null,"abstract":"Automatic classification of skin disease plays an important role in healthcare especially in dermatology. Dermatologists can determine different skin diseases with the help of an android device and with the use of Artificial Intelligence. Deep learning requires a lot of time to train due to the number of sequential layers and input data involved. Powerful computer involving a Graphic Processing Unit is an ideal approach to the training process due to its parallel processing capability. This study gathered images of 7 types of skin disease prevalent in the Philippines for a skin disease classification system. There are 3400 images composed of different skin diseases like chicken pox, acne, eczema, Pityriasis rosea, psoriasis, Tinea corporis and vitiligo that was used for training and testing of different convolutional network models. This study used transfer learning to skin disease classification using pre-trained weights from different convolutional neural network models such as VGG16, VGG19, MobileNet, ResNet50, InceptionV3, InceptionResNetV2, Xception, DenseNet121, DenseNet169, DenseNet201 and NASNet mobile. The MobileNet model achieved the highest accuracy, 94.1% and the VGG16 model achieved the lowest accuracy, 44.1%.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125730969","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: 1

Image Processing System for the Detection of Recyclable Solid Waste 可回收固体废物检测的图像处理系统

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_04

Brian Meneses Claudio, Juan Saberbein Muñoz, E. L. Huamani, María Salina Cruz, Melissa Yauri Machaca, William Henao Ruiz

{"title":"Image Processing System for the Detection of Recyclable Solid Waste","authors":"Brian Meneses Claudio, Juan Saberbein Muñoz, E. L. Huamani, María Salina Cruz, Melissa Yauri Machaca, William Henao Ruiz","doi":"10.46338/ijetae0423_04","DOIUrl":"https://doi.org/10.46338/ijetae0423_04","url":null,"abstract":"One of the problems that affects everyone is solid waste pollution, which has spread to various parts of the surface and has put the planet in trouble because of the enormous amount of waste that has accumulated over time, affecting the habitats of various marine and terrestrial animals. These damages on the environment, reflect the lack of culture human about recycling, just 10% worldwide recyclemore frequently in homes. Nowadays, the change of materials to biodegradable has been made so that their decomposition is early, accumulations of recyclable materials are still observed in various parts of the surface and observing that it does not solve the contamination. With the exposure of this problem, this article developed an image processing system for the detection of recyclable solid waste that through an automatic analysis will allow the identification of recyclable solid waste materials, such as plastic, cardboard and glass bottles, helping to detect those solid waste that canbe reuse and avoid accumulation of them per ton. Through its development, it was observed that the system identifies the various recyclable materials that are distributed throughout the street with an efficiency of 97.99%, standing out for its efficiency and precision in the analysis of recyclable materials.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116084313","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

An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique 使用优化深度学习技术的离线手写年龄范围预测系统

International Journal of Emerging Technology and Advanced Engineering Pub Date : 2023-04-05 DOI: 10.46338/ijetae0423_12

Olusanya Olamide Omolara, O. Oyedepo, Elegbede Adedayo Wasiat, Adeola Adetola Olufunmilayo, Ojo Olufemi Samue

{"title":"An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique","authors":"Olusanya Olamide Omolara, O. Oyedepo, Elegbede Adedayo Wasiat, Adeola Adetola Olufunmilayo, Ojo Olufemi Samue","doi":"10.46338/ijetae0423_12","DOIUrl":"https://doi.org/10.46338/ijetae0423_12","url":null,"abstract":"One of the distinguishing features of an individual is handwriting and it has been established that everyone has unique handwriting differing from one another. This unique feature evolves with time and is influenced by a variety of factors such as gender, physical and mental health, and age among others. Also, the recent development in using individual peculiar features for forensic investigations in banks and other allied institutions either for signature verification or identification spelled the need to develop a smart system that can predict the age range with offline handwriting recognition. It is on this background that this research employed an optimized deep learning technique comprising of Gravitational Search Algorithm and Convolutional Neural Network (CNN-GSA) for offline handwriting age range prediction. A local database was populated with samples of the signature captured with a digital camera (5 megapixels), the CNN was employed for feature extraction, GSA was utilized to select optimal CNN parameters used for classification while the combined CNNGSA was utilized for an offline handwritten-based age prediction system. The performance evaluation of the approach proposed was done using sensitivity, specificity, precision, false positive rate, recognition accuracy, and processing time for all the variants, while the superiority of the system developed was ascertained by comparing it with the original CNN.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116169502","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