2021 Emerging Technology in Computing, Communication and Electronics (ETCCE)最新文献_第2页

An absolute Optical Character Recognition system for Bangla script Utilizing a captured image 基于捕获图像的孟加拉文字绝对光学字符识别系统

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689855

Md. Ruhulamin Siddique, Md. Ashiq Mahmood

{"title":"An absolute Optical Character Recognition system for Bangla script Utilizing a captured image","authors":"Md. Ruhulamin Siddique, Md. Ashiq Mahmood","doi":"10.1109/ETCCE54784.2021.9689855","DOIUrl":"https://doi.org/10.1109/ETCCE54784.2021.9689855","url":null,"abstract":"Character recognition from a captured image is a significant field of research because there are 230 million native speakers in Bangladesh and India. In addition, there are many signboards, billboards, and many other image sources that contain Bangla Script. Since mid-1980, researchers started to recognize Bangla characters from scanned images. However, they already tried different kinds of methods to identify characters and examine the performance of recognition. This paper focuses on developing an eclectic OCR system that can recognize and extract Bangla text. This recognition process predestines captured images by digital camera or scanner containing Bangla scripts. Preprocessing steps include binarization, segmentation, noise cleaning, scaling characters by font size, skew detection, and correction. Freeman chain code represents a character from the image after feature extraction from a scaled character. A multilayer feedforward neural network-based recognition scheme is constructed to recognize and classify the unknown character and samples. We concluded that the success rate is approximately 99% in identifying characters and demonstrating the Unicode text from experimental results.","PeriodicalId":208038,"journal":{"name":"2021 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131234520","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 Conceptual Anonymity Model to Ensure Privacy for Sensitive Network Data 一种保证敏感网络数据隐私的概念匿名模型

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689872

N. Arafat, Md. Ileas Pramanik, Abu Jafar Md Muzahid, B. Lu, Sumaiya Jahan, Saydul Akbar Murad

{"title":"A Conceptual Anonymity Model to Ensure Privacy for Sensitive Network Data","authors":"N. Arafat, Md. Ileas Pramanik, Abu Jafar Md Muzahid, B. Lu, Sumaiya Jahan, Saydul Akbar Murad","doi":"10.1109/ETCCE54784.2021.9689872","DOIUrl":"https://doi.org/10.1109/ETCCE54784.2021.9689872","url":null,"abstract":"In today’s world, a great amount of people, devices, and sensors are well connected through various online platforms, and the interactions between these entities produce massive amounts of useful information. This process of data production and sharing appears to be on the rise. The growing popularity of this industry, as well as the required development of data sharing tools and technology, pose major threats to an individual’s sensitive information privacy. These privacy-related issues may elicit a regularly strong negative reaction and restrain further organizational invention. Researchers have identified the privacy implications of large data collections and contributed to the preservation of data from unauthorised exposure to solve the challenge of information privacy. However, the majority of privacy strategies concentrate solely on traditional data models, such as micro-data. The academe and industry are paying more attention to network data privacy challenges. In this paper, we offer (ℓ, k)-anonymity, a novel privacy paradigm for network data that focuses on maintaining the privacy of both node and link information. Here, original network data will turn to attribute generalization nodes through a complex process, where several algorithms, clustering, node generalization, link generalization and ℓ-diversification will be applied. As a result, (ℓ, k)-anonymous network will be generated and will filter original network data to ensure publishable (ℓ, k)-anonymize data. Hopefully, this anonymity model will have a stronger role against homogeneity attacks of intruders, which will prevent the unauthorized disclosure of sensitive network data for several areas, such as - health sector. This model will also be cost effective and data loss will be controlled using two different ways.","PeriodicalId":208038,"journal":{"name":"2021 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441603","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

Maximizing the Probability of User Association of a Tier of a Multi-Tier Heterogeneous Network by Optimal Resource Allocation 基于最优资源分配的多层异构网络中各层用户关联概率最大化

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689907

Mobasshir Mahbub, B. Barua, Abdullah G. Alharbi

引用次数: 0

Image Processing Based Water Quality Monitoring System for Biofloc Fish Farming 基于图像处理的生物絮团养鱼水质监测系统

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689904

Mahmuda, Barkatullah, Emranul Haque, A. Al Noman, Feroz Ahmed

引用次数: 2

Design of a Bi-Directional Wireless Data Transceiver for Implantable Biomedical Device 植入式生物医学设备双向无线数据收发器的设计

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689829

Md. Raisul Hassan Khondaker, Md Nasim Reza, S.r. An-Nababi, Md. Piyas, Md. Hasanuzzaman

引用次数: 0

A deep Spatio-temporal network for vision-based sexual harassment detection 基于视觉的性骚扰检测深度时空网络

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689891

Md Shamimul Islam, M. Hasan, Sohaib Abdullah, Jalal Uddin Md Akbar, N. Arafat, Saydul Akbar Murad

{"title":"A deep Spatio-temporal network for vision-based sexual harassment detection","authors":"Md Shamimul Islam, M. Hasan, Sohaib Abdullah, Jalal Uddin Md Akbar, N. Arafat, Saydul Akbar Murad","doi":"10.1109/ETCCE54784.2021.9689891","DOIUrl":"https://doi.org/10.1109/ETCCE54784.2021.9689891","url":null,"abstract":"Smart surveillance systems can play a significant role in detecting sexual harassment in real-time for law enforcement which can reduce the sexual harassment activities. Real-time detecting of sexual harassment from video is a complex computer vision because of various factors such as clothing or carrying variation, illumination variation, partial occlusion, low resolution, view angle variation etc. Due to the advancement of convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM), human action recognition tasks have achieved great success in recent years. But sexual harassment detection is addressed due to presences of large-scale harassment dataset. In this work, to address this problem, we build a video dataset of sexual harassment, namely Sexual harassment video (SHV) dataset which consists of harassment and non-harassment videos collected from YouTube. Besides, we build a CNN-LSTM network to detect the sexual harassment in which CNN and RNN are employed for extracting spatial features and temporal features, respectively. State-of-the-art pretrained models are also employed as a spatial feature extractor with an LSTM and three dense layer to classify harassment activities. Moreover, to find the robustness of our proposed model, we have conducted several experiments with our proposed method on two other benchmark datasets, such as Hockey Fight dataset and Movie Violence dataset and achieved state-of-the-art accuracy.","PeriodicalId":208038,"journal":{"name":"2021 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131182033","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: 3

Depression Analysis from Social Media Data in Bangla Language: An Ensemble Approach 从孟加拉语的社交媒体数据分析抑郁:一个集成方法

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689887

M. B. Mohammed, Abu Saleh Md. Abir, Lubaba Salsabil, Mahir Shahriar, Ahmed Fahmin

{"title":"Depression Analysis from Social Media Data in Bangla Language: An Ensemble Approach","authors":"M. B. Mohammed, Abu Saleh Md. Abir, Lubaba Salsabil, Mahir Shahriar, Ahmed Fahmin","doi":"10.1109/ETCCE54784.2021.9689887","DOIUrl":"https://doi.org/10.1109/ETCCE54784.2021.9689887","url":null,"abstract":"Depression is a mental illness that has been harming individuals in their daily lives. With the advancement of technology, people rely on social media as means of communication. However, even though social media can significantly impact changing lives, the information from this platform is still considered vague and often disregarded. Moreover, with the hashtags and being on-trend, it is challenging to find depressive posts and help those in need. With the advancement of intelligence technology such as natural language processing and other machine learning algorithms, it has become easier to recognize patterns and ensure an effective digitized solution for depression analysis. There have been numerous studies about depression detection and analysis; however, most of them had not achieved a desirable outcome. Our paper intends to propose a model with a new approach for analyzing depression from Bangla social media posts. In our model, we have proposed a modified feature selection method along with different ensemble learning techniques. We have evaluated the performances of these techniques and acquired that the eXtreme Gradient Boost (XGB) Classifier with a 92.80% accuracy is the most suited for our model.","PeriodicalId":208038,"journal":{"name":"2021 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"100 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122226372","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

Transfer learning on VGG16 for the Classification of Potato Leaves Infected by Blight Diseases VGG16上马铃薯叶枯病分类的迁移学习

2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2021-12-21 DOI: 10.1109/ETCCE54784.2021.9689792

J. Akther, Muhammad Harun-Or-Roshid, Al-Akhir Nayan, M. G. Kibria

{"title":"Transfer learning on VGG16 for the Classification of Potato Leaves Infected by Blight Diseases","authors":"J. Akther, Muhammad Harun-Or-Roshid, Al-Akhir Nayan, M. G. Kibria","doi":"10.1109/ETCCE54784.2021.9689792","DOIUrl":"https://doi.org/10.1109/ETCCE54784.2021.9689792","url":null,"abstract":"According to the FAO of the UN, availability, access, utilization, and stability are the four pillars of food security that largely depend on sufficient, safe, and nutritious food. Detecting plant disease in advance might be a measure of resilience to the future disruption or unavailability of food supply. Due to the notable performance through highly accurate mechanization, deep learning-based methods have been applied to automatically identify and diagnose plant disease that can improve efficiency and productivity. The work prioritizes Transfer Learning of VGG16 for predicting potato blight disease. The model’s weights are pretrained on ImageNet, which can be extracted from specific features of small datasets. The implemented approach presents a significant performance improvement on a self-prepared dataset. After completing the necessary training and testing process, 96.88% accuracy was achieved by the model. Experimental results are compared with well-established models, which concludes that the model performs better in classifying potato leaves blight diseases.","PeriodicalId":208038,"journal":{"name":"2021 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132129176","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: 3