2021 4th International Conference of Computer and Informatics Engineering (IC2IE)最新文献_第10页

The Implementation of Unsupervised Learning Techniques as a Data Sharing Model in the Back-propagation for the Classification of Student Graduation 无监督学习技术作为数据共享模型在学生毕业分类反向传播中的实现

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649190

E. Lestari, Mustakim

{"title":"The Implementation of Unsupervised Learning Techniques as a Data Sharing Model in the Back-propagation for the Classification of Student Graduation","authors":"E. Lestari, Mustakim","doi":"10.1109/ic2ie53219.2021.9649190","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649190","url":null,"abstract":"One of the requirements to increase the accreditation of higher education is the percentage of students who graduate on time. Responding to this issue, it is necessary to discover the factors that affect students in completing the Final Project. One of the algorithms that can be used to determine the classification of student graduation is the Backpropagation Neural Network (BPNN). The factors that have the most effect on student graduation, it includes procrastination, total credits and the number of repeat courses. To gain the best accuracy results on the classification technique, it was carried out by experiment of training and testing data sharing by applying the clustering technique. The cluster division consisted of the K-Means and K-Medoid algorithms, had the best cluster validity Davies-bouldin Index (DBI) 0.063 on the K-Means algorithm using 101 training data and 44 testing data. Based on BPPN, data sharing using K-Means had a big impact on the BPNN classification process with an accuracy value of 98% from a learning rate of 0.005 with 1000 iterations.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127251343","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

Biography of Keynote Speakers 主讲人简介

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649407

引用次数: 0

Quantum Resistance Deep Learning based Drone Surveillance System 基于量子抵抗深度学习的无人机监视系统

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649188

F. Kumiawan, N. Cahyani, Gandeva Bayu Satrya

{"title":"Quantum Resistance Deep Learning based Drone Surveillance System","authors":"F. Kumiawan, N. Cahyani, Gandeva Bayu Satrya","doi":"10.1109/ic2ie53219.2021.9649188","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649188","url":null,"abstract":"Some countries have designed anti-drone systems i.e., detecting, jamming, and camera units. It is a multidis-ciplinary experienced system particularly designed to protect regions and people from cyber-terrorist and oppose unauthorized drones. Security and surveillance are two of the leading areas in the growing drone sector. Moreover, machine learning or deep learning could help in object detection because of its high accuracy and acceptable delay performance. Hence, this paper proposed a modified streaming protocol for drone surveillance with post-quantum cryptography that ensures the drone's data confidentiality. This paper also provided a deep learning receiver to perform object detection by using YOLOv2-Tiny, YOLOv3-Tiny, and YOLOv4-Tiny respectively. The 72 experiment results showed that all configurations on the 30-FPS input produced big overhead and huge delay. This leaves the option to set the FPS input to be lower than 30, yet the FPS benchmark result showed that even with the highest FPS configuration, the results were capped at a maximum of 14-FPS. Nevertheless, the results of the proposed methods confirmed the feasibility of using the developed surveillance drone on low-energy architecture.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127544159","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

Deep Residual CNN for Preventing Botnet Attacks on The Internet of Things 防止物联网僵尸网络攻击的深度残差CNN

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649314

D. T. Rahmantyo, B. Erfianto, G. B. Satrya

{"title":"Deep Residual CNN for Preventing Botnet Attacks on The Internet of Things","authors":"D. T. Rahmantyo, B. Erfianto, G. B. Satrya","doi":"10.1109/ic2ie53219.2021.9649314","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649314","url":null,"abstract":"Extensive internet of things (IoT) devices being in-fected by malware are an increasingly important viable objective in IoT cyberattacks e.g., botnet, virus, trojan, etc. The botnets got leverage from unsecured IoT devices (e.g., CCTV, Raspberry Pi, Arduino Uno, ESP8266, etc) that operates by using the Internet traffic. In recent year, the high-profile IoT device’s vendor and the researcher from all over universities are exploring the robustness of IoT devices against botnet attacks. This research uses a deep learning approach to prevent botnet attacks on IoT networks. The deep residual one-dimensional CNN (1DCNN) model as the proposed method is used for botnet traffic detection. Two algorithms are provided: data processing for the N-BaIoT dataset and IoT botnet detection training and testing. For data processing, training, and testing, the datasets were evaluated, and the model was optimized with different optimizers. This research used RMS Prop, ADaDelta, AdaGrad, AdaMax, and Adam as optimizers and the CNN was compared with LSTM, CNN with RNN, and Deep residual 1DCNN, respectively. The results showed that Deep Residual 1DCNN with Adam has the highest training accuracy of 88.67%, 88.67% for validation accuracy, and 88.53%for test accuracy.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129468967","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: 2

Spatio-Temporal Approach using CNN-RNN in Hand Gesture Recognition 基于CNN-RNN的时空方法在手势识别中的应用

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649108

Mochammad Rifky Gunawan, E. C. Djamal

{"title":"Spatio-Temporal Approach using CNN-RNN in Hand Gesture Recognition","authors":"Mochammad Rifky Gunawan, E. C. Djamal","doi":"10.1109/ic2ie53219.2021.9649108","DOIUrl":"https://doi.org/10.1109/ic2ie53219.2021.9649108","url":null,"abstract":"One of the ways of communication in human- computer interaction is by hand gesturing through video, a collection of sequential images, and has a frame per second (fps) configuration so that the existing image could change at any time. Recognize hand gesture videos through the pattern of each frame and its connection. Therefore the recognition views them as images in time sequences. There are several approaches—the single spatial approach by collecting image sequences in large images. Even though it has good accuracy, it will have problems with a less responsive background and fast movement because it captures less information on image pattern changes from adjacent frames. Others take memory. The temporal approach focuses on comparing image patterns between frames but requires spatial information or patterns for each frame. It is not the only initial frame. Hence, it is appropriate to combine the two approaches simultaneously in motion recognition or movement called Spatio-Temporal. Convolution Neural Network (CNN) is good in image recognition. Recurrent Neural Networks (RNN) are usually suitable for recognizing sequences and their relationships. Therefore, for hand gesture recognition, this research used a Spatio-Temporal approach with the CNN-RNN method. CNN with Spatial-Streams get image patterns, and Temporal- Streams use RNN to get connected patterns. The results showed that the combination of CNN and RNN for the Spatio- Temporal approach could recognize one of the four-hand gestures by 96.43%. The experiments resulted in eight CNN convolution layers and two Dense layers in RNN with GRU and LSTM architectures.","PeriodicalId":178443,"journal":{"name":"2021 4th International Conference of Computer and Informatics Engineering (IC2IE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115108971","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

Evaluation of VGG-16 and VGG-19 Deep Learning Architecture for Classifying Dementia People VGG-16和VGG-19深度学习架构对痴呆患者分类的评价

2021 4th International Conference of Computer and Informatics Engineering (IC2IE) Pub Date : 2021-09-14 DOI: 10.1109/ic2ie53219.2021.9649132

Abitya Bagaskara, M. Suryanegara

引用次数: 16