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Simulation of model landslides observation in laboratory used by IoT

AIP Conference Proceedings 2023年第1期2609卷

作者： Made Sutha Yadnya I. G. P. Wedashwara Wedarama I. Wayan Sudiarta 1Electrical Engineering Communication Laboratory Engineering Faculty University of Mataram Mataram Indonesia 2Informatics Engineering Faculty of EngineeringUniversity of Mataram Mataram Indonesia 3Physic Study Program Faculty of Mathematics and Natural Science University of Mataram Mataram Indonesia

This paper is collaboration between BMKG and the University of Mataram to mitigate land slide. For this study using light intensity sensors, air temperature sensors, voltage sensors, current sensors, humidity sensors. This research is on a laboratory scale with a soil slope of 10 degrees, dry soil with a tendency to split (clay) from brittle volcanic rocks. The initial conditions were without rain, then the soil was burdened with rain in low (drizzle), medium (25mm/h) and high (55mm/h) capacities. The second condition of the stage is given a vibration which is a model of the earthquake evenly on that stage. Soil Moisture soil, soil temperature, 3 pieces of vibration sensor switch, piezo electric vibration sensor. The initial height of the soil is 36.78 cm with a width of 79.35cm after being rained for 90 minutes, the final height is 29.93 and 60.25 cm, widening receding to the right and left sides.

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Airplane type identification based on mask RCNN and drone images

arXiv

引用

arXiv 2021年

作者： Alshaibani, Waleed Tawfik Abdullah Helvaci, Mustafa Shayea, Ibraheem Saad, Sawsan A. Azizan, Azizul Yakub, Fitri Institute of Informatics Satellite Communication and Remote Sensing Program Istanbul Technical University ITU Ayazaga Campus Institute of Informatics Building Sariyer Istanbul34469 Turkey Istanbul34469 Turkey Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Negeri Sembilan Bandar BaruNilai71800 Malaysia Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia Advance Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

For dealing with traffic bottlenecks at airports, aircraft object detection is insufficient. Every airport generally has a variety of planes with various physical and technological requirements as well as diverse service requirements. Detecting the presence of new planes will not address all traffic congestion issues. Identifying the type of airplane, on the other hand, will entirely fix the problem because it will offer important information about the plane's technical specifications (i.e., the time it needs to be served and its appropriate place in the airport). Several studies have provided various contributions to address airport traffic jams;however, their ultimate goal was to determine the existence of airplane objects. This paper provides a practical approach to identify the type of airplane in airports depending on the results provided by the airplane detection process using mask region convolution neural network. The key feature employed to identify the type of airplane is the surface area calculated based on the results of airplane detection. The surface area is used to assess the estimated cabin length which is considered as an additional key feature for identifying the airplane type. The length of any detected plane may be calculated by measuring the distance between the detected plane's two furthest points. The suggested approach's performance is assessed using average accuracies and a confusion matrix. The findings show that this method is dependable. This method will greatly aid in the management of airport traffic congestion. Copyright © 2021, The Authors. All rights reserved.

关键词： Aircraft detection

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Security measures and intrusion detection systems for preventing and mitigating SSL renegotiation denial of service attacks

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AIP Conference Proceedings 2024年第1期3135卷

作者： Amalia Nurain Teddy Mantoro Rudy A. G. Gultom Richardus E. Indrajit Cyber Defense Engineering Study Program Faculty of Defense Science and Technology The Republic of Indonesia Defense University Bogor Indonesia Media-Tech Lab Departement of Computer Science Sampoerna University Jakarta Indonesia Pradita University Jakarta Indonesia

Inundating a system, network, or application with traffic is the goal of Denial of Service (DoS) attacks, a disruptive form of attack. SSL DoS attacks, particularly those involving renegotiation, are of particular concern. In order to address these types of attacks, the study proposes several security measures, including rate restriction, session caching, SSL acceleration, and integrating Intrusion Detection Systems (IDS) with a systematic technique method to reduce the risks associated with SSL renegotiation DoS attacks. This study investigates the security measures for preventing and mitigating SSL DoS attacks, with a focus on renegotiation attacks. It offers advice and insights for protecting systems to SSL server administrators and network security professionals by doing rate restriction, session caching, and SSL acceleration to avoid SSL DoS attacks.

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Comparison performance of decision tree classification model for spam filtering with or without the recursive feature elimination (RFE) approach 4

Comparison performance of decision tree classification model...

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4th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2019

作者： Zulfikar, Ahmad Fikri Supriyadi, Dede Heryadi, Yaya Lukas University of Pamulang Faculty of Technique Program Study of Informatics South Tangerang Indonesia STMIK Eresha Program Study of Informatics South Tangerang Indonesia Bina Nusantara University Dept. of Computer Science West-Jakarta Indonesia Faculty of Enginnering Jakarta 12930 Indonesia

ISBN: (纸本)9781728151182

Spam filters have become an important tool for ISP(Internet service Provider) to address the growing spam in cyberspace. Spam filtering is a program used to detect spam, prevent it and enter users' mailboxes. With the rapid growth of Internet technology, email communication is becoming increasingly important in people's daily lives. In addition, the amount of spam contained in emails has increased considerably in recent years. In this study, the authors will compare the performance of the decision tree classification models using or not using Recursive Feature Elimination (RFE) in spam filtering. The results of this test will be compared with the results of level accuracy, recall, precision, and F1 Score using or not using the RFE method. where in the results of this level measurement, the model used can be a reference or solution to filter spam correctly, especially through the use of supervised learning data. © 2019 IEEE.

关键词： Decision trees

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Canopy cover estimation based on support vector regression derived from Lidar & Landsat 8 OLI 40

Canopy cover estimation based on support vector regression d...

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40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019

作者： Hudjimartsu, Sahid Tampinongkol, Felliks Rudianto, Yoga Setiawan, Yudi Prasetyo, Lilik Budi Forests2020 Programme Environmental Analysis and Spatial Modelling Laboratory Department of Forest Resource Conservation Faculty of Forestry Bogor Agricultural University Bogor West Java Indonesia Geoinformatic of Informatics Engineering Study Program Faculty of Engineering and Science Ibn Khaldun University Jalan KH Soleh Iskandar KM 2 Tanah Sereal Bogor West Jawa Indonesia

Light detection and ranging (LiDAR) provides geospatial information with massive amounts of data to use in a variety of applications. Canopy cover percentage of tree crown is one application that is estimated through LiDAR data. This study aims to estimate a canopy cover derived from LiDAR used as reference data with multispectral Landsat 8 OLI. Statistical or machine learning approaches can be used to estimate a canopy cover. In statistical inference, the form of distribution chosen by the analyst and parameter is estimated from the data. This will be a problem in the construction function for the National scale, due to many ecosystems and then will have many functions for each ecosystem. Machine learning (ML) methods, in contrast, are various kinds of algorithms that are used to learn the mapping function or classification rules inductively, directly from the training data. ML can solve problems related to big data and global method and it will be used as a methodical approach to estimate canopy cover on the National scale. In this study, we use a support vector regression (SVR) as ML method to estimate the canopy cover. The estimated percentage of canopy cover will be used as one of the parameters to classify a forest. © 2020 40th Asian Conference on Remote Sensing, ACRS 2019: "Progress of Remote Sensing Technology for Smart Future". All rights reserved.

关键词： Ecosystems

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Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies

arXiv

引用

arXiv 2020年

作者： Ferreira, Mafalda Falcão Camacho, Rui Teixeira, Luís F. Doctoral Program in Informatics Engineering Faculty of Engineering University of Porto INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Informatics Engineering Faculty of Engineering University of Porto Porto - Portugal

Cancer is still one of the most devastating diseases of our time. One way of automatically classifying tumor samples is by analyzing its derived molecular information (i.e., its genes expression signatures). In this work, we aim to distinguish three different types of cancer: thyroid, skin, and stomach. For that, we compare the performance of a Denoising Autoencoder (DAE) used as weight initialization of a deep neural network. Although we address a different domain problem in this work, we have adopted the same methodology of Ferreira et al.. In our experiments, we assess two different approaches when training the classification model: (a) fixing the weights, after pre-training the DAE, and (b) allowing fine-tuning of the entire classification network. Additionally, we apply two different strategies for embedding the DAE into the classification network: (1) by only importing the encoding layers, and (2) by inserting the complete autoencoder. Our best result was the combination of unsupervised feature learning through a DAE, followed by its full import into the classification network, and subsequent fine-tuning through supervised training, achieving an F1 score of 98.04% ± 1.09 when identifying cancerous thyroid samples. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond

arXiv

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arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

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Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining 1

Analysis of Decision Tree and Smooth Support Vector Machine ...

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1st International Conference on computer science and Applied Mathematic, ICCSAM 2018

作者： Sitepu, Natalina Br Sawaluddin Zarlis, M. Efendi, Syahril Dhany, Hanna Willa Informatic Engineering Study Program Magister Faculty of Computer Science and Information Technology Universitas Sumatra Utara Medan Indonesia

This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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Use of the Web API as a basis for obtaining the latest data on bitcoin prices at 30 exchange places

引用

IOP Conference Series: Materials science and engineering 2021年第1期1125卷

作者： Rizky Parlika Arista Pratama Informatics Engineering Study Program Faculty of Computer Science National Development University 'Veteran' East Java Information Systems Study Program Faculty of Computer Science Raya Rungkut Madya Road Gunung Anyar Surabaya

Bitcoin has become a commodity traded by millions of traders from all over the world. This is one of the causes of fluctuating price movements. From the data we got on coinmarketcap, Bitcoin is traded on various cryptocurrency trading exchanges. And at every exchange that has a reputation, of course, has an API service to access historical data about the price movements of all the crypto commodities they have traded from the start. By using the PHP programming language and implementation of the CURL function for JSON readings, we can pull Bitcoin movement data in real time. In this paper, Bitcoin is specifically observed because it is the forerunner and the main cryptocurrency commodity traded and is a determinant of Alternative coin price movements in general. In this paper Bitcoin price monitoring is carried out at 30 reputable exchange places through API access provided by each exchange place. Furthermore, conclusions are drawn about the various variants of how to access the API from the 30 bitcoin exchange places. The program code that is displayed directly in this paper can then be used as an initial reference if you want to develop a Cryptocurrency price movement monitoring application for Bitcoin. At the end of the paper, an example of the application of Bitcoin price monitoring will be presented using a web-based application containing charts and supporting indicators as well as a telegram bot to display price depth charts.

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Electronic-offering of fresh fruit bunches oil palm by independent farmer with a blockchain approach

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AIP Conference Proceedings 2025年第1期3223卷

作者： Harison Marimin Sukardi Faqih Udin Yani Nurhadriyani Engineer Professional Education Study Program Universitas Andalas Padang Indonesia - 25000 Department of Agro-industrial Technology Faculty of Agricultural Engineering and Technology IPB University Bogor Indonesia - 16680 Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia – 16680

Indonesia meets the needs of 50% of the world's palm oil needs. Sources of Indonesian palm oil, 34 % are produced by independent smallholders. The lack of governance of independent smallholders leads to low productivity of their crops. Global market demands for palm oil derivative sources become an obligation to compete. The development of blockchain technology that favors traceability and transparency is applied in the supply agriculture sector, and this is an opportunity for how blockchain technology can help the palm oil supply chain become transparent and can find its source. This research uses the system development life cycle (SDLC) method. And business process model and notation in business model design. The results of the design of the FFB sale and purchase transaction system with blockchain technology have succeeded in connecting farmer transactions as FFB providers with traders and PKS as FFB buyers. Every transaction sent by the farmer will be locked by a hash, as immunity makes the data sent immutable. The system can display the traceability of transactions while maintaining the integrity of member information.

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