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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Corrugated Quad-Ridged Dual-Polarized Wideband Horn Antenna For mm-Wave Applications

Corrugated Quad-Ridged Dual-Polarized Wideband Horn Antenna ...

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National Conference on Radio Science (NSRC)

作者： Mohamed N. Salem Mohamed I. Ahmed Amr H. Hussein Department of Electronics and communication Engineering Institute of Aviation Engineering and Technology (IAET) Giza Egypt Microstrip department Electronics Research Institute El-Nuzha Cairo Electronics and Communication Program Faculty of Engineering Egypt University of informatics New Administrative Capital Knowledge City Egypt Electronics and Electrical Communications Engineering Dept. Faculty of Engineering Tanta University Tanta Egypt Electronics and Electrical Communications Engineering Department Faculty of Engineering Horus University Egypt New Damietta Egypt

This paper showcases the development of a wideband Dual-polarized horn antenna for mm-wave applications. It can be used for reflector feeding, imaging, satellite communication and power transmission due to its enhanced parameters. The working bandwidth is 10 GHz from 4 GHz to 14 GHz (VSWR $\lt1.9)$. This design is formed of three parts: 1) the waveguide is designed to support the entire bandwidth and to improve the impedance matching 2) four ridges to enable Dual-polarized antenna 3) the flared part is corrugated to enhance the side lobe level (SLL). The insertion between the two ports is less than -25 dB for most frequencies. The SLL is less than -25 dB for the low frequencies and -30 dB for the high frequencies. The co- and cross-pol radiation pattern simulation shows that the cross-pol magnitude is 20-30 dB less than the co-pol magnitude. These results have been proved using the CST MWS and Ansys HFSS software.

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Effect of Additive Chemicals on Soil Characteristics 2

Effect of Additive Chemicals on Soil Characteristics

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2nd International Conference on Computer, Science, engineering, and Technology, ICComSET 2019

作者： Sutarman, Encu Widjajani Dwipriyoko, Estiyan Civil Engineering Study Program Engineering Faculty Universitas Langlangbuana Bandung Indonesia Industrial Engineering Study Program Engineering Faculty Universitas Langlangbuana Bandung Indonesia Informatics Study Program Engineering Faculty Universitas Langlangbuana Bandung Indonesia

The occurrence of landslides in the hills of Walahir, Kidang Pananjung village, Cililin subdistrict. Bandung in May 2004 needed attention and research so that the causes of the landslide could be identified. This landslide took many lives and property, heaped around 5 hectares of rice fields and residents' villages below and claimed 15 lives. Based on geotechnical investigations, the slip plane is at a depth of 4 meters from the ground surface and the area of the landslide ranges from 30,000 m2 leaving weathered rock beneath. Is the opening of agricultural land and fields followed by the use of chemical fertilizers is one of the causes of landslides? This study aims to determine the effect of a single chemical fertilizer (Urea, Za, SP36) and compound (Green Giant NPK, MKP) on the soil under conditions of optimum water content and water saturation or soaked which are carried out at the Chemical Laboratory of the Department of Energy and Mineral Resources of Bandung and Mechanics Laboratory Bandung Institute of Technology. Research on the influence of chemical fertilizers which is carried out using percentages of 1%, 2% and 3% with a relatively short ripening time intends to analyze the extent to which chemical fertilizers have an effect. Research is limited to the identification of minerals and chemical elements of original soil, soil physis properties, Atterberg boundaries, soil index properties, soil classification, soil elastic modulus. Hypothesis;The chemical content of fertilizers in the soil causes changes in soil characteristics. The method of taking original soil is by drilling original soil around the area where landslide disaster occurred at the same height as the height of the landslide slope, soil samples taken from depths of 1 meter and 3 meters from the surface of the land or the base layer between soil and rocks based on ASTM D-1452, 1587, 1586. Laboratory data from the original soil and chemical soil mixture of fertilizers is needed as input data for

关键词： Landslides

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Portable Monitoring Systems for Rivers Waste Based on Internet of Things

Portable Monitoring Systems for Rivers Waste Based on Intern...

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International Conference on informatics and Computing (ICIC)

作者： Henderi Mumammad Hudzaifah Nasrullah Laura Belani Nudiyah Po Abas Sunarya Sofa Sofiana Didik Setiyadi Informatics University of Raharja Tangerang Indonesia Management of Retail Study Program University of Raharja Tangerang Indonesia Informatics Engineering Departement Faculty of Engineering Pamulang University Tangerang Indonesia Infotmatics Faculty of Technolgy and Design Universitas Indonesia Mandiri Jakarta Indonesia

ISBN: (纸本)9798350345728

River pollution has become a severe problem in Indonesia. Industrial waste causes water pollution because the waste is not filtered before being discharged into the river. Illegal waste dumping cannot be prosecuted due to insufficient data and unknown disposal times. Therefore, a tool is needed to measure river water waste to measure polluted water waste data. Studies on river water monitoring devices have shown that there are no portable monitoring tools based on the internet of things for river water waste that follows regulatory standards from the Minister of Health. This study aims to produce a portable monitoring tool based on the internet of things for water waste. The system was developed using TDS sensors, turbidity, pH, MQ-135 sensors, and the system is equipped with a GPS module to indicate the location's latitude. The monitoring system is developed through the stages of system design, testing water samples, setting water quality standards and required hardware and components, setting river water quality parameters, and conducting system testing. The test results show that the system can send the data obtained to dashboard things via an internet connection, the TDS sensor error rate is 2.17%, the pH sensor error rate is 1.87%, the turbidity sensor error rate is 1.66%, the MQ135 sensor value is at the quality limit. The internet of things-based portable river waste monitoring system produced through this research helps related parties to identify and handle polluted rivers. Water according to the standards of the Ministry of Health, and the GPS module shows location measurements and information according to the location's latitude on google maps.

关键词： Error analysis Sensor systems Water pollution Sensors Rivers Pollution measurement Internet of Things

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

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Evaluation of Household Waste Image Identification Using Convolutional Neural Network

SSRN

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SSRN 2022年

作者： Sunardi Yudhana, Anton Fahmi, Miftahuddin Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Master Program of Informatics Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Waste identification has been widely carried out using machine learning with various methods. One method that is often used is the Convolutional Neural Network (CNN). Waste identification has a function as machine learning that helps and accelerates the waste management process, especially the waste sorting process. This research was conducted by collecting data by downloading a dataset from Kaggle. The data were then be labeled in a folder according to their type. The dataset folder was entered into a Python machine learning system equipped with the TensorFlow module. The data was preprocessed so that it became simplified and the machine could read the data. The data was trained to be able to distinguish between the types of waste. After that, the CNN method was implemented in machine learning so that the machine could classify the waste image. The results of the CNN implementation show that the system can classify images with the greatest accuracy of 99.83% with a validation accuracy of 76.1% and the smallest loss percentage of about 0.04% at epoch 18. The results show that there was overfitting in the dataset classification process caused by the average accuracy value of the training data and validation data being too large, around 20%. © 2022, The Authors. All rights reserved.

关键词： Waste management

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest

Borneo Smart Forest Information System for Management of Dip...

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Information Technology International Seminar (ITIS)

作者： Masna Wati Novianti Puspitasari Ummul Hairah Joan Angelina Widians Anindita Septiarini Ade Fiqri Tjikoa Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Informatics Universitas Mulawarman Doctoral Program Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350398205

Kalimantan’s tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan’s rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation.

关键词： Seminars Memory Forestry QR codes Vegetation Wood industry Information technology

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Response of the Offshore Jacket Platform at its Ultimate Strength Under Operating and Extreme Loads

SSRN

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SSRN 2022年

作者： Othman, Nor Adlina Mohd, Mohd Hairil Offshore Engineering and Naval Architecture Research Interest Group Maritime Technology Program Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu21300 Malaysia

Before reassessing the reliability condition of an offshore jacket platform, it is essential to understand the behaviours that the offshore platform exhibits while it is at sea, which are measured in terms of environmental loads: under operating and extreme conditions. Metocean data from a 1-year return period (operating condition) and a 100-year return period (storm condition) are used to predict the four-legs jacket platform's behaviour. Also, two values of minimum design water depth (WD) of 105.9 m and maximum design WD of 115.4 m are chosen. The Structural Analysis Computer Software (SACS), a non-linear finite element analysis software, is used to calculate the jacket platform's ultimate strength, called pushover analysis. The analysis result will determine the Reserve Strength Ratio (RSR) as an approach to examining the offshore jacket platform's ultimate strength. Responses to the offshore jacket platform are base shear design, base shear ultimate, rotation, displacement, and moment, which illustrate the behaviour of the offshore jacket platform. Eight combinations of environmental load directions are applied at 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315° to obtain the structure's weakest part. 45° is the critical direction for maximum design WD, while 90° is the critical direction for minimum design WD. © 2022, The Authors. All rights reserved.

关键词： Drilling platforms

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Exploring Security Threats by Hardware-Faults in Approximate Arithmetic Computing

Exploring Security Threats by Hardware-Faults in Approximate...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Morgana M. A. Da Rosa Eduardo A. C. Da Costa Rafael I. Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Approximate computing (AxC) emerged as a design alternative to boost design efficiency by leveraging the inherent error resiliency of many applications. Recent literature shows attackers could exploit some AxC mechanisms to create new attack surfaces. This work proposes integrity checking and exclusive logic-based methods detect attacks by hardware faults on AxC systems. During ASIC-based synthesis, we investigate the security vulnerabilities of the Approximate Parallel Prefix Adder (AxPPA). We compare the expected output of the designer with the output netlists of ASIC-based synthesis. We propose a verification methodology consisting of a golden model (GM) described in a Simulink model and the DUT represented at the netlist level in a standard cell library after logic synthesis. Experimental results show that the AxPPA has an error range of 1.263 × 10 −5 to 0.0098 concerning the designer’s expected output under ASIC-based synthesis attacks.

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