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10th IEEE information Technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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Detection of Kidney Cysts of Kidney Ultrasound Image using Hybrid Method: KNN, GLCM, and ANN Backpropagation

Detection of Kidney Cysts of Kidney Ultrasound Image using H...

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2023 International Conference on computer science, information Technology and Engineering, ICCoSITE 2023

作者： Mardison Yuhandri Universitas Putra Indonesia YPTK Computer System Computer Science Faculty Padang Indonesia Universitas Putra Indonesia YPTK Information Technology Computer Science Faculty Padang Indonesia

ISBN: (纸本)9798350320954

This research is aim to detect kidney cysts from human kidney Ultrasound (USG) 2D Images. This research uses data from Hospital patients as many as 25 Ultrasound images of the human kidney in the format ***. This research uses the K-Nearest Neighbor (KNN) method for image classification of ultrasound images then using Gray Level Co-Occurrence Matrix (GLCM) method for image extraction to detect cyst and non-cyst regions from the result of classification after that using Artificial Neural Network (ANN) method type Backpropagation for image detection to find cysts from human kidney Ultrasound (USG) 2D Image from the result of image extraction. The result of this research is producing the algorithm to implement the method and the tool software application to detect kidney cysts from ultrasound 2D images. The accuracy of this tool is 84% which can detect with accurate 21 kidney cysts from 25 kidney ultrasound 2D images that validate of a Urology Specialist Doctor. © 2023 IEEE.

关键词： Backpropagation

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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic systems

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IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Battery Monitoring system on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

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4th International Conference on Electronic and Electrical Engineering and Intelligent system, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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2023 International Conference on Modeling and E-information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jabbar, Abdul Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia

ISBN: (纸本)9798350369359

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities. © 2023 IEEE.

关键词： Ant colony optimization

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Maintaining Image Size by Developing the Cropping Rectangle Method on CT-Scan Images 12

Maintaining Image Size by Developing the Cropping Rectangle ...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Guslendra Yuhandri Defit, Sarjon Universitas Putra Indonesia Yptk Computer Science Faculty Department of Information System Padang Indonesia Universitas Putra Indonesia Yptk Computer Science Faculty Department of Information Technology Padang Indonesia

ISBN: (纸本)9798350341102

CT-Scan is a computer-based imaging technology, but it presents certain weaknesses, including interpretation challenges, lengthy analysis times, and visualization limitations. The progress in digital image processing science empowers computers to identify objects through recognizing distinctive features. To facilitate further data processing, an image cropping method is essential to narrow down the area of interest, as not all parts of an object require processing. However, using a rectangular cropping method can lead to a reduction in image size, potentially compromising image quality and causing blurring when displayed on the same axes. To uphold image quality and avert blurring, maintaining the cropped image's size becomes crucial. The developed rectangular cropping method successfully preserves the cropped image's size by adjusting the intensity values of pixels surrounding the cutting point. Coordinate points within the cropping rectangle delineate the region where pixel intensity values are modified. In this study, input images were sourced from CT-scan images of M. Djamil Hospital, Padang, West Sumatra, in jpg format. The developed algorithm underwent testing using the Matlab program, employing the rectangle method for image cropping to ensure the image size remained constant. The study's outcomes reveal no disparity in the number of pixels obtained when employing the developed rectangular cropping method. Based on the experiments carried out in this study using 8 input images that were successfully maintained, the size of the image from the cropped image was 8 images and 0 images were unsuccessful, so the accuracy obtained in this study was 100%. © 2023 IEEE.

关键词： Image quality

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Disclosure Privacy information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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