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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Kalpin Erlangga Silaen Meyliana Meyliana Harco Leslie Hendric Spits Warnars Harjanto Prabowo Achmad Nizar Hidayanto Maria Susan Anggreainy Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Information System Faculty of Computer Science Universitas Indonesia Depok Indonesia

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity.

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MIC: Medical Image Classification Using Chest X-ray (COVID-19 and Pneumonia) Dataset with the Help of CNN and Customized CNN

arXiv

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arXiv 2024年

作者： Fahad, Nafiz Ahmed, Rasel Jahan, Fariha Abdullah-Al-Jubair, Md Morol, Md Kishor Faculty of Information Science and Technology Multimedia University Melaka Malaysia Department of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh Department of Computing Science and Information Science Cornell University New York United States

The COVID-19 pandemic has had a detrimental impact on the health and welfare of the world's population. An important strategy in the fight against COVID-19 is the effective screening of infected patients, with one of the primary screening methods involving radiological imaging with the use of chest X-rays. Which is why this study introduces a customized convolutional neural network (CCNN) for medical image classification. This study used a dataset of 6432 images named Chest X-ray (COVID-19 & Pneumonia), and images were preprocessed using techniques, including resizing, normalizing, and augmentation, to improve model training and performance. The proposed CCNN was compared with a convolutional neural network (CNN) and other models that used the same dataset. This research found that the Convolutional Neural Network (CCNN) achieved 95.62% validation accuracy and 0.1270 validation loss. This outperformed earlier models and studies using the same dataset. This result indicates that our models learn effectively from training data and adapt efficiently to new, unseen data. In essence, the current CCNN model achieves better medical image classification performance, which is why this CCNN model efficiently classifies medical images. Future research may extend the model's application to other medical imaging datasets and develop real-time offline medical image classification websites or apps. © 2024, CC BY.

关键词： COVID-19

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Soft Set Based Clustering and Its Comparison on Categorical Data 9

Soft Set Based Clustering and Its Comparison on Categorical ...

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9th IEEE information Technology International Seminar, ITIS 2023

作者： Riyadi Yanto, Iwan Tri Waishiang, Cheah Hidayat, Rahmat Wahyudi, Rofiul Suprihatin Apriani, Ani Universitas Ahmad Dahlan Information System Department Yogyakarta Indonesia Universiti Malaysia Sarawak Faculty of Computer Science & IT Samarahan Sarawak Malaysia Politeknik Negeri Padang Information Technology Department Padang Indonesia Universitas Ahmad Dahlan Islamic Banking Department Yogyakarta Indonesia Institute Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350306835

Categorical data clustering is problematic since it is difficult or complex to determine how comparable the data is. Several methods, most recently centroid-based strategies, have been developed to reduce the complexity of the similarity of categorical data. These methods nevertheless result in lengthy processing durations. Another method, soft set-based clustering (SSC), based on the probability function of multivariate multinomial distributions, is suggested in this article. Soft sets are used to represent the data, and each soft set has a probability for each object. The joint cluster distribution function determines the probability for each object after the multivariate multinomial distribution function. The connected cluster would receive the highest likelihood. Benchmark data sets from UCI machine learning are used to compare the performance of the approach to the baseline techniques. The outcomes demonstrate that the suggested strategy performed better in purity, rank index, and calculation time. © 2023 IEEE.

关键词： Distribution functions

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AI-Enabled Blockchain for Supply Chain in Agriculture

AI-Enabled Blockchain for Supply Chain in Agriculture

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Singh Bist, Ankur Rawat, Bhupesh Prawiyogi, Anggy Giri Wahyuningsih Septiani, Nanda Fakhrezzy, Muhammad Saputra, Dimas Bagus Computer Science Graphic Era Hill University India Buana Perjuangan University Faculty of Teacher Training and Education Karawang Indonesia University of Raharja Management Information System Indonesia University of Raharja Department of Management Indonesia Computer System University of Raharja Jakarta Indonesia Technical Information University of Raharja Indonesia

ISBN: (纸本)9781665468763

The supply chain is a critical process involving farmers, buyers, suppliers, and many more entities. The entire system of farming and agriculture is based on it. The conventional method is very tedious and causes issues at multiple levels. AI and Blockchain are the two most used technologies nowadays. We are proposing an AI-enabled Blockchain ecosystem for the supply chain in Agriculture. A case study is given to explain the possibilities of the proposed model. The entire process of supply chain management has been transformed by artificial intelligence. AI plays a vital role in the warehousing process in collecting and analyzing information and inventory processes, enabling companies to increase efficiency and boost revenue. Precisely, AI predicts demand, modifying orders and rerouting products in transit. Major companies such as Google and Amazon have invested considerably in AI. Blockchain technology has been used in several areas, such as insurance, natural state, and securing personal information. Agriculture is one such field where Blockchain is being used extensively for the development and reformation of its various aspect. Unlike conventional, centralized and monopolistic agricultural management systems have been evolved. Blockchain provides a decentralized data structure to store and retrieve shared data with multiple un-trusted parties. The technology offers several advantages, including eliminating the dependenon the centralized supply chain management database, which is also referred to as single point failure, and the high cost due to verifying and monitoring transactions by third-party users. Figure.4 explains the blockchain process. © 2022 IEEE.

关键词： Smart contract

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A Lightweight CNN-Based Pothole Detection Model for Embedded systems Using Knowledge Distillation 21

A Lightweight CNN-Based Pothole Detection Model for Embedded...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

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Utilization of Augmented Reality-Based Application to Promote Digital Citizenship 6

Utilization of Augmented Reality-Based Application to Promot...

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6th International Conference on information Technology, InCIT 2022

作者： Pamudji, Andre Kurniawan Murniati, Cecilia Titiek Hartono, Heny Riyandari, Angelika Saptaastuti, Rikarda Ratih Soegijapranata Catholic University Faculty of Computer Science Department of Information System Semarang Indonesia Soegijapranata Catholic University Faculty of Language and Arts Department of English Semarang Indonesia Soegijapranata Catholic University Library Semarang Indonesia

ISBN: (纸本)9781665489126

The purpose of this paper is to describe the process of creating an Android-based application with Augmented Reality feature as well as the user's evaluation of the application. The application was created to equip students with some knowledge on digital citizenship. As the users of digital technologies, college students have to have the ability to peruse technology and participate in online activities in a responsible, safe, and legal manner. The augmented reality-based application was designed to teach students the nine elements of digital citizenship. This paper also explains the feedback from the users of the application to further improve the functionalities of the application for future use. © 2022 IEEE.

关键词： Augmented reality

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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft Computing 2023年第11期38卷 153-168页

作者： Ibrahim M.Alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled M.Alalayah Amira Sayed A.Aziz Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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Machine Learning Analysis for Temperature Classification using Bioelectric Potential of Plant 6

Machine Learning Analysis for Temperature Classification usi...

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6th International Conference on information Technology, information systems and Electrical Engineering, ICITISEE 2022

作者： Tahyudin, Imam Akbar Awal Rozaq, Hasri Nambo, Hidetaka Amikom Purwokerto University Faculty of Computer Science Department of Information System Jl. Letjendpol Soemarto Purwanegara Banyumas53127 Indonesia Graduate School of Informatics Faculty of Computer Science Gazi University Yenimahalle Ankara Turkey Division of Electrical Engineering and Computer Science Graduate School of NST Kanazawa University Kakumamachi Ishikawa Kanazawa920-1192 Japan

ISBN: (纸本)9798350399615

Plants can respond to changes in their surroundings, such as temperature, humidity, and human behavior. This ability is due to photosynthesis and respiration in plants, resulting in low electrical signals. We focus on room temperature change by plant bioelectric potential detection. The temperature topic is interesting for the actual research because it is one of important factor in health aspect. This study aims to analysis of temperature of Bioelectric potential of plant using machine learning. This implementation identifies the plants' bioelectric potential in distinguishing three-room temperature categories: A, B, and C temperature. We conducted the experimental in room control. For the transformation process is using wavelet transform. Furthermore, the analysis process, we used the Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighborhood (K-NN), Naïve Bayes, and linear regression. Then, for the evaluation is using confusion Matrix. The result showed that the best level of transformation using Wavelet is 7thlevel for reducing the noise. Furthermore, the best accuracy of analysis process is using Decision tree which obtained 75%. © 2022 IEEE.

关键词： Bioelectric Electric Potential of Plant Machine Learning Temperature Wavelet Transform

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CUDA Parallelization of Teacher-Learner Optimization Algorithm 1

CUDA Parallelization of Teacher-Learner Optimization Algorit...

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1st International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Abbasi, Mahdi Alghanim, Mohammed Sabri, Mohammad Khosravi, Mohammad R. Bu Ali Sina University Engineering Faculty Department of Computer Engineering Hamedan Iran Zarqa University Faculty of Information Technology Jordan Zarqa University Department of Management Information System Jordan Weifang University of Science and Technology Weifang Shandong China

ISBN: (纸本)9798350332742

Evolutionary algorithms are effective methods for solving optimization problems that do not require any special assumptions to find optimal solutions. Optimization based on teaching and learning has been parallelized using CUDA parallelization technology. The results show that the proposed parallel version of the algorithm is fast and efficient in teaching and learning phases. © 2022 IEEE.

关键词： Evolutionary algorithms

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Analyze and designing low-cost network monitoring system using Icinga and Raspberry Pi

Analyze and designing low-cost network monitoring system usi...

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International Symposium of Geoscience, Oil and Gas Engineering, Sustainable and Environmental Technology 2020, GEOSOSTEK 2020

作者： Maulana, H. Al-Khowarizmi Information System Department Faculty of Computer Science and Information Technologies Universitas Muhammadiyah Sumatera Utara North Sumatera Indonesia

The development of information and data communication technology field in the world has now increasingly led to the development of the Internet of Things devices. The development of network technology in an organization/university has triggered the need for a system that can monitor computer networks and their devices in an organization/university environment that is quite extensive and complex. At this time, network monitoring becomes quite difficult to do if the computer network in a corporate environment has become very broad and complex. The wider the network and the complex network and multimedia topology that is built, the more difficult it is for network administrators to monitor the network. However, in its application to build a monitoring system it will require devices and hardware and computer requirements and specifications that are above average, which of course will also require substantial costs and large resources as well. In this research, a network monitoring and bandwidth management system will be built by using Raspberry-Pi and Icinga as the main modules in network management. Raspberry Pi is an alternative of computers that has many capabilities and has a high ability which is generally used as a Mini-Server at a very cheap price when compared with existing servers. And Icinga is an open source-based application platform that functions to monitor various types of variables contained in servers and infrastructure of the network such as availability and performance and provides easy access for network administrators to important data and to improve maintenance of network conditions. So, it is hoped that by applying this, a good and optimal network monitoring and bandwidth management tool can be produced at a low cost. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Costs

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