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information Technology International Seminar (ITIS)

作者： Rizky Parlika Mustafid Basuki Rahmat Department of Doctoral Program of Information System Pascasarjana School Diponegoro University Semarang Indonesia Department of Informatics Faculty of Computer Science University of Pembangunan Nasional “Veteran” JawaTimur Surabaya Indonesia

Many researchers have proposed various theories and techniques to estimate the value of Bitcoin. However, there is still a great opportunity to suggest a new approach to Bitcoin forecasting. This research will examine the concept of forming a patterned dataset to prove the hypothesis regarding the start of a bitcoin price fluctuation until the end of the fluctuation using patterned dataset pairs in conditions of price decline (moon) and in conditions of price decline (crash). Patterned datasets were initially built from data on the movement of all cryptocurrency assets on a digital cryptocurrency trading market. The Patterned Dataset is programmed based on the response of all crypto assets to changes in the price of bitcoin over time. Patterned datasets are better able to describe the conditions of the digital cryptocurrency market, along with patterns of potential increases and decreases in Bitcoin prices in particular and cryptocurrencies in general. To deepen the observations, resampling of the patterned dataset was carried out with a 4-hour timeframe using the mean, median, min, max, and sum filter functions on pairs of crash and moon conditions per month. The results show that filter functions other than sum, namely mean, median, min, and max, provide markings of fluctuation area patterns that are easier to recognize. Two hypotheses related to fluctuations were also successfully proven in this research.

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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International Conference on Data and Software Engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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Automated Model-Based Test Case Generation Using Uml Activity Diagram: A Review 9th

Automated Model-Based Test Case Generation Using Uml Activit...

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9th International Conference on IT Convergence and Security, ICITCS 2021

作者： Nor, Rozi Nor Haizan Monim, Md Abdul Jusoh, Yusmadi Yah Tajuddin, Nur Ilyana Ismarau Department of Software Engineering and Information System Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Tamhidi Centre Universiti Sains Islam Malaysia Nilai Malaysia

ISBN: (纸本)9789811641176

Software or application testing is a process of executing a program with the goal of finding defect to make better system. In software testing phase, writing test cases is one of the important activities. Manually writing test cases approach is lengthy of time period and need more effort to accomplish the process. This paper describes test case, test case generation techniques, different types of software testing approaches and comparison of testing tool. Test cases usually writing at the beginning of testing process from the set of software requirements. Test cases are created by using two different approaches. One is manually written test cases and another is automatically generated test cases. Manually written test cases are a very lengthy process and need to give a lot of effort to make good quality test cases. On the other hand, automatically generated test cases are generated by automatically using some software tools and it saves a lot of time and effort. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Software testing

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On Big Data Forensic and Forensic Cloud Environment

On Big Data Forensic and Forensic Cloud Environment

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International Conference on Smart Communications and Networking (SmartNets)

作者： Oteng Tabona Kopo M. Ramokapane Thabiso M. Maupong Faculty of Computer and Information Systems Higher Colleges of Technology Abu Dhabi United Arab Emirates Department of Computer Science University of Bristol Bristol United Kingdom Department of Computer Science and Information System Botswana International University of Science and Technology Palapye Botswana

ISBN: (数字)9781665487580

ISBN: (纸本)9781665487597

The number of electronic devices involved in cyber related crimes has increased significantly in the last few years. This is primarily due to advancements in technology and increased accessibility to devices. This poses significant challenges with respect to forensic investigations as investigators are faced with processing huge volumes of data from various sources within a limited time. Due to the urgency, frequency, and magnitude of some cases, investigators may miss deadlines, and crucial leads. In this paper, we consider these above stated challenges and propose a new definition for big data forensics, which recognises the time aspect and variety of sources when carrying our digital forensics. This definition will help digital forensic tool designers consider these critical aspects and design comprehensive tools to combat the limitations of the current state-of-the-art. We review an existing tool, the Forensic Cloud Environment (FCE), against the proposed definition. We show how its design leverages the power of Hadoop Distributed File system (HDFS), HBase, and MapReduce to provide a comprehensive solution for big data forensics. In conclusion, we provide an argument for FCE as a solution for resolving big data forensic challenges based on general Scientific Working Group on Digital Evidence (SWGDE) forensic principles.

关键词： File systems Digital forensics Big Data Best practices

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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

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Human-Aware Navigation in Crowded Environments Using Adaptive Proxemic Area and Group Detection

Human-Aware Navigation in Crowded Environments Using Adaptiv...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Carlos Medina-Sánchez Simon Janzon Matteo Zella Jesús Capitán Pedro J. Marrón Faculty of Business Administration and Economics Institute for Computer Science and Business Information Systems (ICB) University of Duisburg-Essen Essen Germany Faculty of Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Department of System Engineering and Automation University of Seville Seville Spain

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

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Predicting Gold Prices with Rolling Average Representation and Machine Learning

Predicting Gold Prices with Rolling Average Representation a...

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Innovative Engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Rajkumar Kannan Frederic Andres Kalaiarasi Sonai Muthu Chutiporn Anutariya Department of Computer Science Bishop Heber College (Autonomous) Tiruchirappalli India Digital Content and Media Science National Institute of Informtics Tokyo Japan Faculty of Information Science and Technology Multimedia University Malaysia School of Engineering and Technology Asian Institute of Technology Pathum Thani Thailand

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

Price prediction and forecasting have been important topics that have been long studied in the financial world. Commodity price prediction is one such highly popular application in stock markets across the world. Among all commodities such as gold, silver, platinum and others, gold has continued to be the prime and forefront commodity which decides the price movements of other commodities. Moving average approach and its variants are the important technical indicators which help financial institutions, investors and traders to understand the movement of gold prices throughout the year. Some of the popular technical indicators are simple moving averages, cumulative moving averages and exponential moving averages. This paper proposes a unique gold price prediction methodology by building popular ensemble machine learning models utilizing all three types of moving average values as feature representation and next day price as the target for prediction. The predicted values based on the three moving average features are presented to users, investors and traders, so that investors and traders will know the gold price for the next day. Extensive experiments are conducted on the Indian gold price daily data from December 2011 to March 2024 by comparing six prominent machine learning models and ARIMA model. We also present a detailed study of different feature importance. Experimental results on the gold price daily data have confirmed that polynomial regression models have given superior performance in terms of regression accuracy and error metrics namely MSE, RMSE and MAE

关键词： Gold Silver Tracking Linear regression Machine learning Predictive models Market research Data models Polynomials Windows

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Smart Lighting system for Children's Therapy to Prevent Nyctophobia Syndrome at Bedtime 4

Smart Lighting System for Children's Therapy to Prevent Nyct...

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4th International Conference on Cybernetics and Intelligent system, ICORIS 2022

作者： Pramono, Andi Munir, Badrul Farchan, Muchammad Budiman, Satrio Arif Azis, Baskoro Putra, Wahyu Waskito Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Brawijaya University Departemen of Neurology Malang Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya Indonesia Brawijaya University Information System Computer Science Faculty Malang Indonesia

ISBN: (纸本)9781665453950

Everyone has a fundamental desire for sleep. By sleeping, humans can conserve energy and replenish their stamina to optimal levels. Lighting is one component that influences the quality of a person's sleep. One of them is a person who sleeps with the lights on, suffers from nyctophobia, has poor sleep quality, and may be responsible for several illnesses. This study aims to develop a smart lighting device that will aid in treating youngsters with nyctophobia to get used to sleeping with the lights off to improve their sleep quality. The case study methodology used in this study entails the development of a prototype. This method employs an oximeter as a heart rate detector and a sensor comparison as the measurement device. Arduino Uno circuit and heart rate sensor are the main devices in this research. At the same time, the NRF 24L01 is a communication medium between the two Arduino Uno boards. The experimental measurements in normal conditions indicate that the heart rate sensor was nearly the same as the value displayed by the oximeter. Similarly, after running for 1 minute, both devices produced almost identical results, with an average difference of 1 Beats Per Minute (BPM). According to experiments with a heart rate sensor, the switch-off condition can be activated if the heart rate hits 90 BPM. In this case, the light will switch off once the heart rate hits 70 BPM, which might be used for sleeping individuals. © 2022 IEEE.

关键词： Oximeters

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Are University Students Independent: Twitter Sentiment Analysis of Independent Learning in Independent Campus Using RoBERTa Base IndoLEM Sentiment Classifier Model

Are University Students Independent: Twitter Sentiment Analy...

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2021 International Seminar on Machine Learning, Optimization, and Data science, ISMODE 2021

作者： Sandra, Lidia Marcel Gunarso, Gatot Fredicia Riruma, Olfien W Psychology Department Faculty of Psychology Krida Wacana Christian University Jakarta11470 Indonesia Information System Department Krida Wacana Christian University Jakarta11470 Indonesia Management Department Faculty of Economics and Business Krida Wacana Christian University Jakarta11470 Indonesia Informatics Faculty of Engineering and Computer Science Krida Wacana Christian University Jakarta11470 Indonesia

ISBN: (纸本)9781665405447

Higher education is one of the fields that is affected by the COVID-19 pandemic. One of the regulations in Indonesian higher education is Merdeka Learning-Independent Campus (MB-KM). In this research article we are aiming to do an analysis of the sentiment using Twitter as the dataset in order to find out the sentiment of university students towards the program. The analysis was being done using RoBERTa Base IndoLEM Sentiment Classifier Model. With the total number of more than 30 thousand positive sentiments and almost 20 thousand negative sentiments found. The result of the model shows that it achieved an accuracy of 91.73%, with a precision of 83.33%, recall 89.74%, and a harmonic mean of the two (F1 score) of 86.42%. Based on the analysis, it also shows the distribution of the sentiment is 63.0% of positive sentiment and 37.0% of negative sentiments. This paper shows that there are more positive sentiments than the negative one. © 2022 IEEE.

关键词： Sentiment analysis

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