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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Jevremovic, Aleksandar D. Singidunum University Faculty of Informatics and Computing Belgrade11000 Serbia

ISBN: (纸本)9798350347029

The rapid development and progress of artificial intelligence algorithms in the last decade has opened up many new possibilities and fields for its application. The field of human-computer interaction is not only not an exception, but it can also be considered a veteran. Among the general population, there is a certain level of apprehension when it comes to introducing artificial intelligence into new areas and aspects of human life. In this paper, we will attempt to shed new light on this issue and the current trend of villainizing AI, as well as present current trends in enhancing user security based on it. We will summarize the current conditions, trends and experiences in this area. © 2023 IEEE.

关键词： Human computer interaction

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Forecasting Solar Energy Substation Voltages Using Metaheuristic Optimized GRU 32

Forecasting Solar Energy Substation Voltages Using Metaheuri...

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32nd Telecommunications Forum, TELFOR 2024

作者： Jovanovic, Luka Jovic, Nikola Petrovic, Aleksandar Budimirovic, Nebojsa Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia

ISBN: (纸本)9798350391053

As global energy demand grows, renewable sources offer a key alternative to fossil fuels. However, integrating these sources into power grids presents challenges, especially with supply unpredictability. This paper demonstrates how AI, specifically using a modified PSO algorithm to enhance GRU models, can predict solar energy output and improve grid balancing. Experiments on public datasets show that the optimized models achieve high accuracy, with the best models demonstrating a mean square error (MSE) of 0.024642 and an mean absolute percentage error (MAPE) of 0.000943 in hourly predictions, facilitating renewable energy integration. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Maximizing complex features to minimize the detectability of content-adaptive steganography

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Multimedia Tools and Applications 2024年 1-19页

作者： Setiadi, De Rosal Ignatius Moses Rustad, Supriadi Andono, Pulung Nurtantio Shidik, Guruh Fajar Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia

Content-adaptive steganography has three rules that need to be fulfilled to maximize the performance of the cost function, thereby increasing resistance to steganalysis attacks. This study proposes a combination method of image sharpening, Fuzzy edge detection, and residual weighting to maximize the performance of the three rules. Image sharpening is found to be useful for detailing complex features as required by the complexity first rule, while fuzzy edge detection is found to be effective in maximizing the spreading second rule. The clustering third rule is also implemented by customizing the cost of the pixels outside the embedding areas. Embedding simulation is applied to a modified MiPOD method, which we named MaxMiPOD, where SRM and maxSRMd2 steganalysis trials are carried out at payloads of 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5 bpp. The proposed method produces a very significant performance increase of undetectability compared to the MiPOD standard and state-of-the-art methods, especially at a payload of 0.05 bpp. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cost functions

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The Application of Computer-Aided Under-Resourced Language Translation for Malay into Kadazandusun

Annals of Emerging Technologies in Computing

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Annals of Emerging Technologies in computing 2023年第5期7卷 11-24页

作者： Sainin, Mohd Shamrie Sintian, Minah Alias, Suraya Tahir, Asni Language Engineering and Application Development Faculty of Computing and Informatics Universiti Malaysia Sabah Malaysia Faculty of Languages and Communication Universiti Pendidikan Sultan Idris Malaysia

A computer-aided language translation using a Machine translation (MT) is an application performed by computers (machines) that translates one natural language to another. There are many online language translation tools, but thus far none offers a sequence of text translations for the under-resourced Kadazandusun language. Although there are web-based and mobile applications of Kadazandusun dictionaries available, the systems do not translate more than one word. Hence, this paper aims to present the discussion of the preliminary translation of Malay to Kadazandusun. The basic word-to-word with dictionary alignment translation based on Direct Machine Translation (DMT) is selected to begin the exploration of the translation domain where DMT is one of the earliest translation methods which relies on the word-to-word approach (sequence-to-sequence model). This paper aims to investigate the under-resourced language and the task of translating from the Malay language to the Kadazandusun language or vice versa. This paper presents the application and the process as well as the results of the system according to the basic Kadazandusun word arrangement (Verb-Subject-Object) and its translation quality using the Bilingual Evaluation Understudy (BLEU) score. Several phases are involved during the process, including data collection (word pair translation), preprocessing, text selection, translation procedures, and performance evaluation. The preliminary language translation approach is proven to be capable of producing up to 0.5 BLEU scores which indicate that the translation is readable, however, requires post-editing for better comprehension. The findings are significant for the quality of the under-resourced language translation and as a starting point for other machine translation methodologies such as statistical or deep learning-based translation. © 2023 by the author(s).

关键词： Computer aided language translation

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PErformance Evaluation of Detection Mechanisms Towards the Slow Ddos Attack of Edge computing 8

PErformance Evaluation of Detection Mechanisms Towards the S...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Lee, Xue Yi Leau, Yu-Beng Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Cybersecurity Research Lab Faculty of Computing and Informatics Kota Kinabalu Malaysia

ISBN: (纸本)9798350310931

Edge computing is a network topology made up of three layers: Cloud Server Layer (CSL), Edge Server Layer (ESL), and Edge Device Layer (EDL). Therefore, it is vulnerable since the processing capacity of loT devices or mobile devices is insufficient to withstand and they are unable to implement high- level security measures such as using robust security protocols like HTTP/HTTPS, FTP, or SMTP at this layer. Hence, it poses considerable security risks, particularly the Slow Distributed Denial of Service (DDoS) attacks. Various detection methods have been investigated in this study, and three machine learning algorithms that are classified as anomaly-based detection techniques have been chosen, such as CDAAE (Conditional Denoising Adversarial AutoEncoder), CNN (Convolutional Neural Network), and deep learning using 'relu' and 'softmax' in detecting slow DDoS attacks. These models are evaluated with the CICIDS2017 dataset and show that CNN has achieved better overall performance with shorter training times and high detection accuracy. © 2023 IEEE.

关键词： Edge computing

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Hybrid Deep Learning Architectures for Histological Image Segmentation 6

Hybrid Deep Learning Architectures for Histological Image Se...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Hasan, Md Jahid Ahmad, Wan Siti Halimatul Munirah Wan Ahmad Fauzi, Mohammad Faizal Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and InformaticsFaculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350344349

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (HandE) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied. © 2024 IEEE.

关键词： Image segmentation

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ERBM:A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials & Continua 2025年第6期83卷 5155-5179页

作者： Arshad Mehmmod Komal Batool Ahthsham Sajid Muhammad Mansoor Alam Mazliham MohD Su’ud Inam Ullah Khan Department of Information Security and Data Science Riphah Institute of Systems EngineeringRiphah International UniversityIslamabad46000Pakistan Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia

Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known ***,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection *** this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify *** ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the *** fuzzy logic sets produce adaptive detection rules by reducing data ***,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical *** extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional *** the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion *** research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.

关键词： Rule based intrusions IoT fuzzy prediction

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Statistical Analysis of CPU Power Consumption for Detecting DGA Botnet Command and Control Communication

Statistical Analysis of CPU Power Consumption for Detecting ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Ibrahim, Zul-Azri Ismail, Saiful Adli Rahim, Fiza Abdul Abas, Muhammad Hazim bin Khairul Anuar, Muhammad Idris bin Azwan, Aiman Harith Bin Institute of Informatics and Computing in Energy College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia Faculty Artificial Intelligence Universiti Teknologi Malaysia Wilayah Persekutuan Kuala Lumpur Kuala Lumpur Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia

ISBN: (纸本)9798331530303

Domain Generation Algorithms (DGA) Botnets represent a significant threat to network security due to their ability to mask Command and Control (C&C) communications with botmaster. Traditional detection methods like domain blacklists have struggle to keep pace with the dynamic nature of these botnets. This research explores an alternative detection approach by investigating the use of CPU power consumption as an indicator for detecting DGA botnet in Internet of Things (IoT) devices. The power consumption data was collected and analyzed from a testbed comprising infected Raspberry Pi devices with DGA-based botnets. Statistical methods that include Analysis of Variance (ANOVA) and post-hoc tests, were used to determine if significant differences in CPU power consumption exist across the botnet phases. The results show that CPU power consumption is significantly higher during both the C&C and attack phases compared to normal operation. Additionally, the attack phase exhibits greater power consumption variance, suggesting unstable resource utilization. These findings highlight the potential of using power consumption data as a key metric for identifying botnet activities in IoT systems. ©2024 IEEE.

关键词： Bot (Internet)

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Analyzing the Efficiency of Software Distribution Systems in AIoT Environments 8th

Analyzing the Efficiency of Software Distribution Systems in...

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8th EAI International Conference on Management of Manufacturing Systems, MMS 2023

作者： Jevremovic, Aleksandar Veinovic, Mladen Peraković, Dragan Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Transport and Traffic Sciences University of Zagreb Zagreb Croatia

ISBN: (纸本)9783031531606

In the past few decades, the capabilities of hardware platforms designed for the development of specialized devices (such as mobile phones or embedded computers) have advanced tremendously. One significant consequence of this evolution is the opening up of possibilities for using standard software (e.g., the Linux operating system, standard libraries, etc.) on these platforms. The latest phase of this evolution is represented by the emergence of AIoT (Artificial Intelligence of Things) devices, specifically IoT devices that have sufficient hardware resources to execute complex and demanding artificial intelligence algorithms locally. In (A)IoT environments, especially in cases where devices are battery-powered and their autonomy is crucial, performance and algorithm optimization can often be more significant criteria than result accuracy and precision. Until recently, there has not been much focus on optimizing the distribution of software solutions. However, due to several factors, such as a shift in software development philosophy toward more frequent and smaller changes, limited resources in IoT environments, the need for automated updates, increasingly bulky software packages, etc., the question of efficient software distribution and related updates is becoming increasingly important. In this chapter, we provide an overview of standard solutions and approaches for software package distribution and management, as well as an analysis of their efficiency. We consider classical package management systems, package distribution systems for specific programming languages, and hybrid systems of this kind. The analysis primarily focuses on IoT systems characterized by the need for efficient utilization of local resources and communication channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence

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ShadowSense: a Multi-annotated Dataset for Evaluating Word Sense Induction 30

ShadowSense: a Multi-annotated Dataset for Evaluating Word S...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Herman, Ondřej Jakubíček, Miloš Lexical Computing Brno Czech Republic Faculty of Informatics Masaryk University Brno Czech Republic

ISBN: (纸本)9782493814104

In this paper we present a novel bilingual (Czech, English) dataset called ShadowSense developed for the purposes of word sense induction (WSI) evaluation. Unlike existing WSI datasets, ShadowSense is annotated by multiple annotators whose inter-annotator agreement represents key reliability score to be used for evaluation of systems automatically inducing word senses. In this paper we clarify the motivation for such an approach, describe the dataset in detail and provide evaluation of three neural WSI systems showing substantial differences compared to traditional evaluation paradigms. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： ShadowSense word sense induction WSI

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