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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Yulianti, Henny Pranoto, Gatot Tri Yaddarabullah Krishnasari, Erneza Dewi Marchenko, Michael Informatics Engineering Universitas Siber Asia Jakarta Indonesia Informatics Engineering Pelita Bangsa University Bekasi Indonesia Universitas Trilogi Department of Informatics Jakarta Indonesia Universitas Trilogi Department of Visual Communication Design Jakarta Indonesia Dnipro National University Dnipropetrovsk Faculty of Physics Electronics and Computer Systems Oblast Ukraine

ISBN: (纸本)9798350359633

Traffic accidents are a problem confronted by means of each a around the arena, Indonesia isn't any exception. The Indonesian national Police recorded an increase inside the number of traffic accidents in 2019 in comparison to 2018. based totally on POLRI information, there had been 107,500 traffic accidents in 2019, an increase from 103,672 incidents in 2018. in particular in Jakarta, that is the capital city of the Republic of Indonesia, traffic accidents are a common phenomenon, in conjunction with the unexpectedly developing use of motorized motors on the dual carriageway. for that reason, a examine is wanted to determine the variety of traffic accidents with the intention to occur in the future. The observe will forecast the number of traffic accidents for every month based totally on datasets from 2015 to 2020 from the Jakarta Metro Jaya Police Traffic Directorate. The approach used on this examine makes use of facts mining methods by using comparing the artificial Neural community Backpropagation, Deep getting to know and Linear Regression algorithms to decide which algorithm is right for predicting the range of traffic injuries inside the Jakarta region in each following month. This examine concludes that the synthetic Neural community Backpropagation set of rules is the first-rate set of rules to be applied in predicting the number of visitors accidents within the Jakarta vicinity with a Root suggest square mistakes (RMSE) of 29.477. studies trying out with the aid of applying the synthetic Neural network Backpropagation algorithm version has verified to expose the price of forecasting the number of traffic accidents in Jakarta with a mean monthly accuracy cost of 97.34%. © 2023 IEEE.

关键词： Forecasting

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Did State-Sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter 9th

Did State-Sponsored Trolls Shape the 2016 US Presidential E...

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Security and Privacy in Social Networks and Big Data - 9th International Symposium, SocialSec 2023, Proceedings

作者： Salamanos, Nikos Jensen, Michael J. Iordanou, Costas Sirivianos, Michael Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus Institute for Governance and Policy Analysis University of Canberra Canberra2601 Australia

ISBN: (纸本)9789819951765

It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called "troll" accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter. We construct and analyze a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques and a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts, and we find that one troll account appears in the top-100 and four in the top-1000. This, combined with other findings presented in this paper, constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that, on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000, respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social aspects

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Improvement of the Control of the Hybrid Photovoltaic System with Storage Battery to Increase the Object's Power Over the Consumption Limit from the Grid 5

Improvement of the Control of the Hybrid Photovoltaic System...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Shavolkin, Olexander Shvedchykova, Iryna Kolcun, Michal Medved, Dusan Marchenko, Ruslan Kyiv National University of Technologies and Design Department of Computer Engineering and Electromechanics Kyiv Ukraine Technical University of Kosice Faculty of Electrical Engineering and Informatics Košice Slovakia

ISBN: (纸本)9798350359787

The improvement of the control of the hybrid photovoltaic system with a storage battery to increase the power of objects over the limit on consumption from the grid is considered. The aim is to ensure the functioning of the system at the deviation of the load schedule from the calculated one and the generation of photovoltaic battery from the forecast value while maintaining the taken limit of the energy storage depth of discharge. The principle of the control has been improved by setting the value of the added power and forming the state of charge graph of the storage battery. The formation of the storage battery state of charge graph with the correction of the value of the added power according to the deviation of the state of charge from the calculated value with a given step of discreteness in time is proposed. The control algorithm has been changed with the introduction of additional limitations and the exclusion of correction at separate time intervals. Changes are taken into account in the 24-hour model of energy processes in the system. Also, the error of the storage battery state of charge estimation is taken into account. The simulation results confirm the possibility of providing the accepted depth of the discharge value for the calculated load schedule when photovoltaic battery generation is below the forecast value of up to 30%. © 2023 IEEE.

关键词： Electric power supplies to apparatus

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Wrapper-Based Feature Selection to Improve The Accuracy of Intrusion Detection System (IDS) 10

Wrapper-Based Feature Selection to Improve The Accuracy of I...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Jupriyadi Budiman, Arief Hamidi, Eki Ahmad Zaki Ahdan, Syaiful Negara, Ridha Muldina Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Gadjah Mada Department Computer Science and Electronics Yogyakarta Indonesia UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350377057

An Intrusion Detection System (IDS) is a system that can detect attacks on a network. IDS systems can be built using machine learning approaches. However, significant network traffic and many features cause machine learning algorithms to work slowly. Not all available features are essential for use as a reference in an intrusion detection system. Feature selection is important because it can reduce the dataset and speed up computing time in building models and detection systems. In this paper, feature selection using the wrapper method is proposed. Its performance is compared with filter-based feature selection, such as chi-square, information gain, gain ratio, and correlation-based feature selection (CFS). Based on the exploration, the results showed that the feature selection algorithm using the wrapper method was superior to other algorithms. The number of critical features discovered and the detection system's precision serve as proof of the wrapper approach. With only three features as a reference, the detection system produces an accuracy of 96.22%. It increases 5.84% from 90.38% to 96.22%. Computation time also increases because the number of features used is tiny compared to other algorithms and uses all features. © 2024 IEEE.

关键词： Network intrusion

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An automated speech analysis system for the detection of cognitive decline in elderly

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International Journal of Speech Technology 2023年第2期26卷 337-353页

作者： Loizou, Christos P. Pantzaris, Marios Faculty of Engineering & Technology Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology 30 Arch. Kyprianos Str. Office 420 Limassol3036 Cyprus Cyprus Institute of Neurology and Genetics P. O. Box 23462 Nicosia1683 Cyprus

The goal of this study is to develop and test an automated integrated speech analysis system for detecting mild cognitive impairment (MCI) and dementia in spontaneous free speech. During the years 2010–2016, speech recordings (N = 2800) were obtained from 200 Greek Cypriots over the age of 65. These were divided into three groups (G1, G2, and G3) based on the results of their Mini-mental state examination (MMSE): G1:95 normal (NOR) individuals with an MMSE greater than 26;G2:65 MCI subjects with 20 ≤ MMSE ≤ 26;G3:40 dementia subjects with 0 ≤ MMSE © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Neurodegenerative diseases

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Comparative Analysis Of Deep Learning Architectures For The Classification Of Eye-Related Diseases 12

Comparative Analysis Of Deep Learning Architectures For The ...

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12th International Conference on Orange Technology, ICOT 2024

作者： Sultan, Muhammad Ali Kaburuan, Emil R. Rasool, Fezan Suwito, Misni Harjo Informatics Engineering Department Mercu Buana University Jakarta Barat Indonesia Computer Science Department Lahore University of Management Sciences Lahore Pakistan Information System Department Mercu Buana University Jakarta Barat Indonesia

ISBN: (纸本)9798331523602

Eye-related disorders are among the driving causes of vision impedances worldwide. Accurate classification of retinal conditions is crucial for adequate treatment and management. This research explores the potential of three advanced deep-learning models, namely—-VGG16, DenseNet, and Swin Transformers, to classify four eye-related disorders utilizing the Optical Coherence Tomography Image Retinal Database (OCTID) taken from ICPSR. The OCTID dataset contains retinal OCT images representing four distinct pathological conditions: age-related macular degeneration, diabetic retinopathy, central serous retinopathy, macular holes, and normal retinas. Evaluation is done based on metrics such as accuracy, precision, recall, and F1 score. The comparison of these models, paired with interpretability techniques such as GRADCAM and Integrated Gradients, reveals that VGG16 achieves the highest F1-score, demonstrating robust classification performance. At the same time, Swin Transformers develops competitive accuracy with practical interpretability through Integrated Gradients. DenseNet showed slightly lower metrics but performed adequately on the GRADCAM outputs. This study underscores the critical role of advanced deep learning frames, which work to enhance diagnostic accuracy for retinal disorders and provide a foundation for future developments in ophthalmic disease classification. © 2024 IEEE.

关键词： Optical coherence tomography

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Large Language Models versus Natural Language Understanding and Generation 23

Large Language Models versus Natural Language Understanding ...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Nikitas Karanikolas Eirini Manga Nikoletta Samaridi Eleni Tousidou Michael Vassilakopoulos Department of Informatics and Computer Engineering University of West Attica Greece Department of Electrical and Computer Engineering University of Thessaly Greece

ISBN: (纸本)9798400716263

In recent years, the process humans adopt to learn a foreign language has moved from the strict "Grammar –Translation" method, which is based mainly on grammar and syntax rules, to more innovative processes, resulting to the more modern "Communicative approach". As its name states, this approach focuses on the coherent communication with native speakers and the cultivation of oral skills, without taking into consideration, at least at the first stages, the rules that govern the language. The same trend seems to have been applied to the way machinery can be "educated" to comprehend and reproduce the unfamiliar, human language. The "rule based" Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms, on one hand, and the "text based" Large Language Models (LLMs), on the other, are two, analogous to the two human foreign language learning processes, subareas of Natural Language Processing (NLP). This paper presents these two alternative approaches, LLMs (a technology having surfaced as an influential catalyst of NLP, during last years) on the one hand and NLG/NLU on the other, highlighting their applications, their technologies, their capabilities, their differences, their strengths and weaknesses and the challenges they present, contributing to a deeper comprehension of the evolving landscape of Artificial Intelligence and human-computer communication.

关键词： Large Language Models Natural Language Generation Natural Language Processing Natural Language Understanding

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Space Division Multiple Access for Cellular V2X Communications

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computers, Materials & Continua 2022年第10期73卷 1195-1206页

作者： Doaa Sami Khafaga Mohammad Zubair Khan Muhammad Awais Javed Amel Ali Alhussan Wael Said Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Information Taibah University Madinah Saudi Arabia Department of Electrical and Computer Engineering COMSATS University Islamabad45550Pakistan Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management *** safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic *** are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc ***,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety ***,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further *** paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node *** proposed protocol works by dividing road segments into clusters and *** sub-frames are allocated to a cluster and these sub-frames are reused after a certain *** a cluster,sub-channels are allocated to *** implement the proposed SDMA protocol and evaluate its performance in a highway vehicular *** results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.

关键词： ITS vehicular network cellular V2X SDMA

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Recognition of human skin diseases using inception-V3 with transfer learning

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3145-3154页

作者： Mamun, Md. Al Kabir, Md. Solaiman Akter, Morium Uddin, Mohammad Shorif Department of Public Health and Informatics Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

Skin disease is an irritable disease and may be the motive of deadly to human life. So, all of us ought to be aware of this alarming health problem. Recognition of skin diseases is a very challenging task because of its various characteristics. To avoid delay in treatment, in this paper, five most common skin diseases: Vascular lesion, Solar lentigo, Actinic keratosis, Squamous cell carcinoma, and Basal cell carcinoma have been investigated through the Inception-V3 with and without transfer learning. An extensive experiment is performed, and the model’s effectiveness is tested through standard metrics such as accuracy, F1 score, and AUC of the Receiver Operating Characteristics (ROC) curve. Inception-V3 with transfer learning has achieved the highest test accuracy of 98.16%. The obtained results are also compared with the state-of-the-art approaches. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Convolution neural network Inception V3 Skin diseases Transfer learning

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An effective intrusion detection system based on feature selection and regularized long short-term memory classifier

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Multimedia Tools and Applications 2025年 1-24页

作者： Chikkalwar, Sudha Rani Garapati, Yugandhar Department of Computer science and Engineering GITAM Deemed to be University Rudraram Hyderabad India Department of Computer science and Informatics Mahatma Gandhi University Telangana Nalgonda India

In recent days, the expansion of Internet of Things (IoT) and the quick advancement of computer system applications contribute to the current phenomenon of data growth. The field of intrusion detection has expanded considerably as a result of the need to protect networks from various attacks. Previously, security has been provided by combining conventional intrusion monitoring systems with a high number of security measures. However, the ability of combined systems to analyze enormous amounts of data has been restricted. Therefore, in this research, open-source and freely available datasets known as NSL-KDD and CSE-CIC-IDS2018 datasets are analyzed. Those datasets contain a class column that splits the attack detection into two classes (normal and anomalous) and four categories (DoS, probe, R2L, and U2R). Then, feature encoding and scaling are used for pre-processing. After that, a SelectPercentile-based feature selection algorithm is suggested to identify the numerous various intrusion attacks and choose the best features. Later, the Regularized Long Short-Term Memory (LSTM) is employed to classify the data. The result analysis verified that Regularized LSTM surpasses the existing models in the NSL-KDD based on accuracy (99.95%), false alarm rate (0.05%), attack detection rate (99.82%), and f1-measure (99.94%). While in the CSE-CIC-IDS2018 dataset, the Regularized LSTM method achieves better accuracy (99.33%), false alarm rate (0.07%), attack detection rate (99.96), and f1-measure (99.95%). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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