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2023 International Conference on Information and Digital Technologies, IDT 2023

作者： Klimo, Marek Antal, Eugen Kvassay, Miroslav Institute of Computer Science and Mathematics Slovak University of Technology Bratislava Slovakia University of Žilina Department of Informatics Žilina Slovakia

ISBN: (纸本)9798350305869

In this work, we focus on supporting online education of courses about Classical Ciphers and promoting historical cryptography to the general public. For this purpose, we designed and developed new modules in 2020 and integrated them into our ongoing HCPortal project. We developed an education module (Angular application) with which we can present a wide range of supporting applications regarding cryptography and cryptanalysis of historical ciphers and visualize their necessary parts. In addition, we also developed a special Virtual museum (using A-Frame technology) to promote public interest in ciphers using modern technologies. These modules have undergone significant change in recent years. They have been expanded with new content, and the structure of the Educational module has also changed. We have included various Chinese and Japanese historical ciphers into these modules due to the lack of availability of resources that specifically address this area of research. We are presenting the new content added to the HCPortal, focusing on these Chinese and Japanese ciphers. © 2023 IEEE.

关键词： Cryptography

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Improving the CNN Model for Arabic Crime Tweet Detection Based on an Intelligent Dictionary 16

Improving the CNN Model for Arabic Crime Tweet Detection Bas...

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16th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Abdalrdha, Zainab Khyioon Al-Bakry, Abbas Mohsin Farhan, Alaa K. Informatics Institute of Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Baghdad Iraq University of Technology Department of Computer Sciences Baghdad Iraq

ISBN: (纸本)9798350381344

Rising crime rates in Iraq and the Arab World pose significant challenges for law enforcement, exacerbated by vast data on criminal activity, technological advances, and high population density. This research aims to extract credible information from this data to understand crime better and assist in future prevention efforts. One challenge is the complexity of the Arabic language in crime detection. The study introduces an innovative approach using deep learning techniques, including creating an intelligent dictionary for detecting crime-related content in Arabic tweets. This method employs the Aho-Corasick algorithm to address the dynamic nature of language and evolving criminal terminology. The methodology applies deep learning and machine learning techniques, emphasizing natural language processing (NLP) for preprocessing and extracting relevant tweet characteristics. Created a labeled tweet dataset and classified it into various crime-related categories. Deep learning models, such as an improved CNN model based on chi-square testing and Grid search, were trained and evaluated using precision, recall, F1-score, accuracy, and MAP accuracy metrics. The analysis of an Arabic tweet dataset, comprising 18,493 tweets with ten features, demonstrated that the improved CNN model achieved a high accuracy of 99.25% and a macro F1-score of 99.9%. These results indicate that the proposed deep learning model outperforms others regarding accuracy. © 2023 IEEE.

关键词： Crime

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CNN Hyper-Parameter Optimizer based on Evolutionary Selection and GOW Approach for Crimes Tweet Detection 16

CNN Hyper-Parameter Optimizer based on Evolutionary Selectio...

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16th International Conference on Developments in eSystems Engineering, DeSE 2023

ISBN: (纸本)9798350381344

Social media is widely utilized for crime, highlighting the need for automated crime detection and prevention systems that can reach a vast audience. There is substantial study on specific social media crimes, but a holistic model to detect them is needed. This article classifies cybercrime tweets using deep neural networks, specifically deep CNNs, a common computer vision method. Although promising, deep neural networks need further research to find their best topology. Manual iteration and intensive testing limit the deep CNN model's practicality. CNN embedding dimensions, dense unit count, learning rate, and batch size affect model performance. Thus, constructing an effective CNN model with little manual intervention and domain expertise is difficult. This study reduces dimensionality and improves model performance by selecting the most relevant and informative dataset features using CNN hyperparameters. Five CNN feature selection methods using optimal hyperparameters are shown. These approaches use CNN, SO-roulette wheel, SO-tournament, SO-linear rank, SO-exponential rank, and Grey Wolf Optimizer to identify and categorize Arabic criminal tweets. The CNN-SO-tournament model recognizes illicit Arabic tweets with 99.60% accuracy. This study substantially improves Twitter crime prevention. © 2023 IEEE.

关键词： Crime

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LightGBM empowered by whale optimization for thyroid disease detection

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International Journal of Information technology (Singapore) 2023年第4期15卷 2053-2062页

作者： Sinha, Bam Bahadur Ahsan, Mohammad Dhanalakshmi, R. Department of Computer Science Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi 834010 India Informatics Cluster School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Department of Computer Science Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620009 India

Thyroid disorder is a significant source of formulation in medical classification and prognosis, with onset being a challenging assumption in medical study. The thyroid gland is a vital organ of our body. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are two of the most prevalent thyroid disorders that affect the thyroid’s ability to regulate the pace of the body’s metabolism. Data cleansing procedures were used to prepare the data to the point where analytics could be performed to determine the risk of individuals developing thyroid disease. In this paper, an optimal framework design employing LightGBM (Light Gradient Boosting Machine), Sequential Backward Selection (SBS), and a metaheuristic technique termed Whale Optimization (WO) plays a decisive role in detecting Thyroid disease. The objective of this research is to provide an impeccably clean and rational way to detecting thyroid disorder in humans. Numerous processes have been applied to thyroid data sets with astounding results, yet the data used for thyroid disease detection is redundant, inconsistent and contains missing feature values, as per our literature survey. The proposed effort would develop an expert advisory system based on the Opti-LightGBM architecture for the purpose of detecting thyroid disorders and initiating necessary medication promptly. The proposed Opti-LightGBM model yields an accuracy of 99.75% on Thyroid dataset and outperforms several state-of-art comparative models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Classification Feature selection LightGBM Optimization Thyroid

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Exploring Network Dynamics: Community Detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Andreas Vonitsanos, Gerasimos Karamitsos, Ioannis Al-Hussaeni, Khalil Ionian University Department of Informatics Corfu Greece University of Patras Computer Engineering and Informatics Department Patras Greece Rochester Institution of Technology Dubai United Arab Emirates Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates

ISBN: (纸本)9798350362480

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network's structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health. © 2024 IEEE.

关键词： Tweets

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Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

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computer Systems Science & Engineering 2022年第7期42卷 15-31页

作者： Monday Eze Charles Okunbor Solomon Esomu Nneka Richard-Nnabu Kayode Oladapo Oghenetega Avwokuruaye Abisola Olayiwola Akpovi Ominike Godwin Odulaja Oluwatobi Akinmerese Department of Computer Science Babcock UniversityOgunNigeria Department of Computer Science Admiralty UniversityDeltaNigeria Department of Computer Science National Open UniversityNigeria Department of Computer Science and Informatics Alex-Ekwueme Federal UniversityEbonyiNigeria Department of Cyber Security Admiralty UniversityDeltaNigeria Department of Computer Science Caleb UniversityImotaLagosNigeria Department of Computer Science and Information Technology Petroleum Training InstituteDeltaNigeria Department of Computer Science Tai Solarin University of EducationOgunNigeria

Knowledge propagation is a necessity,both in academics and in the *** focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study *** practice of knowledge sharing in study groupsﬁnds relevance in conferences,workshops,and class ***-nately,there appears to be only few researches on empirical best practices and techniques on study groups formation,especially for achieving rapid knowledge *** work bridges this gap by presenting a workﬂow driven compu-tational algorithm for autonomous and unbiased formation of study *** system workﬂow consists of a chronology of stages,each made of distinct *** of the most important steps,subsumed within the algorithmic stage,are the algorithms that resolve the decisional problem of number of study groups to be formed,as well as the most effective permutation of the study group participants to form collaborative *** work contributes a number of new algorithmic concepts,such as autonomous and unbiased matching,exhaustive multiplication technique,twisted round-robin transversal,equilibrium summation,among *** concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’performances in an examination,rather than through any external *** part of practical demon-stration of this work,study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty(40)participants,and partially for study groups of 50,60 and 80 *** quantitative proof of this work was done through the technique called equilibrium summation,as well as the calculation of inter-study group Pearson Correlation Coefﬁcients,which resulted in values higher than 0.9 in all *** life experimentation was carried out while teaching Object-Oriented Programming to forty(40)under-graduates between February and May *** result showed

关键词： Knowledge propagation collaborative study group unbiased matching twisted round robin exhaustive multiplication

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

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Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第10期16卷 140-162页

作者： Akintola, Abimbola Ganiyat Balogun, Abdullateef Oluwagbemiga Mojeed, Hammed Adeleye Usman-Hamza, Fatima Enehezei Salihu, Shakirat Aderonke Adewole, Kayode Sakariyau Balogun, Ghaniyyat Bolanle Sadiku, Peter Ogirima Department of Computer Science University of Ilorin Ilorin Nigeria Department of Computer and Information Sciences Universiti Teknologi PETRONAS Perak Malaysia Institute of Technical Informatics and Telecommunication Gdansk University of Technology Gdańsk Poland

Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognizing malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to ML models based on the RUS method. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection. © 2022. All Rights Reserved.

关键词： Machine learning

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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