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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Makarovskikh, Tatiana Salah, Amir Badr, Amr Kadi, Ammar Alkattan, Hussein Abotaleb, Mostafa South Ural State University System of Programming Department Chelyabinsk Russia Ain Shams University Computers and Systems Engineering Cairo Egypt New England University School of Science and Technology ArmidaleNSW Australia South Ural State University Department of Food and Biotechnology Chelyabinsk Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

Recent technological advancements allow deep learning to be employed in practically every aspect of life. Because deep learning techniques are so precise, they can be used in medicine to classify and detect various diseases. The coronavirus (SARSCoV2) epidemic has recently affected global health systems. One of the ways to diagnose SARSCoV2 is with a chest X-ray. This paper proposes a deep learning technique to distinguish SARSCoV2 positive and normal cases. In this study, we fine-Tuned the deep learning models and hyperparameters, and the fine-Tuned deep learning models performed significantly better. To classify X-ray images, we developed a system based on deep learning algorithms that includes five models: Xception, VGG19, ResNet50, DenseNet121, and Inception. We offer deep learning models and algorithms that have been trained and evaluated to support medical efforts and reduce medical staff workload when dealing with SARSCoV2. In addition, the classification model that was proposed yields positive results because it makes use of accurate classification of the SARSCoV2 disease based on medical images. Additionally, the performance of our proposed CNN classification method for medical imaging was evaluated using various edge-based neural networks. The accuracy of tertiary classification with CNN will decrease as the number of classes in the training network grows. In tertiary classification, which includes normal and SARSCoV2 positive images, the proposed model achieved a 0.9897 accuracy. The proposed algorithm achieves a high level of classification accuracy when using the DenseNet121 model for binary classification. © 2022 IEEE.

关键词： COVID-19

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How Indoor Positioning Technology Can Enhance Our Understanding of Communication Patterns in Office Environments

How Indoor Positioning Technology Can Enhance Our Understand...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Saqib Aziz Felix Deiters Markus Jacobi Iris Inokhosa Giovanni Betti Department for Structural Design and Engineering University of the Arts Berlin Berlin Germany Programming & Design Strategy Berlin Germany Design Systems HENN Berlin Germany Department for Sustainability HENN Berlin Germany

This paper presents an early-stage process to track communication patterns in office spaces using a real-time location system with sub-meter accuracy. A custom frontend application is used to assess and map the positioning data, with the goal of gathering empirical evidence on how spatial intervention can improve workplace communication and productivity. The case-study involved equipping an architectural firm’s office and employees with tracking devices to gather data on their communicative behavior. Unsupervised machine learning algorithms are compared to identify location, quantity, and duration of tracked meeting events, providing insights into participants’ communication behavior. While initial results are promising, this is a work in progress. The effect of tracking on individuals is being investigated through self-assessment questionnaires. Further research is needed to understand the potential impact of spatial intervention on workplace communication and productivity. The paper aims to contribute to the development of evidence-based design strategies in architecture and related fields.

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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Computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Methods for Improving the Quality of Classification on Imbalanced Data

Methods for Improving the Quality of Classification on Imbal...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Zozulia Vladislav Nataliia Khatsko Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors.

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Advantages of Applying a Multi-agent Approach in Simulation Modeling: a Data-to-Action Case Study Applied to Electricity Production

Advantages of Applying a Multi-agent Approach in Simulation ...

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National Conference with International Participation (TELECOM)

作者： Krassimir Stoyanov Varbinka Stefanova-Stoyanova Petko Danov Department of Computer Systems Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781665482134

This article examines modern methods for analysis and dynamic process modeling with the context of the capabilities, characteristics and advantages of types of multiagent systems. A practical implementation of the “Data to Action” model is proposed through the research simulation modeling system “Photovoltaic (PV) to Smart Energy Storage (SES) Grid”, developed by the authors using a Multi-Agent System for simulating the process of generating electricity produced by a PV park, its storage with the possibility of Time-Shifting and sale of energy.

关键词： Photovoltaic systems Process modeling Production Data models Telecommunications Multi-agent systems Energy storage

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A lightweight attention-driven YOLOv5m model for improved brain tumor detection

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Computers in Biology and Medicine 2025年 188卷 109893-109893页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Mardieva, Sevara Iskhakova, Nargiza Sultanov, Murodjon Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Seongnam-si461-701 Korea Republic of Department of Information Systems and Technologies of the Tashkent State University of Economic Tashkent100066 Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan

Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases, significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of advancing brain tumor diagnostics, this study introduces a significant enhancement to the YOLOv5m model by integrating an Enhanced Spatial Attention (ESA) layer, tailored specifically for the analysis of magnetic resonance imaging (MRI) brain scans. Traditional brain tumor detection methods, heavily reliant on expert interpretation of MRI, are fraught with challenges such as high variability and the risk of human error. Our innovative approach leverages the ESA layer to acutely focus on salient features, significantly improving the method ability to differentiate between common classes of brain tumors—meningioma, pituitary, and glioma tumors. By processing spatial features with enhanced precision, the model minimizes false positives and maximizes detection reliability. Validated against a comprehensive dataset of 3064 T1-weighted contrast-enhanced MRI images from 233 patients, our modified YOLOv5m architecture demonstrates superior performance metrics compared to the standard model, highlighting its potential as a robust tool in clinical applications for automated and precise brain tumor diagnosis. © 2025 The Authors

关键词： Dynamic contrast enhanced MRI

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An Experiment to Evaluate IoT Application Security Scheme (IoTGazePass)

An Experiment to Evaluate IoT Application Security Scheme (I...

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2022 International Conference on Engineering and MIS, ICEMIS 2022

作者： Keshlaf, Ayad A. Alfard, Faheemah M. Bouzid, Omar M. Sabratha University Faculty of Engineering Computer Eng. and It Department Sabratha Libya Libyan Center for Electronic Systems Programming and Aviation Research University of Zawia Computer Department Faculty of Engineering Zawia Libya University of Gharyan Faculty of Engineering Elec. and Electronic Eng. Dept Gharyan Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

IoTGazePass is a new password scheme that has been designed to tackle the weaknesses of existing types of passwords. It is intended to be suitable for IoT applications. An experiment was conducted to evaluate the strengths and weaknesses of the IoTGazePass scheme (e.g., security, usability and memorability). The evaluation aspects include a comparison with traditional textual and graphical based passwords in terms of memorability and security of the passwords. The results of the evaluation indicate that the IoTGazePass scheme is easy to use, memorable and flexible, as it can generate strong, secure and scalable passwords. The experiment results have also showed that the IoTGazePass scheme have good ability to deal with shoulder surfing attacks. In fact, the experiment results lead to some recommendations and suggestions to improve the planned performance. © 2022 IEEE.

关键词： Authentication

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Classified emotion as implicit recommendation system feedback

Classified emotion as implicit recommendation system feedbac...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Andrii Kuliahin Heorhii Kuchuk Department of Computer Systems Networks and Cybersecurity National Aerospace University “Kharkiv Aviation Institute” Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

the paper proposes the use of 3D convolutional neural network for recognizing user emotions on videos in a recommender system. The data approach aims to use the recognized emotion as important implicit feedback and improve the recommendation result. This is expected to significantly improve the performance of the recommender system.

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Research into the Influence of Informative Speech Parameters on the Reliability of RNN-based Classification of Human Emotional States

Research into the Influence of Informative Speech Parameters...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Alan K. Alimuradov Alexander Yu. Tychkov Mikhail A. Tyurin Dmitry S. Dudnikov Anna N. Tychkova Danil S. Moiseev Maria G. Myasnikova Student Research and Production Business Incubator Penza State University Penza Russia Department of Radio Engineering and Radioelectronic Systems Penza State University Penza Russia Department of Automation and Telemechanics Penza State University Penza Russia Department of Scientific and Innovation Penza State University Penza Russia Department of Information Systems and Programming Leninogorsk Oil College Leninogorsk Russia

Reliable classification of human emotional states from speech is dependent on speech signal processing algorithms and classification methods. The reliability is especially affected by the database of emotional speech informative parameters used to train classification methods. The paper is aimed at studying the influence of informative speech signal parameters on classification reliability of human emotional states based on a Recurrent Neural Network (RNN). The research is to expand the information space for emotional speech parameters through the use of an original signal representation algorithm based on Empirical Mode Decomposition (EMD), and assessing the impact on classification reliability using a five-layer RNN. The article outlines the research relevance and provides a brief overview of related academic papers. Local and global informative speech parameters relevant to human emotional states are considered in detail. The emotional speech database is described, and the speech signal representation algorithm based on modified decomposition methods is characterized. The research results on the method for classifying human emotional states based on a five-layer RNN using original and extended databases of emotional speech informative parameters are presented. Reliability assessment was performed by computing the confusion matrix for multi-class classification. The research results have evidenced an increase in classification reliability of human emotional states by an average of 2.66 % through expanding the information space for emotional speech parameters. The reliability ranges from 97.4 % to 98.3 % for the classification of emotional states, and from 95.8 % to 98.2 % for the classification of emotions.

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Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)

arXiv

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arXiv 2023年

作者： Crook, Barnaby Schlüter, Maximilian Speith, Timo University of Bayreuth Department of Philosophy Bayreuth Germany Technical University Dortmund Chair for Programming Systems Dortmund Germany Saarland University Center for Perspicuous Computing Saarbrücken Germany

Within the field of Requirements Engineering (RE), the increasing significance of Explainable Artificial Intelligence (XAI) in aligning AI-supported systems with user needs, societal expectations, and regulatory standards has garnered recognition. In general, explainability has emerged as an important nonfunctional requirement that impacts system quality. However, the supposed trade-off between explainability and performance challenges the presumed positive influence of explainability. If meeting the requirement of explainability entails a reduction in system performance, then careful consideration must be given to which of these quality aspects takes precedence and how to compromise between them. In this paper, we critically examine the alleged trade-off. We argue that it is best approached in a nuanced way that incorporates resource availability, domain characteristics, and considerations of risk. By providing a foundation for future research and best practices, this work aims to advance the field of RE for AI. © 2023, CC BY-NC-ND.

关键词： Artificial intelligence

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