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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Sergii Kovalenko Svitlana Kovalenko Olena Mikhnova Anton Kovalenko Daniil Pelikh Valeriy Severin dept. of System Analysis and Information-Analytical Technologies National Technical University “KhPI” Kharkiv Ukraine dept. of Software Engineering and Management Intelligent Technologies National Technical University “KhPI” Kharkiv Ukraine dept. of Cyber Security and Information Technologies State Biotechnological University Kharkiv Ukraine

The study proposed an approach to cells classification in a blood smear image, consisting of two stages: object segmentation and application of machine learning methods to determine the cell type. To apply this approach, a dataset consisting of cell type labels and the corresponding cell area was created. Object segmentation was performed using image processing methods: noise removal, image binarization, contouring, and area determination using the Gaussian formula. The obtained data set was pre-processed: duplicate data and outliers were removed, and the data was divided into testing and training sets. Training for Gaussian Naive Bayes Classifier, Decision Tree Classifier, Random Forest, Support Vector Classifier models were performed. It was found that the accuracy of all classifiers is 97-98%. In addition, metrics were calculated to evaluate the effectiveness of the models. The Decision Tree Classifier method was chosen to apply the approach to cell classification on new images. All image processing, data collection and processing, model training and evaluation were performed using the Python programming language and relevant libraries.

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Solving the Two-Criteria Maximum Coverage Location Problem of Continuous Area Monitored by Sensor Network 12

Solving the Two-Criteria Maximum Coverage Location Problem o...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Yakovlev, Sergiy Wojciechowski, Adam Podzeha, Dmytro Illiashenko, Oleg Yakovleva, Iryna National Aerospace University 'KhAI' Kharkiv61070 Ukraine Lodz University of Technology Lodz90-924 Poland Institute of Information Science and Technologies 'Alessandro Faedo' - Isti Cnr Software Engineering & Dependable Computing Laboratory Pisa56124 Italy National University of Urban Economy in Kharkiv Kharkiv61002 Ukraine

ISBN: (纸本)9798350358056

The paper investigates the problem of optimizing a sensor network for monitoring a continuous area, considering the bounded coverage areas of sensors. This task is formulated in terms of the maximum coverage location problem. A mathematical model is proposed as a two-criteria optimization problem. The objective functions are the maximum area of the covered part of the region and the minimum total overlapping of sensor coverage areas. This model is transformed into an elastic (quasi-physical quasi-human) model, which differs from the known one in forming the extrusion potential energy function. To solve the problem, an original approach was implemented, combining local and global optimization stages. At the stage of local optimization, the Broyden-Fletcher-Goldfarb-Shanno method was used, in which the gradients were calculated analytically or from first-order differences depending on the shape of sensor coverage areas. At the stage of local optimization, the multistart method was used. The implementation of the approach has been tested for the polygonal shape of the region and elliptical shapes of the sensor coverage areas. © 2023 IEEE.

关键词： elastic model mathematical model maximum coverage location problem optimization sensor network testing

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The IDC system for Sentiment Classification and Sarcasm Detection in Arabic 6

The IDC System for Sentiment Classification and Sarcasm Dete...

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6th Arabic Natural Language Processing Workshop, WANLP 2021

作者： Israeli, Abraham Nahum, Yotam Fine, Shai Bar, Kfir Data Science Institute Interdisciplinary Center Herzliya Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9781954085091

Sentiment classification and sarcasm detection attract a lot of attention by the NLP research community. However, solving these two problems in Arabic and on the basis of socialnetwork data (i.e., Twitter) is still of lower interest. In this paper we present designated solutions for sentiment classification and sarcasm detection tasks that were introduced as part of a shared task by Abu Farha et al. (2021). We adjust the existing state-of-the-art transformer pretrained models for our needs. In addition, we use a variety of machine-learning techniques such as down-sampling, augmentation, bagging, and usage of meta-features to improve the models performance. We achieve an F1-score of 0.75 over the sentiment classification problem where the F1-score is calculated over the positive and negative classes (the neutral class is not taken into account). We achieve an F1-score of 0.66 over the sarcasm detection problem where the F1-score is calculated over the sarcastic class only. In both cases, the above reported results are evaluated over the ArSarcasm-v2-an extended dataset of the ArSarcasm (Farha and Magdy, 2020) that was introduced as part of the shared task. This reflects an improvement to the state-of-the-art results in both tasks. © WANLP 2021 - 6th Arabic Natural Language Processing Workshop

关键词： Natural language processing systems

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B2A, A Straight-Through Approach from Basics to Advanced Level: Case Study for Python Course 11

B2A, A Straight-Through Approach from Basics to Advanced Lev...

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11th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Shazmeen, Abbasi Dakić, Pavle Zoltan, Alwahab Dhulfiqar Department of Programming Languages and Compilers Faculty of Informatics Eötvös Loránd University 1/C Pázmány Péter st. BudapestH-1117 Hungary Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 Slovakia

In this study, we present a straightforward teaching strategy for teaching the Python programming language. Our method emphasizes a clear progression from fundamental to sophisticated concepts. Beginning with a thorough explanation of core Python ideas, we show a beginner-level script that dissects each line of code and displays the results. One distinguishing feature of our system is that students use ChatGPT to optimize code, which encourages them to improve the core program with fewer lines. After getting the updated solutions, students carefully compare the original and optimized programs to discover improvements and potential flaws. This study underlines the effectiveness of the Straight-Through Approach, which focuses on improving Python understanding while also developing critical thinking and code optimization skills. Our findings show that this strategy is a simple and effective way for teachers to assist students in growing from basic to advanced Python abilities, resulting in a dynamic and successful learning environment. The proposed technique will teach students how to use AI-driven modules correctly, such as ChatGPT. © 2024 Copyright for this paper by its authors.

关键词： Python

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Cybersecurity Enhancement in IoT-Enabled Public Health information systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Mishra, Mahesh Kumar Trivedi, Lalit Mohan Farooq, Mansoor Nimma, Divya Alazzam, Malik Bader Kiran Bala, B. Delta System and Software Inc 2320 Grover Cleveland Dr McKinneyTX United States Department of ASH Moradabad Institute of Technology UP Moradabad India Department of Cybersecurity American University of Bahrain Bahrain University of Southern Mississippi Data Analyst in UMMC United States Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (纸本)9798331536336

The rising use of IoT devices in Public Health information systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats. © 2025 IEEE.

关键词： Cyber attacks

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The Impact of 5G Technology on Agile Project Management: A Cross-Industry Analysis 5

The Impact of 5G Technology on Agile Project Management: A C...

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5th International Conference on Intelligent Communication technologies and Virtual Mobile Networks, ICICV 2024

作者： Mishra, Mahesh Kumar Selvaraj, Krishnamoorthy Santosh, Kathari Aarif, Mohd Suma Christal Mary, S. Kiran Bala, B. Delta System and Software Inc 2320 Grover Cleveland Dr McKinneyTX75072 United States Encloud Service Llc Dept of Erp KellerTX United States Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Iosrp UP Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350385649

Project management and organisation are completely transformed by the introduction of 5G technology, which has brought in a new era of connection. This research investigates how 5G networks will affect Enterprise Resource Planning (ERP), Agile Project Management (APM) and the larger context of the Technology-Organization-Environment (TOE) paradigm through a cross-industry examination. Agile approaches may benefit from improved adaptive design, cooperation and connectivity as a result of 5G technology being included into project management procedures. A team of developers can work together more easily and make decisions more quickly with the real-time data transfer made possible by 5G networks' higher capacity and speed. This study looks at how Agile methods for managing projects are more effective and flexible when using 5G's low latency and high data throughput. This research explores how Enterprise Resource Planning (ERP) systems may be affected by 5G. With 5G networks' enhanced capabilities, current information synchronisation is made possible in new ways, enabling ERP systems to function with increased precision and speed. This study investigates how supply chain administration, resource allocation and general organisational agility are improved by 5G-enabled ERP. This study evaluates how various sectors' organisational structures, procedures, and objectives are affected by the deployment of 5G infrastructure. This research outlines critical elements that influence the successful implementation of 5G into project management and ERP systems, including technology preparedness, organisational culture, and external factors. This research provides important insights into the potential for transformation of 5G technology through a thorough cross-industry assessment. It also gives organisations an established basis on which to adapt their Agile Project Management and Enterprise Resource Planning practises in the dynamic context of today's digital world. © 2024 IEEE.

关键词： Decision making

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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Science China(information Sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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DOA Estimation via Adaptive Weighted Truncated Nuclear Norm

DOA Estimation via Adaptive Weighted Truncated Nuclear Norm

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Chinese Control and Decision Conference, CCDC

作者： Zhen Tian Qile Zhu Shun Fang Qi Wu Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China

Aiming at the large error of traditional direction-of-arrival (DOA) estimation algorithm caused by the low signal-to-ratio and limited sampling. This paper proposes a DOA estimation method based on adaptive weighted truncation nuclear norm. First, a new model which combines γ norm and adaptive weighted nuclear norm is proposed to accurately reconstruct the received signal covariance matrix into a low-rank covariance matrix and a sparse covariance matrix. Next, the generalized alternating direction method of multipliers (GADMM) with an adaptive stopping criterion is used to solve this convex optimization. Finally, based on the reconstructed low-rank noise-free covariance matrix, a multiple signal classification (MUSIC) algorithm is used to achieve DOA estimation. The simulation results are provided to verify the effectiveness and superiority of the proposed method.

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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New Textural Features for Handwritten Signature Image Verification 7

New Textural Features for Handwritten Signature Image Verifi...

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7th International Conference on engineering, Applied Sciences and Technology, ICEAST 2021

作者： Soisang, Suriya Poomrittigul, Suvit Pathumwan Institute of Technology Faculty of Engineertng L Electrical Engineering Department Bangkok Thailand Pathumwan Institute of Technology Faculty of Science and Technology 2 Software Engineering and Information System Department Bangkok Thailand

ISBN: (纸本)9781665441223

In this paper, a new textural feature for solving offline handwritten signature verification is proposed. A new textural features method is developed by combining a Local Binary Patterns (LBP) method and a Gradient Quantization Angle (GQA) method. This proposed method is called Local Binary Patterns with Gradient Quantization Angle (LBPGQA), as developed by heuristic method to improve the precision of verification the offline signature image. The hypothesis for this study is to classify the distinctive handwritten signature individually with the actual signature angle and refraction for enhancing the signature fraud detection. The verification step is achieved by Artificial Neural Network (ANN) classifier and trained on genuine signatures. Furthermore, the test stage is performed on genuine signatures and skilled forgeries. The experiments are conducted on CEDAR datasets. The experimental results show that in the LBPGQA method outperforms classical features such as Histogram of oriented gradients and local binary patterns. Conclusively, this proposed method can verify the individual and distinctive handwritten signature and help to protect the signature fraud by skilled forgeries. © 2021 IEEE.

关键词： Local binary pattern

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