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2025 Joint National Conference on Cybersecurity, ITASEC and SERICS 2025

作者： Alaimo, Rosolino Corallo, Roberto Schilleci, Silvia Dino, Alessandra Mangione, Stefano Tinnirello, Ilenia Garlisi, Domenico University of Palermo Engineering Department Palermo Italy University of Palermo Mathematic and Computer Science Department Palermo Italy CNIT Parma Italy

This paper presents a comprehensive study on selective jamming, focusing on its impact on the synchronization phases of the 5G New Radio (NR). We introduce STORM, a novel framework tool designed to assess jamming attacks on the cell search phases of 5G User Equipment (UE). The research incorporates both simulation and experimental evaluations, providing a detailed account of the methodology employed. A unique aspect of the proposed approach is the implementation of a jamming technique that remains undetectable to external entities due to its synchronization with the gNB’s Synchronization Signal Block (SSB). Furthermore, the implemented jamming operates at a duty cycle of 3.55% of continuous jamming, leading to significant optimization in terms of energy consumption and computational resources. STORM underscores that the success rate of jamming attacks and the necessary Signal to Interference Ratio (SIR) for effective disruption are significantly influenced by the specific configurations of the jamming signal. The paper discovers that the Primary Synchronization Signal (PSS) exhibits a higher degree of resilience compared to the Secondary Synchronization Signal (SSS), requiring a greater jammer transmission power to interfere with the cell search procedure. © 2025 Copyright for this paper by its authors.

关键词： 5G 5G security Jamming Attacks PSS Selective Jamming SSS User Equipment

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Prediction of Drug-Target Interactions Through the Application of Supervised Machine Learning Algorithms 1

Prediction of Drug-Target Interactions Through the Applicati...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Mahammad Rafi, D. Chandrashekar, Rakesh Veena, C.H. Dutt, Amit Waghmare, Shwetambari Pandurang Yamini Supriya, B. Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Bharati Vidyapeeth College of Engineering Department of Mathematic Navi Mumbai India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India

ISBN: (纸本)9798350359756

Numerous machine learning strategies have been utilized and evaluated to improve and optimize predictions of drug-target interactions. The Support Vector Machines (SVM) demonstrated exceptional performance, with an accuracy of 92.3% and an AUC-ROC of 0.97. Random Forests, applying the capability of ensemble researching, established fantastic overall performance with an accuracy of 94.1% and an AUC-ROC of 0.98. The Neural Networks executed a 93.5% accuracy and an AUC-ROC of 0.98 proving its capacity to capture complicated data patterns. Meanwhile, Gradient Boosting Machines (GBM) attained an accuracy of 93.8% and an AUC-ROC rating of 0.97. In addition, three distinct approaches have been tested to benefit comparative insights. Linear Regression (LR) gave an accuracy of 88.7% and an AUC-ROC of 0.94, conveying an understanding on the limitations of linear patterns on this topic. The k-Nearest Neighbors (okay-NN) set of rules done an accuracy of 90.2% and an AUC-ROC of Emphasizing the virtues and ability downsides of instance-primarily based gaining knowledge of, the textual content addresses the significance of 0.95. Decision Trees (DT) finished a 91.5% accuracy price with an AUC-ROC price of 0.96, proving their usefulness in making informed decisions. To recap, at the same time as each set of regulations displayed mind-blowing effectiveness, ensemble solutions, notably Random Forests, only barely passed the overall performance of diverse techniques. The outcomes underscore the modern potential of machine learning algorithms in drug-goal interaction predictions, giving a hopeful trajectory for the destiny of drug discovery. © 2023 IEEE.

关键词： Forecasting

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New insight in cervical cancer diagnosis using convolution neural network architecture

arXiv

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arXiv 2024年

作者： Khozaimi, Ach Mahmudy, Wayan Firdaus Department of Computer Science Faculty of Engineering Universitas Trunojoyo Madura Indonesia Department of Mathematics Faculty of Mathematic and Natural Sciences Brawijaya University Malang Indonesia Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis. © 2024, CC BY-SA.

关键词： Convolutional neural networks

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Causal loop and Stock-Flow Modeling of Signal Transduction Pathways

arXiv

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arXiv 2024年

作者： Sulaimany, Sadegh Bidkhori, Gholamreza Khoshnaw, Sarbaz H.A. Department of Computer Engineering University of Kurdistan Sanandaj Iran Science for Life Laboratory Sweden Department of Mathematic University of Raparin Kurdistan Region Ranya Iraq

System dynamics is a popular approach in many fields of science and technology, but it has not been investigated for cell signaling pathways yet. It is a well formulated methodology used to analyze the components of a system considering the cause-effect relationships. The two main components of system dynamics modeling, Causal Loop and Stocks-Flow diagrams, make possible to model the dynamics of the system with the ability to analyze it both qualitatively and quantitatively. In this paper, after introducing the system dynamics modeling approach, and giving a simple example from its usage for Michaelis Menton reactions, a three-step process is proposed for signal transduction modeling. Then in a complete example, it is applied to a case study in cell signaling pathways, namely the RKIP Influence on the ERK signaling pathway and the results is compared with an integrative modeling approach based on Petri net and ODEs. Computational simulations show the success of the system dynamic in easier and effective modeling of the cell signaling pathways, in addition to its diverse options for understanding and testing the pathway quantitatively and quantitatively at the same time in relation to other methods. More intestinally, the suggested approach here helps one to identify all biochemical reaction paths and loops for complex cell signaling pathways. It will be a good step forward to define dominant systems for such complex cell signaling pathways. © 2024, CC BY.

关键词： System theory

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A BTN-Based Method for Multi-Entity Bitcoin Transaction Analysis and Influence Assessment

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Distributed Ledger Technologies: Research and Practice 2024年第4期3卷 1-23页

作者： Yan Wu Liuyang Zhao Jia Zhang Leilei Shi Lu Liu John Panneerselvam Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhengjiang Jiangsu China and School of Computer Science and Communication Engineering Jiangsu University Zhengjiang Jiangsu China School of Computer Science and Communication Engineering Jiangsu University Zhengjiang Jiangsu China Department of Computer Science University of Exeter Exeter UK School of Computing and Mathematic Sciences University of Leicester Leicester UK

Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the *** coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.

关键词： Blockchain computer forensics Bitcoin

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Develop learning methods imr (integrated material realistic) alternative mathematics learning during a pandemic 7

Develop learning methods imr (integrated material realistic)...

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7th International Conference on mathematics, science, and Education 2020, ICMSE 2020

作者： Rusmana, I.M. Zainuri Wardono Nulhakim, A.L. Doctoral Program Universitas Negeri Semarang Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Indraprasta Pgri Jakarta Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Negeri Semarang Indonesia Department of Informatics Engineering Faculty of Engineering and Computer Science Universitas Indraprasta Pgri Jakarta Indonesia

The learning methods used by teachers in schools in Serang City are very ineffective, especially during the pandemic. This is because not integrated content of material in one subject with other subjects. Never mind between subjects, sometimes the material is not well integrated. The purpose of this study is to provide an alternative learning method during a pandemic, namely a method that integrates material with the realities of life which is abbreviated as IMR (Integrated Realistic Material). The method in this research is Research and Development (RnD) with the ADDIE model. In making this learning method according to the stages from the ADDIE study. The results of this study are in the form of learning methods that are integrated with the realities of life during the pandemic. © Published under licence by IOP Publishing Ltd.

关键词： Learning systems

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Bidirectional Choice for Many-to-many Online Task Assignment in Mobile Crowdsourcing

Bidirectional Choice for Many-to-many Online Task Assignment...

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IEEE International Conference on Web Services (ICWS)

作者： Xinhao Li Yingjie Wang Feng Jiang Yang Gao Chunxiao Mu Zhipeng Cai Yingxin Li Shilong Jin School of Computer and Control Engineering Yantai University Yantai China Chongqing University Chongqing China School of Mathematic and Information Sciences Yantai University Yantai China Department of Computer Science Georgia State University Atlanta GA USA

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

The evolution of 5G and 6G technologies has boosted mobile network speed, reduced delays, and widened coverage, empowering Mobile Crowd Sensing (MCS) to overcome surface and terrain obstacles. However, this advancement brings new hurdles for online task assignment. While most MCS methods suit surface applications, they struggle with allocation in complex environments. This paper focuses on MCS in varied settings like surface, air, and high altitude. Currently, planning-based task assignment works better for one-to-one or one-to-many scenarios, with limited options for many-to-many situations. Improving platform utility, attracting top-quality crowd workers, and enhancing task completion efficiency are vital. To tackle these challenges, the paper introduces a spatial division algorithm using 3D Voronoi diagrams for complex environments. This algorithm utilizes task coordinates to delineate assignment spaces. Additionally, it introduces a two-stage many-to-many online task assignment algorithm (MOTA) that forecasts crowd workers’ arrival probabilities and combines auction-based incentives with differential evolution algorithms. MOTA ensures efficient matching of workers and tasks within spatio-temporal constraints, balancing both parties’ interests. Finally, comparative experiments on real datasets assess the proposed MOTA algorithm’s usability and effectiveness based on overall gain, running time, task count, and assignment rate.

关键词： Crowdsourcing Three-dimensional displays Web services Prediction algorithms Partitioning algorithms Sensors Delays Resource management Usability Mobile computing

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Development of Hybrid Optimum Model to Determine the Brake Uncertainties 2

Development of Hybrid Optimum Model to Determine the Brake U...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Punitha, S. Selvaraj, M. Kumar, N. Anil Nagarajan, G. Kiran, Chappeli Sai Karyemsett, Nagarjuna Vinayaka Mission's Research Foundation [Deemed to Be University Vinayaka Mission's Kirupananda Variyar Engineering College Department of Mathematic Salem India Bule Hora University Department of Mechanical Engineering Bule Hora Ethiopia Sree Vidyanikethan Engineering College Department of Electronics and Instrumentation Engineering Tirupati India Panimalar Engineering College Department of Mathematics Chennai India Cvr College of Engineering Department of Mechanical Engineering Ibrahimpatnam India Koneru Lakhamaiah Education Foundation Department of Computer Science and Engineering Guntur India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

During rapid braking, the braking system must support the stresses exerted by the hydraulic rubber. The friction between the disc brakes and the disc effectively creates pressure. As a result of its rotation, the disks also suffer from rotational body pressures. Deformations resulting from these pressures could lead to material failure. As a result, it is essential to ensure that the groove pressures are kept to a minimum. The whole technique has been programmed into MATLAB to improve modules and operating systems. This involves using optimization methods such as the interior-point approach and also the Multi Parametric Genetic Algorithm (MPGA), and drawing Percentile charts for subsystems & operating system improvement & which require detailed verification. © 2022 IEEE.

关键词： MATLAB

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Prediction of Drug-Target Interactions Through the Application of Supervised Machine Learning Algorithms

Prediction of Drug-Target Interactions Through the Applicati...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Mahammad Rafi D Rakesh Chandrashekar CH Veena Amit Dutt Shwetambari Pandurang Waghmare B Yamini Supriya Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Department of Mathematic Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

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FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients 39

FNIN: A Fourier Neural Operator-based Numerical Integration ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Leng, Jiaqi Ju, Yakun Duan, Yuanxu Zhang, Jiangnan Lv, Qingxuan Wu, Zuxuan Fan, Hao Haide College Ocean University of China China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China School of Computing and Mathematic Sciences University of Leicester United Kingdom Department of Computer Engineering Qingdao City University China College of Computer Science and Technology Ocean University of China China

ISBN: (纸本)157735897X

Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-scale linear solvers. Although recent advances in deep learning, such as photometric stereo, have enhanced normal estimation accuracy, they do not fully address the intricacies of gradient-based surface reconstruction. To overcome these limitations, we propose a Fourier neural operator-based Numerical Integration Network (FNIN) within a two-stage optimization framework. In the first stage, our approach employs an iterative architecture for numerical integration, harnessing an advanced Fourier neural operator to approximate the solution operator in Fourier space. Additionally, a self-learning attention mechanism is incorporated to effectively detect and handle discontinuities. In the second stage, we refine the surface reconstruction by formulating a weighted least squares problem, addressing the identified discontinuities rationally. Extensive experiments demonstrate that our method achieves significant improvements in both accuracy and efficiency compared to current state-of-the-art solvers. This is particularly evident in handling high-resolution images with complex data, achieving errors of fewer than 0.1 mm on tested objects. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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