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Intelligent Framework for Secure Transportation Systems Using software-Defined-Internet of Vehicles

Computers, Materials & Continua 2021年第9期68卷 3947-3966页

作者： Mohana Priya Pitchai Manikandan Ramachandran Fadi Al-Turjman Leonardo Mostarda School of Computing SASTRA Deemed UniversityThanjavur613401India Research Centre for A.I.and IoT Near East UniversityNicosiaMersin10Turkey Department of Software Engineering Camareno UniversityItaly

The Internet of Things plays a predominant role in automating all real-time *** such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic *** vehicles are connected to the internet through wireless communication technologies,the Internet of Vehicles network infrastructure is susceptible to flooding *** the network infrastructure is difficult as network customization is not *** software Defined Network provide a flexible programming environment for network customization,detecting flooding attacks on the Internet of Vehicles is integrated on top of *** basic methodology used is crypto-fuzzy rules,in which cryptographic standard is incorporated in the traditional fuzzy *** this research work,an intelligent framework for secure transportation is proposed with the basic ideas of security attacks on the Internet of Vehicles integrated with software-defined *** intelligent framework is proposed to apply for the smart city *** proposed cognitive framework is integrated with traditional fuzzy,cryptofuzzy and Restricted Boltzmann Machine algorithm to detect malicious traffic flows in software-Defined-Internet of *** is inferred from the result interpretations that an intelligent framework for secure transportation system achieves better attack detection accuracy with less delay and also prevents buffer overflow *** proposed intelligent framework for secure transportation system is not compared with existing methods;instead,it is tested with crypto and machine learning algorithms.

关键词： Internet of things smart cities software-defined network intelligent transportation system fuzzy inference system

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Formal Timing Analysis and Verification of the Interrupt-driven Programs for ISO26262

Formal Timing Analysis and Verification of the Interrupt-dri...

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2025 2nd International Conference on Algorithms, software engineering and Network Security (ASENS)

作者： Qing She He Huang Shenglin Bao Xin Guo National Trusted Embedded Software Engineering Technology Research Center East China Normal University / Shanghai Huayuan Chuangxin Software Co. Ltd China High end Equipment Systems Department Shanghai Huayuan Chuangxin Software Co. Ltd China Basic Software Department East China Institute of Computing Technology China National Trusted Embedded Software Engineering Technology Research Center East China Normal University China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Safety-critical systems refer to software systems whose operation can affect the safety of people and facilities used in the automotive industry, aerospace, and hospital health care systems. The high level of safety of software in the aforementioned fields has often drawn attention. ISO26262 has been issued for the automotive industry, which defines standards and procedures, including software level, software development standards, and software validation standards to ensure the high level of safety of software. In high-safety automotive software, a real-time safeguard requires the analysis of interrupt-driven programming. Such analysis presents a challenging and complex task because of the uncertainty of interrupts. In ISO26262, the formalized method is suggested to verify the process of software development. To achieve the verification goals defined in ISO26262, we use a formal method instead of traditional verification methods to analyze worst-case execution time. We analyze interrupt-driven programs from the perspective of timeliness and present the Interrupt Control Flow Graph (ICFG), which is used to describe the control flow of interrupt-driven programs. The execution of interrupts may largely increase the number of program states, potentially leading to state explosion, resulting in a sizable ICFG. To solve this problem, we propose an algorithm for reducing the size of ICFG. On the basis of the reduced ICFG, the worst-case execution path can be determined and thus be used to verify whether the program execution time exceeds the expected time and identify the execution path that may cause a timeout problem.

关键词： Industries Uncertainty software algorithms software systems Timing Flow graphs Standards Automotive engineering software development management software engineering

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Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anxiety Notification System 9th

Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anx...

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9th International Conference on Computational Science and Technology, ICCST 2022

作者： Abdulmanab, Muhammad Rahiman Nor’Azman, Nur Faidz Hazirah Meon, Hasni Razak, Chempaka Seri Abdul Ab Hamid, Siti Hafizah Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Communication Faculty of Communication Visual Art and Computing Universiti Selangor Bestari Jaya Malaysia School of Pre-University Studies Taylor’s College Lakeside Campus Subang Jaya Malaysia

ISBN: (纸本)9789811984051

As anxiety becomes increasingly prevalent among youths especially university students, early prevention via anxiety disorder profiling is crucial. Nevertheless, most screening tools to date are not automated, labour-intensive and time-consuming. Mental health detection in online social networks (OSNs) using artificial intelligence (AI) can provide means for capturing overlooked behavioural attributes, but few are centred around anxiety detection. Internet of Things (IoT)’s increasing popularity enables bio-signals monitoring to aid in mental health care. However, minimum research has been devoted to IoT-based assistive technologies for anxiety, which currently are only used by individuals already diagnosed and actively monitored. This study proposes a novel approach to anticipatory anxiety detection for Malaysian university students aged between 18 and 25 inclusive as an early notification system using AI and IoT for bilingual Malay and English tweets and vital signs analysis respectively. Our result shows that Logistic Regression with Bag-of-Words (BoW) is the overall best performing model with 81.06% accuracy. The Arduino smartwatch sensor readings from the MAX30102 pulse oximeter sensor are used to determine different body conditions using a rule-based approach. The comparison test result between the prototype and conventional measuring devices shows the prototype’s ability to accurately distinguish vital signs with a relatively low Mean Absolute Percent Error (MAPE) rate of 10.92% and 1.51% for pulse rate and temperature respectively. Together, our proof-of-concept results indicate the potential of AI and IoT in deriving new measures of anxiety, by analysing social data combined with bio-signals monitoring to alert users regarding their anxiety state. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading

The Impact of Transformer Models and Regression Algorithms o...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Abhijith. N. Nampoothiri M. G. Thushara Pradeesh. N Anand Krishna K. R Abhai Santhosh Abhijith Kumar S Suryan Kumar Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Omnex Software Solutions Pvt. Ltd Chennai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models—including BERT, Sentence-BERT, T5, Genism, and XLNet—are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations.

关键词： Support vector machines Measurement Accuracy Education Forestry Transforms Transformers Numerical models Reliability Random forests

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A New Hybrid Transformer Topology for Distribution Networks

A New Hybrid Transformer Topology for Distribution Networks

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Annual Conference of Industrial Electronics Society

作者： Rupert Power Udaya Madawala Craig Baguley Bingkun Song Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Department of Engineering Computing and Mathematical Sciences Auckland University of Technology Auckland New Zealand

The widespread integration of Distributed Generation (DG) and new loads such as Electric Vehicles (EVs) into power distribution networks presents substantial technical challenges for network operators, such as voltage fluctuations and phase unbalance. These can be addressed by many power electronic devices with advanced control strategies, including the Hybrid Transformer (HT). This paper presents a new three-phase HT concept which can perform multiple functions, including voltage regulation and voltage balancing, to address power quality problems in distribution networks. Theory and control strategy are presented, along with simulated results incorporating an unbalance-tolerant control scheme and 3D Space Vector Modulation, to demonstrate the validity of the proposed HT topology. Its advantages include being retrofittable to existing distribution transformers, not requiring bulky low-frequency converter magnetics, having a low semiconductor component count, and allowing reduced DC-link capacitances. This makes it cost-effective, compact and light weight in comparison to the existing topologies that provide similar functionality.

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Reasoning about Strategic Abilities in Stochastic Multi-agent Systems

arXiv

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arXiv 2024年

作者： Zhang, Yedi Song, Fu Chen, Taolue Wu, Xuzhi Department of Computing Science National University of Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom College of Computer Science and Software Engineering Shenzhen University China

Reasoning about strategic abilities is key to AI systems comprising multiple agents, which provide a unified framework for formalizing various problems in game theory, social choice theory, etc. In this work, we propose a probabilistic extension of the alternating-time μ-calculus (AMC), named PAMC, for reasoning about the strategic abilities of agents in stochastic multi-agent systems. We show that PAMC subsumes two existing logics AMC and PμTL (a probabilistic extension of the modal μ-calculus), but is incomparable with the probabilistic alternating-time temporal logic (PATL). We study the problems of model checking and satisfiability checking for PAMC. We first give a model checking algorithm by leveraging algorithms for solving normal-form games and AMC model checking. We establish that the model checking problem of PAMC remains in UP∩co-UP, the same complexity class as the model checking problem for AMC and PμTL. We also provide a new reduction from the satisfiability problem of PAMC to solving parity games, by which we obtain an EXPTIME decision procedure, as well as the small model property which allows to construct a model for each satisfiable PAMC formula. Satisfiability in PAMC has the same complexity as in the modal μ-calculus, unlike PCTL and PATL whose satisfiability checking problems remain open. We have implemented both the model checking and satisfiability checking algorithms as open-source tools. Experimental results are reported, showcasing the practical applications and effectiveness of our approaches. © 2024, CC BY.

关键词： Stochastic systems

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Exploiting the sequential nature of genomic data for improved analysis and identification

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Computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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Developing Trustworthy Reinforcement Learning Applications for Next-Generation Open Radio Access Networks

Developing Trustworthy Reinforcement Learning Applications f...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Ahmad M. Nagib Hatem Abou-zeid Hossam S. Hassanein School of Computing Queen’s University Canada Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Electrical and Software Engineering University of Calgary Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Artificial intelligence is envisioned to transform the design and operation of 6G networks. Reinforcement learning (RL), in particular, has emerged as a fundamental approach toward this goal with strong support from the industry and the Open Radio Access Network (O-RAN) Alliance. While research efforts have demonstrated the potential of RL, the lack of trustworthiness of RL algorithms remains a challenge to its adoption in real-world networks. In this paper, we propose a trustworthy RL framework that addresses the core challenges experienced by RL-based radio resource management applications deployed in O-RANs. We then demonstrate a case study on O-RAN slicing incorporating several modules of the proposed framework. The experimental results show improvements in the average RL convergence rate, initial reward value, percentage of converged scenarios, and reward variance. Hence, the RL-based algorithms exhibit fast convergence and enhanced generalizability, safety, and robustness.

关键词： Industries Open RAN Reinforcement learning Transforms Robustness Safety Resource management

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Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

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Computers, Materials & Continua 2022年第9期72卷 6061-6077页

作者： Muhammad Akram Muhammad Waseem Iqbal Syed Ashraf Ali Muhammad Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science The Institute of Management Sciences(PAK-AIMS)Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

In cipher algorithms,the encryption and decryption are based on the same *** are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes *** paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and *** plaintext character is substituted by an alternative *** mode of substitution is transformed cyclically which depends on the current position of the modified *** keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st *** study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the *** utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

关键词： Encryption decryption cipher symmetric key cryptography single key attack

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models

Explainable AI for Healthcare: An Approach Towards Interpret...

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Information Technology (ACIT)

作者： Kanwal Zahoor Narmeen Zakaria Bawany Usman Ghani Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Institute of Business Administration Karachi Pakistan

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as ‘normal’ or ‘pneumonia’. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness.

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