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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumbhkar, Makhan Shukla, Pranjal Singh, Yashwardhan Sangia, Rohib Adrianto Dhabliya, Dharmesh Christian Eminent College Department of Computer Science Indore India Accenture Solutions Pvt. Ltd. Data Engineering Associate Indore India Iain Ambon Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350398465

In the age of big data, analysing complex and enormous amounts of data costs time and money and might result in mistakes and misunderstandings. Therefore, bad inference and decision-making, and occasionally irreversible and catastrophic occurrences, could result from imprecise and mistaken reasoning. On the other hand, effective administration and use of priceless data can considerably expand knowledge and lower costs through preventive measures. Time-to-event and survival data analysis are the cornerstones of risk assessment in this area and play a crucial part in estimating the likelihood of numerous occurrences, including the failure of a device or component. As a result, before beginning any analytic process, it is desirable to apply appropriate approaches to effectively reduce large-scale, huge, and complex data, especially in terms of variables. To minimize the abovementioned decision-making challenges and to make survival data and failure analysis easier, we offer an applied data reduction strategy in this research that enables us to obtain appropriate multiclass classification in large dimensionality data and huge datasets. In order to estimate the risk in addition make decisions in the context of a complex analysis of big volume in survival analysis, this research presents an applied analysis data and attribute selection methodology. © 2023 IEEE.

关键词： MapReduce

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Autonomous Vehicles for All?

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ACM Journal on Autonomous Transportation Systems 2024年第1期1卷 1-8页

作者： Sakib Mahmud Khan M Sabbir Salek Vareva Harris Gurcan Comert Eric A. Morris Mashrur Chowdhury Intelligent Transportation Systems Lead MITRE Corporation McLean VA Ph.D. Candidate Glenn Department of Civil Engineering Clemson University Clemson SC Vice President for Corporate Engagement O Analytics Incorporated Fairmont WV Associate Professor Computer Science Physics and Engineering Department Benedict College Columbia SC USA Professor Nieri Department of Construction Development and Planning Clemson University Clemson SC USA Professor Glenn Department of Civil Engineering Clemson University Clemson SC USA

The traditional build-and-expand approach is not a viable solution to keep roadway traffic rolling safely, so technological solutions, such as autonomous vehicles (AVs), are favored. AVs have considerable potential to increase the carrying capacity of roads, ameliorate the chore of driving, improve safety, provide mobility for those who cannot drive, and help the environment. However, they also raise concerns over whether they are socially responsible, taking into account issues such as fairness, equity, and transparency. Regulatory bodies have focused on AV safety, cybersecurity, privacy, and legal liability issues, but have failed to adequately address social responsibility. Thus, existing AV developers do not have to embed social responsibility factors in their proprietary technology. Adverse bias may therefore occur in the development and deployment of AV technology. For instance, an artificial intelligence-based pedestrian detection application used in an AV may, in limited lighting conditions, be biased to detect pedestrians who belong to a particular racial demographic more efficiently compared to pedestrians from other racial demographics. Also, AV technologies tend to be costly, with a unique hardware and software setup which may be beyond the reach of lower-income people. In addition, data generated by AVs about their users may be misused by third parties such as corporations, criminals, or even foreign governments. AVs promise to dramatically impact labor markets, as many jobs that involve driving will be made redundant. We argue that the academic institutions, industry firms, and government agencies overseeing AV development and deployment must act proactively to ensure that AVs serve all and do not increase the digital divide in our society.

关键词： Autonomous vehicles Social responsibility Fairness Equity Transparency

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Innovations in Teaching Vibrations and Control - Reinforcing Learning by Combining Portable Lab Equipment and Virtual Labs

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IFAC-PapersOnLine 2024年第28期58卷 66-71页

作者： Britt Walker Fahim Dorsey Vanessa Young Connor Talley Coskun Tekes Tris Utschig Ayse Tekes Undergraduate students (Walker Dorsey and Young) and Ph.D. student (Talley) Department of Mechanical Engineering Kennesaw State University Marietta GA USA Assistant Professor of Electrical and Computer Engineering (C. Tekes) Professor of Mechanical Engineering (Utschig) Kennesaw State University Marietta GA USA Associate Professor of Mechanical Engineering Kennesaw State University Marietta GA USA

Most educational laboratory equipment utilized in vibrations and control labs is heavy and bulky, limiting its use within lab spaces. Further, mechanical engineering students typically gain hands-on experience only in labs that are offered separately from fundamental courses, unlike other engineering programs. Thus, the extent of student learning from labs, given their limited time, remains a concern considering students’ learning opportunities. This study presents the design and development of two portable and low-cost lab equipment devices: a compliant mechanism with unbalanced rotating masses and a DC motor with beam control under disturbances. While the former is utilized to demonstrate fundamentals of vibrations, derivation of the mathematical model, simulation, and comparison of theory and experimental data, the latter can be used for demonstration of disturbance control. Virtual labs are developed for both mechanisms in MATLAB Simscape.

关键词： Lab equipment design virtual lab simulations fundamentals of vibrations

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Enhancing IoT Network Security through AIDriven Intrusion Detection with Hybrid AutoencoderGAN Fusion 23

Enhancing IoT Network Security through AIDriven Intrusion De...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Rupali A. Mahajan Sukhvinder Singh Dari Ritika Dhabliya Gabriela Michael Anil Kumar Nidhi Ranjan Computer Associate Professor COMP VIIT Pune Maharashtra India Computer Associate Professor Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pun India Computer Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra India India Computer Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India India Department of Artificial Intelligence & Data Science Professor Poornima Institute Of Engineering &Technology Jaipur Rajasthan India India Computer Associate Professor Vasantdada Patil Pratishthan?s College of engineering and Visual Arts Mumbai Maharashtra India India

ISBN: (纸本)9798400709418

The rapid growth of Internet of Things (IoT)IoT devices has changed how we connect and interact with our environment. This advancement has also complicated network security. As IoT networks grow, data integrity and confidentiality become more important. This study integrates a hybrid autoencoder and Generative Adversarial Network (GAN) model to use AI to solve security problems. The UNSW-NB15 dataset, a benchmark for network IDSs, is used to rigorously evaluate the proposed model. An autoencoder and GAN combination offers promising IDS for IoT networks. The autoencoder component excels at capturing concise and illustrative data representations, enabling it to spot network anomalies. The GAN component improves the model's ability to generate synthetic data samples that closely resemble network traffic patterns, making it easier to identify complex and novel attacks. The UNSW-NB15 dataset is known for its extensive network traffic data, making it ideal for testing the proposed artificial intelligence-based IDS. The proposed hybrid model achieves 99.13% accuracy. The result outperforms traditional DL methods. The proposed model's increased precision shows its ability to detect and address network anomaly risks. The proposed system is evaluated using precision, recall, F1-Score, AUC-ROC, MCC, and Cohen's Kappa. The thorough evaluation shows that the Proposed hybrid autoencoder-GAN model provides a complete and efficient IoT network security solution in all dimensions. In conclusion, artificial intelligence-based intrusion detection and a hybrid model that combines autoencoder and generative adversarial network techniques improve IoT network security. The model's UNSW-NB15 dataset accuracy shows its ability to protect IoT ecosystems from many threats. This study helps improve the reliability and robustness of networks, boosting confidence in the growing world of interconnected devices.

关键词： Deep Learning

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Identification of Congenital Heart Defects in Ultrasound Images using U-Net Segmentation 1

Identification of Congenital Heart Defects in Ultrasound Ima...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Pandey, Saroj Kumar Diwan, Ritesh Rose, Anthony Singh, Kamred Udham Singh, Teekam Somasekar, J. Gla University Department of Computer Engineering & Apllications UP Mathura India Department of Ele. & Tele. Raipur Institute of Technology Raipur India Pune India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Professor of Cse Department Bangalore India

ISBN: (纸本)9798350330915

In this study, the use of a U-Net segmentation model is measured for the accurate detection of congenital heart abnormalities in newborn ultrasound pictures. The technique includes U-Net model training, sophisticated picture preprocessing, as well as exhaustive dataset gathering. As a result of the model's considerable accuracy, sensitivity, and specificity, it has the potential to become a crucial diagnostic tool. A thorough analysis found both advantages, such as reliable performance, and disadvantages, which include the requirement for a larger and more varied dataset. Enlarging the dataset, addressing equipment variability, real-time clinical application, multidisciplinary cooperation, and ethical issues, including model improvement are among the key recommendations. In order to promote newborn healthcare, future work will require ongoing model development, real-time clinical integration, and ethical adherence. The work advances the development of accurate diagnostics in newborn care. © 2023 IEEE.

关键词： Congenital heart defects diagnostic accuracy medical image analysis neonatal ultrasound pediatric cardiology U-Net segmentation

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Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture

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computers and Security 2022年 116卷

作者： M .P . , Ramkumar Reddy, P.V. Bhaskar Thirukrishna, J.T. Vidyadhari, Ch. Assistant Professor Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India Professor School of Computer Science and Engineering REVA University Karnataka Bengalore India Associate Professor Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore560082 India Assistant Professor Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

With the rapid expansion of Internet services and increasing intrusion issues, conventional intrusion detection techniques cannot work well with several difficult intrusions. Though several intrusion detection methods have been introduced in the past years, developing an Intrusion Detection Systems (IDS) with prevailing intrusion detection strategy is still very much desirable. Hence, a robust and effective intrusion detection approach, named RV coefficient+Exponential Sea Lion Optimization-enabled Deep Residual Network (ExpSLO-enabled DRN) using spark is devised for the intrusion detection. Here, the unique features are selected using proposed RV coefficient-based hybrid feature fusion, which is designed by the incorporation of wrapper, class-wise information gain (CIG), and Canberra distance in slave node. With the distinctive features selected, the process of data augmentation is done using oversampling for making the data more appropriate to perform the further process in the slave node. Moreover, DRN classifier is utilized for detecting the intrusions in the master node where the DRN training is done using devised ExpSLO algorithm, which is the hybridization of Exponentially Weighted Moving average (EWMA) and Sea Lion Optimization (SLnO). Furthermore, the devised method obtained better performance by considering the evaluation metrics, such as precision, recall, and F-measure with the higher values of 0.8800, 0.8845, and 0.8822 based on without attacks using dataset-2. © 2022

关键词： Intrusion detection

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Numerical Simulation of Flow Field around NACA 0012 Airfoil at Low Reynold’s Number Assuming Mars Environment using OpenFoam

Numerical Simulation of Flow Field around NACA 0012 Airfoil ...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Okamoto, Atsushi Yamada, Takafumi Advanced Mechanical and Control System Engineering Course National Institute of Technology Tsuyama College 624-1 Okayama Numa Tsuyama708-8509 Japan Associate Professor Department of Integrated Science and Technology National Institute of Technology Tsuyama College 624-1 Okayama Numa Tsuyama708-8509 Japan

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In this study, incompressible flow fields around a NACA0012 airfoil were numerically simulated at low Reynolds number for a Mars environment. For mesh generation, the standard Open FOAM utilities block Mesh and snappy HexMesh were used. Using those meshes and pisoFoam, which is PISO method-based analysis solver, NASA wind tunnel test conditions were numerically simulated in the Earth environment and obtained a similar lift gradient as in the experiment up to the stall. Then, the flow field of 12,000 Reynolds number, which simulates the Martian environment, was calculated. The variation of lift coefficient with increasing angle of attack was almost consistent with the experimental value in the Earth environment, and the stall angle was 16°. From the results of pressure distribution on the wing surface, it is suggested that compared to the Earth environment, the separation point movesfrom the leading edge to aft of the wing upper surface in the Martian environment. The flow field at the stall angle was similar to that of the Earth environment, and vortices were formed, however, their velocity vectors were very small, and dead water regions were observed near the trailing edge of the wing upper surface. More detailed aerodynamic characteristics should be clarified by evaluating the results using drag and moment coefficients until the SciTech Forum 2025. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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An Efficient Algorithm for Secure Key Management in Cloud Environment

An Efficient Algorithm for Secure Key Management in Cloud En...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： J. Jospin Jeya S. Raja Ratna G. Ganga Devi M. Priya C. Sivasankar Department of Computer Science and Engineering SRM Institute of Science and Technology Professor/CSE Saveetha School of Engineering SIMATS Chennai

Cloud infrastructure is efficient and scalable in all aspects; hence an enhanced method of safety measures is needed for secured data transfer. Security solution for cloud centric PC’s was reliable in providing the on-demand cloud services. The common way to protect file before sending the file is encrypting the file. So safe key generation and allocation to the client for file decryption is a big task. The significant method we proposed is to receive the secret key secretly by the allowed user. We proposed wellorganized algorithm to share out the secret key in cloud computing environment. In this paper the safety method used depends on two things. One is improving the security by using secure socket shell channel and the second one is sharing the secret key to the allocated user using well organized key distribution algorithm.

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Intrusion Detection in Novel WSN-Leach Dos Attack Dataset using Machine Learning based Boosting Algorithms

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Procedia computer science 2023年 230卷 90-99页

作者： Dharini N Jeevaa Katiravan Sruthi Priya D M Sakthi Sneghaa V A Associate Professor Department of Computer Science Engineering(Cyber Security) R.M.K. College of Engineering and Technology Thiruvallur 601206 Professor Department of Information Technology Velammal Engineering College Surapet Chennai 600066 Software Engineer embedUR Chennai 600013 Jr. Customer Service Executive Tata Communications Transformation Services Chennai 600037

Security poses a substantial challenge in the context of wireless sensor networks (WSNs). WSNs, which are frequently employed for real-time monitoring in various Internet of Things (IoT)-based applications, are particularly vulnerable to a range of Denial of Service (DoS) attacks. These attacks represent a significant security concern and are among the most prevalent issues confronting WSNs *** our research, we have introduced a WSN DoS dataset using the Matrix Laboratory (MATLAB) tool. This dataset was developed through the utilization of the Leach Protocol and incorporates various types of DoS attacks, including Wormhole, Blackhole, Grayhole, Flooding, and Time Division Multiple Access (TDMA) attacks. In our study, we assessed the performance of several machine learning techniques using our dataset. We employed various performance evaluation metrics that gauge both the classification accuracy and the complexity of these machine learning models. These metrics included parameters such as the number of accurately classified and misclassified instances, along with the time required for constructing the model. This comprehensive evaluation aimed to demonstrate the effectiveness of our proposed *** results of our analysis revealed that the Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Bagging algorithms exhibited strong performance on our dataset. They achieved impressive accuracy levels of up to 99.16%, 99.16%, and 99.8%, respectively, when using 14 features. Additionally, these algorithms achieved accuracy rates of 99.16%, 98.75%, and 99.8%, respectively, with 5 features, and 95.41%, 95%, and 93.75%, respectively, when considering only 3 features such as node ID and Energy-Consumed, Attack_type. Notably, LightGBM outperformed the others in terms of speed, as it employs a leaf-wise growth strategy, consumes less memory, and requires less time for model construction. Consequently, LightGBM emerged as the most efficien

关键词： Denial of Service (DoS) attacks Intrusion Detection System (IDS) Leach Protocol WSN DoS dataset Ensemble Learning Feature Selection Accurately Classified and Misclassified instance Light Gradient Boosting Machine (LightGBM)

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Collaborative Defense Strategies: AI, ML, and Cybersecurity: A Comprehensive Study 12th

Collaborative Defense Strategies: AI, ML, and Cybersecurity:...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Borah, Samarjeet Dhabliya, Dharmesh Dhage, Prashant Shukla, Jyoti Rai, Sumit Kumar Bhattacharya, Saurabh Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune India Assistant Professor SCSE Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819601462

In the fast-changing field of cybersecurity, the rise of advanced threats requires new and creative defense strategies. This study thoroughly examines collaborative defense strategies, focusing on the utilization of artificial intelligence (AI) and machine learning (ML) techniques. The study aims to evaluate the effectiveness of five prominent machine learning algorithms (random forest, support vector machine (SVM), gradient boosting, isolation forest, and one-class SVM) in the field of cybersecurity. The assessment is carried out utilizing the NSL-KDD dataset, which is a standard in the industry. The study commences by investigating the origins of collaborative defense, analyzing its historical development and fundamental principles. The text explains the importance of AI in collaborative defense, highlighting machine learning algorithms and natural language processing as essential elements. The study explores the uses of artificial intelligence, specifically in identifying, averting, and reacting to threats. A substantial part of the research focuses on evaluating the effectiveness of the five chosen machine learning algorithms. The evaluation includes predictive analysis to forecast cyber threats and proactive defense measures. The study examines how machine learning improves the sharing of threat intelligence by facilitating real-time information exchange between collaborating entities. The challenges and considerations related to incorporating AI and ML into cybersecurity are examined, emphasizing ethical implications, bias, fairness, transparency, and accountability. Case studies and success stories from real-world scenarios showcase examples of successful collaborative defense strategies applied in various industries and sectors. The study evaluates and contrasts the random forest, SVM, gradient boosting, isolation forest, and one-class SVM algorithms based on performance metrics like accuracy, precision, recall, and F1 score, utilizing the NSL-KDD dataset. T

关键词： Adversarial machine learning

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