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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

SN Computer Science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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On Agile Leadership and Project Sustainability

On Agile Leadership and Project Sustainability

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Projektmanagement und Vorgehensmodelle 2023, PVM 2023 - Project Management and Process Models 2023, PVM 2023

作者： Guckenbiehl, Pascal Krieg, Alexander Brandt, Sarah Theobald, Sven TOPdesk Deutschland GmbH Carl-Euler-Straße 8 Kaiserslautern67663 Germany nextOrange GmbH Geschäftsführung Stadtdeich 2-4 Hamburg20097 Germany Fraunhofer-Institute for Experimental Software Engineering Smart City Design Fraunhofer-Platz 1 Kaiserslautern67663 Germany

ISBN: (纸本)9783885797340

Sustainability is the predominant topic right now and will remain so in the coming decades, which adds another layer to the already existing complexity accompanying digitalization. At the same time, many organizations embrace agile leadership on project or organizational level, to better deal with complexity and constant changes. The objective of this paper is to investigate potential impacts of agile leadership on project sustainability. This is done by mapping categories of agile leadership to the three sustainability dimensions "people, profit, and planet" (triple bottom line). While certain sustainability aspects can be addressed primarily by top level decision making and the mindset of leaders, other aspects are directly influenced by an agile environment and way of working. Overall, the results highlight natural synergies between agile leadership and sustainability and provide an impulse for further research. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Sustainable development

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Optimal Strategies for the Game with Two Faster 3D Pursuers and One Slower 2D Evader 41

Optimal Strategies for the Game with Two Faster 3D Pursuers ...

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第41届中国控制会议

作者： Runqi Zhang Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LabCollege of EngineeringPeking University National Engineering Research Center of Software Engineering Peking University Center for Multi-Agent Research Institute for Artificial IntelligencePeking University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A pursuit-evasion differential game with two faster pursuers in 3 D space and one slower evader in a 2 D plane is *** pursuers,which have different speeds,attempt to cooperate to capture the evader while the evader struggles to avoid or delay the *** differential games and the concept of isochron,we present a complete solution to this game and develop the optimal strategies for both the pursuers and the *** order to guarantee the optimality of the proposed strategies,the value function of the game is given and is further proved to be the solution of the corresponding Hamilton-Jacobi-Isaacs ***,simulations are carried out with comparisons between the proposed strategies and other typical strategies,the results of which show the optimality of the proposed strategies.

关键词： Pursuit-evasion differential games multi-agent system optimal strategy

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Potentials and Barriers of the Metaverse for Circular Economy

Potentials and Barriers of the Metaverse for Circular Econom...

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Simulation Winter Conference

作者： Julia Kunert Hendrik van der Valk Hannah Scheerer Christoph Hoppe Chair for Industrial Inform. Management TU Dortmund University Dortmund NRW Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund NRW Germany Dept. of Industrial Manufacturing Fraunhofer ISST Dortmund NRW Germany

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Sustainability is a challenge for society that circular economy tries to tackle. The metaverse, as an emerging technology that incorporates digital twins and simulation in an immersive virtual environment, has not been thoroughly investigated in connection to circular economy. Thus, the purpose of this study is to summarize the potentials and barriers of the use of the metaverse for circular economy. By conducting a structured literature review, this paper categorizes the findings into dimensions that are important for both the metaverse and circular economy. A variety of potentials and barriers that cover different perspectives important for businesses aiming to comply with circular economy principles is discovered. The findings include potentials and barriers in several areas, like the access to the metaverse, connected costs, data, knowledge transfer, collaboration, innovation, product design, production planning, training of employees, and transportation. The results can be used to promote the implementation of circular economy principles.

关键词： Training Technological innovation Costs Metaverse Transportation Collaboration Product design Sustainable development Business Systematic literature review

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Faridoon, Fatima Ali, Raja Hashim Ul Abideen, Zain Shahzadi, Nazia Ijaz, Ali Zeeshan Arshad, Usama Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis. © 2023 IEEE.

关键词： Feature Selection

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

Exploiting Partial Observability and Optimized Simple State ...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Mahmood, Danyaal Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Applications & Nanostructure BCMaterials Basque Center for Materials Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent's performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent's decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game. © 2023 IEEE.

关键词： Autonomous agents

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 IEEE.

关键词： Feature Selection

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TOWARDS DIALOGUE BASED, COMPUTER AIDED software REQUIREMENTS ELICITATION

arXiv

引用

arXiv 2023年

作者： Seibert, Vasiliy Institute for Software and Systems Engineering TU-Clausthal Clausthal-Zellerfeld38678 Germany

Several approaches have been presented, which aim to extract models from natural language specifications. These approaches have inherent weaknesses for they assume an initial problem understanding that is perfect, and they leave no room for feedback. Motivated by real-world collaboration settings between requirements engineers and customers, this paper proposes an interaction blueprint that aims for dialogue based, computer aided software requirements analysis. Compared to mere model extraction approaches, this interaction blueprint encourages individuality, creativity and genuine compromise. A simplistic Experiment was conducted to showcase the general idea. This paper discusses the experiment as well as the proposed interaction blueprint and argues, that advancements in natural language processing and generative AI might lead to significant progress in a foreseeable future. However, for that, there is a need to move away from a magical black box expectation and instead moving towards a dialogue based approach that recognizes the individuality that is an undeniable part of requirements engineering. © 2023, CC BY.

关键词： Requirements engineering

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction

A Comparison of Deep Learning and Machine Learning Methods f...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Srinivasa Gupta B. Valarmathi Ishaan Hiwanj Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Predicting water quality is essential to preserving human health and environmental sustain ability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72 %, 83 %, 69%, 63 %, 66%, 71 %, 63 %, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models - Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest - evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights.

关键词： Support vector machines Adaptation models Accuracy Scalability Water quality Boosting Reliability Random forests Long short term memory Regression tree analysis

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Platoon Trajectory Reconstruction with Conflict Resolution Using Semidefinite Relaxation

Platoon Trajectory Reconstruction with Conflict Resolution U...

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International Conference on Intelligent Transportation

作者： Junyi Ji Yanbing Wang William Barbour Daniel B. Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University

The quality of trajectory data plays a crucial role in the investigation of microscopic traffic flow. However, the challenge of effectively and automatically eliminating false positive collisions from the measured data still remains an unsolved problem. Firstly, this paper formulates the problem of reconstructing multi-vehicle trajectories as a nonconvex quadratic constraint quadratic programming (QCQP) problem. Subsequently, it derives the equivalent semidefinite programming (SDP) problem and develops a semidefinite relaxation technique to e·fficiently obtain the global minimum of the relaxed problem. We then apply a randomization method to obtain a feasible near-optimal solution to the original problem. The proposed approach is validated through experiments conducted on the NGSIM I–80 camera 6 lane 5 data, and the results of these experiments demonstrate that the unreasonable vehicle dynamics are removed and the platoon inconsistency decreased from 0.622% to 0.268% in the experimental data.

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