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arXiv 2022年

作者： Savoia, Dylan Ragno, Alessio Capobianco, Roberto Dept. of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy Sony AI

It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to determine which modifications are more promising or which aspects of a molecule are more influential for the final activity/property. Here we present an automatic process which involves Graph Convolutional Network models and input-attribution methods to generate new molecules. We also explore the problems of over-optimization and applicability, recognizing them as two important aspects in the practical use of such automatic tools. © 2022, CC BY.

关键词： Molecules

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Fog Computing based Privacy Preservation via Distributed Access control System in Blockchain Technology 1

Fog Computing based Privacy Preservation via Distributed Acc...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Thatikonda, Ramya Nandihal, Priya Priyanka, M.S. Mathapati, Mahantesh PhD in Information Technology University of the Cumberlands Williamsburg United States Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dept.of Cse Bengaluru India

ISBN: (纸本)9798350316469

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices. © 2023 IEEE.

关键词： Blockchain Data Privacy Fog computing Homomorphic encryption Internet of Things Malicious Tampering

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Semi-Supervised GCN for learning Molecular Structure-Activity Relationships

arXiv

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arXiv 2022年

作者： Ragno, Alessio Savoia, Dylan Capobianco, Roberto Dept. of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy Sony AI

Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial case studies we apply the method to solubility and molecular acidity while checking its consistency in comparison with known experimental chemical data. As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design. © 2022, CC BY.

关键词： Graph neural networks

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Seva, Banothu Jain, Ratnesh Khare, Neha Choudhury, Hussain Ahmed Chakravarthi, M Ayyappa Tomer, Nitin MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Design M.P Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P Bhopal462044 India Barak Valley Engineering College Department of CSE Assam Karimganj India KKR & KSR Institute of Technology and Sciences Dept. of CSE - Data Science AP Guntur India GGITS Cyber Security & Blockchain Dept of IOT M.P. Jabalpur India

ISBN: (纸本)9798331539948

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. Security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets. © 2024 IEEE.

关键词： Adversarial machine learning

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Direct Data-Driven State-Feedback control of General Nonlinear Systems

Direct Data-Driven State-Feedback Control of General Nonline...

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IEEE Conference on Decision and control

作者： Chris Verhoek Patrick J. W. Koelewijn Sofie Haesaert Roland Tóth Dept. of Electrical Engineering Control Systems group Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc.

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Delamination Detection in CFRP Components from Ultrasound Images Using Convolutional Neural Networks

Delamination Detection in CFRP Components from Ultrasound Im...

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IEEE Instrumentation and Measurement Technology Conference

作者： Tilman Seesselberg Axel Busboom Jonas Welsch Edmond Cretu Robert Rohling Dept. of Engineering and Management Munich University of Applied Sciences Munich Germany Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Carbon fibre-reinforced polymer (CFRP) materials are used for various applications due to their desirable me-chanical properties. Defects, such as delaminations, can occur during production or operation of CFRP components and can compromise their structural integrity. Ultrasonic imaging is a well-established and cost-effective technique for non-destructive testing (NDT) of CFRP components. We evaluate the use of supervised training of artificial neural networks (ANNs) for the automated detection of delaminations from ultrasound B-scans. After hyperparameter optimization, convolutional neural networks (CNNs) performed better than multilayer perceptrons (MLPs). Using a test specimen, we achieved a recall of 100 % and a precision of 94.3 % on unseen test images.

关键词： Training Ultrasonic imaging Imaging Production Multilayer perceptrons Polymers Convolutional neural networks

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Virtual ECU Development for Vehicle Diagnostics Software Testing using UDS Protocol

Virtual ECU Development for Vehicle Diagnostics Software Tes...

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2021 Asian Conference on Innovation in Technology, ASIANCON 2021

作者： Bidkar, Shubham Patil, S.L. Shinde, Pravin College of Engineering Dept. of Instrumentation and Control Pune India Hella India Automotive Energy Management Dept. Pune India

ISBN: (纸本)9781728185835

A modern-day vehicle consists of more than a hundred Electronic control Units (ECU) for controlling various electrical parameters. Hence diagnostics of these ECUs are very tedious and vital to identify the cause of the systems' failure and reduce the risk of failure. Unified Diagnostics Services (UDS) protocol defined in the standard ISO-14229 is used for vehicle diagnostics in the automotive industry. Also, diagnostics software complexity increases due to a rise in the demand for multiple functionalities. This doubles the difficulty of developing and testing software during the product development phase, as many time hardware is not available for validation and testing of the software. Hence testing and validation without hardware in the virtual environment is a must in early development phases to reduce the development period. The proposed tool-chain in this paper focuses on creating the virtual environment for testing and validation of the diagnostics software developed by using AUTOSAR standard in the automotive industry. Diagnostics software is implemented on the virtual ECU generated using UDS protocol, and results are presented. © 2021 IEEE.

关键词： Automotive industry

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Investigating the Role of Block Chain to Secure Identity in IoT for Industrial Automation 2

Investigating the Role of Block Chain to Secure Identity in ...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Veeraiah, Vivek Preetham, Anusha Karthikeyan, P. Kumari, P. Lalitha Mehta, Kamakshi Kumbhkar, Makhan Adichunchanagiri University Dept. of R & D Computer Science Karnataka Mandya India Dayananda Sagar Academy of Technology and Management Dept. of Ise Karnataka India School of Management Studies Kongu Engineering College Perundurai TN Erode India Visakha Institute of Engineering & Technology Dept. of CSE AP Narava Visakhapatnam India Amity College of Commerce Amity University Haryana India Christian Eminent College Dept. of Computer Science Madhya Pradesh Indore India

ISBN: (纸本)9781665437899

Everyone who has an app that can access the block chain can get their hands on the data. There are blocks for each new piece of data that come in. A chronological chain of data is formed when a block is filled with data and chained to the previous block. Smart things are protected from cyber-attacks thanks to the IoT's technology's transparency and virtual incorruptibility. The block chain may be used to enable secure connectivity between IoT devices by storing data in transactions and confirming those transactions with nodes. The Internet of Things (IOT) has helped industrial automation reach new heights. We can improve the security and privacy by adding block chain to it. © 2022 IEEE.

关键词： Internet of things

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Thyroid Health Forecaster: A Cutting-Edge Hybrid Deep Learning Framework for Early Disease Prediction 5

Thyroid Health Forecaster: A Cutting-Edge Hybrid Deep Learni...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Selvamuthu, C.M. Kartheek, G.L.N.V. Chandrashekhara, K.T. Thatipudi, Jayendra Gopal Vadar, Parashuram Shankar Bhattacharjee, Sandeep Tamil Nadu Chennai India Anurag Engineering college Department of Computer science and Engineering Telangana Suryapet India BMS Institute of Technology and Management Department of Information science and Engineering Bangalore India University of North Texas Dept. of Computer Science United States Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Amity University Rajarhat Newtown West Bengal Kolkata700135 India

ISBN: (纸本)9798331530013

Detecting thyroid disease early is crucial for effective management and treatment, as conditions like hypothyroidism and hyperthyroidism can significantly impact a patient's health if left untreated. Traditional diagnostic methods, primarily based on hormone level testing, often fail to detect the disease in its initial stages. This study introduces a hybrid deep learning strategy to predict thyroid disease at an earlier stage, utilizing a combination of ResNet50 for feature extraction and Support Vector Machine (SVM) for classification. The methodology involves preprocessing thyroid image data, including resizing, normalization, augmentation, noise reduction, and enhancement, followed by feature extraction and classification. The hybrid model achieved an accuracy of 95.2%, precision of 94.5%, recall of 93.8%, F1 score of 94.1%, and ROC-AUC of 97%. This approach demonstrates superior performance compared to traditional machine learning models, indicating its potential for reliable early diagnosis of thyroid conditions. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning Analytics and Operations Research: Models, Applications and Managerial Implications 6

Deep Learning Analytics and Operations Research: Models, App...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Vijayalakshmi, K. Kulshreshtha, Kushagra Bhong, Mahesh Singh, Durgeshwar Pratap Kumar, C Praveen Bhutani, Vijay Dean of Research Loyola Academy Alwal Telangana Secunderabad India Institute of Business Management GLA University Uttar Pradesh Mathura India Indira College of Engineering and Management Dept. of Mechanical Engineering Maharashtra Pune India Graphic Era Deemed to Be University Dept of Mechanical Engineering Uttarakhand Dehradun India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Amity University Department of Mechanical Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Business analytics refers to the processes, strategies, and procedures that are utilised to derive value for individuals, organisations, and organisations from data. In today's fast-paced and more globalised digital economy, it is essential for decision-making to be driven more by data and to be supported by artificial intelligence (AI) and machine learning (ML). Deep learning (DL) does offer a number of benefits, but it also has a number of limitations that have, up to this point, prohibited businesses from making widespread use of it. Deep neural networks offer the potential of surpassing models from standard machine learning in terms of prediction accuracy, which is one of the reasons why the introduction of deep learning has led this area to undergo a major transformation. This is one of the reasons why the introduction of deep learning has caused this field to undergo a considerable shift. However, according to the findings of our review of the body of recently published research, the number of papers relevant to our industry that make use of operations research is quite low. Consequently, the objectives of this study provide an overview. In a few different case studies, we look at the value that operations research brings to the table by using real data from real-world business activities. Every one of these instances demonstrates improvements in operations research performance above what is possible with traditional machine learning, which ultimately results in gains in direct value. In this paper, we present academics, managers, and practitioners in the field of operations research who are interested in developing their abilities in deep learning and business analytics with suggestions and implications. Our computational investigations highlight the need of bespoke architectures by recommending a one-of-a-kind deep-embedded network as a solution to the problem that standard, out-of-the-box solutions are often not satisfactory. © 2023 IEEE.

关键词： Artificial Intelligence Deep Learning Deep neural networks Operations Research

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