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Innovation in Technology (INOCON), IEEE International Conference for

作者： Minal A. Zope Rakesh K. Deshmukh Priya Pise Shrikant D. Bhopale Kalinga University Raipur Dept. of Computer Engineering AISSMS’s Institute of Information Technology Pune India Kalinga University Raipur India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr.D.Y.Patil Pratisthan’s Colleg of Engineering Salokhenagar Kolhapur India

While CNN-based Alzheimer’s disease (AD) classification has showed some encouraging results, it will need high accuracy, fast processing time, and generalizability to a number of population types before it can be used in clinical settings. The brain disorder known as Alzheimer’s disease (AD) is progressive and currently has no cure. Alzheimer’s disease (AD) is characterized by a decline in memory and other mental abilities as well as a loss of autonomy in daily life. The results of this study suggest that using statistical learning and ensemble learning strategies, an image feature extraction technique like the Auto color correlogram Filter methods may be used successfully to an Alzheimer’s photos dataset. Dementia caused by Alzheimer’s disease (AD) is a progressive form of the neurodegenerative brain disorder. It’s not always obvious at first since there are usually so few symptoms at the outset, and they might be similar to those of other disorders. Distinguishing Alzheimer’s disease from mild cognitive deterioration, which may be appreciated in normal aging, may be challenging.

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A Symmetric Encryption Scheme with Multi User Searchable Options in the Cloud Based Applications 4

A Symmetric Encryption Scheme with Multi User Searchable Opt...

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4th International Conference on Emerging Research in Electronics, computer Science and Technology, ICERECT 2022

作者： Dubey, Abhishek Kumar, Vijay Singhal, Priyank Savyasachi Ul Islam, Atowar Kuppuchamy, R. Chandigarh Engineering College Department of CSE Jhanjeri India JAIN University Department of CSE Bangalore India Mahaveer University Department of Computing Sciences & IT Moradabad India Lalit Narayan Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India University of Science and Technology Dept. of Computer Science and Electronics Meghalaya Techno City India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India

ISBN: (纸本)9781665456357

Scientists in academia and business have thoroughly studied Encryption Scheme (SE). Whereas many scientific SE methods provide verifiable confidentiality, in order to attain high performance, they typically reveal certain 'trans (such as search and access patterns). Yet, a number of infer methods have taken advantage of this leaking, for instance, a query recovering attack that uses foreknowledge to convert transparent question passageways to their matching keywords. But at the other hand, several more pro - posed SE schemes require substantial modification applications, that also tends to make them less convenient, weak in user experience, and complicated to deploy. In this research, we offer IDCrypt, a safe and useful Is the Enclosure (SSE) scheme with demonstrable security power for azure. IDCrypt uses cryptographic techniques to increase SE's security level and search efficiency. We also highlight the key difficulties in safely sharing decrypted data across different individuals and search across several indexes. We suggest a coin search strategy to maintain the search features across several indexes or a key sharing system that combines Personality Crypto (IBE) with Community Encrypted data to solve the aforementioned problems (PKE). Our empirical findings demonstrate that the number of key agreement's expense is rather modest. © 2022 IEEE.

关键词： Cryptography

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Mitigating Epilepsy by Stabilizing Linear Fractional-Order Systems

Mitigating Epilepsy by Stabilizing Linear Fractional-Order S...

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American control Conference (ACC)

作者： Emily A. Reed Guilherme Ramos Paul Bogdan Sérgio Pequito Ming Hsieh Electrical and Computer Engineering Department University of Southern California USA Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Uppsala Sweden

Epilepsy affects approximately 50 million people worldwide. Despite its prevalence, the recurrence of seizures can be mitigated only 70% of the time through medication. Furthermore, surgery success rates range from 30% - 70% because of our limited understanding of how a seizure starts. However, one leading hypothesis suggests that a seizure starts because of a critical transition due to an instability. Unfortunately, we lack a meaningful way to quantify this notion that would allow physicians to not only better predict seizures but also to mitigate them. Hence, in this paper, we develop a method to not only characterize the instability of seizures but also to leverage these conditions to stabilize the system underlying these seizures. Remarkably, evidence suggests that such critical transitions are associated with long-term memory dynamics, which can be captured by considering linear fractional-order systems. Subsequently, we provide for the first time tractable necessary and sufficient conditions for the global asymptotic stability of discrete-time linear fractional-order systems. Next, we propose a method to obtain a stabilizing control strategy for these systems using linear matrix inequalities. Finally, we apply our methodology to a real-world epileptic patient dataset to provide insight into mitigating epilepsy and designing future cyber-neural systems.

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Construction Site Modeling Objects Using Artificial Intelligence and BIM Technology: A Multi-Stage Approach

Construction Site Modeling Objects Using Artificial Intellig...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Serhii Dolhopolov Tetyana Honcharenko Volodymyr Savenko Olena Balina Iryna Bezklubenko Tamara Liashchenko Dept. of Cyber Security and Computer Engineering Kyiv National University of Construction and Architecture Kyiv Ukraine Dept. of Information Technologies of Design and Applied Mathematics Kyiv National University of Construction and Architecture Kyiv Ukraine Dept. of Organization and Management of Construction Kyiv National University of Construction and Architecture Kyiv Ukraine Dept. of Information Technologies Kyiv National University of Construction and Architecture Kyiv Ukraine

This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an “evolutionary” digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site.

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Efficient COVID-19 Identification Using Deep Learning for IoT 5

Efficient COVID-19 Identification Using Deep Learning for Io...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Veeraiah, Vivek Pankajam, A. Vashishtha, Ela Dhabliya, Dharmesh Karthikeyan, P. Chandan, Radha Raman Adichunchanagiri University Department of R & D Computer Science Karnataka Mandya India Avinashilingam Institute for Home Science and Higher Education for Women Dep of Business Administration Tamil Nadu India University of North Texas Health Science Center Healthcare Consultant TX United States Vishwakarma Institute of Information Technology Dept. of Information Technology Maharashtra Pune India School of Management Studies KonguEngineering College Perundurai Tamil Nadu Erode India Shambhunath Institute of Engineering & Technology Jhalwa Dept. of Computer Science & Engineering Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350398267

Internet of Things (IoT) has made it possible to diagnose and treat patients remotely, as well as to expedite the transportation of essential drugs and medical equipment to locations that are geographically separated. This has occurred at a time when society has become more socially distant. During the Ebola and COVID-19 outbreaks, the Internet of Things (IoT) technology was put to use in remote patient monitoring and the management of the vaccine cold chain. Concurrently, this study reflects on the variables that are required for IoT to scale. Since December 2019, the COVID-19 outbreak on a worldwide scale has developed into a significant problem. In order for medical treatment to be successful, it is essential to make a prompt and accurate diagnosis of persons who may be infected with the COVID-19 virus. In order to put a halt to the spread of COVID-19, it is important to construct an automated system that is based on deep transfer learning and is capable of detecting the virus based on chest X-rays. The authors of this study present an internet-of-things (IoT) system that makes use of ensemble deep transfer learning to diagnose COVID-19 patients at an earlier stage. It is feasible to keep an eye on potentially hazardous COVID-19 incidents as they occur so long as suitable procedures are adhered to. Inceptions A variety of different deep learning models are included into the framework that has been proposed for the Internet of Things. According to the findings of the study, the method that was suggested assisted radiologists in accurately and quickly identifying patients who could have COVID-19. The proposed effort focuses on developing an effective identification system based on the COVID-19 standard for use in an IoT setting. © 2022 IEEE.

关键词： COVID-19

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Security Threats Mitigation for Internet of Vehicles Using Cryptographic Protocols

Security Threats Mitigation for Internet of Vehicles Using C...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Khurram Shahzad Muhammad Raheel Raza Asaf Varol Dept. of Electronic & Comp. Engineering University of Limerick Limerick Ireland Peter Faber Business School Australian Catholic University North Sydney Australia Dept. of Eng. Management & Technology College of Eng. & Comp. Science Uni. of Tennessee at Chattanooga Chattanooga TN US Department of Computer Engineering College of Eng. & Natural Sciences Maltepe University Istanbul Türkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

In the transportation domain, the Internet of Vehicles (IoV) enables vehicles to connect with their surroundings, including other vehicles, infrastructure, and various services, hence promoting information sharing for better user experience, safety, and traffic management. Given the increasing demand for autonomous vehicles, it is critical to address the network's security and privacy vulnerabilities. The paper aims to propose a secure chat protocol specifically tailored for enhancing communication in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) relationships within the Internet of Vehicles (IoV). The protocol establishes a mutually agreed session key (Kabc) among vehicles and infrastructure components, ensuring that all exchanged messages are encrypted to protect sensitive data from eavesdropping and unauthorized access. By employing a certificate-based authentication mechanism, the protocol verifies the identities of communicating entities, thereby preventing impersonation attacks and ensuring trust in the information exchanged between vehicles and infrastructure systems.

关键词： Vehicle-to-infrastructure Prevention and mitigation Transportation Vehicular ad hoc networks Information sharing Network security User experience Safety Cryptography Internet of Vehicles

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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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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

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

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

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.

关键词： Deep learning Adaptation models Limiting Computational modeling Weapons Evolutionary computation Network security Personnel Protection Monitoring

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Inferring Topology of Networked Dynamical Systems by Active Excitations

Inferring Topology of Networked Dynamical Systems by Active ...

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IEEE Conference on Decision and control

作者： Yushan Li Jianping He Cailian Chen Xinping Guan Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Topology inference for networked dynamical systems (NDSs) has received considerable attention in recent years. The majority of pioneering works have dealt with inferring the topology from abundant observations of NDSs, so as to approximate the real one asymptotically. Leveraging the characteristic that NDSs will react to various disturbances and the disturbance’s influence will consistently spread, this paper focuses on inferring the topology by a few active excitations. The key challenge is to distinguish different influences of system noises and excitations from the exhibited state deviations, where the influences will decay with time and the excitation cannot be arbitrarily large. To practice, we propose a one-shot excitation based inference method to infer h-hop neighbors of a node. The excitation conditions for accurate one-hop neighbor inference are first derived with probability guarantees. Then, we extend the results to h-hop neighbor inference and multiple excitations cases, providing the explicit relationships between the inference accuracy and excitation magnitude. Specifically, the excitation based inference method is not only suitable for scenarios where abundant observations are unavailable, but also can be leveraged as auxiliary means to improve the accuracy of existing methods. Simulations are conducted to verify the analytical results.

关键词： Analytical models Network topology Topology Performance analysis Dynamical systems Testing

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Innovative Competency-Based Electrical engineering Curriculum 54

Innovative Competency-Based Electrical Engineering Curriculu...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Rehder, Gustavo P. Serrano, Ariana L. C. Grimoni, José B. Brandão, Anarosa A. F. Silva, Magno M. T. Moriya, Henrique T. Panazio, Cristiano M. Barbero, Edson Alayo, Marco I. Gianesi, Irineu G. N. Seabra, Antonio C. Universidade de São Paulo Electronic Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Electric Automation and Energy Dept. The Escola Politécnica São Paulo Brazil Universidade de São Paulo Computer Engineering and Digital Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Telecommunications and Control Dept. The Escola Politécnica São Paulo Brazil Á lvares Penteado School of Commerce Foundation São Paulo Brazil Instituto Mauá de Tecnologia São Paulo Brazil

ISBN: (纸本)9798350351507

The Electrical engineering undergraduate program of the Escola Politécnica of the Universidade de São Paulo has created an alternative innovative curriculum path for its freshmen students in 2024. The Competency-based Path emphasizes the development of competencies and skills for the unprecedented technological advancements that necessitate a holistic education for future electrical engineers, preparing them with practical skills for real-world challenges. The program was designed using an innovative methodology and centers around semester-long or year-long integrative projects. These projects bridge the gap between theory and practice, as students deal with real-world challenges while fostering social responsibility. © 2024 IEEE.

关键词： Students

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Local Topology Inference of Mobile Robotic Networks under Formation control

arXiv

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arXiv 2022年

作者： Li, Yushan He, Jianping Cai, Lin Guan, Xinping The Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Dept. of Electrical and Computer Engineering University of Victoria BC Canada

The interaction topology is critical for efficient cooperation of mobile robotic networks (MRNs). We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots. This problem faces new challenges brought by the highly coupled influence of unobservable formation robots, inaccessible formation inputs, and unknown interaction range. The novel idea here is to advocate a range-shrink strategy to perfectly avoid the influence of unobservable robots while filtering the input. To that end, we develop consecutive algorithms to determine a feasible constant robot subset from the changing robot set within the observation range, and estimate the formation input and the interaction range. Then, an ordinary least squares based local topology estimator is designed with the previously inferred information. Resorting to the concentration measure, we prove the convergence rate and accuracy of the proposed estimator, taking the estimation errors of previous steps into account. Extensions on nonidentical observation slots and more complicated scenarios are also analyzed. Comprehensive simulation tests and method comparisons corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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