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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Matsumoto, Kaisei Toma, Takao Oshiro, Shiho Wada, Tomohisa Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Area of Computer Science and Intelligent Systems University of the Ryukyus Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

关键词： Quadrature phase shift keying

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Dynamic Modeling and Identification of the COVID-19 Stochastic Dispersion 20

Dynamic Modeling and Identification of the COVID-19 Stochast...

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20th International Conference on Language engineering, ESOLEC 2022

作者： Taher, M. Hedaya, Mohammed M. Bakeer, Bahi Kafrawy, Passant El Zakaria, Mahmoud Nile University Department of Computer Science Cairo Egypt Ain Shams University Department of Design and Production Engineering Cairo Egypt School of Information Technology and Computer Science CIS Research Center Nile University Computer Science Department Egypt

ISBN: (纸本)9781665453226

In this work, the stochastic dispersion of novel coronavirus disease 2019 (COVID-19) at the borders between France and Italy has been considered using a multi-input multi-output stochastic model. The physical effects of wind, temperature and altitude have been investigated as these factors and physical relationships are stochastic in nature. Stochastic terms have also been included to take into account the turbulence effect, and the r and om nature of the above physical parameters considered. Then, a method is proposed to identify the developed model's order and parameters. The actual data has been used in the identification and prediction process as a reference. These data have been divided into two parts: The first part is used to calculate the stochastic parameters of the model which are used to predict the COVID-19 level, while the second part is used as a check data. The predicted results are in good agreement with the check data. © 2022 IEEE.

关键词： COVID-19

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A Comprehensive Framework for Evaluating Cyber-Physical Threats in Energy Internet

A Comprehensive Framework for Evaluating Cyber-Physical Thre...

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

作者： Sahu, Pooja Viji, A. Jeraldine Roy, Vandana Roy, Lipika Manogna, Devolla Vasal, Somil IES College of Technology Department of Computer Science and Engineering MP Bhopal462044 India Mailam Engineering College Dept. of EEE TN Villupuram India GGITS Department of EC M.P. Jabalpur India GGITS Dept. of Computer science and Business Studies M.P. Jabalpur India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Dept of Computer Science and Design M.P. Jabalpur India

ISBN: (纸本)9798331539948

The proposed Energy Internet threat modeling technique identifies system constraints, components, and resources appropriately. Threat Modeling Algorithm classifies and identifies dangers using STRIDE. It assigns relevance and probability to each system vulnerability. Risk and vulnerability assessments can forecast and influence such scenarios. Grouping hazards by significance affects priorities. To demonstrate threat implications, we design and assess instances. Upon reviewing and revising the threat model, we establish risk ratings and determine their importance. Develop the risk model, inform the necessary stakeholders, and then mitigate the risk. A comprehensive risk assessment ensures that all hazards are effectively managed at the end of the activity. The assessment and management of cyber-physical risks in the energy Internet are implemented to enhance system security and resilience. © 2024 IEEE.

关键词： Cyber attacks

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Attention-Based RNN for Alzheimer's Diagnosis: A Multi-Modal Approach with Enhanced Feature Retention Algorithm 1

Attention-Based RNN for Alzheimer's Diagnosis: A Multi-Modal...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Ramalingam, S. Gowsikraja, P. Loganathan, E. Logeswaran, A. Ramkumar, N. Geetha, K. Sri Eshwar College of Engineering Department of ECE Coimbatore India Kongu Engineering College Department of Computer Science and Design India Erode Sengunthar Engineering College Department of CSE Erode India Velalar College of Engineering and Technology Department of IT India SR University Department of ECE Warangal India Excel Engineering College Department of CSE India

ISBN: (纸本)9798331518943

Alzheimer's disease (AD) affects millions, and early detection is key. This paper introduces a new deep learning algorithm, Attention-Based Recurrent Neural Network (ABRNN), to detect AD using multi-modal data, including MRI scans and clinical features. ACO is used for preprocessing to enhance image quality and retain important patterns in MRI scans for diagnosis. SegNet isolates the brain regions that indicate the progression of AD through image segmentation. ResNet is used for feature extraction to capture deep hierarchical features from the segmented MRI scans to identify subtle but important features of brain degeneration. A memory-augmented block is added to store key feature patterns so the network can recall important information for AD diagnosis. A hybrid attention mechanism combines imaging and non-imaging features to focus on the relevant brain regions and clinical indicators. ABRNN is compared to 4 existing algorithms: CNN, LSTM, VGG-16, and Random Forest. In experiments, ABRNN achieved 97.85% accuracy, outperforming CNN (94.2%), LSTM (93.5%), VGG-16 (95.1%), and Random Forest (92.3%). ABRNN's performance is better because it retains important features through memory augmentation, extract features well with ResNet, and segments well with SegNet, so it's more accurate than traditional methods © 2024 IEEE.

关键词： Image segmentation

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An Intelligent Interface of Video Retrieval Based on Summary CARD of Query Words

An Intelligent Interface of Video Retrieval Based on Summary...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Wu, Runxun Lin, Shujin Zhao, Baoquan Zhou, Fan School of Computer Science and Engineering Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University National Engineering Research Center of Digital Life Guangzhou China School of Communication and Design Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University National Engineering Research Center of Digital Life Guangzhou China

ISBN: (纸本)9798331509873

The large amount of video resources on the Internet brings huge challenges to users' retrieval. Therefore, this paper designs an algorithm to automatically generate video card summaries, which contain rich graphic and text materials and can effectively summarize video content. In order to facilitate the division of the hierarchical structure of the video, we also propose a model based on bidirectional transformer for video scene segmentation. Experiments on benchmark datasets show that our method achieves competitive performance. In order to visually display the algorithm, we further design a visual intelligent interface system for news or educational video retrieval results. © 2024 IEEE.

关键词： Video analysis

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A multimodal approach for enhanced disease management in cauliflower crops:integration of spectral sensors, machine learning models and targeted spraying technology

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Frontiers of Agricultural science and engineering 2025年第2期12卷 261-273页

作者： Rohit ANAND Roaf Ahmad PARRAY Indra MANI Tapan Kumar KHURA Harilal KUSHWAHA Brij Bihari SHARMA Susheel SARKAR Samarth GODARA Shideh MOJERLOU Hasan MIRZAKHANINAFCHI Division of Agricultural Engineering ICAR-IARI Vasantrao Naik Marathwada Krishi Vidyapeeth Central Arid Zone Research Institute Division of Vegetable Sciences ICAR-IARI Division of Design of Experiments ICAR-IASRI Division of Computer Applications ICAR-IASRI Department of Agroecology-Entomology and Plant Pathology Aarhus University Electrical Engineering and Computer Science Daktronics Engineering Hall South Dakota State University

This research explored a novel multimodal approach for disease management in cauliflower crops. With the rising challenges in sustainable agriculture, the research focused on a patch spraying method to control disease and reduce crop losses and environmental impact. For non-destructive disease assessment, a spectral sensor was used to collect spectral information from diseased and healthy cauliflower parts. The spectral data sets were analyzed using decision tree and support vector machine（SVM） algorithms to identify the most accurate model for distinguishing diseased and healthy plants. The chosen model was integrated with a low-volume sprayer(50-150 L·ha-1),equipped with an electronic control unit for targeted spraying based on sensor-detected regions. The decision tree model achieved 89.9% testing accuracy, while the SVM model achieved 96.7% accuracy using hyperparameters: cost of 10.0 and tolerance of 0.001. The research successfully demonstrated the integration of spectral sensors, machine learning, and targeted spraying technology for precise input ***, the optimized sprayer achieved a 72.5% reduction in chemical usage and a significant time-saving of 21.0% compared to a standard sprayer for black rot-infested crops. These findings highlight the potential efficiency and resource conservation benefits of innovative sprayer technology in precision agriculture and disease management.

关键词： Disease management site-specific sprayer spectral sensor machine learning models cauliflower crop black-rot disease

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A Comparative Analysis: Autism Spectrum Disorder Among Children Using Classification Models 2

A Comparative Analysis: Autism Spectrum Disorder Among Child...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Srikar, Y. Himaanshu, V. Suryanarayana, G. Lnc Prakash, K. Saketha Rama, B.V. Vardhaman College of Engineering Dept: of CSE Hyderabad India Vardhaman College of Engineering Dept: of Information Technology Hyderabad India Hyderabad India Indian Institute of Information Technology Design and Manufacturing Dept: of Computer Science Engineering Kancheepuram Chennai India

ISBN: (纸本)9798350344882

Autism is a neurological condition marked by challenges with communication and social engagement, some recurring interests, and habits. It is a very complex and diverse condition that affects individuals, and there is no prescribed treatment for autism. Machine learning models have such potential to understand autism and provide more effective interventions for autistic patients. However, learning models are particularly challenging to read, making it challenging to understand how the model makes decisions and identify any biases in the model. These are important in the context of research on autism, where it is possible to assess the effects of inaccurate diagnosis or improper treatment recommendations. In this work, various algorithms are applied to an autism spectrum disorder (ASD) dataset with 931 samples and 103 features. Experimental results show that the predictive model Random Forest gives the highest accuracy, precision, recall, and Fl-Score with 100% compared to LR, GNB, KNN, SVM, and ID3. © 2023 IEEE.

关键词： Diagnosis

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Understanding, Implementing, and Supporting Security Assurance Cases in Safety-Critical Domains

arXiv

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arXiv 2025年

作者： Mohamad, Mazen Department of Computer Science & Engineering Division of Interaction Design and Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

The increasing demand for connectivity in safety-critical domains has made security assurance a crucial consideration. In safety-critical industry, software, and connectivity have become integral to meeting market expectations. Regulatory bodies now require security assurance cases (SAC) to verify compliance, as demonstrated in ISO/SAE-21434 for automotive. However, existing approaches for creating SACs do not adequately address industry-specific constraints and requirements. In this thesis, we present CASCADE, an approach for creating SACs that aligns with ISO/SAE-21434 and integrates quality assurance measures. CASCADE is developed based on insights from industry needs and a systematic literature review. We explore various factors driving SAC adoption, both internal and external to companies in safety-critical domains, and identify gaps in the existing literature. Our approach addresses these gaps and focuses on asset-driven methodology and quality assurance. We provide an illustrative example and evaluate CASCADE's suitability and scalability in an automotive OEM. We evaluate the generalizability of CASCADE in the medical domain, highlighting its benefits and necessary adaptations. Furthermore, we support the creation and management of SACs by developing a machine-learning model to classify security-related requirements and investigating the management of security evidence. We identify deficiencies in evidence management practices and propose potential areas for automation. Finally, our work contributes to the advancement of security assurance practices and provides practical support for practitioners in creating and managing SACs. Copyright © 2025, The Authors. All rights reserved.

关键词： Automotive industry

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Data-Driven Norm-Optimal Control of Discrete Repetitive Processes 63

Data-Driven Norm-Optimal Control of Discrete Repetitive Proc...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Moore, Kevin L. Chu, Bing Rapisarda, Paolo Colorado School of Mines Division of Engineering Design and Society and the Department of Electrical Engineering 1500 Illinois St. GoldenCO80401 United States University of Southampton School of Electronics and Computer Science SO17 1BJ United Kingdom

ISBN: (纸本)9798350316339

We present a data-driven, model-free approach to norm-optimal control of discrete repetitive processes, exploiting Willems' fundamental lemma. The algorithm is described in detail, and a rigorous analysis of its convergence properties is performed. A numerical example is provided to demonstrate the effectiveness of the proposed design methodology. © 2024 IEEE.

关键词： Adversarial machine learning

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Securing Pedestrian Crosswalks in Smart Cities: An Embedded Vision System for Pedestrian Detection and Safety Enhancement 2

Securing Pedestrian Crosswalks in Smart Cities: An Embedded ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Mohan Kumar, S. Reddy, Elangovan Guruva Chandramohan, S. Prabagar, S. Latha, N. CMR University Karnataka Bangalore India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

One of the most severe issues in the context of rapidly growing intelligent cities is the safety of pedestrians at urban crosswalks. To address this issue, the system developed an embedded vision system for real-time pedestrian detection and to enhance safety at crosswalks. A camera module captures real-time video, an image processor analyzes the captured data using a pedestrian detection algorithm, and a microcontroller manages the system's other functions. Our system can accurately identify pedestrians inside the crossing area using deep learning-based object detection models. If pedestrians are detected, an LED warning system alerts drivers to either slow down or stop, drastically decreasing the possibility of an accident. A real-time pedestrian count and any applicable safety warnings are also shown on an LCD screen. By connecting with existing systems, this smart technology may significantly improve the security of smart cities for pedestrians. Preliminary testing has demonstrated the technology has great potential for deployment in real-world urban environments. © 2023 IEEE.

关键词： computer Vision Intelligent Transportation Systems Pedestrian Detection Pedestrian Safety Smart Cities

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