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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Arkashree P. Mishra Suvasini Panigrahi Dept. of Computer Science & Engineering Veer Surendra Sai University of Technology Burla India

Cardio Vascular Diseases (CVDs) are the prime reason of death globally. Particularly, the cases of coronary artery disease (CAD) have been rising in the post Covid era, especially amongst middle-aged people. Thus, the before time detection of the disease might evade the growing number of fatalities and morbidities. Two most common and accurate methods used to detect CAD are angiography and electrocardiography (ECG). While the former is invasive and a risky procedure, the latter one although non-invasive lacks correctness. Over a decade, non-invasive Machine Learning (ML) as well as Deep Learning (DL) methods have shaped the healthcare scenario by facilitating early diagnosis of the diseases through the application of various computer based algorithmic models over clinical data and images. A hybrid Convolutional- Extreme Gradient Boosting (Convolutional XGBoost) model has been proposed to accurately detect CAD by outperforming some of the existing methods.

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A Threat modeling approach to analyze and mitigate WhatsApp attacks: A Review

A Threat modeling approach to analyze and mitigate WhatsApp ...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Pawan Kumar Patidar Deepak Singh Tomar R.K. Pateriya Yogesh Kumar Sharma Dept. of Computer Science Engineering Maulana Azad National Institude of Technology Bhopal India

One of the most popularly used features on smart-phones is WhatsApp. It is a free messaging app available for An-droid, IOS, and all other smartphones. A WhatsApp vulnerability is a hole or a weakness in the app, which can be a design flaw or an implementation bug. Through bug, an attacker can enter into the app and cause harm to the database of an application. A database contains private information like backup files, chatting information, contacts, etc. The most dangerous vulnerability in WhatsApp are Authentication, Account Hijacking, and Message Manipulation. In these vulnerabilities, an attacker can manip-ulate the message but not hijack the entire account. There are some basic requirements for a secure and privacy-preserving chat service, database backup, encrypted database, etc. The awareness about the vulnerability of WhatsApp and its security settings in new versions of WhatsApp. Use WhatsApp security features and secure the services of the application. This paper aims to be aware of the risks and vulnerabilities of WhatsApp and use the Threat modeling method to mitigate its vulnerability. It used threat Modelling steps to help organizations to quantify risks and vulnerabilities, ensuring those that need the most attention and resources do so to minimize their attack surface in a purposeful way.

关键词： Threat modeling Freeware Databases computer bugs Organizations Security Cryptography

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Unified Object Detection Model for Image Style Convertible Modalities

Unified Object Detection Model for Image Style Convertible M...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Mu-Chuan Li Xiu-Zhi Chen Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

Object detection is an important task in computer vision. In earlier studies, object detection mostly works on single-modal. But recently, in order to improve detection efficiency, researchers start adding more modals for improving the robustness of detection result. In this paper, we assume that all input modals which can convert to image style are able to complete object detection task through a unified model. We choose YOLOv7 as our baseline model and use RGB and infrared images from the public dataset Camel to test our hypothesis. Based on our experiment results, training RGB and IR images together obtains better performance than training individually, which shows that all image style modalities are possibly handled by a unified model and even achieve better results.

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Influence of Quantum Computing on IoT Using Modern Algorithms 4

Influence of Quantum Computing on IoT Using Modern Algorithm...

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4th International Conference on Advanced science and engineering, ICOASE 2022

作者： Ageed, Zainab Salih Zeebaree, Subhi R. M. Saeed, Rezgar Hasan Nawroz University Computer Science Dept. Duhok Iraq Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq Duhok Private Technical Institute Information Technology Dept. Duhok Iraq

ISBN: (纸本)9781665472227

Even while the Internet of Things (IoT) already affects our day-to-day activities, its future relevance and potential for transformation remain untapped. Security issues with present communications technology must be solved before secure end-to-end connection between services can be achieved. Internet of Things (IoT) is here to stay. In the next years, it will become an integral part of our everyday life. There is a good chance that sensor-based networks can recognize it as a direct service provider in our surroundings. Even if it's just in the form of a value-added service delivered through cellular networks, it's going to be helpful nevertheless. It is, nevertheless, subject to a wide range of security threats. The current level of security is insufficient for IoT applications in the future. A secure cryptosystem is needed for the Internet of Things. quantum-based security has seen a surge of attention recently. Additional quantum key distribution systems and network services are now supported by this solution. As a result of the quantum computing's unrivaled security level, it has become more popular in recent years. Because every measurement must affect the state of the quantum bit being sent, quantum physics dictates that this must be the case. Regardless of whether the sender or recipient is aware of the change, it is clear. It is thus no longer possible to listen passively. Polarized photons may encode a string of bits using protocols like BB84. Secure cryptographic keys may be generated over an unsafe channel utilizing various key distillation procedures. © 2022 IEEE.

关键词： Internet of things

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A Survey of the AVOA Metaheuristic Algorithm and its Suitability for Power System Optimization and Damping Controller Design 13

A Survey of the AVOA Metaheuristic Algorithm and its Suitabi...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Sabo, Aliyu Odoh, Theophilus Ebuka Habu, Samuel Shahinzadeh, Hossien Ebrahimi, Farshad Department of Electrical AndElectronic Engineering Serdang Malaysia Water Resource Institute Department of Electrical Engineering Kaduna Nigeria Amikabir University of Technology Department of Electrical Engineering Tehran Iran University of Houston Dept. of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350330151

The performance of metaheuristic optimization techniques is system-dependent, it is essential to ascertain the suitability of an algorithm before its application. This study surveys the suitability of AVOA in damping controller design by reviewing the performance of AVOA in used cases of power system optimization problems and damping controller design. Based on the findings, AVOA is recommended as a good optimizer in power system optimization and damping controller design. © 2023 IEEE.

关键词： Damping

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AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding

AIX Implementation in Image-Based PM2.5 Estimation: Toward a...

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15th International Conference on Knowledge and Smart technology, KST 2023

作者： Utomo, Sapdo John, A. Pratap, Ayush Jiang, Zhi-Sheng Karthikeyan, P. Hsiung, Pao-Ann National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India

ISBN: (纸本)9781665477123

In accordance with the Sustainable Development Goals, the exponential expansion of machine learning (ML) and artificial intelligence (AI) presents an excellent chance to build more effective tools and solutions and generate positive social impact. According to the WHO report, global PM pollution causes more than 8 million deaths annually. This is the fundamental reason we are performing this research. This research proposes estimating air quality using deep learning. The proposed model can surpass the state-of-the-art model in terms of RMSE, R-squared, and accuracy, which have respective values of 30.10, 0.83, and 76.92%. In order to explain the model's output, LIME has been implemented. According to LIME's explanation, the proposed model's output is trustworthy. Because it reveals that the sky, and not other places such as buildings, was the source of the most impactful superpixels on the model's decision. We hope that with this discovery, we can contribute to the theme of 'AI for social good,' notably in the domains of the environment and human welfare. © 2023 IEEE.

关键词： Air quality

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MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics

arXiv

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arXiv 2025年

作者： Jin, Haoan Shi, Jiacheng Xu, Hanhui Zhu, Kenny Q. Wu, Mengyue Ant Group Hangzhou China SJTU Shanghai China SJTU: X-LANCE Lab Dept. of Computer Science and Engineering Shanghai Jiao Tong University China FDU: Institute of Technology Ethics for Human Future Fudan University China UTA: Dept. of Computer Science and Engineering University of Texas Arlington United States

Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: knowledge, assessing the models’ grasp of medical ethics principles, and application, focusing on their ability to apply these principles across diverse scenarios. To support this benchmark, we consulted with medical ethics researchers and developed three datasets addressing distinct ethical challenges: blatant violations of medical ethics, priority dilemmas with clear inclinations, and equilibrium dilemmas without obvious resolutions. MedEthicEval serves as a critical tool for understanding LLMs’ ethical reasoning in healthcare, paving the way for their responsible and effective use in medical contexts. © 2025, CC BY.

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Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms 4

Two-Area Power System with Automatic Generation Control Util...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sati, Madan Mohan Kumar, Dinesh Singh, Akhilesh Raparthi, Mohan Alghayadh, Faisal Yousef Soni, Mukesh Graphic Era Hill University Deptt. of Mathematics Bhimtal263136 India Srm Institute of Science and Technology Dept. of Computer Science and Engg. UP Ghaziabad201204 India Uttarakhand Technical University Campus Institute Deptt. of Electrical Engg. Pithoragarh India Alphabet Life Science Software Engineer DallasTX75063 United States Almaarefa Univ Riyadh College of Applied Sci Computer Science and It Department Saudi Arabia Dr. D. Y. Patil Vidyapeeth School of Science and Technology Pune India

ISBN: (纸本)9798350343670

This study aims to present a feasible LFC (Load Frequency Control) model that integrates physical security limitations, namely GRC, GDB, and TD. The presence of this characteristic guarantees that the design is not only accurate but also capable of being materialized in a physical form. It is suggested that the traditional PI controller be replaced with a PID controller in order to handle the issues that are posed by large-scale power systems that are interconnected. Developing extremely efficient soft computing approaches that are capable of producing the necessary results for both individual and extensive power systems while taking into consideration flaws in modeling is the goal of this endeavor. It is important to get the frequency deviation to zero while still following the rules set by real issues and different working conditions. In order to maintain the electrical flow between the inter-area tie lines that are located within the various control zones at the schedule values that have been specified. In order to achieve optimal levels of performance in both steady state and transient conditions. The purpose of this is to obtain valuable multiple solutions with the intention of future planning and design. It is important to simplify the robust load frequency control design problem by changing it into a low-order proportional integral derivative (PID) control synthesis problem in order to address the practical benefits. This approach is commonly employed in real-world power systems applications. © 2024 IEEE.

关键词： and TD (Transmission Delay ANN BBBC FOPID GDB (Generation Demand Balance) GRC (Generation Rate Constraint) Load Frequency Control (LFC) PID PSO

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Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning

arXiv

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arXiv 2024年

作者： Wu, Man Zheng, Xin Zhang, Qin Shen, Xiao Luo, Xiong Zhu, Xingquan Pan, Shirui The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Monash University Melbourne Australia The College of Computer Science and Software Engineering Shenzhen University Guangdong China The School of Computer Science and Technology Hainan University Haikou China The Dept. of Electrical Engineering & Computer Science Florida Atlantic University United States Griffith University QLD Australia

Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data. In reality, the real-world graph data typically show dynamics over time, with changing node attributes and edge structure, leading to the severe graph data distribution shift issue. This issue is compounded by the diverse and complex nature of distribution shifts, which can significantly impact the performance of graph learning methods in degraded generalization and adaptation capabilities, posing a substantial challenge to their effectiveness. In this survey, we provide a comprehensive review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. Concretely, according to the observability of distributions in the inference stage and the availability of sufficient supervision information in the training stage, we categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. For each scenario, a detailed taxonomy is proposed, with specific descriptions and discussions of existing progress made in distribution-shifted graph learning. Additionally, we discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field. The survey is positioned to provide general guidance for the development of effective graph learning algorithms in handling graph distribution shifts, and to stimulate future research and advancements in this area. © 2024, CC BY.

关键词： Graph neural networks

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A Quantitative Analysis of Basic vs. Deep Learning-based Image Data Augmentation Techniques

A Quantitative Analysis of Basic vs. Deep Learning-based Ima...

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2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Ur Rahman, Mohammed Ehsan Anishetty, Hrudheeshta Kollpaka, Arjun Kumar Yelishetty, Aishwarya Ganta, Swetha Reddy Kakatiya Institute of Technology and Science Dept. of Computer Science and Engg. Warangal India

ISBN: (纸本)9781665435215

Our proposed work is a research project that does quantitative analysis of various basic image manipulation techniques as processes for augmentation of image type data on the accuracy of deep learning task of hand-written digit recognition on MNIST dataset. The paper also presents a detailed comparison of various parameters such as computation burden, storage requirements for model storage, accuracy, and loss function value of the results obtained by using basic image manipulation techniques as image data augmentation techniques with those data augmentation mechanisms that are rooted in deep learning. The results that we have obtained on MNIST dataset without data augmentation applied are accuracy of 97.80% and loss of 0.320, whereas the highest accuracy was achieved by adjusting brightness as the data augmentation technique with 98.57% accuracy and 0.301 loss value. In the view of our results, we recommend that basic image manipulation-based data augmentation techniques must be used to address overfitting instead of memory or computationally expensive deep learning-based image augmentation techniques. This strategy also helps enhance the performance of various image data-based deep learning pipelines and makes these models more robust. © 2021 IEEE.

关键词： Convolutional neural networks

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