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Protecting Data Confidentiality and Assuring Integrity, and Preventing Rogue Devices in Medical Body Area Network

Protecting Data Confidentiality and Assuring Integrity, and ...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Rakibul Hasan Md Al Amin Sheikh Dobir Hossain Md. Ibrahim Abdullah Dept of Computer Science and Engineering Islamic University Bangladesh Dept of Computer Science Colorado State University USA Dept of Electrical and Computer Engineering The University of Texas at El Paso USA

The Internet of Things (IoT) is a modern technology that connects physical objects in a given environment over the Internet to make their information available from everywhere and at any time. The popularity of IoT technologies is rapidly increasing in most areas, such as education, health, agriculture, transportation, etc. Medical Body Area Network (MBAN) is an emerging healthcare technology that allows remote monitoring of patient's vital signs, providing physicians with real-time data and facilitating better treatment decisions. However, security concerns are one of the hardest things to solve in IoT systems because they have limited computing power, memory, and energy, among other things. Moreover, all the objects in the IoT system are communicated with, controlled, and monitored over the Internet, and most communications are established via wireless networks. Due to the weakness of the wireless network, it is essential to protect data so that a bad guy can't get to it. This article proposes a model based on a lightweight cryptographic protocol to ensure data confidentiality, integrity, and device authentication for MBAN patients. Experimental evaluations show that the proposed approach is functional with the currently available hardware resources to provide complete cryptographic functionalities, like ensuring patient physiological data confidentiality and upholding data integrity during the exchange between sensor devices and the central coordinator. Also, unauthorized access attempts from malicious devices or attackers are detected through mutual authentication to maintain the integrity of data sources.

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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

作者： Harshith Pallapothu Gopi Krishna G Digvijay Sunil Teja Yadav S Nagasundari S Dept. of Computer Science & Engineering PES University Bengaluru India Dept. of Computer Science & Engineering Research Center for Information Security Forensics and Cyber Resilience PES University Bengaluru India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs.

关键词： Cloud computing Connected vehicles Web services Vehicle safety Propagation losses Real-time systems Reliability Autonomous vehicles Payloads Predictive maintenance

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Investigating the Efficacy of Diverse Machine Learning Classifiers for Parkinson’s Disease Detection

Investigating the Efficacy of Diverse Machine Learning Class...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Sourabarna Roy Tina Debbarma Tannistha Pal Prakash Duraisamy Dept. of Computer Science and Engineering National Institute of Technology Agartala Agartala India Dept. of Computer Science and Engineering University of Wisconsin- Green Bay WI United States

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

To detect Parkinson’s disease, we compare the effectiveness of K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms. Utilizing a dataset with clinical and biomedical features, we preprocess the data to handle missing values and standardize the features. Subsequently, we train each algorithm with the preprocessed data and evaluate their performance using metrics like accuracy, precision, recall, and F1-score. Our results indicate that all four algorithms achieve excellent accuracy in diagnosing Parkinson’s disease, with KNN slightly outperforming the others. However, the selection of the algorithm may depend on specific needs such as interpretability and computational efficiency. Additionally, we conduct a feature importance analysis to identify the most relevant features for Parkinson’s disease identification, offering insights that can aid in early diagnosis and disease management.

关键词： Support vector machines Radio frequency Accuracy Sensitivity Machine learning algorithms Nearest neighbor methods Sensitivity and specificity Vectors Random forests Diseases

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Improved Dynamic Coupled Graph Convolutional Recurrent Networks for Traffic Forecasting

Improved Dynamic Coupled Graph Convolutional Recurrent Netwo...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Han, Seunghoon Kim, Sungsoo Lim, Sungsu Chungnam National University Dept. of Computer Science & Engineering Daejeon Korea Republic of ETRI Smart Data Research Section Daejeon Korea Republic of

ISBN: (纸本)9798350324457

Traffic forecasting is a crucial application of the Intelligent Transportation System (ITS), with research focusing on various methods, from classical statistical approaches to graph-based methods integrated with RNN-based approaches to capture spatial and temporal correlations simultaneously. During the traffic data collection phase, the absence of vehicles on each road or sensor malfunctions can result in the collection of traffic time series data as zeros. However, storing such zero values makes accurate traffic prediction more challenging. To address this challenge, we present a novel model for improving traffic forecasting using graph convolutional recurrent neural networks. The proposed method is evaluated on two real-world public benchmark datasets and compared with six baseline models, showcasing its superior performance. © 2023 IEEE.

关键词： Graph Neural Networks Spatio-Temporal Time-Series Traffic Forecasting

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Building Resilient Rural Communities: Participatory Approaches in Khattukhal Village for Sustainable Development 1

Building Resilient Rural Communities: Participatory Approach...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Makadi, Yila Caiaphas Gadde, Ashok Patel, Aniket Adusumilli, Tarun Paladugu, Suchal Sankranti, Srikar Cornet, Souresh Amrita School for Sustainable Futures Amritaputi Amrita Vishwa Vidyapeetham India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electrical and Computer Engineering Coimbatore India

ISBN: (纸本)9798350306637

The work presents a comprehensive study conducted in a village in Uttarakhand, India namely Khattukhal village, concentrating on the identification of sustainable development concerns and the implementation of participatory solutions. Using a combination of Participatory Rural Appraisal (PRA) tools and Human-Centered Design (HCD) methodologies, the very first stage of work focuses mainly on to analysis, which includes surveys, observations, brainstorming sessions, as well as problem tree analysis, which aims to address the community's pressing problems. The study emphasises the serious issues with river and canal systems, the lack of systems for collecting water and effective irrigation techniques, and insufficient waste management practises. This study report presents practical strategies to minimise these problems and encourage sustainable development in Khattukhal village by proactive community engagement and partnership with local stakeholders. © 2023 IEEE.

关键词： Sustainable development

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S-Band Circularly Polarized Wideband Loaded Slot Antenna for CubeSat Applications

S-Band Circularly Polarized Wideband Loaded Slot Antenna for...

引用

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Hassan, Omar S. Abou-Khousa, Mohamed Al-Wahedi, Khaled Hussain, Rifaqat Khalifa University of Science and Technology Dept. of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

With the recent proliferation of CubeSat missions, there is a critical need for space-ready antennas with a desired set of characteristics. In this paper, a wideband, circularly polarized (CP), S-band loaded slot antenna with a co-planar waveguide (CPW) feed is proposed for CubeSat applications. The antenna geometry is presented, and its performance is validated through extensive numerical electromagnetic simulations and measurements. The proposed antenna is shown to achieve fractional impedance bandwidth of 37.1% centered around 3.5 GHz, axial ratio bandwidth (ARBW) of 18.75%, and 7-8 dBi gain all while being lower profile and constructed on space-qualified substrate (i.e., Rogers RO4350B). © 2022 IEEE.

关键词： Circular polarization

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Analysing Breast Cancer Classification Using Explainable Artificial Intelligence

Analysing Breast Cancer Classification Using Explainable Art...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Kazi Fardeen Harshadeep Bhaduri Soham Hazra Praloy Mondal Sarbajit Manna Tapas Si Dept. of Computer Science & Electronics Ramakrishna Mission Vidyamandira Howrah West Bengal India Dept. of Computer Science & Engineering University of Engineering & Management Jaipur Rajasthan India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

Worldwide, breast cancer is becoming the most serious illness that affects women. It is believed that early diagnosis and treatment of breast cancer can increase survival rates and decrease the need for surgery. Machine Learning model is very reliant on features for their proper training. However, understanding how a prediction is being affected by specific features is very important for a model’s interpretation. Understanding what features support a prediction is important as it provides some transparency to the inner workings of the model. Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are used to classify the breast cancer data, and accuracy, sensitivity, specificity, false-positive rate, precision, F 1 -score, and Geometric-Mean (GM) are used for the performance assessment. Furthermore, Multi-Criteria Decision Making (MCDM) is used to evaluate overall performance assessment based on the aforementioned performance measures and DT is found to be best among all the classifiers. Finally, an Explainable AI model namely LIME is used to interpret the predicted outcomes and impact of the different features of the data on the model’s prediction.

关键词： Support vector machines Radio frequency Analytical models Accuracy Explainable AI Predictive models Nearest neighbor methods Breast cancer Data models MCDM

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Firefly Based Node Localization Algorithm 2

Firefly Based Node Localization Algorithm

引用

2nd International Conference on Informatics, ICI 2023

作者： Kaur, Amanpreet Aggrawal, Niyati Maheshwari, Aastha Rathee, Anu Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Noida India Maharaja Agrasen Institute of Technology Dept of IT Delhi India

ISBN: (纸本)9798350343830

Node localization is an important requirement in the area of wireless sensor networks. Numerous proposals have been put forward in this area, but still all these proposals are in babyhood. The main limitation of node localization algorithm is accuracy. In order to achieve this objective, an improved node localization algorithm is proposed that applies one of the popular algorithms that is Firefly Optimization Algorithm. Simulation Results prove this fact. © 2023 IEEE.

关键词： Optimization

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EFFICIENT PARALLEL SIMULATION OF NETWORKED SYNCHRONOUS DISCRETE-EVENT SYSTEMS

EFFICIENT PARALLEL SIMULATION OF NETWORKED SYNCHRONOUS DISCR...

引用

2024 Winter Simulation Conference, WSC 2024

作者： Karanjkar, Neha Desai, Madhav Kushe, Akhil Natekar, Anish School of Mathematics and Computer Science IIT Goa Goa India Dept. of Electrical Eng IIT Bombay Mumbai India Dept. of Electronics and Telecomm Eng Goa College of Engineering Goa India

ISBN: (纸本)9798331534202

We present Sitar, an open-source, general-purpose modeling framework consisting of a custom modeling language and a simulation kernel, designed for efficient parallel simulation of networked Synchronous Discrete-event Systems (SDES) in applications such as communication networks and computer systems design. A unique feature of Sitar is its two-phase, cycle-based simulation algorithm that allows an efficient, race-free parallel execution on shared-memory systems. This is achieved by imposing a mild restriction on the set of SDES that can be described within the framework. The modeling language is designed for describing complex, networked systems with a static interconnection structure. We demonstrate Sitar’s modeling capability, performance and scalability through a detailed performance evaluation and a comparison against SystemC and SimPy. Our results show that Sitar’s single-threaded performance is better than that of SimPy and comparable to that of SystemC, whereas a multi-threaded execution shows near-linear scaling for several benchmark configurations. © 2024 IEEE.

关键词： Discrete event simulation

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Envisioning Revolutionary Technological Changes: The Future Terrain of Fuzzy Logic

Envisioning Revolutionary Technological Changes: The Future ...

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Disruptive Technologies (ICDT), International Conference on

作者： Sarvani Chalamalasetty Thulasi Bikku Papiya Mukherjee Nitin Tyagi Jyoti Kesarwani Abhishek Kesharwani Dept of Computer Science and Engineering KL University Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amaravati Andhra Pradesh India Dept of Computer Science and Engineering Dronacharya Group of Institutions Gr. Noida Uttar Pradesh India Dept of Computer Science and Engineering GL Bajaj Institute of Technology & Management Greater Noida Uttar Pradesh India Dept of Computer Science and Engineering United College of Engineering and Research UPSIDC Industrial Area Naini Prayagraj Uttar Pradesh India Dept of Computer Science ABES Engineering College Ghaziabad Uttar Pradesh India

The realm of intelligent systems empowers the development of applications that play a vital role in our real-world scenarios. Fuzzy logic, a valuable asset in contemporary times, finds application across diverse industries. This research aims to delve into the trends of fuzzy logic, examining current advancements through the application of fuzzy theory and computational capabilities. Additionally, the study offers recommendations for future research endeavors. The survey culminates with insights into potential design and development considerations for the future.

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