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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Aditya Pathak Mahek Kamani R. Priyanka Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

Biomedical imaging is a growing domain in the field of medical science. The automation of tumour segmentation can be achieved by the utilisation of artificial intelligence and deep learning techniques. Brain tumour is considered to be one of the potentially lethal medical conditions, characterised by a relatively high fatality rate. Therefore, the detection and segmentation of such tumors becomes important at an early stage. The process of tumour segmentation entails acquiring a three-dimensional anatomical image of the brain through the use of magnetic resonance imaging (MRI), which is composed of many slices. The process of manually segmenting the tumour is a time-consuming activity that requires significant effort, leading to potential variations in observations across different individuals. The automated segmentation would be swift and accurate, allowing for early detection of cancer. This research study proposes the utilisation of a ResUNET model for the purpose of segmenting brain tumours. The proposed network employs the Tversky loss function as a means to address the challenge of class imbalance present in the dataset. Consequently, the model achieves a dice score of 0.95 and an intersection-over-union of 0.90, enabling it to generate a segmentation map that matches the dimensions of the original input image. which attests to the model's robustness and the high IoU indicating a better alignment of the predicted and actual regions. The model would perform better when compared to the existing UNET/ modified UNET models.

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Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Bhanu Pratap Singh Rathore Rutul Dave Dhanasekaran K Priyadarsini K Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively.

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Blockchain-enabled Device Authentication and Authorisation for Internet of Things

Blockchain-enabled Device Authentication and Authorisation f...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Raman Singh Sean Sturley Bhisham Sharma Imed Ben Dhaou School of Computing Engineering and Physical Sciences University of the West of Scotland United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Engineering Computing and Informatics Dar Al-Hekma University Saudi Arabia

The Internet of Things (IoT) is the network of multiple devices known as “things” which includes sensors, security cameras, smart lights, smart TV, traffic lights etc. in the smart home or industrial environment. In many applications, these IoT devices are installed in open areas for example traffic lights/ security cameras in a smart city. Strong authentication and authorisation for these devices need to be deployed to ensure trust among IoT networks. IoT devices produce and forward security-sensitive data and hence confidentiality, authentication and proper authorisation should be the primary priority of an IoT system. Implementing Certificate Authority-based digital certificate solutions is costly because of the number of devices involved in IoT networks. Blockchain is a decentralized ledger-based technology which can help to provide seamless yet cost-effective solutions for confidentiality, authentication, and authorisation for IoT environments. A blockchain-based system for device registration, authentication, authorisation, and data confidentiality is proposed. The paper shows the methodological and procedural details of the proposed security scheme.

关键词： Authorization Technological innovation Smart cities Supply chains Authentication Cameras Blockchains

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FPPT and Fixed-time Sliding Mode Approaches for a Single-stage Grid-integrated PV System

FPPT and Fixed-time Sliding Mode Approaches for a Single-sta...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Pooyan Alinaghi Hosseinabadi Sajib Ahmed Hemanshu Pota Saad Mekhilef Georgios Konstantinou School of Engineering and Information Technology UNSW Canberra Power Electronics and Renewable Energy Research Laboratory Universiti Malaya School of Science Computing and Engineering Technologies Swinburne University of Technology School of Electrical Engineering and Telecommunications UNSW Sydney

This article introduces a flexible power point tracking (FPPT) algorithm for a single-stage grid-integrated photovoltaic (PV) system. The proposed FPPT algorithm utilizes the scanning approach, which can operate under partial shading conditions. This algorithm cooperates with the voltage controller, current controller, and phase-locked loop (PLL) to ensure extracting a predefined power (within the maximum available power) from the solar PV array and delivering the extracted power into the grid. To make this possible, the use of new forms of robust fixed-time sliding mode approaches is proposed. A chattering-free fixed-time sliding mode voltage controller is defined to track the DC-link voltage reference provided by FPPT. A simple fixed-time sliding mode current controller is designed to maintain the unity power factor and to track the current reference provided by the voltage controller. A PLL algorithm based on a novel fixed-time sliding mode approach is defined to detect the grid voltage information and provide it to the inverter controllers. Stability analysis of the closed-loop system is given using the Lyapunov theorem. The effectiveness of the suggested scheme is demonstrated using simulation results in comparison with the conventional schemes.

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MODERATE DEVIATION PRINCIPLES FOR A REACTION DIFFUSION MODEL IN NON-EQUILIBRIUM

arXiv

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arXiv 2024年

作者： Zhao, Linjie School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

We study moderate deviations from hydrodynamic limits of a reaction diffusion model. The process is defined as the superposition of the symmetric exclusion process with a Glauber dynamics. When the process starts from a product measure with a constant density, which is a non-equilibrium measure for the process, we prove that the re-scaled density fluctuation field satisfies the moderate deviation principle. Our proof relies on the so-called main lemma developed by Jara and Menezes [5, 6]. © 2024, CC BY.

关键词： Equilibrium constants

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Predictive Modelling of Diabetes Complications: Insights from Binary Classifier on Chronic Diabetic Mellitus

Predictive Modelling of Diabetes Complications: Insights fro...

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Communication, Computer Sciences and engineering (IC3SE), International Conference on

作者： Smita Panigrahy Sachikanta Dash Sasmita Padhy Naween Kumar Yajnaseni Dash Computer Science and Engineering GIET University Gunupur Odisha School of Computing Science and Engineering VIT Bhopal University Bhopal MP School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Chronic diabetes Mellitus (DM) leads to severe consequences such as nephropathy, neuropathy, and retinopathy. Various learning algorithms are employed to forecast and categorize eight issues, encompassing metabolic syndrome and dyslipidemia. Machine learning is revolutionizing various domains by enabling algorithms to extract models from data. The main objective of this proposed study is to utilize binary classifiers to predict and categorize eight problems associated with DM. For this study we have used the dataset obtained from the Jeevandeep Hospital and Research Centre (JHRC) in Baripada, Mayurbhanj, Odisha, India. It includes 1296 records and has nine input variables and eight output categories that indicate different issues. Multiple models were constructed to address problems like Nephropathy, neuropathy, dyslipidemia, diabetic foot, metabolic syndrome, retinopathy, hypertension, and obesity. Different preprocessing operations on missing data, unbalanced data, and feature selection are employed. Binary classifiers were developed for each type of complication, with the number of records varied for each. Subsequently, an evaluation was conducted. The models achieved a maximum of 95.7% accuracy and 97.9% F1 score. The study demonstrates the effectiveness of using selected features to build accurate classifiers for predicting diabetes complications.

关键词： Hypertension Machine learning algorithms Retinopathy Predictive models Prediction algorithms Feature extraction Data models

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FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET

FADA: Flooding Attack Defense AODV Protocol to counter Flood...

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International Conference on Advances in Electronics, Computers and Communications (ICAECC)

作者： Kiruthika M Lalitha Krishnasamy Prithi Samuel Kaavya Kanagaraj Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Computer Science and Engineering School of Engineering and Technology Christ University Bengaluru Karnataka

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration.

关键词： Protocols Accuracy System performance Simulation Throughput Security AODV

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A Hierarchy-Based Dynamic Routing Protocol for LoRa Mesh Networks

A Hierarchy-Based Dynamic Routing Protocol for LoRa Mesh Net...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Liyuan Feng Haoyuan Yu Min Jia School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China

In this paper, we present a new routing protocol for LoRa mesh networks, the hierarchical, dynamic, and energy-efficient routing protocol (HBDR). We have developed a proof-of-concept implementation of the protocol and have shown its effectiveness in both laboratory tests and a field trial in a real-world LoRa deployment, which shows better performance than the traditional AODV protocol.

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A Pseudonym Exchange-Based Traceable Location Privacy Protection Scheme for IoV

A Pseudonym Exchange-Based Traceable Location Privacy Protec...

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International Conference on Networking and Network Applications (NaNA)

作者： Weigang Ma Yaping Yu Yichuan Wang Xiaoxue Liu Zhoukai Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology

Internet of Vehicles (IoV) is a network built by communicating entities such as vehicles and roadside infrastructure, etc. The entities can communicate with each other, sharing data and providing services. However, the openness and interoperability (location services) of IoV makes the communication entity vulnerable to attacks. when the vehicle requests location services, it will expose its own location information, which will cause serious personal safety problems. To address the location privacy leakage problem in the IoV, this paper proposes a pseudonym exchange-based traceable location privacy protection scheme for IoV. Our scheme not only provides location privacy protection, but has the following properties: First, the whole pseudonym exchange process does not require trusted third-party participation; Second, each vehicle performs a series of hash operations on several dynamic anonyms and obtains a corresponding fixed-length hash value as the corresponding account in order to cut off the connection between the real identity, which is different in each run. If a vehicle is compromised and utilized to launch attack, the trusted center (CA) can also recover his/her real identity; Last, the experimental results show that the pseudonym exchange process and pseudonym update process cost less time compared to relevant literature. Hence, these features make our scheme very suitable for computation-limited mobile devices compared with other related existing schemes.

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A Power Efficient Solution to Determine Red Blood Cell Deformation Type Using Binarized DenseNet 1st

A Power Efficient Solution to Determine Red Blood Cell Defor...

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1st International Conference on Advances in computing Research, ACR’23

作者： Reza, Md Tanzim Dipto, Shakib Mahmud Parvez, Mohammad Zavid Barua, Prabal Datta Chakraborty, Subrata Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Information Technology APIC Adelaide Australia Information Technology Torrens University Adelaide Australia Peter Faber Business School Australian Catholic University Blacktown Australia School of Computing Mathematics and Engineering Charles Sturt University Bathurst Australia University of Southern Queensland Toowoomba Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England Armidale Australia Cogninet Australia Pty Ltd Surry Hills Australia

ISBN: (纸本)9783031337420

Red Blood Cells (RBCs) play an important role in the welfare of human being as it helps to transport oxygen throughout the body. Different RBC-related diseases, for example, variants of anemias, can disrupt regular functionality and become life-threatening. Classification systems leveraging CNNs can be useful for automated diagnosis of RBC deformation, but the system can be quite resource-intensive in case the CNN architecture is large. The proposed approach provides an empirical analysis of the application of 28 and 45-layer Binarized DenseNet for identifying RBC deformations. According to our investigation, the accuracy of the 45-layer binarized variant can reach 93–94%, which is on par with the results of the conventional variant, which also achieves 93–94% accuracy. The 23-layer binarized variant, while not on par with the regular variant, also gets very close in terms of accuracy. Meanwhile, the 45-layer and 28-layer binarized variant only requires 9% and 11% storage space respectively to that of regular DenseNet, with potentially faster inference time. This optimized model can be useful since it can be easily deployed in resource-constrained devices, such as mobile phones and cheap embedded systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blood

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