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Intensify Stroke Prediction Model Associated with Hypertensive State 3

Intensify Stroke Prediction Model Associated with Hypertensi...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md. Rabiul Rahman, Mst. Sahela Paul, Radha Rani Hosen, Md. Fahad Khairunnahar, Mst. Nayeem, Md. Julker Pundra University of Science & Technology Dept. of Computer Science & Engineering Bogura5800 Bangladesh

ISBN: (纸本)9798350388282

Hypertension and stroke are significant worldwide health issues with high prevalence and complex health hazards. Hypertensive state implications include death or major complications such as myocardial infarction. This study leverages data from the Internet to foresee the prognosis for individuals with hypertensive strokes. There are two main components to the clinical outcome prediction in this study. The value of various variables is ranked by our model through the use of cross-validation techniques, recursive feature disposal, and classification algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). Our empirical cross-validation results demonstrate the remarkable predictive reliability of both LR and RF once repetitive features are removed, even with a smaller set of features and classifiers. Particularly in terms of accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), logistic regression performs better than the other two classifiers. Likewise, we conducted independent prediction analyses for hypertension, hypertensive stroke, and stroke. The highest result of 96.15% is obtained using logistic regression for stroke prediction. The random forest classifier has an accuracy of 91.25% for hypertension, and combining stroke with a hypertension support vector classifier gives 91.72% accuracy. This demonstrates how well it might provide patients with hypertensive strokes with precise and trustworthy projections. Finally, our study illuminates the potentially useful prediction powers of particular algorithms in determining the prognosis of patients with hypertensive strokes. © 2024 IEEE.

关键词： Logistic regression

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Automated Lung Segmentation Using Deep Leaning in Lung CT Images 1

Automated Lung Segmentation Using Deep Leaning in Lung CT Im...

引用

1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Kumar, Arun Mahapatra, Rajendra Prasad SRM Institute of Science & Technology Dept. of Electronics and Communication Engineering Ghaziabad India SRM Institute of Science & Technology Dept. of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798331532956

Lung segmentation algorithms are crucial for accurately delineating the areas of infection within the lungs. The main aim of this research work is to generate a highly efficient and reliable deep learning model for lung segmentation. This will be achieved by utilizing chest computed tomography (CT) images and DeepLabV3+ networks. The model will be designed to classify the images into two classes (background and lung field). This work examines the efficacy of the DeepLab V3+network using three pre-trained networks: Xception, ResNet-18, Inception-ResNet-50, MobileNet-v2. The evaluation of the segmentation performance has been conducted using five performance metrics: Intersection of Union (IoU), Weighted IoU, Balance Fl score, pixel accuracy, and global accuracy. The empirical findings of this study validate that the DeepLabv3+ network, when coupled with ResNet-18 and a batch size of 8, exhibits superior performance in two-class segmentation. In the proposed work the mean accuracy and Mean IoU is 99.59% and 98.89% respectively. The automated segmentation model we have built can be enhanced to serve as a clinical diagnosis method for lung diseases. © 2024 IEEE.

关键词： Image segmentation

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A Graph Based Approach For Detecting Sybil Node In Online Social Networks 15

A Graph Based Approach For Detecting Sybil Node In Online So...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandra, Sucheta Das, Priyanka Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350370249

Sybil attacks pose a significant threat to the security and integrity of online social networks by allowing malicious actors to create multiple fake identities to manipulate network behavior. The abstract outlines a method for detecting Sybil attacks in online social networks using graph analysis. It involves constructing a graph representation of the network, calculating node centrality measures such as in-degree and out-degree, and setting a degree threshold to identify potentially malicious nodes. Additional parameters like clustering coefficient and community structure are also considered to improve detection accuracy. The proposed approach aims to enhance the security of online social networks by identifying and isolating Sybil identities, thus preserving network integrity and trustworthiness. The authors' firmly assert that employing graph-based theory for Sybil node detection in online social networks stands out as one of the most effective methods. This approach not only offers robustness but also scalability, allowing for efficient detection even in large-scale networks. With its ability to unveil intricate relationships, anomalies, and patterns within the network, graph-based theory emerges as a cornerstone in the fight against Sybil attacks. © 2024 IEEE.

关键词： Cyber attacks

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Machine Learning-Powered Defense Against Phishing Websites

Machine Learning-Powered Defense Against Phishing Websites

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Dwivedi, Dipesh Ahmed, Sajjad Patel, Adarsh Azath, H. Vit University Dept. of Computer Science and Engineering Sehore466114 India

ISBN: (纸本)9798350348460

The proliferation of phishing URLs has experienced rapid growth in recent years, necessitating urgent attention to phishing attack detection in cybersecurity. In response, we introduce an improved predictive model that leverages both machine learning and a Deep Learning Model. Our dataset consists of 88,674 instances, encompassing 112 features, including the class level. Notably, this dataset comprises 58,000 legitimate instances and 30,647 phishing *** proposed method incorporates six distinct algorithms: Decision Tree, Support Vector Machine, K-nearest neighbors, Logistic Regression, Ensemble Classifier, and Neural Network algorithm. we systematically evaluated total 28 models performance with different types of feature changes. Following enhancements, every algorithm demonstrated an accuracy performance surpassing 93.16%. Among them, the Ensemble Classifier emerged as the most effective, boasting an accuracy 97.45%, MCC 94.37%, precision a96.35%, and an F1 Score 96.31%. © 2024 IEEE.

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Blockchain Based Security System for Health Care 2

Blockchain Based Security System for Health Care

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Ankit Singh, Rishipal Guru Jambheshwar University of Science & Technology Dept. of Computer Science & Engineering Hisar India

ISBN: (纸本)9798350383652

Security in healthcare systems based on blockchain technology is the subject of ongoing research. Block Chain technology has the potential to revolutionize many different sectors since it provides a decentralized, incontestable, and publicly verifiable record of every transaction activity. Bitcoin's foundational mechanism is referred to as 'block chain technology.' This technology allows for completely decentralized transaction processing. Healthcare aims to diagnose, treat, relieve, and cure any and all bodily and mental health issues, as well as to promote health and prevent sickness. Since all transactions in a blockchain are encrypted, it provides the highest level of security possible. Blockchain networks provide the same assurance of security for medical records. The integrity of the network's ledgers may be checked by checking file signatures on each node to make sure no alterations have been performed. The signature will no longer be valid when the record has been modified. The future of blockchain and big data applications is promising. Big Data technology can manage any kind, amount, or speed of data, while blockchain enhances efficiency across all industries. It has been noticed that medical records are maintained on a blockchain for reasons of security. Proposed system has evaluated healthcare data for security. When compared to the results of cutting-edge research, however, the scalability and adaptability of traditional methods are clearly lacking. Better performance and security utilizing blockchain technology is essential, hence developing such a system is a priority. © 2024 IEEE.

关键词： Electronic health record

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Efficient Hyperspectral Data Compression using 3D Convolutional Autoencoder 3

Efficient Hyperspectral Data Compression using 3D Convolutio...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Afrin, Afsana Mamun, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

Hyperspectral image has large size due to abundance of spectral bands preventing efficient storage and transmission in real-time applications. This paper introduces a 3dimensional convolutional autoencoder (CAE) for hyperspectral data compression, utilizing CAE for both compression and subsequent decompression. Our method achieves improved Compression Ratio (CR) values of 982 and 960 on the KSC and Botswana datasets, along with better Peak Signal-to-Noise Ratio (PSNR) results of 55.29 and 52.64, surpassing existing 2D convolutional autoencoder-based HSI compression. Our approach also shows negligible differences in classification accuracy between original image and the reconstructed version of the compressed image, confirming its effectiveness in real-world applications. © 2024 IEEE.

关键词： Convolution

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A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Model with Beam Search

A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Mod...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Javed City University Dept. of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331529765

Chatbots, also known as talkbots or interactive agents, are software applications designed to facilitate communication between humans and machines. While most students in Bangladesh currently waste their valuable time engaging in casual conversations with each other, using a Bengali educational chatbot for such interactions would provide them with a more beneficial use of their time by providing hand note-wise answers for physics. The challenge faced by a Bengali chatbot lies in the necessity of creating a dataset in Bengali, given the scarcity of available Bengali datasets online. We have developed a physics-focused educational chatbot, akin to personalized hand notes, offering comprehensive answers to aid students in learning and revising complex physics concepts. The development of chatbots involves diverse techniques, including rule-based systems, machine learning-based approaches, and generative models. Our proposed system implements the seq2seq model with a beam search decoding algorithm, featuring BiLSTM layers for enhanced context understanding and efficient exploration of potential output sequences. This approach outperforms traditional seq2seq models, ensuring higher accuracy and delivering high-quality responses. Finally, we obtained the highest BLEU1, BLEU2, BLEU3, and BLEU4 scores, respectively, of 0.52, 0.41, 0.29, and 0.23. © 2024 IEEE.

关键词： Chatbots

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Preliminary Design of Hybrid Simulation Framework for Workload Analysis in Automotive Edge Computing 15

Preliminary Design of Hybrid Simulation Framework for Worklo...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kwon, JiHyun Park, SangEun Chwa, Hoon Sung Kwak, Jeongho Choi, Ji-Woong Kim, BaekGyu Dgist Dept. of Electrical Engineering and Computer Science Daegu Korea Republic of

ISBN: (纸本)9798350364637

Many vehicular tasks shall be completed in a timely manner as they move. In automotive edge computing, a vehicle can offload such tasks to an edge server in proximity to process them faster than using the cloud computing model. However, such an edge server has limited communication coverage and relatively smaller computing power compared to the general cloud server, it is important to distribute the offloaded workloads appropriately. One needs to understand such workload patterns and quantitatively analyze them for such workload distribution. We propose a preliminary hybrid simulation framework that generates compute workloads via a combination of the virtual vehicular traffic and the physical edge-server platforms. © 2024 IEEE.

关键词： Computation offloading

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PARKIT: An Android-based Real Time Smart Parking System using IoT 10

PARKIT: An Android-based Real Time Smart Parking System usin...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Agarwal, Shruti Gupta, Shagun Agarwal, Piyush Sharma, Garima Tripathi, Vikas Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

Internet of Things (IoT) connects individual devices, software, and sensors over a network without misnomers of a public domain. It is a very common scenario in designing better low-cost enterprise solutions for smarter homes, cities, and healthcare. One key issue smart cities are dealing nowadays is car parking. It directly affects traffic congestion, costing people time and fuel. In this work, we solve the current problem by the implementation of an android application with the on-site deployment of an IoT module, where users can search for nearby parking spaces, reserve multiple slots according to their preference, and navigate to the selected location. Also, the application is capable of automatically notifying users of nearby parking locations in real time. Additionally, for efficient parking management, users will get an alert until the parking of a car is not done within the allotted boundaries. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Cardiovascular Disease Prediction Utilizing Machine Learning and Feature Selection with Clonal Selection Algorithm 5

Cardiovascular Disease Prediction Utilizing Machine Learning...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Wahidur Aneek, Riasaad Haque Moinuddin, Md. Sakib, Md. Shadman Iqbal, Md. Sadiq Rahman, Mohammad Motiur Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Z.H. Sikder University of Science and Technology Dept. of Computer Science and Engineering Bhedarganj Shariatpur Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Mawlana Bhashani Science and Technology University Dept. of Computer Science and Engineering Tangail Bangladesh

ISBN: (纸本)9798350394290

A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases. © 2023 IEEE.

关键词： Feature Selection

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