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Intensify Stroke Prediction Model Associated with Hypertensive State 3

Intensify Stroke Prediction Model Associated with Hypertensi...

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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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Seamless Service Evolution: Enhancing Customer Satisfaction Using AWS-Driven AI Chatbots for Restaurant Ordering 4

Seamless Service Evolution: Enhancing Customer Satisfaction ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ali, Mohammed Jaffer Vyshnavi, M.R.V. Kumar, Konda V.S. Harshith Vishnubhatla, Sravya Rajagopal, Shinu M. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

In the current restaurant business environment, factors have emerged in the manner in which consumers are attended to and the ways orders are placed. As good as these traditional systems may be, they do not offer the optimum flexibility and consumer convergence that today's client demands for fast and convenient services. Recent approaches using only basic form of automation and rule-based chatbots do offer some enhancements but lack adequate capabilities to deal with huge volumes of orders, custom recommendations and scalability according to demand. Further, the existing systems have problems with scalability, versatility to different customers' trends and needs, and real-time interactions. To overcome these challenges our work, will utilize modern advances in artificial intelligence and cloud technology. The proposed system uses Amazon Lex for NLU, A WS Lambda for processing multiple booking requests simultaneously without any intervention from the users and Amazon Polly for making interaction more interactive with personalized audio. In addition, Amazon Rekognition and A WS S3 for images will be used for analysis of images and proper storage of data respectively, enhancing the customer satisfaction by recommending products based on a past order. All these technologies integrated with A WS ecosystem in our solution will provide convenience, efficiency and passion to restaurant business proprietors to fulfill the increasing demands of modern customer. The system also supports linkage with some commonly used applications such as WhatsApp, guaranteeing that the customers receive timely updates. Thus, this solution is designed to create an improved status of restaurant ordering and eliminate the problematic experiences placed in current schemes. © 2024 IEEE.

关键词： Scalability

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VGGAE-Net: A Two Stream Ensemble CNN for Diabetes Retinopathy Classification 4

VGGAE-Net: A Two Stream Ensemble CNN for Diabetes Retinopath...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Girepunje, Shashank Singh, Pradeep National Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350349900

Diabetic retinopathy is a retinal disease that leads to vision impairment in people suffering from diabetes mellitus. Early detection of disease requires expert supervision and computer diagnosis systems to avoid vision loss. Diabetic Retinopathy grading is suffering from small lesions and data imbalance problems. In this work, we proposed a VGG based attentive ensemble Network (VGGAE-Net) for the classification of diabetes Retinopathy. We have also utilized the two attention blocks channel attention module and class attention module with extracted feature maps from the CNN. Then their prediction outputs are combined to provide the final output for the classification task. The experiment shows that the ensemble approach of two attention blocks with VGG model gives 96.66% accuracy as compared to the existing recent models. © 2024 IEEE.

关键词： class attention module diabetes retinopathy global attention module

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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...

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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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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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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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Securely Transmit Data over Long Distances Using Quantum Key Distribution Based on E91 Protocol 1

Securely Transmit Data over Long Distances Using Quantum Key...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： RajKumar, V. Priyadharshini, G. Dept. of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350336009

At its core, quantum computing is a fastdeveloping technology that has the potential to process massive volumes of data at high speeds. Some factoring issues may be difficult for the classical computer to solve because of the nature of the factoring. Quantum computing data connections are carried out using optical fiber at a distance as short as possible. While using the E91 protocol for quantum key distribution in a wireless network communication channel, we may extend the distance over which keys are sent. How to design a wireless communication channel for use in quantum computing applications. Using this technology, we are now investigating the development of a broadcast channel that will enhance the data transmission range while maintaining high security and minimizing time consumption. In our technique, we employed polarization multiplexing to communicate across a broadcast channel satellite connection, which allowed us to share data between the transmitter and receiver at the same time. The transfer key is protected by the use of polarization multiplexing and the E91 protocol, both of which are implemented. In a security network, it is used to create an extended communication channel distance between two nodes. In the end, we can meet our goal in this study by comparing the existing method to the performance analysis shown in a graph. © 2023 IEEE.

关键词： Quantum cryptography

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Blockchain Based Security System for Health Care 2

Blockchain Based Security System for Health Care

引用

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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Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs

Estimating Post-Synaptic Effects for Online Training of Feed...

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2024 International Conference on Neuromorphic Systems, ICONS 2024

作者： Summe, Thomas M. Schaefer, Clemens JS Joshi, Siddharth University of Notre Dame Dept. of Computer Science and Engineering United States

ISBN: (纸本)9798350368659

Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data streams in real-time. Although forward-mode differentiation enables online learning, its computational requirements restrict scalability. This is typically addressed through approximations that limit learning in deep models. In this study, we propose Online Training with Postsynaptic Estimates (OTPE) for training feed-forward SNNs, which approximates Real-Time Recurrent Learning (RTRL) by incorporating temporal dynamics not captured by current approximations, such as Online Training Through Time (OTTT) and Online Spatio-Temporal Learning (OSTL). We show improved scaling for multi-layer networks using a novel approximation of temporal effects on the subsequent layer's activity. This approximation incurs minimal overhead in the time and space complexity compared to similar algorithms, and the calculation of temporal effects remains local to each layer. We characterize the learning performance of our proposed algorithms on multiple SNN model configurations for rate-based and time-based encoding. OTPE exhibits the highest directional alignment to exact gradients, calculated with backpropagation through time (BPTT), in deep networks and, on time-based encoding, outperforms other approximate methods. We also observe sizeable gains in average performance over similar algorithms in offline training of Spiking Heidelberg Digits with equivalent hyper-parameters (OTTT/OSTL - 70.5%;OTPE - 75.2%;BPTT - 78.1%). © 2024 IEEE.

关键词： Encoding (symbols)

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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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