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14th International Conference on Cloud Computing, data science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Diwakar, Manoj Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

With employing the Internet of Things (IoT) sensors and modern-day machine learning algorithms, this study presents a modern inquiry at the intersection of healthcare and era. This have a look at studies the capability of Long ShortTerm Memory (LSTM) and Artificial Neural Network (ANN) fashions in forecasting affected person fitness consequences by utilising the exploitation of sensor facts inclusive of temperature, blood strain, ECG, EEG, and pulse price. The study extensively examines the models' performance utilising a broad variety of performance benchmarks. Results demonstrate that once compared to the LSTM model, the ANN version performs better in terms of prediction accuracy, precision, do not forget, and Fl score. This enhanced accuracy indicates how effectively the ANN model can spot challenging patterns in the dataset. The predicted fitness possibilities for 15 human individuals also are proved in a result table, reaffirming the ANN models constant advantage in prediction. This look at proactive patient care, more intelligent treatment regimens, and improved overall patient outcomes is driving the business and is a significant step towards personalised, data-driven healthcare solutions. © 2024 IEEE.

关键词： Forecasting

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Integrated Web-Based Platform for AI-Enhanced Autism Assessment 2

Integrated Web-Based Platform for AI-Enhanced Autism Assessm...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Yasir Abdullah, R. Rk, Kamesh Kavin Prabu, S. Sri Subiksha, T. Venkatraman, P. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798331534950

This research presents a novel web-based platform that leverages facial and vocal tracking to enhance the preliminary screening process for Autism Spectrum Disorder (ASD). By incorporating advanced machine learning algorithms to analyze multimodal data, the platform offers a non-invasive, accessible, and cost-effective solution for initial ASD evaluation. The system demonstrates high diagnostic accuracy, achieving 90% in facial pattern analysis, 85% in vocal pattern analysis, and 95% accuracy when both analyses are combined. Additionally, it includes communication features designed to foster a supportive environment for individuals with ASD. This paper provides an overview of the platform's development, functionality, and future potential, with an emphasis on its capacity to improve existing diagnostic methods and its accessibility for underserved communities. © 2024 IEEE.

关键词： Adversarial machine learning

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Cluster-Based One Shot Deeplabv3+ for Bird Image Segmentation

Cluster-Based One Shot Deeplabv3+ for Bird Image Segmentatio...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Chandrashekar, B.S. Nagendraswamy, H.S. Pavan Kumar, M.P. University of Mysore Department of studies in computer science Karnataka Mysuru India Jawaharlal Nehru National College of Engineering Information Science and Engineering Karnataka Shivamogga India

ISBN: (纸本)9798331528126

In this work, clustering based one shot approach for segmentation of bird images from complex background using Deeplabv3+ is proposed. Hierarchical clustering technique employed with average linkage is used to obtain clusters of bird images consisting of a particular class. The concept of clustering is exploited to capture intra-class variations and to train the learning model more efficiently and more effectively. Mobilenetv2 architecture is explored to extract features from each bird image, which are used for clustering. After obtaining clusters for each bird's species (class), one representative bird image from each cluster is considered for training the model. The remaining samples in the cluster are used for testing the model's performance in segmenting the bird image from complex background accurately. The proposed segmentation approach is tested on the benchmark bird image dataset called CUB-200-2011 dataset. The experimental results have proved that the proposed cluster-based one shot Deeplabv3+ approach has produced good results with less training. © 2024 IEEE.

关键词： Zero-shot learning

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A Secure and Transparent Voting System Framework Using Finger Vein and Blockchain Technology 16

A Secure and Transparent Voting System Framework Using Finge...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Padal, Chittapuli Bhupathi Vatsavayi, Valli Kumari Andhra University College of Engineering Department of Computer Science and Systems Engieering Visakhapatnam530003 India

ISBN: (纸本)9798331505264

The traditional voting system faces widespread mistrust due to its vulnerability to exploitation, lack of transparency, and limited reliability, leading to concerns about the security of democratic rights. Attempts to digitize voting have also faced challenges, with current digital systems often failing to ensure the transparency and security necessary for fair elections. Both traditional and current digital voting systems are susceptible to exploitation, and their inherent weaknesses often lead to mistrust and vulnerability. This proposed solution is secure and transparent voting system framework that leverages finger vein recognition technique integrated with block chain technology to ensure the integrity and confidentiality of votes. Blockchain technology to create a more secure, transparent and reliable voting system. By utilizing blockchain, we aim to address the issues in both physical and digital voting systems, ensuring a fair election process that upholds voters' democratic rights. Blockchain technology offers a platform that maximizes transparency and reliability, fostering a trustworthy relationship between voters and election authorities. each Voter will be registered with Finger Vein as the password and this finger vein will be encrypted and secured by using Pepper-Salt algorithm. Our proposed framework allows voting activities to be conducted entirely online through blockchain, removing the need for physical polling stations. The system employs a scalable blockchain supported by flexible consensus algorithms, with a Chain Security Algorithm to enhance transaction security. © 2024 IEEE.

关键词： Voting machines

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Empirical Analysis of Digital Currency Herding and Contagion Effect Using Deep Learning Approaches 3

Empirical Analysis of Digital Currency Herding and Contagion...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Tripathy, Nrusingha Hota, Sarbeswara Mishra, Debahuti Dash, Biswajit Nayak, Subrat Kumar Tripathy, Jogeswar Deemed to Be University Department of Computer Science and Engineering Siksha 'O' Anusandhan Odisha Bhubaneswar India Deemed to Be University Department of Computer Application Siksha 'O' Anusandhan Odisha Bhubaneswar India Raajdhani Engineering College Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

In recent years, the use of virtual or cryptocurrency-based money has grown. Blockchain, a digital accounting system, is the foundation of cryptocurrency. The markets for cryptocurrencies are eminent for their high volatility and quick price swings. People tend to be more likely to follow the lead of others at times of notable price fluctuations out of a concern of losing money or missing out on possible gains. In this work, we taking the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), the hybrid combinations of LSTM-Convolutional Neural Network (CNN), LSTM-Gated Recurrent Unit (GRU) and for Facebook Prophet (Fb-Prophet) model smoothing, triple exponential smoothing (Holt-Winters Method) is utilized, which adds seasonality into the forecasting model. In this work bitcoin exchanges providing real-time information casing the period from January 2012 to July 2020 is utilized. The price of cryptocurrencies fluctuates significantly throughout a range of time periods, from short-term intraday swings to long-term patterns that last for weeks or months. By altering the network layer design and the duration of the input sequences, LSTM-GRU models configured to adapt to various time horizons. In contrast to previous iterations, the hybrid LSTM-GRU performs well. When compared to other models, the model's Mean Absolute Error (MAE) score of 0.056 and Root Mean Square Error (RMSE) score of 0.073 are quite acceptable. Forecasting the value of cryptocurrencies will help traders and investors in making better-informed choices when purchasing, disposing of, and keeping assets. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-based System for Multi-Head Dual Attention Generative Adversarial Network-based Soil Nutrition and Plant Disease Detection in Smart Agriculture 8

IoT-based System for Multi-Head Dual Attention Generative Ad...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Shukla, Arvind Kumar Krishnaraj, Kowsalyadevi Venkatesh, R.J. Santhanalakshmi, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India Sri Venkateswara College of Engineering Department of Eee Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Dindigul India

ISBN: (纸本)9798350376425

The Internet of Things (IoT) has been increasingly popular in recent years due to its many uses. Regarding the agricultural industry, automated systems for identifying plant diseases offer mixed benefits and some drawbacks. Hence Multi-Head Dual Attention Generative Adversarial Network (MHDAGAN) based plant disease detection in IoT is proposed. In order to address the absence of infrastructure in this area, the proposed approach seeks to identify stress to plants caused by crop diseases, environmental imbalance, and soil fertility. The proposed scheme uses nodes to capture plant leaf images, with a sink node collecting information from an automated module for IoT-based monitoring. The Combined Bilateral Parametric Filtering (CBPF) is used to pre-process the nodes' images so they are appropriate for the plant disease detection. After pre-processing, DenseNet201is proposed for extracting the Statistical features of the images. Then Multi-Head Dual Attention Generative Adversarial Network (MHDAGAN) is proposed for segmenting and classifying the plant leaf as healthy and disease. In order to improve the classification accuracy, Humboldt Squid Optimization Algorithm is proposed. Utilizing criteria such as sensitivity, accuracy, energy of nodes and specificity on various IoT environments, the detection performance is verified overall. According to the simulation findings, the suggested strategy performs better in terms of detection, with an accuracy of 99.46%. © 2024 IEEE.

关键词： Plant diseases

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ECDSA-based tamper detection in medical data using a watermarking technique

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 78-87页

作者： Ch, Rupa Vivek K, Naga Srivastava, Gautam Reddy Gadekallu Department CSE VR Siddhartha Engineering College Andhra Pradesh Vijayawada520 007 India Dept of Math and Computer Science Brandon University MB Canada Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara Punjab Rajpura140401 India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Lovely Professional University Division of Research and development Phagwara India Jiaxing University College of Information Science and Engineering Jiaxing China

Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the or

关键词： Mean square error

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Prediction of Diabetics Using Hybrid Feature Selection with KNN and ANN 1

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Sandhya, G. Kamalie, K. Kesini, K. Mohanapriya, G. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India

ISBN: (数字)9789819983490

ISBN: (纸本)9789819983483

The chronic disease which is affecting people among world countries is diabetes. In the early days, only aged people were affected by diabetes. But as the food style and the lifestyle of the people changes, even small age children are now a days affected by diabetes. This makes the world panic about this disease. At present, many are unaware about the causes of this chronic disease. People cannot even predict and tell that they are free from this disease. If this disease is unnoticed by the patient for the long time, then it results in vulnerable conditions. To examine and create the diabetes prediction model, a branch of Artificial Intelligence (AI) which is called by the name Machine Learning (ML) is applied. The system uses Hybrid Optimisation with Ant Colony Optimisation (ACO) with Simulated Annealing (SA) for the selection of the best features and K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) for the purpose of classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ant colony optimization

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Privacy-Preserving Federated Learning for Predictive Healthcare on Heterogeneous Medical data 2

Privacy-Preserving Federated Learning for Predictive Healthc...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Erukala, Mahender Sanampudi, Suresh Kumar Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Department of Information Technology Telangana Jagtial India

ISBN: (纸本)9798331534950

In modern healthcare, preserving patient privacy while utilizing data for predictive analytics remains a critical challenge. Federated Learning (FL) provides an efficient strategy by providing decentralized model training around different institutions without requiring the transfer of sensitive patient in- formation. This study investigates the implementation of privacy- preserving FL for predictive healthcare, specifically targeting heart disease, kidney disease, and diabetes. Each client in the FL network trains a neural network locally on disease-specific datasets, followed by model aggregation using ensemble learning and knowledge distillation to enhance global model performance while ensuring data confidentiality. Empirical results reveal high predictive accuracies: 79.39% for heart disease, 92.45% for kidney disease, and 70.36% for diabetes. The ensemble models, which synthesize knowledge between clients, maintain these accuracy levels, underscoring the efficacy of FL in delivering robust, privacy-preserving diagnostic models. These findings demonstrate the potential of FL to securely integrate data from multiple healthcare institutions, offering a pathway for scalable, decentralized predictive models in clinical settings. Further studies include the incorporation of more advanced deep learning architectures, the development of personalized federated models tailored to individual patient populations, and the extension of FL to a wider array of medical conditions. Additionally, addressing challenges related to model interpretability and managing client heterogeneity will be essential for broader implementation. This work lays the groundwork for the advancement of privacy- preserving AI solutions in healthcare, with profound implications for regulatory-compliant machine learning applications. © 2024 IEEE.

关键词： Federated learning

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Early Prediction of Chronic Obstructive Pulmonary Disease: A Deep Transfer Learning Approach 2

Early Prediction of Chronic Obstructive Pulmonary Disease: A...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Rukumani Khandhan, C. Gothai, E. Arun, G. Bharathi Priya, R. Kongu Engineering College Department of Computer Science & Engineering Perundurai Erode638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Pg Chennai600062 India Kongu Engineering College Department of Computer Technology -PG Tamil Nadu Perundurai638060 India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368413

Chronic Obstructive Pulmonary Disease (COPD) is a prevalent, crippling lung ailment that often progresses undetected until its advanced stages, leading to significant morbidity and mortality. Early prediction and interference are crucial to improving patient outcomes. Here, a novel approach for the early prediction of COPD using deep transfer learning is proposed. Three cutting edge models from deep transfer learning, namely Inception, ResNet, and VGGNet, are trained to extract and classify features from audio recordings of breathing sounds. These models are fine-tuned on a dataset consisting of sounds from both healthy individuals and those with COPD, along with six other lung disorders. The extracted features are used to predict the presence of COPD, enabling early detection and intervention. The outcome of the proposed methodology demonstrates the efficiency of the model's capability to predict COPD, with the deep transfer learning models achieving high precision in distinguishing COPD patients from non-COPD individuals. This approach shows the potential for cost-effective and early prediction of COPD, facilitating timely interventions and improved management of the disease. © 2024 IEEE.

关键词： Lung cancer

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