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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Bhargava, Misha Mahi, Samarjit Rao, Vidya Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9781032911571

With its vast population and extensive healthcare requirements, India is moving toward digitalizing its health records. Safeguarding Electronic Health Records (EHRs) is crucial given the increase in security breaches and attacks, with India’s healthcare sector at the forefront. This study provides an overview of EHR security in India by exploring the EHR adoption rate in India and the barriers to its use. It focuses on the significant security challenges they bring, and the initiatives currently being implemented in India, such as the Ayushman Bharat Digital Mission (ABDM) and the recently released National Accreditation Board for Hospitals (NABH) Digital Health Standards. Integrating Blockchain in current EHR systems to resolve the observed challenges is then explored. The findings of this study show the urgency of integrating emerging technologies such as blockchain which have the potential for patient-controlled data sharing and data immutability possibly within the framework of the ABDM. This study aims to serve as a valuable resource for healthcare professionals, researchers and other regions seeking to understand and improve EHR security. © 2025 the Author(s).

关键词： Electronic health record

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An Efficient Image to Speech Generation System for Visually Impaired Individuals Using Deep Learning Techniques 10

An Efficient Image to Speech Generation System for Visually ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Priyanka, G. Nivaashini, M. Shree Akshaya, B. Subashini, M. Ritish, G. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

As the world is becoming more visually oriented, visually impaired people are struggling to access and understand visual content. This paper aims to represent the model created to assist blind and visually impaired people. This method leverages deep learning techniques to convert images into meaningful audio outputs. It involves capturing input images and processing them using advanced deep-learning algorithms. The extracted features are translated into speech. The resulting auditory output provides an alternative sensory experience for individuals with visual impairments, enabling them to perceive and interpret visual information through sound. This study implements four deep learning models namely CNN-LSTM, Resnet34, YOLOv5 and Vision Transformer to process sequential visual data capturing intricate patterns and dependencies within images. By integrating these models in this work, the Image to audio converter not only achieves superior accuracy in image recognition but also enhances the richness and depth of the auditory output, creating a nuanced and immersive sensory experience for users. The performance of the proposed models was evaluated using several metrics and it was noted that vision transformer outperformed the remaining models. This seamless integration of deep learning models amplifies the study's effectiveness in translating diverse visual inputs into detailed and coherent auditory output, thereby enhancing accessibility and inclusivity for individuals with visual impairments. © 2024 IEEE.

关键词： Image enhancement

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Corner Key Point Extracted Deep Learning Framework Based Birds Species Classification 8

Corner Key Point Extracted Deep Learning Framework Based Bir...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Jamal Mohammed, R. Hemanth, S. Harish Raj, B. Gajapathy, R. Kamalesh, P. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798331505462

Climate change and habitat destruction have caused the extinction of many bird species. Classifying bird species is a crucial aspect of ornithology, as it aids in the comprehension of species distribution, habitat needs, and environmental changes that impact bird populations. Ornithologists can use variations in bird species distributions to measure the health of a particular environment. Expert ornithologists have relied heavily on visual inspection, which can be tedious, time-consuming, and unreliable when classifying bird species. The development of technology can be used to categorize the bird species. This paper proposes Corner Key Filtered VGG16 (CKP-VGG16) that categorizes eight bird species with high accuracy. The execution was carried out using the 4000 bird images from the KAGGLE Birds species dataset. First, the CKP-VGG16 model separates the images of birds, and then it labels each image. After data augmentation on the labeled images, 84,000 bird images are produced. The augmented images are transformed into grayscale versions, which are then used in filtering techniques to produce Corner Key Point Filtered (CKP) bird image. The performance is analyzed by applying the CKP filtered bird images to the current CNN models. The application demonstrates that the suggested CKP-VGG16 model performs significantly well in classifying bird species, with a high accuracy of 99.7%. © 2024 IEEE.

关键词： Birds

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving Antibody-Antigen Interaction Prediction Through Flexibility with ESMFold

Improving Antibody-Antigen Interaction Prediction Through Fl...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Joubbi, Sara Maccari, Giuseppe Ciano, Giorgio Micheli, Alessio Milazzo, Paolo Medini, Duccio Department of Computer Science University of Pisa Pisa Italy Data Science for Health Lab Fondazione Toscana Life Sciences Siena Italy

Antibodies are essential proteins in the immune system due to their capacity to bind to specific antigens. They also play a critical role in developing vaccines and treatments for infectious diseases. Their complex structure, with variable regions for antigen binding and flexible hinge regions, presents challenges for accurate computational modeling. Recent advancements in deep learning have revolutionized protein structure prediction. Despite these advancements, predicting interactions between antibodies and antigens remains challenging, mainly due to the flexibility of antibodies and the dynamic nature of binding events. This study uses fingerprint-based methodologies that incorporate ESMFold confidence scores as a flexibility feature to model Ab-Ag interactions. Our methodology shows how including flexibility has improved Ab-Ag interactions by 3%, achieving an AUC-ROC of 91%. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Vaccines

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Barrier Function-Based Backstepping Fractional-Order Sliding Mode Control for Quad-Rotor Unmanned Aerial Vehicle under External Disturbances

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IEEE Transactions on Aerospace and Electronic Systems 2024年第1期60卷 716-728页

作者： Liu, Bowen Wang, Yingxun Mofid, Omid Mobayen, Saleh Khooban, Mohammad Hassan Beihang University School of Automation Science and Electrical Engineering Beijing100191 China University of Zanjan Department of Electrical Engineering Zanjan45371-38791 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliou64002 Taiwan Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark

The main control objective for the quad-rotor system is the attitude and position tracking control which is accomplished in this article using the backstepping fractional-order sliding mode control approach combined with the adaptive tuning based barrier function theory. The quad-rotor's dynamical model is obtained at the appearance of the disturbance that is entered to the quad-rotor system by an exterior force. Hence, the attitude and position tracking errors between the quad-rotor's actual states and their desired trajectory are defined. Afterward, the fractional-order sliding surfaces are defined to guarantee finite-time reachability of the defined tracking error. The designed fractional-order surface eases the control process due to the removal of the high-derivative of virtual controllers. Then, the extended adaptive barrier function laws named fractional-order-based adaptive barrier function laws are employed to eliminate the knowledge of the upper bounds of external disturbances. The Lyapunov theory concept mixed with the backstepping control strategy is used to demonstrate that the fractional-order sliding surface reaches the neighborhood of origin in the finite time. Finally, simulation results in various scenarios on the MATLAB/ Simulink environment are provided to show that the designed control procedure in this article is an efficient control technique to assure that attitude and position's trajectories of the quad-rotor system track desired trajectories properly in the existence of the external disturbances. Moreover, the designed method is compared to the existing method to validate the efficiency of the proposed method. Lastly, experimental results using the Speedgoat real-time target machine is implemented to assure validity of the recommended method. © 1965-2011 IEEE.

关键词： Backstepping

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ResNet Blended Five Dense Layered Deep Fully Connected Neural Network based Road Damage Classification 2

ResNet Blended Five Dense Layered Deep Fully Connected Neura...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Keerthivasan, R. Jai Harshan, H. Kamalesh, P. Harishraj, B. Hibban Rashid, M.D. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

Operating a vehicle on a deteriorated roadway causes an enormous threat to safety, particularly for self-driving automobiles. Deep learning-based image processing technology has made it possible to study models that assess roadway conditions. Disparity of roadway damage and complicated backgrounds in roadway shots makes it an extremely difficult challenge classifying the road damage. To address this problem, this paper recommends ResNet Blended Five Dense Layered Deep Fully Connected Neural Network (RB-5DFCNN) that classifies the road damage with high efficiency. The KAGGLE Road Damage dataset, which consists of 2074 road snapshots, is used by the proposed RB-5DFCNN model. In order to classify road damage, the RB-5DFCNN model first preprocesses the images before fitting ResNet CNN, which consists of five steps of convolutional block and identity block processing. After the ResNet model completed its task, five dense layers were applied to the DFCNN in order to classify the state of road damage. The classification of road damage condition and the determination of the necessary number of dense layers to identify road damage with high accuracy are the innovative aspects of the proposed RB-5DFCNN. To test the effectiveness of the road damage categorization, the Road Damage dataset was applied to both the proposed RB-5DFCNN model and the current CNN approaches. Python execution on a GTX Geforce Tesla V100 Nvidia graphics card server over 64 and 150 training intervals shows that the suggested RB-5DFCNN model projects an accuracy of 98.77% in comparison to the state-of-the-art CNN models. © 2024 IEEE.

关键词： Statistical tests

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Emotion Recognition Using Focal Network and Resnet

Emotion Recognition Using Focal Network and Resnet

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Meena, T. Balasai, Sigireddy Anandh Duggireddy Kumar, P. Phaneendhra Velagapudi Ramakrishna Siddartha Engineering College Department of Computer Science and Engineering India Velagapudi Ramakrishna Siddartha Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350382693

Image emotion recognition involves finding the emotions from visual data, usually done through convolutional neural networks (CNN) or deep neural networks (DNN). The existing methodologies are often high complex or time complex and it is not giving the good considerable results and computationally expensive due to high number of parameters. In this paper we try to accomplish a better output by using focal network which stood among other transformers architecture due to the unique property of the dynamic attention Recent studies have shown the efficacy of transformer architectures in various deep learning tasks and challenged the convolution neural networks on various tasks. but, It has a tradeoff between reduced parameter complexity and the need for extensive training data. Drawing inspiration from recent publications, the study adopts a focal network for training, demonstrating its effectiveness in enhancing the model's attention and optimizing emotional feature extraction. The proposed methodology aims to do the process of image emotion recognition by using the advantages of transformers while mitigating the computational problem associated with the deep neural networks. The incorporation of a focal network addresses attention-related challenges, and it contributes to the development of more efficient emotion recognition systems. This research offers insights into the evolving landscape of image emotion recognition methodologies and underscores the potential of transformer architectures in advancing this field. © 2024 IEEE.

关键词： Convolution

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Machine Learning Approaches for Stroke Detection and SMOTE for Imbalanced data 8th

Machine Learning Approaches for Stroke Detection and SMOTE f...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Ruchitha, H.K. Sharma, B. S. Sanjana Sneha Rao, Trupthi Kodipalli, Ashwini Devamane, Shridhar B. Department of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bangalore India Department of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India

ISBN: (纸本)9789819976324

The majority of strokes will be caused by an unanticipated blockage of pathways by the heart and brain. To provide analytical data backing for timely, patient stroke prevention and detection, by creating a highly accurate and well-risk prediction model based on stroke risk factors using machine learning models (ML). We used numerous ML algorithms to analyse influencing factors to perform early detection, timely diagnosis, and timely treatment. In this paper, we have applied Decision Tree Classifier (DTC), K-Nearest Neighbours (KNN), and Logistic Regression (LR) for the analysis. The results were misleading because the original dataset had a data imbalance. To tackle this issue, the synthetic minority oversampling technique (SMOTE) was used in a sampling technique. There is also the use of k-fold cross-validation to extract more information about the performance of the algorithm and additionally comparing the accuracy of the three models. Therefore, this model can be used to support the decision-making of health care professionals. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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Regularized Dense Layered LeNet towards Mushroom Type Classification 5

Regularized Dense Layered LeNet towards Mushroom Type Classi...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Devi, M. Shyamala Harivishnu, K.C. Suriya, Jaya V. Jayasuriya, K. Jason, Infant G.A. Farih, M.D. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350361155

Among the edible varieties of fungi, mushrooms possess the strongest nutrients found in the plant. Nevertheless, because of their great demand for food and significant benefits to medical research, it is imperative to identify the different varieties of mushrooms within the current species. The deep learning technology has recently rendering the CNN models towards classification of various object in interdisciplinary fields. This paper proposes Regularized Dense Layered LeNet (RDL-LeNet) that categorizes the mushroom types with high accuracy. The Mushrooms Classification dataset, which includes 3150 mushroom pictures was used for this execution. The Mushrooms Classification dataset was fitted with the existing CNN models to select the best CNN model. To determine the most efficient CNN framework, the Mushrooms Classification dataset was fitted using the available CNN models. When compared to the other CNN model, the LeNet performs better, with an accuracy rate of 90.67%. LeNet has now been chosen to adjust the quantity of dense layers. LeNet typically consist of three convolutions, two pooling layers, and two dense layers. In order to suggest the recommended framework RDL-LeNet, the LeNet is now changed by adding an additional dense layer that is made up of a single block of batch normalization, relu optimization, and drop out at the end that further compresses the output into vector size of 84. The RDL-LeNet is made up of three convolutional layers, two pooling layers, and three dense layers. The third dense layer consists of a single block that is made up of batch normalization, relu optimization, and dropout, ultimately leading to the final output layer. The suggested RDL-LeNet and the current CNN have been implemented with the Mushroom Classification dataset. The experiment demonstrates that the suggested RDL-LeNet model works better in the categorization of mushroom types, with a high accuracy of 98.68%. © 2024 IEEE.

关键词： Fungi

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