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Machine Learning-Driven Approaches for Dermatological Disease Diagnosis 2

Machine Learning-Driven Approaches for Dermatological Diseas...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Saini, Dimple Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Sri Balaji University Pune India Parul Institute of Technology Parul University Computer Science and Engineering Engineering and Technology India Pune India Chennai Institute of Technology Deparment of Computer Science and Engineering India

ISBN: (纸本)9798350373110

Recent advancements in machine learning (ML) have significantly enhanced the accuracy and efficiency of diagnosing dermatological diseases, surpassing conventional methods. This paper presents a detailed exploration of ML-driven approaches, employing sophisticated algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Decision Trees to develop a predictive model for various skin conditions. Our methodology involved collecting and preprocessing a comprehensive dataset of dermatological images, followed by applying these algorithms to classify and predict skin diseases. The model was rigorously evaluated against traditional diagnostic methods using metrics such as accuracy, precision, recall, and F1 score. Results demonstrate that our ML-driven model, particularly the CNNs, achieved superior diagnostic performance with an accuracy of 94%, precision of 92%, recall of 93%, and an F1 score of 92%. These findings highlight the potential of ML to revolutionize dermatological diagnostics, offering a more accessible, efficient, and precise approach to identifying skin diseases. The study underscores the importance of integrating artificial intelligence in healthcare, paving the way for advancements in dermatological diagnostic tools and contributing to the broader discussion on AI in medical applications. This work not only enhances diagnostic capabilities but also opens avenues for more equitable healthcare solutions by reducing reliance on limited dermatological expertise. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Learning Approach for Marine Animal Classification: Enhancing Taxonomic Identification and Conservation Efforts 3

A Deep Learning Approach for Marine Animal Classification: E...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Tiwari, Raj Gaurang Gautam, Vinay Trivedi, Naresh Kumar Jain, Anuj Kumar Sharma, Vikrant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Deparment of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350302288

Classification of marine animals is essential for studying marine ecosystems, keeping track of biodiversity, and developing efficient conservation plans. The success of deep learning methods in picture classification tasks has increased dramatically in recent years. In this research, we propose a systematic investigation of the use of several deep learning models for the categorization of marine organisms;these models include Inception, ResNetV, ResNet50, VGG16, InceptionV3, Xception, DenseNet, MobileNet, and NASNet, among others. Models originally built on ImageNet were then fine-tuned using transfer learning before being applied to our marine animal dataset for training. Training and validation accuracy were used to rank the models, and the models' overall performance was compared to find the best one. Inception outperformed the other models in tests by a wide margin, with a training accuracy of 95.85% and a validation accuracy of 96.75. Inception's greater performance in marine animal categorization may be attributed to its deeper design and its ability to grasp complex spatial connections. When deep learning models are used to correctly categorize marine species, it may greatly improve taxonomic identification, aid conservation efforts, and make ecological study much easier. Additionally, the results highlight the significance of constructing domain-specific deep learning models for fields like marine biology, where distinct obstacles exist. © 2023 IEEE.

关键词： Image recognition

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Artificial Intelligence Based Approach to Identify Plastic Bottle for Environment Sustainability 3

Artificial Intelligence Based Approach to Identify Plastic B...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Gautam, Vinay Tiwari, Raj Gaurang Trivedi, Naresh Kumar Jain, Anuj Kumar Sharma, Vikrant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Deparment of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350302288

Several classification problems have been resolved with computer vision and deep learning technology. Despite this, no dedicated work has been carried out to classify plastic bottles from non-bottle objects. Therefore, this paper is concerned to resolve issue to classify waste plastic bottle from non-bottle objects. Here a dataset of 52000 images of bottle and non-bottle has been used to classify different objects based on their size. Each image contains a single bottle and non-bottle object. In this paper, various pre-trained models have been employed to classify these objects based on their size. The pretrained models have been trained with bottle dataset and attained 97%, 92% and 91 % accuracy. The comparison shows that the ResNet-18 outperforms. © 2023 IEEE.

关键词： Image classification

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Preserving India's Rich Dance Heritage: A Classification of Indian Dance Forms and Innovative Digital Management Solutions for Cultural Heritage Conservation

Preserving India's Rich Dance Heritage: A Classification of ...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Tiwari, Raj Gaurang Gautam, Vinay Sharma, Vikrant Jain, Anuj Kumar Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Deparment of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350341454

Deep connections exist between dance and cultural heritage. Dance is frequently passed down through generations as an essential component of a culture's identity and as a means of maintaining and honoring that culture's distinctive traditions and customs. A culture's history, beliefs, and values can be powerfully expressed via dance, which can also be used for communication and storytelling. For the purpose of protecting and promoting India's cultural legacy, it is crucial to comprehend how Indian dance styles are categorized. This paper proposes a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) deep learning approach for the accurate classification of Indian dance style categories. The proposed model combines the strengths of both CNN and RNN to leverage spatial and temporal information, respectively, resulting in enhanced performance and improved accuracy. Extensive experiments were conducted to evaluate the performance of the proposed approach. The results demonstrate that the hybrid CNN-RNN model achieved an impressive accuracy of 97.74%, outperforming traditional methods and single-model architectures. © 2023 IEEE.

关键词： Recurrent neural networks

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Extension of localized routing to PMIP-SAE based mobile networks 87

Extension of localized routing to PMIP-SAE based mobile netw...

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87th IEEE Vehicular Technology Conference, VTC Spring 2018

作者： Gohar, Moneeb Muzammal, Muhammad Rahman, Arif-Ur Choi, Jin-Ghoo Koh, Seok-Joo Deparment of Computer Science Bahria University Islamabad Pakistan Department of Information an Communication Engineering Yeungnam University Korea Republic of School of Computer Science Kyungpook Nationl University Korea Republic of

ISBN: (纸本)9781538663554

In existing PMIP-SAE based mobile networks, all the communication traffic go through the centralized Local Mobility Anchor (LMA). Hence the central node, LMA, suffers from huge packet processing overhead and the traffic is delivered via non-optimal routes. In order to overcome these drawbacks, we apply the localized routing to mobile networks based on PMIP-SAE mobility management scheme, while introducing additional control messages of Localized Routing Initiation (LRI) and Local Routing Acknowledgment (LRA). Numerical analysis shows that our proposed scheme performs better than the existing scheme in packet delivery cost, tunneling overhead, and throughput. © 2018 IEEE.

关键词： Wireless networks

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Recognition of Secure Data Transmission in Cloud Platform using Deep Learning 3

Recognition of Secure Data Transmission in Cloud Platform us...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bhavya, R. Kumar, M. Guru Vimal Ramya, U.M. Janagi, R. Ganesan, M. Deepa, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Rangarajan Dr. SangunthalaR&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Deparment of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Hindusthan College of Engineering and Technology Deparment of Information Technology Coimbatore India Kongu Engineering College Perundurai Department of Computer Technology-PG Erode638060 India

ISBN: (纸本)9798350375190

The recognition of secure data transmission in a cloud platform requires the integrity of sensitive information with higher confidentiality. This is obtained using the aid of deep learning techniques. The robustness of the system is improved using a random forest algorithm. In secure data transmission, the deep learning algorithms are trained to adopt various patterns of communication with the identification of certain anomalies. They result in security threats. They involve suspicious user behaviour with unexpected information exchange. The overall accuracy and interpretability of the system are achieved using a random forest algorithm which combines multiple decision trees. They are used for handling the high-dimensional data and obtaining the relationship between complex patterns. They helps in the identification of various security threats in the data communication through cloud platforms. This helps in the automatic decision-making process during malicious behaviour. The obtained dataset contains both the secure and insecure datasets. The extracted features acts as an input for the random forest classifier. These features are analyzed by the random forest and provides information that helps to influence the decision-making process. The integrated approach improves the accuracy of the security threats with enhancing transparency in the automatic decision-making techniques. They provide trust in the security measures in the cloud platforms. The collaboration of deep learning with a random forest algorithm helps in obtaining secure data transmission. © 2024 IEEE.

关键词： Behavioral research

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An improved MRI reconstruction method based on table-lookup gridding

An improved MRI reconstruction method based on table-lookup ...

引用

2009 International Conference on Image Analysis and Signal Processing, IASP 2009

作者： Peng, Yanni Qing, Huo Liu College of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing 400067 China Deparment of Electrical and Computer Engineering Duke University Durham NC 27705 United States

ISBN: (纸本)9781424439867

Radial encoding is often performed for fast data acquisition in magnetic resonance imaging (MRI). Therefore, the gridding method, which is a kind of algorithm for Fast Fourier Transform, is widely used in fast MRI reconstruction. In this paper, an improved gridding method has been utilized to 2D MRI reconstruction, which precomputes an 1D finely sampled table of a high-accuracy interpolator and then uses separable bilinear interpolation by look-up table. Experimental comparisons are made by assessing the performance of the direct reconstruction, the conventional KB gridding and the proposed method. The experimental results show that the proposed method provides high accuracy, fast computation, and dramatic memory saving. ©2009 IEEE.

关键词： Magnetic resonance imaging

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Enrichment of Antenna Gain of a Biconvex Patch with a Novel Superstrate

Enrichment of Antenna Gain of a Biconvex Patch with a Novel ...

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2020 International Conference on computer science, engineering and Applications, ICCSEA 2020

作者： Panda, Ribhu Abhusan Krishna Senapaty, Murali Rath, Premansu Sekhar Mishra, Debasis GIET Univarsity Department of Computer Science Engineering Gunupur India Veer Surendra Sai University of Technology Deparment of ELTC Burla India

ISBN: (纸本)9781728158303

This paper illustrates the mathematical derivation for the biconvex shaped patch which can be used for the 5G application. The gain enhancement has been done by using the superstrate with metal blocks. A 40mm × 40 mm substrate has been taken in which FR4 Epoxy dielectric is used for the substrate with a height 1.6 mm. Air gap between the superstrate and the substrate is also 1.6 mm. The conformal rounded patch has been altered in such a way that it will be shaped as a biconvex lens. The parameters like S-Parameter, SWR, antenna gain etc have been found out and a comparison has been done taking the proposed antenna with and without superstrate. The effect of metal blocks that are used on the superstrate is considered for gain enrichment of the projected model. The return loss ( © 2020 IEEE.

关键词： Substrates

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Spectral interpretation of a handover overflow process in a hiearchical cellular network

Spectral interpretation of a handover overflow process in a ...

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2004 9th IEEE Singapore International Conference on Communication Systems, ICCS

作者： Seo, Jun-Bae Lee, Seung-Que Park, Nam-Hoon Lee, Hyong-Woo Cho, Choong-Ho ETRI Deparment of Wireless Syst. Research Korea University Dept. of Electronics Engineering Korea University Department of Computer Science

ISBN: (纸本)0780385497

In this paper, queueing effects on handover calls in a hierarchical cellular network are examined by varying call holding times, cell dwell times, signal degradation interval and queue size, especially in terms of handover call dropping probability and the power spectral density (PSD) of handover overflow process. Using the PSD, it is found in the numerical examples that queue for handover calls and large signal degradation interval, that is, large handover region, can remove occurrence of the short overflow periods which are related to low dropping rate of handover calls and that handover calls with high mobility induce the long overflow periods which are related to high dropping rate. It is also concluded that large queue size over two, can not give significant enhancement on call performance, because long overflow periods remain to be the dominant factor on the performance.

关键词： Wireless telecommunication systems

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Enhancing Medical Imaging Trust with an Efficient Deep Learning System for Forgery Detection 2

Enhancing Medical Imaging Trust with an Efficient Deep Learn...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Saini, Dimple Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media India Sri Balaji University Pune India Parul Institute of Technology Parul University Computer Science and Engineering Engineering and Technology India Pune India Chennai Institute of Technology Deparment of Computer Science and Engineering India

ISBN: (纸本)9798350373110

The integrity of medical images is critical to accurate diagnosis and treatment, but the proliferation of sophisticated editing tools has heightened the risk of forgery, compromising the trustworthiness of medical imaging. This paper introduces an efficient deep learning system designed to safeguard medical imaging by identifying alterations such as splicing, copy-move, and retouching forgeries. Our proposed solution utilizes a convolutional neural network (CNN) architecture enhanced with attention mechanisms, optimized to detect subtle changes in medical images. The development process involved meticulous data preparation, augmentation, and the implementation of advanced mathematical models to refine the system's ability to differentiate between authentic and manipulated images. Evaluation was conducted on a diverse dataset encompassing various imaging modalities like MRI, CT scans, and X-rays, with both genuine and tampered instances. The proposed system delivered superior performance, achieving an accuracy of 97.5%, a precision of 96.8%, a recall of 98.2%, and an F1 score of 97.5%, significantly outperforming existing methods. These results highlight the system's robustness in distinguishing real images from forged ones, thanks to its attention mechanism that allows the model to focus on key areas within the images. Ultimately, this system offers a highly accurate and efficient tool for enhancing the security of medical imaging, reinforcing the potential of deep learning in tackling healthcare challenges and ensuring reliable diagnostics and treatment planning. © 2024 IEEE.

关键词： Convolutional neural networks

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