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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Suchithra Dhruthi, P.A. Somashekhar, B.M. Rani, N. Shobha Lin, Hong Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science Mysore India Gitam School Of Technology Gitam University Department Of Computer Science And Engineering Bengaluru India University Of Houston Department Of Computer Science And Engineering Technology Houston United States

ISBN: (纸本)9798350371567

Plant species classification holds immense importance across diverse sectors such as agriculture, environmental conservation, and botanical research. Accurate identification of plant species is fundamental for effective ecological management, agricultural practices, and biodiversity preservation. In this context, the research addresses the critical task of fine-grained plant classification, specifically emphasizing vegetable and fruit plants. Recognizing the significance of visual similarity analysis in plant sciences, a robust methodology leveraging Convolutional Neural Networks (CNN) is introduced. The hybrid CNN model combines the interpretability of VGG16 with the gradient-stabilizing benefits of ResNet50's residual connections, enabling nuanced classification of 23 plant classes. Achieving an outstanding 99% accuracy in plant class prediction, the model is not only highly accurate but also interpretable. Additionally, the incorporation of cosine similarity enhances the robustness of the approach, facilitating a leaf similarity search application. This innovative feature strengthens the identification of visually similar plant images based on extracted features, significantly contributing to the advancement of plant species classification methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Performance Analysis of Deep Learning Models for Lung Disease Prediction using Chest X-Ray Images 6

Comparative Performance Analysis of Deep Learning Models for...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Vasamsetti, Samuel Shreyas, G.S.S. Chemboli, Vaibhav Thota, Srikanth Department of Computer Science & Engineering GITAM School of Technology GITAM University Visakhapatnam India

ISBN: (纸本)9798350398496

In recent times, lung diseases like COVID-19, Tuberculosis, and Pneumonia have been identified as significant public health issues. Early detection is a crucial factor for effective treatment in such cases. Previous studies have provided binary classifications of these illnesses with a high accuracy of 98%. However, adding additional classes to the classification process has made it more complex. Therefore, this research aims to investigate Chest X-Ray images of patients to detect potential ailments and compare them to pre-trained architectures such as VGG16, EfficientNetB7, and others. Additionally, various normalization techniques are employed to pre-process the data and X-ray image properties. Deep learning technology has emerged as a reliable and practical approach to accurately identify such illnesses. Convolutional Neural Network (CNN) algorithms is employed to obtain detection findings to analyze the dataset containing scenarios. This research aims to create a better and simpler model to improve the detection process of these classes. Therefore, this research presents a comprehensive investigation of the detection of lung diseases using deep-learning techniques and explores the potential of pre-trained models in addressing the complexities involved in the detection of these illnesses. The findings of this research can be useful for improving the accuracy and efficiency of the diagnosis of lung diseases, leading to better patient diagnosis outcomes. © 2023 IEEE.

关键词： COVID-19

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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Privacy-preserving explainable AI: a survey

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science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Estimating the Surface Roughness of a Drilled Surface Using the Edge Detection Technique 1

Estimating the Surface Roughness of a Drilled Surface Using ...

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1st International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2023

作者： Karegoudra, Shilpa Yendapalli, Vamsidhar GITAM School of Technology GITAM University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350306637

Drilling is a preliminary operation in many machining operations. Surface characteristics of the drilled surface play an imperative role in determining the performance and quality of the final parts. The texture of the surfaces can be identified by the appearance of the surface in images. In the proposed method we are taking the image of the drilled surface and estimating the roughness of the surface based on irregularities on the surface. Irregularities or scratches formed during the machining are extracted as edges in image analysis. In this work, we are dealing with the detection and localization of the roughness of the drilled hole image using the Sobel Edge Detection Method. The surface texture of the drilled hole is estimated based on the edges detected by the edge detection method. the surface is rough if more edges are identified. Using the contact method surface roughness values of the same workpiece with different speed and feed is estimated. For the speed 2000 and feed 25 using the contact method roughness value is 0.5588 μm, for the speed 3000 and feed 75 using the contact method roughness value is 0.3827 μm, for the speed 4000 and feed 75 using the contact method roughness value is 0.3110 μm. From the proposed system we got more edges detected for speed 2000 and feed 75 and fewer for speed 3000 and feed 75 and very fewer edges are detected by the edge detection method for speed 4000 and feed 75. hence, edge detection is an accurate approach to estimating surface texture. © 2023 IEEE.

关键词： Surface roughness

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An Empirical Study of Image based Steganography and Watermarking

An Empirical Study of Image based Steganography and Watermar...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Padma, R. Yendapalli, Vamsidhar Jyothi, A.P. Gitam University Computer Science and Engineering Gitam School of Technology Bengaluru India Ramaiah University of Applied Sciences Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

In the current technological landscape, the transmis-sion of data through communication channels has experienced significant advancements. The rapid growth in communication technology has brought about improved outcomes. However, challenges arise in ensuring the security and integrity of the transmitted data. It becomes crucial to safeguard from unauthorized access or users without proper authorization, it is important to implement various security parameters and best practices. This need has led to the development of various data hiding techniques. The objective of this paper is to conduct a comprehensive study on the taxonomy of Steganography and Watermarking types, as well as the taxonomy of techniques employed in Steganography and Watermarking. The paper aims to provide an overview and classification of different types of Steganography and Watermarking techniques. It also focuseson analyzing the advantages and disadvantages associated with these *** examining the taxonomy and assessing the pros and cons of Steganography and Watermarking techniques, this paper aims to contribute to a better understanding of their applications and implications in data security. The findings of this study can serve as a valuable resource for researchers and practitioners working in the field of data hiding and information security. © 2024 IEEE.

关键词： Discrete Fourier transforms

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Next-Gen Image Shielding: Deep Learning's Stealthy Image Encryption for Health-Tech IoT

Next-Gen Image Shielding: Deep Learning's Stealthy Image Enc...

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2023 International Conference on Recent Advances in science and engineering technology, ICRASET 2023

作者： Nadhan, Archana S Jacob I, Jeena Gitam Deemed to Be University Gitam School of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350306927

The healthcare sector has undergone a transformation with the advancement of the Internet of Things, facilitating the seamless incorporation of medical imaging devices within the industry's data systems, leading to faster diagnostics and treatments. Maintaining the privacy of patients is crucial and complex, especially when medical images can reveal intimate and private details about a patient's medical history. We explore the usage of advanced Deep Learning methods for encrypting medical diagnostic images, leveraging a cryptographic-based image encryption and decryption framework. The Proposed approach uses a simple key learning model built on ResNet-50 architecture to convert one visual format into another. This model specifically incorporates a domain known as "hidden factors"during the encryption phase. To revert the encrypted image to its original form, we employ reconstructive networks. By extracting relevant data from the encrypted content and enabling direct data mining from the personal data ecosystem, we offer a system that ensures a good return on investment. Additionally, the suggested method incorporates the Enhanced Encryption technique using a stream cipher. We validated the new approach using two publicly available datasets. Preliminary evaluations suggest that the method provides superior security and impressive operational efficiency. © 2023 IEEE.

关键词： Cryptography

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A Review on Deep Learning Based Question Answering with Natural Language Processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Javeed, Abdul Saleem Gopala Krishnan, C. Gitam School of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350359688

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics. © 2024 IEEE.

关键词： and Medical Data Deep Learning Healthcare Natural Language Processing Question Answering

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DESIGN OF RF FEEDING NETWORK [1:8] USING UNEQUAL AMPLITUDE HYBRID COUPLER FOR LOW SLL X-BAND RADAR APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 1-19页

作者： Kothapudi, Venkata Kishore Sunayana, Lella Divya, Shaik Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St. ColumbiaSC29204 United States

The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports operating at 9.3 GHz. The overall goal is to design and analyze an uneven power feeding network (1:8) for low SLL x-band radar applications employing a hybrid coupler. The specific goal is to create a hybrid coupler with equal amplitude, a hybrid coupler with uneven amplitude [Taylor’s amplitude = –17 dB], and a feeding network using hybrid couplers. The goal of this research is to improve the ability of a single transmitter to provide power to more than one antenna, all at a time either equally or unequally, and this can be achieved by designing a desired number of hybrid couplers and connecting them to the specific number of antennas at the output end and to the single transmitter at the input end. © 2025 by Begell House, Inc.

关键词： Transmitters

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Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

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SN computer science 2023年第1期4卷 72页

作者： Theerthagiri, Prasannavenkatesan Ruby, A. Usha Vidya, J. Department of Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru Bengaluru India School of Computing Science and Engineering VIT Bhopal University Bhopal India

Diabetes mellitus is characterized as a chronic disease that may cause many complications. Machine learning algorithms are used to diagnose and predict diabetes. The learning-based algorithms play a vital role in supporting decision-making in disease diagnosis and prediction. This paper investigates traditional classification algorithms and neural network-based machine learning for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation of patients who possibly suffer from diabetes in the future. This work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with the lowest MSE of 0.19. The MLP gives the lowest false-positive and false-negative rates with the highest area under the curve of 86%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification of diabetes Diabetes prediction Machine learning algorithm MLP

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