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ExpACVO-Exponential Anti Corona Virus Optimization Enabled Hybrid Deep learning for Tongue Image Segmentation Towards Diabetes Mellitus Detection Via CNN-LSTM Network 1

ExpACVO-Exponential Anti Corona Virus Optimization Enabled H...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Mathew, Jimsha K Sathyalakshmi, S. Hindustan Institute of Technology & Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350330915

The surprising headways in biotechnology and public medical services foundations have prompted a pivotal creation of basic and touchy medical care information. By applying canny information examination methods, many fascinating examples are distinguished for the early and beginning discovery and avoidance of a few deadly infections. Diabetes Mellitus (DM) is a very dangerous illness since it adds to other deadly infections, i.e., heart, kidney, and nerve harm. The following stages have been proposed in this paper for the order, beginning phase ID, and expectation of diabetes: a) Data collection from tongue dataset of many images, b) Preprocessing using the gaussian filter for noise removal, c) Segmentation using U-Net-CRFRNN for segmenting region of interest and finally d) classification using the integration of Exponential Weighted Moving Average (EWMA) and Anti Corona Virus Optimization (ACVO) and also to increase the efficiency of the network, use of CNN-LSTM. The suggested architecture is more effective than other state-of-the-art models on many different parameters, based on the outcomes (accuracy: 0.94, sensitivity: 0.96, specificity: 0.96) © 2023 IEEE.

关键词： Anti Corona Virus Optimization Classification CNN-LSTM Deep learning Diabetes Mellitus Exponential Weighted Moving Average Tongue Segmentation

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Efficient Fetal Health Monitoring and Classification with Machine Learning 7

Efficient Fetal Health Monitoring and Classification with Ma...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Singh, Kabir Shyry, Prayla Franklin, Ramya G Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

The goal of the research on fetal health categorization using machine learning is to create a model that can precisely predict the condition of a fetus during pregnancy. This is crucial because prompt action following early identification of fetal health issues might enhance the pregnancy's outcome. This research study suggests classifying fetal health status using machine learning approaches based on ultrasound images and other clinical parameters. To train and test our model, we will gather a sizable dataset of ultrasound images and clinical characteristics from pregnant women. To categorize the state of fetal health, the proposed model will employ machine learning algorithms and image processing techniques. Our approach should categorize fetal health status with a high degree of accuracy, and it will help obstetricians and gynecologists provide better treatment for pregnant patients. © 2023 IEEE.

关键词： Genetic programming

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Vision Based Detection and Analysis of Human Activities 7

Vision Based Detection and Analysis of Human Activities

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Ravipati, Abhiram Kondamuri, Rakesh Krishna Mary Posonia, A. Albert Mayan, J. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397284

These days, it's not uncommon to see video cameras installed to keep an eye on pedestrians and motorists alike in a variety of public spaces. The proliferation of camera footage necessitates the creation of some means of deducing the nature of the activity captured on film. Due to the widespread availability of acquisition devices like handsets & camcorders, HAR may be used in a wide variety of contexts. The proliferation of electronic gadgets and software has been matched by a revolution in data extraction made possible by breakthroughs in AI. Difficulties such backdrop congestion, partly blockage, variations in size, perspective, illumination, & look, make it difficult to recognize human actions in video frame or still photographs. Multimodal recognition is necessary in several fields, such as CCTV, human computer interaction for characterizing human behavior. This study surveys the most current and cutting-edge findings from the study of how to categorize human actions. This research proposes a taxonomy for studying human activity and explore the benefits and drawbacks of this diverse set of approaches. Disabled people's daily routines, including resting, moving, traveling down & up the stairs, speaking, & laying, have been frequently tracked using cellphones. Human motion detection often makes use of well-established ML & DL techniques include CNN, & LSTM Network. © 2023 IEEE.

关键词： Video cameras

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Top-Performing Unifying Architecture for Network Intrusion Detection in SDN Using Fully Convolutional Network 5

Top-Performing Unifying Architecture for Network Intrusion D...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Roy, Bishwarup Acharya, Indranil Papalkar, Darshil Joseph, Mary Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

Internet's omnipresent adoption over the course of many years leading up to the current era along with Wireless Sensor Networks' (WSN) limitations has only increased the security threats to these high-speed networks. As a result, it is critical to deploy Intrusion Detection Systems (IDS) to monitor malicious activities in the network framework. Software Defined Network (SDN) has served its purpose to a certain extent by enabling centralized control and management of WSN and providing ways to implement IDSs more efficiently. However, like any emerging technology, SDN also has its share of vulnerabilities as there exists a gap for contemporary architectures, which can exploit global network assets and balance network resource consumption while providing programmability in terms of security of the network. To tackle this problem, a model has been developed that uses a fitting feature selection method to cut down on the count of features extracted and minimize the data volume relayed over the routes. Conclusions drawn from the scheme's trials demonstrate the proposed IDS technology's potency and effectiveness. The openly accessible University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD) was used to evaluate the intrusion detection algorithm involving the Fully Convolutional Network (FCN) architecture and its effect using a statistical technique in detecting different types of attacks. © 2023 IEEE.

关键词： Deep neural networks

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Machine Learning Algorithmsfor Diagnosis of Parkinson's disease Based on Voice Characteristics 2

Machine Learning Algorithmsfor Diagnosis of Parkinson's dise...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Manoj, Polina Sai Reddy, Kondreddy Sumanth Kumar Sambath, M. Thangakumar, J. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Bradykinesia, tremors, rigidity, and postural instability are symptoms of a degenerative neurological condition called Parkinson's disease (PD) affecting the motor movements. Deep brain stimulation, medication, and other therapies can all be used to treat theParkinson's disease symptoms, yet as of today, there is no effective treatment. Parkinson's disease must be recognized as early and precisely as feasible for effective disease treatment and the development of new therapies. The goal of this work is to create a model that can detect Parkinson's disease based on relevant clinical and demographic variables. It employs a number of machinelearning techniques, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Random Forest Classifier, and ensemble method, which is a voting classifier. A significant dataset that contains data on changes in speaking patterns was used to train the model. Using measures for accuracy, precision, recall, and F1-score, the machine learning model's performance is assessed and contrasted with that of the most widely used techniques for Parkinson's disease diagnosis. © 2023 IEEE.

关键词： Support vector machines

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Land Cover Extraction from Satellite Imagery using U-Net Model 18

Land Cover Extraction from Satellite Imagery using U-Net Mod...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, K. Pushpa Sreedevi, Pogula Dastagiriah, C. Rao, K. Srinivas Subbaraju, G.V. Padmavathi, Jasti Mlr Institute of Technology Dept. of Computer Science and Engineering Hyderabad India G. Pulla Reddy Engineering College Dept. of Computer Science and Engineering Kurnool India Anurag University Dept. of Computer Science and Engineering Hyderabad India Scetw Dept. of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9789380544519

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. Data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. The trained model achieved an F1 score of 92% and an impressive precision score of 95%. This automated method can be scaled to large sets of data, reducing the need for human labeling. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Data augmentation Deep learning Machine learning Multiscale feature learning U-Net architecture

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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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Efficient conversion of acoustic vortex using extremely anisotropic metasurface

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Frontiers of physics 2024年第4期19卷 257-264页

作者： Zhanlei Hao Haojie Chen Yuhang Yin Cheng-Wei Qiu Shan Zhu Huanyang Chen Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyXiamen UniversityXiamen 361005China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3117583Singapore Department of Mechanical and Electrical Engineering Xiamen UniversityXiamen 361005China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361005China

Vortex wave and plane wave,as two most fundamental forms of wave propagation,are widely applied in various research ***,there is currently a lack of basic mechanism to enable arbitrary conversion between *** this paper,we propose a new paradigm of extremely anisotropic acoustic metasurface(AM)to achieve the efficient conversion from 2D vortex waves with arbitrary orbital angular momentum(OAM)to plane *** underlying physics of this conversion process is ensured by the symmetry shift of AM medium parameters and the directional compensation of ***,this novel phenomenon is further verified by analytical calculations,numerical demonstrations,and acoustic experiments,and the deflection angle and direction of the converted plane waves are qualitatively and quantitatively confirmed by a simple *** work provides new possibilities for arbitrary manipulation of acoustic vortex,and holds potential applications in acoustic communication and OAM-based devices.

关键词： efficient wave conversion vortex wave plane wave orbital angular momentum acoustic metasurface

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Damaged Image Repair using Masks with computer Vision Inpaint Method 2

Damaged Image Repair using Masks with Computer Vision Inpain...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ramakrishna, Doddi Purna Gana Emmanuel, G. S. Michael Selvan, Mercy Paul Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Image inpainting is the technique used to automatically fix damaged areas using data from sections that have been saved. With the development of deep learning in recent years, image drawing performance has substantially increased. This research study reviews the main methods used for automating image inpainting. This research study provides a brief overview of traditional techniques while concentrating on deep learning-based inpainting techniques, covering model categorization, strengths and drawbacks, scope of application, and performance comparison. Finally, the challenges and trends surrounding automated image inpainting are examined and foreseen. A tool called image inpainting uses the data from the remaining components to repair damaged areas. With the advancement of society, image inpainting has become a vital research area in the field of computer vision. It is extensively used in culture, daily life, and security, including object removal and the preservation of digital cultural assets. Conventional methods build geometric models based on geometric consistency and image content similarity, or they use texture generation to patch up small sections of damaged images. It partially solves the problem of loose coupling between high-level semantics and low-level image properties, enabling deep learning to gradually overtake traditional methods in computer vision. © 2023 IEEE.

关键词： computer vision

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Augmented Reality-based Indoor Navigation using Unity Engine

Augmented Reality-based Indoor Navigation using Unity Engine

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Rokesh Maran, B. Giridharan, L. Krishnaveni, R. Hindustan Institute of Technology and Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350333602

The evolution of technology with the introduction of mobile devices has provided users with day-to-day advancements in existing technologies. Augmented Reality (AR) is combined with an Artificial Intelligence navigation agent to trackthe environment that is provided specifically. Unity 3D engine provides an inbuilt AR framework easily accessible and the navigation mesh agent tracks penetrating paths with segregated walkable areas and areas that cannot be accessed. The current machine learning systems use inertial sensors and high-end cameras for computer vision-based motion tracking. Whereas, this system does not require external hardware as the environment model is constructed with provided measurements in Unity Engine and virtual trackers, which can be functioned in any AR-enabled device. The proposed model aims to provide the framework with real-time virtual visualization for a better user experience. © 2023 IEEE.

关键词： Navigation

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