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Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network

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SN computer Science 2024年第8期5卷 992页

作者： Sonavale, Amruta Chakkaravarthy, Midhun Srinivasa Rao, Surampudi Salleh, Hishamuddin Bin M. Jadhav, Jagannath Department of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Faculty of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia ECE Malla Reddy College of Engineering and Technology Hyderabad India Faculty of Electronics and Communication Engineering KLE College of Engineering and Technology Chikodi India

Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is *** process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The propos

关键词： Adaptive median filtering Flood detection LSTM Neural network algorithm Structural object son the RSI Versatile Linear Edge Detection (VLED) algorithm

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NSER: Development of Novel Speech based Emotion Recognition System by using Hybrid Learning Technique

NSER: Development of Novel Speech based Emotion Recognition ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Nirmala, P. Vanathi, A. Karunakaran, A. Benisha, M. Thiyaneswaran, B. Gunasekaran, G. Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of ECE Rajalakshmi Institute of Technology Chennai India Department of ECE S.A. Engineering College Chennai India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Kunnam Chennai631604 India Department of ECE Sona College of Technology Salem India Department of Computer Science and Engineering Dr.M.G.R.Educational and Research Institute Chennai India

ISBN: (纸本)9798350305708

For a long time, emotion identification from speech signals has served as an important area of study for HMI applications. Emotion detection systems have already been created for a wide variety of signals. The training and evaluation subsets were manually created using the open-source RAVDESS dataset, which contains acted voice recordings from both male and female speakers of English. Mel-frequency cepstral coefficients (MFCCs) were employed to represent vocal tract information of the speakers. These coefficients were extracted from audio samples within the training dataset. Additionally, feature extraction was carried out on recorded natural speech. The resulting feature vectors were directed to a Novel Hybrid Learning Technique. The initial processing stage of this technique involves a Convolutional Neural Network (CNN), which facilitates the extraction and transformation of audio features through convolutional layers. These layers are intentionally designed to capture pertinent patterns and features in audio signals, a crucial aspect for comprehending emotions. Incorporating Support Vector Machine (SVM) algorithm, known for its efficiency in handling high-dimensional feature spaces, enhances the system's performance in tasks like audio emotion detection. As a result, the proposed model stands out with an outstanding accuracy rate of 96.75%, underscoring its remarkable capability for precise emotion recognition. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Hybrid Learning Technique Speech based Emotion Recognition System Support Vector Machine (SVM)

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Efficient Routing Using Improved Ant Colony Optimization for Vehicular Communication in Urban Scenarios

Efficient Routing Using Improved Ant Colony Optimization for...

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

作者： Gobinathan, B. Keerthika, A. Venkat, S. Gopila, M. Karthikeyan, G. Abijith, G.R. Jaya Engineering College Department of Cse Chennai India Aarupadai Veedu Institute of Technology Department of Ece Chennai603 104 India S.A. Engineering College Department of Ece Chennai India Sona college of Technology Department of Electrical and Electronics Engineering Salem India Department of Vlsi Microelectronics Chennai India St.Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331543617

VANET is crucial for the progress of smart city transportation, allowing immediate communication between vehicles. However, the dynamic and fast-changing nature of VANETs presents significant challenges in routing efficiency. This paper introduces an Enhanced Routing with Improved Ant Colony Optimization (ERIACO) Approach to address these challenges. ERIACO leverages ACO for optimal route discovery, adapting to the dynamic network topology. Additionally, the approach incorporates Effective Link Prediction to preemptively manage potential link failures, enhancing network reliability. Through comprehensive ns3 network simulations, the performance of the ERIACO is assessed according to essential metrics including data delivery ratio, network throughput, routing overhead, energy efficiency, and energy consumption. Comparative analysis with existing methods, including IORDM, ITDMP, and OPRPW, demonstrates the superior efficiency and effectiveness of ERIACO in VANET environments. The results showcase that ERIACO achieves significantly higher data delivery rates, network throughput, and energy efficiency, making it a promising solution for the optimization of VANET routing and communication. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Multi-Dimensional Recurrent Neural Networks Hyperspectral image classification

Multi-Dimensional Recurrent Neural Networks Hyperspectral im...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sangeetha, V. Padmakala, S. Mahaboob John, Y.M. Elayaraja, P. Parthiban, K.G. Saveetha School of Engineering Dept of Cse Chennai India Department of Computer Science and Engineering Chennai India Mahendra College of Engineering Department of Ece Salem India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350375442

Classifying hyperspectral images (HSIs) is a top priority for the remote sensing field. Discriminative feature extraction of HSI using conventional ML techniques is difficult because of the abundance of strongly correlated bands and the scarcity of training data. The deep learning approach has recently achieved state-of-the-art results in a number of visual identification tasks and is widely regarded as a potent tool for feature extraction. The use of deep learning in HSI categorization was spurred by these fruitful uses, and the results have been impressive. Joint spectral-spatial feature extraction for HSI classification has piqued the attention of researchers in convolutional neural network (RNN) based classification techniques among other deep learning approaches. There have been claims in the literature that convolution operations on non-informative spectral bands can cause RNN-based joint feature extraction to underutilize spectral information. In order to minimize information loss, a model was built that extracts spectral and spatial characteristics independently. Then, to take use of the semantic information, the extracted features are fused. Using the datasets from Kennedy Space Center (KSC), Indian Pines (IP), University of Pavia (UP), and Salinas (SA), our model has attained 99.17%, 98.70%, 99.18%, and 99.24% overall accuracy, respectively. For HSI's limited training samples and high dimensional characteristics, researchers have conducted analyses on band selection and training sample quantity, respectively. In comparison to both older and more modern fusion-based approaches, the suggested model has attained state-of-the-art performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Music Genre Classification Using Long Short-Term Memory with Gated Recurrent Unit 2

Music Genre Classification Using Long Short-Term Memory with...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sumalatha, K. Nagendar, Y. Sharma, Jaya Padma, C.R. Alsalami, Zaid Department of Ece Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India Gitam University Department of Cse Hyderabad India Dayananda College of Engineering Department of Medical Electronics Engineering Karnataka India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The music genre classification is an important part in data retrieval for the association of increasing music collections. It is one of the challenging tasks for classifying music with reliable accuracy. Numerous methods are utilized as handcrafted features for identifying individual patterns which are unable to define the actual characteristics of music. To overcome these issues, this paper proposed a Deep Learning (DL) based technique for classifying music genre. The Long Short-Term Memory with Gated Recurrent Unit (LSTM+GRU) is used to classifying the music genre. The GTZAN dataset is used in this paper which collects 1000 clips from 10 balanced genres music data. It is preprocessed by clip splitting procedure which is utilized to convert 30s music clip into 3s which helps to enhance the classification performance. Then, the Mel Frequency Cepstral Coefficients (MFCC) is used for extracting relevant features from input signal and LSTM+GRU is used for classifying the music genres. The accuracy, recall, precision and f1-score are used for calculating model performance. The LSTM+GRU attains accuracy 93.65%, recall 92.38%, precision 92.57% and 92.18% f1-score when compared to existing techniques like ResNet50 and Convolutional Neural Network with Bidirectional GRU (CNN+BiGRU). © 2024 IEEE.

关键词： Long short-term memory

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Revolutioning IIot: A Smart Appliance of an Internet of Things over Industrial Manufacturing Unit to Make Error-Free Circumstances

Revolutioning IIot: A Smart Appliance of an Internet of Thin...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Ravi, Kiran Chand Vaishnavi, T. Princy, Anni Kandaswamy, Vijay Anand Dinesh, M. Lakshmisridevi, S. MLR Institute of Technology Department of ECE Hyderabad India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of ECE Tamil Nadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Department of VLSI Microelectronics Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542344

A future where cyberspace and the real world are inseparable is the goal of the Internet of Things (IoT). The potential for the development of robust industrial services and applications, colloquially known as the Industrial Internet of Things (IIoT), is enormous. From the acquisition of raw materials to the provision of customer service and support, the Internet of Things (IoT) was utilized across numerous sectors by industries. The Internet of Things (IoT) offers a great opportunity to develop significant manufacturing-related services and applications, and it is widely regarded as a crucial technology of the industrial revolution. In addition, it facilitates communication between intelligent devices, allowing them to exchange information and data-crucial for complex systems to make decisions in the here-And-now. In this paper, a new method called Error-Free Smart Industrial Control System (EFSICS) is introduced to improve the efficiency of industrial operations through the use of IIoT. To test how well this method works, it is cross-validated with the traditional model called Classical Industrial Control System (CICS). Regarding the manufacturing aspects in particular, the suggested method is well-suited to the effects of the Internet of Things on sustainable development. Researchers are now at the forefront of cyber integrated manufacturing research, which will further illuminate the present state of the internet of things (IoT) in manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

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Evaluating Generative Adversarial Networks for Virtual Contrast-Enhanced Kidney Segmentation using Res-UNet in Non-Contrast CT Images

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Multimedia Tools and Applications 2025年第18期84卷 20121-20144页

作者： Syamala, Maganti Chandrasekaran, Raja Balamurali, R. Rani, R. Hashmi, Arshad Kiran, Ajmeera Rajaram, A. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur District Andhra Pradesh Vaddeswaram522502 India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai India Centre for non linear systems Chennai institute of technology Chennai India Department of ECE Vemu Institute of Technology Tirupathi-chittoor Highway P. Kothakota Chittoor District AP Pakala India King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Computer Science and Engineering MLR Institute of Technology Dundigal Telangana Hyderabad500043 India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam611002 India

This study addresses the challenges of accurately segmenting kidneys in non-contrast CT images;where Convolutional neural networks (CNNs) perform best. The main goal is to investigate how well a Generative Adversarial Network (GAN) performs in producing virtual contrast-enhanced (vCECT) images from non-contrast sources. A GAN model is trained to generate simulated contrast-enhanced images that preserve pertinent information throughout translation by utilizing a dataset of 286 paired non-contrast and contrast-enhanced images. Following, 20 pairs of non-contrast and vCECT pictures are used to assess the performance of a unique segmentation model named Res-U-Net. The Res-U-Net architecture, which combines a U-Net design with residual connections, is optimized to efficiently extract detailed information from original and vCECT images. Image preprocessing methods like Hounsfield windowing with histogram equalization are used to improve the quality of the data before it is fed into the deep neural network. Dice similarity coefficient (DSC) is used to measure Res-U-Net's performance and acts as a benchmark against the most advanced kidney segmentation techniques. ResU-Net's performance is evaluated using the DSC. When compared to state-of-the-art methods for kidney segmentation recognition, the ResU-Net systems with intact connections achieved a DSC value of 0.97 percent for organs recognition and 0.83 percent for segmentation approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Fault Detection and Classification in Semiconductor Manufacturing for Sensor Screening Using Multi-Layer Deep Neural Network 2

Fault Detection and Classification in Semiconductor Manufact...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Reddy, S. S. Mohan Rakesh, K. Aluvala, Srinivas Bindu, G. Husseen, Ahmad Srkr Engineering College Department of Ece Andhra Pradesh Bhimavaram India Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The fault detection and classification are a prediction model that employs the sensor equipment data to detect if each wafer is healthy or faulty which is significant to attain a greater yield. However, the manufacturing of semiconductors has difficulties in maintaining high-quality production because of potential defects. Hence, effective fault detection and classification is essential to overcome this issue. In this research, the multi-layer Deep Neural Network (DNN) is proposed for fault detection and classification in semiconductor manufacturing for sensor screening. Initially, the image is acquired from a benchmark dataset of Printed Circuit Board (PCB) dataset. The median filter is employed to eliminate the noise in the acquired image. Then, the feature extracted is established using a Gray Level Co-occurrence Matrix (GLCM). Finally, the multi-layer DNN is used to detect and classify as healthy or faulty in semiconductors effectively. The existing techniques like Convolutional Neural Network-VGG16 (CNN-VGG16), Global Contextual attention augmented You Only Look Once (GCC-YOLO), DL-based Path Aggregation Network (PANet), and improved fully CNN are used to compare with proposed technique. When compared to this existing technique, the multi-layer DNN achieves a better accuracy of 99.89% respectively. © 2024 IEEE.

关键词： Printed circuit boards

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Efficient Multiple UAV Positioning and Resource Allocation in Intelligent Wireless Network

Efficient Multiple UAV Positioning and Resource Allocation i...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Rayen, Sonia Jenifer Harirajkumar, J. Vijay, Sreeja Vijayalakshmi, S. Benisha, M. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Electronics and Instrumentation Engineering Salem India S.A. Engineering College Department of ECE Chennai India R.M.K. Engineering College Department of ECE Chennai Kavaraipettai India Jeppiaar Institute of Technology Department of ECE Kunnam Tamilnadu Chennai India

ISBN: (纸本)9798350319200

Intelligent Wireless Network (IWN) is a trending technology and it is used in maximum of intelligent communication in order to improve its performance Unmanned Aerial Vehicles (UAVs) is incorporated with the IWS network. In recent times UAVs handles certain drawbacks like and predictable mobility in the random topology. In order to overcome this drawback's in this article Efficient Multiple UAV Positioning and Resource Allocation (EMURA) is developed. The core idea of this process is the creation of effective system model, channel model, queue based resource allocation and effective multi UAV positioning process. Through this processes an effective communication can be attained by the reduction of delay and power utilization among the devices. The model is implemented using ns2, and the study focuses on calculating the end-to-end delay and as well the vehicles energy consumption characteristics. From results it is proven that the proposed EMURA attains lower delay and energy consumption which leads to improve the communication quality of the proposed EMURA. © 2023 IEEE.

关键词： Efficient Multiple UAV Positioning and Resource Allocation Intelligent Wireless Network (IWN) Unmanned Aerial Vehicles (UAVs)

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Design and Development of Human Detection Robot to Rescue People from Disaster Environment by using Internet of Things

Design and Development of Human Detection Robot to Rescue Pe...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Kavitha, M.S. Bharathi, S. Krishnakumari, S. Shankar, S. Vijay Thyagarajan, C. R.M.K. Engineering College Department ECE Chennai India S.A. Engineering College Department of EEE Chennai India Loyola ICAM College of Engineering and Technology Department of ECE Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Panimalar Engineering College Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350319200

In disaster-stricken environments, the rapid and efficient rescue of individuals is of paramount importance. This abstract presents a novel approach to enhance disaster response efforts through the development of a Human Detection Robot (HDR) designed to locate and rescue people in disaster-affected areas. The proposed robot integrates Light Dependent Resistors (LDR) and the HC-SR501 Passive Infrared (PIR) sensor to detect the presence of human beings in low-visibility and challenging environments. The Human Detection Robot is equipped with a robust mechanical structure and a combination of sensors, including LDRs and the HC-SR501 PIR sensor. LDRs are employed to detect variations in ambient light, while the PIR sensor is used to detect human body heat and motion. These sensors work together to ensure reliable and accurate human presence detection, even in environments with low light conditions or obstructions such as dust, smoke, or debris. The robot's design also includes a microcontroller unit (MCU), which processes sensor data and controls the robot's movements. The MCU enables real-time decision-making and navigation to reach and rescue individuals in disaster-stricken areas. The robot is equipped with a communication system that allows it to relay information to the disaster response team, providing them with crucial data about the location and condition of the people in need of rescue. The HDR's rapid response time, when coupled with its robust mechanical design and communication system, positions it as a game-changing asset in disaster management and search and rescue operations. © 2023 IEEE.

关键词： HC-SR501 Passive Infrared (PIR) sensor Human Detection Robot,Internet of Things (IoT) Light Dependent Resistors (LDR)

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