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Flood Susceptibility Map of Periyar River Basin Using Geo-spatial Technology and Machine Learning Approach

Remote Sensing in Earth Systems Sciences 2024年第1期8卷 1-21页

作者： Sreekala, S. Geetha, P. Madhu, Dhanya Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri Coimbatore India Center for Computational Engineering & Networking Amrita Vishwa Vidyapeetham Coimbatore India Department of Physics Amrita Vishwa Vidyapeetham Amritapuri Coimbatore India

Floods are among nature’s most destructive disasters because they create extremely extensive damage to structures, the environment, and people. Therefore, it is important to determine the causes of floods as well as areas that are vulnerable to flooding, which can be done by performing a flood susceptibility model. This research identified flood-prone locations in the Periyar River Basin using historical flood records from 2000 to 2020 and some of the conditioning features. The ten variables considered in the present study include elevation, slope, aspect, flow direction, drainage density, rainfall, Normalized Difference Water Index (NDWI), Stream Power Index (SPI), Sediment Transport Index (STI), and Topographic position Index (TPI). In order to create a flood susceptibility map and examine the correlation between flood incidence, the logistic regression (LR), support vector machine, naive Bayes, random forest, AdaBoost, gradient boost, and extreme gradient boost models were developed and validated. The model accuracies were measured using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). In addition to this, some other indices like precision, recall, sensitivity, specificity, F1 score, and overall accuracy matrices are used for model evaluation. The results demonstrated that every model can identify flood-prone locations with reasonable accuracy. However, compared to other models, the random forest model showed a better performance and prediction rate (AUC = 94). Furthermore, all models indicated that low-lying places near water bodies and in the western region of the study area had the largest probability of flooding. According to the study, machine learning techniques are a useful tool for mapping and predicting flood-prone areas and for creating flood mitigation strategies and plans. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Feature importance Flood inventory map Flood susceptibility map (FSM) GIS

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AI4BCancer: Breast Cancer Classification using AutoML - TPOT with PCA 2

AI4BCancer: Breast Cancer Classification using AutoML - TPOT...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Jain, Charvi Srinet, Shaurya Singh Goyal, Tarush Kumar Karpagam, M. Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331528713

Breast cancer is the most common cancer diagnosed in women and is a significant cause of cancer-related deaths among women worldwide. It demands accurate and timely detection methods for improved patient outcomes. This study proposes a novel machine learning approach to enhance breast cancer classification. Emphasizing the urgency of early diagnosis, we compare existing models and find AutoML - TPOT with PCA achieving the highest accuracy among all models tested. This proposed model integrates advanced algorithms and PCA to improve accuracy and computational efficiency. Through rigorous evaluation on the Wisconsin Breast Cancer Dataset, this model demonstrates superior performance, achieving an accuracy of 98.6%. This highlights its potential to significantly impact breast cancer diagnosis and treatment planning, contributing to ongoing efforts to improve patient care. Future work will focus on integrating Explainable AI to enhance model transparency and clinical trust. © 2024 IEEE.

关键词： Lung cancer

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Glaucoma Detection Using CNN and Study on Class Imbalance Problem 6th

Glaucoma Detection Using CNN and Study on Class Imbalance Pr...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： U, Nitish Prem Kumar, Pradeesh M, Nimal Madhu V, Hareesh Variyar, V. V. Sajith Center for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Electrical Engineering National Institute of Technology Calicut Kerala Kozhikode India

ISBN: (纸本)9783031503269

Glaucoma refers to eye disorders that result in harm to the optic nerve, leading to either vision loss or blindness. Without treatment glaucoma tends to worsen gradually over time, making early detection and treatment crucial. Deep learning based on CNN was used for the detection of glaucoma using fundus images. But deep learning requires datasets which contain huge amounts of labelled high-quality images to predict with high accuracy. It is because of the need to extract important and complex patterns or features from the images for tuning its parameters and to reduce the model’s loss. But often the availability of such datasets is scarce and sometimes the datasets available will be highly imbalanced. In this paper, the issue of an imbalanced dataset has been addressed by using 2 sets of data augmentation techniques and GAN to make the dataset balanced and thereby increase the size of the dataset. The first set includes operations such as changing rotation, height, flipping etc. The second set includes operations such as changing hue, saturation, and contrast. The second set of augmentation provided the best result with an accuracy of 0.821 when compared with the rest of the approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Robust Feature Extraction Technique for Hand Gesture Recognition System 6th

Robust Feature Extraction Technique for Hand Gesture Recogni...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Yadukrishnan, V. Anilkumar, Abhishek Arun, K.S. Madhu, M. Nimal Hareesh, V. Center for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Electrical Engineering National Institute of Technology Calicut Kerala Kozhikode India

ISBN: (纸本)9783031503269

Hand gesture recognition (HGR) is a vital and versatile technology widely employed in various applications such as gaming, robotics, and interactive media. Beyond its broad utility, HGR systems play a crucial role in facilitating interactions for individuals with disabilities, empowering them with newfound capabilities. This paper introduces an innovative approach to feature extraction, leveraging Autoencoders to derive essential characteristics from the data. The extracted features are subsequently utilized for a comparative analysis of the accuracy achieved across different data sizes within the same dataset. By mapping the visual vocabulary of images, the Autoencoder generates a unified vector, which is then fed into Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (KNN) classifiers. Through this research, we aim to enhance the efficiency and effectiveness of hand gesture recognition systems, fostering advancements in human-computer interaction and augmenting the quality of life for individuals with disabilities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Deep Learning-Based Time Series Forecasting for CO2 Emission 6th

Deep Learning-Based Time Series Forecasting for CO2 Emission

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Anilkumar, Abhishek Yadukrishnan, V. Nimal Madhu, M. Hareesh, V. Premjith, B. Center for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Electrical Engineering National Institute of Technology Calicut Kerala Kozhikode India

ISBN: (纸本)9783031362453

The research paper provides a comprehensive and effective comparison of various deep learning (DL) models applied to univariate data of carbon dioxide (CO2) concentrations spanning from 1870 to 2021, focusing on five European countries: the United Kingdom (UK), Germany, France, Ukraine, and Italy. The primary aim of the study is to contribute to a better understanding of climate change and to facilitate the development of policies and actions to mitigate CO2 emissions. Three DL models, namely long short-term memory (LSTM), convolutional neural network-long short-term memory (CNN-LSTM), and dynamic mode decomposition (DMD), are employed and thoroughly evaluated in this research. A novel aspect of this paper lies in the spatiotemporal modeling and forecasting capabilities, which enable simultaneous analysis for multiple countries using DMD. The results show that DMD outperforms other DL models, such as LSTM and CNN-LSTM, in accurately capturing data trends and forecasting future CO2 values with the highest precision. The findings presented in this research paper have significant implications for advancing our understanding of climate change dynamics and guiding more effective measures to tackle CO2 emissions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Long short-term memory

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Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired

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Computers, Materials & Continua 2022年第2期70卷 4027-4051页

作者： Palli Padmini C.Paramasivam G.Jyothish Lal Sadeen Alharbi Kaustav Bhowmick Department of Electronics&Communication Engineering Amrita School of EngineeringBengaluruAmrita Vishwa VidyapeethamIndia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringCoimbatoreAmrita Vishwa VidyapeethamIndia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Electronics and Communication Engineering PES UniversityBengaluruIndia

The present work presents a statistical method to translate human voices across age groups,based on commonalities in voices of blood *** age-translated voices have been naturalized extracting the blood relation features e.g.,pitch,duration,energy,using Mel Frequency Cepstrum Coefficients(MFCC),for social compatibility of the *** system has been demonstrated using standard English and an Indian *** voice samples for resynthesis were derived from 12 families,with member ages ranging from 8–80 *** voice-age translation,performed using the Pitch synchronous overlap and add(PSOLA)approach,by modulation of extracted voice features,was validated by perception *** translated and resynthesized voices were correlated using Linde,Buzo,Gray(LBG),and Kekre’s Fast Codebook generation(KFCG)*** translated voice targets,a strong(θ>∼93%andθ>∼96%)correlation was found with blood relatives,whereas,a weak(θ<∼78%andθ<∼80%)correlation range was found between different families and different gender from same *** study further subcategorized the sampling and synthesis of the voices into similar or dissimilar gender groups,using a support vector machine(SVM)choosing between available voice ***,∼96%,∼93%,and∼94%accuracies were obtained in the identification of the gender of the voice sample,the age group samples,and the correlation between the original and converted voice samples,*** results obtained were close to the natural voice sample features and are envisaged to facilitate a near-natural voice for speech-impaired easily.

关键词： Blood relations KFCG LBG MFCC vector quantization correlation speech samples same-gender dissimilar gender voice conversion PSOLA SVM

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A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors

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Computers, Materials & Continua 2022年第6期71卷 4523-4554页

The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages,captured by four oral sensors *** an experimental demonstration of the system used in the oral cavity,a prototype tooth model was *** on the principle developed in a previous publication by the author(s),the proposed system has been implemented using the oral cavity(tongue,teeth,and lips)features alone,without the glottis and the *** positions of the sensors in the proposed system were optimized based on articulatory(oral cavity)gestures estimated by simulating the mechanism of human *** system has been tested for all English alphabets and several words with sensor-based input along with an experimental demonstration of the developed algorithm,with limit switches,potentiometer,and flex sensors emulating the tongue in an artificial oral *** system produces the sounds of vowels,consonants,and words in English,along with the pronunciation of meanings of their translations in four major Indian languages,all from oral cavity *** experimental setup also caters to gender mapping of *** sound produced from the hardware has been validated by a perceptual test to verify the gender and word of the speech sample by listeners,with∼98%and∼95%accuracy,*** a model may be useful to interpret speech for those who are speech-disabled because of accidents,neuron disorder,spinal cord injury,or larynx disorder.

关键词： English vowels and consonants oral cavity proposed system sensors speech-disabled speech production vocal tract model

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An Efficient AI Based System for the Detection of Diabetic Maculopathy Using Colour Fundus Images

An Efficient AI Based System for the Detection of Diabetic M...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Bhoopalan, Ramasubramanian Priyadharshini, S. Anbalagan, Sriram Mathu, Venkateswaran Sabpathy, Sundersan. SRMTRP Engineering College Dept. of ECE Tamilnadu Tiruchirappalli India SRM TRPEngineering College Tamilnadu Tiruchirappalli India Lendi Institute of Engineering and Technology Dept.of EEE Vizianagaram India School of AI Department of Computational Engineering and Networking Amrita Viswa Vidyapeetham Tamilnadu Coimbatore India

ISBN: (纸本)9798331540685

Among people with diabetes, diabetic maculopathy, a dangerous consequence of diabetic retinopathy is the primary cause of visual impairment. Effective treatment and management depend on early and precise identification. In this research, datasets from Kaggle, DRIVE, CHASE, STARE and DIARETDB1 is employed and a Convolutional Neural Network (CNN) based method is used for recognizing the presence of diabetic maculopathy. Multiple convolutional layers make up the model architecture, which is then followed by pooling and fully connected layers. A SoftMax method is then used to categorize the data and is extensively tuned for hyperparameters. By incorporating transfer learning from pre-trained models, the performance is further improved and a significant reduction in training period and processing resources is required. Using a variety of datasets guarantees a thorough assessment of the model, improving its dependability and generalizability. The model's capacity to retain excellent performance across several data sources is confirmed by independent testing and cross-validation on each dataset. By facilitating prompt medical intervention, the suggested CNN model might possibly improve patient outcomes by aiding in the early detection of diabetic maculopathy. With a sensitivity of 90%, specificity of 98%, precision of 97.8%, accuracy of 94%, and F1 score of 93.8%, our model performed well. These measurements highlight the promise of deep learning approaches in improving medical imaging diagnostics by demonstrating the model's resilience and efficacy in diagnosing diabetic maculopathy. Accuracy obtained using completed Local Binary Pattern (CLBP) and Deep Learning-Based Framework for Automatic Diabetic Retinopathy Detection is found to be 90% and 91 %, whereas the proposed method offers an accuracy of 94% which is better and optimum. © 2024 IEEE.

关键词： Convolutional neural networks

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An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

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Journal of Safety Science and Resilience 2025年第1期6卷 93-104页

作者： M.Sinthuja S.Pravinthraja B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal Department of ISE M.S.Ramaiah Institute of Technology BengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of CSE Presidency UniversityBengaluruIndia Department of Computer Science and Engineering BMS institute of Technology&ManagementBengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data *** is the case with Association Rules,a tool for unsupervised data mining that extracts information in the form of IF-THEN *** various approaches for extracting frequent itemset(prior step before mining association rules)in extremely large databases have been presented,the high computational cost and shortage of memory remain key issues to be addressed while processing enormous *** objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent *** performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max *** was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithm outperform the existing FP-max algorithm in terms of runtime and memory consumption.

关键词： Clustering Data mining Frequent itemset mining Linear prefix tree Maximal frequent itemset mining

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Path Planning Solution for Intelligent Robots Using Ex*RRT Algorithm in Disaster Relief 5

Path Planning Solution for Intelligent Robots Using Ex*RRT A...

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5th IEEE International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Hariprasath, V.Y. Mukesh, S. Aruna, T.N. Gayana, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. SRM Institute of Science and Technology Department of Computational Intelligence Chennai India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Tiruchirappalli India KGiSL Institute of Technology Department of Information Technology Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350351378

Unmanned Ground Robots (UGVs) are increasingly becoming vital tools in disaster relief operations, offering a means to navigate through hazardous environments where human intervention may be risky or impossible. These intelligent robots are designed to perform a variety of tasks, such as searching for survivors, delivering supplies, and assessing damage. However, the effectiveness of UGVs in such critical missions largely depends on their ability to navigate complex and unpredictable terrains, a challenge that necessitates advanced path-planning strategies. The paper introduces a novel approach to path planning for UGVs in disaster relief scenarios. This approach combines the strengths of advanced rapidly Rapidly-exploring Random Trees (RRT) algorithms with the capabilities of YOLOv4, a state-of-the-art technique for object detection, specifically tailored here for human detection. RRT algorithms are known for their efficiency in exploring complex spaces, making them ideal for navigating through the unpredictable terrains typical of disaster zones. Meanwhile, YOLOv4 enhances the UGVs' ability to detect and respond to human presence, a critical requirement in rescue operations. The UGVs equipped with this novel path-planning strategy were able to navigate safely and efficiently, adapting to the dynamic nature of the environment. More importantly, the accuracy in detecting human presence was significantly improved, a crucial factor in ensuring that aid and rescue efforts are directed where they are most needed. By enhancing the path planning capabilities of UGVs with advanced algorithms and human detection technology, this approach holds the potential to revolutionize disaster response operations, making them more efficient and impactful, ultimately leading to faster and more effective aid delivery to those in dire need. © 2024 IEEE.

关键词： Motion planning

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