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2024 Transactions of the American Nuclear Society on Winter Conference and Expo, ANS 2024

作者： Prantikos, Konstantinos Pallikaras, Venetis Pantopoulou, Maria Tsoukalas, Lefteri H. Heifetz, Alexander School of Nuclear Engineering Purdue University West LafayetteIN47906 United States Nuclear Science and Engineering Division Argonne National Laboratory ArgonneIL60439 United States College of Computing Georgia Institute of Technology AtlantaGA30332 United States

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Oral Cancer Detection and Classification Using Deep Learning with DenseNet121-CatBoost Classifier 2

Oral Cancer Detection and Classification Using Deep Learning...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Anitha, D. Soujanya, Thota Chakraborty, Subhra Alkhayyat, Ahmad Revathi, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Sr University School of Agricultural Sciences Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350372892

Oral cancer is a growing universal health challenge, which is rising in the current situation. Early detection is vital for effective treatment and better patient results. However, current diagnostic methods, notably manual histopathological analysis, suffer from subjectivity and time limitations due to classifying the images according to their type. The dependence on manual analysis can lead to delays in accurately diagnosing oral cancer. The proposed method DenseNet121-CatBoost classifier addresses this challenge by using the Histopathological Images dataset. The DenseNet121 for the extracting process and the CatBoost classifier for classifying the images according to their classes. DenseNet121 is utilized to extract features and patterns from images and the CatBoost classifier in the classification process helps to classify the dataset according to its classes using the extracted information. The performance of the DenseNet121-CatBoost method is analyzed using accuracy, specificity, sensitivity, f1score, precision, and Area Under Curve (AUC). This paper shows that the proposed DenseNet121-CatBoost outperforms the existing methods such as ResNet50+CatBoost, DenseNet201+SVM (Support Vector Machine), and GoogLeNet+XGBoost in terms of accuracy of 97.62%, specificity of 98.96%, sensitivity of 94.55%, f1-score of 93.87%, precision of 98.65%, and AUC of 95.26%. © 2024 IEEE.

关键词： Gabor filters

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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An Efficient Novel Approach for Iris Recognition and Segmentation Based on the Utilization of Deep Learning

An Efficient Novel Approach for Iris Recognition and Segment...

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Pasha, Syed Nawaz Anitha, D. Anbunathan, R. Gobinath, C. Madhura, G.K. Sr University School of Computer Science & Artificial Intelligence Warangal India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sa Engineering College Department of Mathematics Chennai India Reva University School of Computing and Information Technology Bangalore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Iris recognition stands out as one of the most stable and accurate biometric identification systems such as facial, voice, fingerprint, handwriting, and iris recognition itself. Deep Learning (DL) plays a pivotal role in enhancing security by leveraging Convolutional Neural Networks (CNNs) for iris recognition. The acquisition of iris images and real-time processing have become focal points demanding heightened attention in the realm of DL. Iris recognition is prized for its high level of protection, making it challenging to reconstruct iris data easily from the database. The CASIA-Iris Dataset serves as a benchmark to verify the uniqueness of iris patterns and evaluate the performance of iris recognition. However, due to limitations in iris image access, the application of CNNs in the segmentation approach remains critical for iris localization and pupil boundary identification, ensuring both efficiency and specificity in iris images. The results obtained from this study reveal that the proposed CNN, utilizing DL models, achieves an impressive accuracy of 99.89% on the CASIA-Iris dataset, better performance of other existing models, such as U-Net and VGG-Net, and ensuring accurate classification. © 2023 IEEE.

关键词： Contrast-Limited Adaptive Histogram Equalization Convolution Neural Network Deep Learning Iris Recognition and Support Vector Machine

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A Fertilizer Recommendation System for Rice Leaf Plant

A Fertilizer Recommendation System for Rice Leaf Plant

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International Conference on Communication and Electronics Systems (ICCES)

作者： Subhiksha Murugan A. Manikandan S. Hariprasad Department of Electronics and Communication Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

The secret to increasing crop productivity and sustainability is precision farming. Optimizing fertilizer use is essential in rice cultivation to satisfy the unique nutrient requirements of crops while reducing environmental effect. Conventional techniques focus on general principles, which frequently result in reduced yields, deteriorated soil, and inefficient nutrient usage. The AI-based fertilizer recommendation system RiceFert, created for rice leaf plants, is presented in this work. The system examines crop-specific information, soil characteristics (such as type and nutrient levels), and environmental data (temperature, humidity, and wetness) using a Random Forest (RF) model and deep learning techniques. Data preparation, feature extraction, and model training are all part of the process, which compares classifiers such as Random Forest, SVM, Gradient Boosting, and Naive Bayes. The Random Forest model is superior at recommending fertilizer based on performance criteria like accuracy, precision, recall, and F1-score. This focused strategy reduces excessive or inadequate fertilizer use, increasing rice output and promoting sustainable farming. RiceFert demonstrates the potential of AI in precision agriculture by offering farmers personalized guidance that improves crop output, lowers costs, encourages environmental conservation, and helps farmers make better decisions.

关键词： Training Temperature distribution Crops Soil Nutrients Data models Random forests Recommender systems Fertilizers Farming

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Application of Artificial Intelligence Technique to Detect Alzheimer's Disease

Application of Artificial Intelligence Technique to Detect A...

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2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Arulprakash, A. Ponmaniraj, S. Gokul Rajan, V. Divya, A. Sathyamaba Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Simats University Department of Computational Intelligence Thandalam602 105 India School of Computing Science and Engineering Galgotias University Greater Noida India Vandayar Engineering College Department of Computer Science and Engineering Pulavar Naththam Thanjavur India

ISBN: (纸本)9798350394580

Aeras of investigational and clinical study have demonstrated several pathways in the progression of Alzheimer's disease (AD), but no treatments have been developed. Patient lifestyle, clinical, and biological data are increasingly being collected through open data-sharing programmes, and the resulting data set may be infinite in size, far exceeding the capacity of humans to make sense of it. Recent advances in artificial intelligence have opened the door to the possibility of using analysis of medical imagery to aid in the analysis of Alzheimer's disease. A variety of methods, including those based on artificial intelligence, have been devised and used in several research to aid in the CAD of AD identification. The use of AI methods to solve Alzheimer's disease diagnosis challenges in brain imaging will be a major focus of future research in this field. This article delivers an impression of the many images processing, feature extraction, and machine learning approaches that are used to solve the AD detection problem. The final section of the study will include a synopsis of the existing body of knowledge as well as a critical appraisal of potential future research avenues and open questions. © 2023 IEEE.

关键词： Alzheimer's disease detection Artificial intelligence diagnosis machine learning U-Net

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Trust-aware Routing Protocol using Modified Sand Cat Swarm Optimization in Iot-Based WSN

Trust-aware Routing Protocol using Modified Sand Cat Swarm O...

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Anitha, D. Mahaboob John, Y.M. Jamuna Rani, M. Sowmya, M. Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Mahendra College of Engineering Ece Department Salem India Sona College of Technology Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

The trust-aware secure route selection leverages trust-based metrics for evaluating the credibility of nodes and routes which enables the selection of the most reliable and secure paths for data transmission. Secure routing is of major significance for preventing wireless devices from other threats and effective characteristics in WSN attain an efficient secure routing selection. This research proposed the trust secure route selection utilizing Modified Sand Cat Swarm Optimization (TSRS-MSCSO) for efficient route selection. Initially, the secure nodes are identified by using energy and delay of the fitness function. After identifying secure nodes, the route selection is performed through the Modified Sand Cat Swarm Optimization algorithm. The performance of the proposed model is estimated by metrics like packet delivery ratio, throughput, delay and energy consumption with various nodes of 50, 100, 150, and 200. The proposed model attained a high packet delivery ratio of 98.8%, less delay of 3s, high throughput of 96.27%, and less energy consumption of 2.14J for 50 nodes which ensures better performance than other existing methods like Dynamic Deep Neural Network-based Particle Swarm Optimization (DDNN-PSO), Chaotic Bumble Bees Mating Optimization- Trust Sensing Model (CBBMOR-TSM) and fuzzy TOPSIS based hybrid Jarratt Butterfly Optimization (FTOPSIS-HJBO) models. © 2023 IEEE.

关键词： Energy Efficiency Internet of Things Route Selection Sand Cat Swarm Optimization Secure nodes Throughput Wireless Sensor Networks

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Automatic Title Generation with Attention-Based LSTM 3rd

Automatic Title Generation with Attention-Based LSTM

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3rd International Conference on Information technology, InCITe 2023

作者： Dhilsath Fathima, M. Seeni Syed Raviyathu Ammal, M. Singh, Prashant Kumar Shome, Sachi Kharsyienlieh, Manbha Hariharan, R. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Mohamed Sathak Engineering College Tamil Nadu Kilakarai India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819959969

The objective of this article is to develop a generalised architecture that can generate headings for any type of document, including articles, book paragraphs, newspaper articles, and legal documents. The title of a document provides a concise overview of its content, allowing readers to quickly understand the main idea without focusing on the specifics. The more attractive the title is, the more the reader is bound to read it. Title generation will be helpful to the editors of newspapers or technical writers to easily find the central theme of the whole article. The Sequence-to-Sequence (Seq-to-Seq) model is implemented in this proposed work for generating the title for the project, article, and research articles. The Seq-to-Seq model utilizes an encoder-decoder recurrent neural network that has an attention layer and Long Short-Term Memory (LSTM). The main strength of the developed framework is the adoption of an attention layers to the encoder-decoder architecture, which assigns more weightage to background words. This proposed model achieves 90% prediction accuracy by including an attention layer into the LSTM. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Water Quality Analysis Using Machine Learning Techniques 2nd

Water Quality Analysis Using Machine Learning Techniques

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2nd International Conference on Deep sciences for computing and Communications, IconDeepCom 2023

作者： Kumar, M. Gobinath, T. Karthikeyan, M. Saravana Arun, C. Chettinad College of Engineering and Technology NH 67 Karur-Trichy Higways Puliyur CF Po Tamilnadu Karur639114 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9783031689048

The most crucial resource that must be preserved by humans is water. Due to numerous environmental and societal causes, the quality of the water is deteriorating day by day. In order to take remedial action in the best way possible, it is essential to regularly evaluate the quality of the water we consume. The purpose of this study is to use supervised machine learning techniques to examine the quality of water. Many supervised machine learning methods are employed in these applications. The relationship between the features and their related labels, the size of the datasets, and the applications all affect how well the machine learning technique performs. This study considers the K Nearest Neighbor method, Support Vector Machine algorithm, Logistic Regression technique, and Random Forest Classifier algorithm when evaluating the water quality. The Random Forest method performs better in terms of training and testing accuracy than other algorithms among the several supervised machine learning approaches used in our work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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