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Active Learning Using Multifeature Kernel Sparse Representation Classifier for Hyperspectral Image Classification 3

Active Learning Using Multifeature Kernel Sparse Representat...

引用

3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

作者： Bortiew, Amos Patra, Swarnajyoti Tezpur University Dept. of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350325591

Active learning is a popular technique that mitigates the need for large training samples for hyperspectral image classification. The success of this technique is dependent on the query function designed to select informative samples for labelling. In the literature, most of the query functions are defined by exploiting spectral information. In this research, a novel query function for active learning is defined that considers both the spectral and spatial information of the hyperspectral image using multiple feature sets. The multiple feature sets provided complementary information for selecting those informative samples which are not only informative in spectral perspective but also from spatial perspective. Experiments conducted using two real hyperspectral datasets show that the proposed technique outperformed many state-of-the-art techniques. © 2023 IEEE.

关键词： Image classification

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A Code-Based Multiple Watermarking Scheme for the Preservation of Authenticity of Forensic Images 14

A Code-Based Multiple Watermarking Scheme for the Preservati...

引用

14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Chroni, Maria Nikolopoulos, Stavros D. Polenakis, Iosif Vouronikos, Vasileios University of Ioannina Dept. of Computer Science & Engineering Ioannina Greece

ISBN: (纸本)9798350318067

In this work we present watermarking technique for the preservation of authenticity property of forensic images. The proposed technique has its basis on the utilization of watermarks by the embedding of Self-Inverting Permutations by their 2D representation in the frequency domain of forensic images. We propose the embedding of multiple integers through a code-sequence structure, where the robustness and the imperceptibility of the proposed technique has been attested and particular settings on their application e.g. the lower their imperceptibility the higher the fragility of a watermark is, cooperate on the development of the watermarking technique that can be utilized for the originality of the image. The effectiveness of proposed technique is validated throughout an experimental evaluation deploying various attacks, while the achieved results prove the potentials of the proposed model in the preservation of forensic Image authenticity. © 2023 IEEE.

关键词： Embeddings

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Enhancing Biomedical Literature Analysis to Predict Drug Interactions Using Multi Criteria Decision Making and LSTM-CNN Model

Enhancing Biomedical Literature Analysis to Predict Drug Int...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798350315127

The extraction of information from unstructured text has become a crucial task in the biomedical literature field. Specifically, the analysis of various relationships such as Drug Food, Drug Disease, Drug non-prescription and Drug Drug interactions has gained significant importance. To accomplish this, a combination of Multi Criteria Decision Making (MCDM) and machine learning techniques has been employed. A dataset has been curated and utilized to train a model based on Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). The trained model enables accurate predictions and identification of relevant information pertaining to drug interactions in biomedical literature. This approach enhances the understanding and knowledge extraction from vast amounts of unstructured text data, leading to improved insights and potential advancements in the field of biomedical research. © 2023 IEEE.

关键词： Long short-term memory

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Performance and Evaluation of Classification Algorithms Using Machine Learning with Comparative Study for Heart Disease Prediction

Performance and Evaluation of Classification Algorithms Usin...

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2023 IEEE Asia-Pacific Conference on computer science and data engineering, CSDE 2023

作者： Hossain, Md. Manik Ashraful Alam, Md. Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

The leading cause of unseasonable death worldwide is heart complaints. Day by day the causes of heart disease are increasing at a rapid-fire rate and it's veritably important and concerning to prognosticate any such disease beforehand. Predicting how illness will affect a person is a delicate challenge. Machine learning is being applied in different fields around the world. In the healthcare sector, there is no exception. data classification models and machine learning algorithms stoutly introduce diagnostic guidelines and enable experts to increase the effectiveness of the diagnostic process. The body's remaining organs are given advanced precedence over the nucleus. It provides the body with oxygen. The distribution of heart illnesses among medical practitioners can be estimated via data exploration. Medical facilities can examine various disorders and evaluate emerging diseases thanks to data collecting. Predicting the condition based on recent medical research has the biggest impact. To learn statistics more effectively, a variety of methods are being investigated in the scientific community. The renovation has had a big impact on the metropolitan community's way of life in addition to improving it. In this situation, it's crucial to offer a comprehensive tool that will enable medical professionals to foresee the sickness. Different machine learning applications suggest varying prediction precision. It should be analyzed with Logistic Regression, KNN, Decision tree, Random Forest, SVM, Gaussian NB, Ada Boost Classifier Gradient Boosting Classifier, Quadratic Discriminant Analysis, and MLP Classifier with comparative mean of three data sets. It will be better to investigate the accuracy of prediction from the most concerning algorithms of Machine learning with recall and f-score of the heart data and present it in the table with visual representation. This underpinning research has shown that multilayer perceptron with cross-validation has surpassed all other alg

关键词： Heart

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Blockchain Unleashed: Empowering Information-centric Network Computing in Cloud, Fog, and Edge Services 14

Blockchain Unleashed: Empowering Information-centric Network...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Zambre, Pranav Panchal, Mitali Chouhan, Ankit Parul University Dept. of Computer Science and Engineering Vadodara India

ISBN: (纸本)9798350335095

Blockchain technology has been gaining a lot of attention due to its potential to improve data security, privacy, and decentralization. In this paper, we explore the use of blockchain in cloud, fog, and edge computing services. We analyze the unique characteristics of these services and how blockchain technology can enhance them. One of the benefits of blockchain in cloud computing is that it allows for secure and transparent data sharing among multiple parties. Blockchain can ensure the authenticity and integrity of data with smart contracts, which can automate processes and eliminate the need for intermediaries. This can result in cost savings and increased efficiency. In fog computing, blockchain can be used to enable secure and real-time data processing at the network edge. This can improve latency and reduce bandwidth consumption by processing data closer to the source. Additionally, blockchain-based fog computing can provide better data privacy and security for connected devices. Edge computing can benefit from blockchain by providing a trustless environment for data sharing and storage. Edge devices can act as nodes in a blockchain network, allowing for decentralized control and increased security. This can enable new use cases such as peer-to-peer transactions and autonomous decision making. Overall, blockchain has the potential to empower information-centric network computing in cloud, fog, and edge services by providing increased security, privacy, and efficiency. However, there are still challenges that need to be addressed such as scalability, interoperability, and governance. © 2023 IEEE.

关键词： Smart contract

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Machine Learning Advances in Diagnosing Attention Deficit and Hyperactivity Disorder 14

Machine Learning Advances in Diagnosing Attention Deficit an...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Deepika Sharma, Meghna Arora, Shaveta The Northcap University Dept. of Computer Science and Engineering Gurugram India

Attention deficit and hyperactivity disorder (ADHD) is worldwide prevalent neurobehavioral ailment affecting kids and adolescents. ADHD is mainly characterized by behavioral and mental abnormalities with its root lying in genetics and neural impairments. Despite prescription of various diagnosis guidelines by American psychiatric association (APA), there is no standard biomarker for ADHD detection which sometimes causes misdiagnosis/overdiagnosis of the symptoms. So, there is crucial need for an accurate and reliable diagnostic standard for ADHD for timely and effective treatment. This paper reviews various machine learning and deep learning models proposed for ADHD diagnosis in recent times. It also lists different factors supporting the use of machine learning for ADHD detection, brain imaging modalities used, novel execution strategies along with the shortcomings of these methods. The conclusion part talks about future scope of machine learning in ADHD diagnosis. © Grenze Scientific Society, 2023.

关键词： Brain mapping

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Tagging Speech for Words in Low Resourced Monolingual Contexts of Sanskrit Shlokas

Tagging Speech for Words in Low Resourced Monolingual Contex...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Hegde, Thanmayi S. Vindhya, V. Badri Prasad, V.R. Dept. Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798350325188

Natural Language Processing is widely used in Machine translation which helps in replacing many tedious and difficult tasks and producing qualitative and efficient results. Sanskrit is one of the primitive languages of India. It is also the oldest, purest and most systematic language in the world. It is a magical language which helps in generating different words from a single word by prefixing and suffixing the word with different helper words to same root word. Shloka in Sanskrit are phrases or words that represent a poem or a hymn. The famous texts written entirely in shlokas are the "Ramayana" and "Mahabharata." This paper aims to generate the POS tags for each word in Sanskrit Shlokas using various machine learning and deep learning algorithms thus helping in understanding the actual verbal beauty of Sanskrit Shlokas in widely spoken language English. The proposed work implements Parts of Speech Tagging for words in Sanskrit Shlokas using Conditional Random Field, Gaussian NB and Bi-directional LSTM models. Of the implemented approaches, Bi-directional LSTM gives the best result. © 2023 IEEE.

关键词： Bidirectional Long Short-Term Memory Networks Character Embeddings Conditional Random Fields Corpus building for Sanskrit data Preprocessing Deep learning Gaussian Navie Bayes Machine translation systems Natural Language Translation Parts of speech tagging (POS)

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A Study of Transliteration Approaches 4

A Study of Transliteration Approaches

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chaudhary, Anu Shekhar, Shashi GLA University Dept. of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350306118

In the present era, text generated by users is becoming increasingly popular on the Internet. People rely on search engines for their information. The transliterated text is one of the major concerns to search the relevant information for a user query. There is a corresponding effort to handle transliterated text in order to take advantage of this huge knowledge source. In this paper we review the most recent findings in the domain of transliteration. Firstly, start with an introduction to transliteration and its challenges, then outline several transliteration methods and discuss new research efforts in information search for transliterated text. In this study, the authors present various approaches/techniques, algorithms, and evaluation approaches of previous studies, identify some important issues in machine transliteration approaches/models, and provide future guidance for researchers in this field. © 2023 IEEE.

关键词： Search engines

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Enhancement of Accuracy for Iris Presentation Attack

Enhancement of Accuracy for Iris Presentation Attack

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Priyanka, V. Shyam, Gopal K. Presidency University Dept. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300826

Biometrics is the assessment and statistical examination of an individual's distinctive physical and behavioural traits. Identification, access control, and identifying those who are being monitored are the major areas where technology is focused. In security systems, biometric-based recognition has been substituting traditional recognition techniques. Iris recognition (IR) is more utilized in today's biometric technologies, which are applied to various devices for various security concerns. The effectiveness of IR systems has significantly improved during the past ten years. This is due to recent developments in deep convolutional neural networks (CNNs) and computer vision, as well as the availability of enormous training datasets. A presentation attack is a kind of attack where an imposter creates artificial or fake out of genuine. This paper proposed an effective approach to increase the accuracy in detecting iris presentation attacks and design the state-of-the-art of existing CNN techniques from 2015 to 2023. The proposed method is a Dual Channel Convolutional Neural Network Presentation Attack Detector(DC-CNNPAD) for increasing the accuracy of detecting real iris. The experiment was performed on the sample LivDet-2017 dataset to identify the effectiveness of the model in order to detect the artifact. The results obtained from the detection model on the sample dataset are extremely good. © 2023 IEEE.

关键词： Biometrics

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Performance Evaluation of Machine Learning Algorithms for Lung Nodule Detection using Multi-Modal Imaging - A Hybrid Approach 2

Performance Evaluation of Machine Learning Algorithms for Lu...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Puneeth, R.P. Amogha Mayya, K.S. Shetty, Agnesh Rao, Akash Sooda, Aditya A. Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350347579

Lung cancer is a major contributor to global mortality rates and identification is critical to improve patient outcomes. In recent years, machine learning algorithms have demonstrated promising results in identifying lung nodules from medical images. The most compelling area of research for scientists is the early detection of lung cancer. This study is a method for lung nodule detection using CT images. The study incorporates a hybrid model that combines multiple machine learning algorithms including CNN, SVM, DTC, ANN, and KNN to improve the accuracy of nodule detection. The hybrid model demonstrated high accuracy in identifying various types of lung nodules, including Adenocell carcinoma, squamous cell carcinoma, and large cell carcinoma. Specifically, the model achieved an accuracy rate of over 90% in detecting and differentiating normal lung tissue and Adenocell carcinomas. Accuracy graphs and priority setting were utilized to assess the model's capability in accurately predicting the presence of lung cancer. Additionally, the efficiency of the hybrid model was compared with other machine learning algorithms, including SVM, Random Forest, and Decision Trees. A large dataset of CT scans was collected for training and evaluation purposes. The results demonstrated the advantages of the suggested hybrid model in terms of accuracy and efficiency. This study highlights the importance of early lung nodule identification using CT scans and demonstrates the effectiveness of the hybrid model in accurately identifying different types of lung nodules. © 2023 IEEE.

关键词： computerized tomography

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