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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Yuvaraj, K. Setia, Nipun Sangeetha, S. Goyal, Prakhar Thomas, P.K. Meshram, Vishal A. Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Quantum University Research Center Uttarakhand Roorkee247667 India School of Management - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350388916

Since more and more information is being stored in digital formats (such as databases, journals, libraries, and papers), it is crucial that this information be put to good use. However, it might be challenging to extract meaningful insights from the data because of its poor organisation, ambiguity, and lack of precision. Data may be correctly categorised to allow for separate research on different types of data, which will help with this problem. Because of this, researchers in disciplines like mathematics and computer science have been trying to figure out how to fix the issue. Material mining is a branch of machine learning that helps cope with unstructured and disorganised data by sifting through it for useful insights. Text mining is a subfield of data mining that focuses on discovering patterns and extracting structured attributes from unstructured text. To successfully analyse such data, supervised learning is combined with unsupervised learning. It has become common practise to employ Latent Dirichlet Allocation (LDA), a probabilistic approach to topic modelling, when classifying documents. As an unsupervised method, however, traditional LDA's findings may shift depending on which articles come first in the analysis. In this study, we provide a comprehensive strategy for avoiding the order effect as well as the unsupervised probabilistic character of the problem. The proposed framework achieved a success rate of 95.56% & 95.23% when evaluated on two separate datasets, demonstrating its better classification capabilities. Furthermore, the subjects that were extracted from the classified clusters are highly relevant to the domain classifications. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman Vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

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Wavelet Transform Based Feature Extraction for Fault Diagnosis in Machinery 15

Wavelet Transform Based Feature Extraction for Fault Diagnos...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Vyshnavi, A. Mali, Manisha P. Hemelatha, S. Gupta, Mohit Sindhu, M. Singla, Abhishek School of Mangement - Ug Management Karnataka Bangalore India Vishwakarma Institute of Technology Computer Engg Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350370249

The wavelet remodel is a popular sign-processing technique used for feature extraction in fault prognosis in equipment. In this paper, we present an outline of the wavelet transform primarily based on function extraction techniques for fault prognosis. We discuss the standards and advantages of the wavelet remodel and its applicability to machinery fault diagnosis. We gift an in-depth evaluation of the diverse stages of feature extraction along with de-noising, time-frequency distribution, characteristic selection, sample popularity, and category. We additionally talk about the application of the facts-driven methods along with clustering techniques and neural networks for special equipment fault diagnosis troubles. Finally, we provide an outline of numerous case research that has used the wavelet rework based totally feature extraction for fault diagnosis in diverse styles of machinery. This evaluation highlights the usefulness of the wavelet remodel for extracting fault capabilities from equipment with the purpose of, in the long run, helping to enhance the accuracy of fault prognosis and to lessen protection prices. © 2024 IEEE.

关键词： Characteristic Equipment Extraction Machinery Popularity Prognosis Protection Remodel

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The emergence of moral foundations in child language development 44

The emergence of moral foundations in child language develop...

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Ramezani, Aida Liu, Emmy Pinto, Renato Ferreira Lee, Spike W.S. Xu, Yang Department of Computer Science University of Toronto Canada Language Technologies Institute Carnegie Mellon University United States Rotman School of Management Department of Psychology University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

One of the most influential modern theories of morality, Moral Foundations Theory, proposes that morality is formed on innate and shared modular foundations. Psychologists have studied the conceptual development of these moral foundations in childhood, but there exists no comprehensive effort on characterizing the early emergence of moral foundations in naturalistic settings. We explore the emerging order of moral foundations through child and caretaker speech. Using computational methods, we contribute an annotated dataset of moral utterances and find that the individualizing foundations emerge earlier than the binding foundations. Furthermore, caretakers tend to talk more about fairness and degradation, while children talk more about cheating. These results are robust across child gender, family's social class, and race. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Foundations

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A Unified Approach Toward Security Audit and Compliance in Cloud Computing

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 733-750页

作者： Rajesh, Y.S. Kumar, V. G. Kiran Poojari, Asmita MS UpGrad Education Private Limited Nishuvi 75 Dr. Annie Besant Road Worli Mumbai400018 India PSOM Technologies Pvt Ltd Bengaluru India Department of Electronics and Communication Engineering A J Institute of Engineering and Technology Kottara-Mangalore575006 India Department of Computer Science and Engineering N M A M Institute of Engineering and Technology Karkala India

The use of cloud computing has become a reliable information technology solution over the past decade by providing numerous services and resources on a pay-as-you-use basis and evolving from concept to reality. Despite increasing demand and popularity, the adoption of the cloud is hindered mainly by security concerns. Successful cloud adoption and uncomplicated operation are possible if users, intermediaries, and service providers act reliably according to regulations by assuring shared responsibility policy. The biggest challenge in the audit and assurance of cloud computing is the lack of a de facto approach to meet an enterprise’s requirements. The primary focus is on the challenges and effectiveness of audit and assurance by conducting an exploratory study on industry best practices, auditing standards, required certifications, and regulatory compliance frameworks like GDPR, Sarbanes–Oxley, ENISA, ISO 27001, NIST, EU-SEC, OWASP, BSI C5, CIS, ANSSI, HIPAA, CCM, CSA STAR, PCI DSS, COBIT, and SOC in a cloud environment and classifying on basis of applicability. In this research paper, we provide general guidelines on auditing standards by referring to threads and vulnerabilities analyzed and suggesting a unified approach toward audit considerations in cloud computing environments. © The Institution of Engineers (India) 2024.

关键词： Cloud security

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Identifying and Categorizing Alzheimer's Disease with Lightweight Federated Learning Using Identically Distributed Images 11

Identifying and Categorizing Alzheimer's Disease with Lightw...

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11th International Conference on Reliability, Infocom technologies and Optimization, ICRITO 2024

作者： Trivedi, Naresh Kumar Jain, Sachin Agarwal, Sumit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Delhi Institute of Higher Education Department of Management Greater Noida West India

ISBN: (纸本)9798350350357

Among the leading causes of mortality in developed countries are neurological diseases like Alzheimer's disease (AD). Amazing results have come out of studies that have used algorithms powered by computers. Deep models are all the rage right now, especially in the world of image processing. Many academic studies have focused on deep learning for Alzheimer's disease detection since 2018. This paper introduces a simplified framework for federated deep learning to classify Alzheimer's diseases while ensuring the security of private information. This work uses IID (Independent and Identically Distributed) to test federated deep learning models using a distributed client-server architecture to safeguard client data. The framework was tested with one and numerous clients in traditional and federated learning situations. AlexNet's accuracy of 98.53 percent made it our standard after analyzing other pre-trained models. IID datasets were then evaluated using federal learning (FL). The framework's advanced features make it the optimal choice for early-stage diagnosis and classification of Alzheimer's disease. © 2024 IEEE.

关键词： Classification (of information)

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⁠Climate Change Prediction in Sustainable Healthcare Systems for Biodiverse Ecosystem Based on Satellite Data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 283-293页

作者： Jadhav, Makarand Mohan Agarwal, Pankaj Umadevi, B. Khatibi, Ali Akhila, N. Sandeep, Kasturi Sai Banerjee, Sudipta Department of Electronics and Telecommunication NBN Sinhgad Technical Institutes Campus Pune 411041 India School of Engineering and Technology K.R. Mangalam University Gurgaon India Department of Mathematics JSS Academy of Technical Education Karnataka Bengaluru 560060 India Management and Science University Selangor Shah Alam Malaysia Department of Computer Science & amp Engineering Aditya University Surampalem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist.Vaddeswaram Andhra Pradesh 522302 India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Pune India

Every area of human life is being impacted by climate change more and more. According to recent studies, there is a strong association between temperature and human health. It is predicted that by 2100, fluctuations in temperature will cause 73 deaths for every 100,000 people worldwide. Algorithms for machine learning (ML) have made significant progress, leading to innovations in other fields of study and, most recently, being proposed as a tool to support climate analysis. In order to mitigate these risks, a proximity environmental feature-based tree health assessment (PTA) methodology is suggested, which offers recommendations for early warning systems for possible poor tree health. Tree health is determined and assessed in PTA development using proximity environmental characteristics (PEFs). This research proposes novel technique in climate change-based health data analysis using satellite data analysis for biodiverse ecosystem in sustainable modelling. Here, the input is collected as regional climate change data and analysed for feature selection with extraction. This data has selection carried out using kernel adversarial Gaussian linear regression model;the selected features show the abnormality in health issues. Then, the selected features have been extracted using convolutional U-net extreme attention neural network. The extracted features show healthcare data analysis based on climate changes. The simulation analysis is carried out for various climate change-based healthcare analysis dataset in terms of detection accuracy, normalised error, precision, F-measure, and recall. The suggested method produced 96% detection accuracy, 56% normalised error, 90% precision, 95% recall, and 92% F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biodiverse ecosystem Climate change Health data analysis Machine learning Satellite data analysis

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3D-CNN Architecture to Improve the Classification Accuracy of the Real-Time Images from IOT Devices

3D-CNN Architecture to Improve the Classification Accuracy o...

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2023 International Conference on Disruptive technologies, ICDT 2023

作者： Kiran Kumar, C. Aruna Devi, B. Maguluri, Lakshmana Phaneendra Naruka, Mahaveer Singh Codecraft Technologies Data Science Bangalore Karnataka India Dr.NGP Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Guntur India Gl Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798350323887

The classification of real time images from the fast data capturing devices in Internet of Things (IoT) environment is a critical task. It requires suitable processing and development of a model for increased accuracy in classifying the objects in real-time. Therefore, the necessity in improving the accuracy of classifying the instances is needed post performing the modelling, building and development of a model. In this paper, a three-dimensional (3D) Convolutional Neural Network (CNN) is developed to increase the process of classification for the objects in the real-time environment. The objects needed to train the classifier is supplied and the model is built in python environment. The results show an increased classification accuracy in detecting multi-objectives than the state-of-art models. © 2023 IEEE.

关键词： Internet of things

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A Novel Hybrid Framework for Energy-Efficient Clustering and Routing in Heterogeneous IoT-Driven Wireless Sensor Networks 7

A Novel Hybrid Framework for Energy-Efficient Clustering and...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gupta, Gopal Kumar Jain, Arpit Sharma, Khemraj Ashok Kumar, P.M. Gupta, Shubhi Agarwal, Sheenu Nagpur Campus Pune440008 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering AP Vaddeswaram522502 India Nanhua University Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Uttarakhand Technical University Department of Electronics and Communication Engineering U.K. Dehradun India

ISBN: (纸本)9798350350067

Smart computing has been particularly notable in the development of wireless sensor networks (WSNs), which have many applications. Battery-powered, self-configuring sensor nodes are the basis of these networks. Energy and resources are limited when it comes to sensors. Unbalanced nodes in the network consume more power, which adversely affects the network's lifespan. An improved energy-efficient cluster-based routing protocol for heterogeneous wireless sensor networks (WSNs) based on the Internet of Things is proposed in this study. Several performance metrics were used to evaluate the proposed model's effectiveness, including energy efficiency, alive nodes, dead nodes, network lifetime, and residual energy. The results of the comparison were compared with existing methodologies, such as LEACH, PSO, and hybrid PSO. The proposed approach outperforms the existing model by a substantial margin in networks with 50 and 100 nodes, according to the simulation results. © 2024 IEEE.

关键词： Sensor nodes

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Ultrasound-Based Ovarian Cysts Detection with Improved Machine-Learning Techniques and Stage Classification Using Enhanced Classifiers

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SN computer science 2023年第5期4卷 571页

作者： Athithan, Senthil Sachi, Savya Singh, Ajay Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Information Technology Lalit Narayan Mishra College of Business Management Bihar Muzaffarpur 842001 India UIE-Chandigarh University Mohali India

Infertility is a global issue. Total fertility rate declined from over 5 live births per woman in 1950–1955 to 2.5 births per woman in 2010–2015. Female infertility is 37% globally and 12.5% in India. Female infertility can be reduced by identifying and treating the underlying cause early. Ovulatory diseases contribute 25% globally to female infertility. Various female pelvic imaging methods can diagnose ovulatory problems. Diagnostic ultrasound is chosen because it is radiation- and contrast-free and cost-effective. The intensity-based grouping and textural data were used for the detection of follicles and cysts in the ovary, which is based on machine learning (ML). Ovarian diagnosis was given a major boost thanks to the application of machine-learning algorithms, which permitted a success rate of 97% and significantly improved the overall quality of the process. Standard machine-learning strategies have been looked into for the purpose of ovarian classification. In order to determine which method of classification produces the most accurate findings, we constructed three distinct models utilising artificial neural networks, discriminant classifiers, and support vector machines. When the results of the various created classifiers were compared, it was discovered that SVM had the highest accuracy (98.5%). This ingenious tool, which may be classified as a decision support system, will assist the attending physician in reaching the appropriate determination and preventing an error in his or her interpretation. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Demographic and diagnostic features Image processing K-means clustering Machine learning Medical diagnostics Ovarian detection Texture features

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