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2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 IEEE.

关键词： Robot vision

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Smart Precision Farming Using Internet of Things and Machine learning 1

Smart Precision Farming Using Internet of Things and Machine...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Yadav, Devendra Kumar Das, Subha Ranjan Sahoo, Satyam Barua, Ipsita Sahoo, Dibyam XIM University Computer Science And Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

Machine learning (ML) and Internet of Things (IoT) together have a revolutionary impact on agriculture, such as precision farming, which uses cutting-edge technology to address the challenges of unsupervised and random farming practices. Soil testing is a major component of farming that provides knowledge regarding the properties of soil. By measuring the pH level and nutrient deficiencies, the outcome of this test provides knowledge about the fertility and health of the soil. This involves the sampling and testing of each nutrient with certain chemicals, which are further analyzed by experts. This paper presents an innovative framework that harnesses the fusion of IoT and ML to redefine traditional farming methods. In addition, a prototype was created with a combination of various sensing devices, and various analyses of algorithms were performed on the gathered dataset for recommendation. The findings highlight a potential crop suitable for a particular set of soil parameters. © 2024 IEEE.

关键词： Internet of things

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Exploring Quantum Machine learning Algorithms for Enhanced Data Classification and Clustering in Complex Systems - Integration with Gradient Boosting and K-means for Improved Performance

Exploring Quantum Machine Learning Algorithms for Enhanced D...

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2024 international conference on Cybernation and Computation, CYBERCOM 2024

作者： Babu, M. Arun, M. Pushparani, S. Muthukrishnan, A. Poongavanam, N. Rajalakshmi Engineering Çollege Department of Information Technology Thandalam Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

This research focuses on the implementation of quantum machine learning with the classical models such as Gradient Boosting and K-means clustering for efficient classification and clustering of most complex datasets in advanced systems. Taking advantage of the large datasets that are handled by the qc, this work examines Quantum Support Vector Machines (QSVM), Quantum Boosting, and Quantum K-means. To compare the efficiency of these hybrid models three datasets - the healthcare, the financial, and the cyber-security datasets - are used. Our findings indicate that quantum optimization algorithms achieve better accuracy in clustering in comparison to the classical counterparts and are computationally faster. This work shows how quantum machine learning can lead to the enhancement of difficult data processing tasks, a major issue in the quantum computing domain, with specific reference to resources and high-dimensional data. The opportunity to integrate quantum and classical models successfully is the best basis for further developments in machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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CDBODenseNet: CAViaR Dung Beetle Optimization Enabled Deep learning for Diabetic Retinopathy Classification 6

CDBODenseNet: CAViaR Dung Beetle Optimization Enabled Deep L...

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6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Sagvekar, Vidya Joshi, Manjusha Ramakrishnan, Minu Dudani, Ajay Mukesh Patel School of Technology Management and Engineering India Department of Electronics and Communication Engineering India Hospital and Research Centre Department of Ophthalmology K.J Somaiya Medical College Mumbai22 India

ISBN: (纸本)9798350306613

Diabetic Retinopathy (DR) occurs as a degrading infection that affects the eyes as well and it is an effect of Diabetes Mellitus (DM), where soaring levels of blood glucose provoke lesions on the eye retina. In this paper, CAViaR Dung Beetle Optimization-DenseNet (CDBODenseNet) is proposed for DR classification. At first, an image obtained from mentioned dataset is given towards a pre-processing module to enhance the image employing Mean Filter. Afterward, lesion segmentation is performed using Deep Joint Segmentation. Similarly, process of classifying artery and vein is performed using AVNet. Subsequently, feature extraction process extracts some essential features. Finally, DR is classified by DenseNet which is tuned with CDBO. CDBO is designed by integrating DBO with CAViaR technique. Additionally, CDBO DenseNet accomplished high accuracy, sensitivity and specificity of 94 %, 92.9%, and 93.4% respectively. © 2024 IEEE.

关键词： AVNet Deep joint segmentation Deep learning (DL) Dung Beetle Optimization (DBO) Mean filter

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System simulation of multimedia English teaching based on network virtual resource sharing

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international JOURNAL OF SYSTEM ASSURANCE engineering AND MANAGEMENT 2023年 1-9页

作者： Lihua, Shi Xi, Yang Shijiazhuang Informat Engn Vocat Coll Dept Basic Educ Shijiazhuang 050000 Hebei Peoples R China

The emergence of cloud computing, greatly changed the way of data processing, it makes the data center from an independent individual to a large-scale, automated direction. Virtual network takes virtual machine as the carrier and connects through its own unique topology. These virtual machines and topology form a logical virtual data center. The application of the Internet is becoming more and more extensive, and its data is also growing explosively. Due to the structure and scale of the data center, there are many problems in the cloud resource network sharing and isolation methods, which limit the performance of cloud network resource sharing. In addition, this paper also proposes a solution that can realize embedded device voice solidification and broadcast. This method can not only change the current communication configuration, but also increase the voice configuration, so that voice broadcasting can better meet the personal habits and actual needs. This personalized voice broadcast greatly improves the efficiency of users' daily maintenance, and teachers can carry out teaching activities anytime and anywhere. In addition, multimedia teaching can enrich the content of English classroom effectively and make teachers' teaching more dynamic. Therefore, College English teachers should realize personalized teaching plan through multimedia according to students' personal information and learning status, so that multimedia teaching and efficient English teaching can be perfectly integrated.

关键词： Virtual network Resource sharing Voice broadcast Multimedia application English teaching

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Deep learning based Terrain Classification of Mars Raw Images using UNet and FCN Models 18

Deep Learning based Terrain Classification of Mars Raw Image...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Samhitha, D. Sai Ashwini, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9789380544519

The exploration of Mars has consistently been of utmost significance in understanding planetary development, suitability for life, and the possibility of establishing civilizations. Precise classification of Martian terrain is essential for effective navigation, identification of resources, and prevention of potential dangers, to enhance the effectiveness of Mars missions. Traditional methods of terrain categorization frequently depend on manual examination and have constraints in terms of scalability and precision. This paper introduces an innovative method that utilizes deep learning algorithms to completely transform the process of classifying Martian terrain. This study employed the Unet and FCN models to categorize the Martian surface by utilizing a wide variety of images obtained from the AI4MARS dataset, from which a collection of 5K to 15K images are extracted. The results show the effective performance, the FCN model attained an Intersection over Union (IoU) score of 73%, while the Unet model acquired a score of 60%. The results emphasize the capacity of deep learning to revolutionize the classification of the Martian landscape, providing a more precise and adaptable answer in contrast with traditional approaches. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AI4MARS Deep learning Fully Convolution Network (FCN) Mars Exploration ResNet50 Terrain Recognition UNet

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Cyberbullying Detection on Multiclass Data Using Machine learning and A Hybrid CNN-BiLSTM Architecture

Cyberbullying Detection on Multiclass Data Using Machine Lea...

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2024 international conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Balaji, Peddi Gowtham Katariya, Priyanka Prakash Sruthi, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Cyberbullying has emerged as a significantly concerning online societal issue in the past few years with the proliferation of social media platforms. To address and overcome this pressing communal concern and successfully detect cyberbullying across various categories, this study proposes a novel approach that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks, alongside six traditional machine learning techniques, driven by the purpose of enhancing the accuracy and robustness of cyberbullying detection across diverse cultural and textual landscapes. To highlight the multiclass aspect, the model is trained and tested on a dataset containing instances categorized into Age, Religion, Ethnicity, Gender, Other Cyberbullying, and Not Cyberbullying. Through rigorous experimentation, the proposed deep learning model's proficiency results in a 0.83 accuracy and F measure, showcasing its efficacy in capturing the nuances of various categories. Meanwhile, among the six machine learning models employed, Random Forest emerges as the top performer with values 0.94 and 0.93 for the accuracy and F measure respectively. This comprehensive methodology underscores its ability to effectively discern between the different classes and offers a promising solution to combat cyberbullying across diverse demographics and thematic dimensions. © 2024 IEEE.

关键词： Adversarial machine learning

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Neuroendocrine Tumor Detection System 1

Neuroendocrine Tumor Detection System

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Kidwai, Mohd Umair Misha, Anuradha Srivastava, Atul Sharma, Vijay Shankar Amity School of Engineering and Technology AUUP Lucknow India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350353648

Advancements in machine learning have catalysed a transformative shift in healthcare, enabling the development of predictive models for early disease detection. This paper introduces a cutting-edge system, the Neuroendocrine Tumor Detection System (NETDS), which leverages machine learning to anticipate the presence of neuroendocrine tumors based on comprehensive data encompassing medical history with precision, lifestyle factors, and demographic information. NETDS represents a proactive approach to addressing the burgeoning challenges in healthcare by prioritizing early detection and prevention of neuroendocrine tumors. Key components of NETDS include meticulous data preprocessing, strategic feature selection, rigorous model training, and precise disease prognosis. While offering significant benefits such as early intervention and cost-effectiveness, ethical considerations pertaining to data privacy, model interpretability, and ongoing refinement are paramount for the responsible and effective deployment of NETDS in clinical settings. Consequently, NETDS holds immense promise for improving public health outcomes and mitigating the burdens associated with neuroendocrine tumors. © 2024 IEEE.

关键词： clinical settings Data Analytics Demographic early intervention Neuroendocrine

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Performance Metrics and Accuracy Assessment of Machine learning Models for Crop Recommendation

Performance Metrics and Accuracy Assessment of Machine Learn...

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2024 IEEE international conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Senthil, K. Teja, G. Srudhakerthi, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In this work, five machine-learning techniques are compared on crop recommendation. The Random Forest model achieved an accuracy of 99.09%, with both precision and recall at 99.19%. In comparison, the Decision Tree reached an accuracy of 98.33%, but demonstrated higher precision and the Support Vector Machine (SVM) also performed well but slightly less accurately than the Random Forest, with an accuracy of 97.27%. KNN showed an accuracy of 96.06% and performed well, but the ensemble approaches surpassed it. Despite being simpler and more interpretable, Logistic Regression had the worst accuracy of 95.91%. Results: These results indicate strong performance of ensemble methods on complex classification problems. Results were consistent across Decision Tree and SVM while comparable but notably weaker in KNN and Logistic Regression. The comparative analysis highlights the primacy of Random Forest on resilience and precision with an affirmation that simpler models such as Logistic Regression still have high utility in certain circumstances. Future research could focus on fine-tuning model hyperparameters, and exploring additional methodologies to improve classification performance. © 2024 IEEE.

关键词： Support vector regression

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Predictive Modeling for Type-3 Diabetes: A Machine learning Approach for Healthcare Management 1

Predictive Modeling for Type-3 Diabetes: A Machine Learning ...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Sharma, Harshita Devarajan, Ganesh Gopal SRM Institute of Science & Technology Department of Computerscience & Engineering Uttar Pradesh Ghaziabad201204 India SRM Institute of Science & Technology Faculty of Engineering and Technology Department of Computerscience & Engineering Uttar Pradesh Ghaziabad201204 India

ISBN: (纸本)9798350353648

Diabetes Mellitus (DM) is a prevalent metabolic illness that needs to be accurately diagnosed and predicted in order to be effectively managed. Using datasets including the NC State University Diabetes Dataset and the Ohio Longitudinal 2023 Dataset, this work examines predictive modeling techniques for diabetes classification and Type-3 Diabetes (T3DM) forecasting. We use an assortment of machine learning methods, preprocess data, and perform exploratory data analysis. The hybrid technique achieves the most accuracy of 81.1% for diabetes classification. It does this by combining the predictions of gradient boosting and logistic regression classifiers. Furthermore, we create a T3DM predictive model that has outstanding predictive ability to identify people who are at risk. The hybrid algorithm's application demonstrates how well it works to increase prediction accuracy over the use of separate models. Our findings highlight the value of predictive modeling in managing diabetes, assisting in the making of healthcare decisions, and enhancing patient outcomes. Subsequent investigations may concentrate on incorporating supplementary data sources and utilizing sophisticated techniques to improve clinical relevance and predictive precision in the treatment of diabetes. © 2024 IEEE.

关键词： Prediction models

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