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Enhancing Histopathological Image Classification through InceptionResNetV2: A Deep Learning Approach 2

Enhancing Histopathological Image Classification through Inc...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mahesh, R.T. Shenbagavalli, P. Mohanraj, T. Amala, P. Anitha, K. Thakur, Arastu Department of Computer Science and Engineering Bangalore India Rathinam Technical Campus Department Of Artificial Intelligence And Data Science Coimbatore India Karpagam Academy of Higher Education Coimbatore India Erode Sengunthar Engineering College Erode India CSI College of Engineering Department of Information Technology Nilgiris India

ISBN: (纸本)9798331534950

Histopathological image analysis is a critical task in medical diagnostics, aiding in the accurate classification and staging of diseases. Traditional image analysis methods, while effective, often lack the precision and scalability provided by modern deep learning techniques. This study presents a sophisticated use of the InceptionResNetV2 architecture that has been optimized for the categorization of histological pictures from the colon and lungs. The InceptionResNetV2 model was trained on a comprehensive dataset of histopathological images of lung and colon cancer, which were pre-processed and augmented to enhance their quality and diversity. The model was adjusted to operate without the top layer and included additional dense layers with dropout for regularization to prevent overfitting. A custom learning rate adjustment callback was implemented during training to optimize key performance metrics. The final model achieved remarkable classification accuracy, with precision and recall rates exceeding 99%. This performance underscores the capability of InceptionResNetV2 in handling complex image features associated with histopathological data. The research showed that deep learning might significantly increase medical image analysis's prediction accuracy and efficiency, providing useful assistance for clinical decision-making. The results imply that these models may be expanded to include more medical imaging and diagnostic processes, offering a scalable solution for global healthcare systems. © 2024 IEEE.

关键词： Lung cancer

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

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Investigation of Air Effluence Using IoT and Machine Learning 6th

Investigation of Air Effluence Using IoT and Machine Learn...

引用

6th International Conference on Emerging Technologies in Computing, iCETiC 2023

作者： Shakil, Shalah Uddin Perbhez Kashem, Mohammod Abul Islam, Md. Monirul Nayan, Nasim Mahmud Uddin, Jia Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Birulia Savar Dhaka1216 Bangladesh Dhaka1212 Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031502149

Air pollution poses a significant issue in numerous cities worldwide, impacting public health and the environment. We study three significant cities under the Dhaka division, including Kuril Bishow Road, Uttara, and Tongi. Traditional air quality monitoring methods often need more coverage and accuracy. Leveraging Internet of Things (IoT) technology as well as machine learning (ML) algorithms, this study deploys an IoT-based sensor network using Arduino boards and various devices, including MQ135, DHT22, PM2.5, MQ9, and dust sensors to gather real-time values on air pollutants. The gathered data, including sulfur dioxide, ozone, particulate matter 2.5, nitrogen dioxide, particulate matter 10, as well as carbon monoxide, provides a comprehensive view of city pollution levels. ML models such as linear regression, decision trees, K-Nearest Neighbors (KNN), Naive Bayes (NB), Gradient Boosting (GB), and Random Forest classifiers are applied to predict pollution levels using environmental parameters. The Random Forest classifier achieves an impressive prediction accuracy of 97.2 %. Evaluation metrics, including precision, recall, F1 score, Kappa score, mean square error (MSE), root mean square error(RMSE), along mean absolute error (MAE), are used to assess the performance of the models. This study demonstrates the potential of IoT technology along with ML algorithms in accurately predicting air pollution levels, aiding in environmental management and public health efforts in urban areas. © 2024, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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An Efficient Shortest Path Optimized Algorithm for Vehicle Adhoc Networks System 7

An Efficient Shortest Path Optimized Algorithm for Vehicle A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Shalini, K. Reddy, K. Harinath Punati, Kondalarao Kavin, K.S. Veena, P. Pushpalatha, K. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Shamshabad India Annamacharya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Rajampet India Hyderabad Institute of Technology and Management Faculty of Electronics and Communication Engineering Hyderabad500090 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India N. S. S. Polytechnic College Department of Computer Engineering Pandalam Pathanamthitta India Coimbatore Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641109 India

ISBN: (纸本)9798350372816

VANET (Vehicular ad-hoc network) is a network of advanced automobiles designed to solve the problem of mobility and, as a result, reduce the number of accidents. Because VANET is a type of MANET (mobile ad hoc network) surrounded by human-driven nodes, the social concept can be used to explain VANET routing decisions. This motivates academics to look into the possibilities of using Social Network Analysis (SNA) to create routing strategies. The most critical problem in any system is parcel drop. Social characteristics-based architectures encounter comparable challenges, resulting in inefficient message delivery. In this research, the dragonfly method is used to the shortest path methodology for fixed line VANET with the purpose of reducing package drop and increasing network reliability. This is made achievable by sculpted system of values centralization analysis, which improves the decision via dragonfly optimization methodology. The behavior of the using nodes capability then be determined by the importance computation. In this study, the sustainability of the optimized shortest path system is measured in terms of End to End delay, Energy Consumption, Packet Loss, and Throughput. The dragonfly algorithm's throughput evaluation is 650 kbps as compared to the traditional way. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints 41

Fundamental Limits of Distributed Covariance Matrix Estimati...

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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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Advanced Machine Learning Approaches to Combat Fake News in the Digital Era 4

Advanced Machine Learning Approaches to Combat Fake News in ...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Kaur, Tejinder Kumar Gantayat, Pradosh Das, Swatismita Majhi, Madhusmita Dept. of Mmictbm Mullana Haryana Ambala133207 India Department of Artificial Intelligence and Data Science Telangana Hyderabad501203 India Raajdhani Engineering College Bhubaneswar India Department of Computer Science and Engineering Telangana Hyderabad501203 India

ISBN: (纸本)9798350387490

This paper aims to provide the sustainable design for the user to provide them with ability to check whether the information is fake or real by providing the accuracy of the news using Machine learning via various algorithms with the help of database, which will respond as whether the news is accurate or not. Fake news has become a prevalent issue in today's digital age, causing significant societal impacts by spreading misinformation. This paper explores the application of machine learning techniques for detecting fake news. The paper also addresses challenges such as data scarcity, model interpretability, and the dynamic nature of fake news. We conclude with recommendations for future research directions, emphasizing the need for real-time detection systems and cross-disciplinary collaboration to enhance the effectiveness of fake news detection using machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification of Breast Cancer using Jelly fish Based Deep Wavelet Autoencoder 9

Identification of Breast Cancer using Jelly fish Based Deep ...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Anbumani, K. Balamanigandan, R. Kumar, T. Rajesh Sasi Kumar, A. Mahaveerakannan, R. Sri Sai Ram Engineering College Department of EIE Chennai India Saveetha School of Engineering Computer Science and Engineering India SIMATS Saveetha School of Engineering Computer Science and Engineering Tamil Nadu Chennai602105 India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Mangalore India

ISBN: (纸本)9798350397376

Mammography, an imaging technology used to detect breast cancer, has recently received more interest from scientists. Screening mammography is the best way to detect breast cancer and other abnormalities. Breast tumours often have two distinct characteristics: mass lesions and microcalcification. A gathering of appropriate picture pre-processing, proceduresare essential for accurate cancer detection and classification in mammograms. Image processing is widely employed in the medical field to improve the diagnostic and treatment processes. When it comes to classification and segmentation, deep neural networks (DNN) have excelled so far. With this in mind, the authors of this paper suggest a deep wavelet autoencoder (DWA) method for image compression by fusing the autoencoder's basic feature reduction function with the picture decomposition capabilities of the wavelet transform. The best attributes for identifying breast cancer are chosen in this study by using the Jelly Fish Search Algorithm (JFSA). When used together, they have a dramatic impact on reducing the size of the feature set required to persist through a subsequent classification job using DNN. The suggested DWA-DNN image classifier was evaluated on two public datasets of breast cancer pictures. The DWA-DNN classifier's performance criteria was compared to that of other existing classifiers, and it was found that the suggested technique excels in comparison across a range of measures. © 2023 IEEE.

关键词： Deep neural networks

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Fuel Monitoring in Bike Vehicles Based on IoT

Fuel Monitoring in Bike Vehicles Based on IoT

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

作者： Yuvaraj, G. Nithya, M. Shiny, A. LakshmiNarayanan, S. Vinothini, A. Selva Vignesh, S.M. Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Artificial Intelligence & Data Science Avadi India

ISBN: (纸本)9798350394580

In this work, we explore the feasibility of automatically dispensing gasoline using a prepaid RFID card. Individual RFID cards loaded with predetermined quantities will be provided to each user. The gasoline station's dispenser has an RFID reader that can scan the card and update the available balance on an LCD screen. The user inputs the required fuel quantity via keypad, and the system then determines how long the electrical fuel pump needs to run for and begins distributing the gasoline. When the system detects that the target level has been reached, it will immediately turn off the pump. It utilizes a Raspberry Pi module to alert the user through their smartphone. In addition, there is a fire sensor and a fuel purity sensor in this system to prevent fires and ensure clean fuel. As a result, the gasoline bunk management system will be decentralized and need fewer people to operate. © 2023 IEEE.

关键词： ADC 0804 buzzer fuel level sensor GSM/GPRS module Raspberry pi sim808

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Recommendation System and Its Techniques in Machine Learning: A Survey 1

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Banwala, Neeru Singh, Gurpreet Singh, Jaspreet Goswami, Vishwajeet Shankar Bagnia, Aashima Data Science Department of Mathematics Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Mohali India Department of Mathematics Chandigarh University Mohali India

ISBN: (数字)9789811998584

ISBN: (纸本)9789811998577

Machine learning-based recommendation systems are formidable tools that target specific customers with tailored product and content recommendations based on their user data and behavioural patterns. Due to the abundance of information available today, it is now challenging to separate out and provide the user with the most pertinent information. Therefore, using recommendation systems to reduce time and costs for both the business and the user is now necessary to address the issue of information overload. Based on a user’s interest and past preferences, these systems might suggest suitable products to them, thus increasing revenue. This paper seeks to clarify the idea of the recommendation systems built upon, the many methods used to create these systems and the necessity of using these systems in the current and future environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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Evaluation of Deep Learning Technique on Working Model of Self-driving Car—A Review 8th

Evaluation of Deep Learning Technique on Working Model of...

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Sangwan, Somin Singh, Gurpreet Bangia, Aashima Goswami, Vishwajeet Shankar Data Science Department of Mathematics Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Mohali India Department of Mathematics Chandigarh University Mohali India

ISBN: (纸本)9789811998577

Artificial intelligence is revolutionising the way we are living. Autonomous vehicles are going to play a major role in that. According to US department of Transportation, Ohio University and The UK Economic Opportunity self-driving cars will reduce traffic deaths up to 90%, drop of 60% in harmful emissions and reduction in travel time by 40%, respectively. Its a key technology which will help us out in reducing various problems to a larger extent. This is a self-explanatory paper which provides an overview of the functioning of self-driving cars with the use of deep learning. This paper will first make you understand what basically we mean by when we call a car self-driving along with the meaning of deep learning. This will be followed by providing the details on how deep learning is involved at various steps like how the car is able to perceive, localise, use various deep learning algorithms like Markov decision process (MDP) and neural networks like convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc., to predict various possible actions it can take based on the situation and finally with the help of Bayesian optimisation choosing the best possible outcome. This paper will easily make you understand the functioning of self-driving cars and technology behind them. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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