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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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applications in Medical Technology for Optimized Convolutional Neural Network Using Differential Evolutionary Algorithm 4th

Applications in Medical Technology for Optimized Convolution...

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4th international conference on Communication, Devices and Computing (ICCDC 2023)

作者： Buradkar, Vanita S. Ambhaikar, Asha Department of Computer Science and Engineering Kalinga University Naya Raipur Raipur India

ISBN: (纸本)9789819927098

Overall, in the world, most deaths are caused by heart attacks and cancer. Development in medical imaging technology using deep learning algorithms and optimizers is becoming more and more possible. It is a growing area for analytics in the medical field. Medical imaging classification uses convolutional neural networks. It is a difficult effort to choose their hyperparameters and architecture. the CNN is optimized using a variety of evolutionary algorithm versions. Among the evolutionary algorithms are the genetic algorithm, particle swarm optimization, and differential evolutionary algorithms. A straightforward and effective population-based method is a differential evolutionary one to optimize the CNN model, we are using a different evolutionary algorithm. Due to the natural intricacy of medicine, optimization has become an important technique. this article's objective is to provide a thorough summary of applications, methods of CNNs, DE optimizer, and their different versions. the planned algorithm is discussed, and an effort will be made to increase the algorithm's performance to over 90%. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Optimising DOS Attacks Using Machine learning algorithms and Securing IOT Devices from Attacks 4

Optimising DOS Attacks Using Machine Learning Algorithms and...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Siwal, Rahul Sivakumar, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385793

In today's hyperconnected world, identifying and defending against denial-of-service (DoS) attacks and securing Internet of things (IoT) devices are increasingly crucial. Recognizing distributed denial-of-service (DDoS) attacks is essential for network security enhancement and IoT device security. this research explores various methods to mitigate the impact of DDoS attacks, aiming to reduce the likelihood of such attacks. We intend to employ machine learning strategies such as the Random Forest Algorithm, ADAboost, Blockchain, Gradient Boost, and Extra Tree Algorithm to prevent malicious network traffic from overloading servers and causing outages. the initiative offers significant advantages, particularly in theory and statistics. We will test our system using the NSL_KDD dataset, initially training the data and then subjecting it to a series of tests. Data will be presented using tables, graphs. © 2024 IEEE.

关键词： Random forests

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L-BFGS optimization in GANs for Enhanced Pulmonary Disease Prediction from X-ray Images 4

L-BFGS Optimization in GANs for Enhanced Pulmonary Disease P...

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Indumathi, V. Siva, R. School of Computing Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350343670

In the realm of medical imaging for pulmonary disease diagnosis, this research addresses the critical need for accurate and timely predictions from X-ray images. Pulmonary diseases pose significant health challenges, necessitating advanced methodologies for early detection and intervention. this study introduces a novel approach, "Optimizing Pulmonary Disease Prediction with GANs and L-BFGS Algorithm,"which leverages Generative Adversarial Networks (GANs) in conjunction with the L-BFGS optimization algorithm. the proposed model aims to surpass existing techniques by enhancing accuracy, reducing prediction time, and enabling early detection. through comprehensive experimentation on the MIMIC-CXR chest X-ray dataset, the model outperforms traditional methods, demonstrating superior accuracy, sensitivity, specificity, and Matthews Correlation Coefficient (MCC). Beyond its success in pulmonary disease prediction, the proposed model showcases potential applications across diverse medical imaging modalities. the study not only contributes a cutting-edge approach to pulmonary disease diagnosis but also underscores the broader implications of integrating advanced deep learning techniques into medical imaging. the insights gained from this research pave the way for more effective interventions, improved patient outcomes, and continued advancements in the field of medical image analysis for pulmonary diseases. © 2024 IEEE.

关键词： Early Detection GANs L-BFGS optimization Medical Imaging Pulmonary Diseases

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Next-Gen Healthcare: Blockchain-Fuelled Federated learning for IoMT Security 4

Next-Gen Healthcare: Blockchain-Fuelled Federated Learning f...

引用

4th international conference on Artificial Intelligence and Signal Processing

作者： Mohapatra, Subasish Mishra, Ayashkant Mohanty, Subhadarshini OUTR Sch Comp Sci Bhubaneswar Odisha India

ISBN: (纸本)9798350350661;9798350350654

the Internet of Medical things (IoMT) is revolutionising the healthcare landscape by seamlessly integrating medical devices, sensors, and healthcare information systems. this interconnected network of devices is designed to improve patient outcomes, enhance healthcare delivery, and streamline medical processes. However, as IoMT continues to evolve, it introduces new challenges related to data security, privacy, and interoperability. Blockchain technology has emerged as a promising solution to address these challenges, offering a decentralised and secure framework for managing health-related data in IoMT applications. this research aims to implement a blockchain-enabled network within a Federated learning-based Internet of Medical things (IoMT) environment. the proposed framework features a centralized server hosting a global machine learning model. IoMT devices operate with local models that run concurrently with the global model, incorporating device-specific data. Simulations and comparisons have been conducted on the predominant consensus models, namely Proof of Stake and Proof of Work. these ongoing initiatives aspire to play a role in enhancing the security and privacy aspects of the latest developments in the Internet of Medical things.

关键词： Blockchain Decentralisation Federated learning Consensus Algorithm

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Leveraging Rough Set theory to Enhance the Performance of Financial Statement Fraud Detection Model 24

Leveraging Rough Set Theory to Enhance the Performance of Fi...

引用

4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Niu, Chengzhi Zhu, Yougan He, Hong Li, Boyang Ren, Yijia Capital University of Economics and Business Beijing China Beijing Institute of Fashion Technology Beijing China ISM International School of Management Hessen Germany

ISBN: (纸本)9798400710247

In this study, we have developed a hybrid financial statement fraud detection model by combining rough set theory and ensemble learning. In this research, we have developed a pre-processing filter utilizing rough set theory to assist the model in selecting the most appropriate features. Additionally, we have also incorporated a feature extracted from the text information of the Management Discussion and Analysis (MD&A) section in the financial report, which effectively captures the positive tone expressed by the management. To address the challenge of imbalanced classes in fraud detection, we have applied the Synthetic Minority Oversampling Technique (SMOTE) algorithm. the optimization procedures significantly improved the performance of our model. We have compared our model to other regular machine learning methods and observed its superiority. the results demonstrate that the integration of rough set theory attribute reduction algorithms has substantially enhanced the performance of the ensemble learning model. Furthermore, the inclusion of the feature extracted from text information has proven to be effective in detecting financial fraud behaviors. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive learning

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Quantum-Enhanced Deep Q learning with Parametrized Quantum Circuit 4

Quantum-Enhanced Deep Q Learning with Parametrized Quantum C...

引用

4th IEEE international conference on VLSI Systems, Architecture, Technology and applications, VLSI SATA 2024

作者： Manjunath, T.D. Bhowmik, Biswajit National Institute Of Technology Karnataka Maharshi Kanad Qc Lab Brics Lab Department Of Computer Science And Engineering Mangalore Surathkal575025 India

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362268

Quantum Computing (QC) is fastly growing to replace current computing systems for many high-performance applications. Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning, i.e., Quantum Machine learning (QML), will enhance the performance in multiple ways. Subsequently, Deep Q learning is a prominent approach to reinforcement learning used in solving complex applications for a desired performance. But this performance can be improved using a quantum computer. this paper proposes a quantum-enhanced Deep Q learning algorithm to see its advantages over its classical counterparts. We implement our approach using parametrized quantum circuit (PQC). the evaluation of the proposed method achieves the metrics rewards and episodes up to 500 points and 1200, respectively. the performance shows that it collects roughly four times more rewards in a given time and takes significantly fewer episodes to converge compared to the classical approach. © 2024 IEEE.

关键词： Reinforcement learning

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Accessing the Students' Perception Towards Cloud Computing applications in their learning Process in Higher Education Institutions: Role of Trust and Security in Technology Acceptance Using Smart Education 4

Accessing the Students' Perception Towards Cloud Computing A...

引用

4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Sandhu, Inderbir Kaur Malhotra, Manisha Randhawa, Praneet Rangi Uic Chandigarh University Mohali Punjab Gharaun India Chandigarh India

ISBN: (纸本)9798350309508

the study aims to find the factors that influence the perception of students of higher education institutions in Punjab towards the use of Cloud Computing applications in their learning environment. the objective of the study includes: (a) Role of Trust factor in students' intentions to use cloud computing technology in their learning environment (b) Role of Security factor in student's intentions to use cloud computing technology in their learning environment. A total of 761 students from various colleges and universities took part in the study by filling online questionnaire based on the extended Technology acceptance model. Results obtained using PLS-SEM revealed that both trust and security played a significant role in the student's perceptions of using cloud computing applications in their educational settings. the study findings are useful for researchers, educators, and cloud providers in implementing the cloud environment in the educational sector. © 2024 IEEE.

关键词： Students

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Automated Skin Lesion Analysis and Classification Using AI 4

Automated Skin Lesion Analysis and Classification Using AI

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Pattnayak, Parthasarathi Patnaik, Sanghamitra Patra, Sudhansu Shekhar Gourisaria, Mahendra Kumar Singh, Satyendr Barik, Lalbihari School of Computer Applications Kiit Deemed to be University Bhubaneswar India School of Law Kiit Deemed to be University Bhubaneswar India School of Computer Engineering Kiit Deemed to be University Bhubaneswar India Bml Munjal University Computer Science & Engg Department Gurugram India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Rabigh21911 Saudi Arabia

ISBN: (纸本)9798331540364

Skin lesions are becoming a major global health concern, and survival rates can be greatly increased by employing dermoscopic pictures for early identification. It is a difficult undertaking to classify these skin lesions from dermoscopic images. the application of deep learning models has significantly improved this classification's accuracy. Because convolutional neural networks (CNNs) are better at extracting features, which improves classification results, they have been shown to be quite useful in this field. In light of this, the article attempts to create deep learning (DL) algorithms using artificial intelligence (AI) to differentiate between skin lesions as benign or malignant in dermoscopic pictures. the study presents OSSAE-BPNN, an automated method for the detection and classification of skin lesions. this method uses a backpropagation neural network (BPNN) for classification and an optimized stacked sparse autoencoder (OSSAE) for feature extraction. To determine the damaged lesion region, the multi-level thresholding segmentation methodology is part of the proposed OSSAE-BPNN method. the OSSAE performs the feature extraction, and the BPNN handles the categorization. Furthermore, the SGO (seagull optimization) technique is used to optimize the SSAE model's parameters. Extensive testing were conducted on a standard dataset to show the OSSAE-BPNN method's efficacy. the OSSAE-BPNN methodology fared better than other current methods across a range of evaluation parameters, according to the results. © 2024 IEEE.

关键词： Convolutional neural networks

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A Ensembled Model to Detect COVID-19 and Pneumonia Using Chest X-Ray Images 4th

A Ensembled Model to Detect COVID-19 and Pneumonia Using Che...

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4th international conference on Advanced Engineering optimization through Intelligent Techniques

作者： Prince, Master Verma, Pooja Rajitha, B. Motilal Nehru Natl Inst Technol Allahabad Prayagraj India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

the novel coronavirus (COVID-19) has caused global outbreaks, leading to an epidemic with severe respiratory implications and high mortality rates. Chest X-rays are crucial tools for detecting COVID-19 infection at any stage, potentially reducing the death rate. this research focuses on proposing an optimized deep learning approach to automatically classify and diagnose COVID-19 and pneumonia using X-ray images. A globally available dataset from Kaggle containing a large number of chest X-ray images was utilized, consisting of normal, COVID-19, and viral pneumonia categories. Data augmentation techniques were applied to increase data size and prevent overfitting. the DL approach involves three stages: image enhancement, data augmentation, and transfer learning algorithms. By applying transfer learning with ensemble models, such as MobileNet and EfficientNet, on augmented enhanced images with frozen weights, the study achieved a high classification accuracy of 98%. Future work aims to further improve model efficiency, which is encouraging.

关键词： Artificial intelligence (AI) COVID-19 Deep learning Chest X-ray CNN Pneumonia Classification

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Time Series Anomaly Detection Using LSTM and Attention 4

Time Series Anomaly Detection Using LSTM and Attention

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4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Li, Guangyao Dai, Yawen School of Information Engineering Wuhan University of Technology Wuhan430070 China Faculty of Science Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510681767

Time series data anomaly detection is to identify observations or patterns in some chronologically ordered points which are significantly inconsistent with expected patterns or normal behavior. these patterns may indicate unexpected events, unusual behavior, failures, or other unusual conditions in the system. Time series anomaly detection has important applications in many fields, including industry, finance, medical care, etc. We designed a time series anomaly detection algorithm using LSTM(Long Short-Term Memory) and attention. By adding an attention layer after the LSTM network, the network can pay more attention to more relevant features in the input multivariate time series, thereby improving accuracy and recall. In the data preprocessing process, the decision tree algorithm is used on the original data set to remove features that have little impact on the anomaly detection results, so as to reduce the computational complexity of anomaly detection, and improve the efficiency of anomaly detection. Experiments on the industrial time series data set SWaT data set show that the LSTM-Attention model proposed in this article is better to the basic LSTM network in terms of precision, recall, F1score and other indicators, and achieves good time series data anomaly detection results. © 2024 SPIE.

关键词： Data modeling Education and training Decision trees Performance modeling Process modeling Statistical modeling Data processing Detection and tracking algorithms Deep learning Mathematical optimization

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