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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Verma, Aryan Mansoori, Sejal Srivastava, Adithya Rathee, Priyanka Singh, Nagendra Pratap Computer Science and Engineering Department National Institute of Technology HP Hamirpur India Department of Computer Science Indraprastha College for Women New Delhi Delhi India

ISBN: (纸本)9781032669663

Malaria has remained a recurrent challenge in subtropical and tropical areas across the globe. Malaria is caused by plasmodium parasites and can become fatal for the infected person;hence, its precise diagnosis within a short time frame becomes crucial in ending or controlling it. Malaria is analyzed by conventional microscopy, but this methodology has its draw-backs. It is time-consuming and requires an efficient skill set that is often unavailable in many health facilities in several malaria-endemic countries. Hence, tools like computer-aided diagnosis are momentous in analyzing cell images in the modern era of technology. In this paper, we employ deep neural networks to detect malaria presence in the cell images. Both uninfected and parasitized image samples are used to train the models. We use the holdout Validation technique with 15% and 20% validation data to train and compare four variant transfer learned models, VGG19, Resnet50, InceptionV1, and InceptionV3. We employ data augmentation techniques and early stopping callbacks for training the final model Inception V3, which receives 91.40% accuracy, which is better than most of the existing studies in the domain. © 2024 The Author(s).

关键词： computer aided diagnosis

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Citation Prediction Analysis Using Deep Learning Techniques 15

Citation Prediction Analysis Using Deep Learning Techniques

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

作者： Mahalakshmi, G.S. Vinitha Kumari, R. Jaysankar, Pranav Sendhilkumar, S. Anna University Department of Computer Science and Engineering Chennai India Anna University Department of Information Science and Technology Chennai India

ISBN: (纸本)9798350370249

In scientific research, the evaluation of significant articles has traditionally relied on metrics such as Journal Impact Factor and citation counts, but these measures often miss the true essence of an article's impact. But this viewpoint has changed with the introduction of approaches for machine learning and text mining, which let academics examine the subtleties in an article's semantics and so obtain a deeper comprehension of their content. Consequently, the impact of an article's citation count is increasingly driven by its quality and substantive contribution rather than mere popularity metrics. By prioritizing the semantic richness of research articles, scholars can make more informed decisions about which works to engage with and follow, thereby enriching their own research endeavors. This research investigates differences in analysis techniques for deep-text and full-text articles by identifying trends and conducting semantic analysis. Furthermore, the number of citations for every article is predicted using a variety of deep learning techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal Key Generation using Support Vector Machine for Health Records

Optimal Key Generation using Support Vector Machine for Heal...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Himanshu Punhani, Akash SRM Institute of Science and Technology Department of Computer Science and Engineering Delhi-NCR Campus Ghaziabad201204 India

ISBN: (纸本)9798350376470

As the digital age continues to progress, the demand for advanced data privacy techniques has become increasingly vital. With the rise of large-scale collection of data and the proliferation of machine learning algorithms, ensuring privacy while extracting valuable insights has become a challenging situation. This research paper proposes an approach that leverages machine learning-based techniques to achieve scalable privacy preservation, while also providing an optimal key generation mechanism for data encryption and decryption. The proposed approach is evaluated on a real-world healthcare dataset, and the experimental results demonstrate that the proposed approach provides a significant improvement in privacy preservation, while maintaining the performance and accuracy of the system. Healthcare data is among most sensitive one for this scenario as this data encompasses a vast array of information related to patient health, medical conditions, treatments, outcomes, and more. Leveraging this data in a responsible and privacy-preserving manner holds tremendous potential for improving healthcare outcomes, advancing medical research, and enhancing the overall quality of patient care. © 2024 IEEE.

关键词： Electronic health record

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A Review of Conventional Techniques used for Optimized Utilization of Resources in Blockchain Network

A Review of Conventional Techniques used for Optimized Utili...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kuhar, Sunny Singh, Sukhdip Deenbandhu Chhotu Ram University Of Science And Technology Department Of Computer Science And Engineering Sonepat Haryana Murthal India

ISBN: (纸本)9798350306446

Blockchain technology is a game-changing technology that can completely overhaul number of domains by proposing decentralized, transparent, and unchangeable transaction ledgers. The purpose of this overview of the literature is to methodically examine previous studies that have attempted to maximize resource usage in blockchain networks. The initial section of the survey explains the basic ideas of blockchain technology as well as the essential hardware requirements, such as processing power, storage, bandwidth, and energy. After that, it investigates other optimization strategies that have been put out in the literature, including resource allocation plans, network protocols, consensus algorithms, and data compression. This literature review offers an in-depth examination of peer-reviewed articles, conference papers, and technical reports, offering valuable perspectives on resource usage optimization inside blockchain networks. It points up areas in need of investigation, obstacles to overcome, and potential paths for academics and business experts. © 2024 IEEE.

关键词： Network layers

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Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks

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Digital Communications and Networks 2022年第4期8卷 415-421页

作者： Dan Deng Xingwang Li Varun Menon Md Jalil Piran Hui Chen Mian Ahmad Jan School of Information Engineering Guangzhou Panyu PolytechnicGuangzhou410630China School of Physics and Electronic Information Engineering Henan Polytechnic UniversityJiaozuo454150China Department of Computer Science and Engineering SCMS School of Engineering and TechnologyIndia Department of Computer Science and Engineering Sejong UniversitySouth Korea Department of Computer Science Abdul Wali Khan University MardanPakistan

Non-Orthogonal Multiplex Access(NOMA)can be deployed in Unmanned Aerial Vehicle(UAV)networks to improve spectrum *** to the broadcasting feature of NOMA-UAV networks,it is essential to focus on the security of the wireless *** paper focuses on maximizing the secrecy sum rate under the constraint of the achievable rate of the legitimate *** tackle the non-convexity optimization problem,a reinforcement learning-based alternative optimization algorithm is ***,with the help of successive convex approximations,the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization ***,through plenty of explorations of the wireless environment,the Q-learning networks approach the optimal location transition strategy of the UAV,even without the wireless channel state information.

关键词： Unmanned aerial vehicle(UAV) NOMA Reinforcement learning Secure communications Deep Q-learning

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CNN Models Transfer Learning Based Ship Classification for Intelligent Ship Routing

CNN Models Transfer Learning Based Ship Classification for I...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Tiwari, Brij Kishor Munagala, Rohini Sai Mannarugudi, Sai Dheeraj Vignan's Foundation for Science Technology and Research Department of Advanced Computer Science and Engineering Guntur522213 India

ISBN: (纸本)9798350376876

Collision free autonomous navigation is challenging area of interest for the shipping industry, and an accurately classifying the ships according to their size and type classes is a crucial undertaking in marine operations. Accurate prediction and identification will improve the maritime security and navigation safety. However, automatic ship classification is a difficult subject because of the wide variety of ship designs and the complexity of visual data. We developed a deep learning model using transfer learning from pre-trained models in order to classify five different kinds of ships. We performed the experiment using different pre-trained convolution neural network model of the deep learning. In the transfer learning we considered the training of the dataset for 100 epochs. In this study, we investigated four deep learning model ResNet 50, VGG 16, VGG 19, and Inception V3. We carried out the identical procedure for training of each model by removing the top layer of pre-trained model and adding three dense layers for the top layers. The neuron counts are 1024, 512, 256 respectively followed by dropout layer of 0.5. Results of our model shows the maximum obtained accuracies are 86.66 % and from these we can observed that the developed model can be used for ship identification and classification and to take the decisions for collision free and smooth navigation. © 2024 IEEE.

关键词： Transfer learning

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Adverse Drug Reaction (ADR) Prediction using Integrated Machine Learning and Deep Learning Approaches 4

Adverse Drug Reaction (ADR) Prediction using Integrated Mach...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Prabhu, K. Kanagasabapathy, T. Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Pollachi India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

The prediction of adverse drug reactions (ADRs) is paramount in mitigating risks to patient safety and enhancing pharmacovigilance efforts. While both machine learning (ML) and deep learning (DL) techniques have demonstrated efficacy in individual ADR prediction tasks, they possess distinct advantages and limitations. This study proposes an integrated approach that harnesses the synergies between ML and DL to improve ADR prediction accuracy and feature representation. Our hypothesis posits that by integrating traditional ML models with DL's feature extraction capabilities, we can achieve enhanced ADR risk assessment and gain deeper insights into the complex relationships among drug-related factors. This integrated framework holds promise for revolutionizing ADR prediction and proactive pharmacovigilance initiatives. © 2024 IEEE.

关键词： Adversarial machine learning

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Pneumonia Detection Software 4

Pneumonia Detection Software

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Patil, Parth Inamdar, Vibhav Powar, Rohan Patil, Atharv Mulla, Amaan Jadhav, Ranjita D. Y. Patil College of Engineering & Technology Department of Computer Science and Engineering Kolhapur India

ISBN: (纸本)9798350360165

Pneumonia is an infection that leads to inflammation and fluid accumulation within the pulmonary system. Pneumonia can be caused by bacterial, fungal or viral infection. Pneumonia causes breathing difficulties as the air sacs in the lungs might be filled with fluid or pus. It can lead to fever and coughing, often accompanied by the production of yellow, green or even bloody mucus. Pneumonia is a major cause of death throughout the globe. Pneumonia diagnosis is done using an X-ray of the lungs. Pneumonia detection software can detect pneumonia in early stages which can lead to lower mortality rates. With advanced deep learning neural networks such as CNN, Xray images can be classified as pneumonic or non-pneumonic. This can be integrated into a software package that can work across different systems to help people detect pneumonia with just their lung X-rays. © 2024 IEEE.

关键词： Software packages

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Children Hematopoietic Stem Cell Transplant Survival Status Prediction using Machine Learning

Children Hematopoietic Stem Cell Transplant Survival Status ...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Islam Rifat, Ariful Hossain, Mehrab Nahid, Nafiz Akter, Sharmin Islam, Ashraful American International University Department of Computer Science and Engineering Dhaka Bangladesh Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh Noakhali Science and Technology University Computer Science and Telecommunication Engineering Noakhali Bangladesh University of Regina Department of Computer Science Regina Canada

ISBN: (纸本)9798350303452

A surgical treatment known as a bone marrow transplant (BMT) or hematopoietic stem cell transplant (HSCT) successfully treats bone marrow diseases. However, the treatment has a number of risk factors that may reduce long-term survival. This research aimed to predict the survival of children receiving Hematopoietic Stem Cell Transplantation (HSCT) or bone marrow transplant (BMT) using different machine learning classifier models, including Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), AdaBoost (AdB), Gradient Boosting Classifier (GBC), and XGBoost (XGB), using a publicly available dataset. The study involved preprocessing the data and balancing it using SMOTEENN to address the class imbalance issue. Feature selection was also performed using chi-square and correlation methods, resulting in the use of 19 out of 39 features. The data was then split into a 70-30 train-test ratio and trained using the aforementioned machine learning classifier models. The results showed that the decision tree classifier had the highest accuracy rate of 96.77%. These findings suggest that machine learning models can be utilized to predict the survival of children undergoing HSCT, with the decision tree classifier being the most accurate. So, this research provides a foundation for future studies that aim to improve the accuracy of survival predictions for children undergoing HSCT. Additionally, the findings may be utilized to aid in treatment decision-making and counseling for patients and their families. © 2023 IEEE.

关键词： Decision trees

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