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An Intelligent Fusion of machine learning and Optimization for Performance Improvisation in Wireless Sensor networks 7

An Intelligent Fusion of Machine Learning and Optimization f...

引用

7th International conference on image Information processing, ICIIP 2023

作者： Thakur, Puja Sidhu, Jagpreet Kanwar, Kushal Cse Juit Solan Waknaghat India Stme Nmims Chandigarh Chandigarh India

ISBN: (纸本)9798350371406

In present days, Wireless Sensor networks (WSNs) have become a significant scientific advancement with a wide range of applications, spanning from environmental monitoring to industrial automation. However, the obstacles presented by restricted resources of energy, ever-changing settings, and fluctuating communication situations require inventive strategies to optimize their effectiveness. This work introduces a novel integration of machine learning and optimization methodologies with the objective of enhancing the efficiency of WSNs. Here, we used the concept of a Clustering-based routing mechanism with the Cuckoo Search Algorithm (CSA) and neural Network (NN) as a machine learning approach. This work aims to examine the incorporation of machine learning techniques and optimization methodologies in order to tackle the complex issues encountered by WSNs. The evaluation of the suggested methodology in comparison to standard methodologies involves the utilization of performance measures such as energy consumption, network lifetime, and packet delivery ratio. The findings indicate significant improvements in energy efficiency, network stability, and data throughput, hence confirming the effectiveness of the integrated approach using machine learning and optimization. © 2023 IEEE.

关键词： Wireless sensor networks

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Exploration of Membership Inference Attack on Convolutional neural networks and Its Defenses

Exploration of Membership Inference Attack on Convolutional ...

引用

International conference on image processing, Computer Vision and machine learning (ICICML)

作者： Yao, Yimian Chaminade Coll Preparatory West Hills CA 91304 USA

ISBN: (纸本)9781665464680

Recently, machine learning has been frequently applied in multiple areas, like image classification and object recognition. However, some machine learning applications train their models on sensitive data that could leak personal information, leading to potential privacy issues. Many privacy attacks against deep learning models have been proposed. A membership inference attack was demonstrated to be a type of effective attack and a real threat to privacy in machine learning. Here, we trained three different convolutional neural networks for image classification with the CIFAR10 dataset and examined the effects of a membership inference attack on these models. We also explore different defense strategies, including differential privacy against membership inference attack. We showed that membership inference attack was more effective against overfitting models and defense strategies like differential privacy and regularization can significantly lower the attack accuracy.

关键词： machine learning deep learning privacy attacks membership interference attack differential privacy

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Research on Intelligent System of Medical image Recognition and Disease Diagnosis Based on Big Data 2

Research on Intelligent System of Medical Image Recognition ...

引用

2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Zi, Yun Cheng, Xiaohan Mei, Taiyuan Wang, Qi Gao, Zijun Yang, Haowei College of Computing Illinois Institute of Technology Chicago United States Northeastern University D'Amore-McKim Boston United States Khoury College of Computer Sciences Northeastern University Boston United States Cullen College of Engineering University of Houston Houston United States

ISBN: (纸本)9798350360240

This paper designed and developed a mobile intelligent diagnosis system for gallstone disease based on deep learning. It can be used offline on Android phones to realize automatic recognition of medical images of gallstone disease. This project intends to take Xilinx company's newly released ZYNQ Super Scale+ MPSoC as the research object, and innovatively introduce it to the hardware and software platform. ARM and FPGA (Field Programmable Logic Gate Array) work together to achieve high-performance computing capabilities that Gpus common to machine learning cannot match. Experiments have proved that the Google network trained by transplantation can classify gallstones with an accuracy of more than 98%, which is comparable to experienced doctors. It can realize a full identification of gallstone disease and can be completed in 2 seconds. Compared with GPU, this algorithm has the advantages of fast processing speed, low power consumption, and good user experience. It is easy to carry and has a wide range of applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced image Forensics: Unveiling Forgeries with Transfer learning and YoloV5 6

Enhanced Image Forensics: Unveiling Forgeries with Transfer ...

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6th International conference on Cybernetics, Cognition and machine learning applications, ICCCMLA 2024

作者： Sivanagireddy, K. Jagadeesh, S. Tulasiram, K. Chandrika, B.A. Kranthikumar, K. Sridevi Women's engineering college Department of electronics and communication engineering Telangana Hyderabad India

ISBN: (纸本)9798331505790

This paper introduces an innovative approach to improving the detection of forged images by leveraging deep learning and transfer learning techniques, effectively identifying two common types of image manipulation simultaneously. Addressing the prevalent issue of malicious software forging digital images for disseminating fake information on social media platforms, the proposed approach simultaneously identify both image splicing and copy-move forgeries, thus reflecting real-life scenarios more accurately. The study utilizes the CASIAV2 dataset, comprising 4795 images, with 1701 authentic and the remainder forged. In our experiments, we utilized the CASIA 2.0 dataset, the same one used in. This dataset, consists of 1 2, 6 1 4 images, comprising 7,491 originals and 5,123 forgeries. The forgeries include 3,274 copy-move and 1,849 spliced images, all in JPEG, Deep learning methods like Convolutional neural networks (CNN) for image classification and YOLOv5 (You Only Look Once) for detecting forged regions are employed. Sequential, MobileNetV2, and Xception models are fine-tuned for the task. The experimental results demonstrate an impressive accuracy of 99.3 %. This research contributes to advancing digital image forgery detection underscores the potential of deep learning techniques in addressing complex real-world problems in digital media forensics. © 2024 IEEE.

关键词： Convolutional neural networks

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processing and Classification Algorithms of Medical images 6

Processing and Classification Algorithms of Medical Images

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Seitaj, Oltiana Mechanics University of Roma Tre Department of Industrial Engineering Electronics Rome Italy

ISBN: (纸本)9798331506520

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional neural networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation. © 2025 IEEE.

关键词： Convolutional neural networks

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Insights Into YOLOv9-Based Breast Mass Detection Using Ultrasound images 13

Insights Into YOLOv9-Based Breast Mass Detection Using Ultra...

引用

13th International conference on image processing Theory Tools and applications

作者： Erragzi, Nouhaila Moussaid, Abdelghani Zrira, Nabila Benmiloud, Ibtissam Sebihi, Rajaa Ngote, Nabil Mohammed V Univ Fac Sci LPHE Modeling & Simulat Rabat Morocco Natl Super Sch Mines Rabat CPS2E Lab MECAtron Team Rabat Morocco Natl Super Sch Mines Rabat LISTD Lab ADOS Team Rabat Morocco Natl Super Sch Mines Rabat Abulcasis Int Univ Hlth Sci Rabat Morocco

ISBN: (纸本)9798331541859;9798331541842

The most prevalent form of cancer globally is breast cancer, which predominantly impacts women. Early detection ensures successful treatment of breast cancer, significantly improving patients' survival chances. Various imaging modalities, including mammography and ultrasound, are utilized for breast cancer screening. Incorporating new technologies is essential for better patient management, particularly for those with malignant masses. Artificial intelligence can assist radiologists by training neural networks to detect breast lesions on mammograms or ultrasounds using deep learning techniques. In this article, the YOLOv9 network is trained on two public ultrasound databases, UDIAT and BUSIS. The network successfully localized malignant and benign masses with a precision of 83%, a recall of 82%, and a mAP of 87% in the UDIAT dataset. In the BUSIS dataset, our model achieved a precision of 75%, a recall of 88%, and a mAP of 90%. Furthermore, we used real Moroccan cases to evaluate the model's performance.

关键词： Ultrasound Artificial Intelligence Deep learning Breast Lesion Detection YOLOv9

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Hypernetworks Build Implicit neural Representations of Sounds

Hypernetworks Build Implicit Neural Representations of Sound...

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5th International Workshop on learning with Imbalanced Domains - Theory and applications / European conference on machine learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Szatkowski, Filip Piczak, Karol J. Spurek, Przemyslaw Tabor, Jacek Trzcinski, Tomasz Warsaw Univ Technol Warsaw Poland Jagiellonian Univ Fac Math & Comp Sci Krakow Poland IDEAS NCBR Warsaw Poland Tooploox Wroclaw Poland UES Ltd Krakow Poland

ISBN: (纸本)9783031434204;9783031434211

Implicit neural Representations (INRs) are nowadays used to represent multimedia signals across various real-life applications, including image super-resolution, image compression, or 3D rendering. Existing methods that leverage INRs are predominantly focused on visual data, as their application to other modalities, such as audio, is nontrivial due to the inductive biases present in architectural attributes of image-based INR models. To address this limitation, we introduce HyperSound, the first meta-learning approach to produce INRs for audio samples that leverages hypernetworks to generalize beyond samples observed in training. Our approach reconstructs audio samples with quality comparable to other state-of-the-art models and provides a viable alternative to contemporary sound representations used in deep neural networks for audio processing, such as spectrograms. Our code is publicly available at https://***/WUT-AI/hypersound.

关键词： machine learning Hypernetworks Audio processing Implicit neural Representations

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Survey of Deep learning Models for image-Based Disease Detection in Plants 7th

Survey of Deep Learning Models for Image-Based Disease Detec...

引用

7th International conference on Soft Computing: Theories and applications, SoCTA 2023

作者： Mathur, Abhishek Shrivastava, Shailendra Ku. Department of Information Technology Samrat Ashok Technological Institute Vidisha India Samrat Ashok Technological Institute Vidisha India

ISBN: (纸本)9789819720880

Crop diseases pose a severe risk to global food supplies, but accurate and timely diagnosis is hampered by a lack of resources in many regions. As the number of people with access to smartphones continues to rise throughout the world, and as recent advancements in computer vision allowed by deep learning pave the way, we may soon be able to use them to aid in the detection of diseases. Acquiring pictures, preprocessing, segmenting, and extracting features are all part of the image processing pipeline for disease detection in plants. In this review study, we take a look back at some of the most popular methods for classifying plant diseases: "Convolutional neural Network (CNN)", "Support Vector machine (SVM)", "K-Nearest Neighbors", and "Artificial neural Network (ANN)". The results of the study show that the convolutional neural network method outperforms the more conventional techniques in terms of accuracy. This review study discusses the use of DL models to depict different plant diseases and gives a detailed description of their operation. Furthermore, several research gaps are noted from which better clarity may be obtained in regard with early detection of plant diseases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial neural network Convolution neural network Deep learning image processing Plant disease detection Recurrent neural network

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PointCU: Multiscale Sparse Convolutional learning for Point Cloud Color Upsampling

PointCU: Multiscale Sparse Convolutional Learning for Point ...

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2024 conference on Visual Communications and image processing

作者： Kathariya, Birendra Li, Zhu Akthar, Anique Van Der Auwera, Geert Univ Missouri Kansas City Kansas City MO 64110 USA Qualcomm San Diego CA USA

ISBN: (纸本)9798331529543;9798331529550

Upsampling a sparse point cloud is a common operation in various applications, such as high-quality reconstruction, rendering. Point-cloud geometry upsampling has been extensively studied for this purpose. However, when a sparse point cloud includes attributes, such as color, their upsampling must also be carried out in addition to geometry. Despite the apparent need of such work, not a lot of effective solutions are developed that can work with real world large scale point cloud. In this work, we propose a novel solution called PointCU, which is a sparse convolution learning-based Point cloud Color Upsampling method that enables high-fidelity dense point color reconstruction from sparse point color. The proposed method first prepares multiple representations of a dense point cloud through voxelization at different scales, then transfers the color from sparse to the newly created point clouds including the dense point cloud itself through devoxelization. Then by learning feature on these multiple point cloud representation through sparse convolution neural network (SparseCNN) while also expanding and fusing the feature to higher scale, PointCU achieves an excellent color super-resolving capability. Our experimental results on four times (4x) and eight times (8x) upsampling tasks demonstrate that the color upsampling performance of the proposed method is superior to the previous known color upsampling schemes by a large margin.

关键词： point cloud color-upsampling multi-scale learning neural networks feature fusion sparse convolution

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LoockMe: An Ever Evolving Artificial Intelligence Platform for Location Scouting in Greece 1

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24th International conference on Engineering applications of neural networks (EANN)

作者： Trivizakis, Eleftherios Aidonis, Vassilios Pezoulas, Vassilios C. Goletsis, Yorgos Oikonomou, Nikolaos Stefanis, Ioannis Chondromatidou, Leoni Fotiadis, Dimitrios I. Tsiknakis, Manolis Marias, Kostas Fdn Res & Technol Hellas FORTH Computat BioMed Lab CBML Iraklion 70013 Greece Fdn Res & Technol Hellas FORTH Biomed Res Inst BRI Ioannina 45110 Greece Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst Ioannina 45110 Greece Univ Ioannina Dept Econ Lab Business Econ & Decis Ioannina 45110 Greece Hellen Mediterranean Univ Dept Elect & Comp Engn Iraklion 71410 Greece

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

LoockMe is an artificial intelligence-powered location scouting platform that combines deep learning image analysis, cutting-edge machine learning natural language processing (NLP) for automated content annotation, and intelligent search. The platform's objective is to label input images of local landscapes, and/or any other assets that regional film offices want to expose to those interested in identifying potential locations for the film production industry. The deep learning-based image analysis achieved high classification performance with an AUCscore of 99.4%. Moreover, the state-of-the-art machine learning NLP module enhances the platform's capabilities by analyzing text descriptions of the locations and thus allowing for automated annotation, while the intelligent search engine combines image analysis with NLP to extract relevant context from available data. The proposed artificial intelligence platform has the potential to substantially assist asset publishers and revolutionize the location scouting process for the film production industry in Greece.

关键词： artificial intelligence deep learning transfer learning natural language processing location scouting film production search engine

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