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Deep Learning Frameworks for Histopathological Image processing in Colorectal Cancer Diagnostics 5th

Deep Learning Frameworks for Histopathological Image Proces...

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5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Frasca, Maria Cutica, Ilaria Pravettoni, Gabriella La Torre, Davide University of Milan Milan Italy SKEMA Business School Sophia Antipolis Valbonne France European Institute of Oncology Milan Italy

ISBN: (纸本)9789819638628

the use of Artificial Intelligence (AI) for analyzing histopathological images has emerged as a dynamic field in medical diagnostics, particularly in pathology. Traditionally, the analysis of histopathological images requires significant effort from pathologists to accurately assess cellular characteristics. However, the advent of AI and machine learning has led to notable improvements in diagnostic reliability through automated and simplified analysis processes. In this study, we implemented and compared two convolutional neural network models trained on a multiclass dataset of histopathological images associated with colorectal cancer. Following evaluation, the VGG-19 model demonstrated superior performance, achieving a precision rate of 98%. the researchers employed the "Grad-CAM" technique, a Python-based graphical user interface, to understand the model’s classification process and highlight the salient areas during training. Furthermore, the application of AI to colon histopathological images holds promise for enhancing early cancer diagnosis and improving patient outcomes. By integrating AI with intuitive user interfaces like Miky, pathologists can streamline their analysis workflows and augment diagnostic accuracy. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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24th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

24th International Conference on Parallel and Distributed Co...

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24th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

ISBN: (纸本)9789819982103

the proceedings contain 28 papers. the special focus in this conference is on Parallel and Distributed Computing, Applications and Technologies. the topics include: Insider Trading Detection Algorithm in Industrial Chain Based on Logistics Time Interval Characteristics;link Attributes Based Multi-service Routing for Software-Defined Satellite networks;A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets;SSR-MGTI: Self-attention Sequential Recommendation Algorithm Based on Movie Genre Time Interval;fine Time Granularity Allocation Optimization of Multiple networks Industrial Chains in Task processing Systems;Ε-Maximum Critic Deep Deterministic Policy Gradient for Multi-agent Reinforcement Learning;effective Density-Based Concept Drift Detection for Evolving Data Streams;an End-to-End Multiple Hyper-parameters Prediction Method for Distributed Constraint Optimization Problem;Formalization and Verification of the Zab Protocol Using CSP;dynamic Priority Coflow Scheduling in Optical Circuit Switched networks;Deep Reinforcement Learning Based Multi-WiFi Offloading of UAV Traffic;Triple-Path RNN Network: A Time-and-Frequency Joint Domain Speech Separation Model;design of Query Based Gallery Selector and Mask-Aware Loss for Person Search;a Privacy-Preserving Blockchain Scheme for the Reliable Exchange of IoT Data;R-RPT-A Reliable Routing Protocol for Industrial Wireless sensor networks;action Segmentation Based on Encoder-Decoder and Global Timing Information;Security Challenges and Lightweight Cryptography in IoT: Comparative Study and Testing Method for PRESENT-32bit Cipher;the Prediction Model of Water Level in Front of the Check Gate of the LSTM Neural Network Based on AIW-CLPSO;Using MPIs Non-Blocking Allreduce for Health Checks in Dynamic Simulations;parallelizable Loop Detection using Pre-trained Transformer Models for Code Understanding;list-Based Workflow Scheduling Utilizing Deep Reinforcement Learning.

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Advanced Deep Learning Model for Enhanced Kidney Disease Detection 5

Advanced Deep Learning Model for Enhanced Kidney Disease Det...

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5th international conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Davamani, K. Anita Dhanya, D. Pushparani, S. Vidhya, R. Mohan, Geetha Jani Anbarasi, L. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Agni College of Technology Department of Information Technology Chennai India Institute of Applied Technology Abu Dhabi United Arab Emirates Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Automated Chronic Kidney Disease (CKD) is a swiftly growing non-communicable condition that contributes to a substantial number of deaths and illnesses worldwide. Affecting over 10% of the global population, CKD results in millions of fatalities each year. In this study, an advanced deep learning model for kidney disease detection is proposed that combines convolutional and involution layers to improve classification accuracy. the model processes input images by first applying a 2D convolution with 32 filters of size 3x3, followed by ReLU activation to extract key features. Max-pooling layers are then used to reduce spatial dimensions while retaining crucial information. the convolutional layers are sequentially stacked with larger filter sizes, resulting in progressively richer feature representations. To enhance feature extraction, the model integrates custom involution layers, which extend traditional convolution operations and are particularly effective for processing large-scale image data. these involution layers are applied after the convolutional steps, utilizing 128 and 256 filters of size 3 × 3, followed by max-pooling to further refine the extracted features. A comparative analysis was carried out between the proposed custom Convolutional Neural Network (CNN) model and previous research works, utilizing metrics such as Recall, Precision, F1-Score, Accuracy, Loss, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves. the results indicate that the proposed model achieves superior classification prediction performance, with an accuracy of 99% and reduced computational complexity. Additionally, it demonstrates a enhanced F1-Score of 100%, 98%, 98% and 100% for Cyst, Normal, Stone and Tumor respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites processing Case Study 5

A Data-driven Multi-fidelity Physics-informed Learning Frame...

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IEEE 5th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Ramezankhani, Milad Nazemi, Amir Narayan, Apurva Voggenreiter, Heinz Harandi, Mehrtash Seethaler, Rudolf Milani, Abbas S. Univ British Columbia Mat & Mfg Res Inst Kelowna BC Canada Univ British Columbia Dept Comp Sci Kelowna BC Canada German Aerosp Ctr DLR Inst Struct & Design Stuttgart Germany Monash Univ Dept Elect & Comp Syst Engn Clayton Vic Australia

ISBN: (纸本)9781665497701

Despite the successful implementations of physics-informed neural networks in different scientific domains, it has been shown that for complex nonlinear systems, achieving an accurate model requires extensive hyperparameter tuning, network architecture design, and costly and exhaustive training processes. To avoid such obstacles and make the training of physics-informed models less precarious, in this paper, a data-driven multi-fidelity physics-informed framework is proposed based on transfer learning principles. the framework incorporates the knowledge from low-fidelity (auxiliary) systems and limited labeled data from target (actual) system to significantly improve the performance of conventional physics-informed models. While minimizing the efforts of designing a complex task-specific network for the problem at hand, the proposed settings guide the physics-informed model towards a fast and efficient convergence to a global optimum. An adaptive weighting method is utilized to further enhance the optimization of the model's composite loss function during the training process. A data-driven strategy is also introduced for maintaining high performance in subdomains with significant divergence between low- and high-fidelity behaviours. the heat transfer of composite materials undergoing a cure cycle is investigated as a case study to demonstrate the proposed framework's superior performance compared to conventional physics-informed models.

关键词： multi-fidelity learning physics-informed neural network adaptive weighting smart manufacturing composites processing

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Directing Natural Language processing Text Similarity Challenges in Social Media with AI Techniques 5th

Directing Natural Language Processing Text Similarity Challe...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Madhan, N. Dheva Rajan, S. Jain, Madhuri University of Technology and Applied Sciences Al Musannah Muladdah Oman Banasthali Vidyapith Rajasthan Sikar India

ISBN: (纸本)9789819761050

Text similarity is important in natural language processing (NLP), including information retrieval, text categorization, and plagiarism detection. this study investigates the field of NLP, especially addressing the growing problem of plagiarism enabled by social networking (SN) platforms. Plagiarism, a global issue, damages academic and professional integrity, with social media serving as a fertile ground for uncredited material duplication. the investigation thoroughly assesses a variety of text similarity metrics, including Jaccard, Cosine, Euclidean, and Hamming distances, as well as novel techniques such as Minhash, and N-gram analysis. the approaches are used to find and measure similarities between documents, which adds to the arsenal of plagiarism detection algorithms. this study identifies essential variables in constructing effective plagiarism detectors, such as proper text representation, similarity metric selection, parameter tuning, and ensemble approaches. the study stresses the significance of strong algorithms in fighting plagiarism, given the huge terrain of digital information. the findings demonstrate limits in existing metrics, with each technique highlighting strengths and flaws. the Jaccard and Cosine similarity emphasizes exact overlaps but ignores semantic subtleties, whereas Euclidean distance and Hamming distance struggle with variable-length texts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hamming distance

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Deep neuroevolution to predict astrocytoma grade from functional brain networks 5

Deep neuroevolution to predict astrocytoma grade from functi...

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5th international conference on Intelligent Medicine and Image processing, IMIP 2023

作者： Stember, Joseph Jenabi, Mehrnaz Pasquini, Luca Peck, Kyung Holodny, Andrei Shalu, Hrithwik Memorial Sloan Kettering Cancer Center Department of Radiology New York United States Indian Institute of Technology Madras Department of Aerospace Engineering Chennai India

ISBN: (纸本)9798350337396

Whereas MRI produces anatomic information about the brain, functional MRI (fMRI) tells us about neural activity within the brain, including how various regions communicate with each other. the full chorus of conversations within the brain is summarized elegantly in the adjacency matrix. Although information-rich, adjacency matrices typically provide little in the way of intuition. Whereas trained radiologists viewing anatomic MRI can readily distinguish between different kinds of brain cancer, a similar determination using adjacency matrices would exceed any expert's grasp. Artificial intelligence (AI) in radiology usually analyzes anatomic imaging, providing assistance to radiologists. For non-intuitive data types such as adjacency matrices, AI moves beyond the role of helpful assistant, emerging as indispensable. We sought here to show that AI can learn to discern between two important brain tumor types, high-grade glioma (HGG) and low-grade glioma (LGG), based on adjacency matrices. We trained a convolutional neural networks (CNN) with the method of deep neuroevolution (DNE), because of the latter's recent promising results;DNE has produced remarkably accurate CNNs even when relying on small and noisy training sets, or performing nuanced tasks. After training on just 30 adjacency matrices, our CNN could tell HGG apart from LGG with perfect testing set accuracy. Saliency maps revealed that the network learned highly sophisticated and complex features to achieve its success. Hence, we have shown that it is possible for AI to recognize brain tumor type from functional connectivity. In future work, we will apply DNE to other noisy and somewhat cryptic forms of medical data, including further explorations with fMRI. © 2023 IEEE.

关键词： Brain

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A Hybrid Approach for Enhanced Detection of Fake Profiles in Online Social networks 1

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Bhosale, Rohini Mane, Vanita Department of Computer Engineering Ramrao Adik Institute of Technology D. Y. Patil Deemed to be University Nerul Navi Mumbai India Department of Computer Engineering Pillai HOC College of Engineering and Technology University of Mumbai Mumbai India

ISBN: (数字)9789819761036

ISBN: (纸本)9789819761029

the proliferation of information sharing on social media platforms has attracted attackers who often use fake profiles to commit cybercrimes. this work presents a hybrid methodology that addresses the limitations of current techniques by integrating the advantages of deep learning and machine learning algorithms. the suggested technique employs an improved feature extraction layer and a hybrid classifier to optimize the performance of false profile detection. the methodology synergistically integrates the advantages of content-based and behavioral-based analysis methodologies, utilizing a wide range of features that improve accuracy. Content-based analysis involves the examination of textual and multimedia content linked to user profiles. It utilizes natural language processing to identify abnormalities and conflicts. the behavioral-based study centers on examining user interactions, utilizing machine learning algorithms to identify trends in user activity, relationships, and posting behavior. Experimental studies carried out on real data sets obtained from Twitter demonstrated a notable enhancement in the precision of detection when compared to conventional approaches. A combination of these methods shows potential for improving the security and reliability of online social networks, offering an essential tool for platforms and users to reduce the dangers linked to fake profiles. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Multimodal Approaches for Natural Language processing in Medical Data 5

Multimodal Approaches for Natural Language Processing in Med...

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5th international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Basystiuk, Oleh Melnykova, Nataliia Lviv Polytech Natl Univ UA-79000 Lvov Ukraine

Nowadays, Artificial Intelligence became widespread and deeply integrated into our life routines. One of the most interesting and fast-growing technology in the Artificial Intelligence is speech recognition, and it's a part of the multimodal data concept, which includes voice, audio, and text data. the paper overview the possibilities of multimodal approaches for natural language processing problem based on audio data in the field of medical data. Generally, there are there main concepts: Sequence-to-Sequence, Deep Neural networks based on Hidden Markov Model, and Connectionist Temporal Classification based on the End-to-End model. In this research we review the possibility to utilize natural language processing methods in the medical sphere, to increase the overall time efficiency of medical workers, and optimize mechanical work related to fulfilling information or transforming it from audio to text data. Furthermore, it was realized a comparative analysis of the existing approaches, to select the most advanced and reliable for building robust multimodal audio-to-text systems and conducting future research. this research in the future could be utilized to create wide range Speech-to-Text models for specific medical fields which will improve speech translation tasks, reduce workload and improvise the time efficiency of medical workers.

关键词： Speech-to-Text speech recognition deep neural network sequence-to-sequence connectionist temporal classification

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A DV-Hop Localization Algorithm Using Classifying Average Hop Distance in Wireless sensor networks 22

A DV-Hop Localization Algorithm Using Classifying Average Ho...

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5th international conference on Computer Science and Software Engineering, CSSE 2022

作者： Yang, Di Zhao, Xuanzhi Zhang, Wen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781450397780

DV-Hop localization algorithm contains a straightforward structure and is widely employed in wireless sensor network node localization. the DV-Hop localization algorithm has a fundamental error within the average hop distance calculation. thus this paper proposes a DV-Hop localization algorithm using classifying average hop distance(CADV-Hop algorithm). We provide a CADV-Hop algorithm to calculate the typical hop distance by categorizing utterly different hop counts. the simulation results show that the improved algorithm will improve the localization accuracy compared to the DV-Hop algorithm. © 2022 ACM.

关键词： sensor nodes

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MAKE:Knowledge Graph Embedding via Multi-Attention neural network 5

MAKE:Knowledge Graph Embedding via Multi-Attention neural ne...

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5th international conference on Machine Learning and Natural Language processing (MLNLP)

作者： Liu, Denghui Wang, Yanna Zhou, Zili Dong, Zhaoan Qufu Normal Univ Sch Cyber Sci & Engn Qufu Shandong Peoples R China Qufu Normal Univ China Sch Comp Sci Qufu Shandong Peoples R China

ISBN: (纸本)9781450399067

Knowledge graph embedding is a popular method to solve the incompleteness of knowledge graph. At present, research on knowledge graph embedding based on neural network has achieved remarkable results, but most models ignore the influence of the correlation among with subject entity and relation and object entity within triple. Existing attention-based models take into account the effect of correlation, but perform moderately. In this paper, we propose a multi-attention neural network-based embedding model, named MAKE, which utilizes a novel multi-attention mechanism to generate feature maps of triples by computing correlations within triple. To fully exploit the performance of the multi-attention mechanism, MAKE uses a trainable batch normalization method and a novel composite loss function to improve the model learning ability. Evaluation results on FB15K-237 and WN18RR standard datasets show that our MAKE achieves better performance than previous state-of-the-art knowledge graph embedding models.

关键词： knowledge representation knowledge graph embedding neural networks

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