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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Boesso, Simone Farina, Lorenzo Petti, Manuela Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350386226

Radiomics represents a specialized branch of medical imaging where quantitative features are extracted from images. Performing a classification using radiomics means solving two common problems: the imbalanced setting, and the large number of features that would increase the risk of overfitting. Moreover, since its main application and impact are in clinical field, there is the need of interpretable models for explaining their results. The aim of this study is to compare two modelling approaches: one based on a logistic regression model, known for its simplicity and interpretability, and RUSBoost, an ensemble method designed to handle class imbalance with potentially higher complexity, in order to answer the question whether higher complexity and lower interpretability are justified when dealing with radiomics data. Additionally, due to the large literature suggesting it, we analyze the impact of a feature selection step applied to these two classifiers. Test performances measured across 20 repeated splits on two datasets show how the RUSBoost approach is able to capture more detailed patterns of the data but this is highly dependent on the dataset at hand. © 2024 IEEE.

关键词： Logistic regression

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Optimal control Strategy to Reduce HIV-1 Infection in the Brain 9

Optimal Control Strategy to Reduce HIV-1 Infection in the Br...

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Baiocchi, Sofia Blengini, Federica Caleno, Francesca Di Giamberardino, Paolo Iacoviello, Daniela Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350311402

The study of Human Immunodeficiency Virus (HIV) and of its progression to Acquired Immune Deficiency Syndrome (AIDS) has been a challenge by scientists since the early 1980s;the improvements in the knowledge of its transmission and of the evolution of the disease have strongly reduced the mortality, even if up to know an effective vaccine is not available. Nevertheless, the Highly Active Antiretroviral Therapy (HAART) allows a high life expectancy. Some of the mechanisms of HIV reproduction are under investigation and recent observations are suggesting the existence of virus reservoir in the brain, the liver, the lungs and the gut. In particular, due to untreated amounts of virus, neurocognitive disorders as well as motor nerves diseases and weakness in muscles are improved in patients already weakened. In this paper it is considered a mathematical modelling to study the within-host viral dynamics, analysing the dynamics of the CD4-T cells, of the macrophages both inside and outside the brain, uninfected and infected, along with the virions carrying on the infection. An optimal control strategy is proposed and implemented,aiming at maximizing the number of healthy cells, while reducing the number of infected ones, scheduling suitably the therapy. © 2023 IEEE.

关键词： Diseases

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AI-based Visual Attention Scenario Identification Model in Military Environment 4

AI-based Visual Attention Scenario Identification Model in M...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sapariya, Ankur Ravikumar, R.N. Bhatt, Urvi Singh, Suraj Prakash Wanglen, Soram Singh, Sushil Kumar Marwadi University Dept. of Computer Engineering Rajkot India School of Management Sciences Dept. of Computer Engineering Varanasi India

ISBN: (纸本)9798350343670

In response to the evolving landscape of modern military operations, where drone technology now constitutes an estimated 70% of Indian military missions, as evidenced by recent statistics, our proposed AI-based Visual Attention Scenario Identification Model takes center stage. This groundbreaking model is meticulously designed to augment the safety and efficiency of military endeavors in challenging terrains. Our model harnesses advanced deep-learning CNN techniques by processing real-time images from various sources, including drones, strategically focusing on critical details within these visuals. This approach enables the timely detection of potential dangers, even in complex environments, with the primary objective of significantly enhancing situational awareness for military personnel. Positioned as a valuable asset in the military's toolkit, our model, with 85% accuracy, proves particularly effective in identifying potential threats in forested and mountainous regions, ultimately minimizing risks faced by soldiers on the ground. © 2024 IEEE.

关键词： CNN Deep Learning Image Processing Military Operations Visual Attention AI model

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Prediction and Classification of Brain Tumor using Modified Transfer Learning 18

Prediction and Classification of Brain Tumor using Modified ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Roy, Sukanya Mandal, Bijoy Kumar Khamaru, Koustov Bhattacharya, Arghya University of Engineering and Management Dept. of Computer Science Engineering Kolkata India Nshm Knowledge Campus Dept. of Computer Science Engineering Durgapur India

ISBN: (纸本)9789380544519

This study uses a novel method of segmenting and computing the tissue area in brain MRI images to predict the presence of brain tumors and classify the type of tumor into common types (pituitary, meningioma, and glioma). The tissues that make up the brain have intricate boundaries. In order to more accurately classify the different types of tumors, we have thus divided the brain tissues into three segments and examined for enlarged segments using Convolutional Neural Networks (CNNs) and a Modified CNN that uses transfer learning and VGGNet. The paper's methodology involves using CNNs with Transfer Learning for efficient image segmentation and clustering, followed by VGGNet for noise reduction and image segmentation refining. We are optimizing the internal parameters of VGGNet in order to outperform the competition. The volume and density of malignant clusters are computed through pixel counting. This kind of method works pretty well for figuring out tissue volume in a wide range of illnesses that affect people of all ages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Brain Tumor Glioma Image-segementation Meningioma Neural Networks. Image classification Pituitary ResNet Transfer Learning VGGNet

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Challenges in AI-supported Process Analysis in the Italian Judicial System: what After Digitalization?

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Digital Government: Research and Practice 2024年第1期5卷 1-10页

作者： Bianchini, Devis Bono, Carlo Campi, Alessandro Cappiello, Cinzia Ceri, Stefano De Luzi, Francesca Mecella, Massimo Pernici, Barbara Plebani, Pierluigi Dept. of Computer Science University of Brescia Via Branze 38 Brescia25123 Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Piazza Leonardo da Vinci 32 Milan20133 Italy Dept. of Computer Control and Management Engineering Sapienza Universitá di Roma Via Ariosto 25 Roma00185 Italy

In this commentary article, we outline research challenges and possible directions for the potential applications of AI in the judicial domain by specifically considering process analysis in the Italian context. Applying AI to process analysis poses several challenges, including information extraction from legacy information systems and analysis of legal documents, process modeling with a particular emphasis on temporal analysis, real-time process monitoring, conformance and compliance checking, predictive techniques for accurate predictions, and analysis of judges' workload. Solutions to these challenges include methods and tools for data identification and collection, innovative approaches to process modeling, reactive techniques for real-time monitoring, conformance checking with explainability, language models adapted to specific domains, and the identification of suitable indicators for the analysis of case handling efficiency and case classification. Copyright © 2024 held by the owner/author(s).

关键词： Process monitoring

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WebTrek Learner : AI Integrated Cloud Based Learning Platform

WebTrek Learner : AI Integrated Cloud Based Learning Platfor...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Bhandari, Arjun Harisha, A. Rishav, Kumar Shifana, Mariyam Sameer, Jazil Sahyadri College of Engineering and Management Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331517892

In an era defined by the rapid proliferation of digital technology and connectivity, the need for virtual education has never been more critical. This article, referred to hereon as 'WebTrek Learner,' is a forward-thinking initiative aimed at catering quality virtual education through a dynamic and engaging approach of gaming. To further enhance the learning experience, WebTrek Learner incorporates cutting-edge LLM based AI technology to curate internal insights and content based on glossary documents. Cloud based a gaming platform enables lightweight experience to all potential learners. © 2024 IEEE.

关键词： Adversarial machine learning

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Graph Based Keywords Extraction from Crime Data 16

Graph Based Keywords Extraction from Crime Data

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Das, Priyanka Munshi, Anuvab Naha, Annwesha Dey, Adrija Chakraborty, Sriparno Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata India

ISBN: (纸本)9798331505264

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crimerelated keywords from in-dept. police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied;following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime. © 2024 IEEE.

关键词： Crime

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Properties of singular solutions in optimal control problems under input dynamic extension 63

Properties of singular solutions in optimal control problems...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Giamberardino, Paolo Di Iacoviello, Daniela Sapienza University of Rome Dept. of Computer Control and Management Engineering Antonio Ruberti via Ariosto 25 Rome00185 Italy

ISBN: (纸本)9798350316339

The paper addresses the problem of optimal control design in presence of singular solutions for single input dynamics. The dynamical extension for systems obtained adding an integrator on the input is addressed and analyzed. The possibility of computing the optimal control for dynamically extended systems from the solution of the initial ones is investigated, as well as the inverse procedure. These relationships are well evidenced for the singular solutions, showing the possibility of simplifying the optimal control computation. An example is introduced to better highlight the presented results. © 2024 IEEE.

关键词： Optimal control systems

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Classification of Foods based on Bijective Soft Set 15

Classification of Foods based on Bijective Soft Set

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

作者： Das, Priyanka Bhattacharya, Rupam Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350370249

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers. ©2024 IEEE.

关键词： bijective soft set classification classification rule generation cuisine ingredients

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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