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A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

computers, Materials & Continua 2021年第9期68卷 2877-2893页

作者： D.Venugopal T.Jayasankar Mohamed Yacin Sikkandar Mohamed Ibrahim Waly Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of ECE KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent *** diagnosis from digital imagery and treatment are deemed to be the right *** Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for *** generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of *** use manual planimetry,a time-consuming process for segmenting CT scan *** Learning(DL)is the most preferred method to increase the efficiency of diagnosing *** this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as *** proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and *** input image is first preprocessed using the Gaussian Filtering(GF)technique to remove ***,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the ***,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful ***,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of *** researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic *** findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its *** future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

关键词： Intracerebral hemorrhage fusion model feature extraction deep features classification

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Post-pandemic social contacts in Italy: implications for social distancing measures on in-person school and work attendance

arXiv

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arXiv 2024年

作者： Lucchini, Lorenzo Marziano, Valentina Trentini, Filippo Chiavenna, Chiara D’Agnese, Elena Offeddu, Vittoria Manica, Mattia Poletti, Piero Balsamo, Duilio Guzzetta, Giorgio Ajelli, Marco Melegaro, Alessia Merler, Stefano DONDENA Centre for Research on Social Dynamics and Public Policy Bocconi University Italy Center for Health Emergencies Bruno Kessler Foundation Italy Department of Economics and Management University of Pisa Italy Department of Statistics Computer Science Applications University of Florence Italy Laboratory for Computational Epidemiology and Public Health Department of Epidemiology and Biostatistics Indiana University School of Public Health United States

The collection of updated data on social contact patterns after the disruptions brought by the COVID-19 pandemic is of critical importance for future epidemiological evaluations and for the assessment of non-pharmaceutical interventions based on physical distancing. Here, we run two waves of the same online survey on March 2022 and March 2023 in Italy, collecting information from a representative sample of the population on direct (verbal or physical interactions) and indirect (prolonged co-location in indoor spaces) contacts. We investigated the determinants of an individual’s total number of social contacts using a generalized linear mixed model and assessed the potential impact of work-from-home and distance learning on the transmissibility of novel respiratory pathogens. We found that in-person attendance to work or school was among the main drivers of social contacts: among adults (age 18 or more), in-person attendees reported a mean of 1.69 (95%CI: 1.56-1.84) times the contacts of adults staying at home;this ratio increased to 2.38 (95%CI: 1.98-2.87) among children and adolescents (less than 18 years old). We estimated that even drastic measures suspending all non-essential working activities, in the absence of any other preventive measures, would result in a marginal reduction of transmissibility. The combination of distance learning covering educational levels from the primary and upwards, with work-from-home for all jobs allowing for it, could reduce transmissibility by up to 23.7% (95%CI: 18.2-29.0%). Additionally suspending early childcare services would result in minimal further gains. These results provide useful data for modelling the transmission of respiratory pathogens in Italy after the end of the COVID-19 emergency. They also provide insights on the potential epidemiological effectiveness of social distancing interventions centred on work and school attendance, supporting considerations on the balance between the expected benefits and their heavy

关键词： COVID-19

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AI-Powered Business Intelligence for Smarter Decision-Making and Growth

AI-Powered Business Intelligence for Smarter Decision-Making...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Franciskus Antonius Alijoyo Rahul Pradhan Satvik Vats V.Kanpur Rani Timur Kholmukhamedov M. Karthik Center for Risk Management and Sustainability Indonesia Lecturer School of Business and Information Technology STMIK LIKMI Bandung Indonesia Department of Computer Engineering and Applications GLA University Mathura India Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of EEE Hindustan Institute of Technology and Science Chennai India Management Economics and Humanitarian disciplines Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Business intelligence (BI) has become a cornerstone for data-driven decision-making in modern enterprises. With the increasing complexity of data, traditional methods often fall short in providing actionable insights. Machine learning techniques, such as Gradient Boosting Machines (GBMs), offer advanced predictive capabilities that can significantly enhance BI outcomes. This paper explores the application of Gradient Boosting Machines (GBMs) in enhancing BI for better decision-making and operational efficiency. GBMs, a powerful ensemble learning technique, are utilized to predict customer churn based on a comprehensive retail dataset encompassing customer demographics, purchase history, engagement scores, and monetary values. The Proposed Method is implemented using Python Software and demonstrates that GBMs achieve exceptional predictive accuracy of 99.2%, significantly outperforming traditional methods like Decision Tree, SVM, BDMS. Feature importance analysis reveals that ‘Recency’ (days since last purchase) is the most influential predictor of churn, followed by ‘Engagement Score’ and ‘Monetary Value’. The findings emphasize the model's capability to enhance customer retention strategies, optimize resource allocation, and improve overall business outcomes. Utilizing GBMs, high-risk customers (top 10% predicted probabilities of churn) were targeted with a specialized retention campaign, which led to a 15% reduction in churn rates among this group. The effectiveness of the campaign validates the model's predictions and highlights the practical benefits of integrating GBMs into BI systems. This research underscores the transformative potential of AI-driven techniques in business intelligence, offering actionable insights for businesses seeking to leverage predictive analytics for competitive advantage.

关键词： Support vector machines Quantum computing Decision making Predictive models Boosting Software Business intelligence Resource management Churn Python

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Incomplete Multi-view Clustering via Diffusion Contrastive Generation 39

Incomplete Multi-view Clustering via Diffusion Contrastive G...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yuanyang Lin, Yijie Yan, Weiqing Yao, Li Wan, Xinhang Li, Guangyuan Zhang, Chao Ke, Guanzhou Xu, Jie School of Computer Science and Engineering Southeast University Nanjing 210096 China College of Computer Science Sichuan University China School of Computer and Control Engineering Yantai University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University Ministry of Education China College of Computer National University of Defense Technology China College of Computer Science and Technology Zhejiang University China Department of Control Science and Intelligence Engineering Nanjing University China School of Economics and Management Beijing Jiaotong University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates;2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Incomplete Multi-view Clustering via Diffusion Contrastive Generation

arXiv

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arXiv 2025年

作者： Zhang, Yuanyang Lin, Yijie Yan, Weiqing Yao, Li Wan, Xinhang Li, Guangyuan Zhang, Chao Ke, Guanzhou Xu, Jie School of Computer Science and Engineering Southeast University Nanjing210096 China College of Computer Science Sichuan University China School of Computer and Control Engineering Yantai University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China College of Computer National University of Defense Technology China College of Computer Science and Technology Zhejiang University China Department of Control Science and Intelligence Engineering Nanjing University China School of Economics and Management Beijing Jiaotong University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

关键词： Contrastive Learning

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Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

Unified View of Damage leaves Planimetry & Analysis Using Di...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Pijush Kanti Kumar DeepKiran Munjal Sunita Rani Anurag Dutta Liton Chandra Voumik A. Ramamoorthy Department of Information Technology Government College of Engineering & Textile Technology Serampore Calcutta India Department of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Department of Engineering Administration Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Economics Noakhali Science and Technology University Noakhali Bangladesh Department of Mathematics Velammal Engineering College Anna University Chennai Tamil Nadu India

The detection of leaf diseases in plants generally involves visual observation of patterns appearing on the leaf surface. However, there are many diseases that are distinguished based on very subtle changes in these visually observable patterns. This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease. Canker is a bacterial infection of leaves. Symptoms of citrus cankers include brown spots on the leaves, often with a watery or oily appearance. The spots (called lesions in botany) are usually yellow. It is surrounded by a halo of the leaves and is found on both the top and bottom of the leaf. This paper describes various methods that have been used to detect citrus leaf canker disease. The methods used are histogram comparison and k-means clustering. Using these methods, citrus canker development was detected based on histograms generated based on leaf patterns. The results thus obtained can be used, after consultation with experts in the field of agriculture, to identify suitable treatments for the processes used.

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Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

arXiv

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arXiv 2023年

作者： Kumar, Pijush Kanti Munjal, DeepKiran Rani, Sunita Voumik, Liton Chandra Dutta, Anurag Ramamoorthy, A. Department of Information Technology Government College of Engineering & Textile Technology Calcutta Serampore India Department of Computer Applications G. L. Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Department of Engineering Administration Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Economics Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science & Engineering Government College of Engineering & Textile Technology Calcutta Serampore India Department of Mathematics Velammal Engineering College Anna University Tamil Nadu Chennai India

关键词： Graphic methods

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The narrative on tourism sustainability in Italian news: A text mining approach

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Big Data Research 2025年 41卷

作者： Galluccio, Carla Beccherle, Paola Petrucci, Alessandra University of Florence Department of Statistics Computer Science Applications "G. Parenti" Viale Giovanni Battista Morgagni 59 Florence50134 Italy University of Florence Department of Economics and Management Via delle Pandette 9 Florence50127 Italy

Tourism sustainability is a complex and multidimensional construct, for which there is no shared definition in the literature. Consequently, there is no standard method for its measurement, and the adoption of sustainable practices often falls short of reached goals. Therefore, contributing to the definition of the concept of sustainable tourism is essential, both for policymakers and academics. In this vein, news media data can represent a key element through which to understand the debate about tourism sustainability. This research aims to exploit the potential of news texts to explore how sustainable tourism is conceived within specific cultural contexts. Focusing on the case study of Italy, we analysed how the concept of tourism sustainability is represented in Italian newspapers, extracting the topics discussed in relation to this theme. From a methodological point of view, we employed a network-based approach for topic extraction. Our study contributes to the literature on tourism sustainability by proposing an innovative method for extracting information from unstructured data sources, such as textual data, providing policymakers with insights about the narrative around this topic. © 2025 The Author(s)

关键词： Electric network analysis

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Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

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computers, Materials & Continua 2020年第10期65卷 87-107页

作者： Bao Le Nguyen E.Laxmi Lydia Mohamed Elhoseny Irina V.Pustokhina Denis A.Pustokhin Mahmoud Mohamed Selim Gia Nhu Nguyen K.Shankar Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow115093Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Mathematics College of Science&Humanities in AlaflajPrince Sattam bin Abdulaziz UniversityAlaflaj16278Saudi Arabia Graduate School Duy Tan UniversityDa Nang550000Vietnam Department of Computer Applications Alagappa UniversityKaraikudi630003India

In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and *** studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in *** this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data *** program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data ***,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning *** this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed *** security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data *** examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI *** simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.

关键词： Blockchain optimization elliptical curve cryptosystem security ant colony optimization multi kernel support vector machine

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A structural equation model to assess the impact of agricultural research expenditure on multiple dimensions

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Quality and Quantity 2019年第4期53卷 2063-2080页

作者： Magrini, Alessandro Bartolini, Fabio Coli, Alessandra Pacini, Barbara Department of Statistics Computer Science Applications University of Florence Florence Italy Department of Agriculture Food and Environment University of Pisa Pisa Italy Department of Economics and Management University of Pisa Pisa Italy Department of Political Science University of Pisa Pisa Italy

The economic crisis and the pressure towards efficient and effective use of public money claim for a higher accountability of research expenditure, as well as for a greater proximity of research to the needs of community. While agricultural productivity represents a worldwide goal for agricultural research as a response to growing food, feed and energy demands, other objectives besides productivity are becoming central. The challenge is to take into account broader impacts that go beyond academic and economic ones, and to improve knowledge on the causal impact-generating mechanisms. In this paper, we adopt a causal perspective and estimate the impact of agricultural research expenditure on multiple dimensions. We develop a structural equation model relating research expenditure, research activity, productivity and multiple impact indicators within a dynamic impact pathway, accounting for existing domain knowledge on causal relationships and their lag structures. The model is applied on EU 15 countries over the period 1980–2014, making use of official statistics from several European databases. © Springer Nature B.V. 2019.

关键词： Directed acyclic graph Dynamic causal effects Polynomial lag shape Productivity Research impact assessment Sustainable development

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