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EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

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Computers, Materials & Continua 2024年第10期81卷 809-825页

作者： Rabia Javed Tanzila Saba Tahani Jaser Alahmadi Sarah Al-Otaibi Bayan AlGhofaily Amjad Rehman Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan Artificial Intelligence&Data Analytics Lab. CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh145111Saudi Arabia

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to *** cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all *** and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival *** examinations for lung cancer detection,however,frequently fall short of detecting small polyps and *** address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients *** research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological *** proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell *** evaluated the performance of the proposed technique using the histopathological(LC25000)lung *** preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer *** dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training *** model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are *** study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

关键词： Colon cancer EfficientNetB1 histopathological image processing transfer learning health risks

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A Novel Multi-Scale Deep Learning Approach for the Early Detection of Alzheimer's Disease Using fMRI 5

A Novel Multi-Scale Deep Learning Approach for the Early Det...

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5th International Conference on Computer, Big data and artificial intelligence, ICCBD+AI 2024

作者： Rehman Butt, Ateeq Ur Hamid, Isma Nawaz, Qamar Mahmood, Tariq Zhang, Xiang Yaqub, Muhammad National Textile University Department of Computer Science Faisalabad Pakistan University of Agriculture Department of Computer Science Faisalabad Pakistan Prince Sultan University Artificial Intelligence and Data Analytics Lab Ccis Riyadh11586 Saudi Arabia Chinese Academy of Sciences Institute of Biophysics Beijing100101 China Hunan University School of Biomedical Sciences Changsha China

ISBN: (纸本)9798331533991

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains challenging to diagnose in its early stages. This study presents a novel 3D multi-scale Convolutional Neural Network (CNN) model that utilizes resting-state functional MRI (rs-fMRI) data to classify different stages of Alzheimer's disease. The proposed model integrates multi-scale feature extraction from the hippocampal region, optimizing the detection of fine-grained and global patterns indicative of AD progression. The model was evaluated on a balanced dataset of 300 subjects, applying cross-validation to ensure robustness across multiple binary classification tasks. Our experimental results demonstrate a classification accuracy of up to 93% for distinguishing Alzheimer's Disease from Cognitively Normal subjects. High sensitivity, specificity, and precision were observed across all classification tasks, indicating the model's effectiveness in early-stage Alzheimer's detection. These findings suggest the proposed approach could enhance clinical diagnosis and facilitate timely interventions for AD patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), International Conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Trust Management Frameworks In Multi-Cloud Environment: A Review

Trust Management Frameworks In Multi-Cloud Environment: A Re...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Arwa Alajroush Rabia Latif Tanzila Saba College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Data Analytics Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

The term "cloud computing" refers to a method of managing and using resources remotely. Networks, servers, applications, machines, and other resources and storage are all part of the shared and virtualized resource pool that is offered. In addition to providing the flexibility, stability, and scalability many organizations need the services of cloud computing, this reduces the need for setting up the private cloud and deploy separate hardware resources. However, multi-could settings presented several security and trust issues. This paper primary contribution is an examination of contemporary, serious trust challenges that arise in a multi-cloud context. And to put out a survey comparing trust management frameworks for Multi-Cloud environment to address the challenges identified. The suggested solution is given to have a more robust and secure trust framework based on data compliance.

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Review on 3D printers

Review on 3D printers

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作者： Hoshang Kolivand Luke Bailey Shiva Asadianfam Amjad Rehman Department of Computer Science Liverpool John Moores University Department of Computer Engineering Islamic Azad University Qom Branch Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University

In this paper of the report,it is all the different research in the that has been done to understand more about the subject of 3D printers and the health *** this paper all the different research that was done was information that is very closely related to subjects previously stated,although these papers are very professional and informative the focus of this part of the paper is to better show all the different gaps in the information and what it could be missing that could possibly be showed in this paper that they have missed out on.

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Analyzing Employee Attrition: A Visual Perspective

Analyzing Employee Attrition: A Visual Perspective

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Maya John Madawi Alharbi Norah Bin Daham Samia Abu Theeb Artificial Intelligence and Data Analytics (AIDA) Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

The resignation of employees from organizations has always been a burden from the management perspective. This is owing to the fact that organizations spend money on employee training, and there is no certainty regarding how fast and well the new recruits will adapt to the organization. In this paper we examined the employee attrition on department basis. Attrition was analyzed in terms of factors such as satisfaction, business travel and some job related features. Visualization-based approach was used for analyzing the data.

关键词： Training Visualization data visualization Companies data science data mining

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Analysis of Airbnb Prices and Factors in Barcelona: data Visualization Approach

Analysis of Airbnb Prices and Factors in Barcelona: Data Vis...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Sara Alqahtani Maya John Amerah Alghanim College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Data Analytics (AIDA) Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

Airbnb is an online platform where people can find peer-to-peer accommodation (homestays) for different duration of stay. A major attraction of these accommodation facilities is that they are priced much less than the hotels offering similar services. This paper deals with the analysis of various factors related to Airbnb accommodation prices in Barcelona. We also carried out a spatial analysis of the homestays. The prices during the weekend were relatively higher than on weekdays. Higher-priced homestays obtained better cleanliness ratings. No apparent relationship was found between guest satisfaction and prices. The people who stayed in accommodations where the host was a super host were more satisfied with the facilities provided.

关键词： data visualization data science Peer-to-peer computing data mining Sustainable development

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Clustering of solutions in the symmetric binary perceptron

arXiv

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arXiv 2019年

作者： Baldassi, Carlo Vecchia, Riccardo Della Lucibello, Carlo Zecchina, Riccardo Artificial Intelligence Lab Institute for Data Science and Analytics Bocconi University Milano Italy Istituto Nazionale di Fisica Nucleare Sezione di Torino Italy International Centre for Theoretical Physics Trieste Italy

The geometrical features of the (non-convex) loss landscape of neural network models are crucial in ensuring successful optimization and, most importantly, the capability to generalize well. While minimizers' flatness consistently correlates with good generalization, there has been little rigorous work in exploring the condition of existence of such minimizers, even in toy models. Here we consider a simple neural network model, the symmetric perceptron, with binary weights. Phrasing the learning problem as a constraint satisfaction problem, the analogous of a flat minimizer becomes a large and dense cluster of solutions, while the narrowest minimizers are isolated solutions. We perform the first steps toward the rigorous proof of the existence of a dense cluster in certain regimes of the parameters, by computing the first and second moment upper bounds for the existence of pairs of arbitrarily close solutions. Moreover, we present a non rigorous derivation of the same bounds for sets of y solutions at fixed pairwise distances. Copyright © 2019, The Authors. All rights reserved.

关键词： Constraint satisfaction problems

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in artificial intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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