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MAPFUNet:Multi-attention Perception-Fusion U-Netfor Liver Tumor Segmentation

Journal of Bionic Engineering 2024年第5期21卷 2515-2539页

作者： Junding Sun Biao Wang Xiaosheng Wu Chaosheng Tang Shuihua Wang Yudong Zhang School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo451460China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia

The second-leading cause of cancer-related deaths globally is liver *** treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT *** improved method based on U-Net has achieved good perfor-mance for liver tumor segmentation,but these methods can still be *** deal with the problems of poor performance from the original U-Net framework in the segmentation of small-sized liver tumors and the position information of tumors that is seriously lost in the down-sampling process,we propose the Multi-attention Perception-fusion U-Net(MAPFU-Net).We propose the Position ResBlock(PResBlock)in the encoder stage to promote the feature extraction capability of MAPFUNet while retaining the position information regarding liver tumors.A Dual-branch Attention Module(DWAM)is proposed in the skip connections,which narrows the semantic gap between the encoder's and decoder's features and enables the network to utilize the encoder's multi-stage and multi-scale *** propose the Channel-wise ASPP with Atten-tion(CAA)module at the bottleneck,which can be combined with multi-scale features and contributes to the recovery of micro-tumor feature ***,we evaluated MAPFUNet on the LITS2017 dataset and the 3DIRCADB-01 dataset,with Dice values of 85.81 and 83.84%for liver tumor segmentation,which were 2.89 and 7.89%higher than the baseline model,*** experiment results show that MAPFUNet is superior to other networks with better tumor feature representation and higher accuracy of liver tumor *** also extended MAPFUNet to brain tumor segmentation on the BraTS2019 *** results indicate that MAPFUNet performs well on the brain tumor segmentation task,and its Dice values on the three tumor regions are 83.27%(WT),84.77%(TC),and 76.98%(ET),respectively.

关键词： Liver tumor segmentation Small tumors Position information Attention Multi-scale features

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Machine Learning Models for Predicting Movie Ticket Pricing

Machine Learning Models for Predicting Movie Ticket Pricing

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Obagbuwa, Ibidun Christiana Mwanza, Alfred John Sol Platjee University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350358155

Predicting the price of a movie ticket is essential in the dispensations of data analytics and changed customer entertainment behavior. Increasing demand for high-quality 3D screen movies is undergirded by advancements in movie digitization technology in visual effects, driving the movie theatre market. Watching movies with family and friends in cinemas is one of the best entertainment mediums after having a hectic schedule. However, it is difficult to go somewhere without knowing how much you will pay for a specific movie, hence the need for tools to address this customer-side demand. People need to see the prices of tickets online at any time. On the other hand, the movie proprietor aims to make maximum revenue. This study proposes a system modeled to predict the price of the ticket for a specific movie and provided with validations to ensure consistency, reliability, and, most importantly, the correctness of the information. Movie ticket pricing was predicted using different machine learning algorithms to model our system and compare performance to determine which algorithm has the most accurate results. Random forest, Decision trees, and KNN had the highest accuracy. © 2024 IEEE.

关键词： Random forests

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OntoDSO: an ontological-based dolphin swarm optimization (DSO) approach to perform energy efficient routing in Wireless Sensor Networks (WSNs)

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International Journal of information technology (Singapore) 2024年第3期16卷 1551-1557页

作者： Boopathi, Mythili Parikh, Swapnil Awasthi, Aishwary Malviya, Ashwini Nachappa, M.N. Mishra, Awakash Shyam, Gopal Krishna Narula, Gagandeep Singh Department of Information Technology Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Mechanical Engineering Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Computer Science Engineering Presidency University Bengaluru India Guru Gobind Singh Indraprastha University Delhi Dwarka India

The aim of the paper is to perform an energy-efficient routing for moving nodes in Wireless Sensor Networks (WSNs) by using an ontological-based dolphin swarm optimization (DSO) approach. It makes use of echolocation and ontology to locate and represent cluster nodes close to the sink thereby maintaining optimal energy consumption during simulation. A comparative analysis for 20 samples (N = 10 each) is performed between OntoDSO, Ad-hoc On-demand Distance Vector (AODV) algorithm and existing literature review studies by taking several parameters namely mean delay, mean packet delivery ratio and mean energy consumption into consideration. The simulation of results is performed using MATLAB Simulink with implementation parameters such as number of nodes (n) and initial battery life. As a result, it is found that the OntoDSO approach identifies a set of optimized routes that can satisfy delay constraints and consume less energy, thereby achieving higher performance than AODV and existing studies in the context of WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Ad-hoc On-demand Distance Vector (AODV) algorithm Dolphin Swarm Optimization (DSO) algorithm Packet delivery ratio and energy consumption Wireless Sensor Network (WSN)

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Optimizing Resource Allocation Framework for Multi-Cloud Environment

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computers, Materials & Continua 2023年第5期75卷 4119-4136页

作者： Tahir Alyas Taher M.Ghazal Badria Sulaiman Alfurhood Ghassan F.Issa Osama Ali Thawabeh Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Skyline University College University City Sharjah1797SharjahUAE Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia

Cloud computingmakes dynamic resource provisioning more *** a functioning service is crucial,and changes are made when particular criteria are *** research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to *** allocation is a complex challenge due to the versatile service providers and resource *** engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of *** objective of a coherent and rational resource allocation is to attain the quality of *** also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud *** three main parameters of the proposed framework are data accessibility,optimization,and *** an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service *** CloudSim simulator has been used to validate the suggested *** experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained *** results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

关键词： Multi-cloud query optimization cloud resources allocation modelling virtualization

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation 2

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Suman, Saurabh Pacharaney, Utkarsha Choudhary, Vishal Kumar Raushan, Rahul Kumar, Abhishek Kumar, Gulshan Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed. © 2024 IEEE.

关键词： Splines

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ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with Uncertainty Data 8

ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with ...

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8th IEEE International Conference on Software Engineering and computer systems, ICSECS 2023

作者： Che Lah, Muhammad Shukri Arbaiy, Nureize Lin, Pei-Chun Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Malaysia Feng Chia University Department of Information Engineering and Computer Science Taiwan

ISBN: (纸本)9798350310931

Public health is seriously threatened by air pollution. systems for early warning are crucial for preventing its negative impacts on humans. But predicting air quality is difficult because it needs precise data from time series gathered at air monitoring sites. Due to things like measurement mistakes, this data may be ambiguous. Using fuzzy symmetry triangular fuzzy numbers, we suggest a strategy in this study for preparing data that contains uncertain information. Linear programming is utilized to obtain the midpoint value for defuzzification. The fuzzy pre-processed data is then used to build a predictive model using ARIMA. Our findings imply that the linear program can greatly lower prediction errors, resulting in more precise predictions. © 2023 IEEE.

关键词： Linear programming

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Prediction of banana leaf disease by intelligent algorithm through digital image using neural network technique

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Multimedia Tools and Applications 2025年 1-17页

作者： Nagu, Bharathiraja Kaur, Gaganpreet Shanmugavelu, Murugesan M.R.M, Veeramanickam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Information Technology Tagore Engineering College Tamil Nadu Chennai India Apex Institute of Technology-Computer Science and Engineering Chandigarh University Punjab India

A lot of research shows that there could be several reasons why the duality of agricultural products has been reduced. Plant diseases make up one of the most important components of this quality. Therefore, the reduction of plant diseases allows for much higher product quality. The automated diagnosis of plant diseases using CNN techniques is presented in this article. The simple approach alters the 25 color channels and 24H histograms. The Banana Leaf Disease Detection Technique (BLDDT), which uses two attention mechanisms to assess, identify and categorize traits, was also suggested in this research. In the analysis phase, the testing carried out using the reference information reveals that the disease, detection system may be reasonably more appropriate than the current techniques because it both identifies and diagnoses the diseased/infected areas. The researchers were able to identify Esca, black decay, and isariopsis ––––with a 99.93% accuracy level using the suggested disease detector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Streamlining Software Release Process and Resource Management for Microservice-based Architecture on multi-cloud 25

Streamlining Software Release Process and Resource Managemen...

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25th International Multi Topic Conference, INMIC 2023

作者： Herath, Isuru Pathum Jayawardena, Samuditha Fadhil, Ahamed Kodagoda, Nuwan Arachchillage, Udara Srimath S.Samaratunge Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Sri Lanka

ISBN: (纸本)9798350317701

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry's automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components. © 2023 IEEE.

关键词： Long short-term memory

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Deep Learning-Based Robust Hybrid Approaches for Brain Tumor Classification in Magnetic Resonance Images

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Journal of The Institution of Engineers (India): Series B 2023年 1-15页

作者： Krishna, S. Rama Ravela, Chittibabu Mantena, Srihari Varma Sirajuddin, Mohammad Deol, G. Joel Sunny Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Vaddeswaram India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of Information Technology Kallam Haranadhareddy Institute of Technology Dasarapalem India Department of Computer Science and Engineering Kallam Haranadhareddy Institute of Technology Andhra Pradesh Dasarapalem India

Classification of brain images is a very challenging problem among the most helpful and commonly employed procedures in the medical system. Deep learning, a subset of artificial intelligence, has pioneered new techniques for automatic medical image investigation. The goal of this study was to create two deep learning-based hybrid models to segment and classify brain MRI data. The first hybrid model is a combination of Fully Convolutional Networks and Residual Networks for segmentation and classification. The second is a hybrid of SegNet for segmentation and MobileNet for classification. The analysis is based on a labelled dataset containing images of glioma, meningioma, pituitary, and no tumor. The hybrid models proposed above are built using Python. The suggested models exhibit a high accuracy of 93.9% and 91.3% for a brain MRI dataset, respectively. Furthermore, the proposed models were compared and evaluated against other existing hybrid models in terms of dice score, Positive predictive value, False predictive value, precision, recall, specificity, and F1 score. As a result, the proposed deep-learning technique can assist doctors as well as radiologists in detecting brain tumors early. © 2023, The Institution of Engineers (India).

关键词： Magnetic resonance imaging

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An Acceptance Model of Using Mobile-Government Services(AMGS)

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computer Modeling in Engineering & sciences 2022年第5期131卷 865-880页

作者： Ahmad Althunibat Mohammad Abdallah Mohammed Amin Almaiah Nour Alabwaini Thamer Ahmad Alrawashdeh Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmmanJordan Faculty of Computer Sciences and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia

In recent years,the telecommunications sector is no longer limited to traditional communications,but has become the backbone for the use of data,content and digital applications by individuals,governments and companies to ensure the continuation of economic and social activity in light of social distancing and total closure inmost countries in the ***,electronic government(e-Government)andmobile government(m-Government)are the results of technological evolution and ***,it is important to investigate the factors that influence the intention to use m-Government services among Jordan’s *** paper proposed a new m-Government acceptance model in Jordan(AMGS);this model combines the information system(IS)Success Factor Model and Hofstede Cultural Dimensions *** study was conducted by surveying different groups of the Jordanian *** questionnaire was used to collect data from203 *** regression analysis has been conducted to analyze the *** results indicate that the significant predictors of citizen intention to use m-Government services in Jordan are information Quality,Service Quality,Uncertainty Avoidance,and Indulgence ***,the results also suggest that Power Distance is not a significant predictor of citizen intention to use m-Government services.

关键词： Acceptance test e-Government m-Government acceptance model IS success factor model Hofstede cultural dimensions theory

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