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Segmentation and Classification of Stomach Abnormalities Using Deep Learning

computers, Materials & Continua 2021年第10期69卷 607-625页

作者： Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistanl 2College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabial 3Department of SoftwareSejong UniversitySeoulKoreaGwangjin-guKoreal 4College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabial 5Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway

An automated system is proposed for the detection and classification of Gi *** proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of Gi abnormalities by deep *** first bleeding region is segmented using a hybrid *** threshold is applied to each channel extracted from the original RGB ***,all channels are merged through mutual information and pixel-based *** a result,the image is *** and deep learning features are extracted in the proposed classification *** transfer learning(TL)approach is used for the extraction of deep *** Local Binary Pattern(LBP)method is used for texture ***,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture *** selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning *** experimental process is evaluated on the basis of two datasets:Private and *** accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASiR data *** can be confirmed that the proposed method is effective in detecting and classifying Gi abnormalities and exceeds other methods of comparison.

关键词： Gastrointestinal tract contrast stretching segmentation deep learning features selection

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Enhanced Word Embedding with CNN using Word Order information for Sentiment Analysis 20

Enhanced Word Embedding with CNN using Word Order Informatio...

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20th international computer Conference on Wavelet Active Media Technology and information Processing, iCCWAMTiP 2023

作者： Atandoh, Peter Fengli, Zhang Hakeem, Paul Atandoh Acheampong, Edward Mensah Addo, Daniel Appiah-Twum, Michael Adu-Gyamfi, Daniel Blankson, Richard Safo Giza Wi, Tohye Tewodros School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Mercer University Department of Mathematics MaconGA31207 United States School of Resources and Environment University of Electronic Science and Technology of China Chengdu China C. K. Tedam University of Technology and Applied Sciences Department of Cyber Security and Computer Engineering Technology Navrongo UE/R Ghana Summit Economics LLC Colorado United States

ISBN: (纸本)9798350318982

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7%, 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE. © 2023 iEEE.

关键词： Sentiment analysis

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Reliability Analysis of a Monitoring System for Extraterrestrial Habitats

Reliability Analysis of a Monitoring System for Extraterrest...

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iEEE international Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Saurabh Band Florian Stechmann Malavika Unnikrishnan Shadi Attarha Christiane Heinicke Andreas Willig Anna Foerster dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. of Computer Science and Software Engineering University of Canterbury New Zealand

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). it uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

关键词： Wireless communication Wireless sensor networks Space missions Redundancy Prototypes Logic gates Reliability theory Reliability engineering Monitoring Strain

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TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy

arXiv

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arXiv 2025年

作者： Valverde, Juan Miguel Koga, Motoya Otsuka, Nijihiko Dahl, Anders Bjorholm Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark A.I. Virtanen Institute University of Eastern Finland Finland Department of Architecture Faculty of Engineering Sojo University Japan

We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions. TopoMortar enables to investigate in two ways whether methods incorporate prior topological knowledge. First, by eliminating challenges seen in real-world data, such as small training set, noisy labels, and out-of-distribution test-set images, that, as we show, impact the effectiveness of topology loss functions. Second, by allowing to assess in the same dataset (TopoMortar) topology accuracy across dataset challenges, isolating dataset-related effects from the effect of incorporating prior topological knowledge. in these two experiments, it is deliberately difficult to improve topology accuracy without actually leveraging topology information, thus, permitting to attribute an improvement in topology accuracy to the incorporation of prior topological knowledge. To this end, TopoMortar includes three types of labels (accurate, pseudo-labels, noisy labels), two fixed training sets (large and small), and in-distribution and out-of-distribution test-set images. We compared eight loss functions on TopoMortar, and we found that clDice generally achieved the most topologically accurate segmentations, Skeleton Recall loss performed best particularly with noisy labels, and the relative advantageousness of the other loss functions depended on the experimental setting. Additionally, we show that simple methods, such as data augmentation and self-distillation, can elevate Cross entropy Dice loss to surpass most topology loss functions, and that those simple methods can enhance topology loss functions as well. clDice and Skeleton Recall loss, both skeletonization-based loss functions, were also the fastest to train, making this type of loss function a promising research direction. TopoMortar and our code can be found at https://***/jmlipman/TopoMortar. © 2025, CC BY.

关键词： Musculoskeletal system

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Smart Water Level Monitoring System Using internet of Things (ioT)

Smart Water Level Monitoring System Using Internet of Things...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Safina Parveen Syed Muhammad Nabeel Mustafa Sanober Soomro Shama Bano Department of Computer Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Department of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Department of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan

Water shortage is the major issue in the modern world. Conservation is urgently needed as a result of the population's growing need for water on a minute-by-minute basis. Every aspect of life has been automated thanks to information and technology. Automation is now simpler because to the expansion of the internet of Things (ioT). it has applications in the monitoring and management of equipment. ioT has given humans the ability to remotely operate a variety of instruments. We provide a model to manage and monitor the water flow in order to conserve water using the wonders of ioT. The proposed methodology uses water tanks with ultrasonic sensors to measure the liquid level and contrast it with the depth of the container. The storage system's water level is detected by the system. in this case, sensors are installed in the tank at various levels. A microcontroller is attached to the sensors. The sensors gauge the water level and transmit that information to the microprocessor, which updates the mobile application and LCD with the tank condition. Using an ultrasonic sensor, the user can manually override the mobile application's operation of the pump. This technology will give users a mobile application and buzzer to let them know how their tanks are doing, making it simple for them to operate. The system can be enhanced to ensure that dams operate effectively.

关键词： Wireless communication Wireless sensor networks Ultrasonic variables measurement Optical switches Water conservation Acoustics Mobile applications

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A Framework for Blockchain-Based Secure Management of Mobile Healthcare (mHealth) Systems

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Journal of Web engineering 2025年第3期24卷 317-354页

作者： Adel Alkhalil Abdul Razzaq Aakash Ahmad Magdy Abdelrhman Yaser Altameemi Mohammed Altamimi Zhang Tao Department of Software Engineering College of Computer Science and Engineering University of Ha'il Ha'il Saudi Arabia School of Software Northwestern Polytechnical University Xi'an Shaanxi P.R. China School of Computing and Communications Lancaster University Leipzig Germany Department of Foundation of Education College of Education New Valley University Egypt Applied College University of Ha'il Ha'il Saudi Arabia

in recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the interPlanetary File System (iPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algori

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Minimal-envy Conference Paper Assignment: Formulation and a Fast iterative Algorithm 5

Minimal-envy Conference Paper Assignment: Formulation and a ...

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5th Asian Conference on Artificial intelligence Technology, ACAiT 2021

作者： Tan, Mao Dai, Zhuocen Ren, Yuling Walsh, Toby Aleksandrov, Martin Xiangtan University Hunan National Center for Applied Mathematics Xiangtan China Xiangtan University School of Computer Science Xiangtan China University of New South Wales School of Computer Science and Engineering Sydney Australia Institute of Software Engineering and Theoretical Computer Science Technical University Berlin Berlin Germany

ISBN: (纸本)9781665426305

in conference review management, it is very common that many papers being assigned are not liked by some reviewers due to their specialties or personal interests. in this paper, we consider a reviewer might only envy other reviewers who get a better set of papers than his. in this context, we proposes a new bi-objective optimization assignment model to efficiently and fairly assign papers to many competing reviewers, in which an index that measures the amount of envy is introduced. A fast iterative algorithm is proposed to solve the problem. The experimental results on real datasets shows that the algorithm achieves efficient and fair results and good computing performance, especially on large scale problems. This work is also meaningful for a number of similar allocation or matching problems. © 2021 iEEE.

关键词： iterative methods

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Multi-task Scheduling Algorithm Based on Self-adaptive Hybrid iCA–PSO Algorithm in Cloud Environment 8th

Multi-task Scheduling Algorithm Based on Self-adaptive Hybri...

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8th international Workshop on Soft Computing Applications, SOFA 2018

作者： Tabrizchi, Hamed Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Cloud is a new concept in internet technology and has brought many benefits, particularly, in the field of computing. Cloud has changed how on-demand resources are allocated to the different user requests and has provided more optimal resource utilization. Tasks (user requests) have different kinds, including deadline-based tasks having time-related limitations. This kind of task has various parameters and scheduling them is more challenging than scheduling the individual tasks. in this regard, many papers have been published according to these parameters. The objective of this paper is to propose a novel self-adaptive hybrid iCA–PSO algorithm for dealing with associate multi-task scheduling problem. To improve the exploration, two algorithms, namely, the imperialist competitive algorithm (iCA) and particle swarm optimization (PSO) were combined in this study. The results of the present study outperform those of the existing mechanisms. © 2021, Springer Nature Switzerland AG.

关键词： Scheduling algorithms

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Classification of Plants Leave Using image Processing and Neural Network 8th

Classification of Plants Leave Using Image Processing and Ne...

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8th international Workshop on Soft Computing Applications, SOFA 2018

作者： Bagherinezhad, Hashem Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Koles, ioan E. Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Plants are one of the most widely used resources for humans in different fields. Therefore, the distinction between the plant species is important and it is referred to as the plant detection system. Until now, this task has been done by the expert botanists which is an overwhelming and time consuming task. Moreover, there is a lack of the memory and the human fault, so the researchers endeavored to solve these disadvantages using the Ai algorithms. For this goal, in this paper, a system is proposed that includes four phases: pre-processing, feature extraction, training, and test. in this method, we use the combination of the useful features of the leave shape, the leave texture, the leave color, and we provide a method for the classification of a number of the plant species. Finally, the feature vectors will be created and then, the classification is performed by using feed-forward back-propagation multi-layer perceptron artificial neural network algorithm. Then, the results of this method compare with other methods. The obtained results show the high accuracy of this method for a large number of the species in different conditions (such as pests, season changes and lighting). © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach

Diagnosing Spinal Abnormalities Using Machine Learning: A Da...

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Artificial intelligence (iCAi), international Conference on

作者： Zia Ul islam Nasir Khalil Khan Momna Asghar School of Interdeciplanary Engineering & Sciences(SINES) National University of sciences & Technology (NUST) Isamabad Pakistan Department of Computer Software Engineering & Computer Science Pak-Austria Institute of Applied Science & Technology Haripur Pakistan Department of Electrical Engineering University of Azad Jammu & Kashmir Muzaffarabad A.K

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes.

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