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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat jan nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Liquid crystal immunosensors for the selective detection of Escherichia coli with a fast analysis tool

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Photonics Research 2024年第7期12卷 1564-1573页

作者： SANDRO C.OLIVEIRA MARIA S.SOARES BÁRBARA V.GONÇALVES ANDREIA C.M.RODRIGUES AMADEU M.V.M.SOARES RITA G.SOBRAL NUNO F.SANTOS jan nedoma PEDRO L.ALMEIDA CARLOS MARQUES CICECO-Aveiro Institute of Materials Physics DepartmentUniversity of Aveiro3810-193 AveiroPortugal i3N Physics DepartmentUniversity of Aveiro3810-193 AveiroPortugal Associate Laboratory i4HB-Institute for Health and Bioeconomy School of Sciences and TechnologyNOVA University of Lisbon1099-085 LisbonPortugal CESAM-Centre for Environmental and Marine Studies and Department of Biology University of Aveiro3810-193 AveiroPortugal Department of Telecommunications VSB-Technical University of OstravaOstrava 70800Czech Republic i3N-CENIMAT School of Sciences and TechnologyNOVA University of Lisbon1099-085 LisbonPortugal UnIRE ISELPolytechnic Institute of Lisbon1549-020 LisbonPortugal Department of Physics VSB-Technical University of OstravaOstrava 70800Czech Republic

The consumption of contaminated food may cause serious illnesses,and traditional methods to detect Escherichia coli are still associated with long waiting times and high costs given the necessity to transport samples to specialized *** is a need to develop new technologies that allow cheap,fast,and direct monitoring at the site of ***,in this work,we developed optical immunosensors for the selective detection of ***,based on liquid crystal technology,whose molecules can align in different manners depending on the boundary conditions (such as substrates) as well as the environment that they *** glass substrate was functionalized with *** antibody using cysteamine as an intermediate,and a vertical alignment was imposed on the liquid crystal molecules by using DMOAP during *** presence of bacteria disrupts the alignment of the liquid crystal molecules,changing the intensity of light emerging between cross polarizers,measured using a polarized optical microscope and a *** was possible to detect *** in suspensions in the concentration range from 2.8 cells/mL to 2.8×10^(9) cells∕*** was also evaluated,and the sensors were used to analyze contaminated water samples.A prototype was developed to allow faster,in-situ,and easier analysis avoiding bulky instruments.

关键词： crystal alignment analysis

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Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

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Computers, Materials & Continua 2022年第6期71卷 4659-4676页

作者： Radek Martinek jan Baros Rene Jaros Lukas Danys jan nedoma VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Cybernetics and Biomedical Engineering70800Ostrava-PorubaCzechia VSB Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Telecommunications70800Ostrava-PorubaCzechia

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise *** vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART *** speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground *** article presents the new concept of a hybrid system which is implemented as a virtual *** highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise *** study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual *** average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

关键词： 5G noise reduction hybrid algorithms speech recognition 5G data networks in-vehicle background noise

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Augmented Node Placement Model in t-WSN Through Multiobjective Approach

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Computers, Materials & Continua 2021年第12期69卷 3629-3644页

作者： Kalaipriyan Thirugnansambandam Debnath Bhattacharyya Jaroslav Frnda Dinesh Kumar Anguraj jan nedoma School of Computer Science and Engineering VIT UniversityChennai CampusTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturIndia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of Ostrava70833 Ostrava-PorubaCzech Republic

In Wireless Sensor Network(WSN),coverage and connectivity are the vital challenges in the target-based *** linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement *** this paper,a multiobjective problem on target-based WSN(t-WSN)is derived,which minimizes the number of deployed nodes,and maximizes the cost of coverage and sensing *** Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)is incorporated to tackle this multiobjective problem *** problems are intended to solve different objectives of a problem ***-inspired algorithms address the NP-hard problem most effectively in recent *** NSGA-II,the Non-Dominated sorting preserves the better solution in different objectives simultaneously using dominance *** the diversity maintenance phase,density estimation and crowd comparison are the two components that balance the exploration and exploitation phase of the *** of NSGA-II on this multiobjective problem is evaluated in terms of performance indicators Overall Non-dominated Vector Generation(ONGV)and Spacing(SP).The simulation results show the proposed method performs outperforms the existing algorithms in different aspects of the model.

关键词： Focused wireless sensor network m-coverage k-connectivity problem non-dominated sorting NSGA-II

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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Computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda jan nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Noise Reduction in Industry Based on Virtual Instrumentation

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Computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros jan Baros Lukas Danys Aleksandra Kawala-Sterniuk jan nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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