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Improved Quaternion Discriminant Analysis for Feature Extraction and Classification of Hyperspectral Image

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12925-12945页

作者： Wang, Xinpeng Ling, Bingo Wing-Kuen Yu, Long Dai, Qingyun Liu, Shaopeng Zhao, Jiandong Kuang, Weichao Guangdong Polytechnic Normal University Faculty of Computer Science Guangzhou China Guangdong University of Technology School of Information Engineering Guangzhou China Sun Yat-sen University School of Geography and Planning Guangzhou510275 China Dongguan University of Technology Department of Electronic Engineering and Intellectualization Dongguan China

Real domain discriminant analysis (RDA) can not solve the crosstalk of strong correlation between adjacent spectral bands, and also causes spectral information loss of hyperspectral image (HSI). As a result, the effect of feature extraction is not ideal. Besides, in the case of small samples, using quaternion discriminant analysis (QDA) based on Cos-sin Decomposition, the matrix obtained by the first orthogonal trigonometric decomposition is not a diagonal matrix, that makes the projection axis not optimal. To solve these problems, firstly, a quaternion discriminant analysis method based on stable quaternion Cossin Decomposition (QDASG) is proposed to solve the small sample problem. Secondly, this paper theoretically analyzes that when the number of projection axes in QDASG is smaller than that in traditional DA methods, the discriminant formula can achieve optimal results, and verifies this conclusion through experiments. Then, according to the number of training samples, the implementation process of QDA is discussed in two cases. Finally, spatial filtering is used to extract spatial information, and the distance quotient formula between classes is proposed as the standard for selecting the the optimal number of stacking layers for the filter. Experimental results show that this method is effective compared with deep learning method and and other traditional machine learning methods, especially in the case of small samples. © 2008-2012 IEEE.

关键词： Discriminant analysis

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AI-Powered Developments in Oncological Diagnostic Imaging: Revolutionary Approaches to Cancer Treatment

AI-Powered Developments in Oncological Diagnostic Imaging: R...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Singh, Shweta Ashwini, B. Vivek, V. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

This project aims to demonstrate new uses of deep learning techniques in the detection of cancer. A huge body of examples that show the desired behaviour may be used to train a system using deep learning, which is a family of algorithms. Introduction of deep learning methods to tumour imaging may help pathologists identify and categorise cancer early on so that patients can get the right therapies to improve their chances of surviving or recovering. Utilising statistics and other analytical techniques and ScienceDirect have data, it appears that since the late 1990s, a sharp increase in research on deep learning technologies in medical imaging has been motivated by the exceptionally high mortality rates of some cancer types in order to address the concomitant issues of a more precise recognition and the identification of cancer types to use effective therapies for cancer. This study also identifies the institutions, nations, and intellectual subject areas with the greatest levels of intellectual output in multiple disciplines as the origins of the trajectory of deep learning technology in cancer detection. According to Amara's law, this novel technique may result in a change in the way that cancer diagnoses and treats its many forms. When they can submit digital photos to laboratories in other developed nations to have malignancy kinds diagnosed, this fresh innovation may also aid developing regions, narrowing the present healthcare divide as much as feasible. © 2023 IEEE.

关键词： Cancer Detection Cancer Types Deep Learning Early Diagnosis Machine Learning Medical Imaging Metastatic Cancer Pathologists and Clinical Oncology Therapies Tumour Imaging

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Air Quality Monitoring in Ulaanbaatar Using Fog Computing and IoT, Looking for the Correlation Between Air Pollution and Public Health 28

Air Quality Monitoring in Ulaanbaatar Using Fog Computing an...

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28th International Scientific Conference on Transport Means 2024

作者： Patlins, A. Nyamkhuu, B. Kunicina, N. Riga Technical University Faculty of Computer Science Information Technology and Energy Institute of Industrial Electronics Electrical Engineering and Energy 10 Zunda Embankment RigaLV-1048 Latvia

In the research under discussion, the authors put forth a novel approach to air quality monitoring in Ulaanbaatar, Mongolia’s capital, by leveraging the power of fog computing. This innovative method involves the collection of air pollution data in a manner that is both cost-effective and efficient, thereby making it a viable solution for a city where air pollution is a significant concern. The primary objective of this approach is to estimate the impact of air pollution on public health. This is achieved through the application of statistical methods to the collected data, which allows for the understanding of the health risks associated with poor air quality. By doing so, it is hoped that this research will contribute to raising awareness about the dangers posed by air pollution, a problem that is often overlooked but has far-reaching implications for public health. One of the key features of the proposed approach is its focus on expanding the coverage of air quality monitoring. This is particularly relevant in the Ger areas of Ulaanbaatar, where pollution levels tend to spike during the winter months. The inhabitants of these areas, who often rely on coal for heating, are among the most affected by the adverse effects of air pollution. Therefore, by extending the reach of monitoring efforts to these areas, the research aims to provide a more accurate and comprehensive picture of the city’s air quality. In addition to the above, the research also takes into account the role of transportation in contributing to air pollution. In Ulaanbaatar, as in many other urban centers around the world, transportation is a one of major sources of air pollution. This includes not only public transport but also private vehicles, which collectively contribute a significant amount of pollutants to the city’s air. By considering the impact of transportation on air quality, the proposed approach acknowledges the multifaceted nature of the air pollution problem and underscores the need

关键词： Air quality

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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

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computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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The Innovative Cloud Security Environment for Data Privacy

The Innovative Cloud Security Environment for Data Privacy

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Divya Rani, M.S. Rajesh, A. Agarwal, Ankita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

The use of online computing has grown quickly across a range of institutions and businesses. The storage of information in the clouds, which enables individuals and businesses to store their personal information largely in the cloud and not on local computers, is one of the main benefits of cloud computing. Safety of data maintained in the cloud is still a major worry, however. We provide a novel security architecture in this study that is intended to improve data confidentiality and safety. Data is separated into blocks of two bytes each, and each multi-byte bit sequence is subjected to a separate evolutionary process in the revised security framework. The end product of each genetic algorithm's operation is ciphertext, which is made up of two different sets of bits. The ciphertext for these encrypted messages is difficult to find since it is stored in many places across the cloud. Additionally, the newly developed safety architecture uses algorithms that evolve on fewer data blocks to improve data security. A capability list is also used to guarantee granular and secure access to information. © 2023 IEEE.

关键词： access control Cloud security cloud service providers (CSPs) data confidentiality data encryption data privacy data protection Genetic Algorithm (GA) Internet of Things (IoT) security architecture

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An Exhaustive Meta-Analysis on Consumer Retention Forecasting Using Advanced Machine Learning Techniques 3

An Exhaustive Meta-Analysis on Consumer Retention Forecastin...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Shreenidhi, H.S. Yadav, Dhyan Chandra Swetha, G. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka562112 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

In this meta-analysis, we present a robust approach for consumer retention forecasting by integrating advanced machine learning algorithms. The method comprises three core algorithms: Consumer Behaviour Neural Network (CBNN), Retention Decision Trees (RDT), and Long Short-Term Memory for Retention (LSTM-R). The objective is to model intricate relationships within consumer behaviours and accurately predict their retention probabilities. CBNN a key component, adeptly captures intricate patterns within consumer behaviour data. It involves multiple hidden layers with activation functions, enabling accurate prediction of retention probabilities based on input features. The flowchart for CBNN showcases the training process involving forward and backward passes, underlining its deep learning approach. RDT this method utilizes decision tree algorithms to predict consumer retention decisions. By recursively splitting the dataset based on information gain, RDT efficiently models consumer behaviour, as depicted in the flowchart. information gain is maximized to create a robust predictive model. LSTM-R tailored for temporal data in consumer retention prediction, leverages specialized LSTM architecture. It effectively processes sequential consumer interactions, crucial for accurate prediction of consumer retention probabilities. The proposed method integrates these algorithms, leveraging historical consumer data, and conducts a comprehensive meta-analysis to evaluate their predictive performance. © 2023 IEEE.

关键词： Decision trees

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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

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Iran Journal of computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Understanding the Journey to 6G Development

Understanding the Journey to 6G Development

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agrawal, Trapty Kumar, Ajit Gowrishankar, J. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The fifth-generation (5G) mobile communication technology is now being widely deployed in a number of nations, along with a significant rise in the number of 5G customers. However, it is becoming more and more clear that it is time for both academics and business to shift their emphasis towards the next generation as the mobile telecommunications market continues to change. At this point, it is crucial to provide a thorough review of the state of the art and look forward to the future of communications. This article tries to achieve this goal by providing a thorough analysis designed to outline the sixth generation (6G) system's characteristics. The motivations, use cases, use scenarios, needs, key performance indicators (KPIs), architecture, and enabling technologies that will characterize 6G are all covered in this investigation. © 2023 IEEE.

关键词： 6G AI architecture disruptive services enabling technologies generation Key Performance Indicators (KPIs) latency mobile communication mobile traffic mobility network efficiency peak data rate performance indicators technology terahertz communications use cases user-experienced data rate

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Aspect Level Songs Rating Based Upon Reviews in English

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computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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

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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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