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37th International Conference on Advanced Information Networking and applications, aiNA 2023

作者： Salama, Ramiz Al-Turjman, Fadi Culmone, Rosario Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Near East University Nicosia Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey Computer Science Division School of Science and Technology University of Camerino Camerino62032 Italy

ISBN: (纸本)9783031286933

The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top issues for people living in big cities, and everyone wants to feel completely secure when traveling around every single day. In this research, we will look into how CEOs in affluent cities use and value robots, especially in terms of security. To understand the robot security the board stream, some approaches and intricacies are used. Following that is a discussion of issues with urban security and the application of artificial intelligence to drones as a management tool. The use of cutting-edge technology, such as blockchain, to support smart urban community management is covered in the last part. The smart city idea and all of its benefits for local inspection are supported by robotic use. The idea of thriving urban areas is spreading around the world and is crucial in the context of developing economies. We examined how to leverage cutting-edge technologies in this project to make it feasible. Artificial intelligence, robotics breakthroughs, and blockchain technologies all have significant effects. This research highlights their significance to analysts and how they consider them when assessing prospects. This project will be very beneficial for analysts and experts in a relevant subject. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

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2024 IEEE International Conference on E-Health Networking, application and Services, HealthCom 2024

作者： Liu, Xinkai Peng, Boyuan Zhu, Xin Iwasaki, Tsuyoshi Sato, Atsuya Kazama, Junichiro James Huang, Jie Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan

ISBN: (纸本)9798350350548

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM’s segmentation accuracy for renal pathology. © 2024 IEEE.

关键词： Contrastive Learning

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Identifying Source Speakers for Voice Conversion Based Spoofing Attacks on Speaker Verification Systems 48

Identifying Source Speakers for Voice Conversion Based Spoof...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cai, Danwei Cai, Zexin Li, Ming Duke University Department of Electrical and Computer Engineering Durham United States Duke Kunshan University Data Science Research Center Kunshan China

ISBN: (纸本)9781728163277

An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person's speech signal to make it sound like another speaker's voice and deceive the speaker verification system. Most countermeasures for voice conversion-based spoofing attacks are designed to discriminate bona fide speech from spoofed speech for speaker verification systems. In this paper, we investigate the problem of source speaker identification - inferring the identity of the source speaker given the voice converted speech. To perform source speaker identification, we simply add voice-converted speech data with the label of source speaker identity to the genuine speech dataset during speaker embedding network training. Experimental results show the feasibility of source speaker identification when training and testing with converted speeches from the same voice conversion model(s). In addition, our results demonstrate that having more converted utterances from various voice conversion model for training helps improve the source speaker identification performance on converted utterances from unseen voice conversion models. © 2023 IEEE.

关键词： Speech recognition

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAai Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Blockchain-Based Cognitive Computing Model for data Security on a Cloud Platform

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computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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Mobile edge fog, Blockchain Networking and Computing-A survey 1

Mobile edge fog, Blockchain Networking and Computing-A surve...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatla, Shruti Mishra, Divya Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

Mobile edge computing (MEC) and blockchain technology are two rapidly evolving areas in computer science that have the potential to significantly impact the design and operation of distributed systems. MEC involves the deployment of computing resources and services at the edge of the network, closer to the end users, enabling lowlatency, high-bandwidth applications such as augmented reality and machine learning. Blockchain technology, on the other hand, is a distributed database that allows for secure, transparent, and immutable record-keeping, with potential applications in a wide range of industries. In this project, we explore the integration of MEC and blockchain technology for the management of distributed computing resources at the edge of the network. We review the state of the art in these two areas, including existing projects and prototypes, and describe our materials and method for evaluating their potential for integration. Our results show that the combination of MEC and blockchain has the potential to enable new applications and services that require both low latency and secure, decentralized data management. We conclude by summarizing the main contributions of our work and discussing future directions for research in this area. Our findings have implications for the design and operation of distributed systems, and have the potential to enable new applications and services in a wide range of industries. © 2023 IEEE.

关键词： Blockchain

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Domain Aware Multi-task Pretraining of 3D Swin Transformer for T1-Weighted Brain MRI 17th

Domain Aware Multi-task Pretraining of 3D Swin Transformer ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9789819609000

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

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Analysis of Knee Osteoarthritis Severity: A Deep Learning Paradigm for Automated Assessment from Plain Radiographs 8th

Analysis of Knee Osteoarthritis Severity: A Deep Learning Pa...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Panwar, Punita Chaurasia, Sandeep Gangrade, Jayesh Bilandi, Ashwani Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Artificial Intelligence and Data Science Jaipur Engineering College and Research Center Rajasthan Jaipur India Department of Orthopedic Mahatma Gandhi Medical College Rajasthan Jaipur India

ISBN: (纸本)9789819766741

Knee osteoarthritis (OA) is a chronic disease, also known as progressive joint disease which occurs due to continuous loss of cartilage. It generally affects the older person with age above 45 years. It is also known as "wear and tear" joint disease because it mostly affects the joints of humans like knee, hand, hips and spine. Osteoarthritis symptoms frequently grow over the time and result in worsening of articular cartilage. Pain, stiffness, tenderness, swelling, and bone spur are most common signs of osteoarthritis. Doctors normally use radiographic X-ray images for identification of OA. In medical field this is a highly time-consuming process;hence, to automate this process and rapidly diagnose, deep learning algorithms such as convolutional neural networks (CNN), are employed for the identification of knee osteoarthritis (OA) severity in X-ray scans, using the Kellgren-Lawrence (KL) grading scheme. The dataset utilized for this research was sourced from Kaggle, consisting of knee X-ray images. The dataset was partitioned into a training set including 5778 images, a testing set including 1661 images, and a validation dataset containing 826 images. The proposed study encompasses the quantification of grade-specific severity in knee osteoarthritis. The measured rates for each severity grade are G0 = 91%, G1 = 1%, G2 = 49.77%, G3 = 67.85%, and G4 = 88.46%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cartilage

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