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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms 2

Medical Image Fusion Solid Works Supporting Multiple Techniq...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Mutha, Rakhi Pathak, Devendra Nath Ahuja, Vandana Bhandari, Prabhakar Ramesh Rahi, Pankaj Mamodiya, Udit Lovely Professional University Department of Computer Application Punjab India Poornima College of Engineering Department of Computer Science and Engineering Rajasthan Jaipur India Swami Vivekanand Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India Sevadal Mahila Mahavidyalaya Department of Microbiology Umred Road Maharashtra Nagpur India Poornima Institute of Engineering and Technology Department of Artificial Intelligence & Data Science Rajasthan Jaipur India Piet Aicte Idea Lab Poornima Institute of Engineering and Technology Department of Electrical Engineering Rajasthan Jaipur India

ISBN: (纸本)9781665491990

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image. © 2023 IEEE.

关键词： Image resolution

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An Image Semantic Communication System Based on GAN and Relative Position-Encoding

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IEEE Communications Letters 2025年第6期29卷 1280-1284页

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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DETR-DC5 Approaches for Improved Spatial Object Detection in Satellite Imagery

DETR-DC5 Approaches for Improved Spatial Object Detection in...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Baskar, Radhika Dwibedi, Rajat Kumar Kumbhkar, Makhan Sah, Swati Patil, Harshal Firos, A. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Aarupadai Veedu Institute of Technology Vmrf Chennai India Indian Institute of Soybean Research Department of Computer Application Indore India School of Set Sharda University Greater Noida India Computer Science and Engineering Pune India Department of Computer Science and Engineering Rono-Hills Arunachal Pradesh Doimukh India

ISBN: (纸本)9798350394580

Satellite spatial object detection is crucial for urban planning, environmental monitoring, and catastrophe management. The DETR-DC5 (Data Efficient Transformer with DC5 Backbone) is a cutting-edge object detection model. This study introduces DETR-DC5 framework-based satellite spatial object detection improvements. DETR-DC5's strengths are used to improve feature extraction and post-processing. First, a specific feature augmentation technique extracts richer and more discriminative spatial features to let the model capture subtle object attributes. Second, a semi-supervised learning training technique is used to address the lack of labeled satellite imagery data and improve the model's generalization. A new satellite imagery post-processing refining method uses non-maximum suppression methods. This refinement stage reduces false positives and improves localization, especially in densely populated metropolitan regions and complicated natural surroundings. On benchmark satellite imagery datasets, the suggested methods outperform baseline DETR-DC5 models in spatial object detection. Experimental findings indicate significant improvements in precision, recall, and F1-score, proving the upgrades work. This study uses the DETR-DC5 architecture, targeted feature augmentation, semi-supervised training, and refined post-processing algorithms to recognize spatial objects in satellite data. These techniques have the potential to dramatically improve satellite-based object detection, which has implications for urban planning, environmental monitoring, and disaster response. © 2023 IEEE.

关键词： DETR-DC5 Feature Augmentation Satellite Imagery Semi-Supervised Learning Spatial Object Detection

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Enhancing Speech Emotion Recognition Combining Silence Elimination and Attention Model with a Novel CNN Architecture 8th

Enhancing Speech Emotion Recognition Combining Silence Elimi...

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

作者： Gawali, Piyush P. Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

A natural interface for human–computer interaction is automatic speech emotion recognition, but it encounters challenges in handling non-emotional speech segments, especially silence, since it is non-emotional speech segmentation that requires recognition. Using a carefully designed attention model and a silence elimination approach, this paper proposes a novel method of enhancing emotion recognition performance by combining silence elimination with attention. In the present study, an enhanced Convolutional Neural Network architecture has been proposed, comprising of nine layers, utilizing a combination of convolutional 1D As a result of the research, CNNs have been employed in a revolutionary speech-emotion detection system that reaches an impressive 96.00% accuracy rate. There is great potential in this breakthrough to develop social robots and conversational robots capable of conveying nuanced human emotions. This breakthrough has marked a significant improvement in accuracy compared to prior models based on the same dataset. In addition to the broader classifications of positive, negative, and neutral emotions, the model's accuracy is evaluated across a wide range of emotion classes, such as anger, calmness, fear, happiness, and sadness for both genders. There is considerable evidence to suggest that combined noise cancellation and attention models are more effective than individual models of noise cancellation or attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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An Effective Diagnosis of Alzheimer's Disease with the Use of Deep Learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

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Time Series Analysis and Forecasting of Water Quality Parameters along Yamuna River in Delhi

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Procedia computer science 2024年 235卷 3191-3206页

作者： Neetu Gupta Surendra Yadav Neha Chaudhary Research Scholar School of Engineering & Technology Career Point University Kota – 324005 India Department of Computer Science and Application Vivekananda Global University Jaipur – 303012 India Department of Computer Science and Engineering Manipal University Jaipur – 303007 India

The extent of pollution in areas near industrial activities and Yamuna River has increased tremendously and to determine the overall water quality status in these locations is utmost important. This paper presents a comprehensive study on assessing the water quality and gain insights into the overall water quality status in the vicinity of industries and along the Yamuna River in Delhi. To achieve this, water samples were collected from Central Pollution Control Board (CPCB) for the last eight years (2013-2021) and was converted into a machine-readable format to facilitate further analysis. These samples were analysed for several water quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), nutrient levels, heavy metals, and other relevant pollutants. By understanding these factors, a deeper understanding of the pollution sources and their impact on water quality is gained. Time series methods are applied to forecast future trends and values of the water quality parameters. This allows for predicting potential changes in water quality over time. A remarkable accuracy of 93.6% is attained in predicting water quality values up to the present time. The analysis reveals that water quality in the NCR region falls below acceptable standards. However, there has been a marginal enhancement in the water quality at Khajori Paltoon (Location L3) post-COVID. To achieve a more significant improvement in water quality, it is imperative to implement new policies.

关键词： Yamuna River Time series forecasting Biochemical oxygen demand chemical oxygen demand pH

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An Advanced Full Stack Decentralized Approach for Secure Election Voting Process 2

An Advanced Full Stack Decentralized Approach for Secure Ele...

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Singh, Neelam Rohila, Prashant Anwar, Kashif Alam, Tanweer Rawat, Vandana Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Islamic University of Madinah Department of Computer Science Saudi Arabia

ISBN: (纸本)9781665474160

Designing a safe electronic voting system offering secrecy with impartial approach while being transparent and flexible is a challenging task. Full Stack decentralized applications are smart contract decentralized application that runs on a Decentralized Blockhain Network. Problems constructing a vote casting software at the Central Web Server are: the votes at the database will be modified and will be counted extra or eliminated entirely, also the supply code at the net server can also be modified at any time. Rather than having a separate central server database we can deploy Blockchain. Blockchain is a network and database which handles all the transaction. Blocks on the network are added if the transaction is valid and the Blocks also ensure that the data distributed across the Blockchain network are valid. In this paper, we will elaborate the design and development of a Decentralized Approach for Election Voting System using Solidity language to make our smart contract. This Decentralized Approach will have the option for the voter to cast a vote in an election by their Ethereum account address which has public and private key address. This approach aims to provide a secure and trusted voting transaction. © 2022 IEEE.

关键词： Blockchain

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Estimating Fetal Gestational Age from Ultrasound Images Using Deep Learning Techniques

Estimating Fetal Gestational Age from Ultrasound Images Usin...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Suganthi, N. Mohana Subhashni, R. Chitra, A. Rajakumari, S. Priya, T. Anitha, P. Caroline Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St.Peter's Institute of Higher Education and Research Department of Computer Applications Avadi Chennai54 India EastPoint College of Higher Education Department of Computer Application Jnana Prabha East Point Campus Karnataka Bengaluru560049 India

ISBN: (纸本)9798331509828

Monitoring fetal development and guaranteeing mother health depend on correct estimate of fetal gestational age. Conventional approaches of gestational age estimate mostly depend on manual estimations from ultrasonic scans, which may be laborintensive and prone to human mistake. Variability in operators, picture quality, and fetal position might potentially affect current methods and cause possible errors and inconsistent outcomes. We offer the Ultrasound Images Using Deep Learning Techniques (UI-DLT) framework, a strong, automated method for fetal gestational age estimate, to handle these problems. UI-DL T analyzes ultrasonic pictures and enhances the accuracy and consistency of gestational age estimations by using deep learning-more especially, convolutional neural networks (CNNs). UI-DLT reduces human error and standards outcomes across several clinical environments by automating the analytic process. CNNs are used in the proposed system to categorize and examine ultrasonic pictures, therefore spotting important information on fetal development and growth. By extracting deep picture elements that match developmental phases, our method guarantees accurate and consistent gestational age estimations, thereby beyond the capacity of conventional manual techniques. Experimental data on ultrasonic image datasets shows significant increases in estimating accuracy of the UI-DLT framework. This approach not only improves the accuracy of gestational age prediction but also helps to progress clinical decision-making and mother-fetal health care. © 2025 IEEE.

关键词： Ultrasonic imaging

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TBNDG: Trust-Based Malicious Node Detection for Grey-Hole Attacks in VANET’s

TBNDG: Trust-Based Malicious Node Detection for Grey-Hole At...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Soumya Ranjan Mohapata Rekha Jain Srinivas Koushik K Sandeep Varma Kalidindi Umashankar Ghugar Govt College of Engineering Keonjhar Odisha Department of Computer Application Manipal University Jaipur Department of Computer Science & Engineering GITAM University Visakhapatnam Department of Computer Science & Engineering OP Jindal University Raigarh Odisha

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Vehicular advertising networks (VANETs) are vulnerable to various security threats due to their dynamic and decentralized nature. One of the most critical threats is the grey hole attack, where malicious nodes selectively drop packets, which poses a significant threat to communication reliability and security in VANETs. This paper proposes a robust approach to detect malicious nodes involved in grey-hole attacks. Our method uses the concept of trust to distinguish between trusted and untrusted nodes in the network. Each node maintains a trust score for neighbouring nodes based on their behaviour and influence. Trust is calculated through direct observation, such as packet forwarding and message integrity checks, and indirect feedback from other nodes in the network. To effectively detect grey-hole attackers, we use a multi-dimensional trust evaluation mechanism that considers several factors, such as packet delivery consistency, message integrity, and node behaviour history. By summing up these factors, we calculate a comprehensive confidence score for each point that allows us to identify potential malicious actors. In addition, we introduced updates to the trust and consensus mechanisms between neighbouring nodes to improve the strength of our approach against reconciliation and false accusations. Nodes periodically exchange trust information and check the consistency of their trust estimates, thus reducing the impact of false positives and negatives. We evaluate the performance of our proposed trust-based detection scheme through extensive simulations using real-world VANET scenarios. The results show that it detects grey-hole attackers with high accuracy, low computational complexity, and effective communication. In summary, our proposed model TBNDG calculates the performance in terms of PDR, End-To-End Delay, Routing Overhead, Packet Loss Ratio and Throughput than the existing model

关键词： Computational modeling Vehicular ad hoc networks Packet loss Throughput Routing Routing protocols Delays Security Reliability Vehicle dynamics

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