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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Mishra, Jalaj Khan, Mohd Jawed Negi, Himanshi Alam, Jitendra Alam, Junaid Singh, Pankaj Pratap Department of Computer Science & Engineering Greater Noida Institute of Technology Greater Noida India Department of Computer Science & Engineering Central Institute of Technology Assam Kokrajhar India

ISBN: (纸本)9781032911571

Detection of road networks using high-resolution aerial or remote sensing imagery constitutes a significant focus within modern research efforts. Currently, deep learning models demonstrate efficiency to a certain degree, they are constrained by a notable reliance on annotated datasets, a practice divergent from optimal methodologies due to the scarcity of extensive, high-quality labeled data. The precision of road prediction is additionally influenced by the latent information embedded within pixels exhibiting diminished confidence values. These pixels play a crucial role in discriminating between road and non-road regions within the images, yet their utilization by deep learning models is suboptimal. To handle these two different issues, opted U-Net as our base model with a sole generator paired with twin dis-criminators and presented this combined setup of U-Net on the Generative Adversarial Network (GAN). At the base of the U-Net architecture, a bottleneck module is incorporated to augment the functionalities of the proposed model. This proposed model attains 98% accuracy on DeepGlobe Road Dataset. Comparative and quantitative assessment demonstrates the enhanced efficiency of our model compared to similar methodologies. © 2025 the Author(s).

关键词： Generative adversarial networks

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Graphene Photodetector for Time-of-Flight Optical Ranging Using Optoelectronic Mixing

Graphene Photodetector for Time-of-Flight Optical Ranging Us...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kienitz, Paul Bablich, Andreas Bornemann, Rainer Müller, Maurice Bolívar, Peter Haring University of Siegen Department of Electrical Engineering and Computer Science Hölderlinstrasse 3 Siegen57076 Germany

A graphene optoelectronic mixer device utilizing indirect Time-of-Flight (ToF) for enhanced 3D imaging applications is presented. The prototype enables ranging up to 1 m with a mean accuracy of 25.6 mm. © Optica ... 详细信息

ISBN: (纸本)9781957171395

关键词： Three dimensional displays

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Detection of diabetic retinopathy from iris image using deep neural network methodologies: A survey

Detection of diabetic retinopathy from iris image using deep...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Kumar, Ajay Bhatt, Dhowmya Kumar, Vimal Department of Computer Science Engineering SRMIST Delhi-NCR Campus Ghaziabad India Department of Computer Science Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032669663

A key contributing factor to diabetic retinopathy, which damages the human retina and causes visual issues and eventual blindness, is diabetes mellitus. According to statistics, 80% of diabetics battle chronic diabetes for fifteen to twenty years before developing diabetic retinopathy. Although manual diagnosis is a viable option for treating this illness, it is also cumbersome and excessive, necessitating the development of a cutting-edge method instead. Therefore, such health conditions need early detection and diagnosis in order to avoid diabetic retinopathy from causing blindness. Deep learning is more effective for small datasets and produces better results in image classification and feature extraction with large datasets, which is why it is advocated together with machine learning models for this task. The reasons, several ML/DL models, an evaluation of these modeling techniques, and opportunities for future early detection of diabetic retinopathy are all outlined in this work. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Large datasets

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Generalizing Vehicle Manoeuvre Prediction Across Diverse Datasets 2

Generalizing Vehicle Manoeuvre Prediction Across Diverse Dat...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Venkatarao, Marella Reddy, Dodda Venkata Rao, S. Siva Nageswara Ul Hassan Khan, Ameen Avinash, Golla Koushik, Pendela Chenchu Narasaraopeta Engineering College Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331534950

Due to the complex nature of automotive components and sensor data, predictive maintenance is essential to ensure the reliability and safety of the vehicle. This work introduces a new predictive algorithm for automotive engine health, designed as it will provide higher accuracy and faster decisions in detecting potential engine failures, linear Through discriminant analysis, Gaussian naive edges, support vector machines, decision trees, random forests, gradient enhancement, and AdaBoost, the program displays patterns and abnormalities that may indicate impending engine *** data set undergoes extensive preprocessing steps such as standardization, handling missing values, and feature engineering to improve model performance. The evaluation criteria used include accuracy, precision, and confusion matrix, with special attention to prevent overfitting through regularization and the early stop method. In the developed model, the group method, especially stacked model 1, obtains impressive results with a model accuracy of 0.99. This high accuracy highlights the effectiveness of the ensemble approach in managing forecasts. The model's ability to deliver real-time analysis and early warning can help significantly reduce maintenance costs, prevent failures, and enhance vehicle safety, resulting in improved vehicle engine health during the maintenance process. © 2024 IEEE.

关键词： Support vector machines

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Securing Electronic Health Records from Insider Threats in Smart City Healthcare Cloud Using Machine Learning Approach 5

Securing Electronic Health Records from Insider Threats in S...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Ramasami, S. Uma Maheswari, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Erode India Anna University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

In recent years, healthcare in smart cities is considered as significant to create a more resilient and well-informed healthcare ecosystem. The integration of cloud computing in healthcare industry has facilitated the storage and sharing of electronic health records (EHRs), enabling seamless access to medical information. However, this transition to cloud-based systems has introduced new security challenges, particularly the risk of insider threats. Insiders with authorized access to cloud-based EHR systems, such as healthcare professionals and cloud service providers, may exploit vulnerabilities or misuse sensitive patient data, leading to privacy breaches and unauthorized disclosure. This research work presents an innovative approach to securing EHRs from insider threats in the cloud using Gaussian Mixture Model and Classification algorithms. These techniques analyze user behaviour patterns and detect anomalous activities that may indicate insider threats. Here UK hospital electronic patient health record data set is used to conduct the experiments. The proposed approach comprises two key stages. In the initial stage, the unsupervised Gaussian Mixture Model is utilized to find the abnormal patterns in the dataset and label each record in the dataset as normal or anomaly. The second stage involves different supervised classification algorithms namely SVM, KNN, DT, NB and RF are used to classify the new instance. The results demonstrate that the Random Forest (RF) achieves high accuracy in detecting insider threats with an accuracy of 99.97%. The findings of this research contribute to the field of healthcare data security by offering an intelligent and proactive solution for mitigating insider threats to cloud-based EHRs in Smart city environments. © 2024 IEEE.

关键词： Support vector machines

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Secure Accelerated Computing: High-Level Synthesis Based Hardware Accelerator Design for CNN Applications 10

Secure Accelerated Computing: High-Level Synthesis Based Har...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Information Technology Department of Computer Science and Engineering Bhopal India Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331533229

This paper introduces a novel methodology for designing secure hardware accelerator tailored for convolutional neural network (CNN) applications, leveraging security-aware high-level synthesis (HLS). The methodology outlined offers comprehensive security measures, including hindrance against reverse engineering (RE) and intellectual property (IP) piracy concerns through architectural transformations and embedded biometric palmprint constraints, respectively. Key contributions of this methodology include: i) robust hindrance against RE attack, ii) robust piracy detection with no additional design overhead, iii) definitive proof of IP ownership by achieving significantly lower probability of coincidence (P), indicating lower probability of false positive, and iv) enhanced tamper tolerance (TT) and resilience against brute force attacks compared to recent methodologies. © 2024 IEEE.

关键词： High level synthesis

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Deep Reinforcement Learning-Based AoI Minimization in UAV-Assisted Multi-Platoon Vehicular Networks 17

Deep Reinforcement Learning-Based AoI Minimization in UAV-As...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Du, Bochun Qu, Long Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350351637

In the current landscape of intelligent transportation, vehicle platooning has become a key strategy for improving traffic efficiency and safety. However, as multiple platoons move at high speeds, the platoon encounters an unpredictable communication environment, which poses a major challenge for coordinated control of multi-platoon vehicles. Meanwhile, the freshness of information plays a pivotal role in ensuring road traffic safety, quantified by the Age of Information (AoI). This study focuses on a UAV-assisted platooning system in Manhattan scenarios, aiming to minimize vehicle AoI while improving the communication success rate between vehicles and UAV nodes. To tackle this challenge, we model this problem as a Partially Observable Markov Decision Process (POMDP). We propose a trajectory and scheduling strategy based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach. Additionally, to better adapt to the dynamic nature of multi-platoon vehicular networks, we integrate the Prioritized Experience Replay (PER) technique to prioritize learning and processing of critical experience samples. Through simulation experiments and comparisons with baseline methods, the superiority of the proposed algorithm is verified. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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AI based Waterfoot Print of Crop 5

AI based Waterfoot Print of Crop

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Geerthana, G. Hamsavarthini, V. Hariprasath, A. Anuradha, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331504403

This research studies analyzes the essential of water footprint data in numerous geospatial applications like water footprint modeling with water footprint of crop unit. It conducts a through regression analysis on water footprint data utilizing Support Vector Machines (SVM), Polynomial, Linear Regression(LR), Random Forest(RF), Decision Tree (DT) and AdaBoost algorithms. The study aims to assess their effectiveness in predicting water footprint values across water footprint of crop unit. By utilizing a diverse water footprint dataset from various geographical locations, the study covers various preprocessing techniques not within dataset cleaning, scaling of feature, and partitioning of datasets. Every algorithm is utilized on the training dataset to generate regression models, and are assessed using testing set. The algorithm gives the root means square and mean square and R - square is utilized for validating the algorithms. © 2024 IEEE.

关键词： Support vector regression

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On Using BERT Embeddings for Text Semantic Communication 21

On Using BERT Embeddings for Text Semantic Communication

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Mahgoub, Asma Yaacoub, Elias College of Engineering Qatar University Department of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350378078

The current technological advancements carry stringent communication requirements. These requirements cannot be achieved by current networks. Therefore, a new paradigm called semantic communication is proposed. Semantic communication is the transmission of the meaning of data. Different semantic communication algorithms were proposed in literature, but they are complex and non-universal. In this work, a text semantic communication algorithm will be modified to be simple and universal. The modified algorithm uses a transformer network with 1 encoder layer and 3 decoder layers. The embedding layer uses pretrained embeddings from a Bidirectional Encoder Representations from Transformers (BERT) model. The algorithm is trained to optimize both the cross-entropy loss and the mutual information. Training and testing are done using the European parliament proceedings dataset. The performance of the modified algorithm is comparable to the original algorithm although it uses 50% of the layers. This work indicates the potential of using pretrained embeddings to simplify and enhance universality of existing semantic communication algorithms. © 2024 IEEE.

关键词： Semantics

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