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检索条件"任意字段=4th International Multi-Conference on Computing in the Global Information Technology"

共 11813 条记录，以下是91-100 订阅

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Enabling technology for Smart and Intelligent Transportation: Analyse the Uses and Impact of Blockchain technology and Artificial Intelligent on Trucking Industry for Better Results 4th

Enabling Technology for Smart and Intelligent Transportation...

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4th international conference on Smart IoT Systems: Innovations and computing, SSIC 2023

作者： Parihar, Mahendra Akshaya Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS University Mumbai India Department of Information Technology Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS University Mumbai India

ISBN: (纸本)9789819751990

As a backbone of our economy, playing a very crucial role in the movement of our economy, the trucking industry still has not come out of the persistent problems it has been facing over the last few decades such as high cost of operation, low efficiency in operation, government issues, regulatory and legislative mechanism, information issues and many more. However, adopting these latest available technologies and their effective use can solve many of these problems significantly. One such is the adaptation of digitalization properly for Digital Governance which further results in Good Governance and another is the adoption of blockchain technology for data and information management to enhance operation efficiency of the trucking industry. Today in most cases especially for small truck operations, the operation is not viable due to poor technology adoption. Hence with this paper, an attempt is being made to initiate these improvements. the methodology adopted for the study is also relevant to the requirement of the work undertaken in the form of a structured approach. It consists of a relevant literature review, primary and secondary sources of information with available data, and a model framework about digitalization, digital governance, and possible application of blocking technology to bring improvement in operations of the trucking segment in our economy. the study found that the adoption of the latest technology in trucking operations in the form of digitization and blockchain reduces the problem of asymmetric information and brings good governance to enhance operation efficiency. So, the study concludes by stating that if the government supports the trucking industry, provides consultation, and coordinates with other stakeholders of the industry regarding technology adaptation and other aspects, the trucking industry can yield better results and solve many of its problems independently. © the Author(s), under exclusive license to Springer Nature Singapore Pte

关键词： Efficiency

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Influencer Ranking Framework Using th-DCNN for influence maximization 4

Influencer Ranking Framework Using TH-DCNN for influence max...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Shelke, Vishakha Jadhav, Ashish Department of Computer Engineering Ramrao Adik Institute of Technology D.Y. Patil Deemed to Be University Nerul Maharashtra Navi Mumbai400706 India Department of Information Technology Ramrao Adik Institute of Technology D.Y. Patil Deemed to Be University Nerul Maharashtra Navi Mumbai400706 India

As the influencer gains more significance in social media marketing, companies raise their budgets for influencer campaigns. With business increasing day by day, finding efficient influencers is becoming the most prominent factor for success, but choosing the right influencer from these social media users is quite a challenge. this manuscript proposes a novel method to rank influencers by their effectiveness based on their posting behavior and social relations over time. Initially, the data from Twitter is collected from the Indian politics tweets and reactions dataset. this raw data undergoes preprocessing using various techniques including, tokenization, stemming, lemmatization, stop word removal, and data normalization using the Min-Max normalization approach to ensure the data is relevant and suitable format for analysis. Next, construct a heterogeneous network to represent the complex interactions between entities like users, tweets, hashtags, and mentions. then Tree Hierarchical Deep Convolutional Neural Network (th-DCNN) is applied to these networks to derive information representation for each influencer at each period. Finally, a Cosine similarity (CS) is used to learn from the network and predict the influencer rankings. the performance metrics such as accuracy, f1-score, mean average precision (MAP), Normalized Discounted Cumulative Gain (NDCG), Receiver Operating characteristic (ROC), Mean Reciprocal Rank (MRR), and Hit Rate are analyzed in experimental evaluations. the proposed method improved the accuracy compared with existing techniques. © 2025 the Authors. Published by Elsevier B.V.

关键词： Tweets

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Towards Adaptive Asynchronous Federated Learning for Human Activity Recognition 24

Towards Adaptive Asynchronous Federated Learning for Human A...

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14th international conference on the Internet of things, IoT 2024

作者： Gajanin, Rastko Danilenka, Anastasiya Morichetta, Andrea Nastic, Stefan Distributed Systems Group TU Wien Vienna Austria Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9798400712852

In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized Machine Learning. Solving this issue is very important in IoT scenarios, where data coming from various sources, collected by heterogeneous devices, serve the learning of a distributed ML model through Federated Learning (FL). Specifically, we focus on the combination of FL applied to Human Activity Recognition (HAR), where the task is to detect which kind of movements or actions individuals perform. In this case, transitioning from centralized learning (CL) to federated learning is non-trivial as HAR displays heterogeneity in action and devices, leading to significant skews in label and feature distributions. We address this scenario by presenting concrete solutions and tools for transitioning from centralized to FL for non-IID scenarios, outlining the main design decisions that need to be taken. Leveraging an open-sourced HAR dataset, we experimentally evaluate the effects that data augmentation, scaling, optimizer, learning rate, and batch size choices have on the performance of resulting machine learning models. Some of our main findings include using SGD-m as an optimizer, global feature scaling across clients, and persistent feature skew in the presence of heterogeneous HAR data. Finally, we provide an open-source extension of the Flower framework that enables asynchronous FL. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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TCN-based end-to-end optimization for IM-DD optical communication system 4

TCN-based end-to-end optimization for IM-DD optical communic...

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4th international Computational Imaging conference, CITA 2024

作者： Zhang, Tianqian Liu, Wu Zhong, Zhiyi Li, Han Luo, Ming Liu, Shouyin National Key Laboratory of Optical Communication Technologies and Networks China Information and Communication Technologies Group Corporation Wuhan430200 China College of Physical Science and Technology Central China Normal University Wuhan430070 China

ISBN: (纸本)9781510688834

In recent years, machine learning has greatly impacted optical communication systems, with end-to-end learning becoming a key focus. Originating from wireless communications, this approach treats the transmitter, channel, and receiver as a single neural network, forming an auto-encoder for global optimization. In this study, we propose a Temporal Convolutional Network (TCN)-based end-to-end framework for intensity-modulation and direct-detection (IM-DD) optical communication systems, targeting the efficient transmission of 32 Gbaud PAM4 signals. the TCN captures the temporal dependencies of the optical channel, while a one-dimensional convolutional neural network (1D-CNN) optimizes signal encoding at the transmitter. At the receiver, a residual CNN (Res-CNN) mitigates noise and distortion. Our approach improves receiver sensitivity by 1.4 dB and 0.88 dB compared to the Volterra-DFE and feed-forward equalization (FFE) at the receiver coupled with 1D-CNN transmitter, demonstrating its potential for future robust optical communications. © 2025 SPIE.

关键词： Convolutional neural networks

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Kalman filtering based preprocessing for secure key generation 4

Kalman filtering based preprocessing for secure key generati...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sarsodia, Tapesh Bhatt, Uma Rathore Upadhyay, Raksha Bhat, Vijay Electronics & Instrumentation Institute of Engineering & Technology Devi Ahilya University Indore India Institute of Advance Computing SAGE University Indore India

the global market of IoT devices is increasing rapidly. Examples of IoT like networks include smart cities, industrial enterprises, agriculture, home automation, healthcare etc. IoT offers efficient resource utilization, enhanced data collection, minimum human efforts etc. although it is constrained by many challenges such as security, privacy, limited interoperability, complexity and integration challenges. Among all, security and privacy are paramount and require efficient techniques with low power and minimum computer complexity as IoT is a power-constrained network. Traditional encryption methods fail to meet these limitations, so physical layer key generation (PLKG) using Received Signal Strength Indicator (RSSI) preprocessing, is a promising approach for securing such wireless networks. In this paper, the use of Kalman filtering for RSSI preprocessing in secure key generation at the physical layer is presented and compared its performance with the existing Principal Component Analysis (PCA) based preprocessing technique. the performance of the proposed approach is evaluated on three fading channels namely Rician, Rayleigh, and Nakagami to highlight its effectiveness in different environments. the results show that the Kalman filtering is significantly better than PCA in terms of Bit Disagreement Rate (BDR), Spearmen rank Correlation Coefficient (SCC) and Entropy, thus providing stronger security guarantees and more reliable key generation. this makes Kalman filtering a potential solution for PLKG in IoT environments, focusing on computing performance and high security. © 2025 the Authors.

关键词： Kalman filters

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A Transformer-based Ground Penetrating Radar Target Detection Model 4

A Transformer-based Ground Penetrating Radar Target Detectio...

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4th international Computational Imaging conference, CITA 2024

作者： Guo, Zhishun Gao, Yesheng Huang, Zicheng Shi, Mengyang Ma, Jiahui The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University 800 Dongchuan RD. Minhang District Shanghai China

ISBN: (纸本)9781510688834

Ground-penetrating radar (GPR) technology has been widely applied in underground target detection and pipeline exploration. However, traditional feature extraction and machine learning-based GPR target detection methods often suffer from low detection accuracy and poor noise resistance. In recent years, deep learning-based object detection models have demonstrated outstanding performance in various fields, providing new solutions for improving GPR target detection. this paper proposes a Vision Transformer (ViT) based GPR target detection mode. the ViT abandons the traditional convolution operation and instead employs a self-attention mechanism to model the long-range dependencies in the images, thereby better capturing the global information of the targets. Specifically, we first partition the input GPR B-scan images into a series of patches and linearly map them to fixed-dimensional embedding vectors. then, these patch embeddings are input to a Transformer-based encoder-decoder network. the encoder utilizes the self-attention mechanism to extract multiscale features, and the decoder generates the target bounding boxes and class predictions based on the features from the encoder. Experiment results show that the Precision of proposed model achieves 93.04%, Recall achieves 83.77% and IoU achieves 78.83%. In summary, the ViT-based GPR target detection model proposed in this paper fully leverages the advantages of Transformer in feature extraction and global modeling, maintaining high detection accuracy while also achieving faster inference speed. this model can be applied not only to landmine detection but also extended to other underground target detection tasks, such as pipeline exploration and geological prospecting. © 2025 SPIE.

关键词： Landmine detection

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Evaluation of multiple Apache Spark Applications using Kubernetes as a Cluster manager on Google Cloud 4

Evaluation of Multiple Apache Spark Applications using Kuber...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Jayanthi, M. Rao, K. Ram Mohan Sukanya, Vuppala Dept of Computer Science and Informatics Mahatma Gandhi University Nalgonda India Dept of Information Technology Vasavi College of Engineering Hyderabad India Dept of CSE Hyderabad India

Big data processing frameworks demands for scalable and efficient cluster management. Apache Spark has emerged as prominent big data processing framework providing high-speed data processing and analytics capabilities for multiple applications. this paper explores the integration of Kubernetes as a cluster manager for Apache Spark applications leveraging its containerization capabilities to improve resource utilization and simplify deployment. In this paper the challenges of deploying spark applications on traditional cluster managers and showcase the advantages of adopting Kubernetes are analysed. the experimental evaluation demonstrates the benefits of Kubernetes as a cluster manager for Apache Spark framework. To execute the multiple Apache Spark applications on Kubernetes a homogenous cluster on Google Cloud is created by History bucket and service account. Finally multiple applications are executed on Google Kubernetes Engine. Output can be shown as the number of executor pods created with the performance metrics can be viewed. In conclusion, this paper compares the performance metrics such as job execution time and resource utilization with the different cluster. © 2025 the Authors. Published by Elsevier B.V.

关键词： information management

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Mitigating Risks in Vehicle Fires: An Integrated Approach for Early Detection and Real-Time Alert System 4th

Mitigating Risks in Vehicle Fires: An Integrated Approach fo...

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4th international conference on Smart IoT Systems: Innovations and computing, SSIC 2023

作者： Suryawanshi, Ranjeetsingh Bhagat, Harshita Karande, Aryan More, Parth More, Vaishali Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819751990

Every year, there are about 170,000–220,000 car accidents each year that involve fire. the cause of the fire ranges from collision, engine malfunction, electric short circuits, and many more. Irrespective of the cause, the damage incurred is close to $2 billion in direct property damage and in severe accidents, even the passengers’ lives. the rapid detection and timely notification of fire incidents are crucial for minimizing property damage and ensuring public safety. the current vehicle fire safety system relies on manual notification, leading to delayed responses and potential risks, especially in low-density areas and nighttime scenarios. Additionally, the lack of proactive fire detection technology hinders early identification, increasing the likelihood of extensive damage and harm to passengers. this paper proposes an integrated alert system automatically notifies fire stations, hospitals, and police upon fire detection, streamlining coordination and reducing response times. Rigorous evaluations are undertaken to assess the system's accuracy, sensitivity, and reliability under various conditions, solidifying its efficacy in addressing vehicle fire incidents. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fires

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Minimizing the Maximum Link Utilization for Traffic Engineering in SDN: A Comparative Analysis 4

Minimizing the Maximum Link Utilization for Traffic Engineer...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Prabu, U. Geetha, V. Department of Computer Science and Engineering Puducherry Technological University Puducherry605014 India Department of Information Technology Puducherry Technological University Puducherry605014 India

Software Defined Network modernizes network architecture by supporting scalability, significant flexibility, and programmability. Its functioning has been extended to several areas such as cloud computing, edge computing, enterprise networks, data centers, and the Internet of things (IoT). It also successfully enhances the functioning of traffic engineering. Traffic engineering involves optimization and dynamic control of traffic flows, minimizing congestion and maximum link utilization, centralized control, and network traffic management. It also enables the effective utilization of resources, boosts the user experience, and increases network performance. Minimizing maximum link utilization implies the distribution of network traffic across respective links or paths to use the offered resources of the network by preventing congestion. Its major goal is to guarantee that none of the individual links gets overloaded. Various approaches have been proposed in the past several years to address this problem. In this article, the state-of-the-art approaches are reviewed and analyzed. the problem formulation of each approach for minimizing the maximum link utilization is detailed with significant importance. the proposed algorithms, its type, and results are also depicted to have a better comparative analysis. Additionally, the type of graph considered while formulating the problem, topologies used to investigate the efficiency of the algorithm, performance metrics, and implementation platforms are listed to have a clear understanding of various approaches. © 2025 the Authors. Published by Elsevier B.V.

关键词： Deep reinforcement learning

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Parkinson’s Disease Detection from Resting State EEG Using multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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