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Advancing Vehicular Ad-Hoc Network Solutions in Emerging Economies: A Comparative Analysis of V2V Protocols Through Simulation Studies

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SN computer science 2024年第8期5卷 1-13页

作者： Raj, Dharm Ather, Danish Sagar, Anil Kumar Department of Computer Science & amp Engineering Sharda School of Engineering & amp Technology Sharda University Greater Noida India Amity University in Tashkent Tashkent Uzbekistan

This paper aims at moving ahead with Vehicular Ad-Hoc Network (VANET) in the emerging economies via protocol analysis of Vehicle-to-Vehicle (V2V) communication through simulation study. Given the distinct challenges in developing regions and the lack of the infrastructure and resources, the research aims to find out the feasible V2V communication protocols, applicable in VANET settings and profitable. Multiple network simulators that are widely accepted were employed in the network to perform an extensive evaluation of the primary V2V protocols, considering their performance, reliability, and scalability under various conditions that are common in emerging economies. This paper ends up with a different outcome of each protocol and some of them show a better resistance compared to the others in high-density traffic scenarios while others are more suitable to the areas with sparse, low-connectivity. The research allows for a complete evaluation of existing V2V protocols for VANET implementations in these regions with guidelines on critical points to focus on in the future deployment of VANET systems in emerging economies. This research is in line with the general efforts of improving Intelligent Transportation Systems for the developing world, which aims to realize coordinated and intelligent traffic management, safer roads and better overall traffic efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Communication protocols Intelligent Transportation Systems (ITS) Network simulation Vehicle-to-Vehicle (V2V) communication Vehicular Ad-Hoc Networks (VANETs)

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MRI-Based Biomarker in the Diagnosis of Alzheimer’s Disease Using Attention-UNet

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SN computer science 2025年第3期6卷 1-15页

作者： Arumugam, J. Prasanna Venkatesan, V. Beigh, Tabiya Department of Computer Science School of Engineering and Technology Pondicherry University Kalapet Puducherry Puducherry 605014 India Department of Banking Technology School of Management Pondicherry University Kalapet Puducherry Puducherry 605014 India

Dementia can occur in various forms;some types of dementia are curable and some are not. Among the non-curable forms, Alzheimer’s disease (AD) is the prominent one. There is no effective treatment to cure it but it may reduce its impact if diagnosed early. Accurate classification of AD is necessary for diagnosing and giving the right effective treatment to patients. Specific regions in the brain serve as hotspots for AD, potential imaging biomarkers that contribute effectively thereby improving classification accuracy. We introduce a new Attention-based ’U’ shaped Convolution Neural Network in this study to identify imaging biomarkers of AD using 3D T1-weighted MRI data, which excels at distinguishing between gray matter, white matter, and cerebrospinal fluid (CSF). Our model is improved UNet with an enhanced convolution block attention module named EnCBAMUNet. This makes it ideal for brain imaging, where the differentiation between these tissues is crucial for identifying pathology, such as in neurodegenerative diseases. Our method is tested on both ADNI and OASIS datasets. Classification accuracy of 99.8% for Healthy as control normal(CN) vs Alzheimer’s disease(AD) is obtained in binary classification and 95.5% for multi-classifier Alzheimer’s Disease, Mild Cognitive Impairment(MCI) is a transient state from healthy to disease state and Control Normal as Healthy(AD vs MCI vs CN) in multiclass classification. We visualize regions (hippocampus, ventricles, and some parts of cortex) of the brain responsible for AD using the Three Dimensional Gradient-weighted Class Activation Mapping (3D-Grad-CAM) method of our model due to the deep learning model(DL) that is black-box in nature. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzhimer disease Classification Deep learning MRI imaging biomarker Visualization

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Energy-Efficient Green Information Centric Networking for Future Wireless Communications

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 149-158页

作者： Girirajan, S. Vinoth Kumar, S. Karthick, M. Saju Raj, T. Ponnaian, Geetha Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Nandha College of Technology Tamil Nadu Erode India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content. © 2024 by the authors of this article.

关键词： energy efficiency (EE) future wireless communications green information-centric networking information-centric networking (ICN) wireless network

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A Hybrid Algorithm based on Autoencoder and Text Convolutional Network for Integrating scATAC-seq and scRNA-seq Data

A Hybrid Algorithm based on Autoencoder and Text Convolution...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, xiaoshu Liu, Jiahui Lan, Wei Teng, Fei Guilin University of Electronic Technology School of Computer Science and Information Security School of Software Engineering Guilin China Guangxi University School of Computer Electronics and Information Nanning China

ISBN: (纸本)9798350386226

Integrating single-cell RNA-seq (scRNA-seq) data and single-cell ATAC-seq (scATAC-seq) data provides a more comprehensive view of cellular heterogeneity. However, the high sparsity in scATAC-seq data presents significant challenges for cell type identification, while scRNA-seq offers richer gene expression information for more accurate annotation. We propose scEDI, a method that integrates scRNA-seq and scATAC- seq data using autoencoders and text convolutional networks. In scEDI, both data types are processed through an autoencoder to create a shared low-dimensional space, followed by a text convolutional network for label transfer. We evaluated scEDI against three state-of-the-art methods on datasets from the adult mouse cerebral cortex and human peripheral blood monocytes. Experimental results demonstrate that scEDI improves label transfer accuracy, particularly in small datasets. © 2024 IEEE.

关键词： Dimensionality reduction

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Toward Privacy-Aware Efficient Federated Graph Attention Network in Smart Cloud 9

Toward Privacy-Aware Efficient Federated Graph Attention Net...

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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Zhou, Jinhao Su, Zhou Wang, Yuntao Pan, Yanghe Pan, Qianqian Liu, Lizheng Wu, Jun Waseda University Graduate School of Information Production and Systems Fukuoka Japan Xi'an Jiaotong University School of Cyber Science and Engineering China The University of Tokyo School of Engineering Japan Fudan University Academy for Engineering and Technology China

ISBN: (纸本)9798350389500

Federated graph attention networks (FGATs), blending federated learning (FL) with graph attention networks (GAT), present a novel paradigm for collaborative, privacy-conscious graph model training in the smart cloud. FGATs leverage distributed attention mechanisms to enhance graph feature prioritization, improving representation learning while preserving data decentralization. Despite their advancements, FGATs face privacy concerns, such as attribute inference. Our study proposes an efficient privacy-preserving FGAT (PFGAT). We devise an improved multiplication triplet (IMT)-based attention mechanism with a hybrid differential privacy (DP) approach. We invent a novel triplet generation method and a hybrid neighbor aggregation algorithm, specifically designed to respect the distinct traits of neighbor nodes, efficiently secures GAT node embeddings. Evaluations on benchmarks such as Cora, Citeseer, and Pubmed demonstrate PFGAT's ability to safeguard privacy without compromising on efficiency or performance. © 2024 IEEE.

关键词： Blending

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Rule-based Classifiers for Suspect Detection from CCTV Footages

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Recent Advances in computer science and Communications 2022年第4期15卷 594-600页

作者： Pathak, Sunil Department Of Computer Science and Engineering Amity School of Engineering and Technology Amity University Rajasthan Jaipur India

Background: A significant work has been presented to identify suspects, gathering information and examining any videos from the CCTV Footage. This exploration work expects to recognize suspicious exercises, i.e., object trade, the passage of another individual, peeping into other's answer sheet and individual trade from the video caught by a reconnaissance camera amid examinations. This requires the procedure of face acknowledgment, hand acknowledgment and distinguishing the contact between the face and hands of a similar individual and that among various people. Methods: Segmented frames have been given as input to obtain a foreground image with the help of Gaussian filtering and background modeling method. Such foreground images have been given to Activity Recognition model to detect normal activity or suspicious activity. Result: Accuracy rate, Precision and Recall are calculated for activities detection, contact detection for Best Case, Average Case and Worst Case. Simulation results are compared with performance parameter such as Material Exchange, Position Exchange, and Introduction of a new person, Face and Hand Detection and Multi Person Scenario. Conclusion: In this paper, a framework is prepared for suspect detection. This framework will absolutely realize an unrest in the field of security observation in the training area. © 2022 Bentham science Publishers.

关键词： Face recognition

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Study on Roller Bearing Power Loss Calculation Considering the Raceway Fault 2nd

Study on Roller Bearing Power Loss Calculation Considering t...

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2nd International Conference of Mechanical System Dynamics, ICMSD 2023

作者： Shi, Zhifeng Liu, Jing Wei, Yongqiao School of Mechanical and Electrical Engineering Lanzhou University of Technology Lanzhou China School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Key Laboratory for Unmanned Underwater Vehicle Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9789819980475

The local fault of the roller bearing changes the contact characteristics between the roller and the raceway, affecting its friction coefficient and power transmission. The power loss of the roller bearing is a key index to assess the energy efficiency of a rotor-bearing-housing system, which affects the lubrication state and bearing operating temperature. To overcome this issue, a cylindrical roller bearing (CRB) tribodynamic model including the raceway faults is established to calculate power loss for the given fault cases. This paper compares the results of the proposed model and experiments with references to validate the model. The study investigates the impact of local fault size, specifically the length and depth, on power loss. The findings demonstrate that this study provides a valuable approach for predicting the power loss of the CRB, both with and without local faults in the raceway. Moreover, the findings may provide some direction for reducing power loss and enhancing the fatigue life of CRBs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Roller bearings

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Maximizing user retention with machine learning enabled 6G channel allocation

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International Journal of Information technology (Singapore) 2023年第4期15卷 2225-2231页

作者： Singh, Pushpa Agrawal, Rajeev Singh, Krishna Kant Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Engineering Lloyd Institute of Engineering and Technology Greater Noida India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

The paper proposes a machine learning-based user retention technique for the 6G network by identifying and classifying loyal users using supervised machine learning algorithms such as Decision Tree, K-Nearest Neighbor, and Support Vector Machine. The study also suggests a threshold-based channel allocation method to allocate network resources primarily to loyal users. The performance of the proposed algorithm is evaluated using SimPy simulation, and the results show that loyal users experience minimum average waiting time and no call drops compared to normal and recent users. The paper highlights the need for new schemes and policies to retain valuable users and proposes a novel approach that leverages machine learning techniques and 6G aspects to achieve this objective. The proposed algorithm's effectiveness is demonstrated through simulation, which provides useful insights into its performance under different network conditions. The research contributes to the ongoing efforts to enhance user experience in the rapidly evolving field of mobile communication networks. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： 6G Channel Loyal user Machine learning Retention Wireless network

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Research Review and Literature Perception Towards Medicinal Plants Classification Using Deep Learning Techniques 4th

Research Review and Literature Perception Towards Medicinal ...

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Diwedi, Himanshu Kumar Misra, Anuradha Tiwari, Amod Kumar Mahmood, Atif Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow India Computer Science and Engineering Rajkiya Engineering College Sonbhadra India Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819942831

The pharmaceutical industry is paying more attention to medicinal plants since they are less likely to cause undesirable responses and are less expensive than current medicines. These features have attracted researchers interest in automatic medicinal plant recognition. There are several ways to improve the powerful medicinal plant classifier that can be useful in real-time classification. This work uses several reliable and effective deep-learning techniques for classifying plants based on leaf images. The recent research work is collected systematically and categorized based on their techniques. Further, the dataset details, performance measures used for evaluation, tools utilized for implementation, and chronological review of the collected works are discussed. Finally, this research is concluded with an analysis of eminent ongoing opportunities and research perspectives for improvement in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Classification (of information)

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Electroencephalogram Emotion Recognition Based on Individual Frontal Asymmetry Hypothesis

Electroencephalogram Emotion Recognition Based on Individual...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Cao, Gang Yang, Liying Ni, Pei Xidian University School of Computer Science and Technology Xi'an China

ISBN: (纸本)9781665468190

The use of Electroencephalogram(EEG) for emotion recognition has tremendous potential across psychology and biomedicine. However, how the brain generates emotions remains unclear. Inspired by neuroscience and psychology, this paper puts forward the individual frontal asymmetry hypothesis and three methods of Electroencephalogram(EEG) emotion recognition based on this potential hypothesis are introduced, which recognizes and classifies the individual's emotion effectively with signals from only four channels out of the total 32 channels. First, all EEG signals are filtered according to the EEG frequency band. Then, taking the filtered left and right frontal lobe signal differences as the input, three different models are used for classification with leave-one-out cross-validation. For each subject, one film is used for testing and the remaining films are used for training. We verify our idea on the public database DEAP, and recognition accuracy reaches 75.39% in the valence dimension and 68.13% in the arousal dimension, respectively. Since only four EEG channels were used, it greatly improves the operation efficiency and saves the running time. This work might be a demonstration that emotion recognition using individual frontal asymmetry hypothesis is effective, and it provides a potential direction for emotion recognition using portable EEG acquisition devices. © 2022 IEEE.

关键词： Electroencephalography

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