Wireless Networks
Neda Sedighian; Abbas Karimi; Javad Mohammadzadeh; Faraneh Zarafshan
Abstract
Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. ...
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Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. However, traditional clustering methods such as K-Means and MFO, which mainly rely on simple metrics like distance or signal strength, fail to deliver optimal performance in dynamic environments with variable network density. The primary objective of this study is to design and evaluate an advanced clustering algorithm called AI_MCA (Artificial Intelligence Multi Clustering Algorithm), leveraging artificial intelligence and multi-criteria decision-making. By considering factors such as signal strength, relative speed, node density, and vehicle movement direction, the proposed algorithm forms clusters with higher stability and efficiency in dynamic and high-density environments.Methods: This study uses simulations to evaluate AI_MCA in VANETs, which facilitate vehicle-to-vehicle communication and are characterized by high mobility and rapid position changes.Results: Simulations in NS3 and SUMO show that AI_MCA reduces latency by 20% (12ms vs. 15ms in MFO) and improves cluster stability by 30% (lifetime of 45s vs. 33s in K-Means) within a 600m range. At a 1000m range with 300 nodes, delay increases to 14ms and PDR drops to 88%.Conclusion: AI_MCA outperforms traditional methods like K-Means and MFO, offering a scalable solution for VANET clustering.
Smart Grid
F. Ahmed Shaban; S. Golshannavaz
Abstract
Background and Objectives: In smart grid paradigm, there exist many versatile applications to be fostered such as smart home, smart buildings, smart hospitals, and so on. Smart hospitals, wherein patients are the possible consumers, are one of the recent interests within this paradigm. The Internet of ...
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Background and Objectives: In smart grid paradigm, there exist many versatile applications to be fostered such as smart home, smart buildings, smart hospitals, and so on. Smart hospitals, wherein patients are the possible consumers, are one of the recent interests within this paradigm. The Internet of Things (IoT) technology has provided a unique platform for healthcare system realization through which the patients’ health-based data is provided and analyzed to launch a continuous patient monitoring and; hence, greatly improving healthcare systems. Methods: Predictive machine learning techniques are fostered to classify health conditions of individuals. The patients’ data is provided from IoT devices and electrocardiogram (ECG) data. Then, efficient data pre-processings are conducted, including data cleaning, feature engineering, ECG signal processing, and class balancing. Artificial intelligence (AI) is deployed to provide a system to learn and automate processes. Five machine learning algorithms, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), logistic regression, Naive Bayes, and random forest, as the AI engines, are considered to classify health status based on biometric and ECG data. Then, the output would be the most proper signals propagated to doctors’ and nurses’ receivers in regard of the patients providing them by initial pre-judgments for final decisions.Results: Through the conducted analysis, it is shown that logistic regression outperforms the other AI machine learning algorithms with an F1 score, recall, precision, and accuracy of 0.91, followed by XGBoost with 0.88 across all metrics. SVM and Naive Bayes both achieved 0.85 accuracy, while random forest attained 0.86. Moreover, the Receiver Operating Characteristic Area Under Curve (ROC-AUC) scores confirm the robustness of Logistic Regression and XGBoost as apt candidates in learning the developed healthcare system.Conclusion: The conducted study concludes a promising potential of AI-based machine learning algorithms in devising predictive healthcare systems capable of initial diagnosis and preliminary decision makings to be relied upon by the clinician. What is more, the availability of biometric data and the features of the proposed system significantly contributed to primary care assessments.
Artificial Intelligence
M. Yousefzadeh; A. Golmakani; G. Sarbishaei
Abstract
Background and Objectives: To design an efficient tracker in a crowded environment based on artificial intelligence and image processing, there are several challenges such as the occlusion, fast motion, in-plane rotation, variations in target illumination and Other challenges of online tracking are the ...
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Background and Objectives: To design an efficient tracker in a crowded environment based on artificial intelligence and image processing, there are several challenges such as the occlusion, fast motion, in-plane rotation, variations in target illumination and Other challenges of online tracking are the time complexity of the algorithm, increasing memory space, and tracker dependence on the target model. In this paper, for the first time, sketch matrix theory in ridge regression for video sequences has been proposed.Methods: A new tracking object method based on the element-wise matrix with an online training method is proposed including the kernel correlation Filter (KCF), circular, and sketch matrix. The proposed algorithm is not only the free model but also increases the robustness of the tracker related to the scale variation, occlusion, fast motion, and reduces KCF drift.Results: The simulation results demonstrate that the proposed sketch kernel correlation filter (SHKCF) can increase the computational speed of the algorithm and reduces both the time complexity and the memory space. Finally, the proposed tracker is implemented and experimentally evaluated based on video sequences of OTB50, OTB100 and VOT2016 benchmarks.Conclusion: The experimental results show that the SHKCF method obtains not only OPE partial evaluation of Out of view, Occlusion and Motion Blur in object accuracy but also achieved the partial evaluation of Illumination Variation, Out of Plane Rotation, Scale Variation, Out of View, Occlusion, In of Plane Rotation, Background Clutter, Fast Motion and Deformation in object overlap which are the first rank compared to the state-the-art works. The result of accuracy, robustness and time complexity are obtained 0.929, 0.93 and 35.4, respectively.
Artificial Intelligence
S. Kianian; S. Farzi; H. Samak
Abstract
Background and Objectives: Simplicity and flexibility constitute the two basic features for graph models which has made them functional models for real life problems. The attributive graphs are too popular among researchers because of their efficiency and functionality. An attributive graph is a graph ...
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Background and Objectives: Simplicity and flexibility constitute the two basic features for graph models which has made them functional models for real life problems. The attributive graphs are too popular among researchers because of their efficiency and functionality. An attributive graph is a graph the nodes and edges of which can be attributive. Nodes and edges as structural dimension and their attributes as contextual dimension made graphs more flexible in modeling real problems.Methods: In this study, a new clustering algorithm is proposed based on K-Medoid which focuses on graph’s structure dimension, through heat diffusion algorithm and contextual dimension through weighted Jaccard coefficient in a simultaneous matter. The calculated clusters through proposed algorithm are of denser and nodes with more similar attributes.Results: DBLP and PBLOG real data sets are applied to evaluate and compare this algorithm with new and well-known cluster algorithms.Conclusion: Results indicate the outperformers of this algorithm in relation to its counterparts as to structure quality, cluster contextual and time complexity criteria.
Computer Vision
M. Fakhredanesh; S. Roostaie
Abstract
Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition ...
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Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition problem. Many of these frameworks suppose that each video sequence includes only one action class. Therefore, we need to break down a video sequence into sub-sequences, each containing only a single action class.Methods: In this paper, we develop an unsupervised action change detection method to detect the time of actions change, without classifying the actions. In this method, a silhouette-based framework will be used for action representation. This representation uses xt patterns. The xt pattern is a selected frame of xty volume. This volume is achieved by rotating the traditional space-time volume and displacing its axes. In xty volume, each frame consists of two axes (x) and time (t), and y value specifies the frame number.Results: To test the performance of the proposed method, we created 105 artificial videos using the Weizmann dataset, as well as time-continuous camera-captured video. The experiments have been conducted on this dataset. The precision of the proposed method was 98.13% and the recall was 100%.Conclusion: The proposed unsupervised approach can detect action changes with a high precision. Therefore, it can be useful in combination with an action recognition method for designing an integrated action recognition system.
