Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance.

Rapid mobility and frequent disconnection in vehicular networks makes multi-hop data delivery challenging. To address this concern, adaptive data forwarding is applied over vehicular networks in a named data networking environment. We have proposed An Interest Chain based Forwarding Mechanism (ICFM), which includes a chain based forwarding mechanism by employing fuzzy logic to evaluate the next chain member. Autonomous vehicles forward a packet to the next promising vehicle and in this way a chain is formed to satisfy interest with the requested data. The proposed fuzzy-based interest chain mechanism is used to forward a packet to destination and receive a corresponding data packet with decreased data delivery delay. Experimental analysis has been performed on ndnSIM to verify the performance of the suggested scheme. The scheme runs on different scenarios and efficiency has been observed in terms of interest satisfaction ratio, average number of interest packets, average number of data packets and average delay.

Autonomous vehicles are a key element of the automotive industry, where the impact of the human factor on the condition of the vehicle and driving is minimized. An important element is the analysis of vehicular condition, which allows maintainence of its value and correct operation. We propose a system based on the analysis of the image of vehicles, which determines whether there is any damage. For this purpose, we propose a new model of a Convolutional Neural Network (CNN) that has 0. 395M trained values. The architecture of the network is adapted to the analysis of spatial features that allow networks to be adapted to analyze primarily vehicular shape and orientation in relation to other objects. The model also implements spatial dropout and regularization techniques for preventing overtraining and maintaining model generalization. The modeled architecture contributes to obtaining high classification accuracy at 94.78% using a public database and exceeding metrics of known transfer learning models.

The recent development of Autonomous Guided Vehicles (AGV) use in industry has resulted in the need to model new solutions based on the latest technological achievements. One of the areas worth attention and development is Quality of Service (QoS) in relation to communication between vehicles. QoS makes it possible to divide the bandwidth in such a way that tasks performed by devices are completed with a certain priority. However, in order to manage these resources effectively, it is necessary to anticipate available network throughput. Therefore, this paper presents a neural-based model to ensure throughput prediction for AGV. The proposed solution assumes the use of information on both historical throughput values and data obtained from other sensors that AGV are equipped with. Therefore, the idea is to integrate two neural networks with another network, which is supposed to predict the result based on these two previously obtained predictions. Ultimately, prediction results with a Root Mean Squared Error (RMSE) of 0.1 for the downlink and 1.6 for the uplink were obtained.

Decolonization and Indigenous education are at the forefront of Canadian content currently in Academia. Over the last few decades, we have seen some major changes in the way in which we share information. In particular, we have moved into an age of electronically-shared content, and there is an increasing expectation in Canada that this content is both culturally significant and relevant. In this paper, we explore the need for Cybersecurity education in rural Indigenous communities in Canada and the importance of community outreach initiatives. Throughout this project we Indigenized Cybersecurity course material, developed educational content and facilitated community engagement through workshops to assess the need for and knowledge of Cybersecurity. Our findings indicate that First Nation communities are very open to and have a strong desire to learn about Cybersecurity and online threats. The continued facilitation of workshops on First Nations reserves and development of educational material related to Cybersecurity is beneficial to Indigenous people and will aid in their pursuit of digital sovereignty as well as bridge the digital divide.

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation Systems (ITS). Transferring measured data demands $\geq$10Gbit/s transfer rate and $\geq$1GHz bandwidth though the data is lost due to unusual data transfer size and impaired line of sight (LOS) propagation. Most existing models concentrated on resource optimization instead of measured data optimization. Subsequently, RSU-LiDARs have become increasingly popular in addressing object detection, mapping and resource optimization issues of Edge-based Software-Defined Vehicular Orchestration (ESDVO). In this regard, we design a two-step data-driven optimization approach called Object-aware Multi-criteria Decision-Making (OMDM) approach. First, the surroundings-measured data by RSUs and OBUs is processed by cropping object-enabled frames using YoLo and FRCNN at RSU. The cropped data likely share over the environment based on the RSU Computation-Communication method. Second, selecting the potential vehicle/device is treated as an NP-hard problem that shares information over the network for effective path trajectory and stores the cosine data at the fog server for end-user accessibility. In addition, we use a nonlinear programming multi-tenancy heuristic method to improve resource utilization rates based on device preference predictions (Like detection accuracy and bounding box tracking) which elaborately concentrate in future work. The simulation results agree with the targeted effectiveness of our approach, i.e., mAP($\geq$71%) with processing delay ($\leq3.5\times 10^{6}$bits/slot), and transfer delay ($\leq$3Sms). Our simulation results indicate that our approach is highly effective.

A serious issue in today’s society is Depression, which can have a devastating impact on a person’s ability to cope in daily life. Numerous studies have examined the use of data generated directly from users using social media to diagnose and detect Depression as a mental illness. Therefore, this paper investigates the language used in individuals’ personal expressions to identify depressive symptoms via social media. Graph Attention Networks (GATs) are used in this study as a solution to the problems associated with text classification of depression. These GATs can be constructed using masked self-attention layers. Rather than requiring expensive matrix operations such as similarity or knowledge of network architecture, this study implicitly assigns weights to each node in a neighbourhood. This is possible because nodes and words can carry properties and sentiments of their neighbours. Another aspect of the study that contributed to the expansion of the emotion lexicon was the use of hypernyms. As a result, our method performs better when applied to data from the Reddit subreddit Depression. Our experiments show that the emotion lexicon constructed by using the Graph Attention Network ROC achieves 0.91 while remaining simple and interpretable.

Vehicular networking systems rely on Internet Protocol to exchange information among vehicles. With the increasing number of vehicles, the communication overhead has increased significantly a nd h as b ecome m ore c ontent centric. To resolve this problem, the Named data networking-based communication model has been used. This communication is completely based upon the content rather than the location and provides better network coverage comparatively. The vehicles used for communication purposes in a network are moving in some specific p atterns, b ased o n h aving t he s ame destination, with the same speed parameters etc. These vehicles which have common interests form a platoon. This vehicular platoon helps in various fields such as safe driving, energy efficiency and road safety. This paper provides a scheme for the applicability of NDN to the vehicular platoon. Special design features are proposed for communication purposes in V-NDN-based vehicular platoons. The backbone platoon network is used for data dissemination between the vehicles on the highway. To check the efficiency of the proposed scheme, extensive simulations have been performed on the ndnSim simulator. More precisely, different scenarios have been used and analyzed their efficiency i n t erms o f d elay and throughput.

Automated guided vehicles (AGV) allow for the automation of operations in warehouse environments. From an application point of view, vehicles can use sensors to move and perform a variety of tasks, including moving objects. In this paper, we focus on the analysis of the environment and the preparation of data for vehicle navigation. The proposed solution is based on two paths of action. In the first, the image of the room is processed by heuristics to locate the robot’s target - the palette. The found pattern allows one to locate the destination as well as create a mask. The mask can be used to train the U-Net network. When a network is trained, the use of heuristics for pallet location can be omitted. Locating the target allows the image to be processed to obtain an AGV navigation map. The proposed solution based on heuristics and U-Net networks has been described and tested in simulations to indicate the potential of the proposed approach.

The Internet of Things (IoT), with the ease of access of Automated Vehicles, is the most reliable technology in the 21stcentury making every possible thing within seconds. The people in this era are blessed with all the new technology, and new gadgets, due to which many things which used to take longer are done within nanoseconds. IoT combines computing devices, mechanical devices, digital machines, objects, and people which possess the ability to transfer data over the network with Unique identifiers (UIDs) without human intervention. Such IoT-enabled Automated Guided Vehicles (AGV) are more reliable on networks for every action. Deep learning and machine learning techniques are pivotal for the successful implementation of IoT-based applications including AGVs. A botnet refers to the attacks which come from robot Network attacks. Recently, many organizations have been compromised using this attack. Most affected devices are connected to IoT as it uses automatically generated data. The ideology of this study is to propose an intrusion prediction system which can predict botnet attacks in AGVs. In this study, N – Balo dataset is used for classification, clustering and prediction. The technique implemented in this paper may provide some roots to develop the most reliable and highly secured AGV network.

This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural network. This methodology is based on frequent pattern extraction methods. The hashtags of the tweets are then learned using the convolution neural network that was applied to the collection of batches of tweets. In addition, a pruning approach should ensure that the learning process proceeds properly by reducing the number of common patterns. Besides, the evolutionary algorithm is involved to extract the optimal parameters of the deep learning model used in the learning process. This is achieved by using a genetic algorithm that learns the hyper-parameters of the deep architecture. The effectiveness of our methodology has been demonstrated in a series of detailed experiments on a set of Twitter archives. From the results of the experiments, it is clear that the proposed method is superior to the baseline methods in terms of efficiency.

High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory of a single machine. In this article, we first develop a parallel and distributed three-stage MapReduce model for mining high-utility sequential patterns based on large-scale databases. Two properties are then developed to hold the correctness and completeness of the discovered patterns in the developed framework. In addition, two data structures called sidset and utility-linked list are utilized in the developed framework to accelerate the computation for mining the required patterns. From the results, we can observe that the designed model has good performance in large-scale datasets in terms of runtime, memory, efficiency of the number of distributed nodes, and scalability compared to the serial HUSP-Span approach.

In this paper, we provide a technique for identifying outliers based on embedding trajectory deviation points and deep clustering. We begin by constructing the network topology and the neighbors of the nodes to create a structural embedding while capturing the interactions of the nodes. We then develop a strategy to determine the hidden representation of distraction points in the road network topology. To create a collection of sequences from a hierarchical multilayer network, a biased random walk is used. This sequence is used to fine tune the embedding of the nodes. The trip embedding was then determined by averaging the node embedding values. Finally, the embeddings are clustered using an LSTM-based pairwise classification strategy based on similarity metrics. The experimental results show that compared to the generic techniques Node2Vec and Struct2Vec, the proposed embedding learning trajectory captures the structural identity and improves the F-measure by 5.06% and 2.4%, respectively.

Over the past decade, high-utility itemset mining (HUIM) has received widespread attention that can emphasize more critical information than was previously possible using frequent itemset mining (FIM). Unfortunately, HUIM is very similar to FIM since the methodology determines itemsets using a binary model based on a pre-defined minimum utility threshold. Additionally, most previous works only focused on single, small datasets in HUIM, which is not realistic to any real-world scenarios today containing big data environments. In this work, the fuzzy-set theory and a MapReduce framework are both utilized to design a novel high fuzzy utility pattern mining algorithm to resolve the above issues. Fuzzy-set theory is first involved and a new algorithm called efficient high fuzzy utility itemset mining (EFUPM) is designed to discover high fuzzy utility patterns from a single machine. Two upper-bounds are then estimated to allow early pruning of unpromising candidates in the search space. To handle the large-scale of big datasets, a Hadoop-based high fuzzy utility pattern mining (HFUPM) algorithm is then developed to discover high fuzzy utility patterns based on the Hadoop framework. Experimental results clearly show that the proposed algorithms perform strongly to mine the required high fuzzy utility patterns whether in a single machine or a large-scale environment compared to the current state-of-the-art approaches.

During our research conducted in this paper, we demonstrate a successful mining progress to mine the sequential high-utility patterns of uncertain databases. A PUL-Chain structure is developed and built in this paper with several pruning methods to decrease the search space of required patterns for mining efficiency improvement. In contrast to the standard HUS-Span, our experimental results show clearly that both in runtime as well as in the number of candidates discovered, the developed algorithms showed the effectiveness of the discovered patterns and its mining efficiency compared to the elder HUS-Span model. We present the details of our research here in this paper and also focus our attention to future directions that this research may take in the years to come.

In this paper, fuzzy-set theory is first used and a new algorithm called efficient fuzzy high-utility itemset mining (EFUPM) algorithm is designed to discover the fuzzy high-utility patterns from a single machine. Two upper-bounds are then estimated to early prune the unpromising candidates in the search space. To handle the large-scale of big datasets, the Hadoop-based fuzzy high-utility pattern mining (HFUPM) algorithm is then developed to discover the fuzzy high-utility patterns based on the Hadoop framework. Experimental results show that the proposed algorithms can perform well to mine the required fuzzy high-utility patterns whether in a single machine or a large-scale environment compared to the state-of-the-art approaches.

In this article, we present an Efficient High Utility Pattern Mining framework to mine high-utility patterns with a reasonable pruning strategy to speed up the mining performance. Concurrently, for solving the problem of excessive data volume in the current era, we applied the developed framework to the MapReduce architecture used for improving the feasibility in practical applications. Our in-depth work in this paper culminates with some experimental results that clearly show that our proposed framework can perform well to mine the required pattern in a big-data dataset and shows great performance in a Hadoop computing cluster.

Social Media platforms have become common venue for sharing experiences and knowledge about health-related topics. This research focuses on examining social media based communication patterns related to diabetes on the Twitter platform. Specifically, we apply an updated methodology to examine changes in the current use of hash-tags, trending hash-tags, and the frequency of diabetes-related tweets using a previous study as a baseline. Our results show significant growth in the diabetes community on Twitter over time and also evidence that this community is increasing in it's capacity to spread awareness around diabetes related health topics. Our methodological contributions include an improved framework for collecting, cleaning and analyzing Twitter data related to diabetes as well as the application of regular expressions to categorize subsets of Tweets.

Communications on the popular social networking platform, Twitter, can be mapped in terms of a hashtag graph, where vertices correspond to hashtags, and edges correspond to co-occurrences of hashtags within the same distinct tweet. Furthermore, a vertex in hashtag graphs can be weighted with the number of tweets a hashtag has occurred in, and edges can be weighted with the number of tweets both hashtags have co-occurred in. In this paper, we describe additions to some well-known link prediction methods that allow the weights of both vertices and edges in a weighted hashtag graph to be taken into account. We base our novel predictive additions on the assumption that more popular hashtags have a higher probability to appear with other hashtags in the future. We then apply these improved methods to 3 sets of Twitter data with the intent of predicting hashtags co-occurences in the future. Experimental results on real-life data sets consisting of over 3,000,000 combined unique Tweets and over 250, 000 unique hashtags show the effectiveness of the proposed models and algorithms on weighted hashtag graphs.

Increasing research on social networks stresses the urgency for producing effective means of ensuring user privacy. Represented ubiquitously as graphs, social networks have a myriad of recently developed techniques to prevent identity disclosure, but the equally important attribute disclosure attacks have been neglected. To address this gap, we introduce an approach to anonymize social networks that have labeled nodes, α-proximity, which requires that the label distribution in every neighbourhood of the graph be close to that throughout the entire network. We present an effective greedy algorithm to achieve α-proximity and experimentally validate the quality of the solutions it derives.

The growing need to address privacy concerns when social network data is released for mining purposes has recently led to considerable interest in various techniques for graph anonymization. In this paper, we study the following problem: Given a social network modeled as an edge-labeled graph G, we aim to make a pre-specifled subset of vertices of G k-label sequence anonymous with the minimum number of edge additions. Here, the label sequence of a vertex is the sequence of labels of edges incident to it. The contributions of this paper are two fold: We provide a framework to show hardness results for different variants of social network anonymization using a common approach. We start by showing that k-label sequence anonymity of arbitrary labeled graphs is hard, and use this result to prove NP-hardness results for many other recently proposed notions of graph anonymization. Secondly, we present interesting algorithms and hardness for bipartite graphs. For unlabeled bipartite graphs, we show k-degree anonymity is in P for all k ≥ 2. For labeled bipartite graphs, we show that k-label sequence anonymity is in P for k = 2 but it is NP-hard for k ≥ 3.