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A wireless sensor network is a wireless communication network, and each sensor node has several sensors to collect environmental information. Wireless sensor network nodes have limited energy resources and need optimal routing protocols to reduce energy consumption. Failure to reduce energy consumption by sensor nodes reduces network life and efficiency. The main problem in routing is finding optimal paths for sending packets by reducing energy consumption in sensor nodes. This paper proposes an optimal routing method to reduce energy consumption in wireless sensor networks. In the first step, wireless sensor nodes are clustered with the Water strider algorithms (WSA), and cluster heads are selected for routing. In the second step, a mobile sink collects the packets from the cluster heads and sends them to the base station. The mobile sink uses the Ant colony optimization (ACO) algorithms to travel a shorter path between the cluster heads. The authors contribute to presenting a discrete version of the WSA algorithm for cluster head selection to reduce energy consumption. The authors contribute by providing a more comprehensive objective function for clustering network nodes considering error rate, energy consumption, PDR rate, and Euclidean distance. Cluster head traversal with a version of the ACO algorithm to reduce energy consumption and cluster head traversal coding like the TSP problem is the contribution of other authors. The paper aims to reduce energy consumption, reduce the error rate of sending packets and increase the lifetime of the wireless sensor network. Experiments are simulated on several simulated scenarios in Matlab. Criteria such as energy consumption, Packet delivery ratio (PDR), package loss rates, and the number of alive nodes to evaluate the proposed method are used. Experiments show that the proposed algorithm reduces the energy consumption and loss rates of packages of the wireless sensor network by optimally selecting cluster heads and increasing the PDR and number of alive nodes. Comparisons show In terms of energy consumption, Packet delivery ratio (PDR), Loss rates of packages, and the number of alive nodes, the proposed method is more efficient than Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), Information-centric wireless sensor networks, and Cluster based routing (CBR) routing methods. The PDR index in the proposed method is equal to 97.3% and is higher than PSO, GWO, and CS algorithms. The delay of the proposed method in routing is 25.97%, 5.78%, and 17.98% less than HHO, WOA, and GWO algorithms, respectively. [ABSTRACT FROM AUTHOR]

An efficient variant of the recent sea horse optimizer (SHO) called SHO-OBL is presented, which incorporates the opposition-based learning (OBL) approach into the predation behavior of SHO and uses the greedy selection (GS) technique at the end of each optimization cycle. This enhancement was created to avoid being trapped by local optima and to improve the quality and variety of solutions obtained. However, the SHO can occasionally be vulnerable to stagnation in local optima, which is a problem of concern given the low diversity of sea horses. In this paper, an SHO-OBL is suggested for the tackling of genuine and global optimization systems. To investigate the validity of the suggested SHO-OBL, it is compared with nine robust optimizers, including differential evolution (DE), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), fitness dependent optimizer (FDO), Harris hawks optimization (HHO), chimp optimization algorithm (ChOA), Fox optimizer (FOX), and the basic SHO in ten unconstrained test routines belonging to the IEEE congress on evolutionary computation 2020 (CEC'20). Furthermore, three different design engineering issues, including the welded beam, the tension/compression spring, and the pressure vessel, are solved using the proposed SHO-OBL to test its applicability. In addition, one of the most successful approaches to data transmission in a wireless sensor network that uses little energy is clustering. In this paper, SHO-OBL is suggested to assist in the process of choosing the optimal power-aware cluster heads based on a predefined objective function that takes into account the residual power of the node, as well as the sum of the powers of surrounding nodes. Similarly, the performance of SHO-OBL is compared to that of its competitors. Thorough simulations demonstrate that the suggested SHO-OBL algorithm outperforms in terms of residual power, network lifespan, and extended stability duration. [ABSTRACT FROM AUTHOR]

A network of wireless sensors (WSN) is an outstanding technology that can aid in the various applications. Batteries run the sensor nodes those are used in WSN. The battery is impossible to charge or repair, so the most valuable resource for wireless sensor networks is power. Over the years, several strategies have been invented and used to preserve this precious WSN resource. One of the most successful approach for this purpose has turned out to be clustering. The aim of this paper is to suggest an effective technique for choosing cluster heads in WSNs to increase the lifetime of the network. To accomplish this task, Grey Wolf Optimizer (GWO) technique has been used. The general GWO was updated in this paper to meet the particular purpose of cluster head selection in WSNs. In this article, we have considered eleven attributes in the fitness function for the proposed algorithm. The simulation is carried out under different conditions. The results obtained show that the proposed protocol is superior in terms of energy consumption and network lifetime by evaluating the proposed protocol (i.e. CH-GWO protocol) with some well-existing cluster protocols. The suggested protocol forms energy-efficient and scalable clusters. [ABSTRACT FROM AUTHOR]

Clustering is a very important method to analyze HIN. Thus, several HIN clustering algorithms have been proposed and all these algorithms are based on meta-paths. Meta-path can be used to describe the relationship of target objects. Even though the relationship of target objects are fully considered by these meta-path based algorithms, the directly connected neighbors of target objects are neglected by them. These neglected directly connected neighbors are not target objects, but they contain plenty of useful information for finding clusters of target objects. So, while performing clustering based on HIN, these neglected neighbors should be considered. To achieve the goal, in this paper, a new HIN clustering algorithm is proposed. The proposed algorithm tries to build a vector for each target object. The clustering task is fulfilled based on these vectors. During the vector building process, the neighbors of all the target objects are considered. As clustering result of HIN is affected by different factors, such as neighbors of target objects and different kinds of meta-paths, several similarity matrices are built in the proposed algorithm. Each matrix is corresponding to a specific factor. Besides, every matrix will be assigned a weight value. These weight values are used to represent the relative importance of factors. At the same time, in the proposed algorithm, a new training method is adopted to calculate the vectors and the weight values. [ABSTRACT FROM AUTHOR]

Clustering validity indices are designed for evaluating the performance of clustering algorithms in terms of quality of clusters. They are also used for detecting the correct number of clusters. Numerous clustering validity indices have been proposed in the literature for different types of data. However, to the best of our knowledge, there are a few well-known clustering validity indices for graph data. In this paper, we propose a new clustering validity index for graph data called the node-based clustering validity index. The main characteristic of our proposed index is that it captures the exclusive contribution of each node to the separation and compactness of the clusters in a graph. This characteristic gives an advantage to the method which is the detection of the correct number of clusters for cases that two sets of nodes are bonded to each other with a single node. We provide an illustrative example to show that many existing validity indices fail to detect the correct number of clusters in such cases. We evaluate the performance of our proposed index with several experiments on different real-world and synthetic graphs. Experiments show that our proposed node-based index outperforms the existing validity indices for different graph data. [ABSTRACT FROM AUTHOR]

This paper presents a novel clustering approach that utilizes variational autoencoders (VAEs) with disentangled representations, enhancing the efficiency and effectiveness of clustering. Traditional VAE-based clustering models often conflate generative and clustering information, leading to suboptimal clustering performance. To overcome this, our model distinctly separates latent representations into two modules: one for clustering and another for generation. This separation significantly improves clustering performance. Additionally, we employ augmented data to maximize mutual information between cluster assignment variables and the optimized latent variables. This strategy not only enhances clustering effectiveness but also allows the construction of latent variables that synergistically combine clustering information from original data with generative information from augmented data. Through extensive experiments, our model demonstrates superior clustering performance without the need for pre-training, outperforming existing deep generative clustering models. Moreover, it achieves state-of-the-art clustering accuracy on certain datasets, surpassing models that require pre-training. [ABSTRACT FROM AUTHOR]

With the increasing deployment of FANETs, efficient routing algorithms play a crucial role in ensuring reliable and optimal communication among UAVs. This paper presents a novel Clustered Learning Automata-based Routing Algorithm (called CLARA) for FANETs. The proposed algorithm is designed to enhance the performance of routing by leveraging the capabilities of learning automata. The CLARA consists of five key phases. In the first phase, actions, states, and a Q-Table are initialized to facilitate the learning process. Subsequently, in the second phase, drones within the FANET are clustered based on their geographic location and connection characteristics. This clustering enables efficient management of UAVs and improves network performance. In the third phase, the algorithm selects the next hop for data packet transmission, considering factors such as connectivity and reliability. This selection process ensures robust and efficient data delivery within the network. The fourth phase focuses on Q-Table updating and energy management, which optimizes resource allocation and prolongs network lifetime. Finally, in the fifth phase, the cluster head is selected based on its remaining energy, ensuring effective leadership within each cluster. This dynamic selection process enables the efficient distribution of responsibilities and enhances network stability. Simulation results demonstrate the superiority of the CLARA approach compared to existing methods, including OLSR, AODV, and Q-FANET. CLARA algorithm shows its superiority in the criteria of control overhead, routing overhead, computational overhead of energy consumption, network lifetime, PDR, and end-to-end delay. [ABSTRACT FROM AUTHOR]

The k-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global k-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but its well-known that requires high computational cost. It partitions the data to K clusters by solving all k-means sub-problems incrementally for all k = 1 , ... , K . For each k cluster problem, the method executes the k-means algorithm N times, where N is the number of datapoints. In this paper, we propose the globalk-means++ clustering algorithm, which is an effective way of acquiring quality clustering solutions akin to those of global k-means with a reduced computational load. This is achieved by exploiting the center selection probability that is effectively used in the k-means++ algorithm. The proposed method has been tested and compared in various benchmark datasets yielding very satisfactory results in terms of clustering quality and execution speed. [ABSTRACT FROM AUTHOR]

The residualization of public rented housing is a prevalent phenomenon throughout Europe, and strongly present in the small and strongly means-tested social housing sector in Norway. In this article, we discuss the contested geographical dimension of residualization. Scientific studies of the geographical and locational aspects of social housing are scare in Norway and modest internationally. Based on qualitative interviews with representatives of social housing administrators in the fifteen largest urban municipalities in Norway, this paper contributes to the literature by exploring how these social housing bureaucrats perceive, reflect on, and respond to, questions related to the spatial localization of residual social housing. Does it matter where social housing is located? What are the consequences of the geography of social housing for tenants, their neighbours, and the wider socio-spatial development of cities? These are questions pondered in the interviews. In our qualitative analysis, we identify three broad themes. First, the theme of the internal social milieu – inclusive communities versus neighbour complaints and conflicts in the public housing projects. Second, the theme of neighbourhood effects; how concentrated poverty is influencing the local community in general and the upbringing of children in particular. Third, the theme of response from external neighbours and communities, in the form of either predominantly exclusive strategies (NIMBYism – Not in My Backyard), but also less prevalent inclusive strategies like (PHIMBYism – Public Housing In My Backyard). [ABSTRACT FROM AUTHOR]

As an extension of Fuzzy C-Means (FCM), Evidence C-Means (ECM) is proposed in the framework of Dempster–Shafer theory (DST) and has been applied to many fields. However, the objective function of ECM involves only the distortion between the object and the prototype, which relies heavily on the initial prototype. Therefore, ECM may encounter the problem of local optimization. To solve this problem, this paper introduces ECM with Particle Swarm Optimization (PSO) initialization to determine the initial clustering centroids, and proposes Particle Swarm Optimization-based Evidential C-Means (PSO-ECM), which reduces the influence of bad initial prototypes and improves the local optimality problem of ECM. PSO-ECM is compared with three other clustering algorithms in four experiments and with ECM on a noise-containing dataset. According to the experimental results, PSO-ECM performs well in terms of different clustering validity metrics compared with existing clustering algorithms, has high stability of clustering, and can effectively and stably cluster noise-containing datasets and accurately identify outlier points. [ABSTRACT FROM AUTHOR]

Money laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods focus on individual-level abnormal behavior while neglecting the community factor that money laundering is a collaborative group crime. Furthermore, the massive volume of transactions and the issue of label shifting also impede the application of supervised or semi-supervised models. To this end, this paper proposes an efficient community-oriented algorithm, namely SEGE, to identify money laundering based on structural entropy minimization (SEM) with graph embedding in an unsupervised approach. Experiments on both a private real-world money laundering network and a public synthetic dataset show that our SEGE algorithm derives prominent performance and outperforms the parameterized learning-based graph representation methods. Moreover, we find that there are pervasive sub-communities in the real-world money laundering network. Based on our local algorithm, we propose a real combat strategy against the money laundering group, in which when we have several scattered suspicious accounts in the transaction network, we are able to retrieve the whole money laundering group by the union of sub-communities with both high precision and high recall rates. [ABSTRACT FROM AUTHOR]

Although artificial intelligence has become part of everyone's real life, a trust crisis against such systems is occurring, thus increasing the need to explain black-box predictions, especially in the military, medical, and financial domains. Modern eXplainable Artificial Intelligence (XAI) techniques focus on benchmark datasets, but the cognitive applicability of such solutions under big data settings is still unclear due to memory or computation constraints. In this paper, we extend a model-agnostic XAI methodology, named Cluster-Aided Space Transformation for Local Explanation (CASTLE), to be able to deal with high-volume datasets. CASTLE aims to explain the black-box behavior of predictive models by combining both local (i.e., based on the input sample) and global (i.e., based on the whole scope for action of the model) information. In particular, the local explanation provides a rule-based explanation for the prediction of a target instance as well as the directions to update the likelihood of the predicted class. Our extension leverages modern big data technologies (e.g., Apache Spark) to handle the high volume, variety, and velocity of huge datasets. We have evaluated the framework on five datasets, in terms of temporal efficiency, explanation quality, and model significance. Our results indicate that the proposed approach retains the high-quality explanations associated with CASTLE while efficiently handling large datasets. Importantly, it exhibits a sub-linear, rather than exponential, dependence on dataset size, making it a scalable solution for massive datasets or in any big data scenario. [ABSTRACT FROM AUTHOR]

Face clustering has applications in organizing personal photo album, video understanding and automatic labeling of data for semi-supervised learning. Many existing methods cannot cluster millions of faces. They are either too slow, inaccurate, or need a lot memory. In our paper, we proposed a two stage unsupervised clustering algorithm which can cluster millions of faces in minutes. A rough clustering using greedy Transitive Closure (TC) algorithm to separate the easy to locate clusters, then a more precise non-greedy clustering algorithm is used to split the clusters into smaller clusters. We also developed a set of omni-supervised transformations that can produce multiple embeddings using a single trained model as if there are multiple models trained. These embeddings are combined using simple averaging and normalization. We carried out extensive experiments with multiple datasets of different sizes comparing with existing state of the art clustering algorithms to show that our clustering algorithm is robust to differences between datasets, efficient and outperforms existing methods. We also carried out further analysis on number of singleton clusters and variations of our model using different non-greedy clustering algorithms. We did trained our semi-supervised model using the cluster labels and shown that our clustering algorithm is effective for semi-supervised learning. [ABSTRACT FROM AUTHOR]

Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET. [ABSTRACT FROM AUTHOR]

The purpose of clustering is to partition data similar with each other into a same group and partition data dissimilar with each other into different groups. However, in most existing fuzzy clustering approaches, the membership degrees of an individual belonging to different clusters are relied on the different distances between the individual and different cluster centroids, the similarity between the individual and data in different clusters are ignored, besides, the outliers cannot be distinguished effectively. For improving the clustering performance and addressing the problems in most existing fuzzy clustering approaches, based on the concept that data close to each other should be grouped together and be assigned to a same cluster, in this paper, we present a novel efficient fuzzy clustering approach, in which the adaptive local neighborhood information of each data referring to different clusters is taken into consideration; an entirely new idea that the membership degree values of an individual referring to different clusters should not only depend on the distances between the individual and different cluster centers, but also rely on the distances between the individual and several nearest neighbor data in different clusters is put forward; a new scheme to search for the outliers is presented, a method for identifying the different importance of different features is introduced. Experiments on synthetic and publicly real-world datasets were conducted, the clustering results show that the approach put forward can yield consistently satisfying clustering performance and has significant advantages. [ABSTRACT FROM AUTHOR]

Given the rise in demand for cloud computing in the modern era, the effectiveness of resource utilization is eminent to decrease energy footprint and achieve economic services. With the emerging machine learning and artificial intelligence techniques to model and predict, it is essential to explore a principal method that provides the best solution for the accurate provisioning of forthcoming requests in a cloud data center. Recent studies used machine learning and other advanced analytics to predict resource usage; however, these do not consider long-range dependencies in the time series, which is essential to capture for better prediction. Further, they show limitations in handling noise, missing values, and outliers in datasets. In this paper, we explored the problem by studying three techniques that enabled us to answer improvements in short-term forecasting of physical machines' resource usage if the above factors are considered. We evaluated the predictions using Transformer and Informer deep learning models that cover the above aspects and compared them with the Long short-term memory (LSTM) model. We used a real-world Google cluster trace usage dataset and employed Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to select heterogeneous machines. The evaluation of the three models depicts that the Transformer architecture that considers long-range dependencies in time series and shortcomings with datasets shows improvement in forecasting with 14.2% reduction in RMSE than LSTM. However, LSTM shows better results for some machines than the Transformer, which depicts the importance of input sequence order. The Informer model, which considers both dependencies and is a hybrid of LSTM and Transformer, outperformed both models with 21.7% from LSTM and 20.8% from Transformer reduction in RMSE. The results also depict Informer model consistently performs better than the other models across all subsets of the dataset. Our study proves that considering long-range dependencies and sequence ordering for resource usage time series improves the prediction. [ABSTRACT FROM AUTHOR]

Spatio-temporal (S-T) analysis is not typical in structural monitoring applications of buildings and infrastructure. However, monitoring always includes the temporal component, and observations are often captured in specific locations. In other words, a monitoring dataset could also be considered a spatio-temporal archive, notwithstanding that not all monitoring applications can benefit from S-T processing methods. The paper discusses spatio-temporal analysis using the structural monitoring dataset of the Cathedral of Milan, which has an archive of vertical settlements collected from more than 50 years of measurements. The proposed methods can be adapted and extended for other structural monitoring applications, including single buildings, infrastructure, and the environmental level. The cases of pure temporal (T) and spatial (S) analyses are also discussed, comparing the different approaches, illustrating the pros and cons, and describing the opportunities of the S-T combined workflow. The paper specifically focuses on different typologies of S-T processing: data visualization and exploration techniques, clustering, change detection, prediction, and forecasting. The proposed algorithms were all implemented within the R open-source programming language. They can be replicated (and adapted) for other structural monitoring datasets featuring spatio-temporal correlation. [ABSTRACT FROM AUTHOR]

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are composed of small sensor nodes that either transmit their sensed data to the sink node directly or transmit it to its respective cluster head, which then transmits it to the sink node. However, this consumes a lot of network bandwidth and energy from the constrained sensor nodes. To address these constraints, Mobile Agents (MA) paradigm can be used in WSNs, which may lead to better energy and bandwidth conservation. When a single mobile agent is insufficient to complete a task, multiple mobile agents can be deployed to perform in parallel and reduce network latency. The set of sensor nodes and their sequence that MAs must migrate to complete a task is called an itinerary. The planning of the itinerary is the most prominent and significant issue related to the MA-based system, including the determination of an appropriate number of MAs to be dispatched, determining the set of sensor nodes and their sequence to be visited by MAs. This paper proposes a fuzzy-based algorithm to partition Wireless Sensor Networks into a set of sensor nodes, called domains, for enhancing the efficiency of the WSN in terms of its prolonged operation. Experimental evaluations are conducted to compare the proposed algorithm with benchmarked algorithms. The paper suggests that the proposed algorithm's integration with MA-based systems can enhance their performance and prolong the WSN's lifetime. [ABSTRACT FROM AUTHOR]

With the advancement of technology such as video monitoring, Internet-of-things, cloud, and machine learning, Industry 4.0 is working continuously to ensure the security of workers. The workers are equipped with sensors to analyze their activities. In general, the recognition of human activities in cloud-based industrial scenario is leveraged to monitor the safety of the workers. This paper introduced a new optimal clustering method for the activity recognition of workers in industry using cloud based IoT environment. The proposed method uses the temporal and spatial features of human workers in industry. The proposed method is tested on publicly available dataset of different activities maintained into three groups, namely movement, gestures, and object handling, in the context of the medium and small industrial environment. The experimental findings validate that the proposed method achieves 80.2 % , 81.05 % and 80.19 % of average accuracy for movement, gesture, and object handling activities, which clearly outperformed the fuzzy c-means, particle-swarm optimization, and HMM-based activity recognition methods. [ABSTRACT FROM AUTHOR]

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn. [ABSTRACT FROM AUTHOR]

The ever-increasing number of cloud services has led to the service's identification problem. It has become difficult to provide users with cloud services that meet their functional and non-functional requirements, especially as many cloud services offer the same or similar functionality but with different execution constraints (cloud characteristics, QoS, price, and so on). Service recommendation systems can solve the service's identification problem by helping users to retrieve the right cloud services according to their desired needs. However, the majority of service recommendation systems rely on user feedback to locate the user's neighbors, predict missing ratings, and rank the recommended services. As a result, users' rating histories might cause three major problems: cold start, data sparsity, and malicious attack. In order to deal with these issues, we propose in this paper a hybrid recommendation approach, called "HRPCS", that provides a list of personalized cloud services to the active user. This approach is based on user and service clustering. In this approach, cloud services are recommended based on the user's needs (functional and non-functional) and QoS preferences. Then, the services are ranked according to their prices and credibility. Further, the proposed approach returns a list of diversified cloud services. The experimental results confirmed our expectations and proved the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper presents a new cluster-based quality of service routing protocol (C-QoSRP) designed for VANET in a highway setting, offering the benefit of wide-range communication facilitated by road-side units (RSUs). The protocol incorporates the K-nearest neighbor (KNN) algorithm to optimize cluster stability by combining mobility and link quality metrics. A noteworthy aspect of this approach is that in the event of a cluster head (CH) failure, C-QoSRP employs RSUs as temporary cluster heads (TCHs). The TCH assumes the CH's responsibilities to ensure reliable communication within the cluster. Unlike traditional protocols that primarily focus on CH stability, C-QoSRP aims to minimize transmission delays and improve overall network stability. Simulation results demonstrate that C-QoSRP protocol surpasses existing ones in terms of QoS parameters such as packet delivery ratio, end-to-end delay, throughput, overhead and Cluster stability. [ABSTRACT FROM AUTHOR]

COVID-19 has triggered fundamental shifts in how people live, work and consume. The crisis stimulated varied responses that reflect consumer adaptations to COVID. These varied adaptations reveal consumer types based on their reactions. Are these reactions reflecting certain personality traces? With this premise, the paper investigates the potential influence of Big-Five Personalities on the consumer types that emerged out of COVID-19-induced circumstances across the globe. The sample size for this study was one hundred and thirty-four respondents. These personality traits and consumer types were identified and then compared. The consumer preferences and their purchase baskets were then analysed. This study identified four clusters of consumers referred to as consumer types, which were: worrier, individualist, activist and rationalist. No strong associations emerged between personality traits and consumer types, except agreeableness emerging as a common trait across consumer types. There was a similarity in consumer preferences and purchase baskets for all clusters and personality types. The behavioural homogeneity of consumers during this phase of COVID across the globe reassures earlier studies that certain environmental and social situations may moderate or reduce individual differences. [ABSTRACT FROM AUTHOR]

Many methods have been proposed to detect the originality of an image. One of the most commonly used method, the Copy - move forgery detection (CMFD), is considered here. The contribution of this paper is the application of the new fuzzy distances in clustering using metaheuristics. The family of the used fuzzy distances satisfies the axioms of the fuzzy metric. CMFD method, which includes Variable Neighborhood Search (VNS) and Bee Colony Optimization (BCO) metaheuristics, has been tested and compared with similar methods. The proposed method with the proposed new metric used in this research gave better results than the existing methods. The proposed fuzzy metrics in this paper as well as the problem of p - median clustering applied to the problem and compared with existing research in this field give better results. [ABSTRACT FROM AUTHOR]

Contrastive learning has become one of the most important representation learning methods because it does not require data to be labeled. However, current contrast learning treats different negative instances equally as being pushed away evenly in the representation space. Intuitively, the impact of different classes of instances in the representation space is different. Therefore, in this paper, we combine contrastive learning and clustering to propose a prototypical contrastive learning (ProCL) for image classification. Specifically, ProCL performs representation learning by clustering semantically similar images into the same group and encouraging clustering consistency between different augmentations of the same image. For a given image, sampling is performed from different clusters in order to ensure semantic differences from negative samples. Moreover, the gaps in semantic information between the prototypes (clustering center) differ, ProCL further weights the negative samples according to the distance between the prototypes, so that those negative samples with appropriate prototype distances have larger weights. This weighting strategy proves to be more effective. The experimental results in several benchmarks demonstrate that ProCL has strong competitive performance. [ABSTRACT FROM AUTHOR]

Phylogenetic trees are key data objects in biology, and the method of phylogenetic reconstruction has been highly developed. The space of phylogenetic trees is a nonpositively curved metric space. Recently, statistical methods to analyze samples of trees on this space are being developed utilizing this property. Meanwhile, in Euclidean space, the log-concave maximum likelihood method has emerged as a new nonparametric method for probability density estimation. In this paper, we derive a sufficient condition for the existence and uniqueness of the log-concave maximum likelihood estimator on tree space. We also propose an estimation algorithm for one and two dimensions. Since various factors affect the inferred trees, it is difficult to specify the distribution of a sample of trees. The class of log-concave densities is nonparametric, and yet the estimation can be conducted by the maximum likelihood method without selecting hyperparameters. We compare the estimation performance with a previously developed kernel density estimator numerically. In our examples where the true density is log-concave, we demonstrate that our estimator has a smaller integrated squared error when the sample size is large. We also conduct numerical experiments of clustering using the Expectation-Maximization algorithm and compare the results with k-means++ clustering using Fréchet mean. [ABSTRACT FROM AUTHOR]

One of the most important problems that arises during the knowledge discovery from data and data mining process in many new emerging technologies is mining data with temporal dependencies. One such application is activity recognition and prediction. Activity recognition is used in many real world settings, such as assisted living systems. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in assisted living settings. We discover temporal relations such as the order of activities, as well as their corresponding start time and duration features. Analysis of real data collected from smart homes was used to validate the proposed method. [ABSTRACT FROM AUTHOR]

Clustering of publication networks is an efficient way to obtain classifications of large collections of research publications. Such classifications can be used to, e.g., detect research topics, normalize citation relations, or explore the publication output of a unit. Citation networks can be created using a variety of approaches. Best practices to obtain classifications using clustering have been investigated, in particular the performance of different publication-publication relatedness measures. However, evaluation of different approaches to normalization of citation relations have not been explored to the same extent. In this paper, we evaluate five approaches to normalization of direct citation relations with respect to clustering solution quality in four data sets. A sixth approach is evaluated using no normalization. To assess the quality of clustering solutions, we use three measures. (1) We compare the clustering solution to the reference lists of a set of publications using the Adjusted Rand Index. (2) Using the Silhouette width measure, we quantity to which extent the publications have relations to other clusters than the one they have been assigned to. (3) We propose a measure that captures publications that have probably been inaccurately assigned. The results clearly show that normalization is preferred over unnormalized direct citation relations. Furthermore, the results indicate that the fractional normalization approach, which can be considered the standard approach, causes specific inaccurate assignments, which we intend to address in this paper. The geometric normalization approach has a similar performance as the fractional approach regarding Adjusted Rand Index and Silhouette width but leads to fewer inaccurate assignments at moderate to high granularity levels. We therefore believe that the geometric approach may be preferred over the fractional approach. [ABSTRACT FROM AUTHOR]

Preference-approval structures combine preference rankings and approval voting for declaring opinions over a set of alternatives. In this paper, we propose a new procedure for clustering alternatives in order to reduce the complexity of the preference-approval space and provide a more accessible interpretation of data. To that end, we present a new family of pseudometrics on the set of alternatives that take into account voters' preferences via preference-approvals. To obtain clusters, we use the Ranked k-medoids (RKM) partitioning algorithm, which takes as input the similarities between pairs of alternatives based on the proposed pseudometrics. Finally, using non-metric multidimensional scaling, clusters are represented in 2-dimensional space. [ABSTRACT FROM AUTHOR]

Vehicle routing problem is a well-known optimization problem in the logistics area. A special case of the vehicle routing problem is the station replenishment problem in which different types of fuel types have to be transported from the depots to the customers. In this paper we study the replenishment problem of a European petrol company. The problem contains several additional constraints such as time windows, different sized compartment vehicles, and restrictions on the vehicles that can serve a customer. We introduce a mixed integer linear programming model of the problem. To reduce the size complexity of the MILP model the customers are clustered and, based on the clusters, additional constraints are added to the MILP model. The resulting MILP model is tested on real problems of the company. The results show that combining the MILP model with clustering improves the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Cluster labeling is a problem of finding optimal clusters by maximizing the similarity within clusters. Generally, classical optimization methods are used for clustering, which depends upon initial estimates. Meta-heuristic optimization techniques came into existence to avoid this shortcoming as they are not problem-specific and do not dependent on the initial estimates. Optimal threshold selection is a widely known problem for image clustering that optimizes the entropy of image clusters. But for region-wise clustering, the sheer amount of literature is available that uses evolutionary techniques. Sub-optimal cluster selection and slow convergence are the problems that arise in meta-heuristics approaches. In this paper, we optimize the region-wise cluster labels for an efficient objective function. In particular, we consider the cluster label optimization problems in tumour-based magnetic resonance (MR) images. In order to solve the problem, a meta-heuristic approaches is used. The stable random walk is incorporated with the BAT algorithm to avoid the potential endangerment of local minima trapping. Further, the objective improvement is carried out by hybridizing the intra-cluster and inter-cluster-based objectives. To assess the efficiency of the proposed approach, we evaluated the proposed method against the recently proposed techniques. The evolutionary technique is well performed in various aspects of cluster label evaluation and paves new ideas for future research lines. [ABSTRACT FROM AUTHOR]

Recommender systems are widely used in many applications. They can be viewed as the predictor systems that are to suggest accurate and highly preferred items to consumers or clients. These systems can be considered to be information filtering systems. They counter some important challenges such as cold start (it means the absence of enough data for a new item to make accurate recommendations), scalability, and sparsity. The memory-based recommender systems have high accuracy but lack scalability. Also, the model-based systems are scalable but not accurate. Current recommender systems use hybrid methods to deal with the most important shortages of traditional filtering approaches. Current recommender systems are usually a hybrid of content-based filtering and collaborative filtering, and so on. In this paper, a hybrid recommender system is presented to meet the stated challenges, increase system performance and provide more accurate recommendations. This system uses both content-based filtering and collaborative filtering. In addition, using an automatically collected wordnet, we create an ontology that has been used in the content-based filtering section of our proposed approach. Furthermore, this framework applies KNN (k nearest neighbors) algorithm and clustering to improve its functionality. The proposed system is evaluated on a real benchmark. The experimentations show the proposed method has a better performance compared with the current superior related methods. The experimentations also show that our recommender system has desirable scalability compared with the state-of-the-art recommender systems. [ABSTRACT FROM AUTHOR]

Federated learning has been shown to be efficient for training a global model without needing to collect all data from multiple entities to the centralized server. However, the model performance, communication traffic, and data privacy and security are still the focus of federated learning after it has been developed. In this paper, a composition–decomposition based federated learning, denoted as CD-FL, is proposed. In the CD-FL approach, the global model, composed of K sub-models with the same framework, will be decomposed and broadcast to all clients. Each client will randomly choose a sub-model, update its parameters using its own dataset, and upload this sub-model to the server. All sub-models, including the sub-models before and after updating, will be clustered into K clusters to form the global model of the next round. Experimental results on Fashion-MNIST, CIFAR-10, EMNIST, and Tiny-IMAGENET datasets show the efficiency of the model performance and communication traffic of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we consider the matter of on-line clustering of hyperplanes within the presence of bounded noise. This is often a challenging problem that involves the segmentation of the data and the estimation of the hyperplanes parameters. The proposed algorithm consists in two successive steps. At whenever, the first step allows to assign the current data point to the most appropriate hyperplane and therefore the second step realizes an update of the parameters of the hyperplane that contains the data point. The second step springs from an Outer Bounding Ellipsoid type algorithm suitable for on-line parameters estimation within the presence of bounded noise. The performance of our algorithm is proven using synthetic and real data. [ABSTRACT FROM AUTHOR]

It is common to observe significant heterogeneity in clustered data across scientific fields. Cluster-wise conditional distributions are widely used to explore variations and relationships within and among clusters. This paper aims to capture such heterogeneity by employing cluster-wise finite mixture models. To address the heterogeneity among clusters, we introduce latent group structure and incorporate heterogeneous mixing proportions across different groups, accommodating the diverse characteristics observed in the data. The specific number of groups and their membership are unknown. To identify the latent group structure, we employ concave penalty functions to the pairwise differences of the preliminary consistent estimators for the mixing proportions. This approach enables the automatic division of clusters into finite subgroups. Theoretical results demonstrate that as the number of clusters and cluster sizes tend to infinity, the true latent group structure can be recovered with probability close to one, and the post-classification estimators exhibit oracle efficiency. We support our proposed approach’s performance and applicability through extensive simulations and analysis of basic consumption expenditure among urban households in China. [ABSTRACT FROM AUTHOR]

Non-negative Tucker decomposition (NTD) is a powerful tool for data representation to capture rich internal structure information from non-negative high-dimensional tensor data. Arguing that NTD methods often give global-like information, graph constraint has been introduced to capture the important local nonlinear structure of data. However, existing methods generally use fixed graphs and lack the ability to adaptively learn the optimal graph that best benefits the learning task at hand. In this paper, we propose an Adaptive Graph Regularized Non-negative Tucker Decomposition (AGRNTD) model. Not only is the new model able to capture the global multilinear structure of tensor data, but also it adaptively learns the optimal graph to capture local manifold information. An updating rule is designed to optimize the new model with the guarantee of local convergence. By mapping and visualizing the features, our method exhibits better feature extraction compared with other algorithms. The clustering results on five real-world datasets demonstrate the effectiveness and robustness of our method. [ABSTRACT FROM AUTHOR]

Clustering is one of the most long-standing fundamental problems in the fields of computational geometry and algorithm design. In this paper, we focus on the variance-based clustering problems, included in which is the widely known k-means clustering. As the main contribution, a so-called semi brute-force search approach is proposed and analyzed from both theoretical and experimental aspects. The proposed approach samples a small percentage from the input dataset and search in a brute-force way for a k sized seed whose resulting Voronoi Diagram gives a good clustering of the original dataset. With high probability, the clustering is provably good to estimate the optimum under certain assumptions. Extensive experiments on both synthetic datasets and real-world datasets show that to obtain competitive results compared with k-means method (Llyod in IEEE Trans Inf Theory 28(2):129–137, 1982) and k-means++ method (Arthur and Vassilvitskii, (in: 18th ACM-SIAM symposium on discrete algorithms (SODA), 2007)), we only need a subset of 7% size comparing with the input dataset. If we are allowed to sample 15% from the dataset, our algorithm outperforms both the k-means method and k-means++ method in at least 80% of the clustering tasks. Also, an extended algorithm based on the same idea guarantees a balanced k-clustering result. [ABSTRACT FROM AUTHOR]

A wireless body area network (WBAN) facilitates data communication between multiple bio-sensing devices that are implanted to monitor vital parameters of the human body. An energy-proficient data routing algorithm is essential for implementing a WBAN as the small bio-sensing nodes possess a low amount of energy. In addition, a WBAN routing protocol must provide at least a 90% network throughput rate to fulfill IEEE 802.15.6 communication standards. This paper proposes the "Fuzzy Energy Budget based VIKOR-MADM Algorithm (EB-FV-MADM)" that accomplishes dynamic election of cluster head (CH) nodes for cluster routing in WBAN. Here each WBAN node proposes an energy budget for its selection as CH. The energy budget is given in terms of three-node attributes: node residual energy, node energy cost, and node CH job count. The energy cost of a node indicates how much network energy will be spent in one transmission round if that node becomes CH. The CH job count of a node tells how many times that node has performed as CH so far. EB-FV-MADM converts the node attributes to fuzzy grades such as low, medium, and high. Further, the EB-FV-MADM computes a compromise rating index for WBAN nodes which reflects the closeness of node attributes to the ideally desired solution. The node whose attributes show the highest closeness to the ideal solution i.e. high residual energy along with low energy cost and low CH job count is selected as CH. Fuzzy transformation compensates for imprecise attribute information. The performance results depict that EB-FV-MADM provides energy-efficient and high QoS data routing, as well as meets the IEEE 802.15.6 standards. [ABSTRACT FROM AUTHOR]

As in the current trend, the virtual reality-based application is very popular in the medical healthcare system to generate a realistic virtual 3-D simulation environment that users can interact with specialized devices. The increasing demand for advancement in the requirement of the virtual environment of healthcare policies as well as the systems needs changes in the simulation environment. In reference to such requirement, the software industry needs improvement in the development process which reduces the effect of software cost, complexity and resource planning. From last few years, optimization in development of simulation environment's cost-benefit aspects is also the challenging area. In view of such issues, there are several guided (supervised) and unguided (unsupervised) algorithms are using evolutionary approaches and nature-inspired self-organized swarm intelligence approaches have been developed by the researchers for virtual real-time healthcare search based software system. However, there is still a gap in the development of such a simulation environment for identification of the software component. This paper proposes a self-organizing component identification technique using medoid based ant colony clustering algorithms. The proposed algorithm has been compared to classical centroid-based clustering (K-means,CRUD, and FCA) and evolutionary approach(genetic algorithm) on the case study of the virtual real-time healthcare system. For the accuracy and precision of the approach two already studied cases have also been used. [ABSTRACT FROM AUTHOR]

Because of the processing of continuous unstructured large streams of data, mining real-time streaming data is a more challenging research issue than mining static data. The privacy issue persists when sensitive data is included in streaming data. In recent years, there has been significant progress in research on the anonymization of static data. For the anonymization of quasi-identifiers, two typical strategies are generalization and suppression. However, the high dynamicity and potential infinite properties of the streaming data make it a challenging task. To end this, we propose a novel Efficient Approximation and Privacy Preservation Algorithms (EAPPA) framework in this paper to achieve efficient data pre-processing from the live streaming and its privacy preservation with minimum Information Loss (IL) and computational requirements. As the existing privacy preservation solutions for streaming data suffer from the challenges of redundant data, we first propose the efficient technique of data approximation with data pre-processing. We design the Flajolet Martin (FM) algorithm for robust and efficient approximation of unique elements in the data stream with a data cleaning mechanism. We fed the periodically approximated and pre-processed streaming data to the anonymization algorithm. Using adaptive clustering, we propose innovative k-anonymization and l-diversity privacy principles for data streams. The proposed approach scans a stream to detect and reuse clusters that fulfill the k-anonymity and l-diversity criteria for reducing anonymization time and IL. The experimental results reveal the efficiency of the EAPPA framework compared to state-of-art methods. [ABSTRACT FROM AUTHOR]

Over the course of this year, more than a billion people have been afflicted by the COVID-19 outbreak. As long as individuals maintain their social distance, they should all be secure at this period. Because of this, there has been a rise in the usage of different online technologies, but at the same time, there has also been a rise in the likelihood of different cyber-attacks. A DDoS assault, the most prevalent and deadly of them all, impairs an online resource for its users. Thus, in this paper, we have proposed a filtering approach that can work efficiently in the COVID-19 scenario and detect the DDoS attack. We base our proposed approach on statistical methods like packet score and entropy variation for the identification of DDoS attack traffic. We have implemented our proposed approach on Omnet++ and for testing its efficiency we have checked it with different test cases. Our proposed approach detects the DDoS attack traffic with 96% accuracy and can also clearly have differentiated the DDoS attack traffic from the flash crowd. [ABSTRACT FROM AUTHOR]

Load balancing is critical for container-based cloud computing environments for several reasons. A lack of appropriate load balancing techniques could result in a decrease in performance and possible service interruptions as some nodes get overloaded, while others are left underutilized. Cloud service providers can reduce latency and boost system performance by strategically placing containers using clustering algorithms. These techniques aid in efficiently using resources and load balancing by clustering related containers together according to their shared attributes. Clustering strategies are effective in allocating and controlling resources to meet the demands of a changing workload. Algorithms for clustering combine related workloads or containers into clusters, improving performance isolation and maximizing resource usage. One popular methodology for data clustering is the K-Medoid Clustering Algorithm. It is especially helpful when working with categorical data or when the dataset contains outliers. K-medoids is an unsupervised clustering approach where the core of the cluster is made up of data points known as “medoids.” A medoid is a location in the cluster whose total distance to every object in the cluster—also known as its dissimilarity—is as small as possible. Any appropriate distance function may be used, such as the Manhattan distance, the Euclidean distance, or another one. Thus, by choosing K medoids from our data sample, the K-medoids method splits the data into K clusters. This work presents the K-Medoid clustering technique for containers, which can enhance load balancing, decrease resource execution times, and increase resource utilization rates all at the same time. The results of the experiment show that the proposed method outperforms MACO and FCFS in terms of throughput by about 70% when number of cloudlets increased. The relative improvement of execution time of the proposed K-medoid algorithm w.r.t FCFS is about 50%. [ABSTRACT FROM AUTHOR]

Pacific islands are widely exposed to several strong wave events all year long. However, comprehensive analyses of coastal vulnerabilities to wave climates and their extremes are often lacking in those islands. In the present paper, the wave climate around the reef of New Caledonia is analyzed using a 28-year simulation performed with the Wave Watch III model, and accounting for realistic wind intensity forcing from tropical cyclones. Four mean wave regimes are defined with clustering methods, and are shown to vary along the reef depending on its main orientation. The western reef is mostly exposed to energetic south-western swells (significant height over 1.5 m, peak period of ~ 12 s) generated in the Tasman Sea that are reinforced during austral winter. The northern sector and the Loyalty Islands, are hit by shorter waves (~ 8 to 9 s period) coming from the south-east to the north-east, with height ranging on average from 0.8 m in the Loyalty Channel to 1.5 m at the northern tip of the Grande Terre reef. These waves mainly result from the south-eastern trade winds that blow over the central south-western Pacific all year long. In austral summer, additional swell remotely generated by both the extra-tropical westerlies and the north-eastern trade winds of the northern hemisphere reach the north-eastern reef of the archipelago. These wave regimes also strongly vary in response to the interannual El Niño-Southern Oscillation. El Niño events tend to increase the frequency of the south-western swell regime in austral spring and fall, and of the south-eastern trade wind waves in austral summer. In contrast, during La Niña, waves generated in the northern hemisphere are more likely to reach New Caledonia all year long. Finally, extreme wave events and their return periods were assessed. Wave amplitude reaching 7 m is estimated to occur every 100 years. On the west and southern tip of the Grande Terre reef, extreme waves are 80% of the time westerly waves generated by storms in the Tasman Sea or in the Coral Sea, while on the eastern reefs (Loyalty Islands and Channel), 70% of the extreme wave episodes are associated to tropical cyclone-induced waves. During La Niña episodes, more tropical cyclones pass by New Caledonia, increasing their contribution to extreme wave events along the western and southern coasts of the island. Conversely, in El Niño conditions, the exposure to tropical cyclone-induced waves is predominantly concentrated on the northeastern side. [ABSTRACT FROM AUTHOR]

The original K-means algorithm is widely applied for clustering in Euclidean spaces. Nevertheless, due to the non-flat characteristics of the Riemannian manifold, standard Euclidean K-means algorithms yield inferior results on such data. To address this issue, this paper presents an intrinsic K-means clustering algorithm on homogeneous manifolds based on the geodesic distance. It allows the development of K-means-based methods for frequently occurring non-vector spaces in robotics, such as directional vector modelling S 2 and pose estimation S 3 . First, the Riemannian metric of the homogeneous manifold is delivered; on this basis, the intrinsic K-means is proposed using Karcher mean, and its convergence is proved. Then, differences between the proposed algorithm and four projection-based algorithms, such as embedding projection, stereographic projection, central projection and logarithmic projection, are discussed by investigating their distance preservation on manifolds. Finally, to evaluate the effectiveness of the proposed algorithm, it is compared with the projection-based algorithms on S n . The results show that the intrinsic K-means achieves better clustering results, where the clustering accuracy of the proposed method is improved by 47% and 27% on average on artificial S 2 and S 3 datasets, respectively. Meanwhile, the noise immunity of the proposed algorithm becomes more evident with the noise ratio increase. [ABSTRACT FROM AUTHOR]

Fingerprint recognition systems hold a pivotal role across various modern applications, such as criminal investigation, civil identification, and access control. Fingerprint segmentation is commonly the first stage of fingerprint recognition systems, focusing on extracting the foreground from captured fingerprint images. This helps to reduce extracting false minutiae, speed up the extraction process, and hence, improve the overall system performance. In this paper, we introduce a novel fingerprint segmentation method by first proposing new frequency and intensity features with employing fuzzy-c-means and genetic algorithm, accompanied by proposing a new cluster assignment strategy that involves features weighting and cluster assignment probabilities thresholding which helps to improve the accuracy compared to the standard cluster assignment method. Finally, we introduce a new morphological post-processing technique in order to minimize the misclassified pixels and obtain the final mask. Furthermore, we carry out a comprehensive performance evaluation against leading fingerprint segmentation methods on various known databases. As a result, the proposed method achieves an average error rate of 2.52%, which is much lower than the corresponding errors obtained in the literature. By these experimental results, we show that the proposed method outperforms the best existing methods. [ABSTRACT FROM AUTHOR]

Design of effective algorithm for reliable and energy optimized secure routing protocol (SRP) for wireless sensor networks (WSNs) is a demanding design issue now. To handle this problem, we propose a trust and encryption-based SRP based on trust modelling with intrusion detection, elliptic curve cryptography (ECC), clustering, fuzzy rules and ant colony optimization (ACO)-oriented SRP for WSN routing. In this paper, an extended convolutional neural networks with Schrodinger equation and particle swarm optimization is proposed for developing and intrusion detection-based trust modelling. Moreover, a new node authentication scheme and an encryption-based secure routing protocol are also proposed in this work for increasing the security. This proposed secure protocol known as trust and ECC encryption-based ACO-SRP (TECC-ACO-SRP) performs authentication and trust analysis on the nodes using intrusion detection, and then, the data are communicated after data encryption using ECC encryption technique. This proposed system combines dominant set clustering with fuzzy rules to make clusters with similar type of nodes as members and then selects cluster heads (CHs) for every cluster. This SRP ensures improved security, reduced delay and energy usage with higher packet delivery ratio than other existing SRPs. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important approaches used to extend the lifetime of Wireless Sensor Networks (WSN). The fundamental metric taken by clustering algorithms is energy enhancement. Moreover, network coverage and load balance are two important approaches that play crucial roles in improving network lifetime and delivery since the former focuses on maximizing the use of all network resources, while the second is based on distributing the load between the nodes to enhance the energy consumption. As the challenge of clustering nodes in an energy-efficient way is an NP-Hard problem, firefly optimization algorithm is used to address this challenge. However, the proposed solutions focus on centralized processing of the algorithm, which makes them unsuitable for large-scale WSN. In this paper, a double firefly based efficient clustering solution is proposed for large-scale WSN which is implemented in a decentralized fashion to improve the lifetime and packet delivery. The first firefly algorithm is used by each node to move to the best initial Cluster Head (CH) by performing a balance of belonging between the clusters, while the second algorithm is used only between the initial CHs to eliminate membership redundancy and optimally construct balanced clusters. The simulation results show that our proposed solution significantly improves the network lifetime as well as the delivery rate. [ABSTRACT FROM AUTHOR]

Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases. [ABSTRACT FROM AUTHOR]