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Sensor data that is often collected in the Internet of Things (IoT) or any smart city environment should be protected against security and privacy concerns. Often, sensor data that is shared by devices in smart cities contains sensitive or private information that can often be shared over different networks and by different smart applications. In the last decade, the area of Privacy-Preserving Data Mining (PPDM) has received a lot of attention as the amount of data received and collected daily is huge. Unfortunately, PPDM mostly applies to binary data. To improve the usefulness of PPDM, we present a more usable version for smart cities called Privacy-Preserving Utility Mining (PPUM), in the form of a Maximal Sensitive Utility-Maximal Sensitive ConflIct (MSU-MSI) algorithm. MSU-MSI finds any conflicting items that may contain sensitive itemsets with high-utility and sanitizes them, stripping them of sensitive and private information while maintaining utility. Any transactions encountered that contain sensitive itemsets are first fed through sanitization processes. This is followed by calculating the total number of items that conflict, and then removing them so sanitization processes can operate more efficiently so as to not redo known sanitization processes. We conduct an in-depth experimental analysis, where our detailed methodology is compared directly with state-of-the-art frameworks such as MSU-MIU, MSU-MAU, HHUIF and MSCIF. Our proposed MSU-MSI shows a higher performance in missing cost, in particular when dealing with highly dense or highly sparse datasets. Moreover, our novel framework is shown to achieve an excellent performance with regards to similarity in database structure and database utility. [ABSTRACT FROM AUTHOR]

There are some problems in abnormal node sensing in regional wireless networks, such as low sensing accuracy and poor judgment results of abnormal states of sensing nodes. Therefore, this paper develops a method for abnormal node sensing in regional wireless networks based on convolutional neural network. In addition, we will analyze the structure of regional wireless network nodes and determine the distribution mode of wireless network nodes. The regional wireless network node data are extracted and the pivot quantity and two-dimensional Gaussian distribution state are constructed using the median to build the regional wireless network node deployment model according to the confidence interval of the data characteristics; analyze the basic principle of convolution neural network, determine the operation mode of convolution kernel, classify the regional wireless network node data using Bayesian network, set a safety distance to determine the abnormal node of the regional wireless network, train the determined abnormal data as the input data of convolutional neural network and input it into the constructed perception model of the abnormal node of the regional wireless network, the loss function is set to continuously update the iterative results to realize the perception of abnormal node in the regional wireless network. The simulation results show that the sensing range of this method is relatively consistent with the range set by the sample, and the sensing accuracy reaches more than 95%, and the abnormal state error of abnormal nodes in the evaluation sample area is always less than 2%, which verifies that this method improves the sensing accuracy, reduces the error, and has higher application value. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes entropy values and a non-dominant sorting algorithm to identify the candidate with the highest entropy value from the dataset. This is followed by merging the training set with adversarial samples, where a mini-batch of the merged dataset is used to update the dense network parameters. This method can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses. To evaluate the efficacy of the proposed model, two datasets, MNIST and COVID, were used with pixel values and without transfer learning. The results showed an increase of accuracy from 0.85 to 0.88 for MNIST and from 0.83 to 0.85 for COVID, which suggests that the model successfully classified images from both datasets without using transfer learning techniques. [ABSTRACT FROM AUTHOR]

Internet‐based information exchange has resulted in the propagation of false and misleading information, which is highly detrimental to individuals and humankind. Due to the speed and volume of social media news production, supervised artificial intelligence algorithms require many annotated data, which is difficult, costly, and time‐consuming. To address this issue, we offer a novel federated semi‐supervised framework based on self‐ensembling that utilizes the linguistic and stylometric information of annotated news articles and searches for hidden patterns in unlabeled data to denoise labels. Self‐ensembling predicts the labels of unlabeled data by using the outcomes of network‐in‐training from earlier epochs. These cumulative predictions should be a stronger predictor for unknown labels than the output of the most recent training epoch; hence, they may be utilized as a substitute for the labels of unlabeled data. The approach is distinctive in collecting all of the outputs from the neural network's past training periods. It utilizes them as an unsupervised target against which to assess the current output prediction of unlabeled articles. We intend to create a dataset centred on denoising to forward the study. The dataset is mapped using (1) the shifting focus time from published news articles and (2) the semi‐supervised method based on coincidence contexts for a neural contrast embedding model for learning low‐dimensional continuous vectors that generate a focus time‐based query in sequential news articles for temporal comprehension. The model achieved 0.83% F‐measure with lexicon expansion semi‐supervised learning. [ABSTRACT FROM AUTHOR]

To solve the problem of low recognition accuracy and long recognition time in conventional methods for digital moving image feature recognition, a method for digital image feature recognition on mobile platforms based on machine learning is proposed. The support vector machine method is used to cluster the dataset, and then the weight reconstruction method is used to reduce the dimension of digital moving image data. The reduced dimension of the digital moving image is embedded in the coordinates to obtain the reduced dimension of the image data information and complete the feature recognition of the digital moving image. The simulation results show that the recognition accuracy of this method in recognizing features of moving digital images is basically stable at more than 95% and the running time is 0.42s. The recognition effect of the proposed method is good, the recognition efficiency is high, and it has some practical application value. [ABSTRACT FROM AUTHOR]

Traditional motion amplitude normalization technology cannot get the whole process of motion amplitude in the time and space domain, and there is a big error in motion image contour extraction, which leads to low accuracy and long time. Therefore, a motion amplitude normalized feature localization technique based on intelligent vision is proposed. The contour of the moving image is extracted by the method of color information segmentation. The motion process in time and space is simulated by intelligent vision, and the feature data are captured by the parameters of the human motion sensor. According to the horizontal rotation record and geomagnetic flux of the sensor, the feature data is processed by normalization, and the moving image features are selected by normalization. The amplitude function is established to optimize the foreground region feature marker of the motion image, and the motion amplitude standard feature location is realized. Experimental results show that the proposed method is more accurate and less time-consuming than the traditional method. [ABSTRACT FROM AUTHOR]

In order to improve the quality of distance education and solve the problem of slow data processing of the teaching system, an intelligent distance education assistance system based on WEB is developed in this paper. After verification, students, teachers and administrators log into the distance intelligent teaching assistance system and transmit various information to the interface of the teaching and administration subsystems. The submitted information is merged using the Bayesian model for integrating educational resources in the digital cloud to create a distance education database that supports the system with data. At the same time, the evaluation of the business logic of the data is performed. After the data is converted to other formats in the subsequent convertible database, it returns to the user interface to provide browsing and consulting functions for users. The experimental results show that the designed system can realize the remote auxiliary function of intelligent education and effectively improve the quality of teaching. The real-time data acquisition rate of the system in this paper is always equal to the set value. The average acceleration of the system in this paper is 5.5 and the data processing efficiency is higher. The minimum safety factor of the system is between 7.8 and 8.5, and the system has high stability. The user satisfaction of the system is over 93%, and the accuracy of the collected data is relatively high. The auxiliary system designed in this paper can provide a stable and efficient application environment for distance education. [ABSTRACT FROM AUTHOR]

Frequent pattern mining (FIM) identifies the most important patterns in data sets. However, due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered as an important research area. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to protect private and sensitive information. This paper combines entropy-based active learning with an attention-based approach to effectively hide sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the effectiveness of decision-making by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve data sanitization by the hiding sensitive items and avoiding to hide the non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches. [ABSTRACT FROM AUTHOR]

Deep learning methods have led to the state-of-the-art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders for further collaboration. However, limited labeled data set limits the deep learning algorithms to be generalized for one domain into another. To handle the problem, meta-learning helps to solve this issue especially it can learn from a small set of data. We proposed a meta-learning-based image segmentation model that combines the learning of the state-of-the-art models and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segment part and remove noise from the new test images. The proposed model can achieve 0.94 precision and 0.92 recall. The ability is to increase 3.3% among the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• Extract depressive symptoms from writings written by patients. • Enables the identification and visualization of depressive symptoms using a deep attention based method. • The framework acts as a decision support system as it incorporates entropy based labeling of data. • Our online interactive tool (ICT) helps provide context as well as visualization of detected depressive symptoms. • Evaluated different architecture design for the attention mechanism combined with fuzzy rules. In this paper, we propose a structure hypergraph and an emotional lexicon for word representation. Our method can solve problems related to vocabulary size, grammatical representation of words, and the lack of an emotional lexicon. Natural Language Processing (NLP) and attention-based curriculum learning are then used in the developed model. The goal is to achieve semantic word representations using a graph model. Later, embedding is used to label the text using clinical procedures. The experimental results show the emotional word representation with the structure hypergraph. The bidirectional Long Short Term Memory (LSTM) architecture with an attention mechanism achieved a Receiver Operating Characteristic (ROC) value of 0.96. The learning method can help psychiatrists in note taking and contributes to the detection rate of depression symptoms. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing images have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this paper, the target recognition algorithm of multi-source remote sensing image based on IoT vision is investigated. The infrared sensor and SAR radars are set in the visual perception layer of the iVIOT. The visual perception layer transmits the collected remote sensing image information to the application layer through the wireless networks. The data processing module in the application layer uses the normalized central moment idea to extract the features of multi-source remote sensing image. Contourlet two-level decomposition is performed on the image after feature extraction to realize multi-scale and multi-directional feature fusion. A two-step method of primary fineness is used to match the fused features and the random sampling consensus algorithm is used to eliminate false matches for obtaining the correct match pairs. After the image feature matching is completed, the BVM target detection operator is used to complete the target recognition of multi-source remote sensing image. Experimental results show that the use of the IoT to visually recognizing the desired remote sensing image target has low communication overhead, and the recognition reaches 99% accuracy. [ABSTRACT FROM AUTHOR]

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system. • Mine required CHUIs in parallel and distributed environments. • Use HG-k-means to cluster highly relevant transactions in a group that ensures fusion. • An interactive three-phases MapReduce model with a DFM-Miner algorithm is presented. • Experiments on various large-scale datasets are conducted to achieve better performance. [ABSTRACT FROM AUTHOR]

• CRFLV-I and CRFLV-II are proposed for sequence labeling. • Encoding schema is administered in the form of latent variables able to capture structures of hidden variables. • The proposed models make use of different schemas in different scenarios. • We show that our schemas holds strong performance against BIO or BILOU encoding schemas. To process data like text and speech, Natural Language Processing (NLP) is a valuable tool. As on of NLP's upstream tasks, sequence labeling is a vital part of NLP through techniques like text classification, machine translation, and sentiment analysis. In this paper, our focus is on sequence labeling where we assign semantic labels within input sequences. We present two novel frameworks, namely SA-CRFLV-I and SA-CRFLV-II, that use latent variables within random fields. These frameworks make use of an encoding schema in the form of a latent variable to be able to capture the latent structure in the observed data. SA-CRFLV-I shows the best performance at the sentence level whereas SA-CRFLV-II works best at the word level. In our in-depth experimental results, we compare our frameworks with 4 well-known sequence prediction methodologies which include NER, reference parsing, chunking as well as POS tagging. The proposed frameworks are shown to have better performance in terms of many well-known metrics. [ABSTRACT FROM AUTHOR]

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques and constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, satisfaction, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented in detail, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field. [ABSTRACT FROM AUTHOR]

Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naïve Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F-measure, precision, recall and R 2 . The optimized and reduced feature subset model achieved a high F-measure of 0.91 and R 2 of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility. [ABSTRACT FROM AUTHOR]

Natural language processing is a useful processing technique of language data, such as text and speech. Sequence labeling represents the upstream task of many natural language processing tasks, such as machine translation, text classification, and sentiment classification. In this paper, the focus is on the sequence labeling task, in which semantic labels are assigned to each unit of a given input sequence. Two frameworks of latent variable conditional random fields (CRF) models (called LVCRF-I and LVCRF-II) are proposed, which use the encoding schema as a latent variable to capture the latent structure of the hidden variables and the observed data. Among the two designed models, the LVCRF-I model focuses on the sentence level, while the LVCRF-II works in the word level, to choose the best encoding schema for a given input sequence automatically without handcraft features. In the experiments, the two proposed models are verified by four sequence prediction tasks, including named entity recognition (NER), chunking, reference parsing and POS tagging. The proposed frameworks achieve better performance without using other handcraft features than the conventional CRF model. Moreover, these designed frameworks can be viewed as a substitution of the conventional CRF models. In the commonly used LSTM-CRF models, the CRF layer can be replaced with our proposed framework as they use the same training and inference procedure. The experimental results show that the proposed models exhibit latent variable and provide competitive and robust performance on all three sequence prediction tasks. [ABSTRACT FROM AUTHOR]

Association-rule mining (ARM) has concerned as an important and critical research issue in the field of data analytics and mining that aims at finding the correlations among the items in binary databases. However, the conventional algorithms considered the frequency of the item(set) in binary databases for ARM, which is not sufficient in real-life situations. Mining of useful information is not an easy task especially if the item(set) consists of the added values. Moreover, the discovered knowledge is not easy to understand if you are not the domain experts. For the past decades, several intelligent systems involved the fuzzy-set theory for many domains and applications due to it is interpretable for human reasoning. Before, the Apriori-based method for discovering fuzzy frequent itemsets (FFIs) based on the type-2 fuzzy-set theory was proposed, which requires the amount of computations with enormous candidates. In this study, we then first present a fast list-based multiple fuzzy frequent itemset mining (named as LFFT2)algorithm under type-2 fuzzy-set theory. It is developed by the type-2 membership functions to retrieve the multiple fuzzy frequent itemsets for presenting more useful and meaningful knowledge for making the efficient strategies or decisions. From the results shown in the experiments, it is clear to see that the developed LFFT2 outperforms the conventional Apriori-based approach regarding the execution time and the number of examined nodes in the search space. [ABSTRACT FROM AUTHOR]

Privacy-preserving data mining (PPDM) has become an important research topic, as it can hide sensitive information, while ensuring that information can still be extracted for decision making. While performing the sanitization progress for hiding the sensitive information, three side effects such as hiding failure, missing cost, and artificial cost happen at the same time. Several evolutionary algorithms were introduced to minimize those three side effects of PPDM using a single-objective function that generates one solution for sanitization. This paper presents a multiobjective algorithm (NSGA2DT) with two strategies for hiding sensitive information with transaction deletion based on the NSGA-II framework. To obtain better balance of side effects, the designed NSGA2DT takes database dissimilarity (Dis) as one more factor to achieve better performance in terms of four side effects. Moreover, instead of a single solution of the sanitization progress, the designed NSGA2DT provides more than one solutions than those of single-objective evolutionary algorithms, which shows flexibility to select the most appropriate transactions for deletion depending on user's preference. A Fast SoRting strategy (FSR) and the pre-large concept are utilized, respectively, in this paper to find the optimized transactions for deletion and speed up the iterative process. Based on the developed NSGA2DT, the set of several Pareto solutions can be easily discovered, thus avoiding the problem of local optimization of single-objective approaches. Besides, the designed NSGA2DT does not require to set initial weights for evaluating the side effects, and thus, the results could not be seriously influenced by the predefined weights. Experimental results show that the proposed NSGA2DT provides satisfactory results with reduced side effects, compared to previous evolutionary approaches with single-objective function. [ABSTRACT FROM AUTHOR]

Natural language processing is a technique to process data such as text and speech. Some fundamental research includes named-entity recognition, which recognizes name entities (i.e., persons, companies) from texts; semantic parsing, which is used to convert a natural language utterance to the representation of logical form; and co-reference resolution, which extracts nouns (including pronouns, noun phrases) pointing to the same reference body. In this paper, we mainly focus on the task of mention extraction, which extract and classify overlapping or nested structure mentions. We proposed a neural-encoded mention-hypergraph (NEMH) model to use hypergraph to model overlapping or nested structure mentions and use neural networks to extract features for hypergraph automatically. Unlike the existing approaches, our hypergraph model can effectively capture nested mention entities with unlimited lengths. Also, the proposed model is highly scalable and the time complexity of the proposed model is linear in the number of mention classes and the number of input words. Extensive experiments are conducted on several standard datasets to demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract Association-rule mining (ARM) or frequent itemset mining (FIM) is the most fundamental task in knowledge discovery, which is used to find the occurrence frequency of the item/sets in transactional database. The other factors such as weight, interestingness or unit profit of the items are not considered whether in ARM or FIM. To reveal more information, high-utility itemset mining (HUIM) was designed to consider both quantity and unit profit of items to discover the high-utility itemsets (HUIs). Several algorithms for FIM or HUIM were extensively studied but fewer works concern both frequency and utility together to provide better solutions in decision-making. In the past, the SKYMINE algorithm was designed to find the skyline frequent-utility patterns (SFUPs). A SFUP is a non-dominated pattern, in which each solution dominates the others by considering the aspects of frequency and utility. The SKYMINE algorithm needs, however, amounts of computation to level-wisely discover the SFUPs. In this paper, an efficient utility-list structure is used instead of the UP-tree structure used in SKYMINE to mine the SFUPs. Two algorithms are respectively designed by using the depth-first search (called SKYFUP-D) and breath-first search (SKYFUP-B) to mine the SFUPs. An efficient structure is also designed to record the maximal utility of the potential itemsets, thus reducing the computations for finding the SFUPs in the search space. Extensive experiments are conducted on several real-world and simulated datasets and the results indicate that the designed two algorithms have better performance than that of the state-of-the-art SKYMINE algorithm in terms of runtime, memory usage, search space size and the scalability. [ABSTRACT FROM AUTHOR]

Chromosome images are commonly used in karyotype analysis to diagnose chromosomal diseases. However, there are often chromosome adhesion and overlaps in chromosome images, so effective chromosome segmentation is conducive to smooth karyotype analysis. To date, some progress has been made in automatic chromosome segmentation, and existing methods can be used to segment overlapping chromosomes in most cases. However, when two or more overlapping regions are too close to each other in the image of overlapping chromosomes, the existing segmentation methods adjust the non-overlapping regions that do not belong to the overlapping region, resulting in incomplete segmentation of chromatids. Therefore, we use a heuristic algorithm to solve this problem from the point of view of mathematics and geometry to improve the segmentation of overlapping chromosomes. Starting from chromosome images, the existing problems and solutions are explained and displayed in the way of visualized interpretable image features, which helps to better understand the algorithm. Our method achieves 92.86% splicing accuracy and 90.44% overall segmentation accuracy on open datasets. The experimental results show that our method can effectively improve the problem of incorrect chromosome segmentation when two or more overlapping parts of overlapping chromosomes are too close to each other. It can accelerate the development of artificial intelligence in computational pathology and provide patients with more accurate medical services. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is an extension of frequent-itemset mining (FIM) but considers the unit profit and quantity of items to discover the set of high-utility itemsets (HUIs). Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. This approach is, however, inappropriate in real-world applications since the utility of the itemset increases along with the number of items within it. High average-utility itemset mining (HAUIM) was designed to provide more reasonable utility measure by taking the size of the itemset into account. Existing algorithms can only handle, however, the static database and unsuitable for the dynamic environment since the size of data is frequently changed in real-life situations. In this paper, an incremental high-average utility pattern mining (IHAUPM) algorithm is presented to handle the incremental database with transaction insertion. The well-known fast updated (FUP) concept in the FIM is modified to adopt the designed algorithm, thus efficiently updating the discovered HAUIs. Based on the designed model for HAUIM with transaction insertion, the proposed IHAUPM algorithm can easily only handle the inserted transactions. Experiments are carried on six datasets and the results showed that the designed algorithm has better performance than the state-of-the-art algorithms performing in the batch manner. [ABSTRACT FROM AUTHOR]

Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items. However, most HUIM algorithms are designed to be applied to static databases. But in real‐world applications such as market basket analysis and business decision‐making, databases are often dynamically updated by inserting new data such as customer transactions. Several researchers have proposed algorithms to discover high‐utility itemsets (HUIs) in dynamically updated databases. Unlike batch algorithms, which always process a database from scratch, incremental high‐utility itemset mining (iHUIM) algorithms incrementally update and output HUIs, thus reducing the cost of discovering HUIs. This paper provides an up‐to‐date survey of the state‐of‐the‐art iHUIM algorithms, including Apriori‐based, tree‐based, and utility‐list‐based approaches. To the best of our knowledge, this is the first survey on the mining task of incremental high‐utility itemset mining. The paper also identifies several important issues and research challenges for iHUIM. WIREs Data Mining Knowl Discov 2018, 8:e1242. doi: 10.1002/widm.1242 This article is categorized under: Algorithmic Development > Association Rules Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Knowledge Representation [ABSTRACT FROM AUTHOR]

From web search and data mining, users’ click and purchase behaviors contain valuable information, thus numerous approaches have been proposed to identify embedded useful knowledge from them. In these real-life situations, each user may perform the same action/event multiple times, and multiple accessed events product different profit. Many utility-oriented data mining approaches thus have been extensively studied. Previous studies have the limitation that the overall utility of traditional pattern is limited since they rarely consider the inherent correlation. For example, from the purchase behavior, the low-utility patterns sometimes with a very high-utility pattern will be considered as a valuable pattern even if this behavior may be not highly correlated. A more intelligent framework that provides non-redundant and correlated behavior based on utility measure is thus desired. In this paper, we first present a novel method to extract non-redundant correlated purchase behaviors considering the utility and correlation factors. The high qualified patterns can be derived with high profit and strong correlation, which can lead to higher recall and reveal better precision. In the proposed projection-based approach, an efficient projection mechanism and a sorted downward closure property are developed to reduce the database size. Several pruning strategies are further developed to efficiently and effectively discover the desired patterns. An extensive experimental study showed that the novel non-redundant correlated high-utility pattern has more effectiveness than the previous knowledge representation. Moreover, the proposed algorithm is efficient in terms of execution time and memory usage. [ABSTRACT FROM AUTHOR]

Privacy preserving data mining (PPDM) is an emerging research problem that has become critical in the last decades. PPDM consists of hiding sensitive information to ensure that it cannot be discovered by data mining algorithms. Several PPDM algorithms have been developed. Most of them are designed for hiding sensitive frequent itemsets or association rules. Hiding sensitive information in a database can have several side effects such as hiding other non-sensitive information and introducing redundant information. Finding the set of itemsets or transactions to be sanitised that minimises side effects is an NP-hard problem. In this paper, a genetic algorithm (GA) using transaction deletion is designed to hide sensitive high-utility itemsets for PPUM. A flexible fitness function with three adjustable weights is used to evaluate the goodness of each chromosome for hiding sensitive high-utility itemsets. To speed up the evolution process, the pre-large concept is adopted in the designed algorithm. It reduces the number of database scans required for verifying the goodness of an evaluated chromosome. Substantial experiments are conducted to compare the performance of the designed GA approach (with/without the pre-large concept), with a GA-based approach relying on transaction insertion and a non-evolutionary algorithm, in terms of execution time, side effects, database integrity and utility integrity. Results demonstrate that the proposed algorithm hides sensitive high-utility itemsets with fewer side effects than previous studies, while preserving high database and utility integrity. [ABSTRACT FROM PUBLISHER]

Due to the rapid growth of resource sharing, distributed systems are developed, which can be used to utilize the computations. Data mining ( DM) provides powerful techniques for finding meaningful and useful information from a very large amount of data, and has a wide range of real-world applications. However, traditional DM algorithms assume that the data is centrally collected, memory-resident, and static. It is challenging to manage the large-scale data and process them with very limited resources. For example, large amounts of data are quickly produced and stored at multiple locations. It becomes increasingly expensive to centralize them in a single place. Moreover, traditional DM algorithms generally have some problems and challenges, such as memory limits, low processing ability, and inadequate hard disk, and so on. To solve the above problems, DM on distributed computing environment [also called distributed data mining (DDM)] has been emerging as a valuable alternative in many applications. In this study, a survey of state-of-the-art DDM techniques is provided, including distributed frequent itemset mining, distributed frequent sequence mining, distributed frequent graph mining, distributed clustering, and privacy preserving of distributed data mining. We finally summarize the opportunities of data mining tasks in distributed environment. WIREs Data Mining Knowl Discov 2017, 7:e1216. doi: 10.1002/widm.1216 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

The discovery of high-utility itemsets (HUIs) in transactional databases has attracted much interest from researchers in recent years since it can uncover hidden information that is useful for decision making, and it is widely used in many domains. Nonetheless, traditional methods for high-utility itemset mining (HUIM) utilize the utility measure as sole criterion to determine which item/sets should be presented to the user. These methods ignore the timestamps of transactions and do not consider the period constraint. Hence, these algorithms often finds HUIs that are profitable but that seldom occur in transactions. In this paper, we address this limitation of previous methods by pushing the period constraint in the HUI mining process. A new framework called short-period high-utility itemset mining (SPHUIM) is designed to identify patterns in a transactional database that appear regularly, are profitable, and also yield a high utility under the period constraint. The aim of discovering short-period high-utility itemsets (SPHUI) is hence to identify patterns that are interesting both in terms of period and utility. The paper proposes a baseline two-phase short-period high-utility itemset (SPHUI T P ) mining algorithm to mine SPHUIs in a level-wise manner. Then, to reduce the search space of the SPHUI TP algorithm and speed up the discovery of SPHUIs, two pruning strategies are developed and integrated in the baseline algorithm. The resulting algorithms are denoted as SPHUI MT and SPHUI TID , respectively. Substantial experiments both on real-life and synthetic datasets show that the three proposed algorithms can efficiently and effectively discover the complete set of SPHUIs, and that considering the short-period constraint and the utility measure can greatly reduce the number of patterns found. [ABSTRACT FROM AUTHOR]

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds. [ABSTRACT FROM AUTHOR]

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up-to-date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high-utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open-source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

Weighted Frequent Itemset Mining (WFIM) has been proposed as an extension of frequent itemset mining that considers not only the frequency of items but also their relative importance. However, using WFIM algorithms in real applications raises some problems. First, they do not consider how recent the patterns are. Second, traditional WFIM algorithms cannot handle uncertain data, although this type of data is common in real-life. To address these limitations, this paper introduces the concept of Recent High Expected Weighted Itemset (RHEWI), which considers the recency, weight and uncertainty of patterns. By considering these three factors, more up-to-date and relevant results are found. A projection-based algorithm named RHEWI-P is presented to mine RHEWIs using a novel upper - bound downward closure ( UBDC ) property. An improved version of this algorithm called RHEWI-PS is further proposed based on a novel sorted upper-bound downward closure ( SUBDC ) property for pruning unpromising candidate itemsets early. An experimental evaluation against the state-of-the-art HEWI-Uapriori algorithm was carried out on both real-world and synthetic datasets. Results show that the proposed algorithms are highly efficient and are acceptable for mining the desired patterns. [ABSTRACT FROM AUTHOR]

Frequent pattern mining (FPM) is an important topic in data mining for discovering the implicit but useful information. Many algorithms have been proposed for this task but most of them suffer from an important limitation, which relies on a single uniform minimum support threshold as the sole criterion to identify frequent patterns (FPs). Using a single threshold value to assess the usefulness of all items in a database is inadequate and unfair in real-life applications since each item is different and not all items should be treated as the same. Several algorithms have been developed for mining FPs with multiple minimum supports but most of them suffer from the time-consuming problem and require a large amount of memory. In this paper, we address this issue by introducing the novel approach named F requent P attern mining with M ultiple minimum supports from the E numeration-tree (FP-ME). In the developed Set- E numeration -tree with M ultiple minimum supports (ME-tree) structure, a new sorted downward closure ( SDC ) property of FPs and the least minimum support ( LMS ) concept with multiple minimum supports are used to effectively prune the search space. The proposed FP-ME algorithm can directly discover FPs from the ME-tree without candidate generation. Moreover, an improved algorithm, named FP-ME DiffSet , is also developed based on the DiffSet concept, to further increase mining performance. Substantial experiments on both real-life and synthetic datasets show that the proposed algorithms can not only avoid the “ rare item problem ”, but also efficiently and effectively discover the complete set of FPs in transactional databases while considering multiple minimum supports and outperform the state-of-the-art CFP-growth++ algorithm in terms of execution time, memory usage and scalability. [ABSTRACT FROM AUTHOR]

In the field of data mining, the topic of high-utility itemset mining (HUIM) has recently gained a lot of attention from researchers as it takes many factors into account that are useful for decision-making by retail managers. In the past, many algorithms have been presented for HUIM but most of them suffer from the limitation of using a single minimum utility threshold to identify high-utility itemsets (HUIs). For real-life applications, finding itemsets using a single threshold is inadequate and unfair since each item is different. Hence, the diversity or importance of each item should be considered. This paper proposes a solution to this issue by defining the novel task of HUIM with multiple minimum utility thresholds (named as HUIM-MMU). This task lets users specify a different minimum utility threshold for each item to identify more useful and specific HUIs, which would generate more profits when compared to HUIs discovered based on a single minimum utility threshold. The HUI-MMU algorithm is designed to mine HUIs in a level-wise manner. The sorted downward closure ( SDC ) property and the least minimum utility ( LMU ) concept are developed to avoid a combinatorial explosion for identifying HUIs and to ensure the completeness and correctness of HUI-MMU for discovering HUIs. Meanwhile, two improved algorithms, namely HUI-MMU TID and HUI-MMU TE , are presented based on the TID-index and EUCP strategies. Those strategies can be used to speed up the mining performance to discover HUIs. Substantial experiments on both real-life and synthetic datasets show that the designed algorithms can efficiently and effectively discover the complete set of HUIs in databases by considering multiple minimum utility thresholds. [ABSTRACT FROM AUTHOR]

High utility itemset mining is an emerging data mining task, which consists of discovering highly profitable itemsets (called high utility itemsets) in very large transactional databases. Many algorithms have been proposed to efficiently discover high utility itemsets but most of them assume that items may only have positive unit profits. However, in real-world transactional databases, items (products) often have positive or negative unit profits. Mining high utility itemsets in a transactional database where items have positive or negative unit profits is a computationally expensive task, and it is thus desirable to design more efficient algorithms. To address this issue, we propose an efficient algorithm named FHN (Faster High-Utility itemset miner with Negative unit profits). It relies on a novel PNU-list structure (Positive-and-Negative Utility-list) structure to efficiently mine high utility itemsets, while considering both positive and negative unit profits. Moreover, several pruning strategies are introduced in FHN to reduce the number of candidate itemsets, and thus enhance the performance of FHN. Extensive experimental results on both real-life and synthetic datasets show that the proposed FHN algorithm is in general two to three orders of magnitude faster and can use up to 200 times less memory than the state-of-the-art algorithm HUINIV-Mine. Moreover, it is shown that FHN performs especially well on dense datasets. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of the designed algorithms have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPE umu -GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approach. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm namely HUIM-BPSO sig is proposed to efficiently find HUIs. It first sets the number of discovered high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of a particle based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles of the designed HUIM-BPSO sig algorithm. Substantial experiments on real-life datasets show that the proposed algorithm has better results compared to the state-of-the-art GA-based algorithm. [ABSTRACT FROM AUTHOR]

High-Utility Itemset Mining (HUIM) is an extension of frequent itemset mining, which discovers itemsets yielding a high profit in transaction databases (HUIs). In recent years, a major issue that has arisen is that data publicly published or shared by organizations may lead to privacy threats since sensitive or confidential information may be uncovered by data mining techniques. To address this issue, techniques for privacy-preserving data mining (PPDM) have been proposed. Recently, privacy-preserving utility mining (PPUM) has become an important topic in PPDM. PPUM is the process of hiding sensitive HUIs (SHUIs) appearing in a database, such that the resulting sanitized database will not reveal these itemsets. In the past, the HHUIF and MSICF algorithms were proposed to hide SHUIs, and are the state-of-the-art approaches for PPUM. In this paper, two novel algorithms, namely Maximum Sensitive Utility-MAximum item Utility (MSU-MAU) and Maximum Sensitive Utility-MInimum item Utility (MSU-MIU), are respectively proposed to minimize the side effects of the sanitization process for hiding SHUIs. The proposed algorithms are designed to efficiently delete SHUIs or decrease their utilities using the concepts of maximum and minimum utility. A projection mechanism is also adopted in the two designed algorithms to speed up the sanitization process. Besides, since the evaluation criteria proposed for PPDM are insufficient and inappropriate for evaluating the sanitization performed by PPUM algorithms, this paper introduces three similarity measures to respectively assess the database structure, database utility and item utility of a sanitized database. These criteria are proposed as a new evaluation standard for PPUM. [ABSTRACT FROM AUTHOR]

Privacy-preserving data mining (PPDM) has become an important research field in recent years, as approaches for PPDM can discover important information in databases, while ensuring that sensitive information is not revealed. Several algorithms have been proposed to hide sensitive information in databases. They apply addition and deletion operations to perturb an original database and hide the sensitive information. Finding an appropriate set of transactions/itemsets to be perturbed for hiding sensitive information while preserving other important information is a NP-hard problem. In the past, genetic algorithm (GA)-based approaches were developed to hide sensitive itemsets in an original database through transaction deletion. In this paper, a particle swarm optimization (PSO)-based algorithm called PSO2DT is developed to hide sensitive itemsets while minimizing the side effects of the sanitization process. Each particle in the designed PSO2DT algorithm represents a set of transactions to be deleted. Particles are evaluated using a fitness function that is designed to minimize the side effects of sanitization. The proposed algorithm can also determine the maximum number of transactions to be deleted for efficiently hiding sensitive itemsets, unlike the state-of-the-art GA-based approaches. Besides, an important strength of the proposed approach is that few parameters need to be set, and it can still find better solutions to the sanitization problem than GA-based approaches. Furthermore, the pre-large concept is also adopted in the designed algorithm to speed up the evolution process. Substantial experiments on both real-world and synthetic datasets show that the proposed PSO2DT algorithm performs better than the Greedy algorithm and GA-based algorithms in terms of runtime, fail to be hidden (F-T-H), not to be hidden (N-T-H), and database similarity (DS). [ABSTRACT FROM AUTHOR]

Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet’s frame rate from 71.2 to 89.17 and VGG19’s frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations. [ABSTRACT FROM AUTHOR]

With the ever increasing number of applications of data mining, high-utility itemset mining (HUIM) has become a critical issue in recent decades. In traditional HUIM, the utility of an itemset is defined as the sum of the utilities of its items, in transactions where it appears. An important problem with this definition is that it does not take itemset length into account. Because the utility of larger itemset is generally greater than the utility of smaller itemset, traditional HUIM algorithms tend to be biased toward finding a set of large itemsets. Thus, this definition is not a fair measurement of utility. To provide a better assessment of each itemset’s utility, the task of high average-utility itemset mining (HAUIM) was proposed. It introduces the average utility measure, which considers both the length of itemsets and their utilities, and is thus more appropriate in real-world situations. Several algorithms have been designed for this task. They can be generally categorized as either level-wise or pattern-growth approaches. Both of them require, however, the amount of computation to find the actual high average-utility itemsets (HAUIs). In this paper, we present an efficient average-utility (AU)-list structure to discover the HAUIs more efficiently. A depth-first search algorithm named HAUI-Miner is proposed to explore the search space without candidate generation, and an efficient pruning strategy is developed to reduce the search space and speed up the mining process. Extensive experiments are conducted to compare the performance of HAUI-Miner with the state-of-the-art HAUIM algorithms in terms of runtime, number of determining nodes, memory usage and scalability. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high-utility itemsets (HUIs) assume that the information stored in databases is precise, i.e., that there is no uncertainty. But in many real-life applications, an item or itemset is not only present or absent in transactions but is also associated with an existence probability. This is especially the case for data collected experimentally or using noisy sensors. In the past, many algorithms were respectively proposed to effectively mine frequent itemsets in uncertain databases. But mining HUIs in an uncertain database has not yet been proposed, although uncertainty is commonly seen in real-world applications. In this paper, a novel framework, named potential high-utility itemset mining (PHUIM) in uncertain databases, is proposed to efficiently discover not only the itemsets with high utilities but also the itemsets with high existence probabilities in an uncertain database based on the tuple uncertainty model. The PHUI-UP algorithm (potential high-utility itemsets upper-bound-based mining algorithm) is first presented to mine potential high-utility itemsets (PHUIs) using a level-wise search. Since PHUI-UP adopts a generate-and-test approach to mine PHUIs, it suffers from the problem of repeatedly scanning the database. To address this issue, a second algorithm named PHUI-List (potential high-utility itemsets PU-list-based mining algorithm) is also proposed. This latter directly mines PHUIs without generating candidates, thanks to a novel probability-utility-list (PU-list) structure, thus greatly improving the scalability of PHUI mining. Substantial experiments were conducted on both real-life and synthetic datasets to assess the performance of the two designed algorithms in terms of runtime, number of patterns, memory consumption, and scalability. [ABSTRACT FROM AUTHOR]

Frequent itemsets are useful for discovering interesting associations hidden in large databases. Many mining algorithms use data with binary attributes to represent the occurrence of items and find frequent itemsets. However, many real-world applications provide a richer source of transactions with quantitative values. The fuzzy frequent pattern tree algorithm was thus proposed for extracting fuzzy frequent itemsets from the quantitative transactions. In this paper, a tree structure called the upper-bound multiple fuzzy frequent-pattern (UBMFFP)-tree is designed for improving the pruning effect in the mining process. A two-phase fuzzy mining approach based on the tree structure is also proposed to obtain the complete fuzzy frequent itemsets from a quantitative database. The proposed fuzzy mining approach recursively and efficiently finds the upper-bound fuzzy counts of itemsets with the aid of the tree structure. It prunes unpromising itemsets in the first phase, and then finds the actual fuzzy frequent itemsets in the second phase. Experimental results indicate that the proposed UBMFFP-tree algorithm has good performance in terms of execution time and number of tree nodes. [ABSTRACT FROM AUTHOR]

High-utility itemsets mining (HUIM) is a critical issue which concerns not only the occurrence frequencies of itemsets in association-rule mining (ARM), but also the factors of quantity and profit in real-life applications. Many algorithms have been developed to efficiently mine high-utility itemsets (HUIs) from a static database. Discovered HUIs may become invalid or new HUIs may arise when transactions are inserted, deleted or modified. Existing approaches are required to re-process the updated database and re-mine HUIs each time, as previously discovered HUIs are not maintained. Previously, a pre-large concept was proposed to efficiently maintain and update the discovered information in ARM, which cannot be directly applied into HUIM. In this paper, a maintenance (PRE-HUI-MOD) algorithm with transaction modification based on a new pre-large strategy is presented to efficiently maintain and update the discovered HUIs. When the transactions are consequentially modified from the original database, the discovered information is divided into three parts with nine cases. A specific procedure is then performed to maintain and update the discovered information for each case. Based on the designed PRE-HUI-MOD algorithm, it is unnecessary to rescan original database until the accumulative total utility of the modified transactions achieves the designed safety bound, which can greatly reduce the computations of multiple database scans when compared to the batch-mode approaches. [ABSTRACT FROM AUTHOR]

High-utility pattern mining (HUPM) is an emerging topic in recent years instead of association-rule mining to discover more interesting and useful information for decision making. Many algorithms have been developed to find high-utility patterns (HUPs) from quantitative databases without considering timestamp of patterns, especially in recent intervals. A pattern may not be a HUP in an entire database but may be a HUP in recent intervals. In this paper, a new concept namely up-to-date high-utility pattern (UDHUP) is designed. It considers not only utility measure but also timestamp factor to discover the recent HUPs. The UDHUP-apriori is first proposed to mine UDHUPs in a level-wise way. Since UDHUP-apriori uses Apriori-like approach to recursively derive UDHUPs, a second UDHUP-list algorithm is then presented to efficiently discover UDHUPs based on the developed UDU-list structures and a pruning strategy without candidate generation, thus speeding up the mining process. A flexible minimum-length strategy with two specific lifetimes is also designed to find more efficient UDHUPs based on a users’ specification. Experiments are conducted to evaluate the performance of the proposed two algorithms in terms of execution time, memory consumption, and number of generated UDHUPs in several real-world and synthetic datasets. [ABSTRACT FROM AUTHOR]

Autonomous transportation in logistics is the prime focus, with further deep dive into its application across various aspects of the supply chain. In the next article, the authors present an optimized least square support vector machine to improve load forecasting in autonomous vehicles. The next article presents a predictive traffic model and intelligent energy management systems for autonomous vehicles. [Extracted from the article]

Ransomware continues to pose a significant threat to cybersecurity by extorting money from users by locking their devices and personal data. The attackers force the payment of a ransom in order to restore access to personal files. Because of the structural similarity, detection of ransomware and benign applications becomes vulnerable to evasion attacks. Ensemble learning can provide countermeasures, while attackers can use the same technique to improve the effectiveness of their respective attacks. This motivates us to investigate whether the distinct ensemble method can achieve better performance when combined with the voting-based method. This research proposes a hybrid approach that examines permissions, text, and network-based features both statically and dynamically by monitoring memory usage, system call logs, and CPU usage. Ensemble machine learning analyzers on static and dynamic features extracted from Android malware applications (ransomware and non-ransomware) are then trained in the designed model. Our experimental results show that the proposed ensemble classification and detection technique can classify unknown static and dynamic ransomware behavior to mitigate adversarial evasion attacks. [Display omitted] • The designed model extracts and analyzes static network-based features. • Two ML-based ensemble models are proposed for static and dynamic feature sets. • The model is for Android ransomware adversarial evasion attacks. [ABSTRACT FROM AUTHOR]