ABSTRACT A fundamental issue in managing parking effectively is optimizing the utilization of existing parking spaces. While advanced artificial techniques have demonstrated remarkable accuracy in classifying parking spots, optimizing their utilization remains a key concern. However, performance degrades during slight interference, obstructions, and diverse lighting conditions such as fog or haze. Most researchers have used deep learning approaches to classify parking spaces for non‐hazy weather conditions only, often prioritizing model performance over training efficiency. Building on this, the main aim of the proposed work is to develop a model for classifying parking spaces under any weather conditions (hazy and non‐hazy). A synthesized parking dataset is designed for hazy weather conditions. Light‐dehazenet (LD‐Net) is applied to counteract the effects of haze in the synthesized dataset. AlexNet is trained on the synthesized and PKLot datasets by applying transfer learning and preparing hazy and non‐hazy feature vectors, respectively. A random forest is applied to select top‐ranked features to avoid overfitting, remove noise from the features, and increase generalization capability. The selected features contribute to the input vector for classification using Multilayer‐ELM (MLELM). The major breakthrough involves replacing the fully connected layer of AlexNet with MLELM to avoid lengthy backpropagation and reduce the training time. The experimental results of AlexNet‐MLELM are compared with lightweight pre‐trained CNN and existing state‐of‐the‐art models. Empirical results suggest that the proposed model provides a viable approach for parking space classification in diverse weather conditions.

Abstract Event detection in real‐time is applied in diverse domains such as detection of fraudulent activities in commercial transactions, detection of faulty systems in industries, and so forth. Businesses and organizations benefit from the actionable information obtained through various techniques available for anomalous event detection. Real‐time event detection is nowadays handled through streaming data frameworks. Traditional approaches effectively handle event detection in real‐time but with more false positives, thus, resulting in false alarms. In this article, an efficient approach comprising two components, an offline model and an online event detection pipeline, is proposed to achieve minimum mean absolute error (MAE). An offline module is developed to investigate a variety of deep learning models that prove suitable for event detection in real‐time. The experiments conducted with PubNub sensors datasets demonstrate that the long short‐term memory unit of recurrent neural networks is the best suitable model for anomalous event detection. The online pipeline module is built using streaming data frameworks to predict the abnormal peaks. It is revealed through the experimental results that the proposed approach efficiently detects anomalous events in real‐time and also eliminates false positives.

Summary The perpetual increase in social media data due to social distancing policy in practice and the low cost of communication has acquired an interest in rumor detection in social media. During the togetherness of the globalized world in the pandemic situation of Covid‐19, social media applications drive excellent value to the analysts and journalists working in different domains. However, the unmoderated nature of social media users often leads to the spread of rumors due to a variety of opinions on government decisions, which becomes notoriously hard to detect. Also, the users may deny rumors as soon as they are debunked. This research extracts diffused information such as word cloud, hashtags, re‐tweets and so forth and uses it as features. These features are applied in the sliding windows of data streams to detect tweets that may be rumors by validating using credible news sources. To address the challenge of veracity, tweets are classified into rumors and nonrumors. A modified unsupervised VADER sentiment classifier is proposed to further classify rumors tweets into amalgamated, unauthoritative, or exaggerated. It is revealed from the results that the proposed classifier is quite efficient in finding and classifying rumorous tweets that arise in critical situations.

SUMMARY With the advent of break‐through sensing technology, performing data capturing and analysis for knowledge engineering has become more opportunistic. The task of efficiently analyzing sensor based data for effective decision making poses a significant challenge. Conventional prediction and recommender systems lack comprehensive analysis of all parameters and aspects, thus compromising prediction results. At the decision‐making level, traditional knowledge driven prediction systems deploy classical ontology for knowledge representation and analysis. However, classical ontologies are not considered as powerful tools due to their inability to handle vagueness in data for real‐world applications. On the contrary, fuzzy ontology deals with the issue of hazy and uncertain data for effective analysis to give promising results. This work presents interval type 2 fuzzy ontological knowledge model that predicts water quality of sensor based water samples and providing solutions with respect to the corresponding quality state. The proposed knowledge model constitutes of two newly developed ontologies: water sensor observations ontology (crisp ontology to model sensor observational data) and water quality ontology (interval type 2 fuzzy ontology for modeling the water quality prediction process). The inference mechanism is based on interval type‐2 fuzzy partitioning and computation. Besides water quality prediction and providing solutions, the proposed model handles the issue of interoperability and exchange of consensual knowledge among multiple disciplines. The proposed knowledge model is validated with real‐life water sensor based parameterized data captured from various geographically dispersed monitoring stations with approximately 50,000 samples at each station.

Summary Medical records contain vital, critical, and confidential information of any person. Paper‐based medical records are being used by many hospitals that suffer from various issues like large physical storage requirements, limited security, and life. Electronic health records (EHRs) can transform this entire system but faces various security threats like unintentional disclosure, tampering, data leaks, lack of security mechanisms, and data access policies. Thus, there arises a need for a decentralized, tamper‐proof, and transparent healthcare network. In this article, a permissioned blockchain‐based framework using Hyperledger Fabric (HLF) is proposed, which provides secure storage, sharing, and querying of EHRs. For better data accessibility and security, algorithms have been designed to define the functionality of patients, doctors, and third‐party administrator using smart‐contracts. In the existing approaches, LevelDB (key store database) is used as a state database that is not suitable for handling complex queries. To overcome this issue, we have used CouchDB (document store database) as a state database where the search is based on keys and data values both rather than only keys. To measure the various performance indicators for this framework, a blockchain benchmark tool “Hyperledger Caliper” is used. The experimental results show the effectiveness and efficiency of the proposed framework.

Malware has already been recognized as one of the most dominant cyber threats on the Internet today. It is growing exponentially in terms of volume, variety, and velocity, and thus overwhelms the traditional approaches used for malware detection and classification. Moreover, with the advent of Internet of Things, there is a huge growth in the volume of digital devices and in such scenario, malicious binaries are bound to grow even faster making it a big data problem. To analyze and detect unknown malware on a large scale, security analysts need to make use of machine learning algorithms along with big data technologies. These technologies help them to deal with current threat landscape consisting of complex and large flux of malicious binaries. This paper proposes the design of a scalable architecture built on the top of Apache Spark which uses its scalable machine learning library (MLlib) for detecting zero-day malware. The proposed platform is tested and evaluated on a dataset comprising of 0.2 million files consisting of 0.05 million clean files and 0.15 million malicious binaries covering a large number of malware families over a period of 7 years starting from 2010.

Abstract Clustering helps in understanding the patterns present in networks and thus helps in getting useful insights. In real‐world complex networks, analysing the structure of the network plays a vital role in clustering. Most of the existing clustering algorithms identify disjoint clusters, which do not consider the structure of the network. Moreover, the clustering results do not provide consistency and precision. This paper presents an efficient parallel fuzzy clustering algorithm named “PFCA” for large complex networks using Hadoop and Pregel (parallel processing framework for large graphs). The proposed algorithm first selects the candidate cluster heads on the basis of their influence in the network and then determines the number of clusters by analysing the graph structure using PageRank algorithm. The proposed algorithm identifies both disjoint and fuzzy clusters efficiently and finds membership of only those vertices, which are the part of more than one cluster. The performance is validated on 6 real‐life networks having up to billions of connections. The experimental results show that the proposed algorithm scales up linearly with the increase in size of network. It is also shown that the proposed algorithm is efficient and has high precision in comparison with the other state‐of‐art fuzzy clustering algorithms in terms of F score and modularity.

Abstract Correlation clustering possibly represents the most intuitive form of clustering construction. It gives solutions that can be approximated while automatically selecting the number of clusters. This approach handles scenarios where the focus is on relationships between the objects instead of on actual representations of the objects. The suitability of this method extends to the structured objects, for which feature vectors are not easy to obtain. Given the increasing scale of data these days, correlation clustering has become a powerful addition to the fields of data mining and agnostic learning. Correlation clustering considers a weighted graphG=(V,E), where the edge weight indicates whether two nodes are similar (positive edge weight) or different (negative edge weight). The task is to find a clustering that either maximizes agreements or minimizes disagreements. Unlike other clustering algorithms, this does not require choosing the number of clusters (k) in advance. The objective to minimize the sum of weights of the cut edges is independent of the number of clusters. Methodologies, such as approximations and linear programming formulations, have been used to approach this problem. This paper focuses on the problem of correlation clustering and lists the solutions proposed by various researchers. These solutions approach the problem using different computational techniques. Correlation clustering‐based applications such as entity de‐duplication, signed social networks, and problem of aggregating multiples have also been discussed.

Correlation analysis is an effective mechanism for studying patterns in data and making predictions. Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient. We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric: independence from sample size, value between −1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration. Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman’s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.

Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.

Relational databases are providing storage for several decades now. However for today's interactive web and mobile applications the importance of flexibility and scalability in data model can not be over-stated. The term NoSQL broadly covers all non-relational databases that provide schema-less and scalable model. NoSQL databases which are also termed as Internetage databases are currently being used by Google, Amazon, Facebook and many other major organizations operating in the era of Web 2.0. Different classes of NoSQL databases namely key-value pair, document, column-oriented and graph databases enable programmers to model the data closer to the format as used in their application. In this paper, data modeling and query syntax of relational and some classes of NoSQL databases have been explained with the help of an case study of a news website like Slashdot.