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The Internet of Medical Things (IoMT) is an extended genre of the Internet of Things (IoT) where the Things collaborate to provide remote patient health monitoring, also known as the Internet of Health (IoH). Smartphones and IoMTs are expected to maintain secure and trusted confidential patient record exchange while managing the patient remotely. Healthcare organizations deploy Healthcare Smartphone Networks (HSN) for personal patient data collection and sharing among smartphone users and IoMT nodes. However, attackers gain access to confidential patient data via infected IoMT nodes on the HSN. Additionally, attackers can compromise the entire network via malicious nodes. This article proposes a Hyperledger blockchain-based technique to identify compromised IoMT nodes and safeguard sensitive patient records. Furthermore, the paper presents a Clustered Hierarchical Trust Management System (CHTMS) to block malicious nodes. In addition, the proposal employs Elliptic Curve Cryptography (ECC) to protect sensitive health records and is resilient against Denial-Of-Service (DOS) attacks. Finally, the evaluation results show that integrating blockchains into the HSN system improved detection performance compared to the existing state of the art. Therefore, the simulation results indicate better security and reliability when compared to conventional databases. [ABSTRACT FROM AUTHOR]

Vehicular delay-tolerant networks follow a store–carry–forward mechanism to overcome the state of broken links between source and destination, which is crucial for autonomous vehicle networks. These networks depend upon the mobility of vehicular-relay nodes, which can adopt the store–carry–forward arrangement and provide consent to utilize their resources for packet forwarding. In reality, vehicular-relay nodes drop packets because of a lack of resources and selfish behavior, leading to potential failures in networks. The reason for this failure in performance is the absence of an efficient relay node selection strategy. Typically, vehicular delay networks can provide communication solutions in challenging conditions when other traditional networks fail to perform due to disconnection. However, if the routing strategy does not consider nodes' selfish nature and relay nodes' inefficiency, packet drops continue, and the approach will fail to perform. This paper proposes a routing strategy, known as the best-choice vehicular-relay selection strategy for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) transmission, to overcome this breakdown situation. The proposed strategy selects the best-performing vehicular-relay nodes and restricts the packet replication to low-performing vehicular nodes. The proposed routing strategy selects the best-choice relay nodes based on their past performance. Numerical analysis and our simulation results show that the proposed approach outperforms some of the leading routing strategies in its class by increasing the delivery probability, decreasing the overhead ratio, average latency, and the number of dropped packets in the vehicular network. [ABSTRACT FROM AUTHOR]

The concept of cloud envisioned cyber-physical systems is a practical technology that allows users to interact with each other while transferring data in the cloud. In cyber-physical systems, cloud storage utilizes data deduplication techniques to improve the performance of its applications. However, this method exposes sensitive data and causes security risks. Various research related to cloud storage has been conducted. Despite the advantages of this technology, it lacks the necessary security features and high performance. In the proposed method, a victim's virtual computer is moved to the cloud without interfering with the other processes running on the network. It protects against the attacks caused by encrypting the data with a cryptographically binding hash. Post-Quantum Cryptographic techniques, such as lightweight Multi-Extractable Somewhere Statistically Binding and Learning With Error authenticate data sharing protocols (MESSB–LWE), have been used. These allow for safe data sharing across geographically scattered physical devices and clients with lightweight concepts. The numerical analysis of MESSB–LWE is carried out in different stages, and the results show that it has incredible performance and practicality when compared to the literature. Finally, the authors have explored a couple of factors that should be considered for future research work in authentication for securing remote systems in the cloud environment. [ABSTRACT FROM AUTHOR]

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]

Consumer sentiment is one of the essential measures of predictive recommendations in travel and tourism. Nowadays, a massive amount of data is available on the online platform related to consumer sentiment, which may help draw insights into how consumers provide feedback and how we can use that feedback to predict recommendations using machine learning techniques. In this study, we have designed a predictive recommendations method that predicts the consumer recommendations in travel and tourism, particularly in the case of the airline. We developed our predictive methods as a multi-label classification system. We implemented K-Nearest Neighbors, Support Vector Machine, Multi-layer Perceptron, Logistic Regression, Random Forest, and Ensemble Learning as basic classification models to train our model. Further, we boosted our predictive model by implementing state-of-the-art partitioning methods to partition the label space in lower spaces and utilized label space partitioning approaches, namely RAkELo and Louvain, as a transformation technique to transform every label set into a multi-class classification problem. The suggested model obtained higher performance in terms of accuracy using various evaluation measures compared to other binary classifications. Furthermore, the valuable traveler may get benefited from this approach in making their predictive decisions before travel. [ABSTRACT FROM AUTHOR]

Outlier detection in the stream data has emerged as the challenging problems with the precipitously growing demand of applications like intrusion detection, sensor malfunctioning, fraud detection, and system failures, etc. To address these problems many density-based algorithms have been proposed for detecting the outliers in stream data. Still, it suffers a serious problem with the degree of outlierness measures on its neighbors. Using a right number k is not straightforward in the stream data since we do not know prior distribution of neighbor's points. And, it must improvise with incoming data as appeared in the stream data. Additionally, stream-based algorithms are not able to detect sequential outliers as well as are having memory constraints. These challenges motivate the authors to propose "Self-Adaptive Density Summarizing incremental Natural Outlier Detection in Data Stream (ADINOF) with skipping scheme and without skipping scheme (ADINOF_NS)" that successfully overcome the challenges. Our comprehensive experimental evaluations demonstrate that ADINOF and ADINOF_NS significantly outperforms the competitive executed algorithms (iLOF, MiLOF, TADILOF, DILOF, and DILOF_NS). [ABSTRACT FROM AUTHOR]

The vehicular delay-tolerant network is the real-life application based area of Delay tolerant network where communication takes place using vehicular nodes and roadside units. The topology used in vehicular networks is highly dynamic by architecture due to the use of moving vehicular nodes. It operates in such a scenario where a direct path between source and destination remains absent on the most piece of the time. In case of non-existence of connected path vehicular delay-tolerant network works opportunistically and uses the same store, carry, and forward paradigm as Delay Tolerant Network. However, the routing protocols designed for vehicular delay-tolerant network faces crucial challenges like inadequate relay node, incomplete data transfer, a large number of packet drop, and uncertain delivery time. In this research paper, we propose a novel routing strategy for the vehicular delay-tolerant network. The proposed routing strategy selects efficient vehicular relay node for complete packet transfer and intelligently reduces the packet drop for timely packet delivery. We implement the proposed routing strategy in the ONE simulator; the ONE simulator provides an opportunistic environment for nodes. We analyze the performance of the proposed strategy under various simulations results using different parameters. The results show that the proposed strategy outperforms standard routing protocols in terms of considered parameters and provide an efficient solution for the problem of disconnection. [ABSTRACT FROM AUTHOR]

Outlier detection has become an important research area in the field of stream data mining due to its vast applications. In the literature, many methods have been proposed, but they work well for simple and positive regions of outliers, where boundary regions are not given much importance. Moreover, an algorithm which processes stream data must be effective and able to compute infinite data in one pass or limited number of passes. These problems have motivated us to propose an outlier detection approach for large-scale data stream. The proposed algorithm employs the concept of relative cardinality, entropy outlier factor theory of information-based system, and size-variant sliding window in stream data. In addition, we propose a new methodology for concept drift adaptation on evolving data streams. The proposed method is executed on nine benchmark datasets and compared with six existing methods that are EXPoSE, iForest, OC-SVM, LOF, KDE, and FastAbod. Experimental results show that the proposed method outperforms six existing methods in terms of receiver operating characteristic curve, precision recall, and computational time for positive regions as well as for boundary regions. [ABSTRACT FROM AUTHOR]

Fault detection in wind turbine which is identified with complete system monitoring under multi-fault scenario is proposed. When a fault is detected, its types and location are recognized for easy maintenance. Fault in wind turbines is caused due to the high speed of gearbox, generator bearing and the failures occurred in various parts. In wind farm, wind turbine condition monitoring is used to reduce the maintenance cost and also improves the accuracy. Generally, in wind turbine gearbox condition monitoring using sensor is a gainful method to monitor wind turbine performance and fault. This paper nominates a method to decide the parameters for support vector machine (SVM) in wind turbine called Cuckoo search optimization (CSO). The combination of optimization technique with classification technique is evaluated. MATLAB platform was used to evaluate the various faults under fixed value and gain factor conditions. Comparing the accuracy with SVM, particle swarm optimized SVM and k-nearest neighbor, the proposed fault detection and fault isolation technique (CSO-SVM) is improved by 2.5%, 3.5% and 6.5%, respectively. The result shows the CSO model based on SVM algorithm accomplishes the most accurate fault detection than the past models. [ABSTRACT FROM AUTHOR]

The sentiment analysis (SA) from the user‐generated reviews is the latest research topic in natural language processing. Nowadays, the extraction of consumer sentiment from the content of the consumer reviews is getting much attention because of its importance in understanding consumers' experiences regarding services or products. Consumer sentiment may be helpful for both consumers and organizations; a consumer can refer to previous consumers' feedback while making their purchase decisions, and organizations can use it in service improvements. For the consumer SA, this article proposed the BERT‐GAN model with review aspect fusion, which improves the fine‐tuning performance of the BERT model by introducing semi‐supervised adversarial learning. For our objective, we extracted various service aspects from consumer reviews and fused them with the word sequences before feeding them into the model. That helps in incorporating aspect representation as well as position information in context with the sentences. Our results analysis and their demonstration show the contribution of the presented model in terms of accuracy compared with the existing models found in the previous work. [ABSTRACT FROM AUTHOR]

• To develop a model for the efficient retrieval of information using Query Expansion. • The model applies fuzzy logic to remove uncertainties and semantic filtering to remove noise. • The model employs the novelty of using fuzzy logic and semantic filtering and BERTscore for extracting more similar terms. • To study and evaluate the suggested model's performance on a variety of datasets. Several factors hinder information retrieval in the medical profession. Consumers (layman people) often struggle to learn medical terms. Because medical terms are more evident to professionals, it is difficult for consumers to construct a query using medical terms. Consumers would find it easier to access relevant medical information if medical words relevant to their query were automatically added. Various kiosks use approaches using machine vision to form the user queries and monitor their health. This work proposes a hybrid approach to term selection by expanding user queries with medical terms relevant to the medical query. The selection of terms is based on fuzzy similarity reasoning based on two primary term selection strategies. WordNet semantic filtering is applied for preventing query drift, followed by the calculation of BERTScore. The retrieved documents were ranked using Okapi-BM25. For evaluation purposes, six benchmark datasets have been used: CACM, CISI, MEDLINE, ADI, FIRE, and TREC (Covid-19). The results indicate that the suggested technique outperforms the current state-of-the-art. [ABSTRACT FROM AUTHOR]

Text summarization is an extraction of important text from the original document. The objective of any automatic text summarization system, especially in legal domain, is to produce a summary which is close to human-generated summaries. In this article, we present the summarization of legal documents as binary optimization problem where fitness of the solution is derived based on the weighting of individual statistical features of each sentence such as length of the sentence, sentence position, degree of similarity, term frequency–inverse sentence frequency and keywords to generate summary of the document. In this paper, a gravitational search algorithm is adopted that works on the basis of the law of gravity to optimize the summary of the document. To show the efficacy of the proposed method, we compare the experimental results with particle swarm optimization, genetic algorithm, TextRank, latent semantic analysis, MEAD, MS-Word, SumBasic using ROUGE evaluation metrics on the FIRE-2014 data set. The experimental results of the proposed method show better than the existing state-of-the-art methods in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

Automatic text summarization is a process of condensing the content of a text document to pursue the most important information. It plays a significant role in various tasks like text categorization, question answering and information retrieval (IR). As legal information retrieval (LIR) is a subfield of IR, the produced summaries are combined into IR system, with the objective of decreasing the length of the document. In this way, we can improve the access time for searching the information, and relevant documents are retrieved. In this article, we present the creation of passage-level summaries (generic and legal) with different compression ratios and evaluate their performance. The generic summaries present the overall description of the essential information of a document and legal summaries, produced by taking into account the domain-specific features that are present in the document. Next, we propose Boosting Okapi BM25 which is the modified model of Okapi BM25 to increase the efficiency of the LIR. We have evaluated proposed LIR approach in terms of MAP and R-precision and summarization approach using ROUGE tool on FIRE2013 and FIRE2014 datasets. To show the efficacy of the proposed system, we compare the experimental results with different IR models like PL2, I n _ e x p B 2 , I n _ e x p C 2 , InL2, D F R _ B M 25 , Okapi BM25 in terms of MAP. The experimental results of the proposed system show better performance than the existing various IR models in terms of various performance metrics. The empirical results also exhibit that the integration of text summarization and IR techniques helps in retrieving relevant information with less access time. [ABSTRACT FROM AUTHOR]

In this study, we present a clustering cost function that uses K-nearest neighbour (KNN) and particle swarm optimisation (PSO) to detect outliers. Here, the Dunn index was used to estimate the ideal number of clusters. The obtained number of clusters was subsequently utilised to cluster data using the PSO algorithm and our proposed cost function. The KNN method was used to determine the top n outliers in the datasets, and data pruning was performed to eliminate any potential inliers. Outlier detection average precision was measured using both the K-means and our proposed algorithm. It was discovered that our proposed technique consistently detects outliers across different types of datasets containing both extremely and marginally distinct outliers from inliers data points. We believe that our proposed algorithm could be a promising technique for the outlier detection problem, with applications in network intrusion, medical diagnosis, banking, cyber-security, optical communication, etc. [ABSTRACT FROM AUTHOR]

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware's unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection. [ABSTRACT FROM AUTHOR]

Digital technology and social media have delivered many advantages in understanding human psychology, which is essential to industrial growth. Skytrax is an online social media platform for travellers to write reviews on airlines. In online reviews, consumer recommendations are critical indicators for service providers to improve their consumer policies as well as service quality. It is also helpful for future customers to help get information on future purchases prior to making them. Therefore, previous consumer recommendations based on online reviews play a vital role in airline recommendations. Our main goal is to use our proposed cuckoo optimized machine learning model to predict airline recommendations. Experimental analysis was implemented using data scraped from the website https://www.airlinequality.com. Our results show that the proposed eXtreme gradient boosting classifier optimized by Cuckoo Search (CS-XGB) outperforms other state-of-the-art techniques. [Display omitted] • Propose a consumer recommendation prediction system using online review. • Acquired consumer online reviews and ratings from Skytrax. • Applied various pre-processing schemes to input data and feed it to the model. • Create a Cuckoo Search (CS) based parameter optimization model. • Proposed CS-XGB outperforms the other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]