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In the current era of digital communication, image security is a top priority as sending images, which often contain personal or confidential information, over the Internet poses security risks. Many encryption algorithms have been proposed in the literature to address this issue. However, these algorithms have limitations, such as low-key space, high computational overhead, and susceptibility to differential attacks. To address these limitations, this paper proposes Z-Crypt, a novel image encryption approach that combines a substitution-permutation network (SPN) and a chaotic logistic map (CLM) with the Chirp Z-Transform (CZT) to enhance security and resist attacks. The CLM-generated matrices introduce confusion and diffusion, while SPN creates a dissociation of the cipher from the plaintext. Finally, the CZT strengthens the encryption by transforming the image into the frequency domain, creating multiple layers of confusion and diffusion to produce a robust encryption algorithm. We have evaluated the proposed Z-Crypt’s security using various metrics, including correlation coefficient, entropy, peak signal-to-noise ratio (PSNR), and key sensitivity analysis. Experimental results verify that the proposed algorithm outperforms existing methods, achieving high security while maintaining computational efficiency.

Abstract Mobile robots are increasingly employed in today’s environment. Perceiving the environment to perform a task plays a major role in the robots. The service robots are wisely employed in the fully (or) partially known user’s environment. The exploration and exploitation of the unknown environment is a tedious task. This paper introduces a novel Trimmed Q-learning algorithm to predict interesting scenes via efficient memorability-oriented robotic behavioral scene activity training. The training process involves three stages: online learning and short-term and long-term learning modules. It is helpful for autonomous exploration and making wiser decisions about the environment. A simplified three-stage learning framework is introduced to train and predict interesting scenes using memorability. A proficient visual memory schema (VMS) is designed to tune the learning parameters. A role-based profile arrangement is made to explore the unknown environment for a long-term learning process. The online and short-term learning frameworks are designed using a novel Trimmed Q-learning algorithm. The underestimated bias in robotic actions must be minimized by introducing a refined set of practical candidate actions. Finally, the recalling ability of each learning module is estimated to predict the interesting scenes. Experiments conducted on public datasets, SubT, and SUN databases demonstrate the proposed technique’s efficacy. The proposed framework has yielded better memorability scores in short-term and online learning at 72.84% and in long-term learning at 68.63%.

Abstract One of the main goals of ecological studies is to disentangle the dynamics that underlie the spatiotemporal distribution of biodiversity and further functions of the ecosystem. However, due to many ecological and geopolitical reasons, many remote areas with high plant species diversity have not been assessed using newly based analytical approaches for vegetation characterization. Here, we classified and characterized different vegetation types (i.e., major plant communities) based on indicator species and on the influence of different environmental gradients in the Himalayan mixed coniferous forest, Pakistan. For that, we addressed the following questions: Does the vegetation composition of the Himalayan mixed coniferous forest correlate with climatic, topographic, geographic, and edaphic variables? Is it possible to identify plant communities through indicator species in relation to environmental gradients using multivariate approaches? Can this multivariate be helpful for conservation planning? During four consecutive years we assessed the vegetation composition and environmental variables (21 variables divided in geographic, climatic, topographic, and edaphic groups) of 156 50 m-trasects between an elevation of 2000–4000 m. Using newly based analytical approaches for community characterization, we found a total of 218 plant species clustered into four plant communities with the influence of environmental gradients. The highest index of similarity was recorded between Pinus-Cedrus-Viburnum (PCV) and Viburnum-Pinus-Abies (VPA) communities, and the highest index of dissimilarity was recorded between PCV and Abies-Juniperus-Picea (AJP) communities. Among these four communities, highest number of plant species (156 species) was recorded in PCV, maximum alpha diversity (H’ = 3.68) was reported in VPA, highest Simpson index (0.961) and Pielou’s evenness (0.862) were reported in VPA and AJP. The edaphic gradients (i.e., organic matter, phosphorous, pH and soil texture) and climatic factors (temperature, humidity) were the strongest environmental gradients that were responsible for structuring and hosting the diverse plant communities in mixed coniferous forest. Finally, the Himalayan mixed coniferous structure is more influenced by the spatial turnover beta-diversity process (βsim) than by the species loss (nestedness-resultant, βsne). Our analysis of the vegetation structure along the environmental gradient in the Himalayan mixed coniferous forest supported by sophisticated analytical approaches reveled indicator species groups, which are associated to specific microclimatic zones (i.e., vegetation communities). Within this focus, we side with the view that these results can support conservation planning and management for similar and different areas providing mitigating and preventive measures to reduce potential negative impacts, such as anthropic and climatic.

Green synthesis of nanoparticles (NPs) has attracted the researcher's attention due to its rapid, cost-effective, sustainable and eco-friendly nature. Current studies were carried out to perform and investigate the green synthesis of NiO NPs with aqueous (NiO(aqueous)) and ethanolic extracts (NiO(ethanolic)) of Acacia nilotica leaves, respectively. The NPs were characterized by spectroscopic, microscopic and thermometric techniques including FTIR, Raman, XRD, SEM, TGA and DSC. The electrochemical properties, antibacterial potential and haemolytic activities of the synthesized NPs were also examined. XRD patterns revealed the face-centred cubic structures of NiO NPs and a high degree of crystallinity. The average crystallite size of NiO(aqueous) NPs was significantly smaller (16 nm) than that of NiO(ethanolic) NPs (28 nm). SEM images show that NiO NPs are spherical. The synthesized NiO NPs demonstrate good electrochemical stability at an operating potential of −0.5-0.5 V. The inhibition effect of NiO NPs(ethanol) against B. subtilis, was comparable to the ciprofloxacin.

The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.

In recent years, the industrial network has seen a number of high-impact attacks. To counter these threats, several security systems have been implemented to detect attacks on industrial networks. However, these systems solely address issues once they have already transpired and do not proactively prevent them from occurring in the first place. The identification of malicious attacks is crucial for industrial networks, as these attacks can lead to system malfunctions, network disruptions, data corruption, and the theft of sensitive information. To ensure the effectiveness of detection in industrial networks, which necessitate continuous operation and undergo changes over time, intrusion detection algorithms should possess the capability to automatically adapt to these changes. Several researchers have focused on the automatic detection of these attacks, in which deep learning (DL) and machine learning algorithms play a prominent role. This study proposes a hybrid model that combines two DL algorithms, namely convolutional neural networks (CNN) and deep belief networks (DBN), for intrusion detection in industrial networks. To evaluate the effectiveness of the proposed model, we utilized the Multi-Step Cyber Attack (MSCAD) dataset and employed various evaluation metrics.

Nowadays, the growth of multimedia content over the web is exponential. The fingerprints are inconspicuously embedded in multimedia content. The fingerprints can be exploited to trace divergent information from multimedia resources. Sampling fingerprints, particularly from multimedia resources, is challenging since they are complex, heterogeneous, and diverse. This research proposed an approach to sample fingerprints from multimedia resources. Our approach partitions the multimedia content space into converged clusters using variations of Canberra distance and identifies the most diverged samples using Kullback-Leibler (KL) divergence. The resultant clusters represent the information belonging to particular concepts and the diverged samples within the clusters represent multimedia fingerprints. The fingerprint sampling process is leveraged using unsupervised learning algorithms, instantiated across various multimedia descriptors, and tested over standard multimedia datasets. The average results obtained over various standard visual and acoustic datasets reveal 80%, 77%, and 78% accuracy, precision, and recall, respectively, surpassing most of the existing baseline clustering methods such as K-Means, Mean-Shift, and DBSCAN. Furthermore, the rigorousness of the proposed algorithm clustering is evaluated using the internal clustering stability silhouette coefficient and the fingerprint diversity scores. The results unveil a maximum of 94% diversity score. The proposed variation of Canberra distance and KL divergence provides the most stable performance (SD=0.02) and creates promising implications in future multimedia retrieval, summarization, and exploration activities.

The increasing dependence on data analytics and artificial intelligence (AI) methodologies across various domains has prompted the emergence of apprehensions over data security and integrity. There exists a consensus among scholars and experts that the identification and mitigation of Multi-step attacks pose significant challenges due to the intricate nature of the diverse approaches utilized. This study aims to address the issue of imbalanced datasets within the domain of Multi-step attack detection. To achieve this objective, the research explores three distinct re-sampling strategies, namely over-sampling, under-sampling, and hybrid re-sampling techniques. The study offers a comprehensive assessment of several re-sampling techniques utilized in the detection of Multi-step attacks on deep learning (DL) models. The efficacy of the solution is evaluated using a Multi-step cyber attack dataset that emulates attacks across six attack classes. Furthermore, the performance of several re-sampling approaches with numerous traditional machine learning (ML) and deep learning (DL) models are compared, based on performance metrics such as accuracy, precision, recall, F-1 score, and G-mean. In contrast to preliminary studies, the research focuses on Multi-step attack detection. The results indicate that the combination of Convolutional Neural Networks (CNN) with Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) provides optimal results as compared to standalone ML/DL models. Moreover, the results also depict that SMOTEENN, a hybrid re-sampling technique, demonstrates superior effectiveness in enhancing detection performance across various models and evaluation metrics. The findings indicate the significance of appropriate re-sampling techniques to improve the efficacy of Multi-step attack detection on DL models.

The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocols such as CoAP, MQTT, DDS, etc. Study shows that these protocols are vulnerable to attack and prove a significant threat to IoT telemetry data. Within a network, IoT devices are interdependent, and the behaviour of one device depends on the data coming from another device. An intruder exploits vulnerabilities of a device's interdependent feature and can alter the telemetry data to indirectly control the behaviour of other dependent devices in a network. Therefore, securing IoT devices have become a significant concern in IoT networks. The research community often proposes intrusion Detection Systems (IDS) using different techniques. One of the most adopted techniques is machine learning (ML) based intrusion detection. This study suggests a stacking-based ensemble model makes IoT devices more intelligent for detecting unusual behaviour in IoT networks. The TON-IoT (2020) dataset is used to assess the effectiveness of the proposed model. The proposed model achieves significant improvements in accuracy and other evaluation measures in binary and multi-class classification scenarios for most of the sensors compared to traditional ML algorithms and other ensemble techniques.

Abstract Background Lysyl oxidase-like 2 (LOXL2) has recently been explored as extremely pivotal protein involved in angiogenesis which results in metastasis of numerous types of cancers. Hence, LOXL2 is an exciting new target for drug development against tumor progression and its spread to distant organs. Newly synthesized derivatives of natural phenolic antioxidant guaiacol (T1 to T8) were evaluated for their potential as anti-angiogenic agents using in silico approach. The drug likeness properties and toxicity of the synthesized derivatives have also been determined. Active binding sites of LOXL2 protein were determined by online server DoGSiteScorer, and lead–target interactions and conformations of pose analysis were done by using AutoDock Vina and Discovery Studio 4.0. The GUSAR model was applied to find the toxicity and ADMET properties. On the other hand, the chemoinformatics prediction was also performed using online FAF Drug Server and Molinspiration online server. Results Lead molecules from T1 to T8 showed promising binding affinity values, especially T5 and T8 showed best fit in the binding pocket of target enzyme (binding energies − 7.9 and 8.0 kcal/Mol, respectively). The stability of docked complexes was further evaluated using molecular dynamic simulation studies using GROMACS force field, and both leads (T5 and T8) were found to be strongly bounded to the active binding sites. The ADMET results revealed that all experimental molecules were virtually nontoxic and showed compliance with rule of five. Conclusion The present work will further enable researchers to understand how computer-aided drug designing tools may help to expedite new drug discovery process in a minimum cost.

The Internet of Things (IoT) is a mesh network of interconnected objects with unique identifiers that can transmit data and communicate with one another without the need for human intervention. The IoT has brought the future closer to us. It has opened up new and vast domains for connecting not only people, but also all kinds of simple objects and phenomena all around us. With billions of heterogeneous devices connected to the Internet, the network architecture must evolve to accommodate the expected increase in data generation while also improving the security and efficiency of connectivity. Traditional IoT architectures are primitive and incapable of extending functionality and productivity to the IoT infrastructure’s desired levels. Software-Defined Networking (SDN) and virtualization are two promising technologies for cost-effectively handling the scale and versatility required for IoT. In this paper, we discussed traditional IoT networks and the need for SDN and Network Function Virtualization (NFV), followed by an analysis of SDN and NFV solutions for implementing IoT in various ways.

The revolution of IoT highly impacts on different applications such as remote sensing, smart cities, and remote digital healthcare. People use IoT devices for performing business transactions, daily tasks, and healthcare monitoring. IoT devices generate huge amounts of data assets that have potential applications. Biometrics is a potential application of sensors data. The traditional biometric methods such as PINs, passwords are exposed to numerous attacks such as replication, repeated passwords, etc. Sensors’ data-based continuous authentication methods are suitable for maintaining users’ privacy and security in mobile IoT systems. Most of the existing authentication methods have applied motion-based sensors for building users’ identification profiles. The proposed method uses motion sensors and biomedical sensors for reliable and multi-factor user authentication. In this article, we have introduced an IoT sensors data analytics framework to construct user authentication models. We apply the fiducial points-based feature extraction method data for extracting discriminative features. These features act as unique user profiles for authentication purposes. We have performed a detailed analysis of the proposed approach using the publically available datasets. The experiments elaborate on the effectiveness of IoTauth for improved authentication results.

Privacy is a serious concern related to sharing videos or images among people over the Internet. As a method to preserve images’ privacy, chaos-based image encryption algorithms have been used widely to fulfil such a requirement. However, these algorithms suffer from a low key-space, significant computational overhead, and a lag in resistance against differential attacks. This paper presents a novel chaos-based image encryption method based on permutation and substitution using a single Substitution Box (S-Box) to address issues in contemporary image encryption algorithms. The proposed encryption technique’s efficiency is validated through extensive experiments as compared to the state-of-the-art encryption algorithms using different measures and benchmarks. Precisely, the collected results demonstrate that the proposed technique is more resilient against well-known statistical attacks and performs well under plaintext attacks. Indeed, the proposed scheme exhibits very high sensitivity concerning the plaintext attack. A minor change in the encryption key or the plain text would result in a completely different encrypted image.

With the exponential rise in government and private health-supported schemes, the number of fraudulent billing cases is also increasing. Detection of fraudulent transactions in healthcare systems is an exigent task due to intricate relationships among dynamic elements, including doctors, patients, and services. Hence, to introduce transparency in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in existing procedures, so that the fraudulent medical billing cases can be accurately identified. Moreover, there is also a need to optimize both the cost burden for the service provider and medical benefits for the client. This paper presents a novel process-based fraud detection methodology to detect insurance claim-related frauds in the healthcare system using sequence mining concepts. Recent literature focuses on the amount-based analysis or medication versus disease sequential analysis rather than detecting frauds using sequence generation of services within each specialty. The proposed methodology generates frequent sequences with different pattern lengths. The confidence values and confidence level are computed for each sequence. The sequence rule engine generates frequent sequences along with confidence values for each hospital’s specialty and compares them with the actual patient values. This identifies anomalies as both sequences would not be compliant with the rule engine’s sequences. The process-based fraud detection methodology is validated using last five years of a local hospital’s transactional data that includes many reported cases of fraudulent activities.

A vehicular fog network is an emerging paradigm adopted to facilitate delay-sensitive and innovative applications. Since vehicular environments are inherently dynamic, it becomes a challenge to effectively utilize all available resources. Often a centralized resource distribution model is adopted for effective resource utilization but this comes with significant overhead. Thus, a distributed model seems more suited for highly dynamic vehicular environments; however, a distributed model without collective intelligence can end up in uneven workload distribution. In this paper, we propose a distributed non-cooperative task offloading framework for efficient resource utilization. Here, vehicles with heterogeneous task requirements interact with one another without directly influencing the actions of the neighboring vehicles. That is, the framework allows vehicles to communicate with neighbors to gather contextual information to revisit their offloading decisions. The shared information includes resource type, task residence time, system cost, and offloading inference. The effectiveness of the framework is evaluated against baseline schemes in terms of service delay, transmission delay, system cost, delivery rate, and system efficiency. The results demonstrate significantly reduced task residence times by 50%, highest throughput at 83 Mbits/s, which in turn contributes to an improved system utilization up to 70% across the resource-sharing network.

Nowadays, exponential growth in online production and extensive perceptual power of visual contents (i.e., images) complicate the users' information needs. The research has shown that users are interested in satisfying their visual information needs by accessing the image objects. However, the exploration of images via existing search engines is challenging. Mainly, existing search engines employ linear lists or grid layouts, sorted in descending order of relevancy to the user's query to present the image results, which hinders image exploration via multiple information modalities associated with them. Furthermore, results at lower-ranking positions are cumbersome to reach. This research proposed a Search User Interface (SUI) approach to instantiate the non-linear reachability of the image results by enabling interactive exploration and visualization options. We represent the results in a cluster-graph data model, where the nodes represent images and the edges are multimodal similarity relationships. The results in clusters are reachable via multimodal similarity relationships. We instantiated the proposed approach over a real dataset of images and evaluated it via multiple types of usability tests and behavioral analysis techniques. The usability testing reveals good satisfaction (76.83%) and usability (83.73%) scores.

The beef tapeworm, also known as Taenia saginata, is a zoonotic tapeworm from the genus Taenia in the order Cyclophyllidea. Taenia saginata is a food-borne zoonotic parasite with a worldwide distribution. It poses serious health risks to the host and has a considerable negative socioeconomic impact. Previous studies have explained the population structure of T. saginata within the evolutionary time scale and adaptive evolution. However, it is still unknown how synonymous codons are used by T. saginata. In this study, we used 90 T. saginata strains, applying the codon usage bias (CUB). Both base content and relative synonymous codon usage (RSCU) analysis revealed that AT-ended codons were more frequently used in the genome of T. saginata. Further low CUB was observed from the effective number of codons (ENC) value. The neutrality plot analysis suggested that the dominant factor of natural selection was involved in the structuring of CUB in T. saginata. Further analysis showed that T. saginata has adapted host-specific codon usage patterns to sustain successful replication and transmission chains within hosts (Bos taurus and Homo sapiens). Generally, both natural selection and mutational pressure have an impact on the codon usage patterns of the protein-coding genes in T. saginata. This study is important because it characterized the codon usage pattern in the T. saginata genomes and provided the necessary data for a basic evolutionary study on them.

Abstract In diabetes, increased accumulation of sorbitol has been associated with diabetic complications through polyol pathway. Aldose reductase (AR) is one of the key factors involved in reduction of glucose to sorbitol, thereby its inhibition is important for the management of diabetic complications. In the present study, a series of seven 4-oxo-2-thioxo-1,3-thiazolidin-3-yl acetamide derivatives 3(a–g) were synthesized by the reaction of 5-(4-hydroxy-3-methoxybenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl acetic acid (2a) and 5-(4-methoxybenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl acetic acid (2b) with different amines. The synthesized compounds 3(a–g) were investigated for their in vitro aldehyde reductase (ALR1) and aldose reductase (ALR2) enzyme inhibitory potential. Compound 3c, 3d, 3e, and 3f showed ALR1 inhibition at lower micromolar concentration whereas all the compounds were more active than the standard inhibitor valproic acid. Most of the compounds were active against ALR2 but compound 3a and 3f showed higher inhibition than the standard drug sulindac. Overall, the most potent compound against aldose reductase was 3f with an inhibitory concentration of 0.12 ± 0.01 µM. In vitro results showed that vanillin derivatives exhibited better activity against both aldehyde reductase and aldose reductase. The molecular docking studies were carried out to investigate the binding affinities of synthesized derivatives with both ALR1 and ALR2. The binding site analysis of potent compounds revealed similar interactions as were found by cognate ligands within the active sites of enzymes.

In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I‐UWSANs and cause high end‐to‐end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z‐Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well‐known algorithms like energy‐aware scalable reliable and void‐hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.

Reduction in friction ensures fuel economy, control on emissions and durability of components in internal combustion engines. A modern gasoline internal combustion engine was instrumented to determine the friction values at the cam–roller interface considering the effects of surface treatment and engine operating state. A series of tests under different operating speeds and lubricant inlet temperatures were undertaken using both an original surface roller and a Wonder Process Craft (WPC) surface-treated engine roller. The results clearly revealed a substantial reduction in friction magnitude for the WPC surface-treated engine roller in comparison to the original roller while operating under similar conditions, indicating their strong potential for employment in engines. An increase in friction with the rise in temperature was also observed for both types of rollers, whereas increased lubricant entraining velocity due to higher operating speed had the opposite impact. A considerable reduction in frictional drive torque ranging from 8% to 28% was observed by employing the WPC-treated roller in comparison to original/untreated roller at various operating conditions, which signifies the strong potential for employment of WPC surface treatment in the roller/follower valve train engines.

Unlike commercial networks, the tactical networks drive in a critical environment and without a backbone infrastructure. These networks involve mission-critical operations that are dependent on the rapid and reliable transfer of delay-sensitive data to conduct command and control (C2). Owing to the decentralized and dynamic nature, tactical networks need to survive by maintaining seamless and simultaneous time-sensitive communication among software-defined radios (SDRs). Under mobility and dynamic network topology, link status continuously changes that cause substantial packet loss, and degrade network performance. It is challenging to maintain the connectivity between communicating nodes and find a suitable time for sending control messages (e.g., packet forwarding and route discovery), known as control phase time (CPT). Given a maximum transmission range for narrowband (NB) and wideband (WB) communication, the knowledge of link duration between communicating radios are of major concern, particularly for low latency and reliable communication requirements. Many existing techniques focus on topology control by exchanging mobility parameters in control transmissions, increasing the delay in data transmission. Therefore, it is a non-trivial task to calculate the expected time for the control transmissions due to the confrontation of speed and random movement of nodes. This paper presents a novel methodology to estimate a suitable time to execute the control phase based on the lifetime of communication links between SDRs in tactical MANETs. It uses stochastic distribution to make a network capable of effectively figuring out operative connectivity. The proposed methodology evaluates the maximum network connectivity based on the distances between communicating radios and radio transmission ranges for different quality-of-service (QoS) requirements. The simulation results validate that the proposed methodology’s CPT estimations are more appropriate for the timely link-formations in tactical radio MANETs. The proposed technique is generic and can be applied to any MANET environment using different mobility models.

Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.

Authorship attribution is the study to identify individuals by their writing styles without knowing their actual identities. This is a challenging task in natural language processing. Most work on authorship attribution focused on English, whereas, the problem is understudied in Arabic language. However, due to the complex and distinct morphological nature of the Arabic language, techniques developed for English are not directly applicable to Arabic. This paper explored the possibility of using state-of-the-art classifiers, Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Random Forest, to predict authorship in Arabic short-microblog text. We employed three commonly used linguistic features, character-, lexical- and syntactic-based, in an incremental manner to predict the accuracy of the selected classifiers. The results elucidate that a systematic combination of linguistic features improves authorship classification. However, an inverse correlation was observed in authorship classification accuracy and the number of authors. Overall, SVM and Random Forest classifier are comparable and attained ~65% accuracy, whereas KNN hardly attained ~35% accuracy. In addition, lexical features offer more discriminatory power as compared to the character and syntactic features.

In IoT based systems, authentication of users and devices is a major challenge where the traditional authentication mechanisms such as login-password are no longer supportable. It requires continuous authentication methods to ensure authenticity of the users and devices specifically for long sessions. The authentication through the IoT-Sensors based data has already gained the attention of a considerable number of researchers as it does not require direct involvement of the users and provide an extra layer of security and user privacy. Due to the instability of IoT-Sensor data, the authentication techniques need to extract a large number of features to produce high-accurate results. Moreover, the limited capabilities of computation, communication, storage, and the small battery power of IoT devices further makes its implementation hard. In this paper, we have introduced a user authentication framework for remote IoT users based on unique walking patterns to extract gait-related features with minimum data samples and cycle length. For an in-depth analysis of the distinctive features of the sensor-based gait profiles, we have applied an ECG signal processing technique. The proposed approach applies to a diverse range of IoT devices such as cellphones, smartwatches, and wearable sensors. The main contribution of the proposed approach is the deeper gait analysis with the least number of features and data for authentication purposes. We have introduced an authentication algorithm for feature comparison. The machine learning models have been applied to the gait based profiles for validation of the proposed approach. The detailed experimental analysis of different data sets has achieved an accuracy of 94% and an equal error rate (EER) of 6% higher than the existing approaches.

With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the delay in emergency services. Many lives could be saved with efficient rescue services. The delay happens due to traffic congestion or unstable communication to the medical units. The implementation of automatic road accident detection systems to provide timely aid is crucial. Many solutions have been proposed in the literature for automatic accident detection. The techniques include crash prediction using smartphones, vehicular ad-hoc networks, GPS/GSM based systems, and various machine learning techniques. With such high rates of deaths associated with road accidents, road safety is the most critical sector that demands significant exploration. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives.

The number of vehicles on the roads has increased proportionally over the last couple of years and this number is likely to rise due to the increase in population growth and the number of vehicles that are manufacturing every day. This high traffic density leads to several problems, from which effectively disseminating the emergency messages is a major concern. Keeping in view the dynamic characteristics of VANETs, significant challenges are faced in disseminating the message across the network. The major challenges are the broadcast storm problem, hidden node problem and the packet collision. Many studies have been performed to devise an effective and reliable mechanism for disseminating emergency messages in a Vehicular ad-Hoc Network (VANET). Researchers have proposed different models to tackle various types of scenarios for emergency message dissemination. This paper not only reviews some recent contributions to emergency message dissemination in vehicular networks but also discusses various proposed methods based on Intelligent Transportation System (ITS), Internet of Things (IoT), Priority messaging, Clustering approach, Software Defined Network (SDN) and Fog Computing. We have also tried to explore the latest developments in emergency message dissemination using 5G networks.

Transactions related to vehicles include manufacturing, buying, selling, paying insurance(takaful), obtaining regular inspection, leasing a vehicle from banks, getting in an accident, engaging in a traffic violation, calculating price predictions and renting a vehicle. Many people perform transactions related to vehicles in their daily life; transportation authorities also perform vehicle transactions as part of managing vehicle fleets. But tracking these transactions is a challenging task. There are countrywide solutions that uses centralized systems. However, these solutions have problems with trust management, transparency, and access control. Therefore, we believe there is still room for integrated automation of various vehicle-related transactions. In this paper, we present a blockchain-based framework for vehicle tracking that incorporates the mentioned features. Moreover, blockchain is customized to enable usage control for additional transactions, such as inspection, renting and islamic insurance. The usage control model is integrated with IoT devices to continuously monitor the vehicles for certain conditions and remotely revoke access if needed. The complete transaction set is recorded over an immutable ledger that provides trust, transparency and a complete history of record. In this paper, we also presents a prototype implementation of a permissioned blockchain, which will be made available under the GNUv3 General Public License. Performance analysis is performed on the newly proposed framework implementation over the permissioned blockchain to measure its adoption and suitability.

Named Data Networking (NDN) is one of the future envisioned networking paradigm used to provide fast and efficient content dissemination with interest-based content retrieval, name-based routing and in-network content caching. On the one hand, this new breed of future Internet architecture is becoming a key technology for data dissemination in the IoT networks; on the other hand, NDN suffers from new challenges in terms of data security. Among them, a content poisoning attack is the most common data security challenge. The aim of this attack is to inject poisoned content with an invalid signature to the network. Therefore, to prevent NDN against possible content poisoning attack, a signature of the contents is appended to each data packet for verifications. In this paper, we propose an identity-based signature scheme for IoT-based NDN networks, with a special emphasis on content integrity and authenticity. The proposed scheme is based on the concept of the Hyperelliptic curves, which provide the same level of security as Rivest-Shamir-Adleman (RSA), Bilinear pairing and Elliptic Curve Cryptosystems (ECC) with lower-key size. The proposed scheme is subject to both formal and informal security analysis in order to show the feasibility of our scheme. Finally, the performance of the proposed scheme is analyzed via comparison with the relevant existing schemes that authenticates the superiority of our scheme in terms of security and efficiency.

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.

The internet of things (IoT) is prone to various types of denial of service (DoS) attacks due to their resource-constrained nature. Extensive research efforts have been dedicated to securing these systems, but various vulnerabilities remain. Notably, it is challenging to maintain the confidentiality, integrity, and availability of mobile ad hoc networks due to limited connectivity and dynamic topology. As critical infrastructure including smart grids, industrial control, and intelligent transportation systems is reliant on WSNs and IoT, research efforts that forensically investigate and analyze the cybercrimes in IoT and WSNs are imperative. When a security failure occurs, the causes, vulnerabilities, and facts behind the failure need to be revealed and examined to improve the security of these systems. This research forensically investigates the performance of the ad hoc IoT networks using the ad hoc on-demand distance vector (AODV) routing protocol under the blackhole attack, which is a type of denial of service attack detrimental to IoT networks. This work also examines the traffic patterns in the network and nodes to assess the attack damage and conducts vulnerability analysis of the protocol to carry out digital forensic (DF) investigations. It further reconstructs the networks under different modes and parameters to verify the analysis and provide suggestions to design roubust routing protocols.

The magnetic levitation (MAGLEV) train uses magnetic field to suspend, guide, and propel vehicle onto the track. The MAGLEV train provides a sustainable and cleaner solution for train transportation by significantly reducing the energy usage and greenhouse gas emissions as compared to traditional train transportation systems. In this paper, we propose an advanced control mechanism using an Arduino microcontroller that selectively energizes the electromagnets in a MAGLEV train system to provide dynamic stability and energy efficiency. We also design the prototype of an energy-efficient MAGLEV train that leverages our proposed control mechanism. In our MAGLEV train prototype, the levitation is achieved by creating a repulsive magnetic field between the train and the track using magnets mounted on the top-side of the track and bottom-side of the vehicle. The propulsion is performed by creating a repulsive magnetic field between the permanent magnets attached on the sides of the vehicle and electromagnets mounted at the center of the track using electrodynamic suspension (EDS). The electromagnets are energized via a control mechanism that is applied through an Arduino microcontroller. The Arduino microcontroller is programmed in such a way to propel and guide the vehicle onto the track by appropriate switching of the electromagnets. We use an infrared-based remote-control device for controlling the power, speed, and direction of the vehicle in both the forward and the backward direction. The proposed MAGLEV train control mechanism is novel, and according to the best of our knowledge is the first study of its kind that uses an Arduino-based microcontroller system for control mechanism. Experimental results illustrate that the designed prototype consumes only 144 W-hour (Wh) of energy as compared to a conventionally designed MAGLEV train prototype that consumes 1200 Wh. Results reveal that our proposed control mechanism and prototype model can reduce the total power consumption by 8.3 × as compared to the traditional MAGLEV train prototype, and can be applied to practical MAGLEV trains with necessary modifications. Thus, our proposed prototype and control mechanism serves as a first step towards cleaner engineering of train transportation systems.

Melanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin-related consolidated malignancies. The number of cases is growing among young people, but if it is diagnosed at an earlier stage, then the survival rates become very high. The cost and time required for the doctors to diagnose all patients for melanoma are very high. In this paper, we propose an intelligent system to detect and distinguish melanoma from nevus by using the state-of-the-art image processing techniques. At first, the Gaussian filter is used for removing noise from the skin lesion of the acquired images followed by the use of improved K-mean clustering to segment out the lesion. A distinctive hybrid superfeature vector is formed by the extraction of textural and color features from the lesion. Support vector machine (SVM) is utilized for the classification of skin cancer into melanoma and nevus. Our aim is to test the effectiveness of the proposed segmentation technique, extract the most suitable features, and compare the classification results with the other techniques present in the literature. The proposed methodology is tested on the DERMIS dataset having a total number of 397 skin cancer images: 146 are melanoma and 251 are nevus skin lesions. Our proposed methodology archives encouraging results having 96% accuracy.

The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns.

Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.

Ulmus villosa Brandis ex Gamble, an economically and ecologically important forest tree, is native to the western Himalayas of Pakistan. The long pressure imposed by unsustainable utilization and market demands has resulted in the rapid decline of the U. villosa population in the wild. To date, very limited information on the genomic diversity of U. villosa is available and this can tremendously limit our understanding of distribution and future conservation of U. villosa. Therefore, the current study aimed to assess genetic diversity within U. villosa wild populations of the Hazara Division using four barcoding markers (i.e., rbcL, matK XF + 5R, matK 390F + 1326R and trnH-psbA). A total of six high quality sequences were obtained with rbcL, four with matK XF + 5R, four with matK 390F + 1326R, and fourteen with trnH-psbA. The sequenced regions contained insertions and deletions as well as several SNPs across the length of sequences, and PCR-based markers may be developed from these novel SNPs markers. The phylogenetic results supported the polyphyletic origin of the genus Ulmus, and the data indicated that multiple speciation events may have led to the evolution of U. villosa in this region. For deeper understanding of the origin and evolution of U. villosa, sequencing of the complete nuclear and chloroplast genomes will be pivotal. The results herein provide novel insights into the genomic diversity as well as phylogenetic relationships of U. villosa with other species, and may facilitate both in situ and ex situ conservation efforts for this endangered species.

Rice yellow mottle virus (RYMV), transmitted by chrysomelids, is one of the major viral pathogens that has devastated rice production in Sub-Saharan Africa. RYMV is a member of the genus Sobemoviruses in the family Solemoviridae and harbors a positive-sense single-stranded RNA (+ssRNA). Here, we used 50 RYMV strains, applying the codon usage bias (CUB). Both base content and relative synonymous codon usage (RSCU) analysis revealed that GC-ended codons were more frequently used in the genome of RYMV. Further low codon usage bias was observed from the effective number of codons (ENC) value. The neutrality plot analysis suggested the dominant factor of natural selection was involved in the structuring of CUB in RYMV. Based on RSCU values, the RYMV and its host relationship indicate that the RYMV develops codon usage patterns similar to its host. Generally, both natural selection and mutational pressure impact the codon usage pattern of the protein-coding genes in RYMV. This study is important because it characterized the codon usage pattern in the RYMV genomes and provided the necessary data for a basic evolutionary study on them. Additionally, we recommend that experiments such as whole genome sequencing (WGS) or dual RNA sequencing (DRS) should be considered in order to correlate these in-silico findings with viral diseases in the future.

Smart cities play a vital role to develop a sustainable infrastructure with efficient management of the Internet of things devices. The infrastructure is used to support various applications for smart hospitals, smart factories, and intelligent transportation systems. With the extensive deployment of Internet of things devices, unprecedented growth in data has lead to capacity and transfer issues. In this article, we proposed an efficient data transfer mechanism based on self-sustainable networks over the vehicular environment. Depending on whether the network is connected with vehicles available to support direct connection from the source to destination, we propose end-to-end and hop-by-hop forwarding for vehicular networks that are inherently disconnected. The evaluation results demonstrate that the lifetime of the discovered paths depends on the coverage area, vehicle mobility, and vehicle speed. Therefore, at times redundant disjoint paths are selected for communication. In the proposed work, selected vehicles are used to reach the destination.

The inception of the smart cities concept provides a compelling platform to support innovative applications. It provides distinctive view of cities, where mobile devices, pedestrians, and electronic gadgets can communicate with each other to build an effective urban environment to further improve the living standards. Similarly, the role of the Internet of Things (IoT) and vehicular computing has emerged due to smart cities. This is further complemented by edge and fog computing architectures. The emerging concept of vehicular fog computing has enabled the platform to support delay-sensitive applications and to reduce the workload on the backend networks. Vehicular fog computing is a paradigm that touches the boundaries of thinking vehicles as an infrastructures-as-a-service. The use of vehicles to provide computation on-the-move poses various challenges. The vehicles with onboard computing equipment can facilitate delay-sensitive applications. These vehicles can act as an edge device to reduce the load from a backbone network. However, due to continuous mobility, it is difficult to use traditional frameworks to distribute the computation task among vehicles. In this paper, we propose a framework termed vFog. The vFog is designed to provide computing facilities from nearby fog vehicles. The framework utilizes the onboard computing facility of vehicles without the support of roadside units (RSUs). Moreover, the proposed framework handles churn behavior and supports multi-hop communication to improve the task delivery ratio. The proposed framework allows researchers to benchmark their own task distribution algorithms over the dynamic vehicular networks.

Mobile ad-hoc networks (MANETs) comprise a large number of mobile wireless nodes that can move in a random fashion with the capability to join or leave the network anytime. Due to the rapid growth of devices on the Internet of Things (IoT), a large number of messages are transmitted during information exchange in dense areas. It can cause congestion that results in increasing transmission delay and packet loss. This problem is more severe in larger networks with more network traffic and high mobility that enforces dynamic topology. To resolve these issues, we present a bandwidth aware routing scheme (BARS) that can avoid congestion by monitoring residual bandwidth capacity in network paths and available space in queues to cache the information. The amount of available and consumed bandwidth along with residual cache must be worked out before transmitting messages. The BARS utilizes the feedback mechanism to intimate the traffic source for adjusting the data rate according to the availability of bandwidth and queue in the routing path. We have performed extensive simulations using NS 2.35 on Ubuntu where TCL is used for node configuration, deployment, mobility and message initiation, and C language is used for modifying the functionality of AODV. The results are extracted from trace files using Perl scripts to prove the dominance of the BARS over preliminaries in terms of packet delivery ratio, throughput and end-to-end delay, and the probability of congested node for static and dynamic topologies.

Congestion in vehicular ad hoc networks affects the performance of delay-sensitive applications when exchanging emergency or general information sharing messages. In particular, during emergencies on roads like road accidents and security warnings demand high reliability and low latency. However, in traditional solutions, such messages use the same control channel for transmission leading up to a saturated or congested channel. Furthermore, the highly dynamic nature of vehicular networks leading up to unpredictable routing patterns, subsequently degrading the network performance. An uneven deliverance of these critical messages can be catastrophic for the delay-sensitive applications. Thus, congestion control remains one of the most challenging problems within the domain of vehicular networks. This paper provides a comprehensive overview of the working of vehicular networks, the recent research advances to cater for congestion in such networks, and open problems and challenges relevant to congestion avoidance.

Abstract Background Isoxazole is an important pharmacophore in medicinal chemistry with a wide range of pharmacological activities. The present study deals with the synthesis and evaluation of antinociceptive potential of nine novel 3-substituted-isoxazole-4-carboxamide derivatives. Synthesis In the first step, respective oxime was prepared and further treated with ethylacetoacetate and anhydrous zinc chloride followed by hydrolysis of ester to furnish 3-substituted isoxazole-4-carboxylic acid. The respective carboxylic acids were converted to acid chlorides and condensed with aromatic amines to get the target carboxamide derivatives (A1–A5 and B1–B5). These compounds were characterized by FTIR, 1HNMR, 13CNMR and elemental analysis data and screened for their analgesic activity using acetic acid-induced writhing assay and hot plat test in mice and compared with the standard centrally acting analgesic, tramadol. Results All the synthesized carboxamide derivatives showed low to moderate analgesic activity. Among the synthesized derivatives B2 having methoxy (OCH3) showed high analgesic activity as compared to tramadol both in acetic acid-induced writhing assay and hot plate assay at dose of 6 mg/kg. To examine the involvement of opioidergic mechanism in the mediation of analgesic effects of isoxazole derivatives animals were further treated with non-selective opioid analgesic, naloxone (0.5 mg/kg). The results showed that compounds A3 and B2 follow a non-opioid receptor pathway in the mediation of analgesic effects. Synthesized compounds A3 and B2 were docked against non-opioid receptors COX-1 (3N8X), COX-2 (1PXX) and human capsaicin receptor (HCR, 3J9J) to analyze their binding interactions. They showed binding energies in the range of − 7.5 to − 9.7 kcal/mol. Conclusions The results indicated that isoxazole carboxamide derivatives possess moderate analgesic potential especially compounds A3 and B2 can be considered as lead molecules and explored further for pain management with fewer side effects.

The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.

Wetlands are vital and dynamic components of the global ecosystem, providing a wide range of ecosystem services such as water control and biodiversity. During 2015–2019, plant diversity and distribution of wetlands were studied in forty areas of the Mardan division, Khyber Pakhtunkhwa, Pakistan. A total of 282 vascular wetland plant species were identified, divided into 192 genera and 73 families. Poaceae, with 41 species, is the most diverse family of wetland plants, followed by Asteraceae, with 26 species. In the present study, wetland plants are classified based on the Cook classification system. It is interesting to note that all the life forms were present in the study area except Plankton. Helophyte life form was represented by the highest number of plants with 177 plants and 62.5 percent of total wetland plants, followed by Tenagophyte with 42 plant species (14.8%) and Hyperhydate with 27 plant species (9.5%). Based on the distribution pattern of 282 species in 40 wetland sites, the most common plant species found was Euphorbia helioscopia, recorded from 36 locations, followed by Cynodon dactylon which was found in 32 locations. In Mardan division, the Gulshan Abad Khuwar had the highest number of species occurrences (83 species) accounting for 32% of all species, followed by Alo Khwar (59 species, 23%) and Hathian Khwar (50 species, 19%). The current work presents a comprehensive and field-based detailed survey of wetland plant distribution in this region, filling knowledge gaps in the Himalayas in particular, and having broader implications for wetland management policy and practice. Unfortunately, wetlands of the research area are facing high pressure due to an increase in human population, overgrazing, deforestation, construction of buildings and roads. The filling up of these water bodies is resulting in the loss of hydrophytic species diversity. Therefore, these wetlands need proper attention and effective protection from anthropogenic disturbances.

Due to the overall widespread accessibility of electronic materials available on the internet, the availability and usage of computers in education have resulted in a growth in the incidence of plagiarism among students. A growing number of individuals at colleges around the globe appear to be presenting plagiarised papers to their professors for credit, while no specific details are collected of how much was plagiarised previously or how much is plagiarised currently. Supervisors, who are overburdened with huge responsibility, desire a simple way—similar to a litmus test—to rapidly reform plagiarized papers so that they may focus their work on the remaining students. Plagiarism-checking software programs are useful for detecting plagiarism in examinations, projects, publications, and academic research. A number of the latest research findings dedicated to evaluating and comparing plagiarism-checking methods have demonstrated that these have restrictions in identifying the complicated structures of plagiarism, such as extensive paraphrasing as well as the utilization of technical manipulations, such as substituting original text with similar text from foreign alphanumeric characters. Selecting the best reliable and efficient plagiarism-detection method is a challenging task with so many options available nowadays. This paper evaluates the different academic plagiarism-detection methods using the fuzzy MCDM (multi-criteria decision-making) method and provides recommendations for the development of efficient plagiarism-detection systems. A hierarchy of evaluation is discussed, as well as an examination of the most promising plagiarism-detection methods that have the opportunity to resolve the constraints of current state-of-the-art tools. As a result, the study serves as a “blueprint” for constructing the next generation of plagiarism-checking tools.

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

We propose electronic voting authentication scheme, which is a key management mechanism for electronic voting system intended to limit the number of attacks on a polling station and strengthen the security control. The motivation is to diversify security requirements of messages exchanged between polling stations. There are different types of messages exchanged between polling stations and each type of message has different security needs. A security mechanism developed on the basis of a single key is not enough to ensure the diverse security needs of voting network. In electronic voting authentication scheme, every polling station is responsible to support three different types of keys. These are global key, pairwise key, and individual key. The global keys are public keys shared with all polling stations in the voting network. The pairwise key can be used for communication with polling stations. Individual keys will be used for communication with the server. To ensure authentication of local broadcast, electronic voting authentication scheme uses one-way key chains in a well-organized way. The support of source authentication is a visible advantage of this scheme. We examine the authentication of electronic voting authentication scheme on numerous attack models. The measurement demonstrates that electronic voting authentication scheme is very operative in protecting against numerous elegant attacks such as wormhole attack, Sybil attack, and HELLO Flood attack. The proposed system is evaluated and the results demonstrate that the proposed system is practical and secure as compared to the direct recording electronic and manual systems.

Wireless sensor networks are a cornerstone of the Internet of things with many applications. An important aspect of such applications is target tracking using self-positioned known sensor nodes. Over the years, many schemes have been proposed to locate and track the target path. However, accuracy and reliable tracking remain an open area of research. In this article, we propose a dynamic cooperative multilateral sensing scheme for indoor industrial environments to improve target localization and tracking accuracy. The scheme is designed to select reliable nodes based on the distance between nodes within-cluster and to the target for reduced positioning error. Furthermore, a cluster node is dynamically selected based on distance from the base station. We simulate the proposed technique in scenarios with tracking at regular intervals and with the complete path. Furthermore, the performance of the scheme is also tested under different sensor coverage areas. The results show that the proposed scheme provides better target tracking with up to 19% higher accuracy in comparison to the traditional trilateration scheme.