ABSTRACTThis research addresses critical gaps in Mobile Ad hoc Networks (MANETs) by proposing a hybrid secure cluster-based routing algorithm, focusing on enhancing network security, robustness, and ...reliability through multipath routing. Methodologically, the approach integrates Convolutional Neural Networks (CNN) for optimal path routing and Emperor Penguin Optimization (EPO) for clustering, introducing a novel combination for efficient cluster head selection. A novel contribution lies in the development of a prediction technique utilizing a trust assessment algorithm to calculate direct trust ratings at each node, incorporating fuzzy values between zero and one. Trust values are further influenced by node performance, adding a dynamic dimension to the trust evaluation process. Key novelties include the emphasis on energy efficiency, network longevity, remaining energy, security level, bandwidth, and packet delivery ratio as evaluation criteria. The proposed CNN-EPO model demonstrates superior results compared to traditional routing protocols, achieving a remarkable 95% energy efficiency, a heightened security level of 99%, and a throughput reaching up to 8 Mbps. Additionally, the Packet Delivery Ratio (PDR) attains close to 99% and routing overhead remains below 0.5, ensuring efficiency in challenging network scenarios with 50 adversaries. In summary, this research contributes a comprehensive solution to MANET challenges, introducing a novel hybrid routing algorithm, incorporating advanced methodologies for path optimization and clustering. These outcomes highlight how important the suggested strategy is to improve the existing state of the art in MANETs.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Gold nanoparticles have gained much attention due to their widespread biological and technological applications, and consequently their simpler synthesis via green chemistry has also become of ...foremost importance. We report the room temperature synthesis of spherical gold nanoparticles using curcumin alone as the reducing and stabilizing agent. The pH is found to have an important role in curcumin solubilisation and subsequent formation of curcumin conjugated gold nanoparticles (cAuNPs). UV-visible studies show that the cAuNPs formed are of uniform size and HRTEM studies confirm spheres of average size 18 nm. The DLS measurements show a particle size of 58 nm. The crystallinity has been determined by HRTEM and XRD. The conjugation of stable curcumin on the cAuNPs is indicated by FTIR spectra which also suggest that the phenolic and enolic groups of curcumin bring about the reduction. The zeta potential value of cAuNPs is −23 mV which is stable for up to 6 months at room temperature. The mechanism of cAuNP formation is inferred to be through temporal evolution. This is the first demonstration where curcumin is solubilized at alkaline pH without using any external agent and is used for reducing HAuCl 4 to form cAuNPs. The non toxic nature of the cAuNPs is evidenced through biocompatibility studies using human blood cells.
Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, ...existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
MANETs aredecentralized network that involves mobile nodes. As the overall network is mobile and has no centralization, network management, routing, and security become very challenging. Though many ...works have been presented, still there is a lack in organizing the network due to unauthorized access, centralized security schemes, and the dynamic nature of the nodes. This paper proposed a novel Blockchain-assisted Secure Routing (Block-Sec) protocol for MANETs. All mobile nodes are authenticated by Distributed One-Time Passcode (DOT) based authorization scheme. All authorized nodes are segregated into multiple clusters based on Weight based Dynamic Clustering (WDC) algorithm in which multiple metrics are considered in clustering and re-clustering processes. After cluster formation, each cluster is elected with optimal Cluster Head (CH) by Strawberry Optimization (SBO) algorithm with a new objective function. After cluster formation, the optimal route is selected by Fast Neural Net-assisted Fuzzy (FNNF) algorithm by combining multiple variables. Data transmission is secured by Efficient Elliptic Curve (E2C2) algorithm. With the combined algorithms, the proposed approach obtainedimproved efficiency in packet delivery ratio (PDR), throughput, time analysis, and security level.
Unstable Mobile nodes in the network does not maintain the accuracy of data transmission at the maximum level since the node’s characteristics are updated, then nodes receive data’s are intruded, its ...packet information is missed. Since that time, congestion is made for current routing path, so consider that path is a failure, also provide re transmission. It occupies more energy, and packet drop rate. In proposed Enhanced data Accuracy based Path Discovery (EAPD) technique is used to provide transmitting and receiving data has higher accuracy. It verifies the every node communication in routing path has maximum data accuracy, they are selected, otherwise, communication data have minimum data accuracy is rejected. The backing route selection algorithm is constructed to avoid intrusion for communication period; it’s discovering the path, which does not lose the data from packets, since congestion is easily identified. It reduces energy consumption, and packet drop rate.
Mobile Ad-hoc Networks (MANETs) have emerging applications in real-time with lots of research challenges. Specifically, the dynamic nature of the mobile nodes limits the performance of routing in ...MANET. The existing routing algorithms, such as AODV, DSR, and DSDV, lack performance due to an ineffectual route discovery procedure. When it comes to large-scale applications such as air pollution monitoring, routing becomes more complex and consumes more energy for route selection. This research work aims to increase data delivery while minimizing energy consumption for air pollution monitoring applications. To achieve this, we have proposed a novel Hybrid Optimization methodology for MANETs. First, we partitioned the network into multiple dynamic clusters by using Dual Constraint Clustering (DCC) approach that works upon Mobility Metric (MoM) and Hop Count (HC). In each cluster, the Cluster Head (CH) is selected by Type-II fuzzy approach. Then, routing is performed by Hybrid Cellular Automata and African Buffalo Optimization (HCA2BO) algorithm. The proposed optimization algorithm considers multiple metrics to select an optimum route. The extensive analysis in the ns-3 simulation tool shows enhanced performance in network lifetime, energy consumption, and delay. Also, an air pollution monitoring application is demonstrated in the proposed work.
Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain ...cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, F-measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM).
This paper focuses on achieving high-level security in Mobile Adhoc Networks (MANET) by incorporating Blockchain technology-based Intrusion Detection systems (IDS). The existing works on MANET ...security focus on either security prevention or detection. Thus, the security level attained by the prior works is unable to cope with the increasing attacks. To resolve this main issue, this research paper introduces Lightweight Blockchain assisted Intrusion Detection System (LB-IDS) which jointly prevents and detects the attacks held on mobile networks. Initially, the network nodes are authenticated by a lightweight Blockchain-based Multi-Factor Authentication (LBMFA) scheme. This procedure prevents the malicious nodes entry to the network. Then, data packets are transmitted through the optimal route which is selected by Multi-Objective Strawberry Optimization (MOSO) algorithm. The collected data packets are fed into IDS which classifies the data into normal and malicious packets. For IDS, we proposed Deep Q-Learning (DQL) algorithm which takes actions by learning the environment. As the mitigation step, the Blockchain is updated with the trust value according to the data packet classification. For such continuous monitoring, K-Mode Clustering (KMC) algorithm is proposed. On the whole, the proposed work improves the network security in MANET through Prevention, Detection, and Mitigation. The results of the presented work attains better security level, packet delivery ratio (PDR), energy efficiency, delay, and detection accuracy.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) are all very useful diagnostic techniques. The widespread availability of mobile devices plus the declining cost of ...ECG, EEG, and EMG sensors provide a unique opportunity for making this kind of study widely available. The fundamental need for enhancing a country’s healthcare industry is the ability to foresee the plethora of ailments with which people are now being diagnosed. It’s no exaggeration to say that heart disease is one of the leading causes of mortality and disability in the world today. Diagnosing heart disease is a difficult process that calls for much training and expertise. Electrocardiogram (ECG) signal is an electrical signal produced by the human heart and used to detect the human heartbeat. Emotions are not simple phenomena, yet they do have a major impact on the standard of living. All of these mental processes including drive, perception, cognition, creativity, focus, attention, learning, and decision making are greatly influenced by emotional states. Electroencephalogram (EEG) signals react instantly and are more responsive to changes in emotional states than peripheral neurophysiological signals. As a result, EEG readings may disclose crucial aspects of a person’s emotional states. The signals generated by electromyography (EMG) are gaining prominence in both clinical and biological settings. Differentiating between neuromuscular illnesses requires a reliable method of detection, processing, and classification of EMG data. This study investigates potential deep learning applications by constructing a framework to improve the prediction of cardiac-related diseases using electrocardiogram (ECG) data, furnishing an algorithmic model for sentiment classification utilizing EEG data, and forecasting neuromuscular disease classification utilizing EMG signals.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
In a mobile ad hoc network, packets are lost by interference occurrence in the communication path because there is no backup information for the previous routing process. The communication failure is ...not efficiently identified. Node protection rate is reduced by the interference that occurs during communication time. So, the proposed reliability antecedent packet forwarding (RAF) technique is applied to approve the reliable routing from the source node to the destination node. The flooding nodes are avoided by this method; the previous routing information is backed up; this backup information is retrieved if any interference occurred in the communication period. To monitor the packet flow rate of every node, the straddling path recovery algorithm is designed to provide an interference free-routing path. This path has more number of nodes to proceed with communication. These nodes have a higher resource level and also used to back up the forwarded data; since sometimes routing breakdowns occurred, data are lost, which is overcome by using a backup process. It improves the network lifetime and reduces the packet loss rate.
