Metaverse-based virtual worlds can provide users with an immersive digital experience by utilizing extended reality, IoT, 6G communication, and computing technology. Unlike the multiverse, in which users can access on...
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Unmanned Combat Aerial Vehicles (UCAVs) are becoming a critical part of the military to automate complex missions with minimum risk and increased efficiency. Path planning is a necessary routine for UCAVs to guide the...
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Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compressio...
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Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.
Accurate and reliable wind power forecasting is of great importance for stable grid operation and advanced dispatch planning. Due to the complex, non-stationary, and highly volatile nature of wind power data, Transfor...
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Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as significant research challenges that pose during the design of network protocol...
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Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as significant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identified that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efficiency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.
The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst *** is an increase in the prominence of WSN adaptability to emerging applications like the Internet o...
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The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst *** is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by *** is not much published information on issues related to node mobility and management of energy at the time of aggregation of *** the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efficient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artificial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy *** is avoidance of the local optima problem at the time of solution space search in this proposed *** have been conducted on a large WSN network that has issues with mobility of nodes.
Software-defined networking(SDN) is a trending networking paradigm that focuses on decoupling of the control logic from the data plane. This decoupling brings programmability and flexibility for the network management...
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Software-defined networking(SDN) is a trending networking paradigm that focuses on decoupling of the control logic from the data plane. This decoupling brings programmability and flexibility for the network management by introducing centralized infrastructure. The complete control logic resides in the controller, and thus it becomes the intellectual and most important entity of the SDN infrastructure. With these advantages, SDN faces several security issues in various SDN layers that may prevent the growth and global adoption of this groundbreaking technology. Control plane exhaustion and switch buffer overflow are examples of such security issues. Distributed denial-of-service(DDoS) attacks are one of the most severe attacks that aim to exhaust the controller’s CPU to discontinue the whole functioning of the SDN network. Hence, it is necessary to design a quick as well as accurate detection scheme to detect the attack traffic at an early stage. In this paper, we present a defense solution to detect and mitigate spoofed flooding DDoS attacks. The proposed defense solution is implemented in the SDN controller. The detection method is based on the idea of an statistical measure — Interquartile Range(IQR). For the mitigation purpose, the existing SDN-in-built capabilities are utilized. In this work, the experiments are performed considering the spoofed SYN flooding attack. The proposed solution is evaluated using different performance parameters, i.e., detection time, detection accuracy, packet_in messages, and CPU utilization. The experimental results reveal that the proposed defense solution detects and mitigates the attack effectively in different attack scenarios.
Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein...
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Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-m...
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Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac
An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver *** of malignant growth proximity are identified in an ultrasound filter through image pixe...
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An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver *** of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s *** changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis,suggesting that the cause may be alcohol instead of liver *** Two-Dimensional(2D)ultrasound data sets contain an accuracy rate of 85.9%and a 2D Computed Tomography(CT)data set of 91.02%.The most recent work on designing a Three-Dimensional(3D)ultrasound imaging system in or close to real-time is *** this article,a Deep Learning(DL)model is implemented and modified to fit liver CT segmentation,and a semantic pixel classification of road scenes is *** architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers.A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training *** the normal class,we obtained 100%precision for chronic cirrhosis hepatitis(73%),offset cirrhosis(59.26%),and offensive cirrhosis(91.67%)for chronic hepatitis or cirrhosis(73,0%).The aim is to develop a computer-Aided Detection(CAD)screening tool to detect *** results proved 98.33%exactness,94.59%sensitivity,and 92.11%case with Convolutional Neural Networks(CNN)*** the classifier’s performance did not differentiate so clearly at this level,it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve(AUC)and accuracy.
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