The fifth-generation(5G)wireless technology is the most recent standardization in communication services of interest across the *** concept of Multiple-Input-Multiple-Output antenna(MIMO)systems has recently been inco...
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The fifth-generation(5G)wireless technology is the most recent standardization in communication services of interest across the *** concept of Multiple-Input-Multiple-Output antenna(MIMO)systems has recently been incorporated to operate at higher frequencies without *** paper addresses,design of a high-gain MIMO antenna that offers a bandwidth of 400 MHz and 2.58 GHz by resonating at 28 and 38 GHz,respectively for 5G millimeter(mm)-wave *** proposed design is developed on a RT Duroid 5880 substrate with a single elemental dimension of 9.53×7.85×0.8 mm^(3).The patch antenna is fully grounded and is fed with a 50-ohm stepped impedance microstrip *** also has an I-shaped slot and two electromagnetically coupled parasitic slotted *** design is initially constructed as a single-element structure and proceeded to a six-element MIMO antenna configuration with overall dimensions of 50×35×0.8 mm^(3).The simulated prototype is fabricated and measured for analyzing its performance characteristics,along with MIMO antenna diversity performance factors making the proposed antenna suitable for 5G mm-wave and 5G-operated handheld devices.
In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each *** Diagnosis of the people who are at higher risk of CVDs helps them to...
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In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each *** Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent *** becomes inevitable to pro-pose a solution to predict the CVD with high accuracy.A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm(DNN–BBoA)is *** BBoA is incorporated to select the best *** optimal features are fed to the deep neural network classifier and it improves prediction accuracy and reduces the time *** usage of a deep neural network further helps to improve the prediction accu-racy with minimal *** proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle *** proposed work is compared with different machine learning classifiers such as Support Vector Machine,Random Forest,and Decision Tree Classifi*** accuracy of the proposed DNN–BBoA is 99.35%for the heart dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.
The paper presents a novel idea of proposing the application of Variational Autoencoders (VAEs) in crime detection for predicting face aging and deaging, which is one of the potential challenge of forensic science. VA...
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Accurately predicting pharmacokinetic (PK) parameters such as absorption, distribution, metabolism, and excretion (ADME) is essential for optimizing drug efficacy, safety, and development timelines. Traditional experi...
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The rapid expansion of the Internet of Things (IoT) has led to its widespread adoption across various domains, including smart cities, industry, and agriculture. IoT systems consist of billions of interconnected devic...
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Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on st...
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In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending o...
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In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center;CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing;CSP, Cloud service providers;CSSA, Chaotic Squirrel Search Algorithm;DA, Dragonfly Algorithm;ED, Euclidean Distance;EDA-GA, Estimation Of Distribution Algorithm And GA;FF, FireFly algorithm;GA, Genetic Algorithm;HHO, Harris Hawk Optimization;IaaS, Infrastructure-as-a-Service;MGWO, Modified Mean Grey Wolf Optimization Algorithm;MMHHO, Mantaray modified multi-objective Harris Hawk optimization;MRFO, Manta Ray Forging Optimization;PaaS, Platform-as-a-Service;PM, Physical Machine;PSO, Particle Swarm Optimization;SaaS, Software-as-a-Service;SAW, Sample additive weighting;SLA-LB, Service Level Agreement-Based Load Balancing;TBTS, Threshold-Bas
Researchers and scientists need rapid access to text documents such as research papers,source code and *** research documents are available on the Internet and need more time to retrieve exact documents based on *** e...
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Researchers and scientists need rapid access to text documents such as research papers,source code and *** research documents are available on the Internet and need more time to retrieve exact documents based on *** efficient classification algorithm for retrieving documents based on keyword words is *** traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in *** solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text *** proposed SF-CNN is based on deep semantic-based bag-of-word representation for document *** deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for *** experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN ***-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.
Combining auto encoders and hybrid cellular automata provides a novel way to identify anomalies in structured data in the field of anomaly detection. Dimensionality reduction and extracting the features is one of the ...
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Recent advances in Generative AI, have encouraged many industries to use generative models in their products for generating components such as images, text, audio, or video. Generative foundation models like Large Lan...
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