Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better *** technique provides perspective view of spatial resolution and aids in instantane...
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Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better *** technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its *** are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation *** are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction *** overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved bat optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral *** HDIB,we propose a spontaneous bat optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)***-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking ***,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of ***,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.
Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have ...
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Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimizationalgorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimizationalgorithms.
Explosion detection is one the important issues to protect people's lives from terrorist attacks. Technological advances have significantly reduced the possibility of terrorist attacks. One of the technologies tha...
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Explosion detection is one the important issues to protect people's lives from terrorist attacks. Technological advances have significantly reduced the possibility of terrorist attacks. One of the technologies that improved the explosion detection accuracy is Wireless Sensor Networks (WSN), which has gained researches' attention. WSN is widely used in medical systems, industries and military systems to collect data from sensor nodes placed in particular locations. For precise and high speed communications, optical sensor nodes are widely used recently. In this paper, the main objective is to detect explosion in an optical pressure sensor network. To meet this goal, an evolutionary algorithm, bat optimization algorithm, is employed. The proposed method results in reducing energy consumption and improving the service quality. Simulation results indicates the superiority of the proposed algorithm for explosion detection in compare to previous methods proposed for the same problem.
Mobile Cloud Computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations...
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Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor nodes is one of the major challenges in wireless sensor network. Coverage is main concern in node deployment because it influenc...
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Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor nodes is one of the major challenges in wireless sensor network. Coverage is main concern in node deployment because it influences the performance of wireless sensor network. For better performance of wireless sensor network it is essential to increase the coverage of nodes by locating the nodes at optimum positions with help of efficient optimizationalgorithm. In this paper the sensor nodes are located by using Hybrid fruit fly optimizationalgorithm and bat optimization algorithm in three dimensional environment. The exploration feature of fruit fly optimizationalgorithm is combined with exploitation characteristics of bat optimization algorithm in proposed algorithm. The grid points covered once by sensor node are removed from entire grid points for remaining nodes. With removal of grid points the overload on sensor nodes is reduced in proposed algorithm. The simulation results of Hybrid fruit fly optimizationalgorithm and bat optimization algorithm are compared in terms of variance, standard deviation, coverage rate and coefficient of dispersion with fruit fly optimizationalgorithm and bat optimization algorithm. Moreover, to verify the efficiency of proposed algorithm the results are also compared with other optimizationalgorithms such as artificial bee colony algorithm with dynamic search strategy, grey wolf optimizationalgorithm, enhanced grey wolf optimizationalgorithm, whale optimizationalgorithm, hybrid shuffled frog leaping algorithm and whale optimizationalgorithm, differential evolution algorithm, shuffled frog leaping algorithm, hybrid shuffled frog leaping algorithm and whale optimizationalgorithm based on differential evolution respectively. The simulation result signifies that proposed Hybrid fruit fly optimizationalgorithm and bat optimization algorithm is efficient than above stated existing optimizationalgorithm in terms of average coverage rate.
Diabetic Retinopathy (DR) is one of the most severe sight-threatening disorders resulting from diabetes, and can eventually lead to blindness and visual impairment. Early detection and medical therapy can assist in co...
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In Internet of Things (IoT) and cloud systems, Intrusion Detection (ID) is very vital for protecting the security infrastructures. ID techniques are extensively used to detect and track malicious threats in cloud and ...
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In Internet of Things (IoT) and cloud systems, Intrusion Detection (ID) is very vital for protecting the security infrastructures. ID techniques are extensively used to detect and track malicious threats in cloud and IoT systems. In the IoT based ID, the conventional techniques work based on the manual traffic feature values that increase the complexity of the networks and achieve a limited detection rate on the larger IoT databases. For addressing the above-stated issues and achieving high classification results, an effective deep learning based ID-System (IDS) is implemented in this article. Initially, the IoT data is acquired from the NSW-NB15 and NSL-KDD databases, and then, the standard scaling normalization technique, known as Min-Max normalization, is applied to select the dominant attributes and to eliminate outliers from the acquired databases. Additionally, the optimal features are selected from the rescaled normalized data by implementing the bat optimization algorithm. The selection of optimal features decreases the computational complexity and training time of the IDS. The chosen optimal features are passed into the DenseNet model for carrying out intrusion attack detection. Particularly, in the binary-class classification, the bat-based DenseNet model obtained 98.89% and 98.40% of accuracy on the UNSW-NB15 and NSL-KDD databases, correspondingly. The obtained simulation results prove the higher effectiveness of the current study when it is related to the state-of-the-art classifiers.
Numerous advancements and significant progress have been made in computer methods for medical applications, alongside technological developments. Automatic image analysis plays a crucial role in the realm of medical d...
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Numerous advancements and significant progress have been made in computer methods for medical applications, alongside technological developments. Automatic image analysis plays a crucial role in the realm of medical diagnosis and therapy. Recent breakthroughs, especially in the field of medical image processing, have enabled the automatic detection of various characteristics, alterations, diseases, and degenerative conditions using skin scans. Utilizing image processing methods, skin image analysis is instrumental in the identification and monitoring of conditions manifesting through alterations in skin structure. Notably, accurate segmentation of cancerous regions from the background remains a challenging task in the area of melanoma image analysis. The primary objective of this study is to achieve exceptional precision in delineating melanoma boundaries. Leveraging the bat optimization algorithm, we determine the optimal threshold for melanoma segmentation, effectively identifying the most accurate cancerous area boundaries. To evaluate the results, standard metrics such as accuracy, sensitivity, specificity, Dice coefficient, and F1 score are employed. In this study, we applied the bat optimization algorithm to determine the optimal threshold value for segmenting melanoma skin cancer, effectively identifying the most accurate cancerous area boundaries. For result evaluation, we employed standard metrics including accuracy, sensitivity, specificity, Dice coefficient, and F1 score, which yielded impressive values of 99.8%, 98.99%, 98.87%, 98.45%, and 98.24%, respectively.
The k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of t...
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In this study, a damage identification method is proposed using both the finite element method and the bat optimization algorithm applied to the AS-350 helicopter main rotor blade. First, the structure is numerically ...
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In this study, a damage identification method is proposed using both the finite element method and the bat optimization algorithm applied to the AS-350 helicopter main rotor blade. First, the structure is numerically modeled and evaluated with and without the presence of induced damages. In a second approach, an inverse problem of optimization is constructed in order to identify certain damages in terms of its position and severity level. Three different objective functions are evaluated according to the modal parameters of the rotor blade (vibrations in x, y and z directions). Numerical results, through analysis of variance, showed that local damage significantly modifies the modal response into a non-linear aspect. The modal response used was able to identify, with great efficiency, the actual (noise simulated) damages induced in terms of location and severity. Accordingly, a damage identification method is developed in order to better handle any measurement data (to find/regarding) structural changes (or damages) in complex aerospace structures. The obtained results from these numerical examples indicate that the proposed approach can detect true damage locations and estimate damage magnitudes with satisfactory accuracy, even under high measurement noise. (C) 2020 Elsevier Ltd. All rights reserved.
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