A Combinatorial Optimization Model for Emergency Resource Allocation after Disasters

Mapping Intimacies ◽

10.21203/rs.3.rs-1140300/v1 ◽

2021 ◽

Author(s):

Sehej Jain ◽

Kusum Kumari Bharti

Keyword(s):

Large Scale ◽

Emergency Services ◽

Cuckoo Search ◽

Dynamic Allocation ◽

Emergency Response System ◽

Search Optimization ◽

Proposed Model ◽

Cuckoo Search Optimization ◽

The Given ◽

Np Hardness

Abstract Disasters occur over a short or long period of time and cause large-scale harm to humans, infrastructure, as well as the ecosystem every year. Immediate response after a disaster helps minimize its impact on life and property. Therefore, it is crucial to have an emergency response system ready to handle any emergency that may come up after a disaster. In this paper, a model is proposed to optimize the distribution of emergency services at disaster-struck points. Due to the NP-hardness of the problem, two metaheuristic algorithms, Particle Swarm Optimization and Cuckoo Search Optimization have been used to dynamically allocate the available resources based on the given situation. The proposed model uses the distance between the emergency location and the emergency service provider, and the severity of the emergency as the main metrics for scoring any considered solution. The conducted experiments demonstrate that the model provides effective, efficient, and dynamic allocation service at emergency locations in simulated disaster situations.

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Enhanced Cuckoo Search Optimization and Hybrid Firefly Artificial Neural Network Algorithm for Cyberbullying Detection on Twitter Dataset

International Journal of Scientific Research in Science and Technology ◽

10.32628/cseit217486 ◽

2021 ◽

pp. 455-470

Author(s):

Sherly T. T. ◽

Dr. B. Rosiline Jeetha

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Cuckoo Search ◽

Feature Subset Selection ◽

Experimental Result ◽

Search Optimization ◽

Cuckoo Search Optimization ◽

Artificial Neural ◽

Cyberbullying Detection ◽

The Given

With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. Cyberbullying detection is generally in social networks like Twitter is one of the focussed research area. Cyberbullying is serious and widespread issues affecting increasingly more Internet users. Text mining tools are detecting cyber bullying and deal with several issues. However the existing system has issue with time consumption and inaccurate Cyberbullying detection results for the given Twitter dataset. To avoid the above mentioned issues, in this work, Enhanced Cuckoo Search optimization (ECSO) and Hybrid Firefly Artificial Neural Network (HFANN) algorithm is proposed. The proposed system contains three main phases are such as preprocessing, feature subset selection and classification. The preprocessing is done by using k-means algorithm for reducing the noise data from the given Twitter dataset. It handles the missing features and redundancy features through k-means centroid values and min max normalization respectively. It is used to increase the classification accuracy more effectively. The pre-processed features are taken into feature selection process for obtaining more informative features from the Twitter dataset. It is performed by using ECSO algorithm and the objective function is used to compute the relevant and important feature based on the best fitness values. Then the HFANN algorithm is applied for classification through training and testing model. It classifies the features more accurately using best fireflies rather than the previous algorithms. The experimental result proves that the proposed ECSO+HFANN algorithm provides better classification performance in terms of lower time complexity, higher precision, recall, f-measure and accuracy than the existing algorithms.

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A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

Journal of Cybersecurity and Information Management ◽

10.54216/jcim.000105 ◽

2019 ◽

pp. 54-64

Author(s):

Mohamed Elsharkawy ◽

◽

Ahmed N. Al Masri ◽

◽

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Image Encryption ◽

Cuckoo Search ◽

Content Based Image Retrieval ◽

Query Image ◽

Search Optimization ◽

Proposed Model ◽

Cuckoo Search Optimization ◽

Visual Properties

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.

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