Performance Evaluation of Reactive and Proactive Routing Protocols over MANET

Bhargava, Neeraj; Bhargava, Ritu; Mathuria, Manish; Kumawat, Anchal

doi:10.5120/12718-9540

Cited by 8 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A novel method [64] was constituted with three predictive models for attack type, attack region, and weapon type prediction. Six supervised machine learning classifiers were fitted on the data set which includes support vector machine (SVM) [13], Artificial Neural Networks (ANN), Naïve Bayes (NB) [12], Random Forest (RF) [59], REP Tree [69], and J48 [70]. The data set contains only information about terrorist attacks that happened during the session 2013-2016 over the world and the agreement of attacks over the data set is tested by Cohen's kappa method [71].…”

Section: Terrorist Attack Prediction Methodsmentioning

confidence: 99%

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Leenuse,

Pankaj,

2023

2023 International Conference on Control, Communication and Computing (ICCC)

View full text Add to dashboard Cite

Twitter is one of the prominent social media applications which influence civilians due to its fast information dissemination. Researchers have shown that the majority of the tweets posted involve the current issues prevailing in society. Terrorism is a global threat to the peaceful existence of humanity. Terrorists widely use online social media platforms to communicate and propagate their activities. User-to-user interaction leads a major role in the formation of complex communication in the terrorism domain. This paper aims to discuss the different methods in literature used to identify both single and groups of users who are related to terrorist activities. The study focuses on the different techniques for identifying threatening activities, detecting terrorist attacks and predicting terrorist attacks.

show abstract

Section: Terrorist Attack Prediction Methodsmentioning

confidence: 99%

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Leenuse,

Pankaj,

2023

2023 International Conference on Control, Communication and Computing (ICCC)

View full text Add to dashboard Cite

show abstract

“…After designing models with compressed data, the accuracy of the trained models with compressed data was compared to the accuracy of the trained models with original data. Numerous models employing a variety of categorization techniques (Bayesian Network K2 [28], Bayesian Network TAN [29], J48 decision tree [30], K-Nearest Neighbor [31], Naive Bayes [32], Random Forest [33] and Simple Logistic Regression [34]) have been created in order to study the behavior of each.…”

Section: A: Hill Climbingmentioning

confidence: 99%

Efficient Machine Learning on Edge Computing Through Data Compression Techniques

et al. 2023

View full text Add to dashboard Cite

This paper discusses the increasing amount of data handled by companies and the need to use Big Data and Data Analytics to extract value from this data. However, due to the large amount of data collected, challenges related to the computational capacity of machines often arise when performing this analysis to acquire relevant information for the organization, especially when we are using edge computing. The paper aims to train machine learning models using compressed data, with two compression techniques applied to the original data. The results show that models trained with compressed data achieved similar accuracy to those trained with uncompressed data, and different compression techniques were compared. The research extended a previous study by analyzing the use of autoencoders for compression and reducing both instances and dimensionality of the dataset. The accuracy rate of the models when trained with compressed data instead of original data was maintained.

show abstract

“…Te anxious branch is a clear indication that any potential feature relevance should be discounted [17]. Tis classifer is considered the best among the six methods due to its widespread use and construction on information entropy [18]. With this approach, each aspect of the data might be used to break it down into smaller components, such as tree root nodes.…”

Section: Decision Trees Classifer (J48)mentioning

confidence: 99%

A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification

Joodi

Saleh

Kadhim

2023

Journal of Robotics

View full text Add to dashboard Cite

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven classifiers. A hybrid supervised learning system that takes advantage of rich intermediate features extracted from deep learning compared to traditional feature extraction to boost classification accuracy and parameters is suggested. They provide the same set of characteristics to discover and verify which classifier yields the best classification with our new proposed approach of “hybrid learning.” To achieve this, the performance of classifiers was assessed depending on a genuine dataset that was taken by our camera system. The simulation results show that the support vector machine (SVM) has a mean square error of 0.011, a total accuracy ratio of 98.80%, and an F1 score of 0.99. Moreover, the results show that the LR classifier has a mean square error of 0.035 and a total ratio of 96.42%, and an F1 score of 0.96 comes in the second place. The ANN classifier has a mean square error of 0.047 and a total ratio of 95.23%, and an F1 score of 0.94 comes in the third place. Furthermore, RF, WKNN, DT, and NB with a mean square error and an F1 score advance to the next stage with accuracy ratios of 91.66%, 90.47%, 79.76%, and 75%, respectively. As a result, the main contribution is the enhancement of the classification performance parameters with images of varying brightness and clarity using the proposed hybrid learning approach.

show abstract

Performance Evaluation of Reactive and Proactive Routing Protocols over MANET

Cited by 8 publications

References 11 publications

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Efficient Machine Learning on Edge Computing Through Data Compression Techniques

A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification

Contact Info

Product

Resources

About