Cascading Model in Underwater Wireless Sensors using Routing Policy for State Transitions

Vijayan, K.; Ramprabu, G.; Samy, S. Selvakumara; Rajeswari, M.

doi:10.1016/j.micpro.2020.103298

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…In Android apps, dynamic and static analytics are prominent approaches for detecting harmful apps. Despite the fact that both analyzing methodologies have strengths and drawbacks, a unique fusion malware detection analysis called mad4a was presented to reveal certain undiscovered components of Android malware by combining the benefits of these two techniques (dynamic and static) [9]. Over the last few years [10], Android operating systems are becoming increasingly popular, and the platform has made a name for itself in both mobile and Internet of ings (IoT) platforms.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study on Detection of Malware and Benign on the Internet Using Machine Learning Classifiers

Pavithra

Samy

2022

Mathematical Problems in Engineering

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The exponential growth in network usage has opened the way for people who use the Internet to be exploited. A phishing attack is the most effective way to obtain sensitive information about a target individual without their knowledge over the Internet. Phishing detection has an increased false-positive rate and is inaccurate. The motivation behind the research is to analyze and classify the applications among malware or benign with less time complexity. The main purpose is to find the algorithm which provides better accuracy for detecting the adware. The comparative analysis was made with three machine learning classifiers to find a better one. Random forest, SVM, and naïve Bayes were selected because of the better results obtained in previous research papers. Using a confusion matrix, the classifier methods were evaluated for accuracy, precision, recall, and F-measure with positive rates of both true and false. This research indicates that there are a number of classifiers that, if accurately detected, offer better reliable phishing detection outcomes. Random forest has proven to be an effective classifier with 0.9947 accuracy and a 0.017 false-positive rate. In this study, the comparative analysis reveals that the best ML classifiers have a lesser prediction accuracy for spoofing threat identification, implying that nonphishing programmers can use the best ML classifiers to evaluate the attributes of spoofing threat recognition and classification.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study on Detection of Malware and Benign on the Internet Using Machine Learning Classifiers

Pavithra

Samy

2022

Mathematical Problems in Engineering

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“…Predicting the energy consumption for sensor nodes [17][18][19][20] by making a non stationary signal to a stationary form is being done with Auto Regressive Integrated Moving Average [7]. The estimation is done posterior to calculations of data transfer where relationship which influence the fluctuations such as channel response, back off time are not considered and only statistical characteristics are alone inferred.…”

Section: Related Workmentioning

confidence: 99%

Storage Capabilities in Underwater Wireless Sensor Networks for Energy Balancing and Channel Alignment

Sureshkumar¹,

Raj²,

Selvi³

et al. 2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Underwater sensor node overflow occurs when large amount of sensory data is already stored and it is unavailable to obtain new data when an event happens. This misalignment tends to partitioning of individual node from networks in reporting information to sink. Resilience of individual node to recover from overflow considering channel and storage capabilities is needed. This work tries to avoid unnecessary transmission of the overloaded node. The contemporaneous work of storage and transmission is stated to improve energy efficiency in the desired work using DBR Bayesian network. Two slice Bayesian network is constructed for every node for calculating conditional probability distribution to resurrect links. Number of transmission is being improved considering the facilitation of nodes storage and transit duration reducing the overall energy consumption of a node by constraining unnecessary transmissions.

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“…The LOS is a complicated variable that is affected by a variety of circumstances, including the medical and community setting of the patient and his treatment [ 14 , 15 ]. This type of service to which the patients are admitted influences the prognosis of LOS.…”

Section: Introductionmentioning

confidence: 99%

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic

Samy¹,

Karthick²,

Ghosal³

et al. 2023

Int. j. inf. tecnol.

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The construction sector in a rapidly developing country like India is a very unorganized sector. A large number of workers were affected and hospitalized during the pandemic. This situation is costing the sector heavily in several respects. This research study was conducted as part of using machine learning algorithms to improve construction company health and safety policies. LOS (length of stay) is used to predict how long a patient will stay in a hospital. Predicting LOS is very useful not only for hospitals, but also for construction companies to measure resources and reduce costs. Predicting LOS has become an important step in most hospitals before admitting patients. In this post, we used the Medical Information Mart for Intensive Care(MIMIC III) dataset and applied four different machine learning algorithms: decision tree classifier, random forest, Artificial Neural Network (ANN), and logistic regression. First, I performed data pre-processing to clean up the dataset. In the next step, we performed function selection using the Select Best algorithm with an evaluation function of chi2 to perform hot coding. We then performed a split between training and testing and applied a machine learning algorithm. The metric used for comparison was accuracy. After implementing the algorithms, the accuracy was compared. Random forest was found to perform best at 89%. Afterwards, we performed hyperparameter tuning using a grid search algorithm on a random forest to obtain higher accuracy. The final accuracy is 90%. This kind of research can help improve health security policies by introducing modern computational techniques, and can also help optimize resources.

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Cascading Model in Underwater Wireless Sensors using Routing Policy for State Transitions

Cited by 17 publications

References 11 publications

A Comparative Study on Detection of Malware and Benign on the Internet Using Machine Learning Classifiers

A Comparative Study on Detection of Malware and Benign on the Internet Using Machine Learning Classifiers

Storage Capabilities in Underwater Wireless Sensor Networks for Energy Balancing and Channel Alignment

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic

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