Prediction of influencing atmospheric conditions for explosion Avoidance in fireworks manufacturing Industry-A network approach.

This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the causes of explosion during chemical mixing, drying, and pellet making. The Proposed ANN model predicts the accidents and the session of accidents (FN/AN) based on atmospheric conditions. This prediction takes values from historical accident data due to the atmospheric conditions of Sivakasi (2009-2021). In the development of ANN model, the Feed-Forward Back Propagation (FFBP) with the Levenberg-Marquardt function has been employed with hidden layers of 5 and 10 to train the network. The performance accuracy of both the hidden layers is evaluated and compared with other models like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (K-NN). The accuracy of the proposed model for accident classification is 82.7% and 67.8% for hidden layers 5 and 10, respectively. Also, the model predicts the session of accident with the accuracy of 72% and 54%, specificity of 77.7% and 60.1%, sensitivity of 69% and 52.92% for hidden layers 5 and 10, respectively. It is found that hidden layer 5 gives higher accuracy than hidden layer 10. The proposed ANN model gives the highest accuracy when compared to other models. This study is helpful in the firework industry management, and workers improve safety precautions and avoid explosions due to atmospheric conditions.

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network.

Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller's role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native-Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.

Agricultural Irrigation Recommendation and Alert (AIRA) system using optimization and machine learning in Hadoop for sustainable agriculture.

Internet of Things (IoT) in the field of agriculture promises to continuously provide global access to the farming information. The smart agriculture system either gives alert regarding the farm or it recommends for best agriculture field. This paper addresses both irrigation and alert, i.e., Agricultural Irrigation Recommendation and Alert (AIRA) system that operates individually without any correlation. At first, the IoT users of each farm field registers in HDFS, i.e., Hadoop Distributed File System. All the registered farm field holders will receive alerts for water level status and others. The collected data will be processed in a hybrid classifier that combines k-nearest neighbor with a neural network (k-N4). The classifier classifies into five classes of irrigation alerts: low water level, high water level, maintained water level, low pressure, and cyclonic storm. For faster classification, firstly, the neural network is used. Secondly, the recommendation for agronomists is optimal. The collected data is clustered by modified fuzzy clustering, and then optimal weather conditions are recommended from attractiveness-based particle swarm optimization (APSO) algorithm. The main measurements taken into account from the farms are soil moisture, temperature, humidity, wind speed, and intensity. Also, the access for IoT users is authenticated with identity, password, and biometric. Here, biometric iris is used, which is more secure than the fingerprint. Furthermore, data security is assured based on M-RSA cryptography.