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A large number of online public domain comments are usually constructive, but a significant proportion is toxic. The comments include several errors that allow the machine-learning algorithm to train the data set by processing dataset with numerous variety of tasks, in the method of conversion of raw comments previously feeding it to Classification models using a ML method. In this study, we have proposed classification of toxic comments using a ML approach on a multilinguistic toxic comment dataset. The logistic regression method is applied to classify processed dataset, which will distinguish toxic comments from non-toxic comments. The multi-headed model comprises toxicity (obscene, insult, severe toxic, threat, & identity-hate) or Nontoxicity Estimation. We have implemented four models (LSTM, GRU RNN, and BiLSTM) and detected the toxic comments. In Python 3, all models have a simple structure that can adapt to the resolution of other tasks. The classification problem resolution findings are presented with the aid of the proposed models. It has been concluded that all models solve the challenge effectively, but the BiLSTM is the most effective to ensure the best practicable accuracy.

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Computer vision had reached a new level that allows robots from the limits of laboratories to explore the outside world. Even with progress in this area, robots are struggling to understand their location. The classification of the scene is an important step in understanding the scene. In many applications, a scene classifi- cation can be used such as a surveillance camera, self-driving, a household robot, and a database imaging system. Monitoring cameras are now everywhere installed. The accuracy of scene classification of indoor-outdoor techniques is weak. Using the Convolution Neural Net-work Model in VGG-16, this study attempts to im- prove accuracy. This research presents a new method for classifying images into classes using VGG-16. The algorithm’s outputs are validated using the SUN397 indoor-outdoor dataset, and outcomesdemonstrates that the suggested methodol- ogy outperforms existing technologies for indoor-outdoor scene classification. In this paper, Very Deep Convolutional Networks for Large-Scale Image Recognition” is what we implement. In ImageNet, a dataset of over 14 million images belonging to 1000 classes, the model achieves 92.7 percent top-5 test accuracy. It outperforms Alex Net by sequentially replacing large kernel-sized filters (11 and 5 in the first and second convolutional layers, respectively) with multiple 33 kernel-sized filters. We attain Training loss is 10percent and Training Accuracy is 96 percent in our projected work.

Currently, the Internet is playing a vital role in educating students to boost industrial production. Various network components are employed to give a wide range of options and reliability for internet services. As the Internet continues to develop and expand, network security has become an issue. Many attempts to secure transmission at the application, transport, or network layers have failed because the data connection layer has not been appropriately managed. The DHCP and ARP protocols are critical to the network's ability to function correctly. They were not designed with security precautions in mind. So, they are susceptible to a variety of assaults, including the rogue DHCPS, DHCPS hunger, DHCP hijacking, host impersonation, man in the middle, and DDoS. Here, we are going to examine how Kali Linux handles the aforementioned threats. DHCP hunger and host impersonation attacks could not be prevented by the current ARP and DHCP security measures. LAN assaults may be prevented and mitigated by using a novel method to protect ARP and DHCP. ARP and DHCP communications are protected by the suggested approach, which ensures their integrity and validity. A comparison of the proposed plans' security and performance attributes is carried out and compared to those of similar schemes