The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

Implicit and Explicit aspects extraction is the amassed research area of natural language processing (NLP) and opinion mining. This method has become the essential part of a large collection of applications which includes e-commerce, social media, and marketing. These application aid customers to buy online products and collect feedbacks based on product and aspects. As these feedbacks are qualitative feedback (comments) that help to enhance the product quality and delivery service. Whereas, the main problem is to analyze the qualitative feedback based on comments, while performing these analysis manually need a lot of effort and time. In this research paper, we developed and suggest an automatic solution for extracting implicit aspects and comments analyzing. The problem of implicit aspect extraction and sentiments analysis is solved by splitting the sentence through defined boundaries and extracting each sentence into a form of isolated list. Moreover, these isolated list elements are also known as complete sentence. As sentences are further separated into words, these words are filtered to remove anonymous words in which words are saved in words list for the aspects matching; this technique is used to measure polarity and sentiments analysis. We evaluate the solution by using the dataset of online comments.

Sentiment analysis is one of the most important tasks in natural language processing, as it allows opinions expressed in text to be automatically categorized. This study presents a systematically controlled comparative analysis of classical machine-learning, deep-learning, feature-fusion, and fully fine-tuned transformer models for binary sentiment classification on the Internet Movie Database (IMDb) Movie Reviews dataset. While the individual components of the framework, including Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT) embeddings, feature fusion, and Multi-Layer Perceptron (MLP) classification, are established techniques, this study contributes a comprehensive empirical evaluation of their combined effectiveness for the target classification task, providing insights into the practical benefits of integrating lexical and contextual representations. Under a single experimental framework, all models are evaluated on a commonly held-out test set with 95% bootstrap confidence intervals, paired McNemar significance testing, confusion-matrix and error analysis, and a computational-complexity comparison. The fully fine-tuned transformers achieve the highest performance Robustly Optimized BERT Pretraining Approach (RoBERTa), F1-Score = 0.9427. The TF-IDF and BERT feature-fusion model is significantly more accurate than the classical, deep-learning, and BERT-only baselines (all p < 0.001), but significantly less accurate than the fine-tuned transformers (all p < 0.01); furthermore, concatenating the [CLS] token and mean-pooled representations does not significantly improve upon mean pooling alone ( p = 0.48). The study provides a transparent, evidence-based account of the accuracy and cost trade-offs among these model families.