The propagation of immoral content on social media poses substantial worries to online societal well-being and communication standards. While beneficial, traditional machine learning (ML) methods fall short of capturing the difficulty of textual and sequential data. This work reports this gap by suggesting a deep learning-based technique for detecting immoral posts on social media. The proposed model presents a fine-tuned Bidirectional Encoder representation from Transformers (BERT) with word embedding methods. Word2Vec and Global Vectors for Word Representation (GloVe) are employed to improve the identification of immoral posts on social media platforms to advance detection accuracy and strength. The incentive behind this study stems from the increasing demand for more sophisticated methods to struggle with damaging content. The proposed model is considered to capture the complicated patterns and semantic nuances in immoral posts by decreasing the dependence on manual feature engineering. The model is trained and assessed using benchmark datasets containing SARC and HatEval, which deliver a detailed set of labelled user-generated posts. The proposed model shows the best performance compared to traditional ML approaches. The fine-tuned Bert-based Word2Vec embeddings achieved a precision of 95.68%, recall of 96.85 %, and F1 scores of 96.26% on the SARC dataset. Fine-tuned Bert-based GloVe on the HatEval dataset achieved superior precision of 96.65, recall of 97.75, and F1-score of 97.20. The proposed results highlight the potential of the deep learning (DL) approach and fine-tuned BERT models, considerably refining the detection of unethical content on social networks.

As 5G networks advance, integrating blockchain technology to enhance security and decentralization shows great potential but faces significant scalability challenges. This study focuses on addressing these scalability issues in 5G blockchain networks by analyzing key factors such as transaction throughput, consensus mechanisms, and network latency. We propose several models to improve scalability, including sharding, sidechains, optimized consensus algorithms, and off-chain solutions. These models were evaluated through simulations using real-time data, with accuracy levels of up to 95% in predicting performance improvements across various metrics. The paper investigates the challenges specific to 5G environments, such as high transaction volumes and diverse device requirements, and assesses the strengths and limitations of each scalability technique in this context. Our findings highlighted potential synergies between these solutions and the unique features of 5G network architecture, offering tailored strategies for enhancing blockchain scalability. Additionally, the study addresses energy efficiency concerns associated with blockchain technologies and suggests optimization strategies. Simulated results are compared with real-world data, achieving accuracy rates of over 90% in identifying bottlenecks and validating the effectiveness of the proposed solutions. The insights presented aim to optimize 5G blockchain performance in dynamic, resource-constrained environments, facilitating the adoption of secure and decentralized applications in next-generation networks.

In the ever-evolving landscape of social media, where user-generated content shapes digital discourse, the need for nuanced sentiment analysis and topic extraction is paramount. This paper presents a comprehensive approach utilizing advanced Natural Language Processing (NLP) techniques to enhance user experience and foster a healthier digital environment. Leveraging Long Short-Term Memory (LSTM) networks for sentiment analysis and TextRazor for topic extraction, the system provides insights into emotional tones and key themes within social media discussions. Through intuitive visualizations, users gain awareness of sentiment trends and topic distributions, empowering informed engagement. The results demonstrate high accuracy in sentiment classification with 86% and effective topic identification, contributing to the mitigation of misinformation and negativity online. This research underscores the potential of advanced NLP methods in cultivating constructive digital spaces and sets the stage for further innovation in the field.

Accurate demand forecasting has become very significant, especially in the food sector, since many products have a limited lifespan, and improper management can cause the organization to incur enormous waste and loss. This research focuses on the problem of analyzing accurate food demand and its prediction through the application of machine learning techniques. An ensemble technique such as voting regression is employed, leveraging Random Forest Regressor and Gradient Boosting Regressor, which were the top-performing models. By integrating these two techniques using voting regression, we can leverage their complementary strengths to enhance prediction accuracy. The ensemble aggregates the predictions of both models, typically by averaging, to produce a final prediction. This technique can assist in reducing overfitting and capturing complex relationships in the data, resulting in more robust and accurate forecasts of food demand. The outcomes of the R2-score, Root Mean Square Error (RMSE) and Mean Average Error (MAE) are 0.99, 0.01, and 0.00, respectively.

Music classification is essential for faster Music record recovery. Separating the ideal arrangement of highlights and selecting the best investigation technique are critical for obtaining the best results from sound grouping. The extraction of sound elements could be viewed as an exceptional case of information sound information being transformed into sound instances. Music division and order can provide a rich dataset for the analysis of sight and sound substances. Because of the great dimensionality of sound highlights as well as the variable length of sound fragments, Music layout is dependent on the overpowering computation. By focusing on rhythmic aspects of different songs, this article provides an introduction of some of the possibilities for computing music similarity. Almost every MIR toolkit includes a method for extracting the beats per minute (BPM) and consequently the tempo of each music. The simplest method of computing very low-level rhythmic similarities is to sort and compare songs solely by their tempo There are undoubtedly far better and more precise solutions. work discusses some of the most promising ways for computing rhythm similarities in a Big Data framework using machine Learning algorithms.