This issue consists of four papers. The first paper is "Sentiment Analysis of YouTube Comments Using Deep Neural Networks and Pre-Trained Word Embedding" by A C Nanayakkara and G A D M Thennakoon. It discusses the importance of sentiment analysis for various NLP tasks, particularly in the context of the massive data generated by social media. The study focuses on using Deep Learning models with GloVe word embeddings to handle sentiment analysis. Specifically, the study employs a basic Neural Network (NN), Convolutional Neural Networks (CNNs), and a Long Short-Term Memory (LSTM) NN architecture to categorize the sentiment of comments on a popular YouTube video. Based on the experiment's results, it recommends using an LSTM NN model with GloVe word embeddings to overcome the limitations of traditional recurrent NNs. The performance of the LSTM model with GloVe word embeddings is compared to a densely connected NN model and a CNN model. The evaluation metrics used for comparison include training accuracy, testing accuracy and overfitting indicators. The results indicate that the LSTM model with GloVe word embeddings outperforms both the CNN and simple NN models.

The second paper, "Analysis of GPT Sentiments Using Blog Mining" by Sandeep Bhattacharjee, focuses on comprehending the evolution of language models, particularly Generative Pre-trained Transformers (GPTs), commonly known as GPTs. The author has used text mining analytics in the R4.2.2 console to analyze 72 blogs that were available on the OPENAI website. Through this analysis, he identified key terms related to GPT, such as "learning," "openAI," "models," and "model." Furthermore, the study conducted a correlation analysis, which revealed associations between certain terms like "appreciated," "creativity," "flex," "combine," and "connections." These correlations may provide valuable insights into the relationships between different concepts in the context of language models and their applications. The study appears to be relevant for individuals interested in language models, specifically GPTs, and their potential applications in various fields.

The third paper, "Dzyaloshinskii-Moriya Interactions of Skyrmions and Antiskyrmions in Mimicking Electron-Hole Pairs for Logic and Memory Applications" by Mosiori Cliff Orori presents a model and simulation of magnetic skyrmions stabilized by the Dzyaloshinskii-Moriya interaction. The skyrmions are described as tiny whirls of magnetic configurations. Skyrmions are swirling quasi-particles that exhibit a unique magnetic texture. They are characterized by their topological charge and can be thought of as localized magnetic knots or whirls. Antiskyrmions are the counterparts of skyrmions with opposite topological charges. These structures can be stabilized in certain materials by the Dzyaloshinskii-Moriya interaction. The growing demand for novel memory and logic devices in the field of communication and information technology has led to increased interest in utilizing skyrmions and antiskyrmions. Their topological properties and stability make them potential candidates for ultra-dense magnetic memories. Recent laboratory observations of skyrmions and antiskyrmions at room temperature have spurred further research into their transport and dynamic properties. There are also efforts to explore their applications in reservoir computing, a field that requires large memory storage and fast access capabilities. These recent findings suggest that skyrmions and antiskyrmions could be used as memory elements and even potentially in logic elements.

The last paper, "A Comparative Analysis of Traditional and Lightweight Algorithms", by Rimpi Rani and R K Bathla, discusses the importance of cryptography algorithms in safeguarding sensitive data and communications. It highlights that these algorithms can be compared based on various criteria. The aim is to conduct a comparative analysis between traditional cryptography algorithms such as AES, RSA and SHA, and new lightweight algorithms like Speck, Simon and Celfi. The paper acknowledges that the choice of algorithm should be tailored to the specific application and the security requirements. Traditional algorithms like AES, RSA, and SHA are considered to be suitable for scenarios that demand high levels of security. On the other hand, lightweight algorithms like Speck, Simon and Celfi are better suited for environments with limited resources, where efficiency and lower computational complexity are essential. Overall, the paper contributes to the field of cryptography by providing valuable insights into the strengths and limitations of both traditional and lightweight algorithms, helping users make informed decisions about selecting the most appropriate algorithm for their particular use cases.