A 'peer reviewed' journal indexed on Cabell's Directory, and also distributed by EBSCO and Proquest Database
It is a quarterly journal that publishes research papers on state-of-the-art Computing techniques and Encryption techniques; Computational problems; Artificial Intelligence; Cloud Computing; Databases; Algorithms and data structures; Cybernetics; Firewall techniques; Chip designing, Logic circuits and Software engineering; Programming techniques; Computer architecture; Neural networks, Machine Learning etc.
Sentiment Analysis of YouTube Comments Using Deep Neural Networks and Pre-Trained Word Embedding
Textual content sentiment analysis is important for a wide range of natural language processing activities. Particularly, the growth of social media stimulates a huge need for sentiment analysis, which is used to extract relevant statistics from massive data on the Internet. The study focused on handling the sentiment analysis problem by employing Deep Learning models with GloVe word embedding, which was motivated by the accomplishments of Deep Learning. A basic Neural Network (NN) was utilized as the experiment's foundation, together with Convolutional NNs (CNNs) and a Long Short-Term Memory (LSTM) NN architecture to categorize the tone of comments left on one of the most popular YouTube videos. The study recommends NN model with LSTM to overcome the shortcomings of the traditional recurrent neural community. In the comparison test between the densely connected neural network model, the CNN model, and the LSTM model, based on training accuracy (81%, 86%, 87%), testing accuracy (64%, 74%, 84%), and over fitting indicators (15, 12, 3), the LSTM model with GloVe word embedding outperformed both the CNN and simple NN. Future studies are encouraged to test different embedding methods with diversified datasets.
Analysis of GPT Sentiments Using Blog Mining
Language models are thought to be a mechanism for machines to comprehend and anticipate human languages as a component of contextually appropriate human communication. The paper attempts to comprehend the development of language models, commonly referred to as Generative Pre-trained Transformers (GPTs). Using text mining analytics in R4.2.2 console, the study attempts to locate keywords in 72 GPT blogs that appeared on the OPENAI website. Among them, the terms found were "learning," "openAI", "models" and "model." Moreover, correlation analysis revealed associations between terms like "appreciated," "creativity," "flex," "combine," and "connections." Academics, researchers and professionals working on business and information technology applications can all benefit greatly from this study.
Dzyaloshinskii-Moriya Interactions of Skyrmions and Antiskyrmions in Mimicking Electron-Hole Pairs for Logic and Memory Applications
The demand for novel memory and logic devices has grown in recent days with technology advancement. Particular attention has been drawn to the use of skyrmions and antiskyrmions in memory access and storage. The findings in most recent laboratory observation at room temperature further encourage more studies. So far, some investigations have pointed on skyrmions for reservoir computing applications which in most applications require very large memory storages and fast access capabilities. It is only recently that material physicists proposed skyrmions for ultra-dense magnetic memories, though it has not been implemented. In this paper, we present the model, simulation and discuss the findings obtained by simulating a magnetic skyrmion model. The findings suggested that a magnetic skyrmion with antiskyrmion has a capacity to act as memory element. As a result, adopting them for memory applications can simplify the fabrication process of logic elements if their magnetic spin textures are taken into account. These findings form a promising pointer for future application of skyrmion and antiskyrmion.
A Comparative Analysis of Traditional and Lightweight Algorithms
Cryptography algorithms are an essential tool for protecting sensitive data and communications. They can be compared based on several criteria. The paper compares some of the traditional AES, RSA and SHA with lightweight Speck, Simon and Clefia cryptography algorithms based on key size, block size, rounds and security. It is worth noting that the choice of algorithm depends on the specific use case and the level of security. In general, traditional algorithms may be more appropriate for applications that require high levels of security, while lightweight algorithms may be more suitable for resource-constrained environments.
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