Volume 23, Nº 1 (2024)

Natural language processing (NLP) is a subset of artificial intelligence demonstrating how algorithms can interact with individuals in their unique languages. In addition, sentiment analysis in NLP is better in numerous programs, including evaluating sentiment in Telugu. Several unsupervised machine-learning algorithms, such as k-means clustering with cuckoo search, are used to detect Telugu text. However, these techniques struggle to cluster data with variable cluster sizes and densities, slow search speeds, and poor convergence accuracy. This study developed a unique ML-based sentiment analysis system for Telugu text to address the shortcomings. Initially, in the pre-processing stage, the proposed Linear Pursuit Algorithm (LPA) removes words in white spaces, punctuation, and stops. Then, for POS tagging, this research proposed a Conditional Random Field with Lexicon weighting; following that, a Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC) is proposed to classify the sentiments in Telugu text. Consequently, the method we propose produces efficient outcomes in terms of accuracy, precision, recall, and f1-score.

Jumping motion recognition via video is a significant contribution because it considerably impacts intelligent applications and will be widely adopted in life. This method can be used to train future dancers using innovative technology. Challenging poses will be repeated and improved over time, reducing the strain on the instructor when performing multiple times. Dancers can also be recreated by removing features from their images. To recognize the dancers’ moves, check and correct their poses, and another important aspect is that our model can extract cognitive features for efficient evaluation and classification, and deep learning is currently one of the best ways to do this for short-form video features capabilities. In addition, evaluating the quality of the performance video, the accuracy of each dance step is a complex problem when the eyes of the judges cannot focus 100% on the dance on the stage. Moreover, dance on videos is of great interest to scientists today, as technology is increasingly developing and becoming useful to replace human beings. Based on actual conditions and needs in Vietnam. In this paper, we propose a method to replace manual evaluation, and our approach is used to evaluate dance through short videos. In addition, we conduct dance analysis through short-form videos, thereby applying techniques such as deep learning to assess and collect data from which to draw accurate conclusions. Experiments show that our assessment is relatively accurate when the accuracy and F1-score values are calculated. More than 92.38% accuracy and 91.18% F1-score, respectively. This demonstrates that our method performs well and accurately in dance evaluation analysis.

The huge volume of data produced by IoT procedures needs the processing power and space for storage provided by cloud, edge, and fog computing systems. Each of these ways of computing has benefits as well as drawbacks. Cloud computing improves the storage of information and computational capability while increasing connection delay. Edge computing and fog computing offer similar advantages with decreased latency, but they have restricted storage, capacity, and coverage. Initially, optimization has been employed to overcome the issue of traffic dumping. Conversely, conventional optimization cannot keep up with the tight latency requirements of decision-making in complex systems ranging from milliseconds to sub-seconds. As a result, ML algorithms, particularly reinforcement learning, are gaining popularity since they can swiftly handle offloading issues in dynamic situations involving certain unidentified data. We conduct an analysis of the literature to examine the different techniques utilized to tackle this latency-aware intelligent task offloading issue schemes for cloud, edge, and fog computing. The lessons acquired consequently, from these surveys are then presented in this report. Lastly, we identify some additional avenues for study and problems that must be overcome in order to attain the lowest latency in the task offloading system.