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The main goal of the research is to make agricultural yield predictions more accurate through the use of the Naive Bayes (NB) method with a Recurrent Neural Network (RNN). There were 42 samples used in all, with each group consisting of 21 samples. The initial group applied the NB (NB) technique, but the subsequent group employed the RNN technique. The study was planned with a power of statistics of 80% using G-power. The statistically significant levels were set at an alpha of 0.05 and a beta of 0.10. The results show that the NB algorithm outperforms the RNN algorithm in terms of precision, with a rate of 88% compared to 83% for the latter. The average precision detection is within a vary of ±2 standard variations. The sample-independent t-test yielded a significance value of p = 0.010 (p < 0.05), indicating statistical significance. [ABSTRACT FROM AUTHOR]

The Specialized processes are never to be automated and mechanized. Some of the manufacturing processes possess prerequisites that can only be achieved with manpower. Thus, it is crucial to ensure the safety of the manpower. Manufacturing is principally a complex activity and value-added process. The employee needs to go along with the safety measures. Safety measures given precautions consider wearing gloves as a mandatory measure to safeguard the hands of the employees during the manufacturing process. Traditionally, the employees have used the cotton glove over the recent years throughout the manufacturing process and quality assurance. Even so, cotton gloves are worn by employees extensively during manufacturing by the employees, it has certain snag which include low heat resistance and can bruise the hands of employees causing skin infections, etc. Considering and overcoming these flaws, this article modifies the glove through research and analysis, providing an alternative way to implement using a double-layered wield glove, which will protect the hands and eliminate irritations or heat damage. [ABSTRACT FROM AUTHOR]

In today's world, a means to communicate with virtually no loss is highly essential, with innovations in SDR and improvements in cognitive computing, we could achieve a better way of communication than existing ones, and newer algorithms make it less hard to develop and test these systems. The cognitive radio, which allows dynamic allocation of the spectrum, could serve as a viable solution to congestion in the network and paves the way for dynamic spectrum allocation. The Cognitive Radio may be implemented, may vary with location and architecture, but the most common Software-Defined Radio could be converted into Cognitive Radio CR by a simple predictive and decisive algorithm. Here, we aim to analyze one of the techniques that could improve spectrum sensing of SDR implementation of CR. SDRs can tune to any wireless standard with modulation type, bandwidth, and variable carrier frequency; This paper proposes a Goodness of Fit technique to re-enforce and predict better channels for transmission or reception. [ABSTRACT FROM AUTHOR]