Journal of ICT Standardization

Security Monitoring and Early Warning of Negative Public Opinion on Social Networks Under Deep Learning

2024-09-08T06:56:06+02:00

With the continuous development of social networks, negative social network public opinion appears frequently, which is particularly important for its safety monitoring and early warning. Taking Sina microblog as an example, this paper crawled texts from the platform, used BERT to generate word vectors, combined the bidirectional gated recurrent unit (BiGRU) and attention mechanism to design an emotion tendency classification method, and realized the classification of positive and negative emotion texts. Then, TCN was used to predict the negative emotion text to realize public opinion safety monitoring and early warning. It was found that BERT had the best performance. Compared with other deep learning methods, BERT-BiGRUA had a P value of 0.9431, an R value of 0.9012, and an F1 value of 0.9217 in the classification of emotion tendency, which were all the best. In the prediction of negative emotion text, TCN obtained a smaller mean square error and a higher R² than long short-term memory and other methods, showing a better prediction effect. The results verify the usability of the approach designed in this paper for practical safety monitoring and early warning of public opinion.

Application of Lenstra–Lenstra–Lovasz on Elliptic Curve Cryptosystem Using IOT Sensor Nodes

2024-07-07T10:11:49+02:00

The Internet of Things (IoT) model is presented in this paper with multi-layer security based on the Lenstra-Lenstra-Lovasz (LLL) algorithm. End nodes for the Internet of Things include inexpensive gadgets like the Raspberry Pi and Arduino boards. It is not practical to run rigorous algorithms on them, as opposed to computer systems. Therefore, a cryptography procedure is required that could function on this IOT equipment. Bitcoins and Ethereum are examples of cryptocurrency and Ripple employs techniques such as elliptic curve digital signature, Elliptic-Curve Diffie-Hellman (ECDH), and algorithm to sign any cryptocurrency on SECP256k1 elliptic curves transactions. By using Lenstra-Lenstra-Lovasz on a real-world Bitcoin blockchain and applying it to multiple dimensions, such as nonce leakage and weak nonces across several elliptic curves with different bit sizes on a Raspberry Pi, we can demonstrate the security of elliptic curve cryptosystems. Public key encryption techniques are seriously threatened by the development of quantum computing. Therefore, employing lattice encryption with Nth Degree Truncated Polynomial Ring Units (NTRU-NTH) on the Bitcoin blockchain will increase the resistance of Bitcoin blocks to quantum computing assaults. The execution time taken on SECP256k1 is 131.7 Milli seconds comparatively faster than NIST-224P and NIST-384P.

Comparison of Different Machine Learning Algorithms in the Mental Health Assessment of College Students

2024-10-15T19:58:59+02:00

This paper assesses college students’ mental health based on the symptom checklist 90 (SCL-90). In view of the assessment data processing and analysis, the performance of different machine learning algorithms, including random forest (RF), LightGBM3, extreme gradient boosting (XGBoost), in the classification of college students’ mental health samples was compared. Moreover, the effect of different hyperparameter optimization methods (grid search, Bayesian optimization, and particle swarm optimization) was compared. The experiment on the SCL-90 assessment dataset found that the optimization effect of grid search was poor, and the highest F1 value and area under the curve (AUC) of the RF algorithm were 0.8914 and 0.9384, respectively, the highest F1 and AUC values of the XGBoost algorithm were 0.9166 and 0.9551, respectively. The LightGBM algorithm optimized by particle swarm optimization showed the best performance in the classification of mental health samples, with an F1 value of 0.9790 and an AUC of 0.9945. It also achieved optimal results when compared to machine learning algorithms such as naive Bayes and the support vector machines. The results prove the reliability and accuracy of the particle swarm optimization-improved LightGBM algorithm in the analysis of college students’ mental health assessment data. The algorithm can be applied in practice to provide an effective tool for the analysis of the mental health assessment data of college students.

A Study on the Translation of Spoken English from Speech to Text

2024-10-30T18:16:59+01:00

Rapid translation of spoken English is conducive to international communication. This paper briefly introduces a convolutional neural network (CNN) algorithm for converting English speech to text and a long short-term memory (LSTM) algorithm for machine translation of English text. The two algorithms were combined for spoken English translation. Then, simulation experiments were performed by comparing the speech recognition performance among the CNN algorithm, the hidden Markov model, and the back-propagation neural network algorithm and comparing the machine translation performance with the LSTM algorithm and the recurrent neural network algorithm. Moreover, the performance of the spoken English translation algorithms combining different recognition algorithms was compared. The results showed that the CNN speech recognition algorithm, the LSTM machine translation algorithm and the combined spoken English translation algorithm had the best performance and sufficient anti-noise ability. In conclusion, utilizing a CNN for converting English speech to texts and LSTM for machine translation of the converted English text can effectively enhance the performance of translating spoken English.