http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Machine Learning Algorithm Accuracy for Code-Switching Analytics in Detecting Mood
Latib, Latifah Abd,Subramaniam, Hema,Ramli, Siti Khadijah,Ali, Affezah,Yulia, Astri,Shahdan, Tengku Shahrom Tengku,Zulkefly, Nor Sheereen International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.9
Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.
Transcranial Direct Current Stimulation in Schizophrenia
Sri Mahavir Agarwal,Venkataram Shivakumar,Anushree Bose,Aditi Subramaniam,Hema Nawani,Harleen Chhabra,Sunil V. Kalmady,Janardhanan C. Narayanaswamy,Ganesan Venkatasubramanian 대한정신약물학회 2013 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.11 No.3
Transcranial direct current stimulation (tDCS) is an upcoming treatment modality for patients with schizophrenia. A series of recent observations have demonstrated improvement in clinical status of schizophrenia patients with tDCS. This review summarizes the research work that has examined the effects of tDCS in schizophrenia patients with respect to symptom amelioration, cognitive enhancement and neuroplasticity evaluation. tDCS is emerging as a safe, rapid and effective treatment for various aspects of schizophrenia symptoms ranging from auditory hallucinations−for which the effect is most marked, to negative symptoms and cognitive symptoms as well. An interesting line of investigation involves using tDCS for altering and examining neuroplasticity in patients and healthy subjects and is likely to lead to new insights into the neurological aberrations and pathophysiology of schizophrenia. The mechanistic aspects of the technique are discussed in brief. Future work should focus on establishing the clinical efficacy of this novel technique and on evaluating this modality as an adjunct to cognitive enhancement protocols. Understanding the mechanism of action of tDCS as well as the determinants and neurobiological correlates of clinical response to tDCS remains an important goal, which will help us expand the clinical applications of tDCS for the treatment of patients with schizophrenia.