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All Journal Jurnal Riset Kimia PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Khalid Hussain
Universiti Teknologi Malaysia
Chemistry Computer Science & IT

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IN VITRO ANTIANGIOGENESIS ACTIVITY OF STANDARDIZED EXTRACTS OF Piper sarmentosum Roxb Hussain, Khalid; Ismail, Zhari; Sadikun, Amirin; Ibrahim, Pazilah; Malik, Amin
Jurnal Riset Kimia Vol 1, No 2 (2008): March
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jrk.v1i2.50

Abstract

 ABSTRACT This study was undertaken to investigate the antiangiogenesis activity of standardized extracts/fractions of the leaf of Piper sarmentosum, using rat aorta model. The pulverized leaf was extracted sequentially and methanol extract was further fractionated with hexane, chloroform and ethylacetate. Both extracts and fractions were standardized by reverse phase HPLC with UV detection at 260 nm, using two markers, sarmentine and sarmentosine. Chloroform and methanol extracts have exhibited antiangiogenesis activity of 100% and 20% respectively. Antiangiogenesis activity of hexane and chloroform fractions was found to be 10% and 90% respectively, while ethylacetate fraction was found to be inactive. The analysis of most active extract and fraction has exhibited different profile by HPLC on the basis of amides. This study indicates that chloroform extract and fraction have promising antiangiogenesis activity and have potential for diseases involving angiogenesis. Keywords : antiangiogenesis activity, Piper sarmentosum Roxb.
Machine Learning Framework for Early Detection of Mental Health Conditions from Textual Data Riskhan, Basheer; Hadi, Abdullah Al; Saky, S M Asiful Islam; Arefin, Md Saiful; Hussain, Khalid
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.613

Abstract

Mental health disorders significantly affect global populations, placing heavy burdens on healthcare systems worldwide. Traditional diagnostic methods, mainly clinical assessments and self-reports, lack real-time monitoring, are prone to biases, and often result in delayed interventions. Recent advancements in machine learning (ML) offer promising opportunities to enhance mental health detection through behavioural and physiological data analysis. This study evaluates four widely used machine learning algorithms—Support Vector Machines (SVM), Logistic Regression, Naïve Bayes, and Random Forests—in identifying early indicators of mental health conditions from textual data. A dataset of 27,978 textual records from the “Analysis and Modelling on Mental Health Corpus” was analysed. Data preprocessing involved normalization, stop word removal, lemmatization, and TF–IDF vectorization to prepare robust features for model training. Model performance was assessed using accuracy, precision, recall, and F1-score metrics. Results showed that SVM and Logistic Regression outperformed other models, achieving accuracy rates of 92% and 91%. These findings demonstrate the potential of ML-based frameworks to support earlier and more accurate mental health interventions. Integrating such techniques into clinical practice can improve diagnostic accuracy, reduce healthcare workload, and enhance patient outcomes.
Co-Authors Amin Malik Amirin Sadikun Arefin, Md Saiful Hadi, Abdullah Al Pazilah Ibrahim Riskhan, Basheer Saky, S M Asiful Islam Zhari Ismail