Adverse drug events are pharmacologic impacts past the restorative reason that happens after the use of a drug. Drugs can potentially cause unwanted side effects or Adverse Drug Reactions (ADRs). Serious adverse drug events are evaluated to be the fourth driving cause of passing within the United States coming about in 100,000 passings per year. The main difficulty in finding adverse drug events through experiments is that designing clinical trials to discover side effects is typically very expensive, time-consuming, and most importantly, remains very challenging in terms of cost and efficiency. To cut down on the time-consuming and expensive process of drug discovery, machine learning about potential side effects must be used early. This study aims to develop a model that can predict drug side effects. The model will use the Gravitational Search Algorithm (GSA) method to choose the most relevant data or features, and the Support Vector Machine (SVM) method to build the prediction model. Hyperparameter tuning is performed with Linear, Polynomial, and RBF kernels to enhance the model performance. The best model is the RBF kernel with good performance having an accuracy value of 0.6391 and an F1-Score value of 0.7163.
Identifying the encryption algorithm is a crucial part of extracting the SSH session keys. Knowledge of the encryption algorithm is required since it involves the decryption attempt of ciphertext. Memory heap dumps contain valuable information, such as cryptographic keys, that can be obtained through memory forensics. This study proposes SSH session keys extraction tool to assist digital forensics investigators. The first step is to determine the SSH encryption algorithm by inspecting SSH communication in the network capture. Second, the SSH session keys on the memory heap dump are localized using SmartKex model. Finally, the tool made an attempt to decrypt the SSH session keys. The test results demonstrate that the proposed tool successfully identifies nearly all the keys in the test dataset while delivering efficient performance.
Digital transformation (DT) is an essential need for Higher Education Institution (HEI) in the era of modern technology to improve the quality of education and operational efficiency. This research develops a DT model using Object-Process Methodology (OPM), which integrates objects and processes into a single structured framework. This research utilized the results of a systematic literature review that had been carried out in previous studies to identify key components and challenges in DT. In detail, the OPM model was developed to map the interactions between technology infrastructure, business processes, human resources, and change management. Further, the model validation was conducted through three experts’ judgments. Therefore, the results show that our OPM model helps HEI to plan and implement DT strategies more effectively, improving the efficiency and quality of education. Hence, the author encourages HEI to use OPM model like in this study as a strategy to implement DT.
This study investigates the spatial distribution of tetanus cases in Central Java, Indonesia, using a Bayesian spatial method with a Binomial-Poisson model. The analysis addresses the challenge of excess zeros in the data, common in rare diseases like tetanus, by incorporating a zero-inflated Poisson distribution. The Bayesian framework utilizes the Besag-York-Mollié (BYM2) model to account for spatial autocorrelation and employs complexity-penalized priors for the latent level process. The study leverages the Integrated Nested Laplace Approximation (INLA) for efficient and accurate inference. Results provide insights into the spatial patterns of tetanus cases in Central Java, potentially informing targeted interventions and public health strategies. The suggested Bayesian mixed models for Tetanus cases in 2022 across 36 districts/cities in Central Java, Indonesia, incorporating explanatory variables from various dimensions, including health, demographics, industry, and education. Significant dimensions with Tetanus cases are predominantly found in demographic and industrial aspects. Through the Deviance Information Criterion (DIC), the Binomial-Poisson mixture distribution is identified as the most suitable for the data, with an excess of zero values at approximately 62.8% and significant extreme observations in specific regions.
The rapid expansion of the freelancing sector has underscored the necessity for more efficient project matching systems. Traditional methods, which largely depend on keyword searches and manual reviews, are often slow and error-prone. This paper is developing and evaluating Indonesian chatbot powered by Bidirectional Encoder Representations from Transformers (BERT), aimed at enhancing freelancer project matching. The proposed chatbot utilizes advanced natural language processing (NLP) techniques to deliver personalized project recommendations based on users' skills and experiences. It leverages BERT’s bidirectional training approach for better context understanding and integrates Long Short-Term Memory (LSTM) networks to maintain context over extended interactions. The system’s adaptability allows for continuous learning and updating, accommodating new fields and trends. Data was collected using ChatGPT 4.0 and was processed to create a dataset focused on Information Technology, Business Management, and Graphic Design. The models, including Recurrent Neural Networks (RNN) and LSTM, demonstrated impressive performance with LSTM showing superior results in 97% accuracy and context retention in the chatbot. This research highlights the advantages of using BERT with deep learning models for accurate and efficient project matching, providing a scalable solution for the evolving freelancing landscape. Future work will explore expanding the system to other languages and domains to further enhance its applicability.
