Titlebook: Complex, Intelligent and Software Intensive Systems; Proceedings of the 1 Leonard Barolli Conference proceedings 2024 The Editor(s) (if app

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,A Method for?Job Management Using MPI Profiling Interface,estimating the overhead associated with job switching and determining its timing. To assess the usefulness of our system, we evaluated the overhead caused?by job switching. The evaluation results showed a 12.8% reduction?in overhead when switching when considering of the job’s running status, compared to switching without consideration.

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,A Systematic Review of?the?External Influence Factors in?Multifactor Analysis and?the?Prediction of-stationary and non-linear characteristics. This is compounded by the lack of a data-driven analysis of external factors that impact the future price of carbon. This article addresses this gap by conducting a comprehensive systematic literature review to identify the significant factors that contrib

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Stock Market Prediction Using Social Media Sentiments,ating sentiment analysis in stock market prediction, which uses Twitter data as the source of sentiment information. The proposed model is validated by applying it to predicting the stock movement of the National Stock Exchange of India (denoted NIFTY 50). Our proposed approach consists of capturing

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,Investigation of Location Problem in Logistics Centers Using?ADMM Algorithm,ement. A suitable logistics center location model is vital for the efficient functioning of the entire supply chain. In this study, we establish a decision model for logistics center location based on the operational process of the logistics center, with the sum of the construction cost, storage cos

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Stock Price Prediction Based on FinBERT-LSTM Model,nese language-specific, finance-oriented pre-trained model, is utilized to extract sentiment from financial texts. This sentiment data is combined with historical stock price data and input into an LSTM network to predict future stock prices. The model is trained and tested on the real data set with

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Interpreting Large-Scale Attacks Against Open-Source Medical Systems Using eXplainable AI,ncerns. Integrating machine learning algorithms for predicting and identifying potential cyber threats represents a promising advancement. They, however, were not widely accepted in medical practice because of their inherent complexity and lack of explainability. These constraints make implementing

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