Samti Ahmed1, 2, Semeh ben salem1, 2, Sami Naouali1, 2, 1Sciences and Technologies for Defense (STD), Military Academy of Fandouk Jedid, Tunisia, 2Military Research Center (MRC), L’Aouina Military Base, Tunisia
The explosion of social network activity in recent years has led to massive volumes of user data, including status updates, posts, blog articles, forum entries, recommendations, login requests, and suggestions. This has given rise to new topics, including social media analytics and social network analysis. Analyzing online data to uncover terrorist trends is an essential task. It not only aids in comprehending terrorist events, including the actors, communities, methods, and operational tactics involved but also assists in predicting future attacks. However, this process remains challenging and error-prone, as terrorist events often deviate from conventional attack patterns. This paper introduces a scoring model, the Keyword Feature Score (KFS), for collecting data from social networks. The KFS model aims to assist investigators in conducting focused and specific analyses. Researchers can employ the KFS model to score and categorize suspicious comments related to homeland security within the Online Social Network (OSN) dataset, thereby further strengthening the model’s robustness.
Text scoring, natural language processing, Text classification, open source intelligence, social Network, homeland Security, text mining.
Hanna Abi Akl, Data ScienceTech Institute, 4 Rue de la Collégiale, 75005, Paris, France
We introduce SHADOW, a fine-tuned language model trained on an intermediate task using associative deductive reasoning, and mea- sure its performance on a knowledge base construction task using Wiki- data triple completion. We evaluate SHADOW on the LM-KBC 2024 challenge and show that it outperforms the baseline solution by 20% with a F1 score of 68.72%.
Knowledge Graphs, Large Language Models, Ontologies.
Lishuo Tao1, Samuel Silverberg2, 1La Jolla Country Day School, 9490 Genesse Ave, San Diego, CA 92037, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This paper explores the development and evaluation of a mental health tracking app designed to monitor mood patterns and provide personalized support [1]. The app integrates AI technology to offer real-time guidance and recommendations based on user inputs, while a calendar feature visualizes mood trends over time [2]. We conducted two experiments to assess the accuracy of the AI responses and the effectiveness of the calendar in capturing mood patterns, finding both to be effective, although improvements in empathy and user engagement are needed. By comparing our approach with other mental health apps, we demonstrate the apps unique strengths in offering a tailored, interactive experience. Limitations such as reliance on user participation and AI empathy were identified, but proposed enhancements suggest potential for improved functionality. Our app presents a promising tool for mental health management, blending technology and self-reflection to foster better emotional well-being [3].
Mental Health Tracking, AI Integration, Mood Patterns, Mobile Health (mHealth), Personalized Support.
Ebtesam Hussain Almansour, Computer Science Applied collage, Najran University, Najran 66462, Saudi Arabia
The growing acceptance of social media networks as a platform to share opinions on several feature semerged opinion mining or sentiment analysis (SA) as an active investigation part. In recent times, SA has attracted significant attention owing to its various applications in different features of our lives. SA is one of the Natural Language Processing (NLP) that purposes to analyze and process data that is transcribed in human languages. Even though the Arabic language is the most extensively spoken language utilized for content sharing through social media, the SA on Arabic content is restricted owing to numerous challenges with the language’s morphologic structures, the dialects variabilities, and the absence of the proper corpora. In recent times, deep learning (DL) and machine learning (ML) have demonstrated extraordinary achievements in the field of SA for Arabic tweet classification in social media platforms. In this manuscript, we design and develop an Integrated Deep Learning with Natural Language Processing Models for Sentiment Analysis and Classification (IDLNLPM-SAC) technique. The IDLNLPM-SAC model presents a sentiment analysis and classification using Arabic tweets. The presented IDLNLPM-SAC model follows different levels of data preprocessing to transform the raw Arabic tweet data into a compatible format. For the process of word embedding, the latent semantic analysis (LSA) technique can be deployed. Besides, the hybrid of parallel temporal convolutional network–gated recurrent unit (PTCN-GRU) classifier can be implemented for the classification process. Eventually, the parameter choice of the PTCN-GRU algorithm can be implemented by the design of the improved marine predator algorithm (IMPA). The simulation evaluation of the IDLNLPM-SAC technique takes place using the Arabic tweets database. The experimental results pointed out the heightened solution of the IDLNLPM-SAC technique compared to recent approaches.
Sentiment Analysis; Deep Learning; Arabic Tweet; Latent Semantic Analysis; Marine Predator Algorithm.
Jiadong Gu1, Moddwyn Andaya2, 1Bellevue high school, 9235 NE 25th street, Clyde Hill, WA 98004, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
My research focuses on a gamified approach to teaching financial education, targeting students aged 10-18 [1]. I outline a method involving a simulated retail environment where players manage a retail store, enabling them to understand economic concepts through interactive gameplay. In my method analysis, I discuss three key algorithms: ordering items, tracking sales, and evaluating business performance. Each algorithm incorporates real-time data and complex calculations to simulate realistic retail operations, such as inventory management and sales probabilities based on customer foot traffic, location premium, and time of day. I aim to assess student preferences for this gamified learning model compared to traditional platforms like textbooks or Khan Academy, using a system of surveys to gather demographic information and feedback that provided excellent and satisfactory results [2]. The issue of financial literacy is urgent, highlighting statistics that reveal a significant knowledge gap in our youth.
Financial education, Gamified education, Retail simulation.
Michael Jin , Andrew Park,1Lexington High School, 251 Waltham Street, Lexington, MA 02421,2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This project addresses the challenge of simulating rocket landings across different planetary environments by using Unity ML-Agents to train AI models [1]. The reusability of rockets, critical for space exploration, requires precise control and adaptability to varying gravitational conditions. We proposed a solution combining AI-driven controls with interactive user input to create a flexible and realistic rocket landing simulator. The methodology incorporated machine learning to train models for complex control tasks, applying reinforcement learning to adjust for Earth, Mars, and Moon environments. Our experiments focused on testing the model’s adaptation to these environments and assessing how rocket parameters like mass and thrust affected performance [2]. The most significant finding was that the AI performed well on Earth and the Moon but required further tuning on Mars due to faster descent speeds [3]. Our approach provides an engaging and educational platform for studying reusable rocket technology, making it a valuable tool for both academic and practical applications.
Machine Learning, Rockets, Landing, Reinforcement Learning.
Mesay Moges Menebo1,1Associate professor, University of Southeastern Norway, Campus
Background Influenza presents a significant public health challenge globally, with recurrent seasonal outbreaks straining healthcare systems, particularly during peak seasons. Internet search data has emerged as a valuable source for real-time forecasting of influenza trends, offering potential improvements over traditional surveillance systems. This study aimed to assess the effectiveness of using Google Trends search query data, alongside influenza-like illness (ILI) incidence, to forecast influenza trends in Norway using various machine learning models.
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