October 19 ~ 20, 2024, Sydney, Australia
Tadashi Ogino, Department of Information Science, Meisei University, Tokyo, Japan
SHONAN, an advanced system harmonizing human capabilities and information technology (IT), was introduced in light of the COVID-19 pandemic, which prompted the shift of office workers and school students to online platforms. In addition, a specific application of SHONAN, referred to as the narrow area communication system (NAMI), was previously implemented, exclusively sharing text-based information. Further, NAMI uses Bluetooth low energy (BLE) to exchange messages; however, it is difficult to exchange large data, and therefore, we have confirmed that it is possible to exchange large data using Wi-Fi. Thus far, all experimental systems have been designed on paper in advance. This is insufficient for actual dynamic systems. In this paper, we considered a method that can allow NAMI functions to continue to be used even when devices and edges move and the network configuration changes dynamically. Further, we implemented and confirmed the functions in the prototype.
IoT, Sustainable System, Multimedia Data, Autonomous Configuration, Ad Hoc Network.
Swapna Krishnakumar Radha, Andrey Kuehlkamp, and Jarek Nabrzyski, Center for Research Computing, University of Notre Dame,Notre Dame Indiana, USA 46556
Attestation of documents like legal papers, professional qualifications, medical records, and commercial documents is crucial in global transactions, ensuring their authenticity, integrity, and trustworthiness. Companies expanding operations internationally need to submit attested financial statements and incorporation documents to foreign governments or business partners to prove their businesses and operations’ authenticity, legal validity, and regulatory compliance. Attestation also plays a critical role in education, overseas employment, and authentication of legal documents such as testaments and medical records. The traditional attestation process is plagued by several challenges, including time-consuming procedures, the circulation of counterfeit documents, and concerns over data privacy in the attested records. The COVID-19 pandemic brought into light another challenge: ensuring physical presence for attestation, which caused a significant delay in the attestation process. Traditional methods also lack real-time tracking capabilities for attesting entities and requesters. This paper aims to propose a new strategy using decentralized technologies such as blockchain and self-sovereign identity to overcome the identified hurdles and provide an efficient, secure, and user-friendly attestation ecosystem.
Attestation, Blockchain technology, Self-sovereign Identity technology.
Olalekan M. Durojaiye, Ramanjit K. Sahi, Department of Mathematics & Statistics, Austin Peay State University, TN, USA
The loan data simulated with Monte Carlo approach and analyzed in the research work provides valuable insights into the borrowers’ financial positions and loan performance. By calculating the debt-to-income ratio (DTI), we identified 122 (50.8%) loans that were at high risk of default. We also used risk-based pricing (RBP) to assign higher interest rates to riskier loans, helping to mitigate the risk of default. The data analysis showed that a higher DTI is associated with a higher risk of default, and a higher RBP is associated with a higher interest rate. Therefore, it is essential to use these metrics when assessing loan applications to ensure a healthy loan portfolio. This analysis can be used to inform loan officers, risk analysts, and other stakeholders involved in the lending process.
Loan Simulation, Interest rate, Risk Mitigation, Debt-To-Income Ratio, Risk-Based Pricing.
Houssam Hamici , Hani Ahmad and Hamido Hourani, Department of Electrical Engineering, PSUT, Amman, Jordan
This work presents a brain tumor classification survey utilizing Convolutional Neural Networks and Vision Transformers methods. The classification is based on a brain tumor dataset comprising 7023 MRI images with four classes present in the dataset: No tumor, Pituitary, Glioma, and Meningioma. The models used for the classification are ResNet101V2, VGG19, MobileNetV2, InceptionV3, Xception, and Vitb16. Two types of experiments were conducted with and without pre-trained weights to classify the dataset with intended models. Since the dataset is relatively small, CNN performs better than ViT since ViT relies on a vast pre-trained dataset to perform very well. The best results were obtained by Inceptionv3 and Xception architectures, both achieving an accuracy of around 98.6%.
BrainTumor(BT), Classification, Convolutional Neural Networks (CNN), Vision Transformers (ViT).
Jesús María Velásquez-Bermúdez, Founder and Chief Scientific Officer, HYPOTHALAMUS Artificial Intelligence Inc., USA
The document explores the advanced integration of Artificial Intelligence within Enterprise Optimization Systems. The core innovation presented is the transition from traditional Decision Support Systems (DSS) to Enterprise-Wide Optimization Systems (EWOS), which are designed to optimize organizational decision-making processes holistically and autonomously. The Enterprise Artificial Brain concept, inspired by the human brains structure, incorporates artificial components like the neocortex, hypothalamus, and hippocampus to manage, produce, and store knowledge. This integration allows for Autonomous Real-Time Distributed Optimization, significantly enhancing the efficiency and effectiveness of business operations. The document further discusses the application of these principles in various industrial contexts, particularly in the oil and gas sector. HAI’s research underscores the evolution from mental planning models to sophisticated, mathematical optimization models, facilitating integrated business planning/scheduling, and decision-making. By employing technologies such as OPTEX, Optimization Expert System, a generative AI system, HAI demonstrates how artificial brains can autonomously manage complex industrial processes, thereby reducing development time and increasing decision-making accuracy. This approach aims to emulate human cognitive functions through artificial mathematical systems, providing organizations with robust tools for navigating dynamic and uncertain environments.
Bringing these components together enables HAI to start thinking about a revolutionary idea: the Artificial Brain.
Ravi Kumar and Ayushi Kumari, Department of Computer Science and Engineering, Arya College of Engineering and Research Center, Jaipur, Rajasthan India
The integration of Natural Language Processing (NLP) in chatbot technology has revolutionized the healthcare sector, offering innovative solutions for patient care and management. This paper explores the diverse applications of advanced NLP-based chatbots in smart healthcare. These applications include providing medical information, assisting in disease diagnosis, supporting mental health, managing chronic diseases, and enhancing patient engagement. We discuss the underlying technologies, benefits, challenges, and future directions for NLP-based healthcare chatbots.
Natural Language Processing, Smart Healthcare, BERT, Chatbots, Mental Health, Sentiment Analysis.