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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 718 条记录，以下是531-540 订阅

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Exploratory Inference Chain: Exploratorily Chaining Multi-hop Inferences with Large Language Models for Question-Answering

Exploratory Inference Chain: Exploratorily Chaining Multi-ho...

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international conference on Semantic computing

作者： Shosuke Haji Keiichi Suekane Hirofumi Sano Tomohiro Takagi Meiji University Kanagawa Japan

Successful few-shot question-answering with large language models (LLMs) has been reported for a variety of tasks. In the usual approach, an answer is generated by a single call to an LLM, but it has been pointed out that the performance of multi-hop inference by LLMs is not sufficient. Thus, an LLM is unable to perform the complex processing necessary to get an answer, which leads to poor performance. Moreover, the inference process is opaque. Against this, approaches that call an LLM multiple times have been proposed, but many of these approaches can only be used for a limited number of effective tasks, and LLMs essentially require complex *** address these problems, we propose the Exploratory Inference Chain (EIC) framework that combines the implicit processing of LLMs with explicit inference chains, and this is based on the dual process theory of human cognitive processes. The EIC framework first generates the information needed to answer a multi-hop question as keywords and then performs 1-hop inference for each keyword. If the inference is not sufficient, additional inferences are performed. This process is repeated, and when sufficient inferences are obtained, they are aggregated, and the final answer is generated. This makes the information per inference by LLM simplified, and logical inference is achieved through an explicit inference *** conducted experiments on two multi-hop QA datasets and confirmed through a quantitative evaluation that our EIC framework performed better than existing approaches. Moreover, a qualitative evaluation confirmed that our approach can effectively perform inference so as to get closer to the answer in question-answering tasks that require knowledge. In addition, compared with existing approaches, the EIC framework improves the interpretability of the output.

关键词： cognitive processes Computational modeling Semantics Task analysis

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A Dynamic Deep Learning Ecosystem for Real-Time cognitive data Processing on Industrial Edge Devices

A Dynamic Deep Learning Ecosystem for Real-Time Cognitive Da...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Sidhant Das Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The capacity to manage cognitive data in real time is crucial in the rapidly increasing field of industrial edge computing. This criterion must be fully completed to proceed. Our research resulted in the creation of a new platform dubbed the Dynamic Deep Learning Ecosystem, or DDLE, which strives to achieve these goals. The Deep Learning Edge (DDLE) is a strong architecture that ensures deep learning models run as well as possible on edge devices. The dynamic data-driven learning environment (DDLE) includes approaches such as dynamic model scaling (DMS), edge-based pruning (EBP), and real-time adaptive inference (RAI). The entire goal of the operation is dependent on these algorithms accomplishing all this work. Deep learning models may face dynamic changes in complexity and size if DMS is used. The way these changes are implemented is determined in part by the type of data and the amount of processing capacity presently available. The proposed DDLE has been thoroughly tested, and the results demonstrate that it beats more standard approaches in certain crucial measures, such as accuracy and latency. The findings of this study highlight the necessity of deep learning at the edge as well as the potential benefits of DDLE. Furthermore, it demonstrates that DDLE can be a viable option for businesses looking for a real-time cognitive data processing solution.

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Comparison Between Explainable AI Algorithms for Alzheimer’s Disease Prediction Using EfficientNet Models 16th

Comparison Between Explainable AI Algorithms for Alzheimer...

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16th international conference on Brain Informatics, BI 2023

作者： Jahan, Sobhana Saif Adib, Md. Rawnak Mahmud, Mufti Kaiser, M. Shamim Department of Computer Science and Engineering Bangladesh University of Professionals Dhaka Bangladesh Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Institute of Information and Technology Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9783031430749

Alzheimer’s disease (AD) is a common form of dementia that affects brain regions that control cognition, memory, and language. Globally, more than 55 million people are suffering from dementia. Given these troubling statistics, predicting AD is critical for future medications and treatment. Mild cognitive Impairment (MCI) is a vital stage for patients because from this stage a majority of patients turn into AD patients. Early MCI (EMCI) and Late MCI (LMCI) are vital two stages of MCI. Successful prediction of cognitive Normal (CN), AD, MCI, EMCI, and LMCI stages is a big challenge. Although there are models for predicting these stages but all models are not near to accurate. One major concern for these lacking is not having sufficient datasets to train the model. data augmentation can be a solution to create an abundance of MRI data. One major issue regarding Machine Learning (ML) is the black box nature. Due to this limitation, user satisfaction as well as trust in the model’s prediction is missing. Explainable Artificial Intelligence (XAI) is the torchbearer approach and through this, the reason behind every decision can be observed by the user. This paper proposes a comparison between four XAI models named Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, Score-weighted CAM (Score-CAM), and Faster Score-CAM. For performing the five classes (CN, AD, EMCI, MCI, and LMCI) prediction, EfficientNet models are used because these models have performed well on the augmented dataset. Among EfficientNet models (B0-B7), EfficientNetB7 has performed the best. The testing accuracy and loss are 96.34% and 0.12, respectively. After that, the last layer of the EfficientNetB7 model is passed to the XAI models. Comparing the four XAI models it is observed that Grad-CAM++ and Score-CAM are better performing than others. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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1st international conference on Applied Mathematics and Computational Intelligence, ICAMCI 2020

1st International Conference on Applied Mathematics and Comp...

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1st international conference on Applied Mathematics and Computational Intelligence, ICAMCI 2020

ISBN: (纸本)9789811981937

The proceedings contain 18 papers. The special focus in this conference is on Applied Mathematics and Computational Intelligence. The topics include: Steady and Unsteady Solutions of Free Convective Micropolar Fluid Flow Near the Lower Stagnation Point of a Solid Sphere;low-Light Image Restoration Using Dehazing-Based Inverted Illumination Map Enhancement;mathematical Modeling of Probability and Profit of Single-Zero Roulette to Enhance Understanding of Bets;Nonlinear Computational Crack Analysis of Flexural Deficit Carbon and Glass FRP Wrapped Beams;Economic Benefit Analysis by Integration of Different Comparative Methods for FACTS Devices;optimal Pricing with Servicing Effort in Two Remanufacturing Scenarios of a Closed-Loop Supply Chain;a Review on Type-2 Fuzzy Systems in Robotics and Prospects for Type-3 Fuzzy;Optimization of Palm Oil Mill Effluent (POME) Solubilization Using Linguistic Fuzzy Logic and Machine Learning Techniques;analysis of Heat Transfer Coefficients and Pressure Drops in Surface Condenser with Different Baffle Spacings;integration of Big data and Internet of Things (IoT): Opportunities, Security and Challenges;intuitionistic Fuzzy Metrics and Its Application;complex Structure of Number in Language Processing;digital Newspaper Using Augmented Reality;nonlocal Fuzzy Solutions for Abstract Second Order Differential Equations;performance Assessment of Routing Protocols in cognitive Radio Vehicular Ad Hoc Networks;infinite System of Second Order Differential Equations in Banach Space c0.

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Embracing Emerging Technologies: Insights from the 6th Workshop for Young Scientists in Computer Science & Software Engineering 6

Embracing Emerging Technologies: Insights from the 6th Works...

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6th Workshop for Young Scientists in Computer Science and Software Engineering, CS and SE@SW 2023

作者： Semerikov, Serhiy O. Striuk, Andrii M. Kryvyi Rih State Pedagogical University 54 Universytetskyi Ave. Kryvyi Rih50086 Ukraine Institute for Digitalisation of Education The NAES of Ukraine 9 M. Berlynskoho Str. Kyiv04060 Ukraine Zhytomyr Polytechnic State University 103 Chudnivsyka Str. Zhytomyr10005 Ukraine Kryvyi Rih National University 11 Vitalii Matusevych Str. Kryvyi Rih50027 Ukraine Academy of Cognitive and Natural Sciences 54 Gagarin Ave. Kryvyi Rih50086 Ukraine

The 6th Workshop for Young Scientists in Computer Science & Software Engineering showcases cutting-edge research from emerging talents. This volume comprises diverse papers illuminating emerging technologies' profound impact across various domains. Several contributions underscore the pivotal role of telemetry, graph theory, and machine learning in optimising distributed systems, detecting anomalies, and streamlining processes. Others delve into acoustic surveillance techniques for UAV detection, genetic algorithms for university scheduling, and neural network-driven optimisation of chemical synthesis. The proceedings also highlight novel approaches to assessing software architecture reliability, implementing ERP systems, and designing information systems for viral infection data analysis. Thermal resistance calculation software, multimodal distribution data processing methods, and high-performance computing energy consumption modelling are also explored. Moreover, the importance of user experience research in cross-platform application development is emphasised, alongside the design of virtual physics laboratories and Python learning game applications. Notably, predatory conferences are addressed, proposing robust conference management platforms to uphold research integrity. Collectively, these papers exemplify young scientists' innovative spirit and determination to tackle real-world challenges and push the boundaries of their disciplines. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Software reliability

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Sentiment Analysis across Modalities: A Comprehensive Review of Text and Audio Approaches

Sentiment Analysis across Modalities: A Comprehensive Review...

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international conference on Recent Trends in Advance computing (ICRTAC)

作者： Krisha Elle Nikita Dhobale Pratiksha Deshmukh Sakshi Nirne Ashwini Jarali Department of Computer Engineering International Institute of Information Technology Pune India

Sentiment analysis, an essential component of natural language processing, reveals the complex web of human emotions and viewpoints present in a variety of communication formats. Text-based as well as audio-based sentiment analysis are two areas where this survey that closely examines sentiment analysis. The digital era’s explosion of textual and auditory data has sparked a great deal of research in these fields, producing a wide range of methods and models. Sentiment analysis algorithms have become increasingly complex as a result of the availability of textual data from sources like social media and news articles. Concurrently, there has been a surge in interest in audio-based sentiment analysis because of the increasing use of audio content in podcasts, customer service interactions, and multimedia platforms. This survey provides an in-depth understanding of the recent methods, techniques and approaches in sentiment analysis using text and audio. It carefully describes in detail various developments in these fields, encouraging interdisciplinary idea-sharing. This paper presents a comprehensive overview of sentiment analysis advancements by offering a comprehensive review of research papers, with a focus on relevant models and techniques.

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Analysis of Students Performance in Flipped Classroom-based Vocational Education Using data Mining Tools

Analysis of Students Performance in Flipped Classroom-based ...

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IEEE international conference on Intelligent computing and Information Systems (ICICIS)

作者： Yousra O. Kamal Eldeen Mohamed AbdelRaheem Taysir Hassan A. Soliman IT Department Egyptian-German College of Technology Misr International Technological University Assiut Egypt Electrical Engineering Department Assiut University Assiut Egypt Information Science Department Faculty of Computers and Information Assiut University Assiut Egypt

This research investigates the impact of applying a Flipped Classroom Approach (FCA) in Vocational Education and proposes an improved approach that integrates cognitive Theory of Multimedia Learning (CTML) with flipped learning based on Revised Bloom’s taxonomy. Educational data mining (EDM) using statistics and data analysis was applied to transform large educational raw data into useful and meaningful information. This information can be applied to gain insights into students and their learning environments, enhance teaching support, and optimize decision-making in educational systems. EDM was performed on the student activities recorded on online learning management system (Moodle) including, pre-class activities, attendance, and after-class assignments. A correlation analysis was performed to explore the relationship between their activities in Moodle and academic performance. Results show that students who followed the flipped learning strategy outperformed those who did not, and the video designed according to CTML had a positive impact on student outcomes. The study also revealed that after-class assignments had a significant influence on students’ results, and that the students’ final results could be predicted through their assignment performance.

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Web-Based Optimization and Expansion of Multimodal High School Entrance Examination data Visualization

Web-Based Optimization and Expansion of Multimodal High Scho...

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Information and Communication Technology (ICTech), international conference of

作者： Wei Zhang Duan Jian HongLiang Wang YongHong Zhang Shengyang Institute of Computing Technology University of Chinese Academy of Sciences Beijing China Big Data Technology and Cognitive Intelligence Laboratory Shandong University Beijing China

The reform of the new college entrance examination system makes students and their parents pay more attention to the feasibility and accuracy of the college entrance examination. The college entrance examination informatization can help the local admissions departments to mine and analyze the data rules from the massive college entrance examination data, and provide help and guidance for each link of volunteer application. Therefore, this paper, in order to comply with the development trend of the new college entrance examination information construction, proposes the optimization method of data visualization chart rendering and innovative combination of ideas to improve the Web rendering performance in order to enhance the interactive experience of users and the platform.

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Building cognitive Cities in Developing Countries: Ecuador Case Study

Building Cognitive Cities in Developing Countries: Ecuador C...

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international conference on eDemocracy & eGovernment (ICEDEG)

作者： Diana Martinez-Mosquera Lorena Recalde Christian Tipantuna Departamento de Informática y Ciencias de la Computación Escuela Politécnica Nacional Quito Ecuador Departamento de Electrónica Telecomunicaciones y Redes de la Información Escuela Politécnica Nacional Quito Ecuador

Humans as sensors is the paradigm for transforming the idea of a “smart city” into the concept of a “cognitive city”. While the concept of smart cities is based on the notion of using technology to improve city life, cognitive cities take it a step further by utilizing the large amounts of data generated by people to make informed decisions. In developed countries, numerous projects have been successfully implemented using cognitive computing models in areas such as health, transport, and energy sustainability. However, in developing countries such as Ecuador, the collection of data is often a challenge, making it difficult to implement these types of projects. Therefore, in this paper, we present a comprehensive approach to identify available data sources, including open data, social media, and IoT networks, to start the path toward cognitive cities. Our main objective is to present and facilitate the understanding of those datasets by classifying them according to their context of use around their importance in a cognitive city. In this sense, this document consolidates 547 datasets from open portals and describes several proposals in which sensors generate data as part of the Internet of Things infrastructures. Besides, we corroborated that Twitter, Facebook, and Youtube are the most used online social networks in terms of e-participation mainly. We then identified the most relevant categories based on the data available from the analyzed data sources for cities in Ecuador. The results presented establish the need to understand, retrieve and use these data by government, academic, industrial, or research entities. Thus, decision-making processes may be implemented to improve the quality of life of citizens on the basis of available data and aim at building a cognitive country.

关键词： Video on demand Social networking (online) Smart cities Soft sensors Buildings Developing countries Proposals

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cognitive Defense Cyber Attack Prediction and Security Design in Machine Learning Model

Cognitive Defense Cyber Attack Prediction and Security Desig...

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international conference on Contemporary computing and Informatics (IC3I)

作者： P.T. Devadarshini B Chandrashekar Sumit Pundir Mohit Tiwari Ravikiran Madala E. Indhuma Department of Artificial Intelligence and Data Science Easwari Engineering College Chennai TamilNadu India Department Of Computer Science and Engineering St. Martin's Engineering College Kompalli Secunderabad Telangana Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina US Department of Computer Science and Engineering St.Martln's Engineering College Secunderabad Telangana India

Companies of all sizes in today's linked digital world are susceptible to cyber dangers. In this study, we provide a novel approach to cyber defence that integrates machine learning with cognitive defensive techniques to identify and prevent cyber attacks in their tracks. We built three ML models that use cognitive defensive strategies and other cutting-edge security measures like encryption and intrusion detection. A 95% accuracy rate was reached by our models, which is far greater than where things stand right now models. Our work has the potential to have a huge effect since the models we've developed may one day help businesses take a more preventative stance against cyber threats. Our findings may potentially be used to fields outside than cyber security. Overall, our study shows that machine learning and cognitive defense are useful for foreseeing and stopping cyber threats, and points the way toward fruitful future study in this area.

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