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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Gawali, Anita Diliprao Gunjal, Baisa Laxman MET's Institute of Engineering Savitribai Phule Pune University Department of Computer Engineering Bhujbal Knowledge City Nashik Pune India

ISBN: (纸本)9798350322842

Text mining and information retrieval are fundamentally dependent on the selection of text feature items. Features must be created manually for use in traditional feature extraction methods. While it may take a lot of effort to manually build an effective feature, deep learning makes it possible to automatically learn new representations of successful features from training data in the context of new applications. Text mining has made a lot of progress thanks to deep learning, which is a new way to find features. Deep learning is different from more traditional methods because it automatically learns features from large datasets, while the latter adopts handcrafted features, which rely heavily on the prior knowledge of designers and thus make it nearly impossible to take advantage of large datasets. Feature representation in deep learning may be automatically taught using big data, which typically consists of millions of parameters. This thesis starts with a brief overview of the most common ways to extract text features. It then talks about the widespread use of deep learning methods in text feature extraction and their applications. Finally, it makes some predictions about the future of deep learning's role in feature extraction. © 2023 IEEE.

关键词： Feature extraction

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Drawing Order Diagrams Through Two-Dimension Extension

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Journal of Graph Algorithms and Applications 2023年第9期27卷 783-802页

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge & Data Engineering Group Research Center for Information System Design & Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order diagrams provide an important tool for understanding and thus discovering knowledge in such data. Easily readable drawings of such order diagrams are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work, we present the novel algorithm DimDraw to decompose an ordered set (e.g., a concept hierarchy) in linear orders and to produce a corresponding order diagram. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of its transitive incompatibility graph. To evaluate the quality of the algorithm, a user study was conducted where generated drawings were compared with ones from state-of-the-art drawing algorithms. © 2023 Brown University. All rights reserved.

关键词： Set theory

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Evaluation of Modified VGG16 Learning Model for Classifying Skin Cancer Lesions

Evaluation of Modified VGG16 Learning Model for Classifying ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Naser Alaubaidy, Mateen Ismail Abdullah, Abdulqadir Mousa Alwan, Fatimah College of Engineering and Computer Science Lebanese French University Department of Information Technology Kurdistan Region Erbil Iraq College of Engineering Knowledge University Department of Computer Engineering Kurdistan Region Erbil Iraq College of Engineering and Computer Science Bayan University Department of Computer Science Kurdistan Region Erbil Iraq

ISBN: (纸本)9798350300826

One of the most serious types of cancer that is prevalent around the world is skin cancer. It is widely known that skin cancer characterized by uncontrolled cell and tissue growth, presenting a challenge in detecting or diagnosis skin cancer in early stage. Accurate segmentation of skin lesions in dermatoscopic images holds great importance in diagnosis and assessment of skin cancer. Thus, Fast, precise, and real-time algorithms are essential in effectively detecting skin cancer to aid physicians in their treatment decision-making process. In this paper, we evaluated the performance of the modified VGG16 with using three common convolutional neural networks(CNN) in detecting the skin cancer with dermoscopic skin lesions images. In this study, the Kaggle dataset is used to classify skin cancer images. To classify skin lesions, the model modified VGG16 is compared to the most common models such as VGG16, mobileNetV2 and Inception ResNet v2. All models are trained on the same dataset and their performance is evaluated using metrics such as: accuracy, precision, F1-score, and recall. Finally, The comparative analysis revealed a promising performance of the modified VGG16 model in detecting multiclass skin cancer, which highlights its promising potential in classifying. © 2023 IEEE.

关键词： Diagnosis

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Investigating the performance of AI-driven smart building systems through advanced deep learning model analysis

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Energy Reports 2025年 13卷 5885-5899页

作者： Arun, M. Barik, Debabrata Othman, Nashwan Adnan Praveenkumar, Seepana Tudu, Kapura Thandalam602105 India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore641021 India Centre for Energy and Environment Karpagam Academy of Higher Education Coimbatore641021 India Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri44022 Iraq Department of Nuclear and Renewable Energy Ural Federal University Ekaterinburg620002 Russia Department of Mechanical Engineering School of Mechanical Sciences Odisha University of Technology and Research Bhubaneswar751029 India

The significance of this research rests in its potential to revolutionize building management systems, improve energy efficiency, boost occupant comfort, and encourage sustainable urban growth. These include concerns about personal information leakage, the complexity of building systems, and the requirement for powerful computers. To tackle these challenges, a Multi-faceted Smart Buildings using Deep Learning Framework (M-FSB-DLF) is proposed. It combines a comprehensive sensor network for data acquisition, advanced deep learning models like convolutional and recurrent neural networks, and reinforcement learning algorithms for adaptive control systems. The M-FSB-DLF has several potential uses, such as HVAC control, security systems, energy management, and occupancy optimization. By extensively analyzing simulations, this study shows that the proposed methodology improves building performance indicators such as energy efficiency, occupant comfort, resource utilization, security, and operational cost. Energy efficiency, system dependability, and user satisfaction are all shown to have markedly improved by a 20 % reduction in the simulations. In addition to laying the groundwork for future studies in this exciting area, this research highlights the revolutionary effect that deep learning will have on innovative building technology. More smart, efficient, environmentally friendly cityscapes are within reach due to the M-FSB-DLF, a groundbreaking development in smart building technology. The proposed method increases the security system ratio by 94.8 %, energy management system ratio by 96.2 %, occupancy optimization ratio by 74.3 %, energy efficiency ratio by 94.5 %, and sustainable urban growth ratio by 96.2 % compared to other existing methods. © 2025 The Authors

关键词： Smart homes

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Guest Editorial: Special Issue on Evolutionary Computation for Games

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IEEE Transactions on Games 2023年第1期15卷 1-4页

作者： Schrum, Jacob Liu, Jialin Browne, Cameron Ekart, Aniko Gallagher, Marcus Southwestern University Department of Mathematics and Computer Science GeorgetownTX United States Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Aston University Department of Computer Science United Kingdom The University of Queensland School of Information Technology and Electrical Engineering Australia

The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.

关键词： Special issues and sections Evolutionary computation Games

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IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation 22

IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning...

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22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023

作者： Wu, Yu-Hsuan Lin, Ying-Jia Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781959429852

This paper describes the entry by the Intelligent knowledge Management (IKM) Laboratory in the BioLaySumm 2023 task1. We aim to transform lengthy biomedical articles into concise, reader-friendly summaries that can be easily comprehended by the general public. We utilized a long-text abstractive summarization longformer model and experimented with several prompt methods for this task. Our entry placed 10th overall, but we were particularly proud to achieve a 3rd place score in the readability evaluation metric. Our code is available at https://***/IKMLab/ BioLaySumm. © 2023 Association for Computational Linguistics.

关键词： knowledge management

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Performance Enhancement of IoT LoRa Network for Mobile Devices based on Improved AMILCC Technique 1

Performance Enhancement of IoT LoRa Network for Mobile Devic...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Venkatesan, C. Thamaraimanalan, T. Mohammed Ibrahim, M. Vinoly, G. Ramkumar, M. Saranya, K. HKBK College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Tamilnadu Thiruchengode India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

In Internet of Things (IoT) networks, long-range (LoRa) technology is well-known for its wide communication range, but using it with mobile devices presents unique difficulties. Frequently occurring channel attenuation, which causes packet loss, increased energy consumption, and longer transmission times, is one of these difficulties. An improved Adaptive Mobility-based IoT LoRa Clustering Communication (AMILCC) strategy addressed to solve these problems. AMILCC partitions the network into optimal Spreading Factor (SF) areas by using a 2D random waypoint mobility model. The Hybrid Adaptive Data Rate (HADR) technique, which combines intra-and inter-SF region modifications to improve resource allocation for mobile IoT LoRa devices, is a key component of AMILCC. The simulation findings demonstrate the advantages of AMILCC over conventional stationary techniques, even under low mobility conditions (up to 5 m/s). In particular, AMILCC decreases energy usage by 56.5%, improves packet success ratio (PSR) by 72 %, and decreases end-to-end delay by 45.32%. These outcomes demonstrate how well AMILCC works to enhance efficiency and performance for mobile LoRa applications. © 2024 IEEE.

关键词： Resource allocation

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Autism Detection due to Environmental Pollutants Using Supervised Learning Algorithms 7

Autism Detection due to Environmental Pollutants Using Super...

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7th IET Smart Cities Symposium, SCS 2023

作者： Surendran, R. Seena, M.S. Swaminathan, Aravind Julius Babu, Joel Benjamin Vinod, Visal Department of computer science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai India Department of Computer Science Middlesex University Knowledge Park Dubai United Arab Emirates

ISBN: (纸本)9781839539831

Air pollution, a major global concern resulting in numerous annual fatalities, has been associated with various health disorders. This study focuses on understanding the impact of residing in heavily polluted cities on mental and behavioral patterns, specifically exploring the potential link between air pollution and autism. Supervised classification algorithms, including logistic regression, random forest, decision tree, and AdaBoost, were employed to predict the risk of autism in individuals residing in severely polluted countries by 2030. The research aims to identify the relationship between genetic variations in adults with Autism Spectrum Disorder (ASD) and harmful air pollutants, investigate gender differences in autism frequency, and determine the neurotoxicant posing the greatest danger for neurodegeneration and its impact on autistic individuals. This study successfully employed supervised classification models to uncover hidden patterns between air pollution and autism risk. The binary prediction model achieved an average accuracy of 70%, with the AdaBoost algorithm demonstrating the highest accuracy at 73% in predicting autism prevalence. Increased PM2.5 concentrations correlated with higher autism risk compared to other neurotoxicants. These findings underscore the importance of sustainable practices and pollution reduction to safeguard human health. © The Institution of engineering & Technology 2023.

关键词： Adaptive boosting

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IoT-Driven Physical Training and Fitness Management in Colleges 2

IoT-Driven Physical Training and Fitness Management in Colle...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Rajaa, Jasim Mohammed Manee, Mariam J. Mohammed, Marwan Aziz Sharif, Salah Farhan A Al-Nisour University College Baghdad Iraq Uruk University Department of Radiology Techniques Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq

ISBN: (纸本)9798331534950

Increased personalized physical training and fitness demand in colleges propel evolution into physical education. These are tailored according to fitness programs, essential in enhancing the students' physical health through better training experiences. It requires an integrated health data analysis to design training for each student-athlete. Therefore, related needs can be addressed with the help of an IOT-driven physical training and fitness management system by MPES-IoT. To date, a revolution in college fitness management has been proposed to meet educational reform. The proposed IoT-based framework systematically monitors physical activity using adaptive technology for data generation. Based on the input from the college learning management system, a neural network algorithm automatically analyzes the best modes of sports training within the university setting;it is tailored in such a way as to make sports development personalized to every student. Now, the Internet of Things has become a strong means that can be accessed anywhere globally and, consequently, is starting to act as a reliable base for different intelligent educational initiatives taken worldwide. The results of the simulations show that the framework is reliable, with a security rating of 93.13%, a performance rating of 95.5%, and a privacy rating of 96.2%, thereby giving an overall reliability rate of 97.3%. Conclusion The IoT-based fitness management system offers an integrated solution to colleges in efforts to advance physical training to promote health in students. © 2024 IEEE.

关键词： Electronic health record

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Analyzing P300 Signals to Assess Undergraduate Attention Span Using Impulse Neiry BCI

Analyzing P300 Signals to Assess Undergraduate Attention Spa...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Sooda, Kavitha Indiramma, M. Kubakaddi, Sanjeev B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of AI and DS Bangalore India Itie Knowledge Solutions Bangalore India

ISBN: (纸本)9798331540067

The study on brain signals to understand working memory in undergraduate students has yielded promising results. However, the challenge remains in accurately assessing different brain activities. This work seeks to measure concentration in cognitive performance, offering a detailed dataset that tracks how individuals respond to cognitive tasks. A virtual reality-based solution has proven effective in capturing the P300 signal, providing a more focused and accurate measurement platform. The study aims to evaluate the efficacy of P300 signals as indicators of attention and cognitive engagement in educational settings. A brain-computer interface system, Impulse Neiry, captured and analyzed P300 signals from undergraduate students while performing various cognitive tasks. User attention span was evaluated by measuring parameters such as session duration and interaction frequency with the software, offering insights into the system's usability. The results indicated that participants experienced about 35% deeper relaxation, and their attention spans increased by 18.97% compared to baseline engagement levels. © 2024 IEEE.

关键词： Virtual reality

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