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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Srivastava, Arun Pratap Gupta, Priyanka Raj, Vijilius Helena Gupta, Manish Khare, Neha Almusawi, Muntather Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201323 India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M.P. Bhopal India The Islamic University Najaf Iraq

ISBN: (纸本)9798350305463

The Multi-Modal Cross-Attention Network (MCAN) is a revolutionary way to bridge the gap between varied data modalities that has emerged in response to the growing field of cross-modal generative AI and huge models. In this work, we highlight the substantial benefits of MCAN and provide its important contributions, aims, motivations, research issues, and solutions. MCAN has changed the game by dominating key performance indicators. One of its most distinguishing features is its innate accuracy in aligning various data modalities. This alignment is made easier with the use of cutting-edge cross-modal attention techniques, guaranteeing the smooth incorporation of data from many sources like pictures, text, and audio. When it comes to reconstructing information from several sources, The intended meaning is more likely to be preserved since MCAN makes material more semantically consistent. Uniformity is crucial for content-based recall and image-text matching. With its advanced ethical concerns in content creation and greater generalization to unseen material, MCAN is a cutting-edge solution in the rapidly developing area of cross-modal generative AI. When it comes to huge models and cross-modal generative AI, MCAN is a major step forward. © 2024 IEEE.

关键词： Semantics

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems (vol 14, 5434, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Premkumar, Manoharan Sinha, Garima Ramasamy, Manjula Devi Sahu, Santhoshini Subramanyam, Chithirala Bala Sowmya, Ravichandran Abualigah, Laith Derebew, Bizuwork Department of Electrical & Electronics Engineering Dayananda Sagar College of Engineering Kumaraswamy Layout Bengaluru Karnataka 560078 India Department of Computer Science and Engineering Jain University Ramanagaram Bengaluru Karnataka India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering GMR Institute of Technology Rajam Srikakulam Andhra Pradesh India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk 71491 Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia 27500 Petaling Jaya Malaysia College of Engineering Yuan Ze University Taoyuan Taiwan Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Bushira Ethiopia Applied science research center Applied science private university Amman 11931 Jordan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the gre

关键词： Computational intelligence Data mining Grey wolf optimizer K-means clustering Optimization algorithm

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Enhancing Personality Classification through Textual Analysis: A Deep Learning Approach Utilizing MBTI and Social Media Data

Enhancing Personality Classification through Textual Analysi...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： G Bharathi Mohan R Prasanna Kumar Elakkiya R Snehitha Gorantla Department of Computer Science and Engineering - Artificial Intelligence Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Birla Institute of Technology and Science Dubai UAE

Personality computing has grown significantly in use, with useful applications emerging in fields of study like human-robot interaction and recommendation systems. Traditional recommendation systems frequently run into issues with free riders, data scarcity, and a lack of understanding of user preferences. By strengthening the grasp of user preferences and increasing the precision of recommendations, the addition of well-established user personality features aids in resolving these problems. Therefore, the goal of this research is to take advantage of personality computing's numerous advantages while overcoming the drawbacks of current recommendation systems. This model achieved an accuracy of 97% on the test data because it was trained on pre-processed and padded sequences. By utilising LSTM layers, the model is better able to understand contextual information included in the text input and efficiently capture sequential dependencies.

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The Combination of Recognition Technology and Artificial Intelligence for Questioning and Clarification Mechanisms to Facilitate Meaningful EFL Writing in Authentic Contexts 6th

The Combination of Recognition Technology and Artificial Int...

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Proceedings of the 6th International Conference on Innovative Technologies and Learning, ICITL 2023

作者： Hwang, Wu-Yuin Nurtantyana, Rio Lai, Yu-Fu Chiang, I-Chin Nonie Ghenia, George Tsai, Ming-Hsiu Michelle Graduate Institute of Network Learning Technology National Central University Taoyuan City Taiwan School of Computing Telkom University Bandung Indonesia Department of Humanities National Open University New Taipei City Taiwan Department of Computer Science Brunel University London United Kingdom Department of Foreign Languages and Literature Feng Chia University Taichung City Taiwan Department of Computer Science and Information Engineering College of Science and Engineering National Dong Hwa University Hualien County Taiwan

ISBN: (纸本)9783031401121

Most studies of English as a Foreign Language (EFL) writing usually used grammar checking to help EFL learners to check writing errors. However, it is not enough since EFL learners have to learn how to create more meaningful content, particularly using their surroundings in authentic contexts. Therefore, we develop one app, Ubiquitous English (UEnglish), with recognition technology, particularly Image-to-Text Recognition (ITR) texts to provide the vocabulary and description from authentic pictures, and generative-AI that can generate meaningful questions and clarifications to trigger EFL learners to write more. In addition, EFL learners need to answer the question from generative-AI before they receive the clarification. Hence, we proposed a Smart Questioning-Answering-Clarification (QAC) mechanism to help EFL writing. A total of 35 participants were assigned into two groups, experimental groups (EG) with 19 learners and control groups (CG) with 16 learners with/without Smart QAC mechanism support, respectively. In this study, the quasi-experiment was conducted over five weeks and we used quantitative analysis methods. The results revealed that the EG with ITR-texts and Smart QAC had a significant difference with CG in the learning behaviors and post-test. Furthermore, EG could write more meaningful words in the assignments. Therefore, the Smart QAC mechanism could facilitate EFL learners to enhance their EFL writing in authentic contexts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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Optimized BIGRU-Attention Network for OSA and Cardiovascular Disease Prediction with Risk Factor Classification 4

Optimized BIGRU-Attention Network for OSA and Cardiovascular...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Yadav, Sameer Joseph, J. Sharmila Nagar, Mayank Dakshinamurthi, Vidyabharathi Usha, M. Dubey, Vedant Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Vit Bhopal University School of Computing Science and Engineering India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Sona College of Technology Dept. of Cse TN Salem India Measi Institute of Information Technology India Ggits MP India

ISBN: (纸本)9798331518981

Cardiac illness is a rapidly rising worldwide killer. The development of an automated prediction framework for OSA-related cardiovascular problems is tough given that to the extensive information needed to effectively forecast heart illness. Several algorithms have been developed for use in IoT-based medical oversight systems, which utilize sensor data to identify ailments *** deploy new learning models to improve prediction results. The existing methods lack the ability to forecast accurately, making them unsuitable for successful diagnosis. This study proposes BGEAN, a revolutionary framework that combines deep learning with computing devices to identify cardiac issues accurately. Data are pre-processed utilizing Studentized Residual Methods to address missing values and outliers. Improved Gated Recurrent Neural Networks help classify pre-processed data as normal or abnormal. Thorough testing and comparison to top deep learning techniques are conducted. Performance indicators, such as accuracy (99.99%), recall (99.35%), specificity (90.12%), precision (99.5), F1-score (99.99%), and Model Building Time (MBT) (0.212sec), are computed. The findings show that the technology that was suggested surpassed current algorithms in accuracy, error, and reaction time. With its many user situations, BGEAN provides the most diverse environment for forecasting cardiac diseases. © 2024 IEEE.

关键词： Recurrent neural networks

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Automatic Classification of Retinal Fundus Images for Diabetic Retinopathy Detection

Automatic Classification of Retinal Fundus Images for Diabet...

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Jerry, Jiny Mariam Thomas, Teddy Pugalenthi, R. Thomas, Tomcy St. Joseph College of Engineering Omr Department of Computer Science & Engineering Chennai600119 India Cognizant Technology Solutions Department of R&d India National Institute of Epidemiology Indian Council of Medical Research India

ISBN: (纸本)9798350337426

Retinal imaging facilitates the opinion and helps to detect diabetic retinopathy at the early stage. Cardiovascular conditions of the human body can be identified by analyzing the retinal images and the risk is tracked by analyzing blood vessels of the retina. Discovery of any abnormal features in the eye fundus retinal in the before stage is essential as well as experts on this sphere need further acceptable automatic discovery styles to describe the unheroic spots caused by diabetics to control its propagation on the retina. Image analysis of the retina is the fundamental approach to handle the eye diseases and other infections affecting eye fundus. This system introduces a more accurate medical opinion model for diabetic retinopathy discovery in fundus images by automated classification of landmarks. (i) Data preprocessing and augmentation;(ii) Automated classification with deep learning;(iii) Principal feature selection with Whale optimization Algorithm;(iv) Feature extraction;(v) Performance assessment. In the proposed project we used a class of automatic classification algorithms in our system, and the VGG-16 produces better results. The model secured an accuracy of 94 % and loss function of 0.0633. © 2023 IEEE.

关键词： Blood vessels

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Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs

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computers, Materials & Continua 2021年第7期68卷 1219-1233页

作者： Rupkumar Mahapatra Sovan Samanta Madhumangal Pal Jeong-Gon Lee Shah Khalid Khan Usman Naseem Robin Singh Bhadoria Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar UniversityMidnapore721102India Department of Mathematics Tamralipta MahavidyalayaTamluk721636India Division of Applied Mathematics Wonkwang UniversityIksan-SiJeonbuk54538Korea School of Engineering RMIT UniversityMelbourne3001Australia School of Computer Science University of Sydney Sydney2006Australia Department of Computer Science&Engineering Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India

Graph colouring is the system of assigning a colour to each vertex of a *** is done in such a way that adjacent vertices do not have equal *** is fundamental in graph *** is often used to solve real-world problems like traffic light signalling,map colouring,scheduling,***,social networks are prevalent systems in our ***,the users are considered as vertices,and their connections/interactions are taken as *** users follow other popular users’profiles in these networks,and some don’t,but those non-followers are connected directly to the popular *** means,along with traditional relationship(information flowing),there is another relation among *** depends on the domination of the relationship between the *** type of situation can be modelled as a directed fuzzy *** the colouring of fuzzy graph theory,edge membership plays a vital *** membership is a representation of flowing information between end nodes of the *** from the communication relationship,there may be some other factors like domination in *** influence of power is captured *** this article,the colouring of directed fuzzy graphs is defined based on the influence of *** with this,the chromatic number and strong chromatic number are provided,and related properties are *** application regarding COVID-19 infection is presented using the colouring of directed fuzzy graphs.

关键词： Graph colouring chromatic index directed fuzzy graphs

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TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates 4

TRIPORA: Intelligent Machine Learning Solution for Sri Lanka...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Legrand, T.R. Bandara, K.M.R.A.I. Stefania Crishani, J.A.D. Uvindu, L.W.P. Amarasena, Nelum Kasthurirathna, Dharshana Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study. © 2022 IEEE.

关键词： Obsolescence

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Quantum Meets the Minimum Circuit Size Problem 13

Quantum Meets the Minimum Circuit Size Problem

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13th Innovations in Theoretical computer science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Li, Heng Li, Haojin Chen, Jianyu Fu, Huazhu Liu, Jiang The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Institute of High Performance Computing Agency for Science Technology and Research Singapore

Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced in clinical settings due to the scarcity of annotated data resulting from the professional and private nature of medical data. Despite the existence of decent solutions, many of them are hindered in clinical settings due to limitations in data collection and computational complexity. To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain. A frequency filter-based data augmentation strategy is first proposed to promote domain variability within a single-source domain by introducing variations in frequency space and blending homologous samples. Then Gaussian filter-based structural saliency is also leveraged to learn robust representations across augmented samples, further facilitating the training of generalizable segmentation models. To validate the effectiveness of RaffeSDG, we conducted extensive experiments involving out-of-domain inference on segmentation tasks for three human tissues imaged by four diverse modalities. Through thorough investigations and comparisons, compelling evidence was observed in these experiments, demonstrating the potential and generalizability of RaffeSDG. The code is available at https://***/liamheng/NonIID_Medical_Image_Segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

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