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Automatic LSA-based retrieval of synonyms (for search space extension) 1

International Conference on Data engineering and Internet Technology, DEIT 2011

作者： Ekštein, Kamil Krčmář, Lubomír Laboratory of Intelligent Communication Systems Dept. of Computer Science and Engineering University of West Bohemia Plzeň Czech Republic Text-Mining Research Group Dept. of Computer Science and Engineering University of West Bohemia Plzeň Czech Republic

ISBN: (数字)9783642288074

ISBN: (纸本)9783642288067

This paper describes a research, experiments, and theoretical considerations leading towards automatic computational thesaurus construction based upon identification of synonyms in large sets of texts for the needs of question-answering (QA) systems. The method benefits from and is founded on Latent Semantic Analysis (LSA) technique. LSA serves as a hypothesis generator which produces hypotheses about the words that might be synonyms. Subsequently, the generated hypotheses are proven right or wrong by means of examination of morphologic bindings between the two words and of the overall syntactic structure of the context in which they appear, namely the subject-object relation. The retrieved synonyms are used to extend the search space where a QA system mines the answers. © 2013 Springer-Verlag GmbH.

关键词： Semantics

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Image Caption And Speech Generation Using LSTM And GTTS API 2

Image Caption And Speech Generation Using LSTM And GTTS API

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Ram, K. Bhargav Venkatesh, B. Sree, Sala Pooja Sai Anilkumar, Chunduru Reddy, V. Surya Narayana Kodumuri, Bhavya Bvrit Hyderabad College of Engineering for Women Department of Computer Science and Engineering Hyderabad India Systems Engineer Infosys Hyderabad India Gmr Institute of Technology Dept of Information Technology Andhra Pradesh Rajam532127 India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Vidya Jyothi Institute of Technology Department of Computer Science and Engineering HYderabad India

ISBN: (纸本)9798350325799

Image Caption generation is one of the challenging tasks in the field of artificial intelligence. It is used to generate a textual description for a given picture. But due to, the recent advancement in deep learning techniques requires only one single end-to-end model to create a caption for the given image. This paper is intended to provide an image caption and speech generator used to generate a single-line description for a given image and audio/speech for the report generated. The researchers used a sophisticated pipeline of specifically designed models in the previous models. To achieve the proposed model, VGG16 and LSTM models are used to obtain descriptions for the image and GTTS API is used for the audio/speech generation. © 2023 IEEE.

关键词： Long short-term memory

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A Novel Approach for Hashtag Topic Classification with Multimodal Features and VLMs

A Novel Approach for Hashtag Topic Classification with Multi...

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2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Zafar, Hafiza Tayyaba Tahir, Bilal Mehmood, Muhammad Amir University of Engineering and Technology Al-Khawarizmi Institute of Computer Science Lahore54890 Pakistan Islamic University of Madinah Associated with Faculty of Computer and Information Systems Madinah42351 Saudi Arabia

ISBN: (纸本)9798331516055

The rapid expansion of Instagram content marked by a large volume of photos and comments demands effective methods for discovering topics. While hashtags facilitate the grouping of posts, categorizing these hashtags into specific topics is challenging task due to the multimodal and noisy nature of content on the platform. In this paper, we introduce a novel approach that leverages multimodal features and Vision-Language Models (VLMs) for the topic classification of hashtags. To achieve this, we develop a dataset InstaHash which includes 464 hashtags and 12,345 posts spanning five topic categories: army, politics, sports, judiciary, and religion. We extract text features using the TF-IDF algorithm and visual features using HSV, SIFT, ResNet50, and VGG16 algorithms. Additionally, we introduce 34 binary features derived from the VLM of LLAVA through prompt engineering. These prompt engineering features examine the six aspects of images such as people, symbols and objects, buildings, events, uniforms, and embedded text. We train four classifiers of Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Convolutional Neural Network (CNN) for topic classification of hashtags. Our analysis reveals that combining text and visual features yields an accuracy of 0.89 for topic classification with the CNN classifier. However, by incorporating additional prompt features into the model, we achieve the highest accuracy of 0.96. Moreover, we notice that prompt features related to aspects of people and symbols are the most important features for the topic classification of hashtags. © 2024 IEEE.

关键词： Visual languages

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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CloudyPages: A Secure and Scalable Serverless Web-Based Blogging Application 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prakash, Vijay Jaiswal, Shrestha Purohit, Saumya Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MsidaMSD 2080 Malta

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

The CloudyPages app is a precise and trustworthy platform for bloggers to create and effectively manage blogs. The CloudyPages application provides bloggers a platform for creating and managing their blogs. The application can achieve remarkable accuracy, precision, and review rates by utilizing AWS services, assuring minimal errors and enhancing the discoverability and accessibility of bloggers’ content. Collectively, these metrics contribute to an optimized user experience. AWS WAF (Amazon Web Services Web Application Firewall) provides robust protection against common web application vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. By implementing this security layer, the application effectively protects the data of bloggers and readers from potential attacks. The AWS Web Application Firewall (WAF) protects against known attack patterns and continuously monitors and detects harmful activities to prevent unauthorized access. The application's serverless architecture lets bloggers focus on content creation and administration instead of server maintenance. The application's affordable price makes it an appealing, adaptable, and inclusive solution. The application's serverless design and AWS WAF and AWS Open make it flexible, affordable, and accessible for bloggers of all sizes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blogs

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A Novel Vehicle Detection System 13

A Novel Vehicle Detection System

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13th International Conference on computer engineering and systems, ICCES 2018

作者： Zakaria, Yehia Ali, Mohamed Ashraf El Munim, Hossam E. Abd Yousef, Ahmed Hassan Ghoneima, Maged Hammad, Sherif Mechatronics Engineering Department Ain Shams University Faculty of Engineering Cairo Egypt Computer and Systems Engineering Department Ain Shams University Faculty of Engineering Cairo Egypt School of Information Technology and Computer Science Nile University Giza Egypt

ISBN: (纸本)9781538651117

Histogram of oriented gradient (HOG) feature has been widely used in vehicle detection. In this paper, a modified version of HOG is proposed by introducing compass gradient into the HOG calculation. Three different versions of the modified HOG features are used as an input for linear and nonlinear support vector machine (SVM). The modified HOG variants proved to have better classification performance than that of the standard HOG. The classification results of modified HOG and nonlinear SVM are compared to the classification results of YOLO object detector. Finally, a vehicle detection system based on the best performing classifiers is introduced. © 2018 IEEE.

关键词： Support vector machines

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A Low Cost IoT TestBed System for Alzheimer's Early Stages Diagnosis in Health 4.0 Application 7

A Low Cost IoT TestBed System for Alzheimer's Early Stages D...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Kalovrektis, Konstantinos Xenakis, Apostolos Sofianou, Dimitra Simatos, Gavriil Papadopoulos, Georgios Stamoulis, George University of Thessaly Department of Computer Science and Bioinformatics Lamia Greece University of Thessaly Department of Digital Systems Larissa Greece University of Thessaly Department of Electrical and Computer Engineering Volos Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

This paper presents a low cost and novel mechanical system to estimate patients' Alzheimer's disease (AD) early stage. In essence our device measures the time variance between the patient's decisions to take his/her medicine. In more detail, the system calculates the time difference between significant time limits, where the patient is supposed to take the pills. This device may work as a warning tool to proactively diagnose and take medical actions, as it calculates patterns within the time difference in the patients' behavior. To this end, this paper, presents the system testbed, with which we keep track of a patient behavior and warn the treating doctor. © 2022 IEEE.

关键词： Diagnosis

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Automatic discovery of translation collocations from bilingual corpora 16

Automatic discovery of translation collocations from bilingu...

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16th European Conference on Artificial Intelligence, ECAI 2004

作者： Barrachina, Sergio Vilar, Juan Miguel Dpt. of Computer Engineering and Science Universitat Jaume I Castellón Spain Dpt. of Programming Languages and Computer Systems Universitat Jaume I Castellón Spain

ISBN: (纸本)9781586034528

We describe a method to automatically discover translation collocations from a bilingual corpus and how these improve a machine translation system. The process of inference of collocations is iterative: An alignment is used to derive an initial set of collocations, these are used in turn to improve the alignment and this new alignment is used to generate new collocations. This process is repeated until no more collocations are found. The final alignment and the set of collocations are used to train a translation model. We use a model that is based on finite state transducers and word clusters and has been modified to work with collocations in addition to single words. We present experiments in which we show that automatic collocations improve translation quality without prior linguistic information.

关键词： Alignment

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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