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Improved Attentive Recurrent Network for applied Linguistics-Based Offensive Speech Detection

computer Systems science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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Distributed Cryptography for Lightweight Encryption in Decentralized CP-ABE

Distributed Cryptography for Lightweight Encryption in Decen...

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IEEE International Conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Mohammed B. M. Kamel Janneke Van Oosterhout Peter Ligeti Christoph Reich Department of Computer Algebra Eotvos Lorand University Budapest Hungary Institute for Data Science Cloud Computing and IT Security Furtwangen University of Applied Sciences Furtwangen im Schwarzwald Germany Department of Computer Science University of Kufa Najaf Iraq Department of Computer Science University of Twente Enschede The Netherlands Fox Crypto Delft The Netherlands

Decentralized Attribute-based Encryption (DABE) is an extension of public key cryptography that allows the ciphertext to be decrypted by any node that has a predefined set of attributes. DABE can be used to control access to Internet of Things (IoT) devices and data based on attributes such as the type of device, the location, and the role of the user. Due to its heavy computation requirements, the DABE either can not be implemented on the weak devices or will be implemented with significant *** this paper, we study two distributed solutions of lightweight encryption in DABE using secret sharing and outsourcing. The analysis of our results showed that both approaches provided a lightweight encryption property of DABE. The secret sharing outperformed the outsourcing in small number of attributes, and both provided close efficient results in case of high number of attributes.

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Tackling the Curse of Dimensionality with Physics-Informed Neural Networks

arXiv

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arXiv 2023年

作者： Hu, Zheyuan Shukla, Khemraj Karniadakis, George Em Kawaguchi, Kenji Department of Computer Science School of Computing National University of Singapore Singapore119077 Singapore Division of Applied Mathematics Brown University ProvidenceRI02912 United States

The curse-of-dimensionality taxes computational resources heavily with exponentially increasing computational cost as the dimension increases. This poses great challenges in solving high-dimensional partial differential equations (PDEs), as Richard E. Bellman first pointed out over 60 years ago. While there has been some recent success in solving numerical PDEs in high dimensions, such computations are prohibitively expensive, and true scaling of general nonlinear PDEs to high dimensions has never been achieved. We develop a new method of scaling up physics-informed neural networks (PINNs) to solve arbitrary high-dimensional PDEs. The new method, called Stochastic Dimension Gradient Descent (SDGD), decomposes a gradient of PDEs’ and PINNs’ residual into pieces corresponding to different dimensions and randomly samples a subset of these dimensional pieces in each iteration of training PINNs. We prove theoretically the convergence and other desired properties of the proposed method. We demonstrate in various diverse tests that the proposed method can solve many notoriously hard high-dimensional PDEs, including the Hamilton-Jacobi-Bellman (HJB) and the Schrödinger equations in tens of thousands of dimensions very fast on a single GPU using the PINNs mesh-free approach. Notably, we solve nonlinear PDEs with nontrivial, anisotropic, and inseparable solutions in less than one hour for 1,000 dimensions and in 12 hours for 100,000 dimensions on a single GPU using SDGD with PINNs. Since SDGD is a general training methodology of PINNs, it can be applied to any current and future variants of PINNs to scale them up for arbitrary high-dimensional *** Codes 14J60 © 2023, CC BY.

关键词： Stochastic systems

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Paper Book Recommendation System by using Neural Network

Paper Book Recommendation System by using Neural Network

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Faiza Khaliq Amna Ilyas Abd Ur Rehman Syed Shahryar Akbar Omar Satatr Riphah School of Computing & Innovation School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Riphah School of Computing Riphah Interntional University Lahore Pakistan General Education Department Skylin University College University City Sharjah Sharjah UAE

Data mining is the concept of collecting raw data and convert into useful information. Data mining is used for everything from what customers want to buy a thing and which thing is recommended to customers. In this paper, the proposed algorithm used is Neural Network. A Neural Network algorithm is used to recognize the hidden relationship between the data set by adopting the process of how the human brain performs functioning. In this research paper, a different algorithm is also used, and compare the result of this algorithm. The algorithm is applied to the data set of books which may result in the book being recommended or not on the different features and also the Accuracy of the algorithm.

关键词： Data mining Security Biological neural networks Recommender systems Business

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Consensus based optimization with memory effects: random selection and applications

arXiv

引用

arXiv 2023年

作者： Borghi, Giacomo Grassi, Sara Pareschi, Lorenzo RWTH Aachen University Institute for Geometry and Applied Mathematics Aachen Germany University of Ferrara Department of Mathematics and Computer Science Center for Modelling Computing and Statistics Ferrara Italy

In this work we extend the class of Consensus-Based Optimization (CBO) metaheuristic methods by considering memory effects and a random selection strategy. The proposed algorithm iteratively updates a population of particles according to a consensus dynamics inspired by social interactions among individuals. The consensus point is computed taking into account the past positions of all particles. While sharing features with the popular Particle Swarm Optimization (PSO) method, the exploratory behavior is fundamentally different and allows better control over the convergence of the particle system. We discuss some implementation aspects which lead to an increased efficiency while preserving the success rate in the optimization process. In particular, we show how employing a random selection strategy to discard particles during the computation improves the overall performance. Several benchmark problems and applications to image segmentation and Neural Networks training are used to validate and test the proposed method. A theoretical analysis allows to recover convergence guarantees under mild assumptions on the objective function. This is done by first approximating the particles evolution with a continuous-in-time dynamics, and then by taking the mean-field limit of such dynamics. Convergence to a global minimizer is finally proved at the mean-field level. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A Smart Air Pollutants Monitoring System Using IOT Technologies

A Smart Air Pollutants Monitoring System Using IOT Technolog...

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： M Pyingkodi N R Wilfred Blessing K Thenmozhi K Kanimozhi L Pavithira P K Sandhya Balakrishnan Department of Computer Applications Kongu Engineering College Erode India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri(Government) Oman Department of Computer Science Kristu Jayanti College Bengaluru India Department of Information Technology Sri Krishna Adithya College of Arts and Science Coimbatore Department of Computer Science with Data Analytics PSG college of Arts & Science College Coimbatore Department of Computer Science CMS College of Science and Commerce Coimbatore

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of “inventive gadgets” which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device.

关键词： Cloud computing Microprocessors Air pollution Hardware Sensors Servers Internet of Things

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Leaf Disease Segmentation Using Uunet++ Architecture 3rd

Leaf Disease Segmentation Using Uunet++ Architecture

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Farooqui, Nafees Akhter Mishra, Amit Kumar Ray, Kanad Mallik, Saurav Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Karnataka Bengaluru India Department of Physics Amity School of Applied Sciences Amity University Rajasthan Rajasthan Jaipur India Amity Cognitive Computing and Brain Informatics Centre Amity University Rajasthan Rajasthan303002 India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA02115 United States Pharmacology & Toxicology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9789819739363

Segmentation of leaves is an initial step in image-based plant phenotyping, which has received more attention recently. Leaf segmentation shows features of the leaf and growth stages at the leaf level. Due to the significant overlapping between leaves and fluctuating environmental circumstances, such as intensity change and blur due to wind, segmentation of plant tissues like leaves is a most important issue in plant phenotyping. The leaf segmentation work has more complicated problems, such as leaf surface, genotypes, length, structure, and thickness variability. This study proposes an ultra-modern deep gaining knowledge of architectures: UUNet++, a convolutional neural network for advance segmentation. A vital assessment is done using computer Vision for Plant Phenotyping dataset (CVPPP). The plant phenotyping is performed on tobacco and Arabidopsis leaf of RGB images from plant village dataset. The proposed model needs less space as compared to the main UNet architecture. In contrast to typical deep learning image sets, this dataset contains a limited number of samples. Despite that, we achieve exceptional results in leaf segmentation, specifically in distinguishing the interior of leaves from the binary segmentation of the entire leaf. Furthermore, studies are necessary to accurately quantify the number of leaves. Without reducing performance measures, the disease classification inferences time is extremely efficient and accurate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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computer-aided system for extending the performance of diabetes analysis and prediction 7

Computer-aided system for extending the performance of diabe...

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7th International Conference on Software Engineering and computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Murad, Saydul Akbar Azmi, Zafril Rizal M Hakami, Zaid Hafiz Prottasha, Nusrat Jahan Kowsher, Md Universiti Malaysia Pahang Faculty of Computing Malaysia Jazan University Faculty of Computer Science and Information Technology Jazan Saudi Arabia Daffodils International University Department of Computer Science and Engineering Bangladesh Noakhali Science and Technology University Department of Applied Mathematics Bangladesh

ISBN: (纸本)9781665414074

Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients' medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent. © 2021 IEEE.

关键词： Forecasting

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Segment Visibility Counting Queries in Polygons

arXiv

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arXiv 2022年

作者： Buchin, Kevin Custers, Bram van der Hoog, Ivor Löffler, Maarten Popov, Aleksandr Roeloffzen, Marcel Staals, Frank Department of Computer Science TU Dortmund Germany Department of Mathematics and Computer Science TU Eindhoven Netherlands Department of Applied Mathematics and Computer Science TU Denmark Copenhagen Denmark Department of Information and Computing Sciences Utrecht University Netherlands

Let P be a simple polygon with n vertices, and let A be a set of m points or line segments inside P. We develop data structures that can efficiently count the number of objects from A that are visible to a query point or a query segment. Our main aim is to obtain fast, O(polylog nm), query times, while using as little space as possible. In case the query is a single point, a simple visibility-polygon-based solution achieves O(log nm) query time using O(nm2) space. In case A also contains only points, we present a smaller, O(n + m2+Ε log n)-space, data structure based on a hierarchical decomposition of the polygon. Building on these results, we tackle the case where the query is a line segment and A contains only points. The main complication here is that the segment may intersect multiple regions of the polygon decomposition, and that a point may see multiple such pieces. Despite these issues, we show how to achieve O(log nlog nm) query time using only O(nm2+Ε + n2) space. Finally, we show that we can even handle the case where the objects in A are segments with the same bounds. © 2022, CC BY.

关键词： Visibility

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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science China(Information sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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