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Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

computer Modeling in engineering & Sciences 2024年第11期141卷 1651-1671页

作者： Mohamed A.Mahdi Suliman Mohamed Fati Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Information Systems Department College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

关键词： Cyberbullying offensive detection Bidirectional Encoder Representations from the Transformers(BERT) continuous bag of words Social Media natural language processing

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Evolutionary authoring tool for adaptive hypermedia with multimodal navigation

Evolutionary authoring tool for adaptive hypermedia with mul...

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作者： Medina-Medina, Nuria Molina-Ortiz, Fernando Padilla-Zea, Natalia Cabrera-Cuevas, Marcelino García-Cabrera, Lina Parets-Llorca, José Department of Computer Languages and Systems Technical College of Computer and Telecommunications Engineering University of Granada Granada Spain Department of Computer University of Jaen Jaen Spain

This paper discusses the importance of user adaptation and software evolution in hypermedia applications, and reviews some of the most relevant approaches to both fields. The paper describes a model that has been conceived for the development, maintenance and navigation of adaptive hypermedia systems. This model, called SEMantic, systemic and evolutionary model to develop HyPermedia systems (SEM-HP), includes an incremental design process, a layered architecture and an authoring tool integrated with a navigation tool. SEM-HP architecture is composed of four subsystems, which allow the separation of aspects related to knowledge representation, presentation, navigation and user adaptation. In addition, SEM-HP has a higher layer, which acts as a meta-system and allows a consistent evolution of the elements defined in each of the four subsystems, as well as their automatic co-evolution. Regarding user interaction, four alternative ways of navigating hypermedia information are supported. Finally, the paper shows the main results of two case studies carried out with the authoring and navigation tool, JSEM-HP, at the University of Granada, Spain. © 2013 Taylor and Francis Group, LLC.

关键词： Navigation

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Reverse engineering feature models with evolutionary algorithms: An exploratory study

Reverse engineering feature models with evolutionary algorit...

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4th International Symposium on Search Based software engineering, SSBSE 2012

作者： Lopez-Herrejon, Roberto Erick Galindo, José A. Benavides, David Segura, Sergio Egyed, Alexander Institute for Systems Engineering and Automation Johannes Kepler University Linz Austria Department of Computer Languages and Systems University of Seville Spain

ISBN: (纸本)9783642331183

Successful software evolves, more and more commonly, from a single system to a set of system variants tailored to meet the similiar and yet different functionality required by the distinct clients and users. software Product Line engineering (SPLE) is a software development paradigm that has proven effective for coping with this scenario. At the core of SPLE is variability modeling which employs Feature Models (FMs) as the de facto standard to represent the combinations of features that distinguish the systems variants. Reverse engineering FMs consist in constructing a feature model from a set of products descriptions. This research area is becoming increasingly active within the SPLE community, where the problem has been addressed with different perspectives and approaches ranging from analysis of configuration scripts, use of propositional logic or natural language techniques, to ad hoc algorithms. In this paper, we explore the feasibility of using Evolutionary Algorithms (EAs) to synthesize FMs from the feature sets that describe the system variants. We analyzed 59 representative case studies of different characteristics and complexity. Our exploratory study found that FMs that denote proper supersets of the desired feature sets can be obtained with a small number of generations. However, reducing the differences between these two sets with an effective and scalable fitness function remains an open question. We believe that this work is a first step towards leveraging the extensive wealth of Search-Based software engineering techniques to address this and other variability management challenges. © 2012 Springer-Verlag.

关键词： Reverse engineering

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POWER-SYSTEM VOLTAGE CONTROL BY DISTRIBUTED EXPERT systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1990年第3期37卷 236-240页

作者： MATSUDA, S OGI, H NISHIMURA, K OKATAKU, Y TAMURA, S Artificial Intelligence Technology Department Computer and Communication Research Center Tokyo Electric Power Company Limited Japan Systems and Software Engineering Laboratory Toshiba Corporation Japan

A case study of the application of a distributed control scheme to a power system control is presented. In addition, an investigation has been conducted into voltage control. Combined injection of VAr-compensating devices controlled by distributed expert systems has been proposed as a measure to maintain voltage stability in a power system under heavy loading conditions. A simulation study has been carried out by using five workstations that represent a power system and four VAr-compensating devices. The results demonstrate the effectiveness of the proposed system for voltage recovery.< >

关键词： Power system control Power systems Voltage control Expert systems Power system stability Power system simulation Distributed control Power system measurements Control systems Power measurement

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Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling

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Environment International 2025年 199卷 109496页

作者： Ahmed, Maqsood Zhang, Xiang Shen, Yonglin Ahmed, Tanveer Ali, Shahid Ali, Ayaz Gulakhmadov, Aminjon Nam, Won-Ho Chen, Nengcheng National Engineering Research Center of Geographic Information System School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Software College Northeastern University Shenyang110169 China Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Research Center of Ecology and Environment in Central Asia Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi830011 China Institute of Water Problems Hydropower and Ecology of the National Academy of Sciences of Tajikistan Dushanbe734042 Tajikistan School of Social Safety and Systems Engineering Institute of Agricultural Environmental Science National Agricultural Water Research Center Hankyong National University Anseong Korea Republic of

Air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily focuses on single static image analysis, which does not account for the dynamic and temporal nature of air pollution. Meanwhile, research on video-based air quality estimation remains limited, particularly in achieving accurate multi-pollutant outputs. This study proposes Air Quality Prediction-Mamba (AQP-Mamba), a video-based deep learning model that integrates a structured Selective State Space Model (SSM) with a selective scan mechanism and a hybrid predictor (HP) to estimate air quality. The spatiotemporal forward and backward SSM dynamically adjusts parameters based on input, ensures linear complexity, and effectively captures long-range dependencies by bidirectional processing of spatiotemporal features through four scanning techniques (row-wise, column-wise, and their vertical reversals), which allows the model to accurately track pollutant concentrations and air quality variations over time. Thus, the model efficiently extracts spatiotemporal features from video and simultaneously performs regression (PM2.5, PM10, and AQI), and classification (AQI) tasks, respectively. A high-quality outdoor hourly air quality dataset (LMSAQV) with 13,176 videos collected from six monitoring stations in Lahore, Pakistan, was utilized as the case study. The experimental results demonstrate that the AQP-Mamba significantly outperforms several state-of-the-art models, including VideoSwin-T, VideoMAE, I3D, VTHCL, and TimeSformer. The proposed model achieves strong regression performance (PM2.5: R2 = 0.91, PM10: R2 = 0.90, AQI: R2 = 0.92) and excellent classification metrics: accuracy (94.57 %), precision (93.86 %), recall (94.20 %), and F1-score (93.44 %), respectively. The proposed model delivers consistent, real-time performance with a latency

关键词： Air quality

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Leveraging Geographic Information systems and Remote Sensing for Enhanced Water Resource Management in Kazakhstan 4

Leveraging Geographic Information Systems and Remote Sensing...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hamada, Mohamed Ahmed Tolkyn, Orden Hamada, Gehad M. International It University Information Systems Department Almaty Kazakhstan International It University Computer Systems and Software Engineering Department Almaty Kazakhstan

ISBN: (纸本)9798350387490

Kazakhstan's deputy member is vast, and is one of sparse population, scattering and creating an overwhelming challenge to map and assess the water resources. Most of the waters are in remote and poorly equipped areas, making their monitoring by conventional means arduous. This includes costly and inefficient monitoring that results in a blurred condition of the water resources. Such a predicament stands in the way of effective management and decision-making regarding water resources. Therefore, we used Sentinel-3 satellite images to assess the spatiotemporal dynamics of water resources in Kazakhstan and obtained the results through GIS analysis. The main case of the region for case studies reflecting problems in the management of water resources in the country is the northern part of the Aral Sea. It presents a study characterized by in-depth data analysis and mapping that provided insight into the spatial trends of water resources, their variability, and potential areas of concern. The present research found that GIS and remote sensing technologies have emerged as a strong water resource observation platform. They show promise in the resource sector decision support. The development thus justifies the integration of these technology tools within the water resource management practices to ensure sustainable water use in Kazakhstan. The publication has equally availed a model for similarly water-stressed regions regarding technology-driven solutions in matters of environmental sustainability and resource management. © 2024 IEEE.

关键词： Information management

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Facial Emotion Recognition using Deep Learning (FERDL) 25

Facial Emotion Recognition using Deep Learning (FERDL)

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25th International Multi Topic Conference, INMIC 2023

作者： Ramzan, Hafiz Arslan Sohaib, Ahmed Ramzan, Sadia Kalsum, Tehmina Aqeel, Muhammad Majeed, Mohsin Khwaja Fareed University of Engineering and Information Technology Institute of Computer and Software Engineering Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering & Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350317701

Facial Emotion Recognition is one of the in-demand and rapidly growing research topics in the domain of computer Vision (CV) and artificial intelligence (AI). The ability to identify or detect human emotions from real-time facial expressions (FEs) has vast conceivable applications in different domains, such as sentiment analysis, human-computer interaction, human resource management, security, and human psychology. In this paper, a Convolutional Neural Network (CNN) based deep learning model is trained with haar-cascade classifier to recognize the real-time FEs. The suggested model is specially trained to categorize the FEs into one of the seven emotion categories, namely six basic emotions (sad, happy, angry, surprised, disgusted, fear) and a neutral emotion. It includes several convolutional layers, as well as fully connected neurons followed by max-pooling layers, and soft-max activation function with the corresponding seven classes. ReLU activation functions along with various kernels to enhance filtering depth, and extraction of facial features. FER-2013 dataset is used for experimentation purpose. To improve the classification performance and model accuracy, a data augmentation technique is used for rescaling and horizontal flipping. The proposed model outperforms the previous related works by achieving a validation accuracy of 71.96% and training accuracy above 90%, with fewer epochs. © 2023 IEEE.

关键词： Emotion Recognition

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Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach

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WATER SCIENCE AND TECHNOLOGY 2009年第12期60卷 3051-3059页

作者： Zaqoot, Hossam Adel Ansari, Abdul Khalique Unar, Mukhtiar Ali Khan, Shaukat Hyat Department of Chemical Engineering Department of Computer Systems & Software Engineering Environment Quality Authority (Palestinian Authority)-PhD Scholar at the Institute of Environmental Engineering & Management Mehran university of Engineering and Technology and National Institute of Oceanography Oceanography Pakistan E-mail: hanreen2@***

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

关键词： ANNs dissolved oxygen Gaza Mediterranean Sea MLP prediction RBF

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Test-Driven Development (TDD) in Small software Development Teams: Advantages and Challenges 5

Test-Driven Development (TDD) in Small Software Development ...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Ramzan, Hafiz Arslan Ramzan, Sadia Kalsum, Tehmina Institute of Computer and Software Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering and Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

In the context of small software development teams, this research article gives a thorough investigation of the adoption of test-driven development (TDD) approaches. It aims to highlight the benefits that TDD offers, such as improved code quality through modularization and proactive defect spotting which results in effective debugging and development processes. It also discusses the complex issues that arise when TDD is implemented in smaller teams, such as the learning curve and resource constraints. This study significantly advances the understanding of how TDD can be used to optimize software development techniques in organizations or software houses having small development teams. It also explores the possible advantages and challenges. © 2024 IEEE.

关键词： Modular construction

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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

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Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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